Elisa Trasatti; Stefano Sal http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48/changes/07defd9e-e852-4b3b-bd53-d8804eabf75c http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48/changes/51cc4efc-08f0-4f07-9e5f-adf0d894c4af http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf deformation model United Kingdom University of Leeds Fortran code seismic hazard University of LeedsU.K. Fortran Mogi model Tim Wright computer programming computer code volcanology models volcano deformation model system Elisa Trasatti service-account-enrichment http://sandbox.rohub.org/rodl/ROs/Mogi_model/ 2021-05-11T11:11:04.115+02:00 https://plus.google.com/103457129851450819242 http://w3id.org/ro-id/rohub/model#change_specifications/a41f86bc-d941-490c-bd77-0d346847de48 6343 https://api.rohub.org/api/ros/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76/crate/download/ 2021-05-11 09:11:04.115000+00:00 2025-03-05 00:56:57.842643+00:00 2021-05-11 09:11:04.115000+00:00 This is the Mogi (1958) model. Fortran code. application/ld+json https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76 Mogi model (1958) https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76 Elisa Trasatti; Stefano Sal. "Mogi model (1958)." ROHub. May 11 ,2021. https://w3id.org/ro-id/4cc2cb8d-4ace-4fd2-8ea4-674ac0514f76. Biblio tool http://indico.ictp.it/event/a08176/session/82/contribution/62/material/0/0.pdf 2022-03-24 13:14:05.984220+00:00 2022-03-24 13:14:09.157706+00:00 application/pdf presentation of the model 2022-03-24 13:14:05.984220+00:00 1104 https://api.rohub.org/api/resources/a6fe3471-3b69-4508-a607-0851897d612a/download/ 2021-05-11 09:05:12.945000+00:00 2022-03-24 13:14:08.904850+00:00 mogi.f 2021-05-11 09:05:12.945000+00:00 service-account-generation-service https://data.psychosensing.psnc.pl/popane/ http://w3id.org/ro-id/rohub/model#change_specifications/fc6484e8-4439-4613-8d15-829595d4f06b/changes/0bc05a93-4468-4daa-b126-d9d0080793f5 http://w3id.org/ro-id/rohub/model#change_specifications/fc6484e8-4439-4613-8d15-829595d4f06b/changes/18262614-b883-4213-ba15-7efb9ef0c268 http://w3id.org/ro-id/rohub/model#change_specifications/fc6484e8-4439-4613-8d15-829595d4f06b/changes/1a2fb661-520b-4cb3-88fd-21449f291d62 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fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (EKG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including: amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. application/ld+json https://w3id.org/ro-id/6a6127bb-03b0-4d0c-a5a9-52cfd0913fd4 Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants database dataset electrocardiography emotion experience individual information laboratory participant plethysmography response testing environmental sciences Biology Medical procedure-test Psychology Science and technology Psychophysiology of Positive and Negative Emotions data database dataset emotion individual participant life sciences available dataset dataset of psychophysiological response electrodermal activity models of emotion subjective experience To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. http://sandbox.rohub.org/rodl/ROs/POPANE-snapshot/ medicine psychology Szymon KupiÅ?ski. "Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/6a6127bb-03b0-4d0c-a5a9-52cfd0913fd4. Metadata Biblio Raw Data Dataset Data Used Produced Documentation https://data.psychosensing.psnc.pl/popane/ 2022-03-24 13:24:28.053728+00:00 2022-03-24 13:24:29.534204+00:00 https://data.psychosensing.psnc.pl/popane/ 2022-03-24 13:24:28.053728+00:00 service-account-generation-service https://data.psychosensing.psnc.pl/popane/ physiology recorded affect dataset impedance cardiography activity International Crisis Group electrocardiography emotions Szymon KupiÅ?ski service-account-enrichment false http://sandbox.rohub.org/rodl/ROs/POPANE/ 2021-03-11T14:49:03.095+01:00 https://orcid.org/0000-0002-2455-4556 8794 https://api.rohub.org/api/ros/fb39cfdd-d93a-4f89-af5f-2c944b85c05d/crate/download/ 2021-03-11 13:49:03.095000+00:00 2025-03-05 01:17:00.039657+00:00 2021-03-11 13:49:03.095000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (EKG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including: amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. application/ld+json https://w3id.org/ro-id/fb39cfdd-d93a-4f89-af5f-2c944b85c05d Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants database dataset electrocardiography emotion experience individual information laboratory participant plethysmography response testing environmental sciences Biology Medical procedure-test Psychology Science and technology Psychophysiology of Positive and Negative Emotions data database dataset emotion individual participant life sciences available dataset dataset of psychophysiological response electrodermal activity models of emotion subjective experience To the best of our knowledge, Psychophysiology of Positive and Negative Emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their own analyses, corroborate their results, and create robust psychophysiological models of emotions. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. medicine psychology Szymon KupiÅ?ski. "Psychophysiology of Positive and Negative Emotions (POPANE) – a dataset of over 1000 participants." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/fb39cfdd-d93a-4f89-af5f-2c944b85c05d. 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We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. application/ld+json https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a POPANE DATASET - Psychophysiology Of Positive And Negative Emotions http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot-1/ https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a Maciej Behnke. "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/a3a81739-a5b7-4897-b732-7aba23d6fa5a. 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http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot/.ro/annotations/ea4c6885-0798-40bf-81e8-b9a3812205b0 http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot/.ro/annotations/5c63a723-a83c-401a-bcea-04d44ac1f74c psychology physiology popane dataset psychophysiology Of Positive medicine database data electrocardiography dataset neuropsychology DATASET impedance cardiography Of Positive emotion POPANE dataset studies psychophysiology of positive activity plethysmography International Crisis Group experience individual impedance cardiography emotions information response individuals data Maciej Behnke service-account-enrichment false http://sandbox.rohub.org/rodl/ROs/POPANE-1/ 2021-03-11T14:05:41.562+01:00 https://orcid.org/0000-0002-2455-4556 http://w3id.org/ro-id/rohub/model#change_specifications/39096823-9a1d-424b-8c9d-f251f9141b1e 9801 https://api.rohub.org/api/ros/78c3584e-592e-4af6-bf23-605e8a1b84c0/crate/download/ 2021-03-11 13:05:41.562000+00:00 2025-03-05 01:14:12.643536+00:00 2021-03-11 13:05:41.562000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. application/ld+json https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0 POPANE DATASET - Psychophysiology Of Positive And Negative Emotions http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot/ Maciej Behnke. "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/78c3584e-592e-4af6-bf23-605e8a1b84c0. Metadata Dataset Produced Used Documentation Biblio Data Raw Data https://data.psychosensing.psnc.pl/popane/index.html 2022-03-24 13:24:58.732136+00:00 2022-03-24 13:25:00.483590+00:00 text/html https://data.psychosensing.psnc.pl/popane/index.html 2022-03-24 13:24:58.732136+00:00 service-account-generation-service Neurobiology psychology physiology popane dataset psychophysiology Of Positive medicine database data electrocardiography dataset neuropsychology DATASET impedance cardiography Of Positive emotion POPANE dataset studies psychophysiology of positive activity plethysmography International Crisis Group experience individual impedance cardiography emotions information response individuals data Maciej Behnke service-account-enrichment false http://sandbox.rohub.org/rodl/ROs/POPANE-1/ 2021-03-11T14:04:33.909+01:00 https://orcid.org/0000-0002-2455-4556 8553 https://api.rohub.org/api/ros/a415c54e-7d07-43c8-bcbe-1f76220f473f/crate/download/ 2021-03-11 13:04:33.909000+00:00 2025-03-05 01:14:12.144536+00:00 2021-03-11 13:04:33.909000+00:00 Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. Subjective experience along with physiological activity are fundamental components of emotional responding. We present a publicly available dataset of psychophysiological responses to positive and negative emotions of 1157 healthy participants, collected across seven studies. In our studies were continuously recorded affect and physiological activity during resting baseline and emotional responding. We recorded physiological responses using electrocardiography (ECG), impedance cardiography (ICG), electrodermal activity (EDA), photoplethysmography (PPG, the blood pressure measures), respiratory, and temperature sensors. In our studies, we elicited emotions with films, pictures, speech preparation, and expressive writing. We studied a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. To the best of our knowledge, psychophysiology of positive and negative emotions (POPANE) database is the largest, consistent psychophysiological dataset on emotions ever collected and publicly shared. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses, corroborate their results, and create robust psychophysiological models of emotions. The individuals data are openly available in POPANE dataset at https://data.psychosensing.psnc.pl/popane/index.html. application/ld+json https://w3id.org/ro-id/a415c54e-7d07-43c8-bcbe-1f76220f473f POPANE DATASET - Psychophysiology Of Positive And Negative Emotions Maciej Behnke. "POPANE DATASET - Psychophysiology Of Positive And Negative Emotions." ROHub. Mar 11 ,2021. https://w3id.org/ro-id/a415c54e-7d07-43c8-bcbe-1f76220f473f. Biblio Used Metadata Raw Data Dataset Produced Data Documentation https://data.psychosensing.psnc.pl/popane/index.html 2022-03-24 13:25:06.838155+00:00 2022-03-24 13:25:07.593414+00:00 text/html https://data.psychosensing.psnc.pl/popane/index.html 2022-03-24 13:25:06.838155+00:00 service-account-generation-service Antonio Petrizzo Matlab, Taverna, Linux environment http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/20d53a40-6910-4c02-95c1-d23bcd344d32 http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/3134a63c-b0c9-4a2e-8e70-cd527ea27a48 http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/3b51b0a3-9887-4723-8ef4-1081f75c0cb0 http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/8e107b34-13bd-4272-b06c-5ae3f58a7f4e http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/b736aa68-a1d9-4d14-81b4-2cc0ed614040 http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34/changes/f6008087-6209-4593-9324-e6611dc34424 https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ standard deviation of backscatter transports elaborate ASCII file produce a graph SinkTrack It statistics angle standard deviation graph ASCII file net depth angle of net ping net graph with the track statistics backscattering angle of net FM Midwater service-account-enrichment http://ever-est.eu/value#/sinktrack http://ever-est.eu/value#0226e45d-59ef-45dd-992f-824a0dbff431 http://ever-est.eu/value#Work http://sandbox.rohub.org/rodl/ROs/sinktrack/ 2019-12-05T16:29:35.863+01:00 http://everest.psnc.pl/users/antonio.petrizzo http://w3id.org/ro-id/rohub/model#change_specifications/a9f32409-f31b-4b9b-b4ec-5c0526318c34 An ASCII file with beam time series extracted from the water column data, output of FM Midwater, is imported in Matlab where it is read and adpated to be further processed. The noise is filtered setting a threshold for the intensity values. Then the pings are stacked with the all values of the water column backscatter intensity for all beams over time. A graph with the track of the net, pings vs depth, and some statistics (mean and standard deviation of backscatter, depth, angle of net for each ping) are provided as output. The RO consists of three different workflows created through the Taverna Workbench Enterprise application and they are supposed to be run sequentially (“SinkTrackRead”, "sinkTrackGraph", "SinkTrack"). 16324 https://api.rohub.org/api/ros/ed9a1b7c-fc01-4b4a-b274-0708f43b24e2/crate/download/ 2019-12-05 15:29:35.863000+00:00 2025-03-05 01:19:06.048079+00:00 2019-12-05 15:29:35.863000+00:00 It reads and elaborates ASCII file produced with FM Midwater in order to calculate some statistics, mean, standard deviation of backscatter, depth, angle of net for each ping, and to produce a graph with the track of the net application/ld+json EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 https://w3id.org/ro-id/ed9a1b7c-fc01-4b4a-b274-0708f43b24e2 SinkTrack Sea Monitoring http://sandbox.rohub.org/rodl/ROs/sinktrack_ARCHIVE/ Aleksandra Kruss (ISMAR-CNR Venice), Antonio Petrizzo (ISMAR-CNR Venice), and Fantina Madricardo (ISMAR-CNR Venice). "SinkTrack." ROHub. Dec 05 ,2019. https://w3id.org/ro-id/ed9a1b7c-fc01-4b4a-b274-0708f43b24e2. produced components datasets used results setup workflows scripts nested biblio config inputs main web services software 105100 KB https://box.everest.psnc.pl/f/421adc4b01b24d2a906c/ 2022-03-24 13:30:37.089841+00:00 2022-03-24 13:30:38.641614+00:00 text/plain sinkTrackRead.t2flow 2022-03-24 13:30:37.089841+00:00 111274 KB https://box.everest.psnc.pl/f/67b5ddc8a3ac4047b3ef/ 2022-03-24 13:30:37.090235+00:00 2022-03-24 13:30:38.484129+00:00 text/plain sinkTrack.t2flow 2022-03-24 13:30:37.090235+00:00 105094 KB https://box.everest.psnc.pl/f/2a521bb3de8345b1ae9b/ 2022-03-24 13:30:37.089386+00:00 2022-03-24 13:30:38.327875+00:00 text/plain sinkTrackGraph1.t2flow 2022-03-24 13:30:37.089386+00:00 service-account-generation-service WebProcessingServiceExecution 2 http://box.everest.psnc.pl:8000/f/0c4347ad9d/ 2022-03-24 19:49:35.107140+00:00 2022-03-24 19:49:46.065773+00:00 .png cd.png 2022-03-24 19:49:35.107140+00:00 WebProcessingServiceExecution 2018-05-08T16:22:04.503000+00:00 WebProcessingServiceExecution 2 http://box.everest.psnc.pl:8000/f/6157842c73/ 2022-03-24 19:49:35.109031+00:00 2022-03-24 19:49:46.577143+00:00 .tgz cd.tgz 2022-03-24 19:49:35.109031+00:00 WebProcessingServiceExecution 2018-05-08T16:22:04.503000+00:00 985000 http://box.everest.psnc.pl:8000/f/6a67815420/ 2022-03-24 19:49:35.107688+00:00 2022-03-24 19:49:44.444293+00:00 .zip S1A_IW_GRDH_1SDV_20170820T061754_20170820T061819_018003_01E376_9EC3.zip 2022-03-24 19:49:35.107688+00:00 WebProcessingServiceExecution 2 http://box.everest.psnc.pl:8000/f/a25b04c564/ 2022-03-24 19:49:35.108676+00:00 2022-03-24 19:49:49.583999+00:00 .pngw cd.pngw 2022-03-24 19:49:35.108676+00:00 WebProcessingServiceExecution 2018-05-08T16:22:04.503000+00:00 985000 http://box.everest.psnc.pl:8000/f/aa333acec2/ 2022-03-24 19:49:35.108194+00:00 2022-03-24 19:49:48.154486+00:00 .zip S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip 2022-03-24 19:49:35.108194+00:00 Cartography anca popescu EU SatCen EU SatCen POLYGON((-3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252)) WebProcessingService AreaofInterest MasterSentinel-1product Polarization SlaveSentinel-1product SatCen Change Detection Workflow execution Result Files Distribution Package com.terradue.wps_oozie.process.OozieAbstractAlgorithm SatCen Change Detection Workflow POLYGON( ( -3.611394871468983 40.53713623661004, -3.5125914250302666 40.53713623661004, -3.4137879785915506 40.53713623661004, -3.4137879785915506 40.40969285491932, -3.5125914250302666 40.40969285491932, -3.611394871468983 40.40969285491932, -3.611394871468983 40.53713623661004 ) ) SlaveSentinel-1product AreaofInterest Polarization MasterSentinel-1product detection over Madrid Madrid Satcen 2018 Detection earth sciences 17.014721850579107 0.6360710263252258 earth sciences 11.563625766334056 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"Change Detection Data Centric." ROHub. Jun 15 ,2018. https://w3id.org/ro-id/f8fafb66-4349-4d35-a695-0db97605e324. setup components datasets produced web services inputs results biblio main scripts workflows nested used software config 0 https://api.rohub.org/api/resources/0dfaa382-a57d-4f1b-b160-2aed85cb2b36/download/ 2018-05-10 08:21:25.852000+00:00 2022-03-25 15:09:55.345849+00:00 .txt definition.txt 2018-05-10 08:21:25.852000+00:00 4 https://api.rohub.org/api/resources/549abf24-05cc-4ad6-a9f4-1d276eaf0dde/download/ 2018-05-10 08:19:07.994000+00:00 2022-03-25 15:09:57.149262+00:00 .txt workflow.txt 2018-05-10 08:19:07.994000+00:00 ggg 143 https://api.rohub.org/api/resources/5c385343-84f6-4709-8095-e4a2a699cc5f/download/ 2018-05-10 08:09:44.546000+00:00 2022-03-25 15:09:50.104320+00:00 .txt Input-Master.txt 2018-05-10 08:09:44.546000+00:00 11 https://api.rohub.org/api/resources/7d4f074b-cc46-4cad-9b31-79d72d8a1fef/download/ 2018-05-10 10:50:29.452000+00:00 2022-03-25 15:09:52.004094+00:00 .txt Copyright.txt 2018-05-10 10:50:29.452000+00:00 service-account-enrichment service-account-generation-service Chemistry 10.24424/jxpj-vv36 False 2025-07-04 09:08:44.261623+00:00 0 https://api.rohub.org/api/ros/de0b3951-0fa7-4b03-a1fa-d5c4da93a476/crate/download/ 2022-01-12 16:34:39.917729+00:00 2025-10-16 11:15:06.613810+00:00 2022-01-12 16:34:39.917729+00:00 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom. application/ld+json https://w3id.org/ro-id/de0b3951-0fa7-4b03-a1fa-d5c4da93a476 Aromatic compounds - snapshot Aromatic compounds MANUAL Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/jxpj-vv36. arene 7.304347826086956 4.2 aliphatic compound 4.737903225806451 4.7 The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. 21.401515151515152 11.3 Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery oxygen atom 4.032258064516129 4.0 nitrogen 3.9314516129032255 3.9 organic chemistry 65.91639871382637 41.0 oxygen atom 17.794486215538846 7.1 benzene 9.274193548387096 9.2 geochemistry 100.0 0.4569866955280304 heterocyclic compound 9.73913043478261 5.6 chemistry and materials 100.0 0.8506659269332886 aromatic 19.657258064516128 19.5 benzene 12.695652173913043 7.3 monocyclic ring 14.285714285714286 5.7 chemistry and materials (general) 100.0 0.8506659269332886 arene 4.939516129032259 4.9 electron 4.435483870967742 4.4 chemistry 34.08360128617363 21.2 scent 4.536290322580645 4.5 aromatic hydrocarbon 5.94758064516129 5.9 chemical compound 15.826086956521738 9.1 nitrogen atom 29.573934837092732 11.8 aromatic hydrocarbon 8.695652173913043 5.0 ring 3.125 3.1 The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. 39.015151515151516 20.6 organic compound 3.8306451612903225 3.8 chemical compound 10.786290322580644 10.7 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. 39.583333333333336 20.9 carbon atom 15.999999999999998 9.2 aromatic compound benzene 24.81203007518797 9.9 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical heterocyclic compound 6.451612903225806 6.4 aromatic compound 29.739130434782613 17.1 larger compound 13.533834586466165 5.4 earth sciences 100.0 0.4569866955280304 carbon atom 10.786290322580644 10.7 benzene ring 3.528225806451613 3.5 Chemistry 10.24424/070n-rr14 False 2025-07-05 18:47:59.392957+00:00 0 https://api.rohub.org/api/ros/ba53e480-17bb-466f-b789-3533246d7b43/crate/download/ 2022-01-12 16:34:39.917729+00:00 2025-10-16 11:14:31.884055+00:00 2022-01-12 16:34:39.917729+00:00 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom. application/ld+json https://w3id.org/ro-id/ba53e480-17bb-466f-b789-3533246d7b43 Aromatic compounds - snapshot Aromatic compounds MANUAL Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/070n-rr14. chemistry 34.08360128617363 21.2 scent 4.536290322580645 4.5 aromatic 19.657258064516128 19.5 The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. 21.401515151515152 11.3 benzene 9.274193548387096 9.2 carbon atom 10.786290322580644 10.7 geochemistry 100.0 0.4569866955280304 aromatic compound benzene 24.81203007518797 9.9 aromatic compound 29.739130434782613 17.1 larger compound 13.533834586466165 5.4 arene 4.939516129032259 4.9 Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. 39.015151515151516 20.6 arene 7.304347826086956 4.2 oxygen atom 17.794486215538846 7.1 carbon atom 15.999999999999998 9.2 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical electron 4.435483870967742 4.4 aromatic hydrocarbon 8.695652173913043 5.0 chemical compound 15.826086956521738 9.1 benzene ring 3.528225806451613 3.5 heterocyclic compound 6.451612903225806 6.4 aromatic hydrocarbon 5.94758064516129 5.9 nitrogen 3.9314516129032255 3.9 organic compound 3.8306451612903225 3.8 chemistry and materials (general) 100.0 0.8506659269332886 earth sciences 100.0 0.4569866955280304 monocyclic ring 14.285714285714286 5.7 aliphatic compound 4.737903225806451 4.7 benzene 12.695652173913043 7.3 chemical compound 10.786290322580644 10.7 organic chemistry 65.91639871382637 41.0 chemistry and materials 100.0 0.8506659269332886 heterocyclic compound 9.73913043478261 5.6 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. 39.583333333333336 20.9 oxygen atom 4.032258064516129 4.0 nitrogen atom 29.573934837092732 11.8 ring 3.125 3.1 Chemistry https://doi.org/10.24424/x0cn-va37 False 2025-07-05 19:04:55.078129+00:00 0 https://api.rohub.org/api/ros/54c22dc5-ace3-4aaa-be62-b5b4dab97be6/crate/download/ 2022-01-12 16:34:39.917729+00:00 2025-10-16 11:14:13.082777+00:00 2022-01-12 16:34:39.917729+00:00 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom. application/ld+json https://w3id.org/ro-id/54c22dc5-ace3-4aaa-be62-b5b4dab97be6 Aromatic compounds - snapshot Aromatic compounds MANUAL Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/x0cn-va37. chemical compound 15.826086956521738 9.1 The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. 39.015151515151516 20.6 aromatic hydrocarbon 5.94758064516129 5.9 benzene 9.274193548387096 9.2 carbon atom 15.999999999999998 9.2 chemical compound 10.786290322580644 10.7 electron 4.435483870967742 4.4 oxygen atom 4.032258064516129 4.0 arene 4.939516129032259 4.9 chemistry 34.08360128617363 21.2 organic chemistry 65.91639871382637 41.0 chemistry and materials 100.0 0.8506659269332886 scent 4.536290322580645 4.5 heterocyclic compound 9.73913043478261 5.6 benzene 12.695652173913043 7.3 earth sciences 100.0 0.4569866955280304 geochemistry 100.0 0.4569866955280304 The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. 21.401515151515152 11.3 nitrogen atom 29.573934837092732 11.8 aromatic compound 29.739130434782613 17.1 arene 7.304347826086956 4.2 Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery aliphatic compound 4.737903225806451 4.7 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical benzene ring 3.528225806451613 3.5 larger compound 13.533834586466165 5.4 nitrogen 3.9314516129032255 3.9 heterocyclic compound 6.451612903225806 6.4 aromatic hydrocarbon 8.695652173913043 5.0 aromatic 19.657258064516128 19.5 organic compound 3.8306451612903225 3.8 carbon atom 10.786290322580644 10.7 monocyclic ring 14.285714285714286 5.7 chemistry and materials (general) 100.0 0.8506659269332886 aromatic compound benzene 24.81203007518797 9.9 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. 39.583333333333336 20.9 ring 3.125 3.1 oxygen atom 17.794486215538846 7.1 Biology 10.24424/20ms-v465 False 2025-08-12 08:02:25.321821+00:00 0 https://api.rohub.org/api/ros/07b99b7b-a209-44cc-86fd-327339b2599c/crate/download/ 2022-01-19 13:47:59.181939+00:00 2025-10-16 11:12:08.755267+00:00 2022-01-19 13:47:59.181939+00:00 Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate.Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function. For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance. Additionally, it is associated with other mental disorders and substance use disorders. Although it causes impairment, particularly in modern society, many people with ADHD have sustained attention for tasks they find interesting or rewarding, known as hyperfocus. application/ld+json https://w3id.org/ro-id/07b99b7b-a209-44cc-86fd-327339b2599c Attention deficit hyperactivity disorder - snapshot Attention deficit hyperactivity disorder MANUAL Wolniewicz, Małgorzata. "Attention deficit hyperactivity disorder." ROHub. Jan 19 ,2022. https://doi.org/10.24424/20ms-v465. life sciences 100.0 0.989045262336731 distraction 5.919003115264798 5.7 neurodevelopmental disorder 62.65984654731457 49.0 environmental science and management 100.0 0.6445436477661133 behavioural disorder 7.4766355140186915 7.2 environmental sciences 100.0 0.6445436477661133 inattention 9.515260323159785 5.3 substance use disorder 19.565217391304348 15.3 life sciences (general) 100.0 0.989045262336731 diagnosis 6.645898234683282 6.4 medicine 100.0 12.8 individual 4.7767393561786085 4.6 behavioral disorder 12.208258527827647 6.8 individuals with ADHD 7.416879795396419 5.8 mental disorder 3.426791277258567 3.3 problem 9.345794392523365 9.0 attention 5.815160955347872 5.6 impulsiveness 5.815160955347872 5.6 diagnosis 10.23339317773788 5.7 disorder 10.951526032315979 6.1 symptom 4.569055036344757 4.4 attention deficit hyperactivity disorder 21.599169262720665 20.8 Mental and behavioural disorder Health/Diseases and conditions/Mental and behavioural disorder mental disorders 4.731457800511508 3.7 Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate. 59.31758530183727 45.2 difficulty 10.412926391382404 5.8 disorder 12.772585669781932 12.3 For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance. 18.11023622047244 13.8 emotions 4.984423676012462 4.8 School Education/School Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function. 22.572178477690287 17.2 difficulty 6.853582554517134 6.6 attention deficit hyperactivity disorder 32.85457809694793 18.3 school performance 5.626598465473147 4.4 problem 13.824057450628365 7.7 Environmental research Applied sciences Ecology biology conservation strategy ecology Mediterranean Sea endangered species endangered species ecosystem habitat strategy connectivity protected area conservation management result Mediterranean Sea expert evaluation shelf-slope connectivity framework want Integrated Approach Benthic results of a multi-criteria decision analysis efficient set Mediterranean Basin priority POLYGON ((-8.789062500000002 28.459033019728068, -8.789062500000002 48.69096039092552, 38.14453125000001 48.69096039092552, 38.14453125000001 28.459033019728068, -8.789062500000002 28.459033019728068)) -8.789062500000002 28.459033019728068, -8.789062500000002 48.69096039092552, 38.14453125000001 48.69096039092552, 38.14453125000001 28.459033019728068, -8.789062500000002 28.459033019728068 ddbd57b9-a8be-4d53-a229-920d58905c5c POLYGON ((-8.789062500000002 28.459033019728068, -8.789062500000002 48.69096039092552, 38.14453125000001 48.69096039092552, 38.14453125000001 28.459033019728068, -8.789062500000002 28.459033019728068)) service-account-enrichment False https://w3id.org/ro-id/6556cdf7-bcef-43d3-a3ce-3d45d14e4a24 2022-03-24 18:37:26.444410+00:00 https://orcid.org/0000-0002-2736-0052 228075 https://api.rohub.org/api/ros/4fd0f1c2-d58f-4b20-9f12-503c31c607d9/crate/download/ 2022-03-24 17:00:15.895312+00:00 2024-03-05 12:18:48.335914+00:00 2022-03-24 17:00:15.895312+00:00 Benthic habitats of the deep Mediterranean Sea and the biodiversity they host are increasingly jeopardized by increasing human pressures, both direct and indirect, which encompass fisheries, chemical and acoustic pollution, littering, oil and gas exploration and production and marine infrastructures (i.e., cable and pipeline laying), and bioprospecting. To this, is added the pervasive and growing effects of human-induced perturbations of the climate system. International frameworks provide foundations for the protection of deep-sea ecosystems, but the lack of standardized criteria for the identification of areas deserving protection, insufficient legislative instruments and poor implementation hinder an efficient set up in practical terms. Here, we discuss the international legal frameworks and management measures in relation to the status of habitats and key species in the deep Mediterranean Basin. By comparing the results of a multi-criteria decision analysis (MCDA) and of expert evaluation (EE), we identify priority deep-sea areas for conservation and select five criteria for the designation of future protected areas in the deep Mediterranean Sea. Our results indicate that areas (1) with high ecological relevance (e.g., hosting endemic and locally endangered species and rare habitats),(2) ensuring shelf-slope connectivity (e.g., submarine canyons), and (3) subject to current and foreseeable intense anthropogenic impacts, should be prioritized for conservation. The results presented here provide an ecosystem-based conservation strategy for designating priority areas for protection in the deep Mediterranean Sea. application/ld+json https://w3id.org/ro-id/4fd0f1c2-d58f-4b20-9f12-503c31c607d9 Identifying Priorities for the Protection of Deep Mediterranean Sea Ecosystems Through an Integrated Approach - snapshot Identifying Priorities for the Protection of Deep Mediterranean Sea Ecosystems Through an Integrated Approach MANUAL https://w3id.org/ro-id/4fd0f1c2-d58f-4b20-9f12-503c31c607d9/9233b6cc-5495-423f-9af0-b60a83db22c0 Castellan, Giorgio. "Identifying Priorities for the Protection of Deep Mediterranean Sea Ecosystems Through an Integrated Approach." ROHub. Mar 24 ,2022. https://w3id.org/ro-id/4fd0f1c2-d58f-4b20-9f12-503c31c607d9. POLYGON ((-8.789062500000002 28.459033019728068, -8.789062500000002 48.69096039092552, 38.14453125000001 48.69096039092552, 38.14453125000001 28.459033019728068, -8.789062500000002 28.459033019728068)) 265816 https://api.rohub.org/api/resources/54b2de15-6b33-4374-986d-7c20296c8f22/download/ 2022-03-24 17:02:17.046117+00:00 2022-03-24 18:37:25.690510+00:00 image/jpeg fmars-08-698890-t004.jpg 2022-03-24 17:02:17.046117+00:00 https://zenodo.org/record/6382778#.YjykQlXMKHs 2022-03-24 17:04:39.468927+00:00 2022-03-24 18:37:26.331838+00:00 Priority deep-sea areas for conservation in the deep Mediterranean Sea Resources stored in Zenodo 2022-03-24 17:04:39.468927+00:00 Earth sciences geology 100.0 0.8256934881210327 water clarity 30.407523510971785 29.1 earth sciences 100.0 0.8256934881210327 collection 13.091922005571032 9.4 Adriatic Sea https://www.wikidata.org/wiki/Q13924 space 5.153203342618385 3.7 service-account-enrichment False https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140 2022-03-24 18:42:55.013290+00:00 https://orcid.org/0000-0002-2736-0052 533934 https://api.rohub.org/api/ros/28ff4f3e-c3f8-4bf0-8591-8fa36c378faa/crate/download/ 2021-12-14 10:41:17.716553+00:00 2024-03-05 12:16:56.660585+00:00 2021-12-14 10:41:17.716553+00:00 Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea application/ld+json https://w3id.org/ro-id/28ff4f3e-c3f8-4bf0-8591-8fa36c378faa Analysis from satellite data – Environmental monitoring from space - snapshot Analysis from satellite data – Environmental monitoring from space MANUAL https://w3id.org/ro-id/19dabc1d-b24e-4e7b-bbc2-81f95da2f1ad https://w3id.org/ro-id/14888c37-4830-4fd4-9b58-b7364d3b437e https://w3id.org/ro-id/1eddab1d-4fc9-422e-92aa-d523326aa498 https://w3id.org/ro-id/3eaf4867-0bb2-4018-9eac-85eec6ee1309 https://w3id.org/ro-id/44252c81-5879-4430-9255-66dd383ed651 https://w3id.org/ro-id/5d7bef7a-401b-4fa7-a0c0-0960ac13899d https://w3id.org/ro-id/94781348-96cf-459a-85de-405cc09226a3 https://w3id.org/ro-id/a3749fdc-4e70-4f7f-979f-8783827dd636 https://w3id.org/ro-id/af9e7155-28ad-4842-a304-dddf811c4d74 https://w3id.org/ro-id/f100fb36-2a32-44ca-82c2-7076c5835fa5 https://w3id.org/ro-id/0781334a-7a44-4a75-bae6-9b830ce25370 https://w3id.org/ro-id/136ded1a-6eba-470a-bd33-d741aee77ada https://w3id.org/ro-id/6b83b885-0507-4e13-b35c-7aa124395fc2 https://w3id.org/ro-id/3866bcdc-c291-40bf-b3cc-63b22d75e3d5 https://w3id.org/ro-id/386c8b2a-a98e-45da-b12a-8aa8d05d4e3b https://w3id.org/ro-id/6102d84d-2021-4d87-8ef7-17da4930646b https://w3id.org/ro-id/6a7f5310-1a78-453d-ab8b-a5c9b320a4f6 https://w3id.org/ro-id/84271559-7e77-418f-a01c-b38b283b6183 https://w3id.org/ro-id/9cb825ce-e452-4549-abe2-2fdb59ae48b6 https://w3id.org/ro-id/ca4eaea0-0558-4095-b77f-cd7fc8bd1bfa https://w3id.org/ro-id/2c11942c-9936-43aa-a307-fed88ea783c4 https://w3id.org/ro-id/456025f5-a651-498b-9f0a-15e56cd985bc https://w3id.org/ro-id/0980e44a-005d-4804-8a6f-cb82bd6123e9 https://w3id.org/ro-id/75705433-1cc5-403c-baec-1bcd78a366a7 https://w3id.org/ro-id/91973553-ac02-4802-b419-47e6a54e2c06 https://w3id.org/ro-id/9760a79e-83ec-4e0a-9ee6-eb5ed95998c7 https://w3id.org/ro-id/f139b59a-eda3-4aab-ace7-68ad65bc1443 https://w3id.org/ro-id/c7fe0e0f-b774-44ab-9d23-4ef5608890c4 https://w3id.org/ro-id/f8b11e82-83e5-4022-ad0e-7f1229396a5c https://w3id.org/ro-id/fcc1bbf5-28df-4f6e-b562-bc9fa809d641 Castellan, Giorgio. "Analysis from satellite data – Environmental monitoring from space." ROHub. Dec 14 ,2021. https://doi.org/10.5281/zenodo.6383036. Discover, subset, download and visualize satellite data stored in a Data Cube from the ADAM Platform Method Results Results Satellite data on Chl-a and Kd490 Satellite_data 68452 https://api.rohub.org/api/resources/38441693-b827-4108-b4d5-b0d854030c88/download/ 2021-12-14 14:32:01.240770+00:00 2022-03-24 18:42:53.767383+00:00 image/png Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea 2021-12-14 14:32:01.240770+00:00 449579 https://api.rohub.org/api/resources/67b0bcab-22ec-4e02-8f28-37670338943c/download/ 2021-12-14 14:38:21.837867+00:00 2022-03-24 18:42:50.464712+00:00 image/jpeg Analysis from satellite data – Environmental monitoring from space during COVID-19 lockdown 2021-12-14 14:38:21.837867+00:00 https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7 2021-12-14 10:44:17.433059+00:00 2022-03-24 18:42:54.929213+00:00 Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown 2021-12-14 10:44:17.433059+00:00 70005 https://api.rohub.org/api/resources/85c7ebc3-6c32-4573-a828-96044eb3f9a2/download/ 2021-12-14 14:33:06.849172+00:00 2022-03-24 18:42:52.754796+00:00 image/png Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea 2021-12-14 14:33:06.849172+00:00 https://w3id.org/ro-id/34d648b3-0014-4a19-8469-40b9380ca4c3 2021-12-14 10:44:48.894463+00:00 2022-03-24 18:42:51.867845+00:00 Discover and subset satellite data from the ADAM Platform Discover and subset satellite data from the ADAM Platform 2021-12-14 10:44:48.894463+00:00 geosciences 100.0 0.4130299687385559 environmental monitoring 11.413748378728926 8.8 analysis 13.618677042801558 10.5 result 4.735376044568246 3.4 water 11.142061281337048 8.0 geophysics 100.0 0.4130299687385559 lockdown 15.459610027855154 11.1 clarity 12.5810635538262 9.7 collection 11.932555123216602 9.2 Satellite technology Economy, business and finance/Economic sector/Computing and information technology/Satellite technology environmental monitoring from space 11.598746081504702 11.1 satellite data 23.47600518806745 18.1 effects of COVID-19 lockdown 15.256008359456635 14.6 Adriatic Sea 11.142061281337048 8.0 analysis from satellite data 17.03239289446186 16.3 lockdown 14.785992217898833 11.4 analysis 14.902506963788301 10.7 environmental monitoring 11.420612813370472 8.2 Analysis from satellite data – 22.02202202202202 22.0 covid 19 12.191958495460442 9.4 clarity 12.95264623955432 9.3 analysis of satellite data 25.705329153605014 24.6 Environmental monitoring from space. 8.708708708708707 8.7 Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea 69.26926926926926 69.2 Music Classification Study musical genre classification music classification study musical genre classification television musical genre classification warning messsage education Linux http musical genre feature classification Java lib 3 audio install classification by ensemble Music libraries in the lib Taverna Workbench 2.3.0 from http user taverna installation Java Classification version directory release ensemble musical genre classification by ensemble lyrics feature classification by ensembles of audio and lyrics feature service-account-enrichment http://sandbox.rohub.org/rodl/ROs/musicStudy-5/ 2014-07-29T15:05:31.747+02:00 https://www.google.com/accounts/o8/id?id=AItOawl6miGQ2NYnbP2-gtGZcqRkDRukz5GNfGc 4451939 https://api.rohub.org/api/ros/0741085b-b411-4b53-b00e-1311fecc8410/crate/download/ 2014-07-29 12:47:19.237000+00:00 2025-03-05 01:04:17.160177+00:00 2014-07-29 12:47:19.237000+00:00 Musical genre classification by ensembles of audio and lyrics features application/ld+json https://w3id.org/ro-id/0741085b-b411-4b53-b00e-1311fecc8410 Music Classification Study Raul Palma. "Music Classification Study." ROHub. Jul 29 ,2014. https://w3id.org/ro-id/0741085b-b411-4b53-b00e-1311fecc8410. used workflows setup produced main config scripts components nested lib results web services datasets inputs software biblio 160008 https://api.rohub.org/api/resources/02e2583c-cdb9-4948-b919-be41adaab300/download/ 2014-07-29 13:04:04.741000+00:00 2022-03-25 08:54:50.057415+00:00 MusicClassification_WSDL-final.t2flow 2014-07-29 13:04:04.741000+00:00 Raul Palma service-account-generation-service Biosemantics memory epigenetic role deregulate in HD chromatin analysis deregulate in HD gene deregulation gene http research chromatin web service interpretation analysis workflow deregulation system HD participate in epigenetic process HD epigenetic process Genoa participate in epigenetic processes HD gene deregulation chromatin data interpretation epigenetic genetics anni web services research have an epigenetic role aim role information HD chromatin analysis Genoa have an epigenetic role service-account-enrichment http://sandbox.rohub.org/rodl/ROs/data_interpretation-2/ 2014-02-26T14:16:20.355+01:00 https://www.google.com/accounts/o8/id?id=AItOawlLcpRhy-5MtgIVFxuwLWcFFys5ZTC7w2c 81314 https://api.rohub.org/api/ros/d1b1d427-2332-428e-a205-726ac7a0e951/crate/download/ 2014-02-21 13:36:32.163000+00:00 2025-03-05 00:48:41.666693+00:00 2014-02-21 13:36:32.163000+00:00 <p>This research object, was created in order to further analyse and interpret the results from the research object http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/&nbsp; (HD chromatin analysis). The workflows in this research object are using the anni web services, implemented by the Biosemantics group.</p> application/ld+json https://w3id.org/ro-id/d1b1d427-2332-428e-a205-726ac7a0e951 HD data interpretation chromatin data interpretation Eleni Mina. "chromatin data interpretation." ROHub. Feb 21 ,2014. https://w3id.org/ro-id/d1b1d427-2332-428e-a205-726ac7a0e951. data_interpretation 30787 https://api.rohub.org/api/resources/0390ff51-55a1-4af3-b99c-60ef059d0afc/download/ 2014-02-25 16:10:46.463000+00:00 2022-03-25 09:07:52.840714+00:00 This workflow lists all IDs and descriptions of the predefined concept set List Predefined Concept Sets 2014-02-25 16:10:46.463000+00:00 188023 https://api.rohub.org/api/resources/45b81758-2d25-4c33-87c8-cb2968fbd849/download/ 2014-02-25 16:12:14.228000+00:00 2022-03-25 09:07:51.492625+00:00 This workflow can prioritize genes that are related to a specific concept, e.g. HTT. In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against Prioritize gene list related to a concept /list of concepts 2014-02-25 16:12:14.228000+00:00 63 https://api.rohub.org/api/resources/7a3f5ba3-b292-4c80-b0a1-76a15b20e50c/download/ 2014-02-25 16:03:44.993000+00:00 2022-03-25 09:07:53.677489+00:00 text/plain hypothesis.txt 2014-02-25 16:03:44.993000+00:00 203555 https://api.rohub.org/api/resources/7aa50a5a-bc9a-4639-9e04-1fecc9bb5002/download/ 2014-02-25 16:06:54.756000+00:00 2022-03-25 09:07:55.632943+00:00 This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. "5" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services Annotate a gene list with Biological processes 2014-02-25 16:06:54.756000+00:00 69369 https://api.rohub.org/api/resources/99ba79b8-8172-4026-93b2-affd7320122f/download/ 2014-02-26 13:14:06.663000+00:00 2022-03-25 09:07:56.525975+00:00 This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group). This workflow can be used together with other workflows in this pack: http://www.myexperiment.org/packs/282 for functional gene and SNP annotation and knowledge discovery. Explain concept scores 2014-02-26 13:14:06.663000+00:00 67 https://api.rohub.org/api/resources/da150b5d-9fd0-46fc-9745-06460dbed48e/download/ 2014-02-25 16:03:12.283000+00:00 2022-03-25 09:07:49.577479+00:00 text/plain conclusions.txt 2014-02-25 16:03:12.283000+00:00 41692 https://api.rohub.org/api/resources/dcaecdee-b61a-4d22-9610-be3770345a8e/download/ 2014-02-25 16:09:23.429000+00:00 2022-03-25 09:07:54.760474+00:00 This workflow suggests concept ids that match the query term. The user can run this workflow with any term of interest as for example "human", "htt", "Transcription" etc, and will get suggestions for concept ids together with descriptions. Then can choose the concept id that matches the best to her/his needs and use it to the rest of the CPA workflows Get concept suggestions from term 2014-02-25 16:09:23.429000+00:00 39476 https://api.rohub.org/api/resources/f34bc6b5-f6ab-41e7-b2fe-bd83df7041b6/download/ 2014-02-25 16:04:13.823000+00:00 2022-03-25 09:07:50.657598+00:00 Sketch of the workflows and their explanation, for data interpretation, plus the connection to the output from the RO for chromatin analysis image/png workflow sketch data interpretation 2014-02-25 16:04:13.823000+00:00 Eleni Mina Eleni Mina service-account-generation-service D3.1: Workflow Evolution, Sharing and Collaboration Initial Requirements Taverna 2.4 HYPERLEDA. I. Identification and designation of galaxies D4.1: Workflow Integrity and Authenticity Maintenance Initial Requirements D2.1 Workflow Lifecycle Management Initial Requirements Virtual Observatory activities in the AMIGA group D1.2: Wf4Ever Sandbox v1 Python Topcat Capabilities of the HYPERLEDA database from. to. forSb galaxy logr25 tool session Oxford University Lawrence property error value Amiga data revision mathematics United Kingdom astronomy Verley AMIGAsample Poznan Poland Oxford appendix B research object structure Madrid NamesLEDA.txt Leiden Hereafter Edinburgh axis ratio dust extinction coefficient deployment of a Research Object cig SDSS sample Colorado physics lumi nous galaxy Karachentseva HyperLEDA database Netherlands Calzetti statistics galaxy database research data sample red shift text file AMIGA FP ICT workflow value correction opticalAGN ratio information and communication technologies value optical luminosity in B-band luminosity e mail address CIG output Enrique Ruiz Minor Red Cross interactions galaxy University of Manchester Naciones evolution galaxy property mag research object management university inAMIGA Granada Alaska Manchester property value group database Vulkan Shmidta León colors SDSS workflow building sample type coordinate propagation Montenegro appendices database property value error calculation galaxy inclination galaxies galaxy galaxy sample sc galaxy type galaxies inAMIGA Boadilla del Monte propagation of property assigned galaxy type British Telecom evolution galaxy error of the coordinate AKwas Ai AK AGN IAA Pozna Mpc Sect. Spain Golden Exemplar service-account-enrichment http://sandbox.rohub.org/rodl/ROs/Pack585/ 2014-01-27T17:31:15.131+01:00 https://www.google.com/accounts/o8/id?id=AItOawnZHKwZJglv16YBiUjsWEnx39mHA0RB250 http://w3id.org/ro-id/rohub/model#change_specifications/d3cea99d-3ccd-489d-88d2-97c0392888ff 2908927 https://api.rohub.org/api/ros/9faa4bd7-7c10-4a60-a418-b9dc338d966d/crate/download/ 2014-01-24 15:04:31.757000+00:00 2025-03-05 01:16:58.682001+00:00 2014-01-24 15:04:31.757000+00:00 The scientific experiment represented by this research object pertains to the multi-wavelength study for a sample of the most isolated galaxies in the local universe. This study characterizes each galaxy of this sample through both the measurement of basic astrophysical properties:  - The equatorial coordinates in J2000 epoch - The velocities in km/s (v) - The dust extinction coefficient (ag) - The axis ratio of the isophote 25 mag/arcsec2 (logr25) - The apparent total B magnitude (BT) - The morphological type (t) and the calculation of the more complex properties: - The distance in Mega parsecs (D) - The corrected apparent B magnitude (btc) - The optical luminosity in B-band (LB) Specifically, this research object is focused on the calculation of the intrinsic luminosity in the Johnson B-band, in order to achieve it the measurement or calculation of all those astrophysical properties is needed. All the data involved in the characterization of the sample are stored in a local relational MySQL database. To maintain up to date this database and to register all the updates properly is part of this scientific experiment. application/ld+json https://w3id.org/ro-id/9faa4bd7-7c10-4a60-a418-b9dc338d966d Propagation of properties extracted from the HyperLEDA catalog in the calculation of luminosities of galaxies Design Sketch and Experiment hypothesis will improve readability. Consider adding these elements. Please, take into account Hubble constant value in the determination of distances The Hubble constant value has been considered in the script calculateDistance.py http://sandbox.rohub.org/rodl/ROs/Pack585-snapshot/ Jose Enrique Ruiz. "Propagation of properties extracted from the HyperLEDA catalog in the calculation of luminosities of galaxies." ROHub. Jan 24 ,2014. https://w3id.org/ro-id/9faa4bd7-7c10-4a60-a418-b9dc338d966d. used nested config produced scripts software datasets main root results workflow_runs components web_services inputs setup biblio workflows NamesLEDA.txt 733453 https://api.rohub.org/api/resources/04ff3d95-a0d3-4f66-80ad-637d0ded4591/download/ 2014-01-24 15:05:08.250000+00:00 2022-03-25 09:12:12.261743+00:00 Gathering galaxy properties using Hyperleda 2014-01-24 15:05:08.250000+00:00 132151 https://api.rohub.org/api/resources/146edf59-bb21-4430-9917-97cbae988a60/download/ 2014-01-24 15:06:27.574000+00:00 2022-03-25 09:12:00.536760+00:00 application/x-sql bt.sql 2014-01-24 15:06:27.574000+00:00 212329 https://api.rohub.org/api/resources/1d94ef57-ed0c-40b8-9dc9-7fbbc554024b/download/ 2014-01-24 15:06:24.130000+00:00 2022-03-25 09:11:51.326045+00:00 Propagation of physical quantities in the calculation of luminosities of galaxies 2014-01-24 15:06:24.130000+00:00 39043 https://api.rohub.org/api/resources/20ae1815-26a8-4f8c-8ed3-7fba7878bacc/download/ 2014-01-24 15:05:33.094000+00:00 2022-03-25 09:11:47.329135+00:00 text/plain LB3d.txt 2014-01-24 15:05:33.094000+00:00 2948908 https://api.rohub.org/api/resources/21bec57a-00f8-42c6-9d97-da774d800870/download/ 2014-01-24 15:05:31.608000+00:00 2022-03-25 09:11:59.657024+00:00 session.vot 2014-01-24 15:05:31.608000+00:00 morphoNew.txt 887528 https://api.rohub.org/api/resources/2c82e292-ddf9-4def-be1e-f24354a9baef/download/ 2014-01-24 15:05:59.044000+00:00 2022-03-25 09:12:08.867311+00:00 Calculation of distances, magnitutes, and luminosities using Hyperleda 2014-01-24 15:05:59.044000+00:00 1383 https://api.rohub.org/api/resources/37eb901f-e7d0-4edf-8177-5d7f896f9fc1/download/ 2014-01-24 15:05:30.052000+00:00 2022-03-25 09:11:38.384866+00:00 Not working properly with break lines in linux formatted files text/x-python comparing.py 2014-01-24 15:05:30.052000+00:00 142994 https://api.rohub.org/api/resources/5356d55f-09e2-4a0d-af72-07db00734fb8/download/ 2014-01-24 15:04:55.083000+00:00 2022-03-25 09:11:58.122018+00:00 application/x-sql velocity.sql 2014-01-24 15:04:55.083000+00:00 112823 https://api.rohub.org/api/resources/56c5ce79-aa3a-4556-a999-7bc22c03cd0d/download/ 2014-01-27 16:30:26.554000+00:00 2022-03-25 09:11:31.641822+00:00 image/png Schema.png 2014-01-27 16:30:26.554000+00:00 1914881 https://api.rohub.org/api/resources/579dd361-ddf5-45e4-b658-83f1498f4d5a/download/ 2014-01-24 15:05:54.932000+00:00 2022-03-25 09:12:06.825330+00:00 application/pdf D5.3v1: Propagation of interdependent quantities in the calculation of luminosities of galaxies 2014-01-24 15:05:54.932000+00:00 9865 https://api.rohub.org/api/resources/63fd9bb1-4138-4c6b-9629-b5c8fadd142c/download/ 2014-01-24 15:07:10.437000+00:00 2022-03-25 09:11:54.525445+00:00 text/plain RECIPES 2014-01-24 15:07:10.437000+00:00 143737 https://api.rohub.org/api/resources/64994d23-fa5b-484e-b25d-408fb4b21dfa/download/ 2014-01-24 15:05:13.745000+00:00 2022-03-25 09:11:57.237250+00:00 application/x-sql lb.sql 2014-01-24 15:05:13.745000+00:00 8407 https://api.rohub.org/api/resources/7b6f3e2d-34d7-4f12-aed7-e6f522be5a39/download/ 2014-01-24 15:05:15.222000+00:00 2022-03-25 09:12:05.958985+00:00 text/plain NamesLEDA.txt 2014-01-24 15:05:15.222000+00:00 25 https://api.rohub.org/api/resources/84c8166d-8155-41cd-9797-c764ab6e14a9/download/ 2014-01-24 15:04:57.259000+00:00 2022-03-25 09:11:52.208650+00:00 text/plain local.txt 2014-01-24 15:04:57.259000+00:00 145314 https://api.rohub.org/api/resources/99b25137-d453-482b-9531-b478bf886467/download/ 2014-01-24 15:07:01.927000+00:00 2022-03-25 09:11:53.051062+00:00 application/x-sql btc.sql 2014-01-24 15:07:01.927000+00:00 157378 https://api.rohub.org/api/resources/a1df3dff-67e1-406d-a539-8de4ad182b12/download/ 2014-01-24 15:06:00.661000+00:00 2022-03-25 09:12:02.410522+00:00 application/x-sql distances.sql 2014-01-24 15:06:00.661000+00:00 15994 https://api.rohub.org/api/resources/a5f8f3fa-66bd-41a3-ad9f-b4e430159ce8/download/ 2014-01-24 15:06:45.058000+00:00 2022-03-25 09:12:10.525314+00:00 text/plain morphoNew.txt 2014-01-24 15:06:45.058000+00:00 138238 https://api.rohub.org/api/resources/df7721a9-ff51-4ace-9b13-8d922181fc43/download/ 2014-01-24 15:04:59.351000+00:00 2022-03-25 09:12:11.380781+00:00 application/x-sql logr25.sql 2014-01-24 15:04:59.351000+00:00 remote.txt 42662 https://api.rohub.org/api/resources/df86ccfe-ff14-48b8-9034-8776c3367931/download/ 2014-01-24 15:06:03.290000+00:00 2022-03-25 09:12:07.963354+00:00 Comparison and update of values 2014-01-24 15:06:03.290000+00:00 25 https://api.rohub.org/api/resources/e17d348f-a004-4d58-831c-a565ff489bc6/download/ 2014-01-24 15:07:15.314000+00:00 2022-03-25 09:12:03.503387+00:00 text/plain remote.txt 2014-01-24 15:07:15.314000+00:00 501548 https://api.rohub.org/api/resources/e88d881d-7085-49ee-8163-278925cbaff4/download/ 2014-01-24 15:06:10.435000+00:00 2022-03-25 09:11:49.550169+00:00 application/pdf The AMIGA sample of isolated galaxies X. A first look at isolated galaxy colors 2014-01-24 15:06:10.435000+00:00 Jose Enrique Ruiz service-account-generation-service D3.1: Workflow Evolution, Sharing and Collaboration Initial Requirements Taverna 2.4 HYPERLEDA. I. Identification and designation of galaxies D4.1: Workflow Integrity and Authenticity Maintenance Initial Requirements D2.1 Workflow Lifecycle Management Initial Requirements Virtual Observatory activities in the AMIGA group D1.2: Wf4Ever Sandbox v1 Python Topcat Capabilities of the HYPERLEDA database from. to. forSb galaxy logr25 tool session Oxford University Lawrence property error value Amiga data revision mathematics United Kingdom astronomy Verley AMIGAsample Poznan Poland Oxford appendix B research object structure Madrid NamesLEDA.txt Leiden Hereafter Edinburgh axis ratio dust extinction coefficient deployment of a Research Object cig SDSS sample Colorado physics lumi nous galaxy Karachentseva HyperLEDA database Netherlands Calzetti statistics galaxy database research data sample red shift text file AMIGA FP ICT workflow value correction opticalAGN ratio information and communication technologies value optical luminosity in B-band luminosity e mail address CIG output Enrique Ruiz Minor Red Cross interactions galaxy University of Manchester Naciones evolution galaxy property mag research object management university inAMIGA Granada Alaska Manchester property value group database Vulkan Shmidta León colors SDSS workflow building sample type coordinate propagation Montenegro appendices database property value error calculation galaxy inclination galaxies galaxy galaxy sample sc galaxy type galaxies inAMIGA Boadilla del Monte propagation of property assigned galaxy type British Telecom evolution galaxy error of the coordinate AKwas Ai AK AGN IAA Pozna Mpc Sect. Spain Golden Exemplar service-account-enrichment http://sandbox.rohub.org/rodl/ROs/Pack585/ 2014-01-27T17:30:51.771+01:00 https://www.google.com/accounts/o8/id?id=AItOawnZHKwZJglv16YBiUjsWEnx39mHA0RB250 2908816 https://api.rohub.org/api/ros/12f54724-cd7f-4410-b788-3c062fc644f7/crate/download/ 2014-01-24 15:04:31.757000+00:00 2025-03-05 01:16:58.921026+00:00 2014-01-24 15:04:31.757000+00:00 The scientific experiment represented by this research object pertains to the multi-wavelength study for a sample of the most isolated galaxies in the local universe. This study characterizes each galaxy of this sample through both the measurement of basic astrophysical properties:  - The equatorial coordinates in J2000 epoch - The velocities in km/s (v) - The dust extinction coefficient (ag) - The axis ratio of the isophote 25 mag/arcsec2 (logr25) - The apparent total B magnitude (BT) - The morphological type (t) and the calculation of the more complex properties: - The distance in Mega parsecs (D) - The corrected apparent B magnitude (btc) - The optical luminosity in B-band (LB) Specifically, this research object is focused on the calculation of the intrinsic luminosity in the Johnson B-band, in order to achieve it the measurement or calculation of all those astrophysical properties is needed. All the data involved in the characterization of the sample are stored in a local relational MySQL database. To maintain up to date this database and to register all the updates properly is part of this scientific experiment. application/ld+json https://w3id.org/ro-id/12f54724-cd7f-4410-b788-3c062fc644f7 Propagation of properties extracted from the HyperLEDA catalog in the calculation of luminosities of galaxies Design Sketch and Experiment hypothesis will improve readability. Consider adding these elements. Please, take into account Hubble constant value in the determination of distances The Hubble constant value has been considered in the script calculateDistance.py Jose Enrique Ruiz. "Propagation of properties extracted from the HyperLEDA catalog in the calculation of luminosities of galaxies." ROHub. Jan 24 ,2014. https://w3id.org/ro-id/12f54724-cd7f-4410-b788-3c062fc644f7. workflows datasets config used workflow_runs scripts components produced web_services main biblio nested software inputs results root setup local.txt 42662 https://api.rohub.org/api/resources/0f6a16b7-6aeb-4cca-ad41-92803445170f/download/ 2014-01-24 15:06:03.290000+00:00 2022-03-25 09:14:48.242239+00:00 Comparison and update of values 2014-01-24 15:06:03.290000+00:00 112823 https://api.rohub.org/api/resources/11edc8d2-b1df-4726-abe1-95fa73972bed/download/ 2014-01-27 16:30:26.554000+00:00 2022-03-25 09:14:14.355939+00:00 image/png Schema.png 2014-01-27 16:30:26.554000+00:00 212329 https://api.rohub.org/api/resources/1b88d3c0-92d4-4dfb-9ac5-c62b91bf69c3/download/ 2014-01-24 15:06:24.130000+00:00 2022-03-25 09:14:33.481796+00:00 Propagation of physical quantities in the calculation of luminosities of galaxies 2014-01-24 15:06:24.130000+00:00 9865 https://api.rohub.org/api/resources/23f6cea1-0f79-494b-8e6a-37247622766c/download/ 2014-01-24 15:07:10.437000+00:00 2022-03-25 09:14:36.396498+00:00 text/plain RECIPES 2014-01-24 15:07:10.437000+00:00 2948908 https://api.rohub.org/api/resources/4395a71c-5cfb-4502-bd08-18edc3a8993a/download/ 2014-01-24 15:05:31.608000+00:00 2022-03-25 09:14:41.038608+00:00 session.vot 2014-01-24 15:05:31.608000+00:00 1914881 https://api.rohub.org/api/resources/487428fb-373f-4e85-a451-ce191af27c39/download/ 2014-01-24 15:05:54.932000+00:00 2022-03-25 09:14:47.437302+00:00 application/pdf D5.3v1: Propagation of interdependent quantities in the calculation of luminosities of galaxies 2014-01-24 15:05:54.932000+00:00 157378 https://api.rohub.org/api/resources/4b95d337-bb57-4946-bb59-bf4fd6f3e44c/download/ 2014-01-24 15:06:00.661000+00:00 2022-03-25 09:14:43.657464+00:00 application/x-sql distances.sql 2014-01-24 15:06:00.661000+00:00 145314 https://api.rohub.org/api/resources/6897e959-a5eb-4192-9cca-fd51e08831c4/download/ 2014-01-24 15:07:01.927000+00:00 2022-03-25 09:14:35.138531+00:00 application/x-sql btc.sql 2014-01-24 15:07:01.927000+00:00 25 https://api.rohub.org/api/resources/8218a726-7aad-4a00-aa0c-45bf0248d19a/download/ 2014-01-24 15:04:57.259000+00:00 2022-03-25 09:14:34.317084+00:00 text/plain local.txt 2014-01-24 15:04:57.259000+00:00 142994 https://api.rohub.org/api/resources/87c7d0be-53dc-47f9-943d-873775aa84fd/download/ 2014-01-24 15:04:55.083000+00:00 2022-03-25 09:14:39.972234+00:00 application/x-sql velocity.sql 2014-01-24 15:04:55.083000+00:00 25 https://api.rohub.org/api/resources/a002b7dc-4a7c-4e1b-865c-ef8e1fc079b1/download/ 2014-01-24 15:07:15.314000+00:00 2022-03-25 09:14:44.487863+00:00 text/plain remote.txt 2014-01-24 15:07:15.314000+00:00 501548 https://api.rohub.org/api/resources/bce8f604-1df5-42b1-b34e-f26ccfa740a0/download/ 2014-01-24 15:06:10.435000+00:00 2022-03-25 09:14:31.790148+00:00 application/pdf The AMIGA sample of isolated galaxies X. A first look at isolated galaxy colors 2014-01-24 15:06:10.435000+00:00 NamesLEDA.txt 733453 https://api.rohub.org/api/resources/bf716724-d9fc-4310-996c-71b389a00aca/download/ 2014-01-24 15:05:08.250000+00:00 2022-03-25 09:14:52.421044+00:00 Gathering galaxy properties using Hyperleda 2014-01-24 15:05:08.250000+00:00 morphoNew.txt 887528 https://api.rohub.org/api/resources/c585ea92-5c82-4966-9df2-dc8c13b4645f/download/ 2014-01-24 15:05:59.044000+00:00 2022-03-25 09:14:49.313000+00:00 Calculation of distances, magnitutes, and luminosities using Hyperleda 2014-01-24 15:05:59.044000+00:00 138238 https://api.rohub.org/api/resources/d293675e-ef7e-4981-bfeb-a08c1c28eaf7/download/ 2014-01-24 15:04:59.351000+00:00 2022-03-25 09:14:51.438894+00:00 application/x-sql logr25.sql 2014-01-24 15:04:59.351000+00:00 15994 https://api.rohub.org/api/resources/d4938b1d-d4f5-4335-bf90-22cea3aecdaa/download/ 2014-01-24 15:06:45.058000+00:00 2022-03-25 09:14:50.521218+00:00 text/plain morphoNew.txt 2014-01-24 15:06:45.058000+00:00 132151 https://api.rohub.org/api/resources/d9f83555-5069-4e1a-94e1-4f391556a395/download/ 2014-01-24 15:06:27.574000+00:00 2022-03-25 09:14:41.873446+00:00 application/x-sql bt.sql 2014-01-24 15:06:27.574000+00:00 1383 https://api.rohub.org/api/resources/e4e89aac-4342-41c6-8b0a-ae7606dcfbd6/download/ 2014-01-24 15:05:30.052000+00:00 2022-03-25 09:14:21.123593+00:00 Not working properly with break lines in linux formatted files text/x-python comparing.py 2014-01-24 15:05:30.052000+00:00 39043 https://api.rohub.org/api/resources/eecc81f8-f194-448e-af6e-8705c3aff9c6/download/ 2014-01-24 15:05:33.094000+00:00 2022-03-25 09:14:29.690600+00:00 text/plain LB3d.txt 2014-01-24 15:05:33.094000+00:00 8407 https://api.rohub.org/api/resources/f00cdaba-9649-4117-8de6-3b48cb134bd9/download/ 2014-01-24 15:05:15.222000+00:00 2022-03-25 09:14:46.533968+00:00 text/plain NamesLEDA.txt 2014-01-24 15:05:15.222000+00:00 143737 https://api.rohub.org/api/resources/ffae12c4-dbfd-4f42-9708-164fe79505cc/download/ 2014-01-24 15:05:13.745000+00:00 2022-03-25 09:14:39.125914+00:00 application/x-sql lb.sql 2014-01-24 15:05:13.745000+00:00 Jose Enrique Ruiz service-account-generation-service http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/04caa16f-2cf8-4116-88c7-ea2f7e2c06f3 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/147acc3e-9a37-4d2f-b3ce-995d3317d9a2 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/22a7fda1-aeec-4720-9fec-47ff97b2eefe http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/279a00e5-280b-46b2-9bf9-5f33e60c389e http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/38b038fa-2e13-4bb6-8627-440aee9d53c8 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/4cebce3b-4fab-4020-a0e4-a5220b72a1cd http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/4e7d9e49-18c1-429c-a208-e00e41b4ce07 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/52b202e6-206d-4dcb-9b2f-80ddf6b783c0 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/5e7173c2-f3e8-4bc9-98b9-c2cb627f66f1 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/6487d154-1479-4721-bae3-28d63eb37fba http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/698f6b28-40cb-44de-a0b5-87d054681719 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/70ef65f1-fbf8-495c-9ff1-0144b0a49786 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/7555f2f8-6ba2-4993-b90a-1f89ab53c4f6 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/a19ca7be-e526-4ab0-bbd6-12e7313ca8ed http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/a62982a0-158f-4679-851d-1c0c989f9436 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/bc474904-2d2d-410d-9aad-a0e3129f0679 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/d6750f82-981b-411b-a1d6-1b2ecc5e4c49 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/e0457c72-b3ff-429c-a6e3-65ed6f5a9283 http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/f129e0fc-161e-4752-8876-da38688712dc http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/f3d060e3-a438-4e42-b8d4-bfb8275f6cdb http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5/changes/ff7a5d20-fad8-45c8-b3cf-c78bc889b1c3 f5b795c2-763e-4102-9df9-193691cd7bbf.rdf 78aecb3e-37ef-4f00-a649-668d0f418db4.rdf 97793295-96bf-4f5a-b640-25773d659e95.rdf http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/.ro/annotations/b7a5a570-222d-4592-b292-06037a1d42e4 http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/4077d50e-efb4-4061-95d0-6c4cc1198bfa.rdf 1f063ea3-83ac-4dd2-9b3c-a493bd3f842c.rdf http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/5d40cea9-e4f9-49bd-b5e4-ede288278a73.rdf annotations/9f4a5dbd-bce9-4216-a3ed-51a7aeb0ce8d annotations/fc6e78fc-77e5-40c3-9865-2df94702f59f http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/annotate_genes_biological_processes.t2flow annotations/b4acc451-7422-457a-b837-2826910122f9 annotations/f1c7adec-54d4-4e36-a186-56beb260b197 20d14edf-376d-4373-b7bd-5627ecf8e1a5.rdf annotations/ba8a48e9-5156-48c5-a639-e1e7e0a117ec http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/.ro/annotations/3b2e927c-c5d7-4b3e-b2b3-49ec67b2be00 annotate_genes_biological_processes_xpath_cpids.t2flow http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/.ro/annotations/093fc059-3d5a-436e-a0f0-2bf07ce8a30b annotations/6f077a8f-4bf0-4a0d-ac30-5d4dc48f970c http://sandbox.rohub.org/workflows/2725.html http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/c8d59b14-c3be-46d2-88ef-425e608a9d3d.rdf c254eb0d-bebf-4f31-8e76-09afa077dd31.rdf deregulate in HD HD participate in epigenetic processes genetics genes have an epigenetic role Purpose of workflow: This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group). Explain Scores missing link 5.304445274561076 14.2 life sciences (general) 35.87466373187602 0.9233048558235168 have an epigenetic role 0.06863417982155112 0.2 life sciences (general) 35.76177208527657 0.9203993678092957 chromatin 4.039375424304141 11.9 epigenetic process 17.84488675360329 52.0 role 5.491221516623086 14.7 epigenetic role 6.554564172958133 19.1 testing 2.545824847250509 7.5 participate in epigenetic process 0.20590253946465337 0.6 Genoa 20.570264765784113 60.6 epigenetic 1.5274949083503053 4.5 geology 47.691405491207696 0.9814894795417786 HD 3.9596563317146063 10.6 life sciences 28.363564182847412 0.7299919724464417 HD 8.146639511201629 24.0 chromatin 4.59469555472544 12.3 service-account-enrichment http://sandbox.rohub.org/rodl/ROs/data_interpretation/ 2014-02-11T16:28:33.504+01:00 https://www.google.com/accounts/o8/id?id=AItOawnOAeiyuU0cZ91YBD1EW7d43AlWw8xdALU http://w3id.org/ro-id/rohub/model#change_specifications/140a9ab3-c360-4ba4-ba8f-f4df6f060eb5 90137 https://api.rohub.org/api/ros/3a129ea9-7d50-4d4a-bcce-499729c5f3e0/crate/download/ 2013-08-30 16:15:52.229000+00:00 2025-03-05 00:55:18.105872+00:00 2013-08-30 16:15:52.229000+00:00 This RO is an extension of the HD chromatin analysis to interpret the results and reveal missing links that can according to literature explain HD gene deregulation application/ld+json https://w3id.org/ro-id/3a129ea9-7d50-4d4a-bcce-499729c5f3e0 Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/ http://sandbox.rohub.org/rodl/ROs/data_interpretation-snapshot/ https://w3id.org/ro-id/9472831c-c0f7-4736-abb7-1130b6266567 https://w3id.org/ro-id/0ccc06e8-fa83-4607-a51a-d3960f236f2a https://w3id.org/ro-id/1e86dc68-d7a4-4d76-b687-015183bfbbfc https://w3id.org/ro-id/2adb1139-d0a2-4e40-a706-d4193311c6df https://w3id.org/ro-id/2b8b4a6b-4fc5-4f60-8a3b-5798bd7aea12 https://w3id.org/ro-id/36273c7c-826e-426b-97e2-071775328169 https://w3id.org/ro-id/3d7af67d-d31a-4a39-85cf-da169fe3b217 https://w3id.org/ro-id/43eb0d5d-21e6-4061-ad5a-202d747f5c3d https://w3id.org/ro-id/4d53d73d-0372-466d-bccd-001707a529af https://w3id.org/ro-id/59f0f23a-584f-4b2d-87d3-197920b08374 https://w3id.org/ro-id/5a528e2e-c11d-4a5d-a145-b0dce36ee557 https://w3id.org/ro-id/60f9bf78-b591-4188-9863-6c3d8d9cba80 https://w3id.org/ro-id/61868dd9-a2b9-431b-b338-54b4dfff4c6d https://w3id.org/ro-id/6912d56b-a19d-4241-a6fa-7a5591bc976d https://w3id.org/ro-id/7ea94e74-e41d-4867-ab9a-ad911e8f46eb https://w3id.org/ro-id/abde2abd-9c38-421d-a6ea-c99d13195606 https://w3id.org/ro-id/bd06484b-72bb-415a-a5fb-070daf77db0e https://w3id.org/ro-id/d3ab53c4-e61b-4db2-ac6f-1c9027d8b582 https://w3id.org/ro-id/e949bf8b-77ef-4da1-aeac-724b4448ce37 https://w3id.org/ro-id/ef101db2-8b39-4c03-8d81-d8bdc6886b26 https://w3id.org/ro-id/334ba840-d8d4-4c0d-b231-8408fd684615 https://w3id.org/ro-id/3ab8fcb4-b92d-4aad-9efb-048e2597b3c4 https://w3id.org/ro-id/61b3eebb-f4b4-478f-9bb1-be6ac3ff25e2 https://w3id.org/ro-id/6afdce1a-0994-4502-8cc1-ac663cdaa173 https://w3id.org/ro-id/c77eeaa0-6cbb-4eae-9730-17d13d8b508e https://w3id.org/ro-id/cc6d2248-5ba5-481b-bd6b-7be7eaa28110 https://w3id.org/ro-id/55c5275e-f220-4dd2-8e7f-ac8ddd54b9e4 https://w3id.org/ro-id/5d1d6480-6991-4fff-abc6-6c6d8fae6a6c https://w3id.org/ro-id/96636e8f-065e-48eb-bb8d-2eb72469e890 https://w3id.org/ro-id/d47a097c-6764-44a4-abcd-83c5d6b54302 https://w3id.org/ro-id/003d5516-496b-448e-be40-1a9fee828b33 https://w3id.org/ro-id/163a1d35-bb78-4c0d-8f84-825b71393fe0 https://w3id.org/ro-id/3381c808-cd31-49f7-ba8f-63e427655bdd https://w3id.org/ro-id/378be13f-1c57-4e19-bada-f51379af77eb https://w3id.org/ro-id/3a36972b-e7c4-421b-b5eb-7c59f5ac0d98 https://w3id.org/ro-id/52b5ecb5-1fd2-49f6-ae9d-73cf988a26f2 https://w3id.org/ro-id/81827075-1bdc-4069-b65f-c2ba1a3ae182 https://w3id.org/ro-id/86ba70a2-46d2-4500-8717-772f2011064e https://w3id.org/ro-id/8ef34ded-7d1a-4f69-a141-04d88f715dea https://w3id.org/ro-id/9a2f156f-bba1-44b1-85f9-e0e12bcac90b https://w3id.org/ro-id/abced32c-8c02-41af-8736-eef654f6d172 https://w3id.org/ro-id/aecb7a3f-c8fb-4b54-96d0-9d9846e82af8 https://w3id.org/ro-id/ce570653-b842-4017-823e-7b87339a3b69 https://w3id.org/ro-id/0182d2b6-0eb0-4178-b722-fa3d0ab62e43 https://w3id.org/ro-id/0c10fb4c-bf0b-42df-86e1-b01aac9925ca https://w3id.org/ro-id/35b95332-6876-4c12-9cff-2e01851a7d0c https://w3id.org/ro-id/4db8dcbb-0fec-42dc-9e76-ff2b75074831 https://w3id.org/ro-id/77956b14-fcc7-41f1-b9e0-15d0a5aadb21 https://w3id.org/ro-id/e7d9cd96-6b3f-4a49-bf12-3e1c238f2242 https://w3id.org/ro-id/054e977e-3849-4eb0-a305-72e7519b498c https://w3id.org/ro-id/0ffc2e27-e1fa-445c-8be6-d18de1e83a10 https://w3id.org/ro-id/1c15e09e-1005-4e9f-875d-88fc9d7ca868 https://w3id.org/ro-id/25ab1a9f-2b58-46ee-82b6-881a2151fb25 https://w3id.org/ro-id/5466d907-5a67-4665-bd54-b3d55a1275a2 https://w3id.org/ro-id/7ff67d11-e3fe-4e18-8a3e-3b8736918030 https://w3id.org/ro-id/b3c9e0a2-9e14-416e-b2dd-ef8440bac92f https://w3id.org/ro-id/c00f5093-2ca8-42b4-9786-0b35a4ef4a0e https://w3id.org/ro-id/c6355f07-2f22-4e41-8388-a2ff4f90a02f https://w3id.org/ro-id/e771ec0e-560b-4c5b-87b0-9436451f689b https://w3id.org/ro-id/ed3cfcbc-9ead-43b9-98c5-8267c1ca4aff https://w3id.org/ro-id/ee53e862-3f8f-498f-97ee-b20ad91894be https://w3id.org/ro-id/5c589b0d-0fd4-4e10-a949-254f733bad51 https://w3id.org/ro-id/a8bea726-82f1-4049-a93d-facd6d258724 https://w3id.org/ro-id/d80cc364-e913-48af-842d-45af832e8775 Eleni Mina. "Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/." ROHub. Aug 30 ,2013. https://w3id.org/ro-id/3a129ea9-7d50-4d4a-bcce-499729c5f3e0. data_interpretation 41573 https://api.rohub.org/api/resources/4264530f-42e9-45f0-9255-d232a3a558ac/download/ 2013-11-05 13:09:46.561000+00:00 2022-03-25 09:24:37.622734+00:00 Purpose of workflow: This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group). Explain Scores 2013-11-05 13:09:46.561000+00:00 67 https://api.rohub.org/api/resources/4e604e6a-6c92-414d-a49e-4e67a8b39116/download/ 2013-08-31 15:11:30.970000+00:00 2022-03-25 09:24:40.710743+00:00 text/plain conclusion 2013-08-31 15:11:30.970000+00:00 188023 https://api.rohub.org/api/resources/55c4a47f-eb4d-4461-aaf3-f718a38bc803/download/ 2013-11-05 13:07:43.493000+00:00 2022-03-25 09:24:47.224634+00:00 This workflow can prioritize genes that are related to a specific concept, e.g. HTT. In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against Prioritize gene list related to a concept /list of concepts 2013-11-05 13:07:43.493000+00:00 214639 https://api.rohub.org/api/resources/5872d7d2-57d6-4b92-854a-815c2dc665b4/download/ 2014-02-11 15:25:01.110000+00:00 2022-03-25 09:24:49.007856+00:00 This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. "5" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services Annotate a gene list with Biological processes 2014-02-11 15:25:01.110000+00:00 39476 https://api.rohub.org/api/resources/7a77bf73-c2d0-41ca-8a13-b91c31de14d8/download/ 2013-08-31 15:37:06.058000+00:00 2022-03-25 09:24:39.842471+00:00 Sketch of the workflows and their explanation, for data interpretation, plus the connection to the output from the RO for chromatin analysis image/png workflow sketch data interpretation 2013-08-31 15:37:06.058000+00:00 63 https://api.rohub.org/api/resources/97a1ffa9-8d56-4499-827b-8d4e2d313fbc/download/ 2013-08-31 15:04:48.724000+00:00 2022-03-25 09:24:28.805556+00:00 text/plain hypothesis.txt 2013-08-31 15:04:48.724000+00:00 30787 https://api.rohub.org/api/resources/ab98dfb6-be33-4c6d-bb69-1ecccdac03b8/download/ 2013-11-05 13:09:01.493000+00:00 2022-03-25 09:24:44.673277+00:00 This workflow lists all IDs and descriptions of the predefined concept set ListPredefinedConceptSets 2013-11-05 13:09:01.493000+00:00 41692 https://api.rohub.org/api/resources/f0c4d8f6-4ef6-43bd-806f-f6ee04fe6cbf/download/ 2013-08-31 15:02:18.108000+00:00 2022-03-25 09:24:48.176013+00:00 This workflow suggests concept ids that match the query term. The user can run this workflow with any term of interest as for example "human", "htt", "Transcription" etc, and will get suggestions for concept ids together with descriptions. Then can choose the concept id that matches the best to her/his needs and use it to the rest of the CPA workflows Get concept suggestions from term 2013-08-31 15:02:18.108000+00:00 gene 3.399327605528577 9.1 earth sciences 47.691405491207696 0.9814894795417786 role 5.363204344874406 15.8 missing link 4.514596062457569 13.3 analyzation 1.4596062457569585 4.3 life sciences 35.76177208527657 0.9203993678092957 HD 4.632050803137841 12.4 analysis of Ro 3.843514070006863 11.2 Animal Human interest/Animal system 3.6320434487440596 10.7 deregulation 9.911744738628649 29.2 Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/. This RO is an extension of the HD chromatin analysis to interpret the results and reveal missing links that can according to literature explain HD gene deregulation 33.333333333333336 100.0 Economic policy Economy, business and finance/Economy/Economic policy HD 3.8696537678207736 11.4 deregulation 3.598099117447386 10.6 geochemistry 27.692441186701984 0.5699106454849243 hard drive 4.107264086897488 12.1 earth sciences 27.692441186701984 0.5699106454849243 life sciences 35.87466373187602 0.9233048558235168 epigenetic 1.357773251866938 4.0 HD gene deregulation 11.358956760466711 33.1 deregulation 11.31864026895779 30.3 analysis 3.0257751214045574 8.1 Genoa 25.476279417258127 68.2 linguistics 100.0 7.7 Economic policy Economy, business and finance/Economy/Economic policy HD 8.06873365707882 21.6 genes involved in HD gene deregulation have an epigenetic role 33.333333333333336 100.0 deregulation 4.333208815838626 11.6 http 2.0706042090970804 6.1 gene 14.60590212924916 39.1 gene deregulation 1.8188057652711047 5.3 Ro 4.989816700610998 14.7 chromatin analysis 0.857927247769389 2.5 HD gene deregulation 25.806451612903224 75.2 geology 24.616153322090323 0.5066006183624268 earth sciences 24.616153322090323 0.5066006183624268 Ro 5.790063503922301 15.5 outcome 1.2219959266802443 3.6 Language Arts, culture and entertainment/Culture/Language Genes deregulated in HD, are participating in epigenetic processes 33.333333333333336 100.0 HD chromatin analysis 14.13864104323953 41.2 life sciences (general) 28.363564182847412 0.7299919724464417 gene 13.06856754921928 38.5 results from the analysis 1.3040494166094714 3.8 deregulate in HD 16.197666437886067 47.2 gene 2.919212491513917 8.6 Eleni Mina service-account-generation-service involve in HD gene deregulation HD participate in epigenetic processes genetics genes have an epigenetic role Purpose of workflow: This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group). Explain Scores testing 2.545824847250509 7.5 HD 8.06873365707882 21.6 life sciences (general) 35.87466373187602 0.9233048558235168 hard drive 4.107264086897488 12.1 http 2.0706042090970804 6.1 analysis of Ro 3.843514070006863 11.2 Ro 5.790063503922301 15.5 Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/. This RO is an extension of the HD chromatin analysis to interpret the results and reveal missing links that can according to literature explain HD gene deregulation 33.333333333333336 100.0 genes involved in HD gene deregulation have an epigenetic role 33.333333333333336 100.0 deregulation 4.333208815838626 11.6 gene deregulation 1.8188057652711047 5.3 gene 2.919212491513917 8.6 Ro 4.989816700610998 14.7 system 3.6320434487440596 10.7 earth sciences 24.616153322090323 0.5066006183624268 earth sciences 27.692441186701984 0.5699106454849243 HD 4.632050803137841 12.4 epigenetic 1.357773251866938 4.0 geology 47.691405491207696 0.9814894795417786 role 5.491221516623086 14.7 results from the analysis 1.3040494166094714 3.8 Economic policy Economy, business and finance/Economy/Economic policy deregulate in HD 16.197666437886067 47.2 life sciences (general) 28.363564182847412 0.7299919724464417 gene 14.60590212924916 39.1 deregulation 11.31864026895779 30.3 life sciences (general) 35.76177208527657 0.9203993678092957 epigenetic 1.5274949083503053 4.5 geology 24.616153322090323 0.5066006183624268 epigenetic process 17.84488675360329 52.0 service-account-enrichment http://sandbox.rohub.org/rodl/ROs/data_interpretation/ 2014-02-11T16:00:29.893+01:00 https://www.google.com/accounts/o8/id?id=AItOawnOAeiyuU0cZ91YBD1EW7d43AlWw8xdALU 81525 https://api.rohub.org/api/ros/95a9036b-bee9-48fb-81eb-7e9a90014293/crate/download/ 2013-08-30 16:15:52.229000+00:00 2025-03-05 00:55:19.037479+00:00 2013-08-30 16:15:52.229000+00:00 This RO is an extension of the HD chromatin analysis to interpret the results and reveal missing links that can according to literature explain HD gene deregulation application/ld+json https://w3id.org/ro-id/95a9036b-bee9-48fb-81eb-7e9a90014293 Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/ https://w3id.org/ro-id/fa027cf1-adc0-4728-b7ff-e32e144b7729 https://w3id.org/ro-id/049c3132-1ee1-408a-a753-c66351b9e823 https://w3id.org/ro-id/10d69f16-88d4-4fef-b498-9ca9f03584d7 https://w3id.org/ro-id/12dafbe1-09f7-4107-8496-c513f6b97367 https://w3id.org/ro-id/2d7423e4-ac2b-4eb0-81d1-20c2dc99d330 https://w3id.org/ro-id/31088bb6-aac7-4307-9471-48da9891b2dc https://w3id.org/ro-id/433dc648-a386-4922-917e-6ed05d3a97ff https://w3id.org/ro-id/64a17901-a541-4910-bc00-f7bb220ea386 https://w3id.org/ro-id/8c0cc9ba-6303-4cc6-a2ce-78e26ae86db7 https://w3id.org/ro-id/b55d00c3-b2c0-438e-a616-8b64a1273ff7 https://w3id.org/ro-id/bcb1bb76-fd8e-48f2-b107-553a5dcf0c5f https://w3id.org/ro-id/cb1fc5e8-971c-4f0e-bfb7-bc0095ca4e98 https://w3id.org/ro-id/cb39a646-e1d1-4303-a4b4-717b7c663702 https://w3id.org/ro-id/cfb98605-5a52-4da9-b744-e9a354a8fe8c https://w3id.org/ro-id/d30fb3af-cba6-402e-9916-fab7a97104a8 https://w3id.org/ro-id/d91ab93e-d975-4f2c-ae27-06033ff01bf8 https://w3id.org/ro-id/e74a5f9a-7b3b-4a0a-9c2d-e3f7aac43fba https://w3id.org/ro-id/eac3c9d6-6030-40e3-84f0-f55ff8e1c18c https://w3id.org/ro-id/ef870edf-e68d-4428-8a47-640c9c1cb3c4 https://w3id.org/ro-id/f59dceab-2927-465f-979e-2d937576206c https://w3id.org/ro-id/50ef50fa-5449-4de2-b41e-983587738d78 https://w3id.org/ro-id/5b1503b4-4a1f-4abc-a2e5-e7bc4401cd07 https://w3id.org/ro-id/722d8898-aedd-4f21-bdb8-db907db59e5b https://w3id.org/ro-id/9317e0fa-ec42-4a64-81be-7968b0420d7d https://w3id.org/ro-id/afb76586-4c0a-40e6-99e6-c99890200df0 https://w3id.org/ro-id/d182acc9-493a-4d75-9bd4-871e2555265e https://w3id.org/ro-id/7ad072ed-d161-4575-99a5-664cb4eb1b27 https://w3id.org/ro-id/97e1df9f-7ef9-4834-bfbb-dc8843faedab https://w3id.org/ro-id/99fe1429-cbac-4a80-ba71-23c0ce6664ee https://w3id.org/ro-id/dd80c5f0-30e7-46fa-8ec1-57c9c4ab0a29 https://w3id.org/ro-id/0dd15a1b-c0aa-455b-aa18-53b45dc979b4 https://w3id.org/ro-id/19e9bb94-844f-4a46-b361-5cf4e39ff804 https://w3id.org/ro-id/2651bc82-85b6-4992-b676-891270ca6fe1 https://w3id.org/ro-id/5dba2b2f-00f4-4162-9e62-2aa459ba88ea https://w3id.org/ro-id/74c8f2b9-8571-4629-8e0e-82a4b59670be https://w3id.org/ro-id/8611953b-d788-4e73-a8fb-bf132828997e https://w3id.org/ro-id/8941cdba-2596-4fcc-b71a-514afaf40ebc https://w3id.org/ro-id/9ba625ba-e500-4195-815b-54af2656d720 https://w3id.org/ro-id/abfdf7b4-b6dc-42ed-a544-c148ea552e3e https://w3id.org/ro-id/b7572fc6-6c64-4e00-a8d4-0f522fc90b1e https://w3id.org/ro-id/c283c1ef-c3f8-4703-a661-695109f19f88 https://w3id.org/ro-id/c2c10aca-8a73-442c-af9b-441ba68c1098 https://w3id.org/ro-id/dc837b23-a71d-4bd2-b2b4-080528e49fa9 https://w3id.org/ro-id/0ef41513-eaa9-4e76-994a-e90587f4069e https://w3id.org/ro-id/80f188fc-a15a-40e1-9843-303b0be67656 https://w3id.org/ro-id/8978ed0f-74e3-4ca4-bea3-c366b5ac4a27 https://w3id.org/ro-id/b8bcb4cf-ba2b-438b-a8e3-d9e460152a09 https://w3id.org/ro-id/cb11d942-fce9-459d-bfe2-4a4ed9d1fe1d https://w3id.org/ro-id/e24af000-2fbd-4820-bf60-bd01890f4b89 https://w3id.org/ro-id/15ba1d7a-c420-4e56-870a-99df018d8907 https://w3id.org/ro-id/2b8c0d56-d5b4-4689-a663-1752b449dc0a https://w3id.org/ro-id/78cd33c6-3425-4ecf-a833-00cf96855d21 https://w3id.org/ro-id/7f465c71-cf0c-4f7d-8406-3eaef5ad7c0b https://w3id.org/ro-id/93f1ab58-022a-4598-97f7-7807f93f2aa1 https://w3id.org/ro-id/9d438ec7-40ed-4892-a79f-86270050c4f8 https://w3id.org/ro-id/a852c4bf-efeb-436b-951e-b4dc59e1e4b7 https://w3id.org/ro-id/aa30e810-a7cf-4844-9c02-c5f2cb97b671 https://w3id.org/ro-id/af8a543b-f09a-4484-9a01-66fdcadbd631 https://w3id.org/ro-id/d6a3e45a-7fea-4bce-89ce-e0c2574d972f https://w3id.org/ro-id/e907a28b-718c-45b7-9531-0e556e167c0c https://w3id.org/ro-id/f8fb6331-272a-4818-ad9a-1fa334ce7606 https://w3id.org/ro-id/1b53e2a7-75e0-4b9a-97e0-c61af6f89d96 https://w3id.org/ro-id/1da386ca-5f4b-4458-9db0-2dfb98944d22 https://w3id.org/ro-id/fa01854e-8511-436f-9f73-5e8db98dec87 Eleni Mina. "Interpretation of the results from the analysis of RO: http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/." ROHub. Aug 30 ,2013. https://w3id.org/ro-id/95a9036b-bee9-48fb-81eb-7e9a90014293. data_interpretation 67 https://api.rohub.org/api/resources/207ea21e-4180-4f98-a6e9-9906672c0f49/download/ 2013-08-31 15:11:30.970000+00:00 2022-03-25 09:26:52.370638+00:00 text/plain conclusions.txt 2013-08-31 15:11:30.970000+00:00 30787 https://api.rohub.org/api/resources/2328b548-0032-4dfb-97dd-2892668282c5/download/ 2013-11-05 13:09:01.493000+00:00 2022-03-25 09:27:02.866468+00:00 This workflow lists all IDs and descriptions of the predefined concept set ListPredefinedConceptSets 2013-11-05 13:09:01.493000+00:00 41692 https://api.rohub.org/api/resources/574c5f85-a2ea-484f-92e6-f11fe184661d/download/ 2013-08-31 15:02:18.108000+00:00 2022-03-25 09:27:05.517990+00:00 This workflow suggests concept ids that match the query term. The user can run this workflow with any term of interest as for example "human", "htt", "Transcription" etc, and will get suggestions for concept ids together with descriptions. Then can choose the concept id that matches the best to her/his needs and use it to the rest of the CPA workflows Get concept suggestions from term 2013-08-31 15:02:18.108000+00:00 203555 https://api.rohub.org/api/resources/71f79523-cf5b-48fb-b7b9-92ec04636326/download/ 2013-11-05 13:07:17.887000+00:00 2022-03-25 09:27:03.799736+00:00 This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. "5" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services Annotate a gene list with Biological processes 2013-11-05 13:07:17.887000+00:00 39476 https://api.rohub.org/api/resources/a220e0d8-7cdd-40ba-a8a5-6b264086dde3/download/ 2013-08-31 15:37:06.058000+00:00 2022-03-25 09:26:58.184590+00:00 Sketch of the workflows and their explanation, for data interpretation, plus the connection to the output from the RO for chromatin analysis image/png workflow sketch data interpretation 2013-08-31 15:37:06.058000+00:00 188023 https://api.rohub.org/api/resources/a8461986-76a4-49b2-8153-4c899df13854/download/ 2013-11-05 13:07:43.493000+00:00 2022-03-25 09:27:04.663380+00:00 This workflow can prioritize genes that are related to a specific concept, e.g. HTT. In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against Prioritize gene list related to a concept /list of concepts 2013-11-05 13:07:43.493000+00:00 63 https://api.rohub.org/api/resources/e60c67e6-6657-4ddf-99ed-b47687ce684c/download/ 2013-08-31 15:04:48.724000+00:00 2022-03-25 09:26:50.571966+00:00 text/plain hypothesis.txt 2013-08-31 15:04:48.724000+00:00 Language Arts, culture and entertainment/Culture/Language Animal Human interest/Animal analysis 3.0257751214045574 8.1 have an epigenetic role 0.06863417982155112 0.2 participate in epigenetic process 0.20590253946465337 0.6 chromatin analysis 0.857927247769389 2.5 HD 3.9596563317146063 10.6 HD gene deregulation 11.358956760466711 33.1 geochemistry 27.692441186701984 0.5699106454849243 HD 3.8696537678207736 11.4 chromatin 4.59469555472544 12.3 life sciences 35.87466373187602 0.9233048558235168 missing link 4.514596062457569 13.3 Genoa 25.476279417258127 68.2 missing link 5.304445274561076 14.2 life sciences 35.76177208527657 0.9203993678092957 Genoa 20.570264765784113 60.6 gene 13.06856754921928 38.5 analyzation 1.4596062457569585 4.3 earth sciences 47.691405491207696 0.9814894795417786 chromatin 4.039375424304141 11.9 epigenetic role 6.554564172958133 19.1 deregulation 3.598099117447386 10.6 gene 3.399327605528577 9.1 Economic policy Economy, business and finance/Economy/Economic policy life sciences 28.363564182847412 0.7299919724464417 role 5.363204344874406 15.8 HD gene deregulation 25.806451612903224 75.2 outcome 1.2219959266802443 3.6 deregulation 9.911744738628649 29.2 HD 8.146639511201629 24.0 HD chromatin analysis 14.13864104323953 41.2 Genes deregulated in HD, are participating in epigenetic processes 33.333333333333336 100.0 linguistics 100.0 7.7 Eleni Mina service-account-generation-service gene deregulation Huntington's disease gene deregulation HepG Permuted HNF channel subunit cell change mRNAs encod ing proton channel subunit frontal cortex HD brain Johann Sebastian Bach aberrantprotein protein interaction United States of America New Hampshire unfolded protein response protein anatomy enrichment HD enhancers activity genetics caudate nucleus a number of mRNA mRNA change HepG HUVECHSMMNHLFNHEKHMEC chromatin genes motif cell type BA changes cell clusters state islands kB s disease brain disease mRNA disease 2.4802890932982917 15.1 cortices 1.7575558475689883 10.7 epigenetic phenomena 2.56078634247284 9.9 epigenetic dataset 1.0346611484738748 4.0 Genetics Science and technology/Natural science/Biology/Genetics genetics 19.45945945945946 43.2 caudate nucleus 1.3961892247043366 8.5 Economic policy Economy, business and finance/Economy/Economic policy Overall, the regional changes in gene expression areconsistent with the neuropathology in early grade HD, withcaudate being the most affected area, the cerebellum andBA cortex being relatively spared and the BA cortexshowing an intermediate pathology. 2.9443254817987152 11.0 Nova Scotia https://www.wikidata.org/wiki/Q1952 Ro 1.9077901430842608 7.2 cerebellum 1.5768725361366625 9.6 sample 1.806833114323259 11.0 experiment document 2.56078634247284 9.9 workflow 2.3582405935347115 8.9 recentneuroimaging data 1.5002586652871184 5.8 Mental and behavioural disorder Health/Diseases and conditions/Mental and behavioural disorder biology 6.126126126126126 13.6 chemistry 5.585585585585585 12.4 Diseases and conditions Health/Diseases and conditions gene 11.990801576872537 73.0 University of California, Berkeley https://www.wikidata.org/wiki/Q168756 biochemistry 17.34234234234234 38.5 Huntington's disease gene deregulation 44.43869632695292 171.8 Auckland City https://www.wikidata.org/wiki/Q758634 deregulation 9.937582128777924 60.5 Vinh Yên https://www.wikidata.org/wiki/Q36088 geophysics 5.460552638139745 0.20815198123455048 disease 2.888182299947006 10.9 Genetics Science and technology/Natural science/Biology/Genetics change 2.266754270696452 13.8 Mental and behavioural disorder Health/Diseases and conditions/Mental and behavioural disorder Hutchinson https://www.wikidata.org/wiki/Q958555 brain 1.192368839427663 4.5 Wales https://www.wikidata.org/wiki/Q25 geology 72.8531489829241 2.6746557652950287 epigenetic mechanism 1.810657009829281 7.0 Columbia University https://www.wikidata.org/wiki/Q49088 environmental sciences 27.1468510170759 0.9966416358947754 gene deregulation 2.8453181583031557 11.0 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical Huntington's disease 17.6205617382088 66.5 l Globin 1.5002586652871184 5.8 In addition we include all related to our experiment documents, papers and datasets 2.43576017130621 9.1 data comefrom 1.577858251422659 6.1 phenomenon 2.266754270696452 13.8 chicken chicken e Globin c Globin 0.6725297465080186 2.6 Huntington s disease (HD) pathology is well understood at a histological level but a comprehensivemolecular analysis of the effect of the disease in the human brain has not previously been available. 3.747323340471092 14.0 life sciences 94.53944736186025 3.603769540786743 Lausanne https://www.wikidata.org/wiki/Q807 medicine 23.73873873873874 52.7 earth sciences 72.8531489829241 2.6746557652950287 epigenetic information 2.1986549405069837 8.5 Animal Human interest/Animal Massachusetts https://www.wikidata.org/wiki/Q771 Cardiff https://www.wikidata.org/wiki/Q3398450 cerebellum 1.2718600953895072 4.8 mRNA 1.8018018018018018 6.8 Regional and cellular gene expression changes inhuman Huntington s disease brain 6.5310492505353315 24.4 Recently, regions with the sequence characteristics of CpG islands have been found associated with a number of genes, most of which are housekeeping genes (for reviews, see Cooper & Gerber Huber, ; Bird, ). 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"Analyzing gene derulation in Huntington's disease with respect to epigenetic information." ROHub. Aug 30 ,2013. https://w3id.org/ro-id/9c56e407-c5bc-45bd-8636-2f1066e10e75. workflows 91 https://api.rohub.org/api/resources/30874a46-cbfd-424f-aef2-1a674f1d09f9/download/ 2013-08-30 16:20:26.198000+00:00 2022-03-25 09:30:52.544644+00:00 text/plain conclusions.txt 2013-08-30 16:20:26.198000+00:00 2454579 https://api.rohub.org/api/resources/330e91b4-3166-4525-af4f-565788e021f9/download/ 2013-08-30 16:07:34.922000+00:00 2022-03-25 09:30:50.687347+00:00 application/pdf CpG_islandsINVertebrateGenomes.pdf 2013-08-30 16:07:34.922000+00:00 78 https://api.rohub.org/api/resources/394a58eb-e038-47cd-b533-5f4aa6c611a6/download/ 2013-08-30 16:18:12.111000+00:00 2022-03-25 09:30:57.894038+00:00 text/plain hypothesis_data_analysis.txt 2013-08-30 16:18:12.111000+00:00 1162315 https://api.rohub.org/api/resources/4b6d3538-a47a-40d8-8354-fa2c15b3db90/download/ 2013-08-30 16:08:44.993000+00:00 2022-03-25 09:30:56.214880+00:00 text/plain broad_hmm_1_Active_Promoter.txt 2013-08-30 16:08:44.993000+00:00 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"UNAVCO GPS Timeseries." ROHub. Sep 04 ,2017. https://doi.org/10.5072/ro-id.BXIILXKNNW. workflows config produced scripts biblio used nested components main setup 258680 https://api.rohub.org/api/resources/113e436b-d9b3-4ee8-a901-0f8256e26bfe/download/ 2017-05-23 14:58:35.829000+00:00 2022-03-25 15:24:06.450528+00:00 image/jpeg perm_mtolympus1.jpg 2017-05-23 14:58:35.829000+00:00 286993 https://api.rohub.org/api/resources/4570cc2d-8abe-4b2b-9b6b-ec3202b56e97/download/ 2017-05-23 17:16:54.207000+00:00 2022-03-25 15:24:07.383762+00:00 PDF GAGE_GPS_Analysis_Plan_20170315.pdf 2017-05-23 17:16:54.207000+00:00 42242 https://api.rohub.org/api/resources/466843f4-1012-4e98-b597-58fa4412aa91/download/ 2017-05-23 16:39:24.912000+00:00 2022-03-25 15:24:04.020562+00:00 PNG sequenceplotter2.png 2017-05-23 16:39:24.912000+00:00 42055 https://api.rohub.org/api/resources/9c009ab9-7679-44ef-b454-5da224af61c1/download/ 2017-05-23 16:38:24.493000+00:00 2022-03-25 15:24:08.700195+00:00 PNG sequenceplotter.png 2017-05-23 16:38:24.493000+00:00 http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short 2022-03-25 15:23:47.115178+00:00 2022-03-25 15:24:04.092028+00:00 http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short 2022-03-25 15:23:47.115178+00:00 44925 https://api.rohub.org/api/resources/b7e542c5-4dc8-46f6-8a61-9f1ec564e6c5/download/ 2017-05-23 16:40:01.455000+00:00 2022-03-25 15:24:09.614557+00:00 PNG sequenceplotter3.png 2017-05-23 16:40:01.455000+00:00 http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short sequenceplotter2.png 14182 https://api.rohub.org/api/resources/b99161f5-25de-48ce-801a-2c425da85a66/download/ 2017-05-01 19:55:49.788000+00:00 2022-03-25 15:24:10.714638+00:00 audio/midi UNAVCO workflow built around time series data web service 2017-05-01 19:55:49.788000+00:00 603474 https://api.rohub.org/api/resources/bcc9e892-0338-410e-a685-68b4f32d322b/download/ 2017-05-23 17:18:37.911000+00:00 2022-03-25 15:24:05.035080+00:00 PDF GAGE_Velocity_Release_Notes_20161230.pdf 2017-05-23 17:18:37.911000+00:00 earth sciences 27.293525495972574 0.6552424430847168 data from GPS station 8.620689655172413 11.0 UNAVCO GPS Timeseries 10.815047021943572 13.8 process 3.279220779220779 10.1 The process noise statistics are generated from the time series using the GAMIT/GLOBK script sh gen stats based on tsfit fits to the time series with the realistic sigma algorithm used to account for correlated noise. 11.786038077969176 13.0 CWU snx/cwu . .a.rms ../NMT snx/nmt . .a.rms Format Version : . . Release Date : Start Field Description Dot character identifier for a given station GAGE Number sec phase epochs in hours for combined RMS calculation PRMS Root mean square (RMS) scatter of combined phase residuals, mm CWU Number sec phase epochs in hours for CWU RMS calculation CRMS Root mean square (RMS) scatter of CWU phase residuals, mm NMT Number sec phase epochs in hours for NMT or BSL (prior to Feb ) RMS calculation NRMS Root mean square (RMS) scatter of NMT or BSL phase residuals, mm A Coefficient from model fit RMS (elev) A B /sin(elev) where elev is elevation angle, mm B Coefficient from model fit, mm GPSW GPS Week for hour processing day D GPS Day of week for hour processing day YYYYMMDD Year, month, day of month for hour processing day End Field Description Dot GAGE PRMS CWU CRMS NMT NRMS A B GPSW D YYYYMMDD NSU . . . . . ULM . . . . . ODM . . . . . AB . . . . . AB . . . . . ZME . . . . . ZMP . . . . . ZNY . . . . . ZSE . . . . . ZTL . . . . . 3.0825022665457844 3.4 noise 3.4595300261096606 5.3 system 2.3051948051948052 7.1 Scripps Orbit and Permanent Array Center 7.915360501567398 10.1 computer science 15.634218289085547 21.2 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical earthquake file 2.3510971786833856 3.0 communications and radar 41.26573884859473 0.6167286038398743 PBO GKNA PBO TSKF . . . . . . 1.4505893019038985 1.6 National Oceanic and Atmospheric Administration https://www.wikidata.org/wiki/Q214700 mathematics 14.896755162241888 20.2 gauge 4.6103896103896105 14.2 field 4.308093994778068 6.6 velocity 3.6363636363636362 11.2 coordinate file 2.7429467084639496 3.5 Global Geodetic Network 4.112271540469974 6.3 Software Economy, business and finance/Economic sector/Computing and information technology/Software PBO GKNA CWU TSKF 1.8808777429467083 2.4 PBO GKNA PbO 2.037617554858934 2.6 Civilian Conservation Corps https://www.wikidata.org/wiki/Q1094508 root mean square 3.4595300261096606 5.3 geology 41.24948833068723 0.9902867078781128 data 3.2142857142857144 9.9 reference frame 2.741514360313316 4.2 National Aeronautics and Space Administration 2.5974025974025974 8.0 coordinate system 3.7337662337662336 11.5 file 2.9545454545454546 9.1 linguistics 9.660766961651918 13.1 International GNSS Service 4.960835509138382 7.6 GLOBK SINEX combinations, GK ( ) time series analyses using weighted least squares (LS) and ( ) time series analyses using a Kalman filter of the time series (KF). The time series LS analysis is the one that generates the monthly GAGE SNAPSHOT fields. 1.5412511332728922 1.7 geosciences 43.04185052089376 0.643273115158081 UNAVCO 4.046997389033943 6.2 Musical instrument Arts, culture and entertainment/Arts and entertainment/Music/Musical instrument Southern California Integrated GPS Network 3.9164490861618804 6.0 Space programme Science and technology/Research/Scientific exploration/Space programme GPS 5.1566579634464755 7.9 standard deviation 2.93733681462141 4.5 file format 3.133159268929504 4.8 list 3.7206266318537864 5.7 Science and technology Science and technology PBO GKNA PBO GKI G 2.037617554858934 2.6 PbO 9.725848563968668 14.9 GKNA NMT GKNA 2.115987460815047 2.7 PBO GKNA PBO TSLS 3.2915360501567394 4.2 service-account-generation-service Jose Manuel Gomez Perez rms calculation NRMS root mean square mathematics scatter of NMT velocity site coordinate information PBO Central Washington University North America file naming Tom Herring GAGE GPS data analysis plan Analysis estimates geodetic products reference frame files sites mm year GPS data analysis method T. A. Musical instrument Arts, culture and entertainment/Arts and entertainment/Music/Musical instrument gage reference frame 2.3510971786833856 3.0 Jet Propulsion Laboratory https://www.wikidata.org/wiki/Q189325 geology 41.24948833068723 0.9902867078781128 GPS 5.1566579634464755 7.9 PbO 9.725848563968668 14.9 of the Aug-23-2011 netsel.use list 2.664576802507837 3.4 Treaty Politics/International relations/Diplomacy/Treaty Scripps Orbit and Permanent Array Center 7.915360501567398 10.1 list 3.7206266318537864 5.7 mathematics 14.896755162241888 20.2 geology 27.293525495972574 0.6552424430847168 GKNA NMT GKNA 2.115987460815047 2.7 earth sciences 27.293525495972574 0.6552424430847168 field 4.308093994778068 6.6 noise 3.149350649350649 9.7 Data from GPS stations not archived by UNAVCO are obtained from the NOAA National Geodetic Survey (NGS) Continuously Operating Reference Station (CORS) data center, the NASA Crustal Dynamics Data Information System (CDDIS), the U.S. Geological Survey, the Scripps Orbit and Permanent Array Center (SOPAC), and the International GNSS Service (IGS) 22.665457842248415 25.0 GLOBK SINEX combinations, GK ( ) time series analyses using weighted least squares (LS) and ( ) time series analyses using a Kalman filter of the time series (KF). The time series LS analysis is the one that generates the monthly GAGE SNAPSHOT fields. 1.5412511332728922 1.7 database 2.359882005899705 3.2 University Education/School/Higher education/University Global Geodetic Network 4.112271540469974 6.3 data 3.2142857142857144 9.9 The NMT RMS files also contain information on the elevation angle dependence for the phase residuals. 3.445149592021759 3.8 Cwu rms calculation CRMS root mean square 2.664576802507837 3.4 engineering 41.26573884859473 0.6167286038398743 Non-Aligned Movement https://www.wikidata.org/wiki/Q83201 gage GPS Data analysis plan 1.8808777429467083 2.4 NASA Global Geodetic Network 7.68025078369906 9.8 velocity 4.503916449086162 6.9 list 2.272727272727273 7.0 velocity 3.6363636363636362 11.2 system 2.3051948051948052 7.1 discontinuity 3.524804177545692 5.4 The GAGE GPS Analysis Centers process data from more than 2,000 GPS stations. 34.2701722574796 37.8 gauge 4.6103896103896105 14.2 University Education/School/Higher education/University root mean square 3.4595300261096606 5.3 The final entries PBO and PBO are the earlier and PBO full solution generated in November and . is the number of common sites in the solutions. 2.2665457842248413 2.5 mathematics 16.44542772861357 22.3 PBO GKNA CWU TSKF 1.8808777429467083 2.4 aeronautics 15.692410630511509 0.23452769219875336 file 2.9545454545454546 9.1 process 3.279220779220779 10.1 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical time series 4.765013054830288 7.3 noise 3.4595300261096606 5.3 UNAVCO 4.046997389033943 6.2 PBO GKNA PBO GKI G 2.037617554858934 2.6 Chur https://www.wikidata.org/wiki/Q69007 National Aeronautics and Space Administration 2.5974025974025974 8.0 Space programme Science and technology/Research/Scientific exploration/Space programme data from GPS station 8.620689655172413 11.0 National Aeronautics and Space Administration 4.308093994778068 6.6 Virginia https://www.wikidata.org/wiki/Q1370 geophysics 43.04185052089376 0.643273115158081 coordinate file 2.7429467084639496 3.5 statistics 10.103244837758112 13.7 geosciences 43.04185052089376 0.643273115158081 The process noise statistics are generated from the time series using the GAMIT/GLOBK script sh gen stats based on tsfit fits to the time series with the realistic sigma algorithm used to account for correlated noise. 11.786038077969176 13.0 reference frame 2.741514360313316 4.2 service-account-enrichment 10.5072/ro-id.3RRRUMSLRG https://www.unavco.org/data/web-services/documentation/gps-position-documentation.html false http://sandbox.rohub.org/rodl/ROs/UNAVCO_GPS_Timeseries/ 2017-09-04T12:48:59.459+02:00 http://everest.psnc.pl/users/jmgomez 1166176 https://api.rohub.org/api/ros/911a1b7c-c3f8-4a5d-a692-080237dbcf8d/crate/download/ http://connect.unavco.org/display/org253530 2017-09-04 10:48:59.459000+00:00 2025-03-05 02:47:01.120606+00:00 2017-09-04 10:48:59.459000+00:00 The GAGE GPS Analysis Centers process data from more than 2,000 GPS stations. Most of these stations are operated by UNAVCO as part of the PBO, COCONet, TLALOCnet and smaller regional networks. Also processed are stations from the Southern California Integrated GPS Network (SCIGN), the NASA Global Geodetic Network (GGN), the International GNSS Service (IGS) network, the Rio Grande Rift network, the GPS Array for Mid America (GAMA), the Basin and Range Geodetic Network (BARGEN), the Idaho National Laboratory (INL) network, the Pacific Northwest Geodetic Array (PANGA), the Western Canada Deformation Array, SuomiNet, GulfNet, and stations near the epicenter of the 23 August 2011 M5.8 Mineral, VA earthquake. Data from GPS stations not archived by UNAVCO are obtained from the NOAA National Geodetic Survey (NGS) Continuously Operating Reference Station (CORS) data center, the NASA Crustal Dynamics Data Information System (CDDIS), the U.S. Geological Survey, the Scripps Orbit and Permanent Array Center (SOPAC), and the International GNSS Service (IGS). application/ld+json https://w3id.org/ro-id/911a1b7c-c3f8-4a5d-a692-080237dbcf8d GAGE, GPS stations, PBO, COCONet, TLALOCnet, time series data, web services UNAVCO GPS Timeseries GPS time series data web service https://w3id.org/ro-id/15087ccf-09b1-40fa-ada4-592df46d13c4 https://w3id.org/ro-id/26227b8e-135d-418e-8d3f-f83960bcf597 https://w3id.org/ro-id/6975631e-1288-41d2-868f-a9b29d126784 https://w3id.org/ro-id/83ebd15e-e707-48be-8b23-29ecad8c9637 https://w3id.org/ro-id/98ec7caf-bf67-465d-8daa-6a81554dbd32 https://w3id.org/ro-id/9e8db06d-7d2c-4678-93b8-122f3c3d1c71 https://w3id.org/ro-id/bc12e8b8-8f16-470a-ae8a-8fd8edb818ee 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https://w3id.org/ro-id/1de9d863-1909-413e-93dc-052ab58aa39d https://w3id.org/ro-id/1ff83729-2311-485b-a9e5-413beaaf66e0 https://w3id.org/ro-id/3d0d15d6-0256-4b4d-898d-c7460ddf6227 https://w3id.org/ro-id/617b98f5-7455-4cd9-beb4-2b3f35b5c06d https://w3id.org/ro-id/69571445-971c-4605-b7ca-cb7454615956 https://w3id.org/ro-id/8c35bba8-c182-4e3b-9018-2fcd158b3f3d https://w3id.org/ro-id/9c0a0609-b55d-4c9f-b016-f301472c39b8 https://w3id.org/ro-id/b0bf7bab-71d8-4011-87b8-104dce0b688f https://w3id.org/ro-id/d7660396-0834-46d6-86ff-e218052a2254 https://w3id.org/ro-id/f6084908-18fd-4f0c-9ea2-c43f4271e219 https://w3id.org/ro-id/0d0b7a54-e7e4-4e41-a616-ae303fd0d684 Jose Manuel Gomez Perez. "UNAVCO GPS Timeseries." ROHub. Sep 04 ,2017. https://doi.org/10.5072/ro-id.3RRRUMSLRG. produced workflows main nested scripts config biblio setup used components 44925 https://api.rohub.org/api/resources/0343ece9-ca85-4a00-8385-38f0861fb64c/download/ 2017-05-23 16:40:01.455000+00:00 2022-03-25 15:25:33.898979+00:00 PNG sequenceplotter3.png 2017-05-23 16:40:01.455000+00:00 286993 https://api.rohub.org/api/resources/0f448e7a-e009-47f9-8dae-f006cfe30cb1/download/ 2017-05-23 17:16:54.207000+00:00 2022-03-25 15:25:27.280603+00:00 PDF GAGE_GPS_Analysis_Plan_20170315.pdf 2017-05-23 17:16:54.207000+00:00 http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short sequenceplotter.png 14182 https://api.rohub.org/api/resources/47c20188-060b-4393-b3a3-c8d88912f605/download/ 2017-05-01 19:55:49.788000+00:00 2022-03-25 15:25:30.993161+00:00 audio/midi UNAVCO workflow built around time series data web service 2017-05-01 19:55:49.788000+00:00 http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short 2022-03-25 15:25:13.146253+00:00 2022-03-25 15:25:24.869032+00:00 http://web-services.unavco.org/gps/data/position/P378/v3?analysisCenter=pbo&referenceFrame=igs08&starttime=2008-01-01T00:00:00&endtime=2018-03-01T00:00:00&report=short 2022-03-25 15:25:13.146253+00:00 603474 https://api.rohub.org/api/resources/afa5e9de-7ae0-4915-ad66-70d4db5e0f87/download/ 2017-05-23 17:18:37.911000+00:00 2022-03-25 15:25:31.889430+00:00 PDF GAGE_Velocity_Release_Notes_20161230.pdf 2017-05-23 17:18:37.911000+00:00 42242 https://api.rohub.org/api/resources/d380f679-0171-46d2-a663-83c052b2fce2/download/ 2017-05-23 16:39:24.912000+00:00 2022-03-25 15:25:30.136677+00:00 PNG sequenceplotter2.png 2017-05-23 16:39:24.912000+00:00 42055 https://api.rohub.org/api/resources/d96bf278-8e0d-400d-ba69-24d48e266433/download/ 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7.0 coordinate system 3.7337662337662336 11.5 global positioning system 3.0194805194805197 9.3 National Aeronautics and Space Administration https://www.wikidata.org/wiki/Q23548 Science and technology Science and technology PBO velocity field file format 5.407523510971786 6.9 information 2.5324675324675323 7.8 PBO GKNA PBO TSKF . . . . . . 1.4505893019038985 1.6 file format 3.133159268929504 4.8 rms calculation NRMS root mean square 1.9592476489028212 2.5 PBO GKNA PbO 2.037617554858934 2.6 Idaho National Laboratory 8.072100313479623 10.3 computer science 8.185840707964601 11.1 PBO GKNA PBO TSLS 3.2915360501567394 4.2 North America https://www.wikidata.org/wiki/Q49 Southern California Integrated GPS Network 3.9164490861618804 6.0 file format 2.5 7.7 time series 3.4740259740259742 10.7 earth sciences 41.24948833068723 0.9902867078781128 data 5.678851174934725 8.7 Massachusetts Institute of Technology https://www.wikidata.org/wiki/Q49108 U.S. Geological Survey https://www.wikidata.org/wiki/Q193755 statistics 2.272727272727273 7.0 National Oceanic and Atmospheric Administration https://www.wikidata.org/wiki/Q214700 Also processed are stations from the Southern California Integrated GPS Network (SCIGN), the NASA Global Geodetic Network (GGN), the International GNSS Service (IGS) network, the Rio Grande Rift network, the GPS Array for Mid America (GAMA), the Basin and Range Geodetic Network (BARGEN), the Idaho National Laboratory (INL) network, the Pacific Northwest Geodetic Array (PANGA), the Western Canada Deformation Array, SuomiNet, GulfNet, and stations near the epicenter of the 23 August 2011 M5.8 Mineral, VA earthquake. 15.412511332728922 17.0 International GNSS Service 4.960835509138382 7.6 atmospheric sciences 31.456986173340194 0.755195677280426 Civilian Conservation Corps https://www.wikidata.org/wiki/Q1094508 UNAVCO GPS Timeseries 10.815047021943572 13.8 earth sciences 31.456986173340194 0.755195677280426 Non-Aligned Movement https://www.wikidata.org/wiki/Q83201 statistics 6.342182890855457 8.6 time series 2.207792207792208 6.8 New Mexico https://www.wikidata.org/wiki/Q1522 standard deviation 2.9870129870129865 9.2 field 2.6298701298701297 8.1 National Research Council Canada https://www.wikidata.org/wiki/Q1437507 CWU snx/cwu . .a.rms ../NMT snx/nmt . .a.rms Format Version : . . Release Date : Start Field Description Dot character identifier for a given station GAGE Number sec phase epochs in hours for combined RMS calculation PRMS Root mean square (RMS) scatter of combined phase residuals, mm CWU Number sec phase epochs in hours for CWU RMS calculation CRMS Root mean square (RMS) scatter of CWU phase residuals, mm NMT Number sec phase epochs in hours for NMT or BSL (prior to Feb ) RMS calculation NRMS Root mean square (RMS) scatter of NMT or BSL phase residuals, mm A Coefficient from model fit RMS (elev) A B /sin(elev) where elev is elevation angle, mm B Coefficient from model fit, mm GPSW GPS Week for hour processing day D GPS Day of week for hour processing day YYYYMMDD Year, month, day of month for hour processing day End Field Description Dot GAGE PRMS CWU CRMS NMT NRMS A B GPSW D YYYYMMDD NSU . . . . . ULM . . . . . ODM . . . . . AB . . . . . AB . . . . . ZME . . . . . ZMP . . . . . ZNY . . . . . ZSE . . . . . 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"token verifier." ROHub. 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"Land Monitoring Coregistering Step." ROHub. Jul 04 ,2016. https://w3id.org/ro-id/cb7d509a-5048-4007-952b-6f4b2589583c. Documents Wokflow 152 https://api.rohub.org/api/resources/2d2e2ac2-325b-465d-892f-696cdd6ffd2e/download/ 2016-07-04 15:25:13.656000+00:00 2022-03-25 16:06:15.581873+00:00 text/plain output_prodID.txt 2016-07-04 15:25:13.656000+00:00 6227 https://api.rohub.org/api/resources/37cbbda7-30a6-45ae-af64-14f1701ed849/download/ 2016-07-04 15:22:50.594000+00:00 2022-03-25 16:06:18.356879+00:00 workflow.t2flow 2016-07-04 15:22:50.594000+00:00 160 https://api.rohub.org/api/resources/3bcdeef3-54e8-4f93-bf82-3f0f16d6d1a7/download/ 2016-07-04 15:24:48.072000+00:00 2022-03-25 16:06:14.747546+00:00 text/plain input_prodID.txt 2016-07-04 15:24:48.072000+00:00 60 https://api.rohub.org/api/resources/6efdf8c1-107f-4c74-b2ed-72f61419002d/download/ 2016-07-04 15:24:18.971000+00:00 2022-03-25 16:06:13.877443+00:00 text/plain input_AoI.txt 2016-07-04 15:24:18.971000+00:00 3150939 https://api.rohub.org/api/resources/837b574e-8369-4e34-9423-0d57be9c5ff9/download/ 2016-07-04 15:23:34.489000+00:00 2022-03-25 16:06:17.227724+00:00 application/pdf Sentinel-1_User_Handbook.pdf 2016-07-04 15:23:34.489000+00:00 5913 https://api.rohub.org/api/resources/bf97a947-e3f3-41d1-acc8-de0c2b212588/download/ 2016-07-04 15:21:50.980000+00:00 2022-03-25 16:06:16.403918+00:00 image/png sketch.png 2016-07-04 15:21:50.980000+00:00 Monitoring 15.720978656949505 30.2 earth sciences 21.354949506400125 0.43228960037231445 space sciences (general) 8.025722697128423 0.05067460238933563 geosciences 70.04925223661134 0.44229263067245483 step 24.343675417661096 10.2 This RO describes the coregistering step 24.624624624624623 49.2 coregistering step 9.71943887775551 9.7 earth sciences 78.64505049359987 1.5920167565345764 astronautics 21.92502506626024 0.13843512535095215 S1A_IW_GRDH_1SDV_20160128T181049_20160128T181114_009698_00E26E_461A_calibration 26.028110359187924 50.0 S1A_IW_GRDH_1SDV_20151024T181049_20151024T181114_008298_00BB2A_4A13_calibration 26.028110359187924 50.0 land Monitoring Coregistering step 83.46693386773548 83.3 Language Arts, culture and entertainment/Culture/Language Monitoring Coregistering step 5.210420841683367 5.2 Ro 17.959396147839666 34.5 geology 78.64505049359987 1.5920167565345764 describe the coregistering step 0.20040080160320642 0.2 spacecraft propulsion and power 21.92502506626024 0.13843512535095215 Coregistering step 1.402805611222445 1.4 Ro 75.65632458233891 31.7 space sciences 8.025722697128423 0.05067460238933563 Coregistering 14.263404476834982 27.4 S1A_IW_GRDH_1SDV_20151024T181049_20151024T181114_008298_00BB2A_4A13_calibration 25.125125125125123 50.2 earth resources and remote sensing 70.04925223661134 0.44229263067245483 S1A_IW_GRDH_1SDV_20160128T181049_20160128T181114_009698_00E26E_461A_calibration 24.874874874874873 49.7 oceanography 21.354949506400125 0.43228960037231445 Land Monitoring Coregistering Step. 25.375375375375373 50.7 service-account-enrichment service-account-generation-service http://ffoglini.livejournal.com/ elaborate data descriptors marine biology trend trend in the evolution Doctors Without Borders jellyfish 2016-07-04T16:26:20.716+02:00 19908 https://api.rohub.org/api/ros/9c95ec04-1c8b-412b-9d8a-dc23d1103415/crate/download/ 2016-07-04 14:17:38.590000+00:00 2025-10-20 10:41:16.726394+00:00 2016-07-04 14:17:38.590000+00:00 Starting from Jellyfish sightings, we elaborate data to produce explicit geographical information concerning trend about the evolution and distribution of alien species according with MSF directive descriptors. application/ld+json https://w3id.org/ro-id/9c95ec04-1c8b-412b-9d8a-dc23d1103415 Trend in the evolution of invasive jellyfish distribution http://ffoglini.livejournal.com/. "Trend in the evolution of invasive jellyfish distribution." ROHub. Jul 04 ,2016. https://w3id.org/ro-id/9c95ec04-1c8b-412b-9d8a-dc23d1103415. workflows data software documents 10271 https://api.rohub.org/api/resources/2451971d-00d5-4b86-8d91-08bd3b42b1ac/download/ 2016-07-04 14:20:08.546000+00:00 2022-03-25 16:10:44.570490+00:00 trend_invasive.t2flow 2016-07-04 14:20:08.546000+00:00 3057 https://api.rohub.org/api/resources/6808e606-a130-4e0d-b89d-c5372c8b1b59/download/ 2016-07-04 14:25:37.053000+00:00 2022-03-25 16:10:45.551682+00:00 image/png trend_invasive.png 2016-07-04 14:25:37.053000+00:00 118 https://api.rohub.org/api/resources/cddb09db-7a3a-4eb4-b9f0-aea7f3c7831c/download/ 2016-07-04 14:21:14.727000+00:00 2022-03-25 16:10:47.815189+00:00 application/xml wf_trend.xml 2016-07-04 14:21:14.727000+00:00 9994 https://api.rohub.org/api/resources/e6e85a89-bb59-4d12-aded-9a34328db616/download/ 2016-07-04 14:20:43.843000+00:00 2022-03-25 16:10:46.695182+00:00 Workflow1.wfbundle 2016-07-04 14:20:43.843000+00:00 trend 12.533692722371969 9.3 distribution 12.707182320441989 11.5 sighting 7.624309392265193 6.9 geographical 6.077348066298343 5.5 data 21.293800539083556 15.8 geology 100.0 0.6029276847839355 life sciences (general) 100.0 0.9660021662712097 trend 10.497237569060774 9.5 Starting from Jellyfish sightings, we elaborate data to produce explicit geographical information concerning trend about the evolution and distribution of alien species according with MSF directive descriptors. 81.48148148148148 81.4 Doctors Without Borders Animal Human interest/Animal jellyfish sighting 25.675675675675674 20.9 biology 100.0 5.6 distribution of alien species 8.476658476658477 6.9 Trend in the evolution of invasive jellyfish distribution. 18.51851851851852 18.5 MSF directive descriptor 34.15233415233415 27.8 earth sciences 100.0 0.6029276847839355 evolution 10.646900269541778 7.9 directive 7.292817679558011 6.6 jellyfish 17.250673854447438 12.8 evolution 8.839779005524862 8.0 life sciences 100.0 0.9660021662712097 Non-governmental organisation Politics/Non-governmental organisation distribution 15.49865229110512 11.5 Geography Science and technology/Social sciences/Geography jellyfish 14.033149171270718 12.7 trend in the evolution 11.425061425061426 9.3 alien species 10.242587601078167 7.6 information 17.016574585635357 15.4 descriptor 12.533692722371969 9.3 Foreign aid Politics/International relations/Foreign aid subject heading 9.613259668508286 8.7 jellyfish distribution 20.27027027027027 16.5 Health organisations Health/Health organisations Doctors Without Borders 6.298342541436464 5.7 service-account-enrichment service-account-generation-service http://ffoglini.livejournal.com/ data of the hydrodynamic model bathymetric variable hydrodynamic data variable importance Maxent software presence data hydrodynamic variable Mount Wilson Shetlands bathymetric data British Petroleum Maximilian hydrography Area water geography Shetland Islands value range Water Wilson trawl dredges species Study software resolution variables model north east Habitat seabed mounds variables distribution habitat suitability model Elsevier Science Ltd. UV mean corals David variable influence 2016-04-05T13:41:34.053+02:00 46868119 https://api.rohub.org/api/ros/eb7b6b9e-070d-45cc-a3e7-b71c7fe4cb08/crate/download/ 2016-04-05 11:16:51.848000+00:00 2025-10-20 10:38:37.858001+00:00 2016-04-05 11:16:51.848000+00:00 In this RO we derive the MSFD indicator 1.5 (Habitat area) to assess the biological diversity descriptor. To do this in deep sea environment, the scientist (user) needs to implement a habitat suitability model. application/ld+json https://w3id.org/ro-id/eb7b6b9e-070d-45cc-a3e7-b71c7fe4cb08 Deep Sea Habitat Suitabilty Model http://ffoglini.livejournal.com/. "Deep Sea Habitat Suitabilty Model." ROHub. Apr 05 ,2016. https://w3id.org/ro-id/eb7b6b9e-070d-45cc-a3e7-b71c7fe4cb08. Coral Occurences data documents Env Variables software workflows Maxent 3799421 https://api.rohub.org/api/resources/1df7ce2d-3749-47dc-8420-434a655ea9b3/download/ 2016-04-05 11:24:57.936000+00:00 2022-03-25 16:30:53.503314+00:00 application/vnd.openxmlformats-officedocument.presentationml.presentation Use of Maxent for predictive habitat mapping of.pptx 2016-04-05 11:24:57.936000+00:00 39410367 https://api.rohub.org/api/resources/363e66d9-0bc3-4eb8-a662-6e444681c70b/download/ 2016-04-05 11:34:54.908000+00:00 2022-03-25 16:30:54.486785+00:00 application/x-7z-compressed EnvVariable.7z 2016-04-05 11:34:54.908000+00:00 836294 https://api.rohub.org/api/resources/520e2bef-bd4d-4792-b735-c1fdd49242d5/download/ 2016-04-05 11:27:35.281000+00:00 2022-03-25 16:30:55.327005+00:00 application/pdf The-cold-water-coral-Lophelia-pertusa-Scleractinia-and-enigmatic-seabed-mounds-along-the-north-east-Atlantic-margin-are-they-related-_2003_Marine-Poll.pdf 2016-04-05 11:27:35.281000+00:00 2479847 https://api.rohub.org/api/resources/6e2613b6-b2c1-423a-aa43-3ab544abee94/download/ 2016-04-05 11:24:20.214000+00:00 2022-03-25 16:30:52.493837+00:00 application/vnd.openxmlformats-officedocument.presentationml.presentation Habitat suitability models for BARI canyon_2.pptx 2016-04-05 11:24:20.214000+00:00 13787 https://api.rohub.org/api/resources/736bd22b-80ad-4649-97a5-218ec4343dcb/download/ 2016-04-05 11:26:11.633000+00:00 2022-03-25 16:30:49.505037+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document How_to_use.docx 2016-04-05 11:26:11.633000+00:00 5683 https://api.rohub.org/api/resources/c6b8c081-fc28-4067-84cd-f9118c4468e8/download/ 2016-04-05 11:31:07.091000+00:00 2022-03-25 16:30:51.327456+00:00 text/csv CWC_Bari_20m.csv 2016-04-05 11:31:07.091000+00:00 638331 https://api.rohub.org/api/resources/de10459e-db27-4394-8f0a-09a1c5f0fb75/download/ 2016-04-05 11:37:23.476000+00:00 2022-03-25 16:30:50.325625+00:00 application/zip maxent.zip 2016-04-05 11:37:23.476000+00:00 oceanography 22.0931087561896 0.728894829750061 In this RO we derive the MSFD indicator 1.5 (Habitat area) to assess the biological diversity descriptor. 44.617299315494705 71.7 Princeton University Bari 3.627942879197221 9.4 information 9.648784253184099 25.0 earth sciences 22.0931087561896 0.728894829750061 raw data 2.7788498649170204 7.2 sea environment 6.5888240200166805 15.8 16-Nov-17 variable 5.725376031052887 11.8 descriptor 5.403319181783095 14.0 data 12.17855409995148 25.1 bathymetry 3.3478893740902476 6.9 hydrodynamic data 2.9190992493744785 7.0 From Nov-1-2011 to Jun-28-2012 statistics 17.980295566502463 7.3 row data 5.67139282735613 13.6 Geography Science and technology/Social sciences/Geography Tmean 2.37748665696264 4.9 Bari Bari Canyon 2.793994995829858 6.7 trade 1.7241379310344827 0.7 research and support facilities (air) 25.66363931506019 0.5196491479873657 Bari Canyon system 2.2518765638031693 5.4 Software Economy, business and finance/Economic sector/Computing and information technology/Software mathematics 10.591133004926109 4.3 indicator 4.747201852566576 12.3 Language Arts, culture and entertainment/Culture/Language MSFD 6.404657933042213 13.2 Science and technology Science and technology Raw data : is the bathymetry used for the work, and the adelie observations of the CWC i used. 8.400746733042936 13.5 Textile and clothing Economy, business and finance/Economic sector/Process industry/Textile and clothing descriptor 6.986899563318778 14.4 MAxent 3.9786511402231923 8.2 Annaëlle 4.2212518195050945 8.7 Exports Economy, business and finance/Economy/Macro economics/Exports name 3.31918178309533 8.6 Here are all the data of the BARI Canyon used. 7.467330429371499 12.0 earth sciences 59.16887537665321 1.9520968198776245 file 2.161327672713238 5.6 sea habitat Suitabilty model 5.045871559633027 12.1 Habitat 2.8141678796700633 5.8 ecology 12.31527093596059 5.0 distribution 2.4745269286754 5.1 work 2.35430335777692 6.1 variable 8.220764183712852 21.3 AT&T bargain Annaelle 6.630525437864887 15.9 linguistics 2.216748768472906 0.9 earth sciences 18.73801586715719 0.6182037591934204 To Feb-14 Biology Science and technology/Natural science/Biology aeronautics 25.66363931506019 0.5196491479873657 frequency distribution 3.3577769201080656 8.7 environment 6.11353711790393 12.6 scientist 2.1227325357005014 5.5 database 7.8817733990147785 3.2 Hydrodynamic data (Davide) implementation of ROMS for ocean currents, coupled with SWAN within the COAWST modelling system 2.7380211574362168 4.4 computer science 28.32512315270936 11.5 bathymetric variable 2.1267723102585485 5.1 ENFA 6.307617661329452 13.0 CWC occurences 2.335279399499583 5.6 2008 Bologna physics 9.35960591133005 3.8 Values Society/Values pdf name 5.713094245204337 13.7 UVmax 2.4745269286754 5.1 8 months diversity 4.129679660362794 10.7 bargain 2.7171276079573023 5.6 raw data 2.8141678796700633 5.8 The pdf name "methodoly2" is in french, and not finished yet, but explian step by step how to use the ENFA, MAxent and R, and where to find the programs. 7.965152457996266 12.8 life sciences (general) 74.3363606849398 1.505196750164032 species occurrence data 2.6271893244370306 6.3 diversity descriptor 12.677231025854878 30.4 To do this in deep sea environment, the scientist (user) needs to implement a habitat suitability model. 13.378967019290602 21.5 atmospheric sciences 18.73801586715719 0.6182037591934204 Habitat area 3.9616346955796495 9.5 Weather Weather from Jan-25 ENFA model 7.756463719766472 18.6 presence data 4.08673894912427 9.8 environment 4.785796989579312 12.4 Bari geology 59.16887537665321 1.9520968198776245 biology 7.389162561576354 3.0 Nov-25-2015 Bargain Annaelle – 4.107031736154324 6.6000000000000005 Geography Science and technology/Social sciences/Geography data of the BARI Canyon 4.5037531276063385 10.8 bathymetry 2.6244693168660747 6.8 Habitat Suitability model for Bari Canyon with Hydrodynamic variables 2.5513378967019285 4.1 software 2.216748768472906 0.9 diversity 5.240174672489083 10.8 Relate species occurrence data (distribution = biological data) with environmental predictor variables (EGVs = Ecogeographic variables) 1.9912881144990664 3.2 habitat suitability 3.056768558951965 6.3 name 3.5904900533721493 7.4 CWC distribution 1.9182652210175142 4.6 life sciences 74.3363606849398 1.505196750164032 Wireless technology Economy, business and finance/Economic sector/Computing and information technology/Wireless technology Toulon For ENFA, UVmax was not used (eigenvalues too high) marginality (niche position in the ecological space) specificity (niche size) = 1/tolerance 2.6135656502800244 4.2 data of the hydrodynamic model 1.7514595496246872 4.2 data 11.925897336935545 30.900000000000002 winter Deep Sea Habitat Suitabilty Model. 4.16925948973242 6.7 indicator 6.11353711790393 12.6 of november to Jun-28 software 2.199922809725974 5.7 event 3.7051331532226937 9.6 Economic indicator Economy, business and finance/Economy/Macro economics/Economic indicator buy 3.7437282902354294 9.7 habitat suitability model 6.8807339449541285 16.5 specificity 2.392898494789656 6.2 service-account-enrichment service-account-generation-service http://emanuele79.livejournal.com/ 107f503d-cf15-4c10-898f-74967aeed82c.rdf change detection military European Space Agency image processing algorithm Pierre Potin Synthetic aperture radar SAR-based technique Sentinel Products French Guiana Level MISSION Date Reason Segment results image archive small sample size 2016-06-28T11:09:08.412+02:00 3040650 https://api.rohub.org/api/ros/d18e59ae-ff07-4d59-a113-0202d37202aa/crate/download/ 2016-03-11 08:32:14.144000+00:00 2025-10-20 10:37:57.843580+00:00 2016-03-11 08:32:14.144000+00:00 The Land Monitoring RO allows to monitor urban, built-up and natural environments in order to identify certain features and anomalies or changes over Areas of Interest. application/ld+json https://w3id.org/ro-id/d18e59ae-ff07-4d59-a113-0202d37202aa Land Monitoring Workflow http://emanuele79.livejournal.com/. "Land Monitoring Workflow." ROHub. Mar 11 ,2016. https://w3id.org/ro-id/d18e59ae-ff07-4d59-a113-0202d37202aa. Documents Wokflow CalibrationWorkflow.t2flow 7159 https://api.rohub.org/api/resources/2d7a787e-aa3b-4f2d-b83a-f7f403927b77/download/ 2016-03-11 08:35:13.404000+00:00 2022-03-25 16:34:25.931149+00:00 ChangeDetectionWorflow.t2flow 2016-03-11 08:35:13.404000+00:00 6670 https://api.rohub.org/api/resources/40a547f1-04e8-449a-b8c7-990efe8979d9/download/ 2016-03-11 08:34:48.903000+00:00 2022-03-25 16:34:26.928335+00:00 CalibrationWorkflow.t2flow 2016-03-11 08:34:48.903000+00:00 ChangeDetectionWorflow.t2flow 25727 https://api.rohub.org/api/resources/8104d943-ea58-49c7-a701-d30d068523e6/download/ 2016-03-11 08:34:26.608000+00:00 2022-03-25 16:34:23.719861+00:00 LandMonitoringWorkflow.t2flow 2016-03-11 08:34:26.608000+00:00 11466 https://api.rohub.org/api/resources/8594202e-9f60-4e54-ad9f-d2359d60e835/download/ 2016-03-11 11:51:20.645000+00:00 2022-03-25 16:34:24.687253+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land_Monitoring_Workflow_conclusions.docx 2016-03-11 11:51:20.645000+00:00 3150939 https://api.rohub.org/api/resources/b116a635-2cd2-4c3d-babc-a92e87753a67/download/ 2016-03-11 08:40:42.045000+00:00 2022-03-25 16:34:27.793051+00:00 application/pdf Sentinel-1_User_Handbook.pdf 2016-03-11 08:40:42.045000+00:00 LandMonitoringWorkflow.t2flow 54130 https://api.rohub.org/api/resources/d4a24cd7-4e65-4aeb-b751-51c23837b968/download/ 2016-03-11 08:33:08.200000+00:00 2022-03-25 16:34:29.627661+00:00 image/png ChangeDetectionWorkflowChain.png 2016-03-11 08:33:08.200000+00:00 12037 https://api.rohub.org/api/resources/ec23ccfe-ceea-4bc0-b129-75c3238d3d63/download/ 2016-03-11 11:48:32.006000+00:00 2022-03-25 16:34:28.620739+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land_Monitoring_Workflow_hypothesis.docx 2016-03-11 11:48:32.006000+00:00 heritage mission 2.585961921000284 9.1 opposite 1.834862385321101 6.2 geosciences 13.850684028604018 0.31724029779434204 atmospheric sciences 78.1177901124589 2.0325206220149994 sentinel mission data acquisition 2.131287297527707 7.5 European Space Agency 2.2129570237331624 6.9 payload 2.1604024859425865 7.3 size 2.693104468777745 9.1 life sciences (general) 19.373018192810438 0.4437255263328552 impact 5.420141116100063 16.9 Ecosystem Environment/Nature/Ecosystem anomaly 3.91276459268762 12.2 mapping capability 4.2057402671213415 14.8 anomaly 3.1962118970109503 10.8 . . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities ................. 4.057553956834532 14.1 astronautics 40.98360655737705 22.5 instrument payload 2.4154589371980677 8.5 sample size 16.1409491332765 56.8 The Land Monitoring RO allows to monitor urban, built-up and natural environments in order to identify certain features and anomalies or changes over Areas of Interest. 21.8705035971223 76.0 mission 3.3441846700207165 11.3 Synthetic aperture radar (SAR) sensors represent an alternative to optical ones for monitoring environments, especially considering change detection analysis. 13.640287769784173 47.4 communications and radar 66.77629777858554 1.5294647216796875 earth resources and remote sensing 13.850684028604018 0.31724029779434204 sentinel family 3.752405388069275 11.7 geology 21.882209887541087 0.5693458914756775 detection 2.0525978191148173 6.4 Europe Even though the proposed fully automated SAR-based technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based change detection to match the mapping capability of high-quality optical data. 5.72661870503597 19.9 synthetic aperture radar 5.163566388710713 16.1 Electrical appliance Economy, business and finance/Economic sector/Manufacturing and engineering/Electrical appliance physics 4.553734061930784 2.5 tweak the classifier 7.956805910770106 28.0 change detection analysis 4.688832054560955 16.5 land Monitoring workflow 23.245240125035522 81.8 sample 3.9656703166617344 13.4 SENTINEL USER GUIDE ............................................................................. 4.690647482014389 16.3 sample 4.457985888389993 13.9 workflow 4.842847979474022 15.1 Prepared by Sentinel Team 4.575539568345324 15.9 - Tweaking the classifier has potentially more impact than tweaking the features. 11.39568345323741 39.6 feature 7.783367860313701 26.299999999999997 French Guiana 1.8940514945250075 6.4 Monitoring 5.772931366260424 18.0 conclusion 2.5336754329698525 7.9 decision 2.693104468777745 9.1 tweak the feature 3.296391020176187 11.6 aerospace engineering 31.876138433515482 17.5 Software Economy, business and finance/Economic sector/Computing and information technology/Software system 10.59485054749926 35.8 classifier 5.4749926013613495 18.5 sentinel B 2.7280477408354646 9.6 Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring change dynamics in specific areas. 7.798561151079137 27.1 sentinel user guide 1.534526854219949 5.4 sensor 2.020525978191148 6.3 orbit characteristic 3.353225348110259 11.8 natural environment 4.6463450725066595 15.7 European Space Agency 3.9360757620597813 13.3 earth sciences 21.882209887541087 0.5693458914756775 workflow 3.9952648712636876 13.5 Land Monitoring Workflow. 6.877697841726619 23.9 description 2.1808851828094933 6.8 This is in part related to the small sample size. 7.223021582733813 25.1 sentinel data product dissemination 1.9039499857914182 6.7 European Space Agency Monitoring workflow 1.3356067064506962 4.7 French Guiana baseline 4.746632456703015 14.8 feature 6.735086593970494 21.0 Land Monitoring RO 5.965362411802438 18.6 geophysics 9.65391621129326 5.3 sentinel mission guide 4.5467462347257745 16.0 microwave remote-sensing data 2.585961921000284 9.1 Armed forces Politics/Government/Defence/Armed forces life sciences 19.373018192810438 0.4437255263328552 computer science 12.932604735883425 7.1 sentinel satellite 1.953240603728914 6.6 classifier 6.350224502886466 19.8 engineering 66.77629777858554 1.5294647216796875 impact 5.001479727730097 16.9 Book industry Economy, business and finance/Economic sector/Media/Book industry country 2.18999704054454 7.4 technique 3.4316869788325848 10.7 sar-based technique 2.4154589371980677 8.5 Conclusions - The results obtained are better than the baseline, but not statistically significant. 10.129496402877699 35.2 certain feature 2.813299232736573 9.9 natural environment 5.676715843489416 17.7 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences size 2.9185375240538804 9.1 revision 1.9236460491269607 6.5 earth sciences 78.1177901124589 2.0325206220149994 Science and technology Science and technology The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather. 2.014388489208633 7.0 image archive 2.188121625461779 7.7 description 2.3675643681562595 8.0 service-account-enrichment service-account-generation-service http://emanuele79.livejournal.com/ change detection military European Space Agency image processing algorithm Pierre Potin Synthetic aperture radar SAR-based technique Sentinel Products French Guiana Level MISSION Date Reason Segment results image archive small sample size 2016-03-11T15:06:25.156+01:00 3040404 https://api.rohub.org/api/ros/45a828a4-2697-44b1-aca4-c3a1a168cbeb/crate/download/ 2016-03-11 08:32:14.144000+00:00 2025-10-20 10:37:26.631769+00:00 2016-03-11 08:32:14.144000+00:00 The Land Monitoring RO refers to the monitoring of urban, built-up and natural environments to identify certain features and anomalies or changes over areas of interest. application/ld+json https://w3id.org/ro-id/45a828a4-2697-44b1-aca4-c3a1a168cbeb Land Monitoring Workflow http://emanuele79.livejournal.com/. "Land Monitoring Workflow." ROHub. Mar 11 ,2016. https://w3id.org/ro-id/45a828a4-2697-44b1-aca4-c3a1a168cbeb. Wokflow Documents 12037 https://api.rohub.org/api/resources/2b953713-279f-4544-a0b9-5a2e7d86d617/download/ 2016-03-11 11:48:32.006000+00:00 2022-03-25 16:35:39.887280+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land_Monitoring_Workflow_hypothesis.docx 2016-03-11 11:48:32.006000+00:00 3150939 https://api.rohub.org/api/resources/2cb0f945-b4b1-4f64-b377-e98bad06e041/download/ 2016-03-11 08:40:42.045000+00:00 2022-03-25 16:35:39.039269+00:00 application/pdf Sentinel-1_User_Handbook.pdf 2016-03-11 08:40:42.045000+00:00 LandMonitoringWorkflow.t2flow 54130 https://api.rohub.org/api/resources/3ba6a55b-31b4-460c-969d-f6967dc2d87e/download/ 2016-03-11 08:33:08.200000+00:00 2022-03-25 16:35:40.908680+00:00 image/png ChangeDetectionWorkflowChain.png 2016-03-11 08:33:08.200000+00:00 6670 https://api.rohub.org/api/resources/46cde01b-d938-49a1-8265-1b11fd60f2fb/download/ 2016-03-11 08:34:48.903000+00:00 2022-03-25 16:35:38.213623+00:00 CalibrationWorkflow.t2flow 2016-03-11 08:34:48.903000+00:00 ChangeDetectionWorflow.t2flow 25727 https://api.rohub.org/api/resources/b3cb42e0-6429-43f5-9591-318d9833e424/download/ 2016-03-11 08:34:26.608000+00:00 2022-03-25 16:35:35.197940+00:00 LandMonitoringWorkflow.t2flow 2016-03-11 08:34:26.608000+00:00 CalibrationWorkflow.t2flow 7159 https://api.rohub.org/api/resources/dfa63ba7-6079-4610-b293-5752af50b5eb/download/ 2016-03-11 08:35:13.404000+00:00 2022-03-25 16:35:37.108974+00:00 ChangeDetectionWorflow.t2flow 2016-03-11 08:35:13.404000+00:00 11466 https://api.rohub.org/api/resources/e4451f7f-50fd-4585-96b5-4e01bd8319cc/download/ 2016-03-11 11:51:20.645000+00:00 2022-03-25 16:35:36.267868+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land_Monitoring_Workflow_conclusions.docx 2016-03-11 11:51:20.645000+00:00 Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring change dynamics in specific areas. 7.798561151079137 27.1 workflow 3.6689169357205755 12.5 technique 3.4250960307298337 10.7 Conclusions - The results obtained are better than the baseline, but not statistically significant. 10.129496402877699 35.2 sample size 16.1409491332765 56.8 synthetic aperture radar 5.1536491677336755 16.1 baseline 4.737516005121639 14.8 natural environment 3.713188220230474 11.6 Land Monitoring Workflow. 6.474820143884892 22.5 anomaly 4.0653008962868125 12.7 microwave remote-sensing data 2.585961921000284 9.1 land Monitoring workflow 22.165387894288152 78.0 European Space Agency 2.2087067861715752 6.9 sentinel satellite 1.9371881420604637 6.6 certain feature 3.097470872406934 10.9 size 2.670971529204579 9.1 sentinel B 2.7280477408354646 9.6 French Guiana Land Monitoring RO 5.56978233034571 17.4 . . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities ................. 4.057553956834532 14.1 sentinel data product dissemination 1.9039499857914182 6.7 natural environment 3.0818902260052834 10.5 sentinel user guide 1.534526854219949 5.4 country 1.995890813031993 6.8 atmospheric sciences 78.1177901124589 2.0325206220149994 European Space Agency physics 4.553734061930784 2.5 classifier 6.338028169014085 19.8 mapping capability 4.2057402671213415 14.8 Ecosystem Environment/Nature/Ecosystem aerospace engineering 31.876138433515482 17.5 tweak the feature 3.296391020176187 11.6 earth resources and remote sensing 13.850684028604018 0.31724029779434204 mission 3.3167009098914 11.3 Book industry Economy, business and finance/Economic sector/Media/Book industry revision 1.9078368065746991 6.5 sentinel family 3.745198463508323 11.7 Even though the proposed fully automated SAR-based technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based change detection to match the mapping capability of high-quality optical data. 5.72661870503597 19.9 instrument payload 2.4154589371980677 8.5 Monitoring workflow 1.278772378516624 4.5 heritage mission 2.585961921000284 9.1 feature 7.866157910184913 26.799999999999997 sample 4.449423815620999 13.9 European Space Agency 3.903727619606692 13.3 sar-based technique 2.4154589371980677 8.5 feature 6.850192061459667 21.4 geosciences 13.850684028604018 0.31724029779434204 anomaly 3.3167009098914 11.3 astronautics 40.98360655737705 22.5 change detection analysis 4.688832054560955 16.5 earth sciences 21.882209887541087 0.5693458914756775 monitoring 3.1690140845070425 9.9 Prepared by Sentinel Team 4.575539568345324 15.9 Armed forces Politics/Government/Defence/Armed forces image archive 2.188121625461779 7.7 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences description 2.348106838861168 8.0 monitoring 2.6122688582330498 8.9 description 2.176696542893726 6.8 Synthetic aperture radar (SAR) sensors represent an alternative to optical ones for monitoring environments, especially considering change detection analysis. 13.640287769784173 47.4 - Tweaking the classifier has potentially more impact than tweaking the features. 11.39568345323741 39.6 impact 5.409731113956465 16.9 tweak the classifier 7.956805910770106 28.0 size 2.9129321382842512 9.1 life sciences (general) 19.373018192810438 0.4437255263328552 workflow 4.449423815620999 13.9 communications and radar 66.77629777858554 1.5294647216796875 sample 3.9330789550924568 13.4 engineering 66.77629777858554 1.5294647216796875 sentinel mission data acquisition 2.131287297527707 7.5 geology 21.882209887541087 0.5693458914756775 sentinel mission guide 4.5467462347257745 16.0 The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather. 2.014388489208633 7.0 The Land Monitoring RO refers to the monitoring of urban, built-up and natural environments to identify certain features and anomalies or changes over areas of interest. 22.273381294964032 77.4 earth sciences 78.1177901124589 2.0325206220149994 life sciences 19.373018192810438 0.4437255263328552 orbit characteristic 3.353225348110259 11.8 computer science 12.932604735883425 7.1 impact 4.960375697094217 16.9 Software Economy, business and finance/Economic sector/Computing and information technology/Software detection 2.0486555697823303 6.4 geophysics 9.65391621129326 5.3 decision 2.670971529204579 9.1 This is in part related to the small sample size. 7.223021582733813 25.1 SENTINEL USER GUIDE ............................................................................. 4.690647482014389 16.3 conclusion 2.528809218950064 7.9 system 10.507778103903727 35.8 Europe classifier 5.429997064866452 18.5 Electrical appliance Economy, business and finance/Economic sector/Manufacturing and engineering/Electrical appliance payload 2.142647490460816 7.3 Monitoring 5.217669654289373 16.3 Science and technology Science and technology French Guiana 1.8784854710889347 6.4 service-account-enrichment service-account-generation-service http://emanuele79.livejournal.com/ sentinel user guide military sentinel mission data acquisition sentinel satellite title sentinel user handbook European Space Agency Pierre Potin Sentinel Products payload data ground segment French Guiana Level MISSION Date Reason Segment Document satellite description 2016-03-11T15:04:44.660+01:00 3040289 https://api.rohub.org/api/ros/4d228be1-e395-4a7c-a8e4-a704a3556090/crate/download/ 2016-03-11 08:32:14.144000+00:00 2025-10-20 10:37:11.409929+00:00 2016-03-11 08:32:14.144000+00:00 The Land Monitoring RO refers to the monitoring of urban, built-up and natural environments to identify certain features and anomalies or changes over areas of interest. application/ld+json https://w3id.org/ro-id/4d228be1-e395-4a7c-a8e4-a704a3556090 Land Monitoring Workflow http://emanuele79.livejournal.com/. "Land Monitoring Workflow." ROHub. Mar 11 ,2016. https://w3id.org/ro-id/4d228be1-e395-4a7c-a8e4-a704a3556090. Documents Wokflow 11466 https://api.rohub.org/api/resources/2e9a5183-8f61-4912-95c0-26abd64ede42/download/ 2016-03-11 11:51:20.645000+00:00 2022-03-25 16:36:11.744201+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land_Monitoring_Workflow_conclusions.docx 2016-03-11 11:51:20.645000+00:00 3150939 https://api.rohub.org/api/resources/45fad927-133c-4479-91e6-c40125a6efbc/download/ 2016-03-11 08:40:42.045000+00:00 2022-03-25 16:36:14.782702+00:00 application/pdf Sentinel-1_User_Handbook.pdf 2016-03-11 08:40:42.045000+00:00 6670 https://api.rohub.org/api/resources/629578b9-b937-461c-9e1b-3c010f2a2a9b/download/ 2016-03-11 08:34:48.903000+00:00 2022-03-25 16:36:13.489008+00:00 CalibrationWorkflow.t2flow 2016-03-11 08:34:48.903000+00:00 ChangeDetectionWorflow.t2flow 25727 https://api.rohub.org/api/resources/b53c9c8e-4083-47e2-ab85-594d539e2b0c/download/ 2016-03-11 08:34:26.608000+00:00 2022-03-25 16:36:10.820683+00:00 LandMonitoringWorkflow.t2flow 2016-03-11 08:34:26.608000+00:00 CalibrationWorkflow.t2flow 7159 https://api.rohub.org/api/resources/c864feb8-469d-4c9b-8692-1b17fb9aba05/download/ 2016-03-11 08:35:13.404000+00:00 2022-03-25 16:36:12.582734+00:00 ChangeDetectionWorflow.t2flow 2016-03-11 08:35:13.404000+00:00 LandMonitoringWorkflow.t2flow 54130 https://api.rohub.org/api/resources/c9eb5034-4ce2-478e-959c-5cdaf2d900bd/download/ 2016-03-11 08:33:08.200000+00:00 2022-03-25 16:36:16.617160+00:00 image/png ChangeDetectionWorkflowChain.png 2016-03-11 08:33:08.200000+00:00 12037 https://api.rohub.org/api/resources/e8f36e30-98e2-43d9-a79e-941831722ac9/download/ 2016-03-11 11:48:32.006000+00:00 2022-03-25 16:36:15.616142+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land_Monitoring_Workflow_hypothesis.docx 2016-03-11 11:48:32.006000+00:00 aerospace engineering 31.876138433515482 17.5 size 2.9129321382842512 9.1 tweak the feature 3.296391020176187 11.6 sar-based technique 2.4154589371980677 8.5 geosciences 13.850684028604018 0.31724029779434204 computer science 12.932604735883425 7.1 system 10.507778103903727 35.8 European Space Agency description 2.348106838861168 8.0 sentinel user guide 1.534526854219949 5.4 country 1.995890813031993 6.8 sample 4.449423815620999 13.9 sentinel B 2.7280477408354646 9.6 - Tweaking the classifier has potentially more impact than tweaking the features. 11.39568345323741 39.6 sentinel mission data acquisition 2.131287297527707 7.5 Armed forces Politics/Government/Defence/Armed forces classifier 5.429997064866452 18.5 conclusion 2.528809218950064 7.9 heritage mission 2.585961921000284 9.1 sample size 16.1409491332765 56.8 microwave remote-sensing data 2.585961921000284 9.1 earth sciences 21.882209887541087 0.5693458914756775 sentinel family 3.745198463508323 11.7 change detection analysis 4.688832054560955 16.5 French Guiana 1.8784854710889347 6.4 life sciences (general) 19.373018192810438 0.4437255263328552 physics 4.553734061930784 2.5 description 2.176696542893726 6.8 The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather. 2.014388489208633 7.0 classifier 6.338028169014085 19.8 Land Monitoring RO 5.56978233034571 17.4 land Monitoring workflow 22.165387894288152 78.0 sample 3.9330789550924568 13.4 orbit characteristic 3.353225348110259 11.8 engineering 66.77629777858554 1.5294647216796875 technique 3.4250960307298337 10.7 tweak the classifier 7.956805910770106 28.0 Conclusions - The results obtained are better than the baseline, but not statistically significant. 10.129496402877699 35.2 geology 21.882209887541087 0.5693458914756775 Book industry Economy, business and finance/Economic sector/Media/Book industry baseline 4.737516005121639 14.8 The Land Monitoring RO refers to the monitoring of urban, built-up and natural environments to identify certain features and anomalies or changes over areas of interest. 22.273381294964032 77.4 SENTINEL USER GUIDE ............................................................................. 4.690647482014389 16.3 Monitoring 5.217669654289373 16.3 This is in part related to the small sample size. 7.223021582733813 25.1 impact 4.960375697094217 16.9 revision 1.9078368065746991 6.5 Software Economy, business and finance/Economic sector/Computing and information technology/Software communications and radar 66.77629777858554 1.5294647216796875 certain feature 3.097470872406934 10.9 Even though the proposed fully automated SAR-based technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based change detection to match the mapping capability of high-quality optical data. 5.72661870503597 19.9 Ecosystem Environment/Nature/Ecosystem Electrical appliance Economy, business and finance/Economic sector/Manufacturing and engineering/Electrical appliance French Guiana Monitoring workflow 1.278772378516624 4.5 workflow 3.6689169357205755 12.5 European Space Agency 2.2087067861715752 6.9 Science and technology Science and technology anomaly 3.3167009098914 11.3 Prepared by Sentinel Team 4.575539568345324 15.9 earth sciences 78.1177901124589 2.0325206220149994 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences earth resources and remote sensing 13.850684028604018 0.31724029779434204 European Space Agency 3.903727619606692 13.3 feature 6.850192061459667 21.4 natural environment 3.713188220230474 11.6 life sciences 19.373018192810438 0.4437255263328552 atmospheric sciences 78.1177901124589 2.0325206220149994 . . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities ................. 4.057553956834532 14.1 instrument payload 2.4154589371980677 8.5 sentinel satellite 1.9371881420604637 6.6 sentinel mission guide 4.5467462347257745 16.0 mission 3.3167009098914 11.3 Europe Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring change dynamics in specific areas. 7.798561151079137 27.1 feature 7.866157910184913 26.799999999999997 Land Monitoring Workflow. 6.474820143884892 22.5 decision 2.670971529204579 9.1 astronautics 40.98360655737705 22.5 mapping capability 4.2057402671213415 14.8 synthetic aperture radar 5.1536491677336755 16.1 geophysics 9.65391621129326 5.3 natural environment 3.0818902260052834 10.5 sentinel data product dissemination 1.9039499857914182 6.7 detection 2.0486555697823303 6.4 monitoring 3.1690140845070425 9.9 impact 5.409731113956465 16.9 anomaly 4.0653008962868125 12.7 payload 2.142647490460816 7.3 workflow 4.449423815620999 13.9 Synthetic aperture radar (SAR) sensors represent an alternative to optical ones for monitoring environments, especially considering change detection analysis. 13.640287769784173 47.4 size 2.670971529204579 9.1 monitoring 2.6122688582330498 8.9 image archive 2.188121625461779 7.7 service-account-enrichment service-account-generation-service http://emanuele79.livejournal.com/ 65e769be-d0b2-401e-996b-c5a4afb49cb6.rdf annotations/572dfc14-5d3d-4245-bc9b-87e2209cf271 RO samples military data selection SatCen service information social media information archived data programming building industry Sentinel data provenance Elizabeth use user user sensing analysis Everest Monitoring communities LAND land monitoring data case changes data privacy information techniques 2016-02-24T17:27:21.745+01:00 3410577 https://api.rohub.org/api/ros/81063ed1-cac7-4ef6-9e3b-21a2e134ebd2/crate/download/ 2016-02-24 16:08:48.221000+00:00 2025-10-18 11:56:40.477819+00:00 2016-02-24 16:08:48.221000+00:00 This is a RO created for Land Monitoring Activities application/ld+json https://w3id.org/ro-id/81063ed1-cac7-4ef6-9e3b-21a2e134ebd2 Land Monitoring RO http://emanuele79.livejournal.com/. "Land Monitoring RO." ROHub. Feb 24 ,2016. https://w3id.org/ro-id/81063ed1-cac7-4ef6-9e3b-21a2e134ebd2. manuals datasets biblio workflows RO samples 3150939 https://api.rohub.org/api/resources/48b045b1-6e8b-40d6-906a-eae9b700f7ca/download/ 2016-02-24 16:13:07.590000+00:00 2022-03-25 16:36:47.758876+00:00 application/pdf Sentinel 1 Handbook 2016-02-24 16:13:07.590000+00:00 11466 https://api.rohub.org/api/resources/885aee33-f35b-4958-a83c-1a3895f951d6/download/ 2016-02-24 16:23:47.807000+00:00 2022-03-25 16:36:44.706586+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land Monitoring - conclusions.docx 2016-02-24 16:23:47.807000+00:00 450219 https://api.rohub.org/api/resources/c9c7726b-6070-44c4-a40b-402912c646fe/download/ 2016-02-24 16:21:45.123000+00:00 2022-03-25 16:36:45.642194+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document LAND MONITORING-hypotesis.docx 2016-02-24 16:21:45.123000+00:00 24968 https://api.rohub.org/api/resources/ddc168b8-216d-4eb4-b997-111b8f998f5f/download/ 2016-02-24 16:09:17.223000+00:00 2022-03-25 16:36:43.842776+00:00 image/png imgTest.png 2016-02-24 16:09:17.223000+00:00 2020 computer science 41.54929577464788 47.2 description 1.6202049082678105 6.8 land monitoring use case 1.0309278350515463 3.4 heritage mission 2.7592480291085506 9.1 baseline 3.526328329759352 14.8 European Union engineering 44.81040662436906 1.1378629207611084 data 2.7710257656781723 11.4 Sentinel user handbook 1.3341419041843543 4.4 activity 3.0384054448225575 12.5 environmental science and management 57.03765932858587 1.9248507618904114 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences classifier 4.496840058337384 18.5 SENTINEL USER GUIDE ............................................................................. 4.520244037714919 16.3 Monitoring activity 2.031534263189812 6.7 French Guiana Monitoring RO. 1.0006064281382656 3.3 Science and technology Science and technology Monitoring 11.26995472956874 47.3 earth resources and remote sensing 37.71514992396501 0.9576942920684814 impact 4.1079241614000965 16.9 sentinel mission data acquisition 2.274105518496058 7.5 Land Monitoring RO. This is a RO created for Land Monitoring Activities 27.73155851358846 100.0 operational scenario 8.02954491303312 33.7 in 2006 and 2010 politics 2.288732394366197 2.6 feature 3.9863879436071947 16.4 methodology 6.123421491541578 25.7 Ro 5.493437044239184 22.6 land Monitoring activity 10.491206791995149 34.6 in Feb-2016 country 6.0281964025279535 24.8 small sample size 1.0309278350515463 3.4 communications and radar 44.81040662436906 1.1378629207611084 activity 3.454848701453419 14.5 sentinel family 3.8598999285203717 16.2 land Monitoring RO. 16.73741661613099 55.2 aerospace engineering 15.404929577464788 17.5 Prepared by Sentinel Team 4.409317803660565 15.9 atmospheric sciences 42.96234067141413 1.4498507678508759 to create and access data/text mining tools as well as of automatic tools for processing high volumes of data; 1.0537992235163616 3.8 sentinel data product dissemination 2.031534263189812 6.7 instrument payload 2.5773195876288657 8.5 orbit characteristic 3.5779260157671313 11.8 output 1.701507049100632 7.0 mission 1.4772456516559447 6.2 software 8.098591549295772 9.2 Language Arts, culture and entertainment/Culture/Language mission 2.746718522119592 11.3 feature 2.382654276864427 10.0 sentinel mission guide 4.851425106124924 16.0 Ro 6.766738146294973 28.4 life sciences (general) 17.474443451665934 0.4437255263328552 Europe decision 2.2119591638308216 9.1 classifier 4.717655468191565 19.8 size 2.2119591638308216 9.1 workflow 9.64974982130093 40.5 geosciences 37.71514992396501 0.9576942920684814 sentinel B 2.9108550636749544 9.6 tweak the feature 3.51728320194057 11.6 sample 3.3118894448415537 13.9 information 2.527953330092368 10.4 Conclusions - The results obtained are better than the baseline, but not statistically significant. 9.76150859678314 35.2 system 3.2085561497326207 13.2 data 1.5963783654991661 6.7 Armed forces Politics/Government/Defence/Armed forces earth sciences 42.96234067141413 1.4498507678508759 European Space Agency tweak the classifier 8.489993935718617 28.0 land 2.12056230640934 8.9 size 2.1682153919466285 9.1 workflow 12.129314535731648 49.9 sample 3.2571706368497813 13.4 Everest payload 1.7744287797763734 7.3 . . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities ................. 3.910149750415973 14.1 conclusion 1.8822968787228973 7.9 Armed forces Politics/Government/Defence/Armed forces law 12.852112676056336 14.6 description 1.9445794846864366 8.0 European Space Agency 3.232863393291201 13.3 With respect to the use of open data, dedicated satellites under the Copernicus[footnoteRef:1] programme can guarantee open and up-to-date information through a set of services dedicated to environmental and security issues. 0.9983361064891846 3.6 sample size 17.22255912674348 56.8 This is in part related to the small sample size. 6.9606211869107035 25.1 astronautics 19.806338028169012 22.5 The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather. 1.9412090959511923 7.0 life sciences 17.474443451665934 0.4437255263328552 - Tweaking the classifier has potentially more impact than tweaking the features. 10.98169717138103 39.6 sentinel mode 0.9702850212249848 3.2 impact 4.026685727900881 16.9 Software Economy, business and finance/Economic sector/Computing and information technology/Software sentinel user guide 1.6373559733171619 5.4 Book industry Economy, business and finance/Economic sector/Media/Book industry Land monitoring is a issue common to a number of user communities; the main aim is to provide useful information to those entities that have to make informed decisions to address environmental, scientific, humanitarian, health, political and security issues as well as to adopt sustainable management practices. 27.73155851358846 100.0 environmental sciences 57.03765932858587 1.9248507618904114 European Space Agency 1.6440314510364546 6.9 community 1.8473505104521148 7.6 service-account-enrichment service-account-generation-service http://emanuele79.livejournal.com/ 65e769be-d0b2-401e-996b-c5a4afb49cb6.rdf annotations/572dfc14-5d3d-4245-bc9b-87e2209cf271 RO samples military data selection SatCen service information social media information archived data programming building industry Sentinel data provenance Elizabeth use user user sensing analysis Everest Monitoring communities LAND land monitoring data case changes data privacy information techniques 2016-02-24T17:27:21.745+01:00 3410623 https://api.rohub.org/api/ros/2d50b9a1-5eb8-46b2-a135-04c0db5da7e6/crate/download/ 2016-02-24 16:08:48.221000+00:00 2025-10-18 11:56:27.862244+00:00 2016-02-24 16:08:48.221000+00:00 This is a RO created for Land Monitoring Activities application/ld+json https://w3id.org/ro-id/2d50b9a1-5eb8-46b2-a135-04c0db5da7e6 Land Monitoring RO http://emanuele79.livejournal.com/. "Land Monitoring RO." ROHub. Feb 24 ,2016. https://w3id.org/ro-id/2d50b9a1-5eb8-46b2-a135-04c0db5da7e6. datasets RO samples manuals workflows biblio 450219 https://api.rohub.org/api/resources/0182cb87-8d24-4b3a-afcc-2299d138cd6f/download/ 2016-02-24 16:21:45.123000+00:00 2022-03-25 16:37:16.304854+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document LAND MONITORING-hypotesis.docx 2016-02-24 16:21:45.123000+00:00 24968 https://api.rohub.org/api/resources/370f3a5d-aa2a-4a02-af8e-87ac453eee55/download/ 2016-02-24 16:09:17.223000+00:00 2022-03-25 16:37:14.517159+00:00 image/png imgTest.png 2016-02-24 16:09:17.223000+00:00 3150939 https://api.rohub.org/api/resources/b68deac2-deac-47ac-8af0-0c9bd19d3f58/download/ 2016-02-24 16:13:07.590000+00:00 2022-03-25 16:37:18.681313+00:00 application/pdf Sentinel 1 Handbook 2016-02-24 16:13:07.590000+00:00 11466 https://api.rohub.org/api/resources/d5501788-252c-4c8d-b4d0-070356a92b91/download/ 2016-02-24 16:23:47.807000+00:00 2022-03-25 16:37:15.403789+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land Monitoring - conclusions.docx 2016-02-24 16:23:47.807000+00:00 size 2.2119591638308216 9.1 environmental sciences 57.03765932858587 1.9248507618904114 European Space Agency 3.232863393291201 13.3 description 1.9445794846864366 8.0 tweak the feature 3.51728320194057 11.6 Europe operational scenario 8.02954491303312 33.7 Monitoring RO. 1.0006064281382656 3.3 sentinel data product dissemination 2.031534263189812 6.7 impact 4.1079241614000965 16.9 small sample size 1.0309278350515463 3.4 software 8.098591549295772 9.2 land Monitoring RO. 16.73741661613099 55.2 European Union size 2.1682153919466285 9.1 sentinel user guide 1.6373559733171619 5.4 Conclusions - The results obtained are better than the baseline, but not statistically significant. 9.76150859678314 35.2 classifier 4.496840058337384 18.5 environmental science and management 57.03765932858587 1.9248507618904114 in Feb-2016 Everest engineering 44.81040662436906 1.1378629207611084 geosciences 37.71514992396501 0.9576942920684814 life sciences (general) 17.474443451665934 0.4437255263328552 classifier 4.717655468191565 19.8 orbit characteristic 3.5779260157671313 11.8 Ro 5.493437044239184 22.6 system 3.2085561497326207 13.2 The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather. 1.9412090959511923 7.0 conclusion 1.8822968787228973 7.9 sentinel family 3.8598999285203717 16.2 sentinel mode 0.9702850212249848 3.2 SENTINEL USER GUIDE ............................................................................. 4.520244037714919 16.3 Prepared by Sentinel Team 4.409317803660565 15.9 land 2.12056230640934 8.9 Land Monitoring RO. This is a RO created for Land Monitoring Activities 27.73155851358846 100.0 Sentinel user handbook 1.3341419041843543 4.4 Ro 6.766738146294973 28.4 decision 2.2119591638308216 9.1 Science and technology Science and technology to create and access data/text mining tools as well as of automatic tools for processing high volumes of data; 1.0537992235163616 3.8 impact 4.026685727900881 16.9 feature 2.382654276864427 10.0 tweak the classifier 8.489993935718617 28.0 land monitoring use case 1.0309278350515463 3.4 Armed forces Politics/Government/Defence/Armed forces French Guiana mission 1.4772456516559447 6.2 output 1.701507049100632 7.0 Language Arts, culture and entertainment/Culture/Language 2020 sample size 17.22255912674348 56.8 data 1.5963783654991661 6.7 sentinel mission data acquisition 2.274105518496058 7.5 life sciences 17.474443451665934 0.4437255263328552 sample 3.3118894448415537 13.9 activity 3.454848701453419 14.5 methodology 6.123421491541578 25.7 in 2006 and 2010 feature 3.9863879436071947 16.4 Armed forces Politics/Government/Defence/Armed forces With respect to the use of open data, dedicated satellites under the Copernicus[footnoteRef:1] programme can guarantee open and up-to-date information through a set of services dedicated to environmental and security issues. 0.9983361064891846 3.6 - Tweaking the classifier has potentially more impact than tweaking the features. 10.98169717138103 39.6 country 6.0281964025279535 24.8 Book industry Economy, business and finance/Economic sector/Media/Book industry payload 1.7744287797763734 7.3 Software Economy, business and finance/Economic sector/Computing and information technology/Software This is in part related to the small sample size. 6.9606211869107035 25.1 earth resources and remote sensing 37.71514992396501 0.9576942920684814 instrument payload 2.5773195876288657 8.5 atmospheric sciences 42.96234067141413 1.4498507678508759 Land monitoring is a issue common to a number of user communities; the main aim is to provide useful information to those entities that have to make informed decisions to address environmental, scientific, humanitarian, health, political and security issues as well as to adopt sustainable management practices. 27.73155851358846 100.0 aerospace engineering 15.404929577464788 17.5 politics 2.288732394366197 2.6 sentinel B 2.9108550636749544 9.6 workflow 12.129314535731648 49.9 sentinel mission guide 4.851425106124924 16.0 European Space Agency 1.6440314510364546 6.9 workflow 9.64974982130093 40.5 community 1.8473505104521148 7.6 law 12.852112676056336 14.6 Monitoring 11.26995472956874 47.3 description 1.6202049082678105 6.8 . . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities ................. 3.910149750415973 14.1 information 2.527953330092368 10.4 data 2.7710257656781723 11.4 baseline 3.526328329759352 14.8 computer science 41.54929577464788 47.2 activity 3.0384054448225575 12.5 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences land Monitoring activity 10.491206791995149 34.6 mission 2.746718522119592 11.3 Monitoring activity 2.031534263189812 6.7 earth sciences 42.96234067141413 1.4498507678508759 sample 3.2571706368497813 13.4 astronautics 19.806338028169012 22.5 heritage mission 2.7592480291085506 9.1 European Space Agency communications and radar 44.81040662436906 1.1378629207611084 service-account-enrichment service-account-generation-service http://emanuele79.livejournal.com/ military data selection SatCen service information social media information archived data programming building industry Sentinel data provenance Elizabeth use user user sensing analysis Everest Monitoring communities LAND land monitoring data case changes data privacy information techniques 2016-02-24T17:25:00.127+01:00 3409372 https://api.rohub.org/api/ros/94f3ec01-2fc1-4d36-9d33-df988345c96b/crate/download/ 2016-02-24 16:08:48.221000+00:00 2025-10-18 11:56:15.168592+00:00 2016-02-24 16:08:48.221000+00:00 This is a RO created for Land Monitoring Activities application/ld+json https://w3id.org/ro-id/94f3ec01-2fc1-4d36-9d33-df988345c96b Land Monitoring RO http://emanuele79.livejournal.com/. "Land Monitoring RO." ROHub. Feb 24 ,2016. https://w3id.org/ro-id/94f3ec01-2fc1-4d36-9d33-df988345c96b. workflows datasets manuals biblio 24968 https://api.rohub.org/api/resources/7064747c-169d-40fb-9756-15d56b9e574b/download/ 2016-02-24 16:09:17.223000+00:00 2022-03-25 16:37:45.006847+00:00 image/png imgTest.png 2016-02-24 16:09:17.223000+00:00 450219 https://api.rohub.org/api/resources/770a0193-5dc6-4eec-b278-e392994716e0/download/ 2016-02-24 16:21:45.123000+00:00 2022-03-25 16:37:46.854978+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document LAND MONITORING-hypotesis.docx 2016-02-24 16:21:45.123000+00:00 11466 https://api.rohub.org/api/resources/87037202-5ae1-4bfb-8303-a24c0d520357/download/ 2016-02-24 16:23:47.807000+00:00 2022-03-25 16:37:45.836419+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Land Monitoring - conclusions.docx 2016-02-24 16:23:47.807000+00:00 3150939 https://api.rohub.org/api/resources/9afe342c-ac1d-4c8c-89b0-0e9492b685be/download/ 2016-02-24 16:13:07.590000+00:00 2022-03-25 16:37:48.918998+00:00 application/pdf Sentinel 1 Handbook 2016-02-24 16:13:07.590000+00:00 sentinel user guide 1.6373559733171619 5.4 mission 2.746718522119592 11.3 in 2006 and 2010 impact 4.026685727900881 16.9 software 8.098591549295772 9.2 Conclusions - The results obtained are better than the baseline, but not statistically significant. 9.76150859678314 35.2 European Space Agency 3.232863393291201 13.3 Europe environmental science and management 57.03765932858587 1.9248507618904114 - Tweaking the classifier has potentially more impact than tweaking the features. 10.98169717138103 39.6 sentinel family 3.8598999285203717 16.2 Monitoring RO. 1.0006064281382656 3.3 Software Economy, business and finance/Economic sector/Computing and information technology/Software 2020 conclusion 1.8822968787228973 7.9 law 12.852112676056336 14.6 environmental sciences 57.03765932858587 1.9248507618904114 activity 3.454848701453419 14.5 European Space Agency land Monitoring activity 10.491206791995149 34.6 system 3.2085561497326207 13.2 SENTINEL USER GUIDE ............................................................................. 4.520244037714919 16.3 sample size 17.22255912674348 56.8 sentinel mode 0.9702850212249848 3.2 earth resources and remote sensing 37.71514992396501 0.9576942920684814 Book industry Economy, business and finance/Economic sector/Media/Book industry Sentinel user handbook 1.3341419041843543 4.4 Armed forces Politics/Government/Defence/Armed forces instrument payload 2.5773195876288657 8.5 information 2.527953330092368 10.4 operational scenario 8.02954491303312 33.7 The SENTINEL mission comprises a constellation of two polar orbiting satellites, operating day and night performing C band synthetic aperture radar imaging, enabling them to acquire imagery regardless of the weather. 1.9412090959511923 7.0 Land monitoring is a issue common to a number of user communities; the main aim is to provide useful information to those entities that have to make informed decisions to address environmental, scientific, humanitarian, health, political and security issues as well as to adopt sustainable management practices. 27.73155851358846 100.0 computer science 41.54929577464788 47.2 sample 3.3118894448415537 13.9 land Monitoring RO. 16.73741661613099 55.2 size 2.2119591638308216 9.1 Monitoring 11.26995472956874 47.3 astronautics 19.806338028169012 22.5 small sample size 1.0309278350515463 3.4 tweak the classifier 8.489993935718617 28.0 to create and access data/text mining tools as well as of automatic tools for processing high volumes of data; 1.0537992235163616 3.8 data 1.5963783654991661 6.7 aerospace engineering 15.404929577464788 17.5 community 1.8473505104521148 7.6 Ro 5.493437044239184 22.6 classifier 4.496840058337384 18.5 geosciences 37.71514992396501 0.9576942920684814 data 2.7710257656781723 11.4 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences This is in part related to the small sample size. 6.9606211869107035 25.1 Land Monitoring RO. This is a RO created for Land Monitoring Activities 27.73155851358846 100.0 land monitoring use case 1.0309278350515463 3.4 earth sciences 42.96234067141413 1.4498507678508759 French Guiana methodology 6.123421491541578 25.7 Language Arts, culture and entertainment/Culture/Language country 6.0281964025279535 24.8 life sciences 17.474443451665934 0.4437255263328552 . . . . Sentinel Mission Data Acquisition and (NRT) Production .................................................... . . . . Sentinel Collaborative Data Products .................................................................................. . . . . Sentinel Data Product Dissemination and Access ............................................................... . . . . Innovative Tools and Applications ....................................................................................... . . . . Sentinel complimentary Calibration/Validation activities ................. 3.910149750415973 14.1 impact 4.1079241614000965 16.9 land 2.12056230640934 8.9 decision 2.2119591638308216 9.1 Science and technology Science and technology in Feb-2016 output 1.701507049100632 7.0 Ro 6.766738146294973 28.4 size 2.1682153919466285 9.1 sentinel B 2.9108550636749544 9.6 workflow 12.129314535731648 49.9 communications and radar 44.81040662436906 1.1378629207611084 baseline 3.526328329759352 14.8 politics 2.288732394366197 2.6 workflow 9.64974982130093 40.5 Monitoring activity 2.031534263189812 6.7 sentinel data product dissemination 2.031534263189812 6.7 description 1.9445794846864366 8.0 life sciences (general) 17.474443451665934 0.4437255263328552 sample 3.2571706368497813 13.4 mission 1.4772456516559447 6.2 description 1.6202049082678105 6.8 European Union activity 3.0384054448225575 12.5 With respect to the use of open data, dedicated satellites under the Copernicus[footnoteRef:1] programme can guarantee open and up-to-date information through a set of services dedicated to environmental and security issues. 0.9983361064891846 3.6 Prepared by Sentinel Team 4.409317803660565 15.9 engineering 44.81040662436906 1.1378629207611084 sentinel mission data acquisition 2.274105518496058 7.5 European Space Agency 1.6440314510364546 6.9 sentinel mission guide 4.851425106124924 16.0 orbit characteristic 3.5779260157671313 11.8 payload 1.7744287797763734 7.3 heritage mission 2.7592480291085506 9.1 feature 2.382654276864427 10.0 feature 3.9863879436071947 16.4 Armed forces Politics/Government/Defence/Armed forces Everest atmospheric sciences 42.96234067141413 1.4498507678508759 classifier 4.717655468191565 19.8 tweak the feature 3.51728320194057 11.6 service-account-enrichment service-account-generation-service http://tahsl.livejournal.com/ United Kingdom model element GIS raster data workflow description datasets workflow nets impact hazard forecast 2.6887871853546907 4.7 ensemble rainfall 4.628890662410216 5.8 kind 3.4897025171624714 6.1 footprint 5.118961788031724 7.1 environmental sciences 61.02558789258154 0.9478866457939148 List of software used to generate elements in the workflow e.g. G2G modelling of SWF footprint 5.162827640984909 6.5 description 3.032036613272311 5.3 Language Arts, culture and entertainment/Culture/Language hazard impact modelling context 4.628890662410216 5.8 computer science 17.801047120418847 3.4 data 3.6041189931350113 6.3 guidance 3.432494279176201 6.0 Hazard Impact Model Development 15.35688536409517 21.3 ensemble surface runoff forecast 2.5538707102952913 3.2 early warning system 10.011441647597254 17.5 GIS database 2.633679169992019 3.3 software 25.13089005235602 4.8 workflow 4.3258832011535695 6.0 development of early warning systems 13.248204309656826 16.6 Datasets specific to hazard footprints e.g. MetOffice observed rainfall data and ensemble forecast rainfall (GIS gridded raster data) 6.910246227164415 8.7 ensemble 6.350114416475973 11.100000000000001 hazard 13.482335976928624 18.7 dataset 9.153318077803203 16.0 impact 13.501144164759724 23.6 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware impact 13.554434030281184 18.8 geosciences 100.0 0.8209701478481293 earth sciences 38.97441210741846 0.6053743362426758 chance 10.583524027459953 18.5 workflow 4.519450800915331 7.9 geographic information system 3.832951945080091 6.7 early warning system 13.266041816870944 18.4 Weather Weather RO. Ro 3.5913806863527533 4.5 United Kingdom 15.42898341744773 21.4 system 5.892448512585812 10.3 United Kingdom Software Economy, business and finance/Economic sector/Computing and information technology/Software footprint 5.3775743707093815 9.4 element 3.4607065609228553 4.8 Description of datasets and primary purpose in hazard impact modelling context e.g. ensemble rainfall forecast input to G2G to yield ensemble surface runoff forecasts 8.498808578236696 10.7 workflow description 3.750997605746209 4.7 hazard impact modelling workflow 2.3942537909018355 3.0 Hazard Impact Model Development RO. Ro 47.88507581803671 60.0 earth resources and remote sensing 100.0 0.8209701478481293 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences GIS 3.6770007209805335 5.1 database 57.06806282722513 10.9 database 2.6315789473684204 4.6 dataset 4.974765681326605 6.9 impacts within the UK 5.107741420590583 6.4 method 3.893294881038212 5.4 Hazard Impact Model Development RO. RO to facilitate development of early warning systems for natural hazards and their impacts within the UK. 79.42811755361397 100.0 natural hazard 9.577015163607342 12.0 geophysics 38.97441210741846 0.6053743362426758 description 3.4607065609228553 4.8 United Kingdom 11.899313501144164 20.8 environmental science and management 61.02558789258154 0.9478866457939148 2016-02-02T13:43:52.345+01:00 38304 https://api.rohub.org/api/ros/fb83ce4a-bcbd-4708-984a-c67bdbc10bdd/crate/download/ 2016-01-25 15:51:30.922000+00:00 2025-10-18 11:56:03.731636+00:00 2016-01-25 15:51:30.922000+00:00 RO to facilitate development of early warning systems for natural hazards and their impacts within the UK. application/ld+json https://w3id.org/ro-id/fb83ce4a-bcbd-4708-984a-c67bdbc10bdd Hazard Impact Model Development RO http://tahsl.livejournal.com/. "Hazard Impact Model Development RO." ROHub. Jan 25 ,2016. https://w3id.org/ro-id/fb83ce4a-bcbd-4708-984a-c67bdbc10bdd. input data method notes definitions framework workflow publications case study validation risk rainfall observed software generic datasets standards rainfall forecast hazards processing intermediary input used documentation impacts data produced methodology guidance user guides impact library output 20825 https://api.rohub.org/api/resources/d23349d6-dd05-4568-9b79-f78022de0693/download/ 2016-01-25 17:35:00.034000+00:00 2022-03-25 16:40:24.570337+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document NHP Research Object Hackathon v2.docx 2016-01-25 17:35:00.034000+00:00 service-account-enrichment service-account-generation-service http://rapw3k.livejournal.com/ input data Multitemporal InSAR image data SBAS InSAR data processing processing method SBAS method programming velocities GPS telecommunications file residuals ground 2016-01-22T14:47:35.818+01:00 749686 https://api.rohub.org/api/ros/85dd9dc0-7231-454e-b3c6-ca619c8d4df2/crate/download/ 2016-01-22 11:43:56.224000+00:00 2025-10-18 11:55:41.598421+00:00 2016-01-22 11:43:56.224000+00:00 Ground deformation mapping is a typical use case for this VRC. It may be carried out by different researchers on different volcanoes or even on the same volcano application/ld+json https://w3id.org/ro-id/85dd9dc0-7231-454e-b3c6-ca619c8d4df2 Volcano deformation mapping Please make sure that this workflow is executable http://rapw3k.livejournal.com/. "Volcano deformation mapping." ROHub. Jan 22 ,2016. https://w3id.org/ro-id/85dd9dc0-7231-454e-b3c6-ca619c8d4df2. output data figures used connected figures produced web services compilers software manuals publications documentation configuration files main processing workflows annotations scripts input consumption 13779 https://api.rohub.org/api/resources/0640ba54-ab25-47fb-a73a-2bf3762217f6/download/ 2016-01-22 12:00:03.236000+00:00 2022-03-25 16:41:49.808434+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document SBAS InSAR data processing using SarScape.docx 2016-01-22 12:00:03.236000+00:00 13665 https://api.rohub.org/api/resources/38cea6c2-1621-41d2-8eb5-5c028f5dddf4/download/ 2016-01-22 12:17:20.473000+00:00 2022-03-25 16:41:47.859397+00:00 image/jpeg volano-def.jpg 2016-01-22 12:17:20.473000+00:00 12804 https://api.rohub.org/api/resources/52c75270-985b-4705-80ee-053bb3eff72e/download/ 2016-01-22 12:02:23.324000+00:00 2022-03-25 16:41:48.903620+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document CC-BY-NC4 Validation of ground velocities using InSAR ground deformation and GPS.docx 2016-01-22 12:02:23.324000+00:00 702905 https://api.rohub.org/api/resources/61d312ce-035d-4006-9ce0-98afffda9b5d/download/ 2016-01-22 12:23:54.578000+00:00 2022-03-25 16:41:45.477209+00:00 application/zip vel_masked2.zip 2016-01-22 12:23:54.578000+00:00 This is a compressed file of all the output results script 3.0653266331658293 6.1 ground velocity file 7.529055078322385 14.9 SarScape software interface 8.388074785245074 16.6 It may be carried out by different researchers on different volcanoes or even on the same volcano 7.288765088207985 15.7 geosciences 100.0 0.8578767776489258 soil 3.558576569372251 8.9 Volcanic eruption Disaster, accident and emergency incident/Disaster/Natural disasters/Volcanic eruption geology 100.0 0.9990125894546509 use case 3.6683417085427137 7.3 SBAS method 5.305709954522486 10.5 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences software interface 3.0653266331658293 6.1 mapping 12.11515393842463 30.3 same volcano 1.5159171298635674 3.0 Ground deformation mapping is a typical use case for this VRC. 25.580315691736303 55.1 global positioning system 4.998000799680128 12.5 processing SW SarScape 6.2152602324406265 12.3 SarScape 5.8291457286432165 11.6 Natural disasters Disaster, accident and emergency incident/Disaster/Natural disasters data 4.278288684526189 10.7 volcano 4.398240703718512 11.0 processing 2.3990403838464616 6.0 telecommunications 6.578947368421052 2.5 Activities Opens ArcMap interface Load ground velocity file to validate and test files (they should have the same Line of Sight Run scripts to compare two raster ground velocity files and calculate the statistics of residuals. 10.53853296193129 22.7 result 2.4390243902439024 6.1 input data 4.321608040201005 8.6 different researcher 0.8590197069226883 1.7 ground deformation mapping 31.985851440121273 63.3 earth sciences 100.0 0.9990125894546509 ground 8.190954773869347 16.3 line of sight run script 2.2233451237998993 4.4 velocity 5.728643216080402 11.4 ground 4.358256697321072 10.9 WF steps User selects the appropriate InSAR and DEM data for the processing. 4.8282265552460535 10.4 volcano 5.678391959798995 11.3 mapping 15.477386934673367 30.8 volcano deformation mapping 13.996968165740272 27.7 input data Multitemporal InSAR image data 6.06366851945427 12.0 vertical redundancy check 2.5589764094362257 6.4 validation 2.3115577889447234 4.6 Science and technology Science and technology data 4.623115577889447 9.2 law 8.157894736842104 3.1 computer programming 9.210526315789474 3.5 processing 2.613065326633166 5.2 VRC 3.3165829145728645 6.6 The user runs the SarScape software interface and displays one or more menus and graphic windows to select the input data, the processing method (SBAS) and parameters. 9.006499535747444 19.4 velocity 5.557776889244303 13.9 programming interface 2.7988804478208715 7.0 raster 2.7189124350259894 6.8 Discrimination Society/Discrimination Computer crime Crime, law and justice/Crime/Computer crime researcher 4.42211055276382 8.8 computer science 40.26315789473684 15.3 GPS 5.226130653266332 10.4 GPS site velocity 5.811015664477009 11.5 Workflow Title: SBAS InSAR data processing using SarScape 7.98514391829155 17.2 Workflow Title: Validation of ground velocities using InSAR ground deformation and GPS 12.070566388115134 26.0 input file 3.9184326269492207 9.8 raster 2.814070351758794 5.6 deformation 11.959798994974875 23.8 Volcano deformation mapping. 13.50974930362117 29.1 statistics 2.279088364654138 5.7 InSAR ground deformation 4.143506821627084 8.2 user 2.9588164734106357 7.4 deformation 9.636145541783288 24.1 SBAS InSAR 2.71356783919598 5.4 different volcano 1.010611419909045 2.0 rule 2.279088364654138 5.7 Input data Ground velocity file in raster format Ground velocity file(s) resulting from different analysis methods (e.g. PS, SBAS, mixed), different time periods, or different datasets (e.g. Sentinel-1, ALOS 2, GPS, optical levelling) 9.192200557103064 19.8 processing method 3.1328954017180393 6.2 geophysics 100.0 0.8578767776489258 Capital punishment Crime, law and justice/Law enforcement/Punishment (criminal)/Capital punishment script 2.838864454218313 7.1 digital elevation model 2.663316582914573 5.3 velocity file 1.819100555836281 3.6 researcher 3.358656537385046 8.4 software 35.78947368421053 13.6 service-account-enrichment service-account-generation-service memory deregulate in HD HD participate in epigenetic processes genetics research have an epigenetic role aim 3.7729318103149883 10.9 gene deregulation 1.8138261464750172 5.3 genes involved in HD gene deregulation have an epigenetic role 33.34444814938313 100.0 participate in epigenetic process 0.20533880903490762 0.6 web service 4.084458290065767 11.8 have an epigenetic role 0.06844626967830253 0.2 life sciences 100.0 2.662090003490448 chromatin 5.430210325047801 14.2 HD chromatin analysis 10.095824777549623 29.5 http 3.530633437175493 10.2 deregulation 11.586998087954111 30.3 analysis 2.076843198338526 6.0 http 4.053537284894838 10.6 chromatin data interpretation 9.68514715947981 28.3 HD gene deregulation 25.735797399041754 75.2 deregulate in HD 16.1533196440794 47.2 earth sciences 67.21141059758304 1.1682264804840088 epigenetic role 6.536618754277893 19.1 research object 5.6125941136208075 16.4 Animal Human interest/Animal geochemistry 32.78858940241696 0.5699106454849243 Genoa 20.976116303219108 60.6 epigenetic 2.942194530979578 8.5 chromatin 4.7767393561786085 13.8 workflow 3.6711281070745696 9.6 chromatin data interpretation. 4.268089363121041 12.8 geology 67.21141059758304 1.1682264804840088 role 5.469020422291451 15.8 information 2.872966424368294 8.3 system 3.703703703703704 10.7 web service 4.7036328871892925 12.3 earth sciences 32.78858940241696 0.5699106454849243 research 4.091778202676864 10.7 research 4.534440983039114 13.1 HD 12.313575525812622 32.2 role 5.621414913957935 14.7 chromatin analysis 1.0609171800136894 3.1 object 4.130019120458891 10.8 workflow 3.0806507442021465 8.9 interpretation 2.492211838006231 7.2 Science and technology Science and technology life sciences (general) 100.0 2.662090003490448 epigenetic process 17.796030116358658 52.0 anni web services 5.236139630390144 15.3 HD 12.253374870197302 35.4 Genes deregulated in HD, are participating in epigenetic processes 33.34444814938313 100.0 Genoa 26.080305927342256 68.2 gene 14.952198852772467 39.1 deregulation 10.107303565247491 29.2 <p>This research object, was created in order to further analyse and interpret the results from the research object http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/&nbsp; (HD chromatin analysis). The workflows in this research object are using the anni web services, implemented by the Biosemantics group.</p> 29.043014338112705 87.1 data 3.365200764818356 8.8 Economic policy Economy, business and finance/Economy/Economic policy gene 13.326410522672205 38.5 2014-02-26T14:16:20.355+01:00 81201 https://api.rohub.org/api/ros/f84d00ef-47b2-40b3-af26-099ed7e82f5b/crate/download/ 2014-02-21 13:36:32.163000+00:00 2025-10-18 11:54:49.104646+00:00 2014-02-21 13:36:32.163000+00:00 <p>This research object, was created in order to further analyse and interpret the results from the research object http://sandbox.rohub.org/rodl/ROs/HD_chromatin_analysis/&nbsp; (HD chromatin analysis). The workflows in this research object are using the anni web services, implemented by the Biosemantics group.</p> application/ld+json https://w3id.org/ro-id/f84d00ef-47b2-40b3-af26-099ed7e82f5b chromatin data interpretation Eleni Mina. "chromatin data interpretation." ROHub. Feb 21 ,2014. https://w3id.org/ro-id/f84d00ef-47b2-40b3-af26-099ed7e82f5b. data_interpretation 30787 https://api.rohub.org/api/resources/2ea2f7b6-1c15-454f-b62a-e7656bb81db4/download/ 2014-02-25 16:10:46.463000+00:00 2022-03-25 16:50:57.659485+00:00 This workflow lists all IDs and descriptions of the predefined concept set List Predefined Concept Sets 2014-02-25 16:10:46.463000+00:00 69369 https://api.rohub.org/api/resources/473e57ab-4056-427a-a8d9-b2c62e9b34d4/download/ 2014-02-26 13:14:06.663000+00:00 2022-03-25 16:51:01.271983+00:00 This workflow takes two concept ids as input and returns the top ranking "B" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). The B concepts are returned sorted on the percentage of the contributions of the individual concepts to the coherence score (the average of the inner product scores of all possible concept pairs within the group). This workflow can be used together with other workflows in this pack: http://www.myexperiment.org/packs/282 for functional gene and SNP annotation and knowledge discovery. Explain concept scores 2014-02-26 13:14:06.663000+00:00 63 https://api.rohub.org/api/resources/5b168f58-186e-4335-8079-2c917c3173e3/download/ 2014-02-25 16:03:44.993000+00:00 2022-03-25 16:50:58.525827+00:00 text/plain hypothesis.txt 2014-02-25 16:03:44.993000+00:00 203555 https://api.rohub.org/api/resources/7f5b48a1-083d-4891-b311-b4627ceaa7ab/download/ 2014-02-25 16:06:54.756000+00:00 2022-03-25 16:51:00.400167+00:00 This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. "5" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services Annotate a gene list with Biological processes 2014-02-25 16:06:54.756000+00:00 188023 https://api.rohub.org/api/resources/840546e9-eecd-455e-b3f7-b1893b29e083/download/ 2014-02-25 16:12:14.228000+00:00 2022-03-25 16:50:56.779303+00:00 This workflow can prioritize genes that are related to a specific concept, e.g. HTT. In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against Prioritize gene list related to a concept /list of concepts 2014-02-25 16:12:14.228000+00:00 39476 https://api.rohub.org/api/resources/cb796e52-689f-4e9e-8842-09612588af02/download/ 2014-02-25 16:04:13.823000+00:00 2022-03-25 16:50:55.921582+00:00 Sketch of the workflows and their explanation, for data interpretation, plus the connection to the output from the RO for chromatin analysis image/png workflow sketch data interpretation 2014-02-25 16:04:13.823000+00:00 41692 https://api.rohub.org/api/resources/ccd9b022-3e2d-422b-a5c5-1e12af73b5c7/download/ 2014-02-25 16:09:23.429000+00:00 2022-03-25 16:50:59.349734+00:00 This workflow suggests concept ids that match the query term. The user can run this workflow with any term of interest as for example "human", "htt", "Transcription" etc, and will get suggestions for concept ids together with descriptions. Then can choose the concept id that matches the best to her/his needs and use it to the rest of the CPA workflows Get concept suggestions from term 2014-02-25 16:09:23.429000+00:00 67 https://api.rohub.org/api/resources/f97e8e56-b0de-4fda-9e37-352412aa2d3c/download/ 2014-02-25 16:03:12.283000+00:00 2022-03-25 16:50:54.643219+00:00 text/plain conclusions.txt 2014-02-25 16:03:12.283000+00:00 Eleni Mina Eleni Mina service-account-enrichment service-account-generation-service Earth sciences environmental monitoring 11.420612813370472 8.2 environmental monitoring from space 11.598746081504702 11.1 service-account-enrichment False https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140 2022-03-29 07:03:12.340483+00:00 https://orcid.org/0000-0002-2736-0052 533964 https://api.rohub.org/api/ros/15e9432f-53ee-4ea8-b1a3-6fdcaca7cf9e/crate/download/ 2021-12-14 10:41:17.716553+00:00 2024-03-05 12:16:56.530781+00:00 2021-12-14 10:41:17.716553+00:00 Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea application/ld+json https://w3id.org/ro-id/15e9432f-53ee-4ea8-b1a3-6fdcaca7cf9e Analysis from satellite data – Environmental monitoring from space - snapshot Analysis from satellite data – Environmental monitoring from space MANUAL https://w3id.org/ro-id/81696625-5dc7-413b-a2c5-0815dee8dc2a https://w3id.org/ro-id/08e10088-5d06-4d9a-a7a0-7a6bf4a0d199 https://w3id.org/ro-id/4266cc91-2256-44f6-b790-90445838a46e https://w3id.org/ro-id/5549dbaf-583c-4c8c-8dfa-a39ad54b848e https://w3id.org/ro-id/6c915ada-20b3-43cf-a69c-8773c78a0eaa https://w3id.org/ro-id/6f7e5fac-e969-4048-9f37-c6b31a3c612e https://w3id.org/ro-id/70d4f966-c4d0-48f5-9fa5-811f884e7b1e https://w3id.org/ro-id/97bb139d-ef31-4724-96c5-ea9425fc7c37 https://w3id.org/ro-id/d2a7c5cc-1102-43cd-8f62-288373b07b06 https://w3id.org/ro-id/f3594549-487e-496f-9517-0860ce40f088 https://w3id.org/ro-id/74dd5244-227a-4a04-b482-735a591fd8c1 https://w3id.org/ro-id/b72312be-71d7-47a7-b84c-4bfe2e2e23dd https://w3id.org/ro-id/7b96142d-d1dc-4dd2-9f96-5dd9fb851416 https://w3id.org/ro-id/32c4e511-07a6-4aed-8e1e-484785703c49 https://w3id.org/ro-id/3ee3f89c-1f32-41b6-a676-baa8abd9d56f https://w3id.org/ro-id/63bbaf66-12ba-4747-88a4-b84d914c3b7e https://w3id.org/ro-id/ac96b0ba-720f-47cb-a2fe-a19e3e7e7713 https://w3id.org/ro-id/c922ffbe-2d7f-4489-aaa7-b1aae735efc5 https://w3id.org/ro-id/cf67ee27-302a-48bc-a5ee-904c4e50fa3e https://w3id.org/ro-id/d6fe7e21-0924-4201-b407-8df494f726ed https://w3id.org/ro-id/5b45c2e1-bd6d-494f-8f0b-71842e9ff0c6 https://w3id.org/ro-id/aa125d6d-2a3d-4e86-a386-fca35f89cfff https://w3id.org/ro-id/14db60f4-ba75-4054-8b25-9588535e5441 https://w3id.org/ro-id/3a19afd2-96fe-46f3-a8ff-a00267cec7a9 https://w3id.org/ro-id/3bea0d90-d8aa-4e43-9a10-49f192c86cf9 https://w3id.org/ro-id/99a7f0a1-d957-45a2-b2db-21a5a4d52bee https://w3id.org/ro-id/ecb76892-a19f-4944-bdbc-7859c36c5de9 https://w3id.org/ro-id/6b907ad3-41f5-4c92-a60a-fa30c01895d6 https://w3id.org/ro-id/d95b93a0-106a-483a-af39-2d4e03335ad7 https://w3id.org/ro-id/f5db11a5-bf5a-4f0e-8924-4cef9408845f Castellan, Giorgio. "Analysis from satellite data – Environmental monitoring from space." ROHub. Dec 14 ,2021. https://doi.org/10.5281/zenodo.6392772. Discover, subset, download and visualize satellite data stored in a Data Cube from the ADAM Platform Method Results Results Satellite data on Chl-a and Kd490 Satellite_data 70005 https://api.rohub.org/api/resources/190680da-e8f0-45d9-91e0-504523674f2a/download/ 2021-12-14 14:33:06.849172+00:00 2022-03-29 07:03:09.363944+00:00 image/png Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea 2021-12-14 14:33:06.849172+00:00 449579 https://api.rohub.org/api/resources/34811ef9-67ba-4917-a0f5-a601d5d4f582/download/ 2021-12-14 14:38:21.837867+00:00 2022-03-29 07:03:07.276437+00:00 image/jpeg Analysis from satellite data – Environmental monitoring from space during COVID-19 lockdown 2021-12-14 14:38:21.837867+00:00 https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7 2021-12-14 10:44:17.433059+00:00 2022-03-29 07:03:12.216426+00:00 Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown 2021-12-14 10:44:17.433059+00:00 https://w3id.org/ro-id/34d648b3-0014-4a19-8469-40b9380ca4c3 2021-12-14 10:44:48.894463+00:00 2022-03-29 07:03:08.528464+00:00 Discover and subset satellite data from the ADAM Platform Discover and subset satellite data from the ADAM Platform 2021-12-14 10:44:48.894463+00:00 68452 https://api.rohub.org/api/resources/d1404599-300c-4572-971f-90a3a6a5e72a/download/ 2021-12-14 14:32:01.240770+00:00 2022-03-29 07:03:10.497073+00:00 image/png Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea 2021-12-14 14:32:01.240770+00:00 environmental monitoring 11.413748378728926 8.8 effects of COVID-19 lockdown 15.256008359456635 14.6 water clarity 30.407523510971785 29.1 collection 11.932555123216602 9.2 analysis 14.902506963788301 10.7 water 11.142061281337048 8.0 geosciences 100.0 0.4130299687385559 analysis 13.618677042801558 10.5 Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea 69.26926926926926 69.2 collection 13.091922005571032 9.4 space 5.153203342618385 3.7 Adriatic Sea 11.142061281337048 8.0 earth sciences 100.0 0.8256934881210327 Satellite technology Economy, business and finance/Economic sector/Computing and information technology/Satellite technology Adriatic Sea https://www.wikidata.org/wiki/Q13924 clarity 12.95264623955432 9.3 analysis from satellite data 17.03239289446186 16.3 geophysics 100.0 0.4130299687385559 satellite data 23.47600518806745 18.1 geology 100.0 0.8256934881210327 clarity 12.5810635538262 9.7 lockdown 14.785992217898833 11.4 result 4.735376044568246 3.4 covid 19 12.191958495460442 9.4 Analysis from satellite data – 22.02202202202202 22.0 analysis of satellite data 25.705329153605014 24.6 lockdown 15.459610027855154 11.1 Environmental monitoring from space. 8.708708708708707 8.7 Applied sciences Climatology 10.13039/501100000781 European Commission https://doi.org/10.5281/zenodo.4543739 2022-03-28 14:18:39.324751+00:00 2022-03-29 18:08:07.800368+00:00 By deploying JupyterLab with PANGEO, CESM and ESMValTool conda environments as a new Climate Galaxy interactive tool, we are aiming at bridging the gap between climate scientists and non-climate specialists. Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational research. One of the strength of Galaxy is that it does not require programming experience and allow researchers to easily upload data, run complex tools and workflows in a reproducible manner, and visualize results. Galaxy Climate is quite new and aims at offering tools to everyone interested in Climate Science so that they can analyse and visualize climate data produced by climate scientists. However, climate scientists and in particular climate modellers have very different working practices: they often like to use command lines for running climate models and thanks to the PANGEO community (a community platform for Big Data geoscience) the Jupyter ecosystem has become very popular with several deployments of JupyterHubs dedicated to climate data analysis. By deploying JupyterLab with PANGEO, CESM and ESMValTool conda environments as a new Climate Galaxy interactive tool (https://live.usegalaxy.eu/?tool_id=interactive_tool_climate_notebook), we are aiming at bridging the gap between climate scientists and non-climate specialists. On this poster, we will show typical use cases both for research (https://nordicesmhub.github.io/eosc-nordic-climate-demonstrator/02-use-cases/) and for teaching (https://nordicesmhub.github.io/NEGI-Abisko-2019/intro). Climate JupyterLab as an interactive tool in Galaxy 2022-03-28 14:18:39.324751+00:00 https://doi.org/10.5281/zenodo.6394185 2022-03-29 17:55:05.034625+00:00 2022-03-29 18:08:05.535928+00:00 This is a tarball for the Docker climate-JupyterLab image - Version 2021-03-18. To use it: download the image file docker-climate-notebook-2021-03-18.tar load it with docker with the command: docker load --input docker-climate-notebook-2021-03-18.tar launch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook:2021-03-18 start your favorite web browser and go to: http://localhost:7777/ipython/ See https://github.com/NordicESMhub/docker-climate-notebook for more details Docker climate-JupyterLab image Version 2021-03-18 2022-03-29 17:55:05.034625+00:00 https://github.com/NordicESMhub/docker-climate-notebook 2022-03-29 12:01:31.834492+00:00 2022-03-29 18:08:06.488065+00:00 This github repository contains all the sources required for building the docker containers that are made available in Quay Container Registry. Source code for building the docker container (github repository) 2022-03-29 12:01:31.834492+00:00 https://jupyterlab.readthedocs.io/en/stable/ 2022-03-28 14:14:45.648769+00:00 2022-03-29 18:08:02.576584+00:00 Link to the online JupyterLab documentation. JupyterLab Documentation 2022-03-28 14:14:45.648769+00:00 University of Freiburg, Freiburg (Germany) bjoern.gruening@gmail.com Björn Grüning 0000-0002-3079-6586 https://quay.io/repository/nordicesmhub/docker-climate-notebook 2022-03-29 11:58:28.213223+00:00 2022-03-29 18:08:06.281346+00:00 These docker images (different tags) correspond to the docker images built for Galaxy Climate JupyterLab. The docker images can be used within Galaxy and as standalone docker images. You can use the same images we use in Galaxy on your local computer or any other platform: 1. Pull an existing image locally docker pull quay.io/nordicesmhub/docker-climate-notebook 2. Run a pre-build image from docker registry 3. To start your JupyterLab: docker run -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook and you will top open a new terminal and start your favorite web browser. your running Jupyter Notebook instance on http://localhost:7777/ipython/. Remark: for reproducibility purpose, we suggest you use a specific tag e.g. docker pull quay.io/nordicesmhub/docker-climate-notebook:2021-03-18 Then use the same tag when starting your JupyterLab application: docker run -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook:2021-03-18 Docker images for Galaxy Climate JupyterLab (Quay Container Registry) 2022-03-29 11:58:28.213223+00:00 https://raw.githubusercontent.com/NordicESMhub/docker-climate-notebook/ie2/climate-jupyter-galaxy_web.gif 2022-03-29 11:41:30.552728+00:00 2022-03-29 18:08:02.662870+00:00 This is a gif animated image showing how to start the Galaxy Climate JupyterLab in Galaxy Europe image/gif How to start Galaxy Climate JupyterLab (gif animated) 2022-03-29 11:41:30.552728+00:00 https://raw.githubusercontent.com/NordicESMhub/docker-climate-notebook/ie2/map_vis_Galaxy.gif 2022-03-29 11:43:11.075459+00:00 2022-03-29 18:08:03.597880+00:00 This is a gif animated image showing some of the functionalities of the Galaxy Climate JupyterLab image/gif Demo of some of the functionalities of the Galaxy Climate JupyterLab (gif animated) 2022-03-29 11:43:11.075459+00:00 01xtthb56 University of Oslo 04jcwf484 Nordic e-Infrastructure Collaboration 857652 EOSC-Nordic 01840e60-5480-4d82-a6e0-ba8713e1ccc8 POINT (7.8337097307667145 48.01044395569975) 10.766601562500002 59.921531172441085 POINT (10.766601562500002 59.921531172441085) 7.8337097307667145 48.01044395569975 POINT (7.8337097307667145 48.01044395569975) 900c168d-9825-4521-a718-87b8ac6bf711 POINT (10.766601562500002 59.921531172441085) False 2022-03-29 18:08:11.857053+00:00 30283 https://api.rohub.org/api/ros/cb869c7a-7a89-49dd-9038-b8a05a91dc6e/crate/download/ 2022-03-26 09:45:54.364171+00:00 2025-10-18 11:54:14.993216+00:00 2022-03-26 09:45:54.364171+00:00 🐳 🔬 📚 Jupyter running in a docker container. This image can be used to integrate Jupyter into Galaxy. This Jupyter Docker container is used by the Galaxy Project and can be installed from the quay.io index (https://quay.io/repository/nordicesmhub/docker-climate-notebook). application/ld+json https://w3id.org/ro-id/cb869c7a-7a89-49dd-9038-b8a05a91dc6e cesm climate docker esmvaltool jupyterlab pangeo Docker Climate Analysis Jupyter Container - snapshot Docker Climate Analysis Jupyter Container Version 2021-03-18 MANUAL Anne Foilloux, and Björn Grüning. "Docker Climate Analysis Jupyter Container Version 2021-03-18." ROHub. Mar 26 ,2022. https://doi.org/10.24424/6mwg-cq92. POINT (7.8337097307667145 48.01044395569975) POINT (10.766601562500002 59.921531172441085) input tool biblio output 1729 https://api.rohub.org/api/resources/a5017748-4c4f-4546-b555-4b1323fce016/download/ 2022-03-29 12:08:38.781608+00:00 2022-03-29 18:08:11.623739+00:00 Default Jupyter Notebook used when starting Galaxy Climate JupyterLab if no other Jupyter Notebook is passed by the user. Default Jupyter Notebook for Galaxy Climate JupyterLab 2022-03-29 12:08:38.781608+00:00 6716 https://api.rohub.org/api/resources/c8a9d642-c401-43c4-9436-58f866edb277/download/ 2022-03-29 12:06:48.073820+00:00 2022-03-29 18:08:09.556008+00:00 This is the Galaxy Climate JupyterLab tool wrapper used by Galaxy to start the Galaxy Climate JupyterLab on a Galaxy instance. application/xml Galaxy Climate JupyterLab Tool wrapper (xml) 2022-03-29 12:06:48.073820+00:00 29705 https://api.rohub.org/api/resources/f974c6a2-5fb5-45ae-b19f-03968d55060f/download/ 2022-03-29 12:16:49.457762+00:00 2022-03-29 18:08:08.669861+00:00 Most of the resources and information of this Research Object were created from this Jupyter Notebook. Jupyter Notebook used to create/update this Research Object 2022-03-29 12:16:49.457762+00:00 y. This Jupyter Docker container is used by the Galaxy Project and can be installed from the quay.io index (https://quay.io/repository/nordicesmhub/docker-climate-notebo 29.59697732997481 23.5 computer programming and software 100.0 0.7481239438056946 Mar-18-2021 Samsung Galaxy 26.40990371389271 19.2 image 10.178817056396149 7.4 integrate Jupyter 3.556034482758621 3.3 atmospheric sciences 100.0 0.8577955365180969 Docker Climate Analysis Jupyter Container Version 2021-03-18. 39.42065491183879 31.3 http 6.155507559395248 5.7 r. This image can be used to integrate Jupyter into Gala 30.982367758186395 24.6 docker Climate analysis Jupyter container version 7.866379310344827 7.3 container 20.194384449244062 18.7 earth sciences 100.0 0.8577955365180969 Jupyter Docker container 6.142241379310345 5.7 image 8.099352051835854 7.5 loader 8.803301237964236 6.4 Samsung Galaxy 24.622030237580994 22.8 Waterway and maritime transport Economy, business and finance/Economic sector/Transport/Waterway and maritime transport shipping container 21.8707015130674 15.9 docker container 17.672413793103445 16.4 mathematical and computer sciences 100.0 0.7481239438056946 model 11.004126547455295 8.0 Jupyter 25.80993520518359 23.9 Occupations Labour/Employment/Occupations analysis Jupyter container version 64.76293103448276 60.1 http 6.327372764786794 4.6 docker 6.263498920086393 5.8 version 8.855291576673865 8.2 trade 100.0 3.5 container 15.405777166437414 11.2 Wireless technology Economy, business and finance/Economic sector/Computing and information technology/Wireless technology Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 service-account-enrichment Information science Applied sciences Climatology The Nordic e-Infrastructure Collaboration (NeIC) Finnish Meteorological Institute (Finland) antti-ilari.partanen@fmi.fi Antti-Ilari Partanen 0000-0002-0883-8161 NSC (Sweden) struthers@nsc.liu.se Hamish Struthers 0000-0002-4214-2213 NERSC (Norway) yanchun.he@nersc.no Yanchun He 0000-0002-5932-3627 Finnish Meteorological Institute (Finland) tommi.bergman@fmi.fi Tommi Bergman 0000-0002-6133-2231 Norwegian Meteorological Institute (Norway) oskaral@met.no Oskar Landgren 0000-0002-6264-8502 UiO jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 Finnish Meteorological Institute (Finland) risto.makkonen@fmi.fi Risto Makkonen 0000-0002-8961-3393 04jcwf484 Nordic e-Infrastructure Collaboration 200505 NeIC-NICEST2 12.555999755859377 55.67835873246176 POINT (12.555999755859377 55.67835873246176) 289e88e4-f8e2-49f6-ac86-37a26b1d5b72 POINT (12.555999755859377 55.67835873246176) 3262c094-1e9d-47ac-a8a0-64865036b931 POINT (16.18921279907227 58.59026697919618) 525c39e3-74d2-4c7a-ad46-833cb95d16d2 POINT (10.72265625 59.94400716933027) 16.18921279907227 58.59026697919618 POINT (16.18921279907227 58.59026697919618) 85b27ae2-c22f-4adf-b0fa-a6669981ea9a POINT (24.672546386718754 60.203663175350826) 983db612-43b3-482c-b2b6-82b92da71eca POINT (5.328369140625001 60.413852350464936) 24.672546386718754 60.203663175350826 POINT (24.672546386718754 60.203663175350826) 5.328369140625001 60.413852350464936 POINT (5.328369140625001 60.413852350464936) 10.72265625 59.94400716933027 POINT (10.72265625 59.94400716933027) False 2022-04-01 15:21:09.952162+00:00 2627956 https://api.rohub.org/api/ros/ed4e6aa2-9db8-452d-9301-ba1606361034/crate/download/ 2022-04-01 14:09:22.878784+00:00 2025-10-18 11:50:35.391990+00:00 2022-04-01 14:09:22.878784+00:00 NICEST-2 is the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools and it focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives. It builds on previous efforts within NICEST (a 3-year NeIC project as of 2017-01) and NordicESM (3-year NordForsk funded project from 2014-12). NICEST2 activities include: 1) Enhance the performance and optimize and homogenize workflows used, so climate models (like EC-EARTH and NorESM) can be run in an efficient way on future computing resources (like EuroHPC); 2) Widen the usage and expertise on evaluating Earth System Models and develop new diagnostic modules for the Nordic region within the ESMValTool; 3)Create a roadmap for FAIRification of Nordic climate model data. application/ld+json https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034 EC-EARTH HPC NorESM Nordic climate earth system modelling esm NeIC NICEST2 Project - snapshot NeIC NICEST2 Project MANUAL Anne Foilloux, Hamish Struthers, Risto Makkonen, Oskar Landgren, Antti-Ilari Partanen, Elina Miinalainen, Jean Iaquinta, et al. "NeIC NICEST2 Project." ROHub. Apr 01 ,2022. https://doi.org/10.24424/chnf-4g76. POINT (5.328369140625001 60.413852350464936) POINT (24.672546386718754 60.203663175350826) POINT (16.18921279907227 58.59026697919618) POINT (10.72265625 59.94400716933027) POINT (12.555999755859377 55.67835873246176) 438353 https://api.rohub.org/api/resources/4ed7f241-959a-41e5-bfb8-a4d53d3d1fa6/download/ 2022-04-01 14:26:58.230734+00:00 2022-04-01 15:20:39.864804+00:00 Initial Collaboration agreement for the NICEST2 project application/pdf NICEST2 Collaboration Agreement 2022-04-01 14:26:58.230734+00:00 1182910 https://api.rohub.org/api/resources/9ea437fd-1af0-4a1d-bc68-7af1c3bed34a/download/ 2022-04-01 15:18:04.647336+00:00 2022-04-01 15:20:30.258581+00:00 Achievements of the NeIC NICEST2 project at the beginning of January 2022. This slide is part of a presentation that has been shown during the NeIC AHM22. image/png NICEST2 project outcomes (24th January 2022) 2022-04-01 15:18:04.647336+00:00 191598 https://api.rohub.org/api/resources/a51d3ea3-89a6-443b-8d22-06f4c3ec71ee/download/ 2022-04-01 14:27:49.687557+00:00 2022-04-01 15:21:09.531085+00:00 Business Case for the NICEST2 project. application/pdf NICEST2 Business Case 2022-04-01 14:27:49.687557+00:00 610336 https://api.rohub.org/api/resources/cc821096-f0f1-4a81-afd2-e311c899250c/download/ 2022-04-01 14:25:00.118897+00:00 2022-04-01 15:20:37.652753+00:00 The submitted project proposal for the NICEST2 project. application/pdf NICEST2 Project proposal (submitted) 2022-04-01 14:25:00.118897+00:00 667263 https://api.rohub.org/api/resources/fd0ced6b-bfec-49bd-99b8-cb2db9f5c3f9/download/ 2022-04-01 14:22:43.959817+00:00 2022-04-01 15:20:32.410941+00:00 NICEST2 project plan. Please note that some changes may have been agreed during the course of the project. application/pdf Project Plan (as agreed initially) 2022-04-01 14:22:43.959817+00:00 Earth System Modeling Tools 2.7037933817594832 6.7 Greenland Budgets and budgeting Economy, business and finance/Economy/Macro economics/Budgets and budgeting project manager 2.4616626311541565 6.1 Weather Weather obligation 2.58272800645682 6.4 data 4.3205317577548 23.4 European Community 3.1476997578692494 7.8 Weather Weather Earth system model 11.910029498525073 32.3 Project outcome 1.9174041297935105 5.2 Rivers Environment/Natural resources/Water/Rivers physical geography and environmental geoscience 19.57903756400082 0.9382504224777222 diagnostic 2.806499261447563 15.2 NICEST-2 3.510895883777239 8.7 general 26.152918802410237 0.7280471920967102 NICESTThe objective of the NICEST project was to strengthen the Nordic ESM community by supporting the efficientuse of various e infrastructures through competence building, sharing and exchanging knowledge. 2.0846106683016554 3.4 Nordic collaboration 3.0604719764011805 8.3 Intergovernmental Panel on Climate Change ESMValTool 3.430185633575464 8.5 The Nordics and NeICWithin the Nordic ESM modeling community there is significant and sustained support for the concept of aNordic collaboration. 2.575107296137339 4.2 Human resources Economy, business and finance/Business information/Human resources Oslo Weather Weather Europe NICEST-2 is the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools and it focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives. 29.981606376456163 48.9 IS ENES network 2.6179941002949856 7.1 Science and technology Science and technology atmospheric sciences 40.744573414005764 1.9525277018547058 Environmental politics Environment/Environmental politics climate modelling community 2.3230088495575227 6.300000000000001 general (general) 26.152918802410237 0.7280471920967102 business and commercial law 2.1150033046926637 3.2 meteorology and climatology 45.71769468108118 1.2726930975914001 roadmap 2.744148506860371 6.8 earth resources and remote sensing 28.129386516508585 0.7830682694911957 plan 2.0310192023633675 11.0 party s liability 1.9542772861356934 5.3 Strengthen the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoinginitiatives to enable a future joint Nordic Climate Model Intercomparison Project and Nordic Climate services toassist decision making. 2.6364193746167994 4.3 Non-fiction Arts, culture and entertainment/Arts and entertainment/Literature/Non-fiction Coupled Model Intercomparison Project 3.305022156573116 17.9 data 4.923325262308313 12.2 The Climate Community needs to learn and understand FAIR principles to be able to create a roadmap for FAIRification of Nordic climate model data. 2.3298589822194975 3.8 trade 3.833443489755453 5.8 In addition to the comprehensive experiments made available to the community through CMIP, NorESM is also used in Norway to study present and past climate states and variability ranging from seasonal to multi centennial time scales. 1.7780502759043533 2.9 environmental science and management 39.676389021993415 1.901339054107666 Scandinavian 2.2895125553914326 12.4 work 2.935745937961595 15.899999999999999 meteorology 12.954395241242564 19.599999999999998 of 2017 College Education/School/Higher education/College Finland job market 11.235955056179776 17.0 Lead institution Sigma 3.244837758112095 8.8 partner 4.468242245199408 24.199999999999996 from 2014 project 2.7141802067946825 14.7 duty 1.9017725258493352 10.3 result 4.2466765140324965 23.0 With regard to one another, each partner bears responsibility for implementation of the duties and obligations specified in this collaboration agreement and the project description specified for the partner. 6.131207847946046 10.0 North of Sixty politics 6.67547918043622 10.1 Science and technology Science and technology software 2.709847984137475 4.1 finance 2.1150033046926637 3.2 collaboration agreement 2.6548672566371683 7.2 NICEST2 2.744148506860371 6.8 work 3.7530266343825667 9.3 It builds on previous efforts within NICEST (a 3-year NeIC project as of 2017-01) and NordicESM (3-year NordForsk funded project from 2014-12) 14.714898835070509 24.0 partner 2.784503631961259 6.9 geosciences 45.71769468108118 1.2726930975914001 Norway 2.1602658788774 11.7 European Open Science Cloud (EOSC) Nordic aims at bridging e services in the Nordic region with EuropeanOpen Science Cloud (EOSC). The Nordic Climate Community is represented in EOSC Nordic by a few partners ofthe NICEST project. 4.353157572041693 7.1 Norway project manager 1.9017725258493352 10.3 climate 4.726735598227474 25.6 Cicero workflow 2.4556868537666174 13.3 computer science 22.27362855254461 33.699999999999996 geosciences 28.129386516508585 0.7830682694911957 climate modeling data 1.991150442477876 5.4 Environmental politics Environment/Environmental politics title project management 2.2492625368731565 6.1 Students Education/Teaching and learning/Students Sweden The Climate Modelling community is an essential component of joint European efforts to build a European framework of earth system modelling as part of the ENES/IS ENES network (European Network for Earth System Science), through Horizon projects (e.g. 2.7590435315757205 4.5 Iceland law 16.259087904824852 24.6 climate Community 2.101769911504425 5.7 The partners will sign necessary agreements with owners, employees (including individuals with dual employment), partners, sub contractors, and others that are required to fulfil the relevant partner s obligations under this agreement, including measures to ensure the necessary transfer of intellectual property rights. 1.7780502759043533 2.9 community 3.028064992614475 16.4 EC earth 2.6548672566371683 7.2 NeIC project 5.383480825958702 14.6 climate 2.8652138821630344 7.1 earth sciences 19.57903756400082 0.9382504224777222 community 2.784503631961259 6.8999999999999995 The Climate Community needs to work on improving the performance and optimizing/homogenizing workflows used, so that climate models (like EC EARTH and 2.7590435315757205 4.5 environmental sciences 39.676389021993415 1.901339054107666 Earth System Grid Federation 3.1073446327683616 7.7 When appropriate, the project owner enters into a separate agreement with the employer of the project manager in a way that does not violate the terms of this agreement. 2.023298589822195 3.3 project owner 2.2492625368731565 6.1 United States of America Metropolitan Police European Community Danish, Finnish, Norwegian and Swedish modeling groups have committed to participate in phase of CMIP (CMIP ) 6.805640711220111 11.1 Trondheim the economy 19.828155981493722 30.0 project data reference syntax 2.5811209439528024 7.0 roadmap for FAIRification 1.991150442477876 5.4 climate model 4.560129136400323 11.3 in december National Security Council steering group 1.9174041297935105 5.2 Environment Environment EC EARTH and NorESM model throughput on LUMI EC EARTH and NorESM are portable e.g. they can run on different HPC national providers and on Virtual machines (cloud computing). EC EARTH and NorESM can scale on new architectures (performance analysis numbers compared to initially on HPC national providers and LUMI when available). Number of users running EC EARTH and NorESM on their own national facilities. 2.391171060698958 3.9 Norway 2.94592413236481 7.3 Po River Nordic 2.9862792574656982 7.4 Coupled Model Intercomparison Project 2.7441485068603715 6.800000000000001 Nordic 2.5480059084194977 13.8 earth sciences 40.744573414005764 1.9525277018547058 NeIC NICEST2 Project 2.8652138821630344 7.1 climate model 5.059084194977843 27.400000000000002 Esgf system 1.9542772861356934 5.3 climate model data 4.793510324483776 13.0 agreement 3.452732644017725 18.7 If not defined otherwise in the project description the project owner does not require the right to conclude a contract on behalf of the other partners through this collaboration agreement. 2.023298589822195 3.3 Commercial contract Economy, business and finance/Business information/Strategy and marketing/Commercial contract European Community 4.062038404726735 22.0 NICEST2 activities include: 1) Enhance the performance and optimize and homogenize workflows used, so climate models (like EC-EARTH and NorESM) can be run in an efficient way on future computing resources (like EuroHPC); 2) Widen the usage and expertise on evaluating Earth System Models and develop new diagnostic modules for the Nordic region within the ESMValTool; 3)Create a roadmap for FAIRification of Nordic climate model data. 12.875536480686696 21.0 NORCE (Norway) algu@norceresearch.no Alok Kumar Gupta Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 CSC (Finland) elina.miinalainen@csc.fi Elina Miinalainen USIT, University of Oslo (Norway) j.h.nordmoen@usit.uio.no Jørgen Halvorsen Nordmoen CSC (Finland) kimmo.ervasti@csc.fi Kimmo Ervasti USIT, University of Oslo (Norway) maikenp@usit.uio.no Maiken Pedersen Norwegian Meteorological Institute (Norway) oyvind.seland@met.no Øyvind Seland NSC (Sweden) pchengi@nsc.liu.se Prashanth Dwarakanath service-account-enrichment NORCE (Norway) tylo@norceresearch.no Tyge Løvseth Applied sciences Earth sciences Earth observation 10.13039/501100000781 European Commission 10.24424/DXFH-X940 https://doi.org/10.24424/dxfh-x940 2022-04-07 19:22:56.289678+00:00 2022-04-10 17:18:00.927096+00:00 Application of VSM to the M7.1 Van Earthquake (Turkey) of 2011 M 7.1 Van Earthquake (Turkey) 2011 2022-04-07 19:22:56.289678+00:00 10.24424/WESR-P505 https://doi.org/10.24424/wesr-p505 2022-04-07 13:26:39.214963+00:00 2022-04-10 17:18:32.214160+00:00 Data modelling related to the 2021 eruption at Nyiragongo volcano (DR Congo) using VSM Nyiragongo volcano (DR Congo) 22 May 2021 eruption 2022-04-07 13:26:39.214963+00:00 https://github.com/EliTras/VSM 2022-04-07 13:56:11.176692+00:00 2022-04-10 17:18:11.929153+00:00 Link to the GitHub repository with the VSM code VSM code in GitHub 2022-04-07 13:56:11.176692+00:00 https://github.com/EliTras/VSM_test 2022-04-07 13:59:08.278250+00:00 2022-04-10 17:18:01.227419+00:00 Tests of VSM in GitHub using InSAR and GNSS data at Campi Flegrei caldera (Italy) VSM tests in GitHub using InSAR and GNSS data at Campi Flegrei caldera (Italy) 2022-04-07 13:59:08.278250+00:00 101017502 Reliance https://w3id.org/ro-id/a25c47c7-f4dd-44d2-be2c-ab74b6a99070 2022-04-07 14:32:55.665484+00:00 2022-04-10 17:18:06.415001+00:00 Modelling of the 2011-2012 unrest at Santorini (Greece). Santorini (Greece) 2011-2012 unrest 2022-04-07 14:32:55.665484+00:00 False 2022-04-10 17:18:34.475111+00:00 503834 https://api.rohub.org/api/ros/e97e6ada-276b-4406-b2ee-d3ec36e096c3/crate/download/ 2022-04-07 13:12:37.521036+00:00 2025-10-18 11:49:47.795813+00:00 2022-04-07 13:12:37.521036+00:00 Volcanic and Seismic source Modelling (VSM) is an open source Python tool to model ground deformation detected by satellite and terrestrial geodetic techniques. The VSM tool allows the user to choose one or more geometrical sources as forward model among sphere, spheroid, ellipsoid, fault, and sill. It supports multiple datasets from most satellite and terrestrial geodetic techniques: interferometric SAR, GNSS, levelling, Electro-optical Distance Measuring, tiltmeters and strainmeters. Two sampling algorithms are available, one is a global optimization algorithm based on the Voronoi cells and the second follows a probabilistic approach to parameters estimation based on the Bayes theorem. VSM can be executed as Python script, in Jupyter Notebook environments or by its Graphical User Interface. Version 1.0 April 2022. For any inquires, please write to elisa.trasatti@ingv.it application/ld+json https://w3id.org/ro-id/e97e6ada-276b-4406-b2ee-d3ec36e096c3 SAR data deformation modelling geodetic data inversion open science seismic cycle volcanic activity Volcanic and Seismic source Modelling (VSM). The Python toolkit for modelling geodetic data - snapshot Volcanic and Seismic source Modelling (VSM). The Python toolkit for modelling geodetic data MANUAL Trasatti, Elisa. "Volcanic and Seismic source Modelling (VSM). The Python toolkit for modelling geodetic data." ROHub. Apr 07 ,2022. https://doi.org/10.24424/t83f-5t97. Figures Examples VSM_src 9282 https://api.rohub.org/api/resources/42037fe1-50cc-4751-9e39-4ee003202c34/download/ 2022-04-07 13:23:18.481402+00:00 2022-04-10 17:18:08.362148+00:00 image/gif VSM logo 2022-04-07 13:23:18.481402+00:00 3243 https://api.rohub.org/api/resources/d159c0a2-bb4c-4e9b-b558-4f8334993357/download/ 2022-04-07 14:03:09.752317+00:00 2022-04-10 17:18:33.705787+00:00 License of use of VSM License of use of VSM 2022-04-07 14:03:09.752317+00:00 492577 https://api.rohub.org/api/resources/d480c9fe-ccdd-492a-a0c4-609fe30bd217/download/ 2022-04-07 13:15:18.286359+00:00 2022-04-10 17:18:09.966348+00:00 image/png figure2.png 2022-04-07 13:15:18.286359+00:00 sampling 6.392045454545455 4.5 computer operations and hardware 100.0 0.5582033395767212 Software Economy, business and finance/Economic sector/Computing and information technology/Software Language Arts, culture and entertainment/Culture/Language algorithm 13.443830570902394 7.3 VSM tool 28.545119705340703 15.5 deformation 9.208103130755065 5.0 earth sciences 100.0 0.76500004529953 software 27.699530516431924 5.9 computer programming 38.49765258215962 8.2 algorithm 12.357954545454545 8.7 Volcanic and Seismic source Modelling (VSM) is an open source Python tool to model ground deformation detected by satellite and terrestrial geodetic techniques. 59.339080459770116 41.3 Python toolkit 20.626151012891345 11.2 environment 6.818181818181819 4.8 VSM 22.467771639042358 12.2 geophysics 100.0 0.76500004529953 geology 16.901408450704224 3.6 source Modelling 14.917127071823206 8.1 open source Python tool 21.17863720073665 11.5 Volcanic and Seismic source Modelling (VSM). The Python toolkit for modelling geodetic data. 22.55747126436782 15.7 toolkit 10.497237569060774 5.7 optimisation 5.823863636363637 4.1 computer science 16.901408450704224 3.6 1.0 Apr-2022 soil 5.6818181818181825 4.0 Modelling 16.75874769797422 9.1 The VSM tool allows the user to choose one or more geometrical sources as forward model among sphere, spheroid, ellipsoid, fault, and sill. 18.10344827586207 12.6 satellite 9.801136363636365 6.9 toolchain 10.085227272727273 7.1 dataset 5.823863636363637 4.1 mathematical and computer sciences 100.0 0.5582033395767212 spheroid 5.965909090909092 4.2 ellipsoid 5.823863636363637 4.1 Python 16.619318181818183 11.7 optimization algorithm 14.732965009208105 8.0 satellite 10.128913443830571 5.5 Python 17.495395948434624 9.5 deformation 8.806818181818183 6.2 https://w3id.org/ro-id/f60c5109-024f-434a-b724-8aea3091e134 2022-04-07 13:25:22.921145+00:00 2022-04-10 17:17:58.948454+00:00 Modelling of InSAR and GNSS data at Campi Flegrei by VSM Campi Flegrei Caldera (Italy) 2011-2013 unrest 2022-04-07 13:25:22.921145+00:00 service-account-enrichment Earth sciences 10.13039/501100000781 European Commission https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007EDEE6736861726547756964236335616136613735373432353332343532623632653533653738663732373439636834366632233732356634616233366362323664306662666330633132346337373565666565636865653439233435386236633362393566303966653363323935373631346461373539666330636839376163/content 2022-05-01 20:18:43.754840+00:00 2023-05-16 18:07:35.516891+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupyter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - Applied over Spain and variable Nitrogen Dioxide 2022-05-01 20:18:43.754840+00:00 https://datahub.egi.eu/share/00d23664c695cb6ce4c3f0438b1778f5ch5ad3 2022-05-01 20:18:45.772814+00:00 2022-05-01 20:23:03.244378+00:00 Monthly average maps of CAMS Nitrogen Dioxide [µg m-3] over Spain in 2019, 2020 and 2021 Nitrogen Dioxide [µg m-3] over Spain for March 2019, 2020 and 2021 2022-05-01 20:18:45.772814+00:00 https://datahub.egi.eu/share/2a9a7f334fe6e73f55bf83595d9aef84ch59d8 2022-05-01 20:18:36.927892+00:00 2022-05-01 20:22:56.674576+00:00 This dataset is a data-Cube retrieved from the ADAM platform over Spain in March 2019 Data-Cube from ADAM platform over Spain in March 2019 2022-05-01 20:18:36.927892+00:00 https://datahub.egi.eu/share/35c15202651e9a56b5791ddd9897fb33chd796 2022-05-01 20:18:50.139193+00:00 2022-05-01 20:23:06.486372+00:00 netCDF data corresponding to daily average of CAMS Nitrogen Dioxide [µg m-3] over Spain for March 2019, March 2020 and March 2021 netCDF data for daily NO2over Spain in March 2019, 2020 and 2021 2022-05-01 20:18:50.139193+00:00 https://datahub.egi.eu/share/50b107f60f369ff2414e679f6e411575ch4e1a 2022-05-01 20:18:47.735278+00:00 2022-05-01 20:23:06.803802+00:00 Daily average maps of CAMS Nitrogen Dioxideµg m-3] over Spain on March 15, 2021 Nitrogen Dioxide [µg m-3] over Spain on March 15, 2021 2022-05-01 20:18:47.735278+00:00 https://datahub.egi.eu/share/d7fb646b024a1d1f8b285f4ad9f313f6che876 2022-05-01 20:18:39.077503+00:00 2022-05-01 20:22:59.301409+00:00 This dataset is a data-Cube retrieved from the ADAM platform over Spain in March 2020 Data-Cube from ADAM platform over Spain in March 2020 2022-05-01 20:18:39.077503+00:00 https://datahub.egi.eu/share/f79280441b0944a05a60c79f4cc3ef22che4a8 2022-05-01 20:18:41.097182+00:00 2022-05-01 20:22:59.545655+00:00 This dataset is a data-Cube retrieved from the ADAM platform over Spain in March 2021 Data-Cube from ADAM platform over Spain in March 2021 2022-05-01 20:18:41.097182+00:00 https://datahub.egi.eu/share/fa58b7afada92ccf75ff97d1db4c1febch82eb 2022-05-01 20:18:34.542298+00:00 2022-05-01 20:23:06.971009+00:00 Geojson file used for 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-2.04345703125 36.63316209558658, -1.69189453125 37.16031654673677, -1.34033203125 37.43997405227057, -0.439453125 37.49229399862877, -0.59326171875 37.75334401310656, -0.37353515625 38.272688535980976, 0.263671875 38.59970036588819, 0.3955078125 38.839707613545144, 0.06591796875 38.94232097947902, -0.17578125 39.2832938689385, -0.19775390625 39.58875727696545, 0.24169921875 39.977120098439634, 0.68115234375 40.463666324587685, 1.07666015625 40.83043687764923, 1.58203125 41.062786068733026, 2.2412109375 41.178653972331674, 2.83447265625 41.541477666790286, 3.33984375 41.73852846935917, 3.36181640625 42.13082130188811, 3.05419921875 42.601619944327965 False 2022-05-01 20:23:07.050251+00:00 231123 https://api.rohub.org/api/ros/a369aaf0-06f7-441a-9a18-3b79b9d45f8e/crate/download/ 2022-05-01 20:15:42.899723+00:00 2025-10-18 11:43:25.244419+00:00 2022-05-01 20:15:42.899723+00:00 This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of NO2 over a given geographical area, here Spain application/ld+json https://w3id.org/ro-id/a369aaf0-06f7-441a-9a18-3b79b9d45f8e CAMS NO2 Spain air quality copernicus jupyter-notebook Jupyter Notebook Analysing the Air quality during Covid-19 pandemic using Copernicus Atmosphere Monitoring Service - Applied over Spain (March 2019, 2020, 2021) with Nitrogen Dioxide NO2 (March 2019, 2020, 2021) in Spain Jupyter notebook demonstrating the usage of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - snapshot MANUAL Anne Foilloux, Jean Iaquinta, and Simone Mantovani. "Jupyter Notebook Analysing the Air quality during Covid-19 pandemic using Copernicus Atmosphere Monitoring Service - Applied over Spain (March 2019, 2020, 2021) with Nitrogen Dioxide." ROHub. May 01 ,2022. https://doi.org/10.24424/y6d7-b622. 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Applied over Spain (March 2019, 2020, 2021) with Nitrogen Dioxide. 28.128128128128125 28.1 analysis 14.957264957264957 7.0 Mar-2019 Epidemic Health/Diseases and conditions/Communicable disease/Epidemic map 20.512820512820515 9.6 area 11.965811965811966 5.6 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 mantovani@meeo.it Simone Mantovani Raul Palma service-account-enrichment Earth sciences https://doi.org/10.5281/zenodo.6653258 2022-07-05 12:52:22.146283+00:00 2022-07-06 15:27:08.164714+00:00 Full SPL dataset is located in Zenodo Full SPL dataset 2022-07-05 12:52:22.146283+00:00 https://notebooks.egi.eu/user/da47d3640f619a02cb075c15d288fc09e053bf46b90d26ec335392acdfae866b@egi.eu/doc/tree/datahub/Reliance/Soundscape/SPL_PostProcessing_HDF5.ipynb 2022-07-06 15:19:37.054357+00:00 2022-07-06 15:27:07.980852+00:00 Link to EGI Jupyer HUB: It allows to post process spl data and to create graphs/tables Jupyter notebook for SPLs processing 2022-07-06 15:19:37.054357+00:00 https://underwaternoise.ices.dk/continuous 2022-07-05 12:42:32.932255+00:00 2022-07-06 15:27:05.398965+00:00 Continuous Noise Database (https://underwaternoise.ices.dk/continuous), 2022. 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42.84375132629021, 13.99658203125 42.73894375124377, 17.369384765625 43.068887774169625)) False 2022-07-06 15:27:15.164812+00:00 14297708 https://api.rohub.org/api/ros/7b86ece5-b588-416b-9c98-30bb63a5b9bc/crate/download/ 2021-12-13 16:00:05.573722+00:00 2025-10-18 11:31:55.908591+00:00 2021-12-13 16:00:05.573722+00:00 This RO provides the Jupyter notebook used to process the Sound Pressure Levels, SPL, data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded. SPL data were calculated from wav data recorded by Develogic SonoVault Hydrophones (https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501). application/ld+json https://w3id.org/ro-id/7b86ece5-b588-416b-9c98-30bb63a5b9bc Underwater Noise, SPLs, Soundscape Sound Pressure Levels Post Processing within the Soundscape project - snapshot Sound Pressure Levels Post Processing within the Soundscape project MANUAL Petrizzo, Antonio, Fantina Madricardo, Marta Picciulin, and Michol Ghezzo. "Sound Pressure Levels Post Processing within the Soundscape project." ROHub. Dec 13 ,2021. https://doi.org/10.24424/tkqc-zr42. data input input Jupyter notebooks here notebook Here some information metadata Here some results results 5833087 https://api.rohub.org/api/resources/63076219-d95a-4ffa-be04-e8c9ead051b7/download/ 2022-07-06 10:25:59.163869+00:00 2022-07-06 15:27:12.295310+00:00 Jupyter notebook for processing SPL data. application/zip Jupyter notebook for processing SPL data. 2022-07-06 10:25:59.163869+00:00 1095417 https://api.rohub.org/api/resources/7cdcd447-39c2-4d6c-8982-4e3b30a4e216/download/ 2022-07-06 15:07:05.061088+00:00 2022-07-06 15:27:00.351667+00:00 image/png workflowPostProcessing.png 2022-07-06 15:07:05.061088+00:00 4863608 https://api.rohub.org/api/resources/912d02f1-4c08-46ce-8d75-ea2f16520385/download/ 2022-07-05 12:05:48.435779+00:00 2022-07-06 15:27:14.989305+00:00 Example of SPL input file. HDF5 format, according to to ICES (International Council for the Exploration of the Sea) continuous noise data portal specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx). Example of SPL input file 2022-07-05 12:05:48.435779+00:00 1628249 https://api.rohub.org/api/resources/a124318c-b696-4515-a1a1-6b37818b6db0/download/ 2022-07-05 12:44:30.318843+00:00 2022-07-06 15:27:04.720891+00:00 Map of stations with their coordinates image/png Stations map 2022-07-05 12:44:30.318843+00:00 3034992 https://api.rohub.org/api/resources/b0fadc73-e542-4e0e-be88-473cd883bbf9/download/ 2022-07-05 12:33:28.328984+00:00 2022-07-06 15:27:09.185481+00:00 Some examples of output files application/zip Some examples of output files 2022-07-05 12:33:28.328984+00:00 http 8.760330578512397 5.3 data 20.729684908789388 12.5 information 11.074380165289256 6.7 Develogic SonoVault hydrophone 18.2548794489093 15.9 Ro 8.099173553719009 4.9 soundscapes in the North Adriatic sea 7.921928817451206 6.9 Newspaper Arts, culture and entertainment/Mass media/Newspaper Language Arts, culture and entertainment/Culture/Language Mar-2020 - Jun-2021 sound pressure level 10.447761194029852 6.3 http 8.955223880597016 5.4 Soundscape Project 12.603648424543948 7.6 SPL data 46.84270952927669 40.8 Biology Science and technology/Natural science/Biology sound pressure 10.24793388429752 6.2 This RO provides the Jupyter notebook used to process the Sound Pressure Levels, SPL, data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded. 42.08416833667335 42.0 Sound Pressure Levels Post Processing within the Soundscape project. 13.42685370741483 13.4 hydrophone 5.785123966942149 3.5 Jupyter notebook 12.769485903814262 7.7 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware acoustics 16.379310344827587 3.8 soundscape 10.743801652892563 6.5 computer science 44.827586206896555 10.4 sound pressure level 10.082644628099173 6.1 earth sciences 100.0 0.6904757022857666 atmospheric sciences 100.0 0.6904757022857666 noise data 15.040183696900115 13.1 Adriatic Sea 8.264462809917354 5.0 AND 6.446280991735537 3.9 soundscape 11.111111111111112 6.7 database 21.982758620689655 5.1 data 20.49586776859504 12.4 SPL data were calculated from wav data recorded by Develogic SonoVault Hydrophones (https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501) 44.48897795591183 44.4 SPL 23.383084577114428 14.1 life sciences 100.0 0.35060247778892517 physics 16.810344827586206 3.9 sound pressure Levels Post 11.940298507462687 10.4 Adriatic Sea life sciences (general) 100.0 0.35060247778892517 of 1 year https://www.italy-croatia.eu/web/soundscape 2022-07-06 10:06:13.532986+00:00 2022-07-06 15:27:15.088234+00:00 EU-Interreg Italy-Croatia 2014/2020 – CBC Program (Contract number 10043643) Soundscape Project 2022-07-06 10:06:13.532986+00:00 CNR ISMAR Venice antonio.petrizzo@ve.ismar.cnr.it Antonio Petrizzo CNR ISMAR fantina.madricardo@ve.ismar.cnr.it Fantina Madricardo CNR ISMAR marta.picciulin@ve.ismar.cnr.it Marta Picciulin CNR ISMAR michol.ghezzo@ve.ismar.cnr.it Michol Ghezzo service-account-enrichment Earth sciences CNR-ISMAR valentina.grande@bo.ismar.cnr.it Valentina Grande 0000-0002-3489-268X linguistics 5.949367088607595 4.7 Everest https://www.wikidata.org/wiki/Q513 experiment result 2.640845070422535 4.5 engineering 12.302526414166074 0.28509142994880676 system 7.56167894905479 23.6 seabed 3.172060237103492 9.9 bathymetry 11.476355247981544 19.9 Software Economy, business and finance/Economic sector/Computing and information technology/Software input file 2.5632809996795896 8.0 workflow 3.8128804870233894 11.9 hydrography 11.89873417721519 9.4 geophysics 16.368849689426614 0.4702737629413605 National Educational Television https://www.wikidata.org/wiki/Q3873154 engineering 2.755527074655559 8.6 Maritime accident and incident Disaster, accident and emergency incident/Accident and emergency incident/Transport accident and incident/Maritime accident and incident OBIA template matching was applied to the seafloor backscatter mosaic in this area. 2.3778071334214004 3.6 software 3.4177215189873418 2.7 atmospheric sciences 23.085019608000973 0.6632279753684998 seafloor 2.7681660899653977 4.8 data 4.325536686959308 13.5 atmospheric sciences 26.820732092685684 0.77055424451828 Automatic detection of MLs targets from the bathymetry. 26.55217965653897 40.2 European Commission https://www.wikidata.org/wiki/Q8880 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware workflow 7.324106113033449 12.7 ASCII.txt data 2.640845070422535 4.5 early spring survey 3.1141868512110724 5.4 geophysics 39.18410222507813 0.9080290794372559 study 3.5565523870554308 11.1 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware technology 5.017301038062283 8.7 Aquaculture Economy, business and finance/Economic sector/Agriculture/Aquaculture target 6.286043829296425 10.9 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences seafloor survey method 2.640845070422535 4.5 earth sciences 23.085019608000973 0.6632279753684998 Venice https://www.wikidata.org/wiki/Q641 Oceans Environment/Natural resources/Water/Oceans experiment 3.7487984620314 11.7 filed experiment 2.171361502347418 3.7 dedicated workflow 6.748826291079812 11.5 late winter earth sciences 26.820732092685684 0.77055424451828 Geography Science and technology/Social sciences/Geography Mapping and recycling of marine litter and Ghost nets on the sea floor marGnet 5.416116248348744 8.2 earth sciences 33.72539860988673 0.9689239263534546 linguistics 3.670886075949367 2.9 OBIA template matching 2.347417840375587 4.0 ML type 2.464788732394366 4.2 targets from the bathymetry 5.1056338028169 8.7 ml detection 2.992957746478873 5.1 Medieval Latin 2.5953220121755844 8.1 metadata 2.242870874719641 7.0 Microsoft Corporation https://www.wikidata.org/wiki/Q2283 automatic detection 12.38262910798122 21.1 methodology 2.306805074971165 4.0 mathematical and computer sciences 20.683121381477395 0.47929835319519043 seafloor backscatter mosaic 1.9953051643192488 3.4 Natural science Science and technology/Natural science data 2.7875680871515534 8.7 ml type 2.171361502347418 3.7 bathymetry 6.247997436718999 19.5 Report on the survey and elaborted data in the Italian survey area 2.3117569352708056 3.5 A dedicated workflow in ArcGIS was developed to identify targets from the bathymetry within the MAELSTROM Project - Smart technology for MArinE Litter SusTainable RemOval and Management 39.43196829590489 59.7 Science and technology Science and technology removal 4.901960784313725 8.5 hydrography 20.37974683544304 16.1 output file 2.4798154555940024 4.3 target 3.3963473245754563 10.6 The experiment data metadata are saved on an open repository (the data are available on request) and the workflow is executable so that the analysis is completely reproducible. 3.6327608982826947 5.5 marGnet field experiments 2.9342723004694835 5.0 earth resources and remote sensing 27.830249979278406 0.6449216604232788 Geography Science and technology/Social sciences/Geography ML 2.3644752018454436 4.1 instrumentation and photography 12.302526414166074 0.28509142994880676 bathymetry 3.1079782121115023 9.7 geosciences 39.18410222507813 0.9080290794372559 data 2.537485582468282 4.4 Interior https://www.wikidata.org/wiki/Q608427 hydrography 6.8354430379746836 5.4 computer programming and software 20.683121381477395 0.47929835319519043 MAELSTROM Project 6.113033448673587 10.6 removal 2.5953220121755844 8.1 computer science 27.72151898734177 21.9 Venetia https://www.wikidata.org/wiki/Q1243 Environmental pollution Environment/Environmental pollution marGnet 2.5951557093425603 4.5 During the experiment, it was possible to recognise a unique track for as many categories as possible of benthic Marine Litter (ML) and outline their sinking velocity. 4.359313077939234 6.6 Mountains Environment/Natural resources/Land resources/Mountains detection 3.973085549503364 12.4 POLYGON ((12.308206558227539 45.42613630538257, 12.307777404785156 45.42538332041329, 12.315845489501953 45.42345563312358, 12.32764720916748 45.42216043125598, 12.32764720916748 45.42327490906539, 12.323012351989746 45.423425512487384, 12.316746711730955 45.425835112600126, 12.31241226196289 45.424901404761854, 12.308206558227539 45.42613630538257)) 12.308206558227539 45.42613630538257, 12.307777404785156 45.42538332041329, 12.315845489501953 45.42345563312358, 12.32764720916748 45.42216043125598, 12.32764720916748 45.42327490906539, 12.323012351989746 45.423425512487384, 12.316746711730955 45.425835112600126, 12.31241226196289 45.424901404761854, 12.308206558227539 45.42613630538257 c949f07d-ca16-4ba8-a9dc-6107b6c4a10b POLYGON ((12.308206558227539 45.42613630538257, 12.307777404785156 45.42538332041329, 12.315845489501953 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https://w3id.org/ro-id/7038c51a-d9ac-49aa-92a3-05383cd9073a https://w3id.org/ro-id/72644ac9-6da8-4ed2-a175-89931b27b1d9 https://w3id.org/ro-id/73dc398e-7644-4e5c-839d-1ea943dea33b https://w3id.org/ro-id/74e45610-a4f4-4f22-92ef-9a02ab11e804 https://w3id.org/ro-id/7aa67da6-fa7f-4ecd-b154-2aa1eafa8455 https://w3id.org/ro-id/7bc748c3-54d1-4916-a598-4446500b3683 https://w3id.org/ro-id/8660aa0a-e578-4076-89a3-75a745b12774 https://w3id.org/ro-id/b8905408-cdeb-4b15-a9e6-cdc18bbb4ad7 https://w3id.org/ro-id/bc8370b9-4232-4f93-880f-525ce4ba1924 https://w3id.org/ro-id/cab94b47-01a7-4523-92d5-d31c5ebb2e2f https://w3id.org/ro-id/de1202e6-fef8-40d8-b70c-b5ed6ccc6baf https://w3id.org/ro-id/e593338d-ab22-47ae-8d56-eb8c909e2e43 https://w3id.org/ro-id/e5d3809e-08ea-4fe5-a96c-003aa5fe77bf https://w3id.org/ro-id/1dce6c51-6e53-4110-8f85-7ab5b694f80d https://w3id.org/ro-id/31a6ed78-c6c3-4f9a-b3c6-4c96e9cb8eb0 https://w3id.org/ro-id/6820ba72-9f8f-479f-9206-4d8500274ce9 https://w3id.org/ro-id/7f306457-62d2-4dd4-8930-11c3b9129c1d https://w3id.org/ro-id/818b2c20-be83-4c6f-9191-2aab81b47d9b https://w3id.org/ro-id/856d187d-c96a-4da1-8743-3c477106be9b https://w3id.org/ro-id/b05ec41f-e5e5-45d5-8957-17891e714f81 https://w3id.org/ro-id/bc1a37b6-03ee-4000-9f8a-8966cf1f5924 https://w3id.org/ro-id/c050e1ee-d288-4cea-bab6-26c978ade3a3 https://w3id.org/ro-id/d7842191-ae53-44bd-996c-6a56e6dc663c https://w3id.org/ro-id/d9f3778f-9f3d-43e6-9e5a-36b75a4b96ac https://w3id.org/ro-id/45b83962-460b-40a8-8bcb-de6e2ff1294a https://w3id.org/ro-id/64ca15de-e2eb-465e-8469-0c28a6df89ef Petrizzo, Antonio, Valentina Grande, Vanessa Moschino, and Fantina Madricardo. "Automatic detection of MLs targets from the bathymetry." ROHub. May 09 ,2022. https://doi.org/10.24424/w5qx-b223. POLYGON ((12.308206558227539 45.42613630538257, 12.307777404785156 45.42538332041329, 12.315845489501953 45.42345563312358, 12.32764720916748 45.42216043125598, 12.32764720916748 45.42327490906539, 12.323012351989746 45.423425512487384, 12.316746711730955 45.425835112600126, 12.31241226196289 45.424901404761854, 12.308206558227539 45.42613630538257)) Here some results Results Related documents and resources Document Data input Input It contains Jupyter notebook Notebooks 1747374 https://api.rohub.org/api/resources/09e78055-8aad-4a02-8c5d-2574c53c6011/download/ 2022-05-09 10:57:07.579893+00:00 2022-08-30 14:29:55.484341+00:00 application/pdf EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet official document with description of the application of the tool. 2022-05-09 10:57:07.579893+00:00 4845062 https://api.rohub.org/api/resources/13faac2d-213a-45de-8936-09c728df7396/download/ 2022-05-09 10:55:40.290485+00:00 2022-08-30 14:30:06.537698+00:00 image/png Marine Litter Identification from Bathymetry 2022-05-09 10:55:40.290485+00:00 726695 https://api.rohub.org/api/resources/20119b4f-da6f-4723-8c5c-0f3367af1ec6/download/ 2022-05-09 10:57:14.645946+00:00 2022-08-30 14:29:53.745052+00:00 application/pdf EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet official document with description of ROs developed within MarGnet Project. 2022-05-09 10:57:14.645946+00:00 http://gismarcloud.myqnapcloud.com:8080/share.cgi?ssid=d0c16040872e4a47aee5b6664873057f 2022-08-29 09:49:06.931474+00:00 2022-08-30 14:29:48.715389+00:00 Shape file with targets detected from bathymetry Arcgis workflow output 2022-08-29 09:49:06.931474+00:00 3737442 https://api.rohub.org/api/resources/2fcce231-9d47-4ec7-98d0-f11ce56703b4/download/ 2022-05-09 10:57:00.318622+00:00 2022-08-30 14:29:57.372213+00:00 application/pdf EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet official document with description of the tool. 2022-05-09 10:57:00.318622+00:00 1361042 https://api.rohub.org/api/resources/410ea324-cdcb-42ba-b086-b7ccf0b585a1/download/ 2022-05-09 10:55:56.304257+00:00 2022-08-30 14:30:08.149000+00:00 image/png ArcGis workflow scheme 2022-05-09 10:55:56.304257+00:00 9034979 https://api.rohub.org/api/resources/7c020821-7dd4-443a-abb7-e4d578bf91a7/download/ 2022-05-09 10:56:09.722854+00:00 2022-08-30 14:30:03.652469+00:00 image/png Example of targets detection from bathymetry 2022-05-09 10:56:09.722854+00:00 10597825 https://api.rohub.org/api/resources/aa1513e8-fb5d-4e6d-a1a3-2d3eed682878/download/ 2022-05-09 10:56:21.011021+00:00 2022-08-30 14:30:00.441795+00:00 image/png Example of targets detection from bathymetry (zoom) 2022-05-09 10:56:21.011021+00:00 https://doi.org/10.3997/1873-0604.2012018 2022-05-09 10:57:24.554269+00:00 2022-08-30 14:29:49.853481+00:00 This paper presents a semi-automated method to recognize, spatially delineate and characterise morphometrically pockmarks at the seabed Semi-automated characterisation of seabed pockmarks in the central North Sea 2022-05-09 10:57:24.554269+00:00 https://notebooks.egi.eu/user/da47d3640f619a02cb075c15d288fc09e053bf46b90d26ec335392acdfae866b@egi.eu/doc/tree/datahub/Reliance/MarGnet_ML/arcWorkflow.ipynb 2022-05-09 10:55:53.416434+00:00 2022-08-30 14:29:46.615503+00:00 This Notebook provides a workflow of ArcGis toolboxes to identify ML targets from bathynetry. Marine Litter Targets Identification 2022-05-09 10:55:53.416434+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/dd465b46-0217-426a-ba81-4acadf0d12b9 2022-05-31 11:56:50.978305+00:00 2022-08-30 14:29:46.697417+00:00 Bathymetry metadata description Sacca Fisola, Venice, 2021 metadata description 2022-05-31 11:56:50.978305+00:00 https://reliance.rohub.org/overview?7bc38514-796e-47e6-81cb-b8f91247a854&activetab=overview 2022-05-09 10:57:20.433002+00:00 2022-08-30 14:29:49.671093+00:00 Track of a net from water coloumn data RO created wihin EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet 2022-05-09 10:57:20.433002+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/b9f63328-264e-4d28-94b7-397e50cf2dad 2022-05-31 16:19:48.804055+00:00 2022-08-30 14:29:41.142106+00:00 ArcGis workflow output metadata ArcGis workflow output description 2022-05-31 16:19:48.804055+00:00 http://gismarcloud.myqnapcloud.com:8080/share.cgi?ssid=ccf2ae1e2ec14a848b8607fc268d8bea 2022-08-29 09:41:27.976951+00:00 2022-08-30 14:29:47.792135+00:00 Bathymetric data from Sacca Fisola 2021 survey Sacca Fisola, Venice, 2021 data 2022-08-29 09:41:27.976951+00:00 https://reliance.rohub.org/overview?f8a252e5-47da-410b-9096-526bc50d19a3&activetab=overview 2022-05-09 10:57:22.558491+00:00 2022-08-30 14:29:41.054228+00:00 calculates the sink velocity of a net floating in water starting from water column data RO created wihin EASME/EMFF/2017/1.2.1.12/S2/05/SI2.789314 MarGnet 2022-05-09 10:57:22.558491+00:00 workflow in ArcGIS 11.502347417840376 19.6 The methodology proposed by the marGnet project is to use acoustic and video remote sensing on a large scale to map ML on the seafloor and to model the ML hotspot through modelling. 5.3500660501981505 8.1 bathymetry data 2.288732394366197 3.9 Moreover, the deliverable provides a comparison with the data and efficiency of other seafloor survey methods. 2.708058124174372 4.1 output file 2.7875680871515534 8.7 technical terminology 3.7974683544303796 3.0 experiment 4.2099192618223755 7.3 Fishing Lifestyle and leisure/Leisure/Recreational activities/Fishing experiment data 2.112676056338028 3.6 information 2.1147068247356615 6.6 geosciences 27.830249979278406 0.6449216604232788 Synthetic and plastic chemicals Economy, business and finance/Economic sector/Chemicals/Synthetic and plastic chemicals Library and museum Arts, culture and entertainment/Culture/Library and museum marine litter 5.4209919261822375 9.4 MBES 2.306805074971165 4.0 The first operation block (Execute code) creates the work directory, downloads and extracts the input file, downloads and executes the Matlab code (vel) and finally compresses the output file in only one.zip file. 4.425363276089828 6.7 The so created Research Object is composed by two inputs (Workflow input ports in Fig.) two operation blocks (in light blue in Fig.) and one output (Workflow output ports in Fig.) 3.4346103038309113 5.2 software 4.177215189873418 3.3 Matlab code 2.0539906103286385 3.5 data 3.9215686274509802 6.8 experiment location 1.9953051643192488 3.4 detection of ml 10.856807511737088 18.5 study 2.3389939122076258 7.3 Venice https://www.wikidata.org/wiki/Q641 detection 7.439446366782007 12.9 earth sciences 16.368849689426614 0.4702737629413605 computer science 12.151898734177216 9.6 oceanography 33.72539860988673 0.9689239263534546 bathymetry 2.7681660899653977 4.8 CNR ISMAR Venice antonio.petrizzo@ve.ismar.cnr.it Antonio Petrizzo direttore@ismar.cnr.it CNR-ISMAR CNR ISMAR fantina.madricardo@ve.ismar.cnr.it Fantina Madricardo CNR ISMAR Venice vanessa.moschino@ve.ismar.cnr.it Vanessa Moschino Chemistry service-account-enrichment 10.24424/mgch-7b29 False https://w3id.org/ro-id/72dc783e-9ce2-474c-99c0-3969c014c523 2022-09-20 12:43:22.456882+00:00 https://orcid.org/0000-0003-2388-0744 5871 https://api.rohub.org/api/ros/b90bc0b8-2d26-4e0c-b255-c2399b52d45d/crate/download/ 2022-01-19 19:48:03.265688+00:00 2024-03-05 12:17:12.449514+00:00 2022-01-19 19:48:03.265688+00:00 A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion. application/ld+json https://w3id.org/ro-id/b90bc0b8-2d26-4e0c-b255-c2399b52d45d Carboxylic acids - snapshot Carboxylic acids MANUAL alkyl amino acid anion carboxyl carboxylate carboxylic acid chemical formula fatty acid free radical organic acid protonation earth sciences Chemistry Organic chemical amino acid anion carboxyl group carboxylic acid fatty acid group organic acid chemistry and materials carboxylate anion contain a carboxyl group formula of a carboxylic acid include the amino acids protonation of a carboxylic acid A carboxylic acid is an organic acid that contains a carboxyl group (C(O)OH) attached to an R-group. Deprotonation of a carboxylic acid gives a carboxylate anion. The general formula of a carboxylic acid is R? chemistry organic chemistry Wolniewicz, Małgorzata. "Carboxylic acids." ROHub. Jan 19 ,2022. https://doi.org/10.24424/mgch-7b29. Pictures https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Carboxyl-3D-space-filling-labelled.png/300px-Carboxyl-3D-space-filling-labelled.png 2022-01-19 19:49:57.141598+00:00 2022-09-20 12:43:21.522882+00:00 image/png 300px-Carboxyl-3D-space-filling-labelled.png 2022-01-19 19:49:57.141598+00:00 https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Carboxylic-acid.svg/300px-Carboxylic-acid.svg.png 2022-01-19 19:49:17.752475+00:00 2022-09-20 12:43:20.971265+00:00 image/png 300px-Carboxylic-acid.svg.png 2022-01-19 19:49:17.752475+00:00 https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Carboxylate-resonance-hybrid.png/300px-Carboxylate-resonance-hybrid.png 2022-01-19 19:49:42.590787+00:00 2022-09-20 12:43:21.378826+00:00 image/png 300px-Carboxylate-resonance-hybrid.png 2022-01-19 19:49:42.590787+00:00 Chemistry https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Carboxyl-3D-space-filling-labelled.png/300px-Carboxyl-3D-space-filling-labelled.png 2022-01-19 19:49:57.141598+00:00 2022-09-21 19:34:48.406794+00:00 image/png 300px-Carboxyl-3D-space-filling-labelled.png 2022-01-19 19:49:57.141598+00:00 https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Carboxylate-resonance-hybrid.png/300px-Carboxylate-resonance-hybrid.png 2022-01-19 19:49:42.590787+00:00 2022-09-21 19:34:47.123191+00:00 image/png 300px-Carboxylate-resonance-hybrid.png 2022-01-19 19:49:42.590787+00:00 https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Carboxylic-acid.svg/300px-Carboxylic-acid.svg.png 2022-01-19 19:49:17.752475+00:00 2022-09-21 19:34:44.510173+00:00 image/png 300px-Carboxylic-acid.svg.png 2022-01-19 19:49:17.752475+00:00 2022-10-29 10:59:49.415347+00:00 https://doi.org/10.24424/sc7x-ha64 False 2022-09-21 19:35:01.092825+00:00 11900 https://api.rohub.org/api/ros/6aa4b4a0-c7dc-4762-aee1-e8dc94a1705c/crate/download/ 2022-01-19 19:48:03.265688+00:00 2025-10-18 11:21:10.990436+00:00 2022-01-19 19:48:03.265688+00:00 A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion. application/ld+json https://w3id.org/ro-id/6aa4b4a0-c7dc-4762-aee1-e8dc94a1705c Carboxylic acids MANUAL Wolniewicz, Małgorzata, and Paweł Babalski. "Carboxylic acids." ROHub. Jan 19 ,2022. https://doi.org/10.24424/sc7x-ha64. Pictures organic acid 16.073781291172594 12.2 chemistry and materials 100.0 0.7690860033035278 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical carboxyl group 9.617918313570486 7.3 geochemistry 100.0 0.9274783134460449 protonation of a carboxylic acid 30.099228224917308 27.3 chemistry and materials (general) 100.0 0.7690860033035278 carboxylate anion 44.65270121278942 40.5 fatty acid 12.012644889357217 11.4 Chemistry Science and technology/Natural science/Chemistry carboxylate 4.636459430979979 4.4 chemistry 66.81749622926094 44.3 anion 5.690200210748156 5.4 include the amino acids 2.976846747519294 2.7 amino acid 9.16754478398314 8.7 fatty acid 14.756258234519104 11.2 group 8.827404479578393 6.7 free radical 7.79768177028451 7.4 The general formula of a carboxylic acid is R− 18.96551724137931 12.1 Deprotonation of a carboxylic acid gives a carboxylate anion. 26.175548589341695 16.7 organic chemistry 33.18250377073907 22.0 alkyl 3.898840885142255 3.7 amino acid 11.462450592885373 8.7 A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. 54.858934169278996 35.0 earth sciences 100.0 0.9274783134460449 contain a carboxyl group 2.976846747519294 2.7 carboxyl 7.270811380400421 6.9 anion 7.246376811594202 5.5 protonation 5.163329820864067 4.9 organic acid 12.750263435194942 12.1 chemical formula 5.5848261327713375 5.3 formula of a carboxylic acid 19.294377067254686 17.5 carboxylic acid 26.027397260273972 24.7 carboxylic acid 32.01581027667984 24.3 Paweł Babalski service-account-enrichment Chemistry chemistry and materials 100.0 0.7690860033035278 fatty acid 12.012644889357217 11.4 service-account-enrichment https://doi.org/10.24424/k5t9-z972 False https://w3id.org/ro-id/72dc783e-9ce2-474c-99c0-3969c014c523 2022-09-21 19:38:11.999108+00:00 https://orcid.org/0000-0003-2388-0744 7955 https://api.rohub.org/api/ros/0566d7df-d790-44bd-bcd1-fe89c0582a29/crate/download/ 2022-01-19 19:48:03.265688+00:00 2024-03-05 12:17:12.569780+00:00 2022-01-19 19:48:03.265688+00:00 A carboxylic acid is an organic acid that contains a carboxyl group (C(=O)OH) attached to an R-group. The general formula of a carboxylic acid is R−COOH or R−CO2H, with R referring to the alkyl, alkenyl, aryl, or other group. Carboxylic acids occur widely. Important examples include the amino acids and fatty acids. Deprotonation of a carboxylic acid gives a carboxylate anion. application/ld+json https://w3id.org/ro-id/0566d7df-d790-44bd-bcd1-fe89c0582a29 Carboxylic acids - snapshot Carboxylic acids MANUAL https://w3id.org/ro-id/3250ea92-c99b-4cba-8ba4-f8e4a48ef737 https://w3id.org/ro-id/62521849-feb2-4b0b-9456-85d697be1afe https://w3id.org/ro-id/049e52e8-b766-4e5f-ad02-b2363d3dea6a https://w3id.org/ro-id/2ce15c8f-ad5f-4200-9238-2905b84cbfa0 https://w3id.org/ro-id/380db561-5ac9-4a85-a908-3aed3e5a6dc6 https://w3id.org/ro-id/61f7a284-38fd-4a4d-8f16-3e1b2e4772c3 https://w3id.org/ro-id/7ea4f9e6-fb61-4e45-a05e-e66118a8172e https://w3id.org/ro-id/96bbad8b-b913-4009-add7-292d921daba2 https://w3id.org/ro-id/977a7f7a-bb8f-41ae-a367-6e949ac2544e https://w3id.org/ro-id/c3fa043f-d1ef-4842-9243-3a50e0c83c6b https://w3id.org/ro-id/e1245df8-7fd7-4f45-9a95-ee1416387a03 https://w3id.org/ro-id/e3bd229c-f5b6-47e0-a691-05aa38855b6c https://w3id.org/ro-id/f2062f98-e3a4-4338-9ca6-4af2f70f2041 https://w3id.org/ro-id/df8a7871-3966-4e5f-88d2-a299095de34f https://w3id.org/ro-id/e6226e3c-5527-45eb-a70a-366c1817782b https://w3id.org/ro-id/23f1c7b5-75a2-4d33-86e4-57cda4b5da50 https://w3id.org/ro-id/be27129c-cee8-4082-b624-9e1f58322850 https://w3id.org/ro-id/30a6fb45-fb09-4222-a3d6-29ab5eb8b8e1 https://w3id.org/ro-id/334da04f-4a6d-4cda-820d-b885b15232bb https://w3id.org/ro-id/4552d3a0-8964-496b-86fe-022ce1fe9930 https://w3id.org/ro-id/4a7043f6-a428-412f-aa8b-652c3e488cd8 https://w3id.org/ro-id/604f1eb4-aa92-44cd-8e18-d4e78e0887c5 https://w3id.org/ro-id/e006c931-08f9-497d-b8f0-a6c8e2656015 https://w3id.org/ro-id/f85b3b50-8049-409d-a0b9-f24f94040d1e https://w3id.org/ro-id/01978f65-0163-44c7-abd7-31ac90bd9192 https://w3id.org/ro-id/54ba604e-a315-4848-818c-92637a3da799 https://w3id.org/ro-id/0a4adb15-1148-4e64-b29a-3c1bdf878c51 https://w3id.org/ro-id/40ca46c9-c5e0-466c-af9d-c67ca4831b14 https://w3id.org/ro-id/491506bb-7cf8-4bc1-97d2-1629b82d2878 https://w3id.org/ro-id/86e0cffa-a582-4525-ae70-7ef53ea49699 https://w3id.org/ro-id/a902fdde-e9df-45ab-a3f1-4846073a6f92 https://w3id.org/ro-id/98b7de0b-9ce9-4e62-a400-285cd9ac45b7 https://w3id.org/ro-id/ac60b015-30e4-426d-86b4-c398b1224099 https://w3id.org/ro-id/ed0104f9-06e4-4856-961f-eebc89694f76 Wolniewicz, Małgorzata. "Carboxylic acids." ROHub. Jan 19 ,2022. https://doi.org/10.24424/k5t9-z972. Pictures https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Carboxylic-acid.svg/300px-Carboxylic-acid.svg.png 2022-01-19 19:49:17.752475+00:00 2022-09-21 19:38:00.155957+00:00 image/png 300px-Carboxylic-acid.svg.png 2022-01-19 19:49:17.752475+00:00 https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Carboxyl-3D-space-filling-labelled.png/300px-Carboxyl-3D-space-filling-labelled.png 2022-01-19 19:49:57.141598+00:00 2022-09-21 19:38:03.215000+00:00 image/png 300px-Carboxyl-3D-space-filling-labelled.png 2022-01-19 19:49:57.141598+00:00 https://upload.wikimedia.org/wikipedia/commons/thumb/8/8e/Carboxylate-resonance-hybrid.png/300px-Carboxylate-resonance-hybrid.png 2022-01-19 19:49:42.590787+00:00 2022-09-21 19:38:02.473015+00:00 image/png 300px-Carboxylate-resonance-hybrid.png 2022-01-19 19:49:42.590787+00:00 include the amino acids 2.976846747519294 2.7 Chemistry Science and technology/Natural science/Chemistry free radical 7.79768177028451 7.4 carboxyl group 9.617918313570486 7.3 chemistry 66.81749622926094 44.3 carboxylic acid 32.01581027667984 24.3 carboxylate 4.636459430979979 4.4 formula of a carboxylic acid 19.294377067254686 17.5 organic acid 16.073781291172594 12.2 protonation of a carboxylic acid 30.099228224917308 27.3 anion 7.246376811594202 5.5 chemistry and materials (general) 100.0 0.7690860033035278 amino acid 11.462450592885373 8.7 carboxyl 7.270811380400421 6.9 organic chemistry 33.18250377073907 22.0 carboxylic acid 26.027397260273972 24.7 carboxylate anion 44.65270121278942 40.5 amino acid 9.16754478398314 8.7 anion 5.690200210748156 5.4 Deprotonation of a carboxylic acid gives a carboxylate anion. 26.175548589341695 16.7 contain a carboxyl group 2.976846747519294 2.7 A carboxylic acid is an organic acid that contains a carboxyl group (C(O)OH) attached to an R-group. 54.858934169278996 35.0 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical organic acid 12.750263435194942 12.1 earth sciences 100.0 0.9274783134460449 group 8.827404479578393 6.7 protonation 5.163329820864067 4.9 chemical formula 5.5848261327713375 5.3 geochemistry 100.0 0.9274783134460449 The general formula of a carboxylic acid is R? 18.96551724137931 12.1 alkyl 3.898840885142255 3.7 fatty acid 14.756258234519104 11.2 Applied sciences Earth sciences https://discourse.pangeo.io/t/september-1-2022-handling-large-geo-data-with-julia/2656 2022-09-02 19:15:52.939627+00:00 2022-10-05 11:05:10.738946+00:00 You will find here all the information published to advertise the Pangeo Show & Tell Talk frm Felix Cremer on "Handling large geo data with Julia ". Pangeo discourse post announcing 1st September Show & Tell by Felix Cremer. 2022-09-02 19:15:52.939627+00:00 https://github.com/JuliaDataCubes/ESDLTutorials 2022-09-02 19:36:28.455672+00:00 2022-10-05 11:05:08.571565+00:00 This will become a selection of tutorials on the use of ESDL.jl and YAXArrays.jl julia packages for the handling of large scale out-of-core geospatial datasets. github ESDLtutorial Github repository. 2022-09-02 19:36:28.455672+00:00 https://github.com/JuliaDataCubes/ESDLTutorials/raw/main/data/ne_50m_admin_0_countries.dbf 2022-09-02 19:27:25.914754+00:00 2022-10-05 11:04:59.380562+00:00 Part of ne_50m_admin_0_countries shapefile. ne_50m_admin_0_countries.dbf 2022-09-02 19:27:25.914754+00:00 https://github.com/JuliaDataCubes/ESDLTutorials/raw/main/data/ne_50m_admin_0_countries.shp 2022-09-02 19:28:35.477795+00:00 2022-10-05 11:05:01.072396+00:00 Part of ne_50m_admin_0_countries shapefile. application/x-qgis ne_50m_admin_0_countries.shp 2022-09-02 19:28:35.477795+00:00 https://github.com/JuliaDataCubes/ESDLTutorials/raw/main/data/ne_50m_admin_0_countries.shx 2022-09-02 19:29:06.833916+00:00 2022-10-05 11:05:08.283815+00:00 Part of ne_50m_admin_0_countries shapefile. application/x-qgis ne_50m_admin_0_countries.shx 2022-09-02 19:29:06.833916+00:00 https://hackmd.io/@pangeo/showandtell 2022-09-20 12:05:09.775445+00:00 2022-10-05 11:05:12.569218+00:00 This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback. Each Show and Tell has its own sub-section. HackMD Pangeo Show and Tell 2022-09-20 12:05:09.775445+00:00 https://juliadatacubes.github.io/YAXArrays.jl/dev/ 2022-09-02 19:18:10.607898+00:00 2022-10-05 11:05:10.000002+00:00 YAXArrays.jl is another xarray-like Julia package. A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL. Package Features: - open datasets from a variety of sources (NetCDF, Zarr, ArchGDAL) - interoperability with other named axis packages through YAXArrayBase - efficient mapslices(x) operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets) YAXArrays.jl Documentation 2022-09-02 19:18:10.607898+00:00 https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/data/ne_50m_admin_0_countries.README.html 2022-09-02 19:23:40.734491+00:00 2022-10-05 11:05:10.091697+00:00 Admin 0 & Countries | Natural Earth text/html ne_50m_admin_0_countries.README.html 2022-09-02 19:23:40.734491+00:00 https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/data/ne_50m_admin_0_countries.VERSION.txt 2022-09-02 19:24:56.813174+00:00 2022-10-05 11:05:08.830771+00:00 Version text/plain ne_50m_admin_0_countries.VERSION.txt 2022-09-02 19:24:56.813174+00:00 https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/data/ne_50m_admin_0_countries.cpg 2022-09-02 19:26:00.758390+00:00 2022-10-05 11:05:10.411799+00:00 cpg file from shapefile dataset. ne_50m_admin_0_countries.cpg 2022-09-02 19:26:00.758390+00:00 https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/data/ne_50m_admin_0_countries.prj 2022-09-02 19:27:59.472971+00:00 2022-10-05 11:05:12.806251+00:00 Part of ne_50m_admin_0_countries shapefile (projection information). ne_50m_admin_0_countries.prj 2022-09-02 19:27:59.472971+00:00 https://raw.githubusercontent.com/JuliaDataCubes/ESDLTutorials/main/overallintro.ipynb 2022-09-02 19:19:48.682613+00:00 2022-10-05 11:05:09.458760+00:00 Jupyter Notebook used by Felix during the Pangeo Show & Tell to demonstrate how to use EarthDataLab.jl to do large scale computations. To execute this Jupyter Notebook, data contained in the "input folder" is needed (please create a folder called "data" in the folder where you have stored the notebook). How to use EarthDataLab.jl to do large scale computations (Jupyter Notebook) 2022-09-02 19:19:48.682613+00:00 04jcwf484 Nordic e-Infrastructure Collaboration POLYGON ((6.152342408895493 36.11420992771953, 6.152342408895493 46.14432008685165, 19.042966514825824 46.14432008685165, 19.042966514825824 36.11420992771953, 6.152342408895493 36.11420992771953)) 6.152342408895493 36.11420992771953, 6.152342408895493 46.14432008685165, 19.042966514825824 46.14432008685165, 19.042966514825824 36.11420992771953, 6.152342408895493 36.11420992771953 c23c13de-3616-4fe4-9df0-64c0c303b28b POLYGON ((6.152342408895493 36.11420992771953, 6.152342408895493 46.14432008685165, 19.042966514825824 46.14432008685165, 19.042966514825824 36.11420992771953, 6.152342408895493 36.11420992771953)) 10.24424/2byf-7r07 False 2022-10-05 11:05:15.777066+00:00 163759 https://api.rohub.org/api/ros/77a61d94-3318-4d33-a3c0-4730e7026fdb/crate/download/ 2022-09-02 19:02:01.731061+00:00 2024-03-05 12:18:33.627372+00:00 2022-09-02 19:02:01.731061+00:00 This talk is part of the Pangeo Show & Tell series and was given on September 1st 2022 by Felix Cremer. Bio Felix Cremer received his diploma in mathematics from the University of Leipzig in 2014. In 2016 he started his PhD study on time series analysis of hypertemporal Sentinel-1 radar data. He currently works at the Max-Planck-Institute for Biogeochemistry on the development of the JuliaDataCubes ecosystem in the scope of the NFDI4Earth 5 project. Abstract The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data. It is based on the YAXArrays.jl package. YAXArrays.jl provides functionality to deal with labelled arrays, similar to the xarray python package and it also provides efficient and easy multithreading and distributed computation of user defined functions along arbitrary slices of the data. EarthDataLab.jl uses DiskArrays.jl in the backend to deal with out of memory datasets. In this Show-and-Tell Felix is going to give a short introduction into the EarthDataLab.jl package for raster data handling in Julia. application/ld+json https://w3id.org/ro-id/77a61d94-3318-4d33-a3c0-4730e7026fdb geodata julia Video Handling large geo data with Julia - snapshot Handling large geo data with Julia MANUAL Felix Cremer, and Pangeo Europe. "Handling large geo data with Julia." ROHub. Sep 02 ,2022. https://doi.org/10.24424/2byf-7r07. POLYGON ((6.152342408895493 36.11420992771953, 6.152342408895493 46.14432008685165, 19.042966514825824 46.14432008685165, 19.042966514825824 36.11420992771953, 6.152342408895493 36.11420992771953)) output tool biblio input 138593 https://api.rohub.org/api/resources/9b5c569a-f9bd-4147-9844-4d856bd858db/download/ 2022-09-02 19:30:37.195378+00:00 2022-10-05 11:05:15.216316+00:00 Plot from the Julia Jupyter notebook. image/png plot_italy_julia_pangeo_ST.png 2022-09-02 19:30:37.195378+00:00 A community platform for Big Data geoscience pangeo-europe@gmail.com Pangeo https://pangeo.io/ raster data 13.14031180400891 5.9 memory dataset 14.823008849557521 6.7 computer operations and hardware 100.0 0.9168391823768616 on Sep-1-2022 diploma 7.854406130268199 4.1 In this Show-and-Tell Felix is going to give a short introduction into the EarthDataLab.jl package for raster data handling in Julia. 35.1981351981352 15.1 time series 10.727969348659006 5.6 YAXArrays.jl package 24.557522123893804 11.1 earth sciences 100.0 0.9773926138877869 data 18.262806236080177 8.2 Library and museum Arts, culture and entertainment/Culture/Library and museum mathematical and computer sciences 100.0 0.9168391823768616 EarthDataLab.jl 16.70378619153675 7.5 handling 12.694877505567929 5.7 Plovdiv treatment 15.708812260536398 8.2 data 21.839080459770116 11.4 functionality 8.045977011494253 4.2 in 2014 computer science 51.54639175257732 5.0 Science and technology Science and technology other earth sciences 100.0 0.9773926138877869 The Earth Data Lab (EDL) is a data cube framework in Julia for the efficient handling of raster data. 31.934731934731936 13.7 dataset 10.244988864142538 4.6 This talk is part of the Pangeo Show & Tell series and was given on September 1st 2022 by Felix Cremer. 32.86713286713287 14.1 raster data handling 26.106194690265486 11.8 multithreading 6.8965517241379315 3.6 geo data 19.469026548672566 8.8 YAXArrays.jl 13.585746102449889 6.1 In 2016 Felix Cremer 15.367483296213809 6.9 calculation 7.662835249042146 4.0 dataset 12.452107279693488 6.5 parcel 8.812260536398467 4.6 database 48.453608247422686 4.7 series analysis 15.044247787610619 6.8 https://youtu.be/18_e8wmI9Os 2022-09-02 19:13:04.311770+00:00 2022-10-05 11:05:08.693363+00:00 This is the recorded talk from Felix Cremer during the Pangeo Show & Tell in September 1st, 2022. Felix is going through his Julia Notebook and explain us about handling large geo data with Julia. Youtube video "Handling large geo data with julia by Felix Cremer." 2022-09-02 19:13:04.311770+00:00 Max-Planck-Institute (Germany) fcremer@bgc-jena.mpg.de Felix Cremer pangeo.europe@gmail.com Pangeo Europe Applied sciences Earth sciences Earth observation https://discourse.pangeo.io/t/discrete-global-grid-systems-dggs-use-with-pangeo/2274 2022-10-07 12:57:56.628114+00:00 2022-10-25 15:48:28.199436+00:00 Discussion from Pangeo Discourse on DGGS use with Pangeo. discussion Pangeo discourse on "Discrete Global Grid Systems (DGGS) use with Pangeo" 2022-10-07 12:57:56.628114+00:00 https://discourse.pangeo.io/t/october-6-2022-dggs-and-their-potential-impact-in-geoscience-and-geospatial-communities/2759 2022-10-25 15:45:23.831383+00:00 2022-10-25 15:48:40.680580+00:00 Pangeo discourse announcement. discourse Pangeo discourse announcement Show & Tell on "October 6, 2022: DGGS and their potential impact in Geoscience and Geospatial communities" 2022-10-25 15:45:23.831383+00:00 https://github.com/allixender/pangeo_dggs_2022 2022-10-07 12:51:00.692996+00:00 2022-10-25 15:48:26.907094+00:00 Github repository with examples used during the Pangeo Show and Tell - 06. Oct., 2022 on "DGGS and their potential impact in Geoscience and Geospatial" by Alexander Kmoch (Landscape Geoinformatics Lab, University of Tartu, Estonia). Twitter: @Lgeoinformatics │ @allixender jupyter notebook Pangeo Show and Tell : DGGS play ground 2022-10-07 12:51:00.692996+00:00 https://hackmd.io/@pangeo/showandtell 2022-10-25 07:27:19.067533+00:00 2022-10-25 15:48:28.394740+00:00 This is the shared document we use for all the Pangeo Show and Tell. We collect information, Q&A and feedback. Each Show and Tell has its own sub-section. hackmd HackMD Pangeo Show and Tell 2022-10-25 07:27:19.067533+00:00 University of Tartu, Estonia alexander.kmoch@ut.ee Alexander Kmoch 0000-0003-4386-4450 https://raw.githubusercontent.com/allixender/pangeo_dggs_2022/main/environment.yml 2022-10-17 14:04:26.842481+00:00 2022-10-25 15:48:24.056839+00:00 Conda environment for running DGGS notebook examples. environment environment.yml 2022-10-17 14:04:26.842481+00:00 https://raw.githubusercontent.com/allixender/pangeo_dggs_2022/main/h3_intro.ipynb 2022-10-17 14:07:52.027331+00:00 2022-10-25 15:48:40.921243+00:00 Jupyter Notebook demonstrating how to perform Spatial Data Analysis with H3. H3 h3_intro.ipynb 2022-10-17 14:07:52.027331+00:00 post@simula.no 00vn06n10 Simula Research Laboratory A community platform for Big Data geoscience pangeo-europe@gmail.com Pangeo https://pangeo.io/ POLYGON ((-175.78125000000003 -80.21861403809504, -175.78125000000003 84.00379284323029, 191.25002145767215 84.00379284323029, 191.25002145767215 -80.21861403809504, -175.78125000000003 -80.21861403809504)) -175.78125000000003 -80.21861403809504, -175.78125000000003 84.00379284323029, 191.25002145767215 84.00379284323029, 191.25002145767215 -80.21861403809504, -175.78125000000003 -80.21861403809504 c0cb3d9d-0b6e-46b0-8a78-c03449698c8d POLYGON ((-175.78125000000003 -80.21861403809504, -175.78125000000003 84.00379284323029, 191.25002145767215 84.00379284323029, 191.25002145767215 -80.21861403809504, -175.78125000000003 -80.21861403809504)) https://doi.org/10.24424/tg01-kv33 False 2022-10-25 15:48:48.641529+00:00 10306143 https://api.rohub.org/api/ros/d1f369cd-25a2-4fc6-b418-b2e7feed7cde/crate/download/ 2022-10-04 09:22:53.114240+00:00 2024-03-05 12:17:36.266464+00:00 2022-10-04 09:22:53.114240+00:00 A Discrete Global Grid Systems (DGGS) is a unique type of spatial reference system comprising of a hierarchy of uniquely identifiable discrete grid cells that span the globe at multiple resolutions. A DGGS can support efficient management, storage, integration, exploration, mining, and visualisation of large geospatial datasets, and several systems of tesselation and indexing schemes exist. The main topic of this session is to introduce the audience to the theoretical background of Discrete Global Grid Systems (DGGS), current real-world implementations and exemplary use cases. This includes grid generation, data indexing and sampling with DGGRID, and some spatial analysis with with H3 and rHealPix. application/ld+json https://w3id.org/ro-id/d1f369cd-25a2-4fc6-b418-b2e7feed7cde DGGS OGC grid DGGS and their potential impact in Geoscience and Geospatial communities - snapshot DGGS and their potential impact in Geoscience and Geospatial communities MANUAL Kmoch, Alexander, and Pangeo Europe. "DGGS and their potential impact in Geoscience and Geospatial communities." ROHub. Oct 04 ,2022. https://doi.org/10.24424/tg01-kv33. POLYGON ((-175.78125000000003 -80.21861403809504, -175.78125000000003 84.00379284323029, 191.25002145767215 84.00379284323029, 191.25002145767215 -80.21861403809504, -175.78125000000003 -80.21861403809504)) biblio tool input output 9241429 https://api.rohub.org/api/resources/589b6590-c318-4238-89ec-af25eed99be9/download/ 2022-10-07 12:55:23.133040+00:00 2022-10-25 15:48:45.685583+00:00 Slides for the presentation on DGGS given during Pangeo Show and Tell October 6, 2022 by Alex Kmoch. application/pdf pdf slides DGGS and their potential impact in Geoscience and Geospatial (pdf presentation) 2022-10-07 12:55:23.133040+00:00 1164046 https://api.rohub.org/api/resources/99ff71ac-bc69-4810-ba18-fe48605b11d6/download/ 2022-10-07 13:02:09.118273+00:00 2022-10-25 15:48:47.461184+00:00 A Discrete Global Grid System is a spatial reference system that uses a hierarchical tessellation of cells to partition and address the globe. OGC Abstract Specification, 2017 image/png Discrete Global Grid System (DGGS) 2022-10-07 13:02:09.118273+00:00 globe 4.4609665427509295 3.6 cell 11.834319526627219 6.0 A Discrete Global Grid Systems (DGGS) is a unique type of spatial reference system comprising of a hierarchy of uniquely identifiable discrete grid cells that span the globe at multiple resolutions. 59.37873357228196 49.7 Medical procedure-test Health/Health treatment/Medical procedure-test grid generation 21.013412816691506 14.1 issue 6.195786864931846 5.0 This includes grid generation, data indexing and sampling with DGGRID, and some spatial analysis with with H3 and rHealPix. 18.279569892473116 15.3 system comprising 12.816691505216097 8.6 atmospheric sciences 100.0 0.913747251033783 Discrete Global Grid Systems 23.274161735700197 11.8 indexing 20.11834319526627 10.2 visualisation 11.242603550295858 5.7 earth sciences 100.0 0.913747251033783 mining 11.045364891518737 5.6 database 37.5 0.9 indexing scheme 24.888226527570794 16.7 mining 8.550185873605948 6.9 data 7.311028500619578 5.9 grid cell 10.432190760059614 7.0 indexing 15.489467162329616 12.5 dataset 5.700123915737298 4.6 data indexing 30.849478390462 20.7 The main topic of this session is to introduce the audience to the theoretical background of Discrete Global Grid Systems (DGGS), current real-world implementations and exemplary use cases. 22.34169653524492 18.7 generation 10.408921933085502 8.4 computer operations and hardware 100.0 0.2547450661659241 mathematical and computer sciences 100.0 0.2547450661659241 visualisation 8.674101610904584 7.0 comprehension 7.311028500619578 5.9 management 4.584882280049566 3.7 cartography 62.5 1.5 generation 12.82051282051282 6.5 grid 6.07187112763321 4.9 tesselation 6.07187112763321 4.9 cell 9.169764560099132 7.4 comprising 9.664694280078896 4.9 https://youtu.be/kkLRtyZtxs0 2022-10-25 07:25:21.367265+00:00 2022-10-25 15:48:20.916495+00:00 This YouTube video is part of the Pangeo Show & Tell series and was given on October 6 2022 by Alexander Kmoch, Department of Geography of the University of Tartu, (Estonia). show&tell youtube YouTube video "DGGS and their potential impact in Geoscience and Geospatial communities" 2022-10-25 07:25:21.367265+00:00 pangeo.europe@gmail.com Pangeo Europe service-account-enrichment Oceanography Environmental research Earth observation https://210507-004.oceansvirtual.com/view/content/skdwP611e3583eba2b/ecf65c2aaf278557ad05c213247d67a54196c9376a0aed8f1875681f182daeed 2022-01-28 16:07:40.875698+00:00 2022-10-27 21:00:18.383109+00:00 Related publication of the modelling published in OCEANS 2021 Detecting macro floating objects on coastal water bodies using sentinel-2 data 2022-01-28 16:07:40.875698+00:00 https://doi.org/10.5194/isprs-annals-V-3-2021-285-2021 2022-01-28 16:07:43.339740+00:00 2022-10-27 21:00:10.761926+00:00 Publication with further details of the modelling published in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Towards detecting floating objects on a global scale with learned spatial features using sentinel 2 2022-01-28 16:07:43.339740+00:00 https://doi.org/10.5281/zenodo.5827376 2022-01-28 16:07:34.662177+00:00 2022-10-27 21:00:06.699231+00:00 Contains input analysis-ready input images used in the Jupyter notebook of Detecting floating objects using deep learning and Sentinel-2 imagery Input images 2022-01-28 16:07:34.662177+00:00 https://doi.org/10.5281/zenodo.5911143 2022-01-28 16:07:38.160206+00:00 2022-10-27 21:00:18.581833+00:00 Contains outputs, (predictions and interactive figure), generated in the Jupyter notebook of Detecting floating objects using deep learning and Sentinel-2 imagery Outputs 2022-01-28 16:07:38.160206+00:00 https://github.com/Environmental-DS-Book/ocean-modelling-litter-philab/blob/main/.binder/environment.yml 2022-01-31 11:32:03.379546+00:00 2022-10-27 21:00:11.773076+00:00 Conda environment when user want to have the same libraries 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39.03722381230471, 26.52744474524991 39.04105711064335, 26.521543885417145 39.04105711064335, 26.521543885417145 39.03722381230471)) https://doi.org/10.24424/xe24-7z73 False 2022-10-27 21:00:20.753040+00:00 386374 https://api.rohub.org/api/ros/59fb5813-d6c0-41b0-96a8-9ce42df766ee/crate/download/ 2022-01-28 16:07:18.008253+00:00 2024-03-05 12:17:34.761343+00:00 2022-01-28 16:07:18.008253+00:00 The research object refers to the Detecting floating objects using deep learning and Sentinel-2 imagery notebook published in the Environmental Data Science book. application/ld+json https://w3id.org/ro-id/59fb5813-d6c0-41b0-96a8-9ce42df766ee Environmental Science Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book - snapshot Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book MANUAL Raquel Carmo, Jamila Mifdal, and Alejandro 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22:54:53.791364+00:00 The research object refers to the Exploring Land Cover Data (Impact Observatory) notebook published in the Environmental Data Science book. application/ld+json https://w3id.org/ro-id/c60a3a02-d72c-44ec-830a-736fae79158e Environmental Science Jupyter Notebook Exploring Land Cover Data (Impact Observatory) (Jupyter Notebook) published in the Environmental Data Science book - snapshot Exploring Land Cover Data (Impact Observatory) (Jupyter Notebook) published in the Environmental Data Science book MANUAL James Millington, Amandine Debus, and Anne Foilloux. 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post@simula.no 00vn06n10 Simula Research Laboratory 101017502 RELIANCE Development of services for research lifecycle management integrated in European Open Science Cloud (EOSC) for remote sensing data exploitation 9.641255605381165 8.6 computer science 28.71794871794872 5.6 Iceland https://www.wikidata.org/wiki/Q189 metadata 4.9401197604790426 6.6 extraction 5.450500556173527 4.9 RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work. 30.829596412556054 27.5 Weather Weather Research and development Economy, business and finance/Business information/Strategy and marketing/Research and development FDO Conference 2022: FAIR Research Objects for realizing Open Science with RELIANCE EOSC project. 20.291479820627803 18.1 technology 7.110778443113773 9.5 Development of services for research lifecycle management integrated in European Open Science Cloud (EOSC) for remote sensing data exploitation 9.641255605381165 8.6 text mining 6.451612903225806 5.8 platform 7.675194660734149 6.9 Sport organisation Sport/Sport organisation climate change geohazard 7.699711260827718 8.0 Ro model 6.8334937439846 7.1 earth resources and remote sensing 70.59765442880769 0.6432071924209595 aim 3.9670658682634734 5.3 Italy https://www.wikidata.org/wiki/Q38 geosciences 29.40234557119231 0.2678814232349396 politics 21.53846153846154 4.2 data 4.865269461077845 6.5 earth sciences 71.04067086146664 0.994600772857666 data 5.561735261401557 5.0 earth sciences 28.959329138533363 0.405443400144577 Development of services for research lifecycle management integrated in European Open Science Cloud (EOSC) for remote sensing data exploitation 9.641255605381165 8.6 modernisation 3.3682634730538923 4.5 technology 5.450500556173527 4.9 data cubes 11.357074109720886 11.8 research 7.2604790419161676 9.7 geohazard scientific research 5.389797882579403 5.6 geohazard 3.9670658682634734 5.3 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4.491577134467661 52.16031415158596 POINT (4.491577134467661 52.16031415158596) False https://w3id.org/ro-id/640923be-22b3-4418-977d-5a81675862a6 2022-11-05 10:31:51.764686+00:00 mailto:annefou@geo.uio.no 14207037 https://api.rohub.org/api/ros/97352931-9830-4677-ad2a-1a2c7c71bf6c/crate/download/ 2022-10-23 19:32:43.679002+00:00 2024-03-05 12:18:22.752775+00:00 2022-10-23 19:32:43.679002+00:00 The H2020 Reliance project delivers a suite of innovative and interconnected services that extend European Open Science Cloud (EOSC)’s capabilities to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The project has delivered 3 complementary technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining. RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work. RoHub implements the full RO model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable. In our presentation at the 1st international FAIR Digital Object Conference, we will showcase different types of ROs for the 3 Earth Science communities represented in Reliance to highlight how the scientists in our respective disciplines changed their working methodology towards Open Science. application/ld+json https://w3id.org/ro-id/97352931-9830-4677-ad2a-1a2c7c71bf6c FAIR Digital Object FAIR FDO climate change geohazards sea monitoring Conference paper FDO Conference 2022: FAIR Research Objects for realizing Open Science with RELIANCE EOSC project - snapshot FDO Conference 2022: FAIR Research Objects for realizing Open Science with RELIANCE EOSC project MANUAL https://w3id.org/ro-id/97352931-9830-4677-ad2a-1a2c7c71bf6c/c4b78c8f-43f9-499a-a425-8e1e89dc0968 https://w3id.org/ro-id/0b265c9a-8c72-4a62-af1b-474bce328a57 https://w3id.org/ro-id/3c373c51-ef8f-429e-8391-ac3b318db924 https://w3id.org/ro-id/8566e351-97cd-4676-8b9f-5ad8d93c6af4 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https://w3id.org/ro-id/00c3b3c8-ab4a-40a9-b538-8285c4343b04 https://w3id.org/ro-id/152f28a8-049e-497d-845b-e63bfdda9d3f https://w3id.org/ro-id/194db50a-ca8b-41b2-a506-1200e7765271 https://w3id.org/ro-id/1a365d97-9c22-4084-9a8f-388a41ee2613 https://w3id.org/ro-id/4d737ce9-ae9c-44ea-a919-782fd4fbe5a1 https://w3id.org/ro-id/da197f41-c78b-4813-8ded-7c21d8778109 Anne Foilloux, Federica Foglini, and Elisa Trasatti. "FDO Conference 2022: FAIR Research Objects for realizing Open Science with RELIANCE EOSC project." ROHub. Oct 23 ,2022. https://doi.org/10.24424/nz65-v565. POINT (4.491577134467661 52.16031415158596) biblio https://youtu.be/TR47NYd5_yw?t=6255 2022-10-29 16:58:18.851294+00:00 2022-11-05 10:31:46.057686+00:00 Presentation given by Anne Fouilloux during the FDO 2022 Conference at Leiden. youtube Youtube Video: FAIR Research Objects for realising Open Science with RELIANCE EOSC Project 2022-10-29 16:58:18.851294+00:00 3400491 https://api.rohub.org/api/resources/2063808e-5ac7-4691-b95c-deb85074d30e/download/ 2022-11-05 10:32:51.893210+00:00 2022-11-05 10:32:53.696892+00:00 image/jpeg FDO2022-sketch.jpg 2022-11-05 10:32:51.893210+00:00 3400491 https://api.rohub.org/api/resources/2a6a9b36-f741-418a-ad1d-9e666a491a8b/download/ 2022-10-29 17:19:25.539876+00:00 2022-11-05 10:31:51.582821+00:00 image/jpeg FDO2022-sketch.jpg 2022-10-29 17:19:25.539876+00:00 12891281 https://api.rohub.org/api/resources/3af5ce63-e489-4915-86a2-1e92c4063291/download/ 2022-10-29 17:06:30.777253+00:00 2022-11-05 10:31:48.028146+00:00 Slides used by Anne Fouilloux for the presentation of FAIR Research Objects for realising Open Science with RELIANCE EOSC Project. application/pdf Presentation given by Anne Fouilloux at FDO 2022 Conference (slides) 2022-10-29 17:06:30.777253+00:00 https://doi.org/10.3897/rio.8.e93940 2022-10-23 19:38:58.377108+00:00 2022-11-05 10:31:35.850483+00:00 Conference Paper for the 1st International Conference on FAIR Digital Objects, 26-28 October 2022, Leiden (Nederlands). Open Science Conference Abstract: FAIR Research Objects for realizing Open Science with RELIANCE EOSC project 2022-10-23 19:38:58.377108+00:00 https://www.fdo2022.org 2022-10-24 12:26:18.895297+00:00 2022-11-05 10:31:33.153072+00:00 Website for the 1st International Conference on FAIR Digital Objects. WebSite 1st International Conference on FAIR Digital Objects 2022-10-24 12:26:18.895297+00:00 https://www.fdo2022.org/programme/leiden-declaration 2022-10-29 17:11:06.766028+00:00 2022-11-05 10:31:36.622775+00:00 FDO2022 will conclude with the formal signing and publication of the ‘Leiden Declaration on FAIR Digital Objects’, which text you can see below. While the signing will take place at the end of the conference, in Leiden, we would like to invite you to add your name to the movement initiated by FDO2022 by signing the declaration online. It is an opportunity for all of us working in research, technology, policy and beyond to support an unprecedented effort to further develop FAIR digital objects, open standards and protocols, and increased reliability and trustworthiness of data. In short, a new environment that works as a truly meaningful data space. You can sign the declaration using the button at the end of the page. Join us! Leiden Declaration on FAIR Digital Objects 2022-10-29 17:11:06.766028+00:00 12891281 https://api.rohub.org/api/resources/96a6be58-73e9-4cce-9d01-f3448a9fdb3d/download/ 2022-10-29 17:07:24.417771+00:00 2022-11-05 10:31:49.996030+00:00 application/pdf 93940-FDO-AnneFouilloux.pptx.pdf 2022-10-29 17:07:24.417771+00:00 3400491 https://api.rohub.org/api/resources/b1f53ad7-cec8-4798-9712-715caeee0ce1/download/ 2022-10-29 17:18:54.123218+00:00 2022-11-05 10:31:50.641932+00:00 Picture taken during the closing ceremony of the FDO2022 Conference at Leiden. image/jpeg FDO2022 sketch 2022-10-29 17:18:54.123218+00:00 Picture taken during the final ceremony of the FDO 2022 Conference in Leiden. FDO2022-sketch.jpg https://youtu.be/w39xvNrqTR8 2022-10-23 20:53:49.501941+00:00 2022-11-05 10:31:32.348496+00:00 A short video to introduce the 3 RELIANCE services. RoHUB is a Research Object web portal to create and manage Research Objects. Text mining service aims at enriching Research Objects (AI service). And the ADAM platform is a datacube service that enables efficient access to large amount of Earth Observation data such as Copernicus Satellite observations. Introduction to the 3 RELIANCE services 2022-10-23 20:53:49.501941+00:00 geology 28.959329138533363 0.405443400144577 entourage 3.8173652694610776 5.1 Science and technology Science and technology research work 6.9297401347449465 7.2 metadata 6.062874251497006 8.1 exploitation 3.9670658682634734 5.3 metadata extraction 7.8922040423484106 8.2 s capability 9.81713185755534 10.2 research object management platform 24.157844080846967 25.1 The project has delivered 3 complementary technologies: Research Objects (ROs) Data Cubes and AI-based Text Mining. 19.955156950672645 17.8 European Union https://www.wikidata.org/wiki/Q458 geology 71.04067086146664 0.994600772857666 European Union s Horizon research 8.180943214629451 8.5 European Open Science Cloud 10.67853170189099 9.6 technology 4.790419161676647 6.4 text mining 5.538922155688623 7.4 Weather Weather RoHub 7.78642936596218 7.0 management 5.389221556886228 7.2 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 direttore@ismar.cnr.it CNR-ISMAR Applied sciences Earth observation post@simula.no 00vn06n10 Simula Research Laboratory 42eca08c-9d7d-4da3-967a-a62de335f353 POINT (12.67381668003509 41.82716033495593) 12.67381668003509 41.82716033495593 POINT (12.67381668003509 41.82716033495593) https://doi.org/10.24424/pe96-gn27 False 2022-11-05 10:54:35.865610+00:00 4507254 https://api.rohub.org/api/ros/042f0584-c14d-4374-9158-d84f4677c9fe/crate/download/ 2022-11-05 10:39:28.725548+00:00 2024-03-05 12:22:15.118928+00:00 2022-11-05 10:39:28.725548+00:00 Presentation given at ESA-NASA Open Innovation for EO Programmes 2022. This presentation gives an overview of the PAngeo Community and perspectives on creating Open Source user workflows. application/ld+json https://w3id.org/ro-id/042f0584-c14d-4374-9158-d84f4677c9fe earth observation open innovation open source user pathways Presentation Perspective from the Pangeo Community on creating Open Source user workflows - ESA-NASA Open Innovation for EO Programmes 2022 - snapshot Perspective from the Pangeo Community on creating Open Source user workflows - ESA-NASA Open Innovation for EO Programmes 2022 MANUAL Anne Foilloux, and Pangeo Europe. "Perspective from the Pangeo Community on creating Open Source user workflows - ESA-NASA Open Innovation for EO Programmes 2022." ROHub. Nov 05 ,2022. https://doi.org/10.24424/pe96-gn27. POINT (12.67381668003509 41.82716033495593) 283342 https://api.rohub.org/api/resources/962b2a95-0d43-44a6-9739-0e6f04c7b5f4/download/ 2022-11-05 10:42:27.761506+00:00 2022-11-05 10:54:33.520392+00:00 image/png ThePangeoCommunity.pptx.png 2022-11-05 10:42:27.761506+00:00 283342 https://api.rohub.org/api/resources/a6b6f5ee-56b3-4c72-96cb-045316d5aa1e/download/ 2022-11-05 10:42:51.431306+00:00 2022-11-05 10:54:34.343706+00:00 image/png ThePangeoCommunity.pptx.png 2022-11-05 10:42:51.431306+00:00 5222989 https://api.rohub.org/api/resources/a7dcbb78-e00c-44cc-9a8f-4436feef3e4a/download/ 2022-11-05 10:47:35.327706+00:00 2022-11-05 10:54:34.934021+00:00 pdf presentation. Slides used to present the perspectives from the Pangeo Community on creating Open Source User Workflows. This presentation has been given by Anne Fouilloux at the ESA-NASA Open Innovation for EO Programmes 2022 (November 2-4 2022). application/pdf DEI pangeo Slides "Perspective from the Pangeo Community on creating Open Source user workflows" 2022-11-05 10:47:35.327706+00:00 5222989 https://api.rohub.org/api/resources/f7c4b3a0-2173-45c0-8bf0-c2ae3117dfbb/download/ 2022-11-05 10:47:51.193900+00:00 2022-11-05 10:54:35.792343+00:00 application/pdf ThePangeoCommunity-OpenInnovation2022.pdf 2022-11-05 10:47:51.193900+00:00 NICEST-2 - the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives. Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034 A community platform for Big Data geoscience pangeo-europe@gmail.com Pangeo https://pangeo.io/ Roman 4.3964232488822645 5.9 code of conduct.md 13.416149068322982 21.6 Communities Society/Communities http 3.867791842475387 5.5 code of conduct http 5.7142857142857135 9.2 fact 3.055141579731743 4.1 Italy Presentation given at ESA-NASA Open Innovation for EO Programmes 2022. 24.765342960288805 34.3 Food and drink Lifestyle and leisure/Lifestyle/Food and drink European Space Agency 15.499254843517136 20.8 Norway earth observation programmes November Esa esrin Frascati 5.900621118012422 9.5 governance 6.3338301043219065 8.5 ESRIN 5.766526019690576 8.2 geosciences 54.081448369598 0.88709956407547 Italy 5.737704918032787 7.7 workflow 9.423347398030943 13.4 blob 4.843517138599105 6.5 programmes 2022 2.484472049689441 4.0 Diversity, Equity and Inclusion 1.8633540372670807 3.0 governance http 9.813664596273291 15.8 Open Source user workflowsThe Pangeo community 5.217391304347826 8.4 innovation 2.6825633383010428 3.6 Open Innovation for Earth Observation Programmes November ESA ESRIN Frascati (Rm), Italy 9.31407942238267 12.9 conduct 4.3964232488822645 5.9 overview 3.8002980625931437 5.1 EO 4.008438818565401 5.7 code 4.470938897168405 6.0 mathematical and computer sciences 45.918551630402 0.7532033324241638 European Space Agency community 4.321907600596124 5.8 workflow 8.94187779433681 12.0 ESA-NASA Open Innovation 29.503105590062113 47.5 http 4.3964232488822645 5.9 professional 4.470938897168405 6.0 code 3.79746835443038 5.4 earth sciences 100.0 1.4512799978256226 Pangeo Community 8.016877637130802 11.4 Software Economy, business and finance/Economic sector/Computing and information technology/Software aerospace engineering 25.161290322580644 3.9 Italy 4.922644163150492 7.0 geology 100.0 1.4512799978256226 Election Politics/Election Open Source user workflow 14.906832298136646 24.0 Space programme Science and technology/Research/Scientific exploration/Space programme National Aeronautics and Space Administration This presentation gives an overview of the PAngeo Community and perspectives on creating Open Source user workflows. 10.469314079422382 14.5 Frascati 6.39943741209564 9.1 earth resources and remote sensing 54.081448369598 0.88709956407547 November 6.557377049180328 8.8 Discovery and innovation Science and technology/Research/Discovery and innovation computer programming and software 45.918551630402 0.7532033324241638 National Aeronautics and Space Administration 16.095380029806257 21.6 Perspective from the Pangeo Community on creating Open Source user workflows - ESA-NASA Open Innovation for EO Programmes 2022. 36.82310469314079 51.0 National Aeronautics and Space Administration 16.526019690576653 23.5 overview 4.078762306610408 5.8 Open Innovation 8.016877637130802 11.4 Open Innovation for Earth Observation Programmes November ESA ESRIN Frascati (Rm), Italy 18.62815884476534 25.8 November 5.6258790436005635 8.0 perspective from the Pangeo Community 4.472049689440993 7.2 astronautics 74.83870967741936 11.6 Science and technology Science and technology European Space Agency 15.752461322081576 22.4 overview of the Pangeo community 6.708074534161491 10.8 Rm 3.79746835443038 5.4 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 pangeo.europe@gmail.com Pangeo Europe service-account-enrichment Environmental research Applied sciences Earth sciences Climatology Earth observation jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 post@simula.no 00vn06n10 Simula Research Laboratory 10.678710937500002 60.057529736844224 POINT (10.678710937500002 60.057529736844224) c60163e5-b01c-4752-a261-998161d6e80f POINT (10.678710937500002 60.057529736844224) service-account-enrichment https://doi.org/10.24424/xnz3-m908 False https://w3id.org/ro-id/b47069ec-f001-4783-8def-cbd54858f571 2022-12-05 13:24:41.588489+00:00 mailto:annefou@geo.uio.no 16695562 https://api.rohub.org/api/ros/107487d2-a9d5-4224-8b00-b321e133b6c8/crate/download/ 2022-12-05 12:17:46.970937+00:00 2024-03-05 12:22:13.193588+00:00 2022-12-05 12:17:46.970937+00:00 Presentation (slides) and demo (video) by Anne Fouilloux for the RELIANCE use case on Climate Change. The presentation and demo were given during the pan-europeans digital assets supporting research communities. Agenda of the event: On 5-6 December 2022, EOSC Future and the INFRAEOSC-07 projects (C-SCALE, DICE, EGI-ACE, OpenAIRE Nexus, Reliance) are hosting an online use case showcase. Check out the agenda and register for this online interactive event by 4 December, 23.59 CET. Over 2 half-day webinars, actual EOSC users will present how their research communities are using EOSC digital assets to address scientific and societal challenges related to 3 UN Sustainable Development Goals (SDGs): • Climate action (SDG 13) • Industry, Innovation & infrastructure (SDG 9) • Good health & well-being (SDG 3) There will also be a session with use cases related to Open Science more broadly. Why ‘use cases’? The demonstrative, first-hand format of the event will enable real research communities to show how their work can be leveraged by EOSC. Researchers, disciplinary groups and anyone interested to learn about both EOSC-related tools and services for data sharing and discoverability as well as discipline-related solutions are invited to the webinar. Attendees will also hear first-hand accounts from early-adopter communities that have integrated some of these core EOSC services. 1 programme, 2 days Check out the agenda to get a glimpse of the cases in the programme, in addition to a at the users, EU and UN officials who will be weighing in on discussions. Day 1 – 5 December • 09.30-11.00: Digital assets supporting SDG 13: Climate action • 11.15-12.00: Digital assets supporting SDG 3: Good health and well-being • 12:15-13:00: Discovering services for open science Day 2 – 6 December • 09.00-09.45: Digital assets supporting SDG 9 Industry, innovation and infrastructure • 10.00-10.45: Experiences from Early Adopters approaching EOSC: the RELIANCE Open challenge • 11.00-12.00: Lessons Learnt from use cases and Looking forward application/ld+json https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8 deep learning forecasting sea-ice Presentation Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC - snapshot Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC MANUAL https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/1c144684-1ea3-4cb5-93e6-8cffecf3b849 Anne Foilloux, Jean Iaquinta, and Alejandro Coca-Castro. "Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC." ROHub. Dec 05 ,2022. https://doi.org/10.24424/xnz3-m908. POINT (10.678710937500002 60.057529736844224) 14231483 https://api.rohub.org/api/resources/35f2e92c-4366-4f23-bd50-2080645e81dd/download/ 2022-12-10 21:40:02.993477+00:00 2022-12-10 21:40:04.312065+00:00 video/mp4 EOSC-Webinar.mp4 2022-12-10 21:40:02.993477+00:00 378820 https://api.rohub.org/api/resources/584a2c4a-1226-4298-b3ea-12d8b7f05b19/download/ 2022-12-05 12:22:12.769408+00:00 2022-12-05 13:24:37.948951+00:00 image/png reproducible.png 2022-12-05 12:22:12.769408+00:00 14231483 https://api.rohub.org/api/resources/66399eea-b67a-4dca-b280-4408ec6aeb3d/download/ 2022-12-10 21:40:05.324719+00:00 2022-12-10 21:40:06.372640+00:00 video/mp4 EOSC-Webinar.mp4 2022-12-10 21:40:05.324719+00:00 2965182 https://api.rohub.org/api/resources/768ea044-4432-4771-8cab-5e18c98031fd/download/ 2022-12-10 21:40:01.253499+00:00 2022-12-10 21:40:02.721239+00:00 application/pdf ClimateChange-EOSC-RELIANCE.pptx.pdf 2022-12-10 21:40:01.253499+00:00 10.24424/k98q-y763 14231483 https://api.rohub.org/api/resources/8771e967-1344-4704-89e5-50fddc940f7f/download/ 2022-12-05 12:33:43.590961+00:00 2022-12-05 13:24:41.485903+00:00 Demonstration given during the webinar. This demo goes with the presentation (slides) and show how the original work was published s a paper in nature communications. The code and data were available and Alejandro Coca-Castro re-used it to create an executable Research Object with a Jupyter Notebook. This Jupyter Notebook examplifies the use of IceNet (probabilistic deep learning to forecast sea-ice). This executable Research Object was forked and deviated work was created e.g. the Jupyter notebook was updated to make it more accessible to people that are not from the Climate community. We use B2DROP to store the new Jupyter notebook and the results to share while doing. Whenever we update the notebook or add figures, the corresponding Research Object is updated live on RoHub. We are now getting close to Open Science e.g. sharing while doing. video/mp4 mp4 Demo showing the usage of RELIANCE service for seasonal sea-ice forecasting. 2022-12-05 12:33:43.590961+00:00 https://docs.google.com/presentation/d/1QPrWh-PuW514mGsVrc9GEIrKYGY7k0L_/edit?usp=sharing&ouid=117642930190987755261&rtpof=true&sd=true 2022-12-05 12:24:19.690502+00:00 2022-12-05 13:24:34.792481+00:00 Climate change: Collaborative, reproducible and transparent science for seasonal sea-ice forecasting. Digital assets supporting SDG 13: Climate action Climate change presentation (slides) from Google doc 2022-12-05 12:24:19.690502+00:00 378820 https://api.rohub.org/api/resources/cd28444b-2473-4afa-9c9f-367c3b86c82a/download/ 2022-12-10 21:40:01.285478+00:00 2022-12-10 21:40:02.732497+00:00 image/png reproducible.png 2022-12-10 21:40:01.285478+00:00 10.24424/9y70-kv25 2965182 https://api.rohub.org/api/resources/e29d96df-b801-495a-997e-2e0fe9c339e9/download/ 2022-12-05 12:27:21.702898+00:00 2022-12-05 13:24:39.265968+00:00 Presentation (same as the google doc) but in pdf format. application/pdf Climate change presentation (slides) showing the usage of RELIANCE services. 2022-12-05 12:27:21.702898+00:00 The Alan Turing Institute acoca@turing.ac.uk Alejandro Coca-Castro Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 Environmental research Applied sciences post@simula.no 00vn06n10 Simula Research Laboratory Data Managers Network meeting 19.43887775551102 19.4 interest 23.55848434925865 14.3 engineering 100.0 0.74220210313797 Data Managers Network 11.957671957671957 11.3 experience 14.074074074074074 13.3 meeting 8.359788359788359 7.9 The topic of the meeting was "EOSC in practise" where different speakers gave their perspectives and experience with involvement in EOSC. 60.16016016016016 60.1 experience 21.58154859967051 13.1 different speaker 7.615230460921844 7.6 communications and radar 100.0 0.74220210313797 geology 100.0 0.5002459287643433 meeting 14.662273476112025 8.9 6efe5043-da0f-4d4e-be85-5f33175e9d38 POINT (10.737304631620646 59.91051696907157) service-account-enrichment 10.737304631620646 59.91051696907157 POINT (10.737304631620646 59.91051696907157) https://doi.org/10.24424/m77z-a405 False https://w3id.org/ro-id/e4bff7dc-eecc-4a4e-b32c-1c8afe18deb6 2022-12-11 18:55:09.202056+00:00 mailto:annefou@geo.uio.no 6278613 https://api.rohub.org/api/ros/73e65f3b-c8eb-4aca-b7dc-af81e4ec9b4a/crate/download/ 2022-11-21 14:37:49.531319+00:00 2024-03-05 12:18:20.254802+00:00 2022-11-21 14:37:49.531319+00:00 This presentation has been given at the Data Managers Network meeting on Tuesday 22 November 2022. The topic of the meeting was "EOSC in practise" where different speakers gave their perspectives and experience with involvement in EOSC. application/ld+json https://w3id.org/ro-id/73e65f3b-c8eb-4aca-b7dc-af81e4ec9b4a c-scale egi-ace eosc eosc-future eosc-life eosc-nordic eosc-reliance open science Presentation Experiences with involvement in EOSC - snapshot Experiences with involvement in EOSC MANUAL https://w3id.org/ro-id/73e65f3b-c8eb-4aca-b7dc-af81e4ec9b4a/002195f7-9561-4560-a21a-23ce2fa79a63 https://w3id.org/ro-id/0ec00322-f82f-48e4-a421-a83707c7e1c6 https://w3id.org/ro-id/38394c7a-6a80-4ecd-8d51-341e03f2b55c https://w3id.org/ro-id/6b79f2d2-b5e0-431a-b995-79b66de72ae0 https://w3id.org/ro-id/80f52e02-f461-4293-b90d-5c62dea8b20f https://w3id.org/ro-id/f6ef1bbe-f982-48c3-b62f-ebc6487df8e3 https://w3id.org/ro-id/615203c3-b1ef-4d03-9dd5-e76a1c8d90a7 https://w3id.org/ro-id/9a0352a1-5b77-472d-853f-04dcd55104a5 https://w3id.org/ro-id/ace99363-94aa-499c-8ae1-22003b87f7f0 https://w3id.org/ro-id/1c79da79-2a2f-48df-9053-28bc16316e7a https://w3id.org/ro-id/24642550-abbe-419d-a65a-fc6174afa2eb https://w3id.org/ro-id/2dada1a7-7dfa-41b4-9e8a-9f4c9476601d https://w3id.org/ro-id/cabc52f6-bcc2-4b21-8fcc-a668bafc386d https://w3id.org/ro-id/e1bfc592-c069-461f-a28a-061ad482d76b https://w3id.org/ro-id/f219b211-5158-4bd2-a42d-e5b01d2bc2c3 https://w3id.org/ro-id/fe04f709-2bee-41a4-8b3f-8244888fce0d https://w3id.org/ro-id/1290921e-ee4a-4d6b-85d6-ad8e83c42b65 https://w3id.org/ro-id/4d835e29-0edf-424a-84cf-76afc80c1ac4 https://w3id.org/ro-id/0585e6e9-c580-417f-894f-aa84fa3fda0a https://w3id.org/ro-id/46b4dbc9-50aa-4519-bdda-fc4b77de4a2d https://w3id.org/ro-id/d1fdaf06-68c9-40f6-88f0-c03e13f4c3f6 https://w3id.org/ro-id/db61e2a6-9030-4602-b617-cb229a2eaa2c https://w3id.org/ro-id/fb38bc88-13fb-48f3-aed9-25c526750692 https://w3id.org/ro-id/30c0ac24-a69e-4856-875b-390cbec96ed4 https://w3id.org/ro-id/b427380d-ca69-41bf-a153-4ee4b67ea862 https://w3id.org/ro-id/d960895e-d946-49cd-837f-5be67ee479ef https://w3id.org/ro-id/98786acc-9f7a-4fcc-aa21-7db254d0d7e0 Anne Foilloux, and admin NordicESMHub. "Experiences with involvement in EOSC." ROHub. Nov 21 ,2022. https://doi.org/10.24424/m77z-a405. POINT (10.737304631620646 59.91051696907157) biblio 207682 https://api.rohub.org/api/resources/2cc72630-5f90-4284-9f30-ce8b647935b2/download/ 2022-11-21 14:41:58.730765+00:00 2022-12-11 18:55:07.148312+00:00 1st slide of Anne Fouilloux's presentation. image/png EOSC-experienceAF.png 2022-11-21 14:41:58.730765+00:00 7362418 https://api.rohub.org/api/resources/5c0b4dc8-8ddc-4377-8e10-88022bd34ddf/download/ 2022-11-21 14:44:35.309807+00:00 2022-12-11 18:55:08.476244+00:00 Slides for Anne Fouilloux's presentation at UiO Data Manager Meeting on EOSC in practise. application/pdf My experience with EOSC (Slides, pdf) 2022-11-21 14:44:35.309807+00:00 79367 https://api.rohub.org/api/resources/99001ed6-eafc-4363-ad93-b3da218a7187/download/ 2022-11-21 14:42:56.411912+00:00 2022-12-11 18:55:07.733198+00:00 Timeline with Anne Fouilloux's EOSC journey. image/png EOSC-journey.png 2022-11-21 14:42:56.411912+00:00 https://youtu.be/tz0OqxHvnbw 2022-11-21 14:52:01.754759+00:00 2022-12-11 18:54:52.704491+00:00 Demo for EOSC-Future: Turning FAIR and Open Science into Reality. The example shown is about the "impact of the Covid-19 Lockdown on Air quality over Europe using Copernicus and EOSC project services". youtube Turning FAIR and Open Science into Reality (demo, video) 2022-11-21 14:52:01.754759+00:00 https://eoscfuture.eu/newsfuture/answering-research-questions-with-eosc/ 2022-11-21 15:36:19.837234+00:00 2022-12-11 18:54:52.850465+00:00 Answering research questions with EOSC, March 10, 2022. Climate Data Scientist Anne Fouilloux and her team were faced with a research question: In France, have there been changes in air quality over the course of the COVID-19 pandemic? With the help of compute services available through EOSC, Anne was able to search for European air quality data analysis. NAVIGATING EOSC Check our infographic and follow Anne as she: - searches for European air quality data via OpenAIRE|Explore; - selects a software (an EOSC Jupyter notebook); - orders the notebook on the EOSC marketplace; - accesses and aggregates research from the RELIANCE project; - performs data analysis with air quality data in France; - shares a new research object (via a B2Drop folder). Anne Fouilloux is answering research questions with EOSC. 2022-11-21 15:36:19.837234+00:00 speaker 13.01482701812191 7.9 on Tue, Nov-22-2022 earth sciences 100.0 0.5002459287643433 Television Arts, culture and entertainment/Mass media/Television This presentation has been given at the Data Managers Network meeting on Tuesday 22 November 2022. 14.314314314314313 14.3 speaker 8.148148148148149 7.7 EOSC in practise 32.76553106212425 32.7 Experiences with involvement in EOSC. 25.525525525525524 25.5 topic of the meeting 31.86372745490982 31.8 involvement 14.39153439153439 13.6 EOSC 24.973544973544975 23.6 issue 27.182866556836903 16.5 experiences with involvement 8.316633266533067 8.3 topic 18.0952380952381 17.1 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 nordicesmhub@gmail.com admin NordicESMHub Environmental research Earth sciences physics 39.548022598870055 7.0 sound pressure Levels Post 2.774694783573807 2.5 Adriatic Sea 12.96551724137931 9.4 Soundscape Project 11.349693251533742 7.4 Adriatic Sea https://www.wikidata.org/wiki/Q13924 physics 100.0 0.289516806602478 noise 4.689655172413793 3.4 This RO provides the Jupyter notebook used to post process the Sound Pressure Levels (SPLs) data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded. 85.08508508508508 85.0 Soundscape project: Sound Pressure Levels Post Processing. 14.914914914914915 14.9 data 10.620689655172415 7.7 AND 12.116564417177914 7.9 228cf85d-7029-4d31-9670-af3a3287fe05 POLYGON ((13.780514672398569 43.20062965835968, 13.679807111620905 43.40716790033413, 13.615721091628076 43.54668210685353, 13.469236940145493 43.60637529450611, 13.212888166308405 43.70573243086513, 13.038938865065576 43.81813859168376, 12.855832502245905 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16.353147402405742 43.58648414033215, 16.517942324280742 43.49357190127942, 16.655271425843242 43.42711822213271, 16.88415236771107 43.353934989703234, 13.780514672398569 43.20062965835968 service-account-enrichment 10.24424/hrhm-8849 False https://w3id.org/ro-id/2f352829-b3e1-45f3-99f2-786c52587485 2022-12-22 10:56:38.063437+00:00 mailto:antonio.petrizzo@ve.ismar.cnr.it 16469215 https://api.rohub.org/api/ros/4691cb04-70c5-4c7b-aa76-65850ddd509c/crate/download/ 2022-12-22 09:40:51.679517+00:00 2024-03-05 12:23:34.560913+00:00 2022-12-22 09:40:51.679517+00:00 This RO provides the Jupyter notebook used to post process the Sound Pressure Levels (SPLs) data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded. application/ld+json https://w3id.org/ro-id/4691cb04-70c5-4c7b-aa76-65850ddd509c SPLs Soundscape Underwater Noise Soundscape project: Sound Pressure Levels Post Processing - snapshot Soundscape project: Sound Pressure Levels Post Processing MANUAL https://w3id.org/ro-id/4691cb04-70c5-4c7b-aa76-65850ddd509c/2365ba84-88fd-4071-8f99-cc7b9abd7116 https://w3id.org/ro-id/09305b61-efc5-4373-9a25-3c3a49c63e6c https://w3id.org/ro-id/52d27a97-4e3c-4de0-b478-0a20a6d19630 https://w3id.org/ro-id/a8294502-720a-4e7a-bc84-9f313a244dfb https://w3id.org/ro-id/179abe10-923b-4fe6-8eeb-6ecfe59203bf https://w3id.org/ro-id/112323c2-023b-4fce-84a3-354c82d09e8f https://w3id.org/ro-id/2c295fb1-fb33-4946-acbd-1484c02c0db5 https://w3id.org/ro-id/3f0448ff-eca6-43c2-960e-92281402a652 https://w3id.org/ro-id/5171a40a-91dc-4322-8fa7-3df5c31724ee https://w3id.org/ro-id/60ac92c4-ddb1-4105-b385-ab05d8e62987 https://w3id.org/ro-id/624b5e15-55ec-4832-9014-057ba4c3494b https://w3id.org/ro-id/781ccd8f-ec48-4bb0-8041-8a13afc0a215 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https://w3id.org/ro-id/f69867d3-b9ee-4bc0-863a-3f88cff88794 https://w3id.org/ro-id/22fa8fc9-4daa-4c68-9845-c89b406a4d8b https://w3id.org/ro-id/ac46b92f-cc3e-46c3-9cd7-5591a784d992 https://w3id.org/ro-id/10c56ecf-1e86-4fbd-8b37-c6ff08744f5c https://w3id.org/ro-id/5510c5c0-d667-48ff-a23f-9a5279e2b3a9 https://w3id.org/ro-id/5e1fb189-52a2-4c27-bc49-e088a340f5ef https://w3id.org/ro-id/9e284553-8357-4d3e-9b00-e624c5f3b3e4 https://w3id.org/ro-id/df605411-99ff-40bf-ab9f-3e029aa66061 https://w3id.org/ro-id/2dc9c9aa-4b05-454c-b56a-2fa091ec9e26 https://w3id.org/ro-id/2eb2a140-591b-4a4c-bec8-ee583fb023e6 https://w3id.org/ro-id/50e57a4a-474d-43ab-b510-1802be2b073a https://w3id.org/ro-id/b247cd72-cdda-4235-bb3c-7ea7af4f1b28 PETRIZZO, ANTONIO, Fantina Madricardo, Marta Picciulin, and Michol Ghezzo. "Soundscape project: Sound Pressure Levels Post Processing." ROHub. Dec 22 ,2022. https://doi.org/10.24424/hrhm-8849. 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A valid EGI account is required. Jupyter notebook for SPLs processing 2022-12-22 10:33:02.338126+00:00 1111721 https://api.rohub.org/api/resources/240112e9-d3e7-4221-8ea9-fd98ea4a092a/download/ 2022-12-22 09:46:43.417490+00:00 2022-12-22 10:56:32.759217+00:00 image/png sketches.png 2022-12-22 09:46:43.417490+00:00 9146360 https://api.rohub.org/api/resources/2b2bbff5-3a54-42f5-9889-123bacb66828/download/ 2022-12-22 10:00:20.583758+00:00 2022-12-22 10:56:34.505368+00:00 Example of SPLs input file. HDF5 format, according to to ICES (International Council for the Exploration of the Sea) continuous noise data portal specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx). Example of SPLs input file 2022-12-22 10:00:20.583758+00:00 3034992 https://api.rohub.org/api/resources/551e4e13-b14a-42fe-b654-788dd2fa2220/download/ 2022-12-22 10:04:21.100557+00:00 2022-12-22 10:56:35.519720+00:00 Some examples of output files application/zip Some examples of output files 2022-12-22 10:04:21.100557+00:00 912578 https://api.rohub.org/api/resources/9424b92f-ac08-4c1d-b702-49f33508a9ca/download/ 2022-12-22 10:07:51.947557+00:00 2022-12-22 10:56:36.131713+00:00 Map of stations with their coordinates image/png Stations map 2022-12-22 10:07:51.947557+00:00 5832938 https://api.rohub.org/api/resources/94c1d183-c6a0-44db-8ec7-262cb699e822/download/ 2022-12-22 10:24:50.681134+00:00 2022-12-22 10:56:37.830882+00:00 The Jupyter notebook used to post process SPLs data and to create graphs/tables. application/zip Jupyter notebook for processing SPLs data. 2022-12-22 10:24:50.681134+00:00 https://doi.org/10.5281/zenodo.7472152 2022-12-22 09:55:32.356553+00:00 2022-12-22 10:56:21.474116+00:00 20 and 60 seconds SPLs dataset Full SPLs dataset 2022-12-22 09:55:32.356553+00:00 https://underwaternoise.ices.dk/continuous 2022-12-22 10:06:10.843847+00:00 2022-12-22 10:56:25.348517+00:00 Continuous Noise Database (https://underwaternoise.ices.dk/continuous), 2022. ICES, Copenhagen Format of input file 2022-12-22 10:06:10.843847+00:00 https://www.italy-croatia.eu/web/soundscape 2022-12-22 10:07:12.320583+00:00 2022-12-22 10:56:21.257593+00:00 EU-Interreg Italy-Croatia 2014/2020 – CBC Program (Contract number 10043643) Soundscape Project 2022-12-22 10:07:12.320583+00:00 of 1 year impact 5.103448275862069 3.7 computer science 13.559322033898306 2.4 soundscapes in the North Adriatic sea 29.855715871254162 26.9 noise data 41.620421753607104 37.5 http 6.620689655172414 4.8 continuo 7.310344827586207 5.3 resource 4.827586206896552 3.5 Language Arts, culture and entertainment/Culture/Language soundscape 16.413793103448278 11.9 year of continuo 8.213096559378469 7.4 acoustics 46.89265536723165 8.3 physics (general) 100.0 0.289516806602478 earth sciences 100.0 0.41819459199905396 Mar-2020 - Jun-2021 Biology Science and technology/Natural science/Biology Jupyter notebook 16.104294478527606 10.5 atmospheric sciences 100.0 0.41819459199905396 Ro 9.517241379310345 6.9 Adriatic Sea 15.184049079754601 9.9 SPL 11.963190184049079 7.8 AND 10.482758620689655 7.6 sound pressure levels 17.53607103218646 15.8 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware soundscape 19.32515337423313 12.6 data 13.957055214723926 9.1 information 11.448275862068968 8.3 Newspaper Arts, culture and entertainment/Mass media/Newspaper antonio.petrizzo@cnr.it ANTONIO PETRIZZO direttore@ismar.cnr.it CNR-ISMAR CNR ISMAR fantina.madricardo@ve.ismar.cnr.it Fantina Madricardo CNR ISMAR marta.picciulin@ve.ismar.cnr.it Marta Picciulin CNR ISMAR michol.ghezzo@ve.ismar.cnr.it Michol Ghezzo Environmental research Applied sciences https://agu.confex.com/agu/fm22/meetingapp.cgi/Session/165616 2022-12-09 18:50:29.467565+00:00 2022-12-23 17:57:00.777346+00:00 Open science communities are pushing the boundaries of how we approach scientific research. With advancements in computing, software, and data management, the tools are available to transform science into a truly open, collaborative, and inclusive space. By following open science practices, we can increase accessibility of scientific research and findings, improve collaboration, and facilitate high quality, reproducible science. This session will showcase success stories in the Earth and space sciences and highlight a range of open science platforms, datasets, and computational tools. Join this session for real-world examples of how open science practices have empowered and enabled scientists across disciplines to carry out successful research projects. session ED16B - Open Science Practices and Success Stories Across the Earth, Space, and Environmental Sciences IV Oral 2022-12-09 18:50:29.467565+00:00 jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 post@simula.no 00vn06n10 Simula Research Laboratory https://w3id.org/ro-id/18269477-c1b8-4aa8-9b0e-372c7bb6b65c 2022-12-08 08:54:10.991302+00:00 2022-12-23 17:56:59.163701+00:00 This research object is a fork from RO examplifying Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. Its main purpose is to show how to make derivative work and keep all the history of contributions and contributors. Sea ice forecasting using IceNet (Jupyter Notebook) forked from the Environmental Data Science book 2022-12-08 08:54:10.991302+00:00 -87.62586593977177 41.875270331922245 POINT (-87.62586593977177 41.875270331922245) c3b434f0-a692-4b20-ae64-5076d995fca8 POINT (-87.62586593977177 41.875270331922245) https://doi.org/10.24424/qkna-rz18 False 2022-12-23 17:57:11.722765+00:00 28405593 https://api.rohub.org/api/ros/871a1786-bc6a-4e60-a160-3f57e3869d35/crate/download/ 2022-12-08 08:34:52.580430+00:00 2024-03-05 12:16:52.989585+00:00 2022-12-08 08:34:52.580430+00:00 This Research Object aggregates all the different Research Objects and resources used for presenting the Environmental Data Science Book at AGU 2022. The Environmental Data Science book is a living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science. The Environmental Data Science is: a book a community a global collaboration We target to make sense of: environmental systems environmental data and sensors innovative research in Environmental Data Science open-source tools for Environmental Data Science We hope you find the content in the resource helpful. The resource and executable notebooks are free under a CC-BY licence and OSI-approved MIT license, respectively. application/ld+json https://w3id.org/ro-id/871a1786-bc6a-4e60-a160-3f57e3869d35 open science reproducible sea-ice Video AGU 2022 - Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science - snapshot AGU 2022 - Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science MANUAL Anne Foilloux, Alejandro Coca-Castro, Environmental Data Science Book Community, Jean Iaquinta, Tom Andersson, Nick Barlow, and . Scott Hosking. "AGU 2022 - Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science." ROHub. Dec 08 ,2022. https://doi.org/10.24424/qkna-rz18. POINT (-87.62586593977177 41.875270331922245) 150689 https://api.rohub.org/api/resources/71b9b61c-81d7-4da7-bb03-72fbec48e993/download/ 2023-01-17 14:37:57.030511+00:00 2023-01-17 14:37:58.189501+00:00 image/png agu22-presentation_EnvDSBook-2.png 2023-01-17 14:37:57.030511+00:00 150689 https://api.rohub.org/api/resources/7a88f5ae-4de7-438f-8f10-25ba9f3736a2/download/ 2022-12-22 16:28:38.705015+00:00 2022-12-23 17:57:10.622032+00:00 image/png agu22-presentation_EnvDSBook-2.png 2022-12-22 16:28:38.705015+00:00 29440246 https://api.rohub.org/api/resources/89f226c9-2b9b-4921-a3a5-e9f24bf82b64/download/ 2022-12-22 16:33:09.605035+00:00 2022-12-23 17:57:11.424898+00:00 Recording of the presentation given at AGU2022. video/mp4 mp4 AGU presentation (recorded video) 2022-12-22 16:33:09.605035+00:00 29440246 https://api.rohub.org/api/resources/bd41569e-528c-4c05-b870-b405f2f30f9b/download/ 2023-01-17 14:37:58.375885+00:00 2023-01-17 14:37:59.850179+00:00 video/mp4 DSEnvBook-AGU2022.mp4 2023-01-17 14:37:58.375885+00:00 https://w3id.org/ro-id/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef 2022-12-08 08:42:58.948034+00:00 2022-12-23 17:57:01.031243+00:00 Research Object demonstrating sea ice forecasting using IceNet. The corresponding Jupyter Notebook has been published in the Environmental Data Science book. jupyter book Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book 2022-12-08 08:42:58.948034+00:00 Computational notebooks community focused on Environmental Data Science environmental.ds.book@gmail.com Environmental Data Science Book Community https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose software 12.612612612612613 1.4 research in Environmental Data Science 15.866084425036389 10.9 MIT license 17.903930131004365 12.3 community-driven resource 11.353711790393012 7.8 research 15.39855072463768 8.5 environmental science and management 100.0 0.9688456058502197 resource 15.191740412979351 10.3 executable notebook 31.732168850072778 21.8 computer science 87.38738738738738 9.7 resource 10.507246376811594 5.8 notebook 7.669616519174042 5.2 tool 10.914454277286136 7.4 publication of data 23.14410480349345 15.9 documentation and information science 100.0 0.30886638164520264 surface 6.48967551622419 4.4 book 10.471976401179942 7.1 Biology Science and technology/Natural science/Biology data 9.734513274336283 6.6 notebook 9.420289855072463 5.2 This Research Object aggregates all the different Research Objects and resources used for presenting the Environmental Data Science Book at AGU 2022. 41.69230769230769 27.1 aim 7.079646017699115 4.8 Research Object 13.22463768115942 7.3 license 7.374631268436579 5.0 Book industry Economy, business and finance/Economic sector/Media/Book industry tool 13.22463768115942 7.3 research 17.84660766961652 12.1 data 12.13768115942029 6.7 publication 7.227138643067848 4.9 Environmental Data Science 26.08695652173913 14.4 The Environmental Data Science book is a living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science. 23.692307692307693 15.4 We target to make sense of: environmental systems environmental data and sensors innovative research in Environmental Data Science open-source tools for Environmental Data Science 34.61538461538461 22.5 social and information sciences 100.0 0.30886638164520264 environmental sciences 100.0 0.9688456058502197 Environment Environment The Alan Turing Institute acoca@turing.ac.uk Alejandro Coca-Castro Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 Global environmental.ds.book@gmail.com Environmental Data Science Book Community The Alan Turing Institute Nick Barlow he British Antarctic Survey Tom Andersson jask@bas.ac.uk . Scott Hosking service-account-enrichment Applied sciences https://docs.google.com/presentation/d/1jxxwEGLiyuqYRJlH5VswekPJVoe5oxjlxVQrEiOojfI/edit?usp=sharing 2023-01-08 20:47:05.281382+00:00 2023-02-19 13:17:27.238629+00:00 Poster on experiences from the NICEST2 project for NeIC All hands meeting 2023. google-doc Poster on our experiences from the NICEST2 project 2023-01-08 20:47:05.281382+00:00 https://doi.org/10.5281/zenodo.4749515 2023-01-08 21:06:32.125399+00:00 2023-02-19 13:17:19.315916+00:00 Report on the bottlenecks that would hinder the efficient usage of Nordic ESMs on EuroHPC and possible remediation actions (I/Os, adding GPU support, etc.) with clear information on costs in terms of manpower. ESMs used in the Nordic countries are clearly not ready for EuroHPC and very little dedicated funding from the scientific community is used for porting existing codes to future architectures. Providers have hired several specialists to support the scientific community but the commitment from the scientific community is not there. Exchange of knowledge of involved staff (scientists, RSEs, technical support) would be very helpful. For instance, being able to organize meetings/hackathons (online or face to face) with both experts from NorESM and EC-EARTH has been highlighted as an important requirements by those involved in the GPU hackathon. Code refactoring and best software practices are the most important component for efficient usage of new architecture, including EuroHPC. NICEST2 - D4.5: First report on the identified bottlenecks for an efficient usage of Nordic ESMs on EuroHPC 2023-01-08 21:06:32.125399+00:00 https://doi.org/10.5281/zenodo.4944686 2023-01-08 21:03:28.636733+00:00 2023-02-19 13:17:22.369015+00:00 This report summarizes the first NICEST2 hackathon with FAIR experts and Earth System Model specialists to understand what needs to be done to make climate data FAIR. It will help us to define our roadmap for FAIR Climate in the Nordics. NICEST2 - D3.3: Report on NICEST2 FAIR climate data hackathon 2023-01-08 21:03:28.636733+00:00 https://doi.org/10.5281/zenodo.5571344 2023-01-08 21:08:01.968412+00:00 2023-02-19 13:17:27.394767+00:00 The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system Models (ESMs) that is not widely used in the Nordics yet. A hackathon/workshop was held on March 12, 2021 as a joint event between the INES, NICEST2 and IS-ENES3 projects. During this hackathon, we identified the needs for specific diagnostics for the Nordics that could help researchers to diagnose strengths and deficiencies of current ESMs. We also discussed how to better organize access to data and share resources within the Nordics. NICEST2 - D2.1: Short report from the Nordic ESM diagnostics hackathon 2023-01-08 21:08:01.968412+00:00 https://doi.org/10.5281/zenodo.5571416 2023-01-08 21:10:14.737755+00:00 2023-02-19 13:17:27.065885+00:00 The Nordic climate modeling community consists of research groups at universities, national meteorological institutes and research institutes, and holds demonstrable world class excellence in the field. Several of these groups contribute to the development of both global and regional climate models (GCMs and RCMs,respectively) in international projects with collaborations within Europe and the US. However, many users, including PhDs and postdocs, are developing and/or running Earth System Models (ESMs) for more fundamental scientific research and sensitivity studies, and/or cross-disciplinary research (economy & climate, biodiversity, etc.) and they do not always benefit from the advances, technologies or resources leveraged by these large projects/consortiums. One concrete example is IS-ENES project (https://is.enes.org/) where only one university, namely Linköpings Universitet (not part of the NICEST2 consortium) from the Nordics is involved; Nordic contributions are mostly from Meteorological services and Research Institutes. From a practical point of view this translates in a lot of time/energy wasted “reinventing the wheel”, repeated simulations, lack of transparency, suboptimal use of the infrastructures, etc. In this context, supporting these researchers and realizing the benefits of Open Science and EOSC are our priorities within NICEST2 and WP4. NICEST2 - D4.1: Identification of the Nordic ESM community needs for ESM workflows 2023-01-08 21:10:14.737755+00:00 https://nordicesmhub.github.io/NorESM_user_workshop_2021/intro.html 2023-01-08 20:50:23.537300+00:00 2023-02-19 13:17:27.609612+00:00 Training material on containers for ESM. This training material uses the Norwegian Earth System Model (NorESM) and has been delivered in 2021 as part of the NorESM user meeting. text/html training Running NorESM in a container 2023-01-08 20:50:23.537300+00:00 https://nordicesmhub.github.io/nicest2-fair-hackathon/ 2023-01-08 20:52:04.176203+00:00 2023-02-19 13:17:18.829914+00:00 First NICEST2 hackathon to understand the FAIR concept and how they can apply to the Nordic Earth System Modelling Community. NICEST2 hackathon on FAIR climate data 2023-01-08 20:52:04.176203+00:00 https://nordicesmhub.github.io/nicest2/2020/05/04/plan.html 2023-01-09 08:28:36.495799+00:00 2023-02-19 13:17:20.174631+00:00 Link to the NICEST2 project plan. text/html Project plan 2023-01-09 08:28:36.495799+00:00 Finnish Meteorological Institute (Finland) antti-ilari.partanen@fmi.fi Antti-Ilari Partanen 0000-0002-0883-8161 Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 NSC (Sweden) struthers@nsc.liu.se Hamish Struthers 0000-0002-4214-2213 NERSC (Norway) yanchun.he@nersc.no Yanchun He 0000-0002-5932-3627 Finnish Meteorological Institute (Finland) tommi.bergman@fmi.fi Tommi Bergman 0000-0002-6133-2231 Norwegian Meteorological Institute (Norway) oskaral@met.no Oskar Landgren 0000-0002-6264-8502 jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 Finnish Meteorological Institute (Finland) risto.makkonen@fmi.fi Risto Makkonen 0000-0002-8961-3393 post@simula.no 00vn06n10 Simula Research Laboratory 04jcwf484 Nordic e-Infrastructure Collaboration 8fc9a20e-fa82-43a8-93f4-cd4cc4a45ac8 POINT (10.138547627793743 61.47037998202813) 10.138547627793743 61.47037998202813 POINT (10.138547627793743 61.47037998202813) https://doi.org/10.24424/9y3x-hg89 False 2023-02-19 13:17:31.060596+00:00 763258 https://api.rohub.org/api/ros/b62e5267-8fe1-4fe1-ada2-47e484c2b107/crate/download/ 2023-01-08 20:41:20.579173+00:00 2024-03-05 12:18:19.992972+00:00 2023-01-08 20:41:20.579173+00:00 Overview of the NICEST2 project and reflection on the successes, failures and possible improvements for follow-up projects. application/ld+json https://w3id.org/ro-id/b62e5267-8fe1-4fe1-ada2-47e484c2b107 climate e-infrastructure Poster Experience from the NeIC NICEST2 project - NeIC All-Hands meeting (23–26 Jan 2023) Experience from the NeIC NICEST2 project - snapshot MANUAL Iaquinta, Jean, Oskar Landgren, Alok Kumar Gupta, Prashanth Dwarakanath, Anne Fouilloux, Tommi Bergman, Tyge Løvseth, et al. "Experience from the NeIC NICEST2 project - NeIC All-Hands meeting (23–26 Jan 2023)." ROHub. Jan 08 ,2023. https://doi.org/10.24424/9y3x-hg89. POINT (10.138547627793743 61.47037998202813) This folder contains NICEST2 deliverables. deliverables Folder containing training material developed and delivered within the NICEST2 projects (either as training or hackathons). training 771062 https://api.rohub.org/api/resources/565cfd17-f309-4a85-9bdd-c0161f486c55/download/ 2023-01-08 20:45:29.648781+00:00 2023-02-19 13:17:29.630057+00:00 Overall view of the NICEST2 poster for the NeIC all-hands meeting. It is mostly used for the sketch. image/png NICEST2 poster for NeIC AHM2023.png 2023-01-08 20:45:29.648781+00:00 771062 https://api.rohub.org/api/resources/bcfaae33-8fd4-4978-ad7a-e88940436d2d/download/ 2023-02-23 21:50:55.971537+00:00 2023-02-23 21:50:58.608624+00:00 image/png NICEST2 poster for NeIC AHM2023.png 2023-02-23 21:50:55.971537+00:00 771062 https://api.rohub.org/api/resources/f43ff13a-adb1-4dae-8d5d-7de044a923d6/download/ 2023-01-17 14:38:23.066910+00:00 2023-02-19 13:17:30.657154+00:00 image/png NICEST2 poster for NeIC AHM2023.png 2023-01-17 14:38:23.066910+00:00 NICEST-2 - the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives. Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034 experience 10.18735362997658 8.7 success 14.528593508500773 9.4 26 Jan 2023). Overview of the NICEST2 project and reflection on the successes, failures and possible improvements for follow-up projects. 56.35635635635635 56.3 NeIC All-Hands meeting 59.22920892494929 58.4 Experience from the NeIC NICEST2 project - NeIC All-Hands meeting (23– 43.64364364364364 43.6 follow-up 9.484777517564401 8.1 fail 6.0278207109737245 3.9 experience 12.519319938176197 8.1 improvement 10.655737704918032 9.1 reflection 5.4095826893353935 3.5 project 17.330210772833723 14.8 NeIC NICEST2 project 25.354969574036513 25.0 geosciences 100.0 0.22873644530773163 overview of the NICEST2 project 1.5212981744421907 1.5 follow-up project 12.373225152129818 12.2 oceanography 100.0 0.6324363350868225 follow-up 11.5919629057187 7.5 earth resources and remote sensing 100.0 0.22873644530773163 Accomplishment Human interest/Accomplishment NeIC All-Hands 16.15925058548009 13.8 earth sciences 100.0 0.6324363350868225 improvement 13.44667697063369 8.7 Jan-26-2023 success 11.592505854800937 9.9 project 24.884080370942815 16.1 meeting 11.5919629057187 7.5 NeIC NICEST2 24.59016393442623 21.0 improvements for follow-up project 1.5212981744421907 1.5 NORCE (Norway) algu@norceresearch.no Alok Kumar Gupta CSC (Finland) elina.miinalainen@csc.fi Elina Miinalainen CSC (Finland) kimmo.ervasti@csc.fi Kimmo Ervasti USIT, University of Oslo (Norway) maikenp@usit.uio.no Maiken Pedersen Norwegian Meteorological Institute (Norway) oyvind.seland@met.no Øyvind Seland NSC (Sweden) pchengi@nsc.liu.se Prashanth Dwarakanath service-account-enrichment NORCE (Norway) tylo@norceresearch.no Tyge Løvseth Environmental research Applied sciences Earth sciences https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007E46B7736861726547756964236161643239616133666234633734356464393231356539663536613733616366636836643138233732356634616233366362323664306662666330633132346337373565666565636865653439236361386634383464346533366532646439643230336131383431616362656563636834393661/content 2023-01-08 19:37:15.986538+00:00 2023-02-19 13:22:45.129102+00:00 Data at the Acqua Alta oceanographic tower is a collection of physical and biogeochemical observation in the northern Adriatic Sea https://www.comune.venezia.it/it/content/3-piattaforma-ismar-cnr http://www.ismar.cnr.it/infrastrutture/piattaforma-acqua-alta PTF dataset(2009-2020) Piattaforma acqua allta 2023-01-08 19:37:15.986538+00:00 https://doi.org/10.1016%2Fj.marpolbul.2021.112124 2023-01-08 19:24:00.526730+00:00 2023-02-19 13:22:53.090742+00:00 Reduction in the impact of human-induced factors is capable of enhancing the environmental health. In view of COVID-19 pandemic, lockdowns were imposed in India. Travel, fishing, tourism and religious activities were halted, while domestic and industrial activities were restricted. Comparison of the pre- and post-lockdown data shows that water parameters such as turbidity, nutrient concentration and microbial levels have come down from pre- to post-lockdown period, and parameters such as dissolved oxygen levels, phytoplankton and fish densities have improved. The concentration of macroplastics has also dropped from the range of 138 ± 4.12 and 616 ± 12.48 items/100 m2 to 63 ± 3.92 and 347 ± 8.06 items/100 m2. Fish density in the reef areas has increased from 406 no. 250 m−2 to 510 no. 250 m−2. The study allows an insight into the benefits of effective enforcement of various eco-protection regulations and proper management of the marine ecosystems to revive their health for biodiversity conservation and sustainable utilization. Reef fish covid-19 environmental health plastic pollution COVID-19 lockdown improved the health of coastal environment and enhanced the population of reef-fish 2023-01-08 19:24:00.526730+00:00 https://earthobservatory.nasa.gov/images/83394/parting-the-sea-to-save-venice 2023-01-08 19:58:47.516622+00:00 2023-02-19 13:22:55.402245+00:00 The natural-color Landsat images above show some of the MOSE engineering efforts that are visible above the water line near the Lido Inlet. The top image was acquired on June 20, 2000, by the Enhanced Thematic Mapper+ on Landsat 7. The second image, from the Operational Land Imager on Landsat 8, was collected on September 4, 2013. Turn on the image comparison tool to make the changes easier to see. (Note that Landsat 8 has a greater dynamic range than Landsat 7, so the Landsat 8 image is crisper the Landsat 7 image.) Parting the Sea to Save Venice 2023-01-08 19:58:47.516622+00:00 giorgio.castellan@bo.ismar.cnr.it Giorgio Castellan 0000-0001-6084-1504 Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 federica.foglini@ismar.cnr.it Federica Foglini 0000-0002-2736-0052 jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 CNR-ISMAR malek.belgacem@ve.ismar.cnr.it Malek Belgacem 0000-0003-0745-4155 Małgorzata Wolniewicz https://reliance.adamplatform.eu/?dataset=69623:EU_CAMS_SURFACE_NO2_G 2023-01-08 19:40:14.176174+00:00 2023-02-19 13:22:52.387115+00:00 CAMS NITROGEN DIOXIDE 2022-12-27T23:00:00Z NO2 CAMS European air quality forecasts: NO2 2023-01-08 19:40:14.176174+00:00 2018-07-12T00:00:00Z Float32 mailto:govoni@meeo.it [1.354510459350422e-07] [0.0] https://reliance.adamplatform.eu/?dataset=69625:EU_CAMS_SURFACE_O3_G 2023-01-08 19:41:17.789149+00:00 2023-02-19 13:22:49.529744+00:00 CAMS OZONE 2022-12-27T23:00:00Z O3 CAMS European air quality forecasts: O3 2023-01-08 19:41:17.789149+00:00 2018-07-12T00:00:00Z Float32 mailto:govoni@meeo.it [2.2007016298175586e-07] [0.0] https://reliance.adamplatform.eu/?dataset=69627:EU_CAMS_SURFACE_PM25_G 2023-01-08 19:42:25.690080+00:00 2023-02-19 13:22:51.053690+00:00 CAMS SURFACE PARTICULATE METTER D<2.5 2022-12-27T23:00:00Z PM2.5 CAMS European air quality forecasts: PM25 2023-01-08 19:42:25.690080+00:00 2018-07-12T00:00:00Z Float32 mailto:govoni@meeo.it [709.8012084960938] [0.0] post@simula.no 00vn06n10 Simula Research Laboratory https://w3id.org/ro-id/0869e396-3733-4aff-8fb2-94c8937b28aa 2023-01-08 19:15:20.212877+00:00 2023-02-19 13:22:55.556680+00:00 This is a case study of snapshot project http://snapshot.cnr.it/ to investigate the lockdown impact on the water quality at a selected site in the northern Adriatic Sea, precisely in Northern Adriatic Sea, the case of the Gulf of Venice using Machine Learning model. Snapshot 2021 study case: Lockdown impacts on the Northern Adriatic Sea at selected site: AcquaAlta Platform Water quality 2023-01-08 19:15:20.212877+00:00 https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1 2023-01-08 19:14:03.311972+00:00 2023-02-19 13:22:50.947789+00:00 The COVID-19 pandemic has led to significant reductions in economic activity, especially during lockdowns. Several studies has shown that the concentration of nitrogen dioxyde and particulate matter levels have reduced during lockdown events. Reductions in transportation sector emissions are most likely largely responsible for the NO2 anomalies. In this study, we analyze the impact of lockdown events on the air quality using data from Copernicus Atmosphere Monitoring Service over Europe and at selected locations. Impact of the Covid-19 Lockdown on Air quality over Europe 2023-01-08 19:14:03.311972+00:00 https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1/resources/2a2b6f01-be2e-414e-af08-d882aa995a71 2023-01-08 19:21:48.221333+00:00 2023-02-19 13:22:50.794426+00:00 In order to fight against the spread of COVID-19, the most hard-hit countries in the spring of 2020 implemented different lockdown strategies. To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2, SO2, CO, PM2.5 and PM10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown. This work shows the reduction of primary pollutants, especially NO2, is mainly due to lockdown policies. However, preexisting local environmental policy regulations also contributed to declining NO2, SO2 and PM2.5 emissions, especially in Asian countries. In addition, higher rainfall during the lockdown period could cause decline of PM2.5, especially in Johannesburg. By contrast, the changes of AQI in ground-level O3 were not significant in most of cities, as meteorological variability and ratio of VOC/NOx are key factors in ground-level O3 formation. Impact of the COVID-19 Pandemic Lockdown on Air Quality Pollution in 20 Major cities around the World 2023-01-08 19:21:48.221333+00:00 https://w3id.org/ro-id/c2c64bf9-7625-4442-9ca9-dcd978b1d38b 2023-01-08 19:19:35.675216+00:00 2023-02-19 13:22:48.215320+00:00 Integration of data on Air and Water quality in the Venice Lagoon to assess the impact of the Covid-19 Lockdown Impact of the Covid-19 Lockdown on Air and Water quality in the Venice Lagoon 2023-01-08 19:19:35.675216+00:00 The main interest is upon marine litter pollution and in particular ranging from marco to mirco and nano size. In encompasses data from citizen science monitoring and sampling activities in cooperation with research-educational institute and centers. segreteria@plasticfreevenice.org Marine Litter and plastics pollution NICEST-2 - the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives. Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034 POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) -25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997 0d3b6136-ceba-417f-b1be-1629993a9831 POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) 2c143630-6f7d-4317-af35-8977b7f3b8d5 POLYGON ((12.094116155058147 45.146856282945706, 12.094116155058147 45.62908481204897, 12.816467229276897 45.62908481204897, 12.816467229276897 45.146856282945706, 12.094116155058147 45.146856282945706)) POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) -25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997 POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) -25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997 8113b8f8-412d-4b99-b7c0-113e1bebe467 POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) POLYGON ((12.094116155058147 45.146856282945706, 12.094116155058147 45.62908481204897, 12.816467229276897 45.62908481204897, 12.816467229276897 45.146856282945706, 12.094116155058147 45.146856282945706)) 12.094116155058147 45.146856282945706, 12.094116155058147 45.62908481204897, 12.816467229276897 45.62908481204897, 12.816467229276897 45.146856282945706, 12.094116155058147 45.146856282945706 fb3a0ff7-80dd-466f-b2c5-4a4bcfee1770 POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) https://doi.org/10.24424/656f-rf51 False 2023-02-19 13:23:07.855375+00:00 293999 https://api.rohub.org/api/ros/eec6faaa-e133-47d4-b377-44f7d06a9654/crate/download/ 2023-01-08 18:47:51.996769+00:00 2024-03-05 12:17:16.953752+00:00 2023-01-08 18:47:51.996769+00:00 In this study, we focusing on understanding changes in air and water quality during the Covid-19 lockdown in the Venice Lagoon. We are re-using existing Research Objects, and in particular Jupyter Notebooks that were created in previous studies. application/ld+json https://w3id.org/ro-id/eec6faaa-e133-47d4-b377-44f7d06a9654 air water Jupyter Notebook Changes in air and water quality during the Covid-19 Lockdown in the Venice Lagoon - snapshot Changes in air and water quality during the Covid-19 Lockdown in the Venice Lagoon MANUAL Fouilloux, Anne, Federica Foglini, Giorgio Castellan, Malek Belgacem, Jean Iaquinta, and Simone Mantovani. "Changes in air and water quality during the Covid-19 Lockdown in the Venice Lagoon." ROHub. Jan 08 ,2023. https://doi.org/10.24424/656f-rf51. POLYGON ((12.094116155058147 45.146856282945706, 12.094116155058147 45.62908481204897, 12.816467229276897 45.62908481204897, 12.816467229276897 45.146856282945706, 12.094116155058147 45.146856282945706)) POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) tool output input biblio 147673 https://api.rohub.org/api/resources/4ac2a84f-ecf7-4e52-acbc-b0502cc29f6a/download/ 2023-05-24 19:00:14.547814+00:00 2023-05-24 19:00:16.737992+00:00 image/png figure.png 2023-05-24 19:00:14.547814+00:00 39986 https://api.rohub.org/api/resources/7719f92a-5ad6-4314-b8ac-6645255a835f/download/ 2023-01-08 20:25:23.565475+00:00 2023-02-19 13:23:06.902211+00:00 The goal is to compare values of NO2 water quality before and during the covid-19 lockdown. water NO2 water quality in the Venice lagoon between March-June 2019 and 2020. 2023-01-08 20:25:23.565475+00:00 63516 https://api.rohub.org/api/resources/875fafc1-3eb1-4273-8380-da3e51d5399e/download/ 2023-01-08 20:22:56.960407+00:00 2023-02-19 13:23:06.267913+00:00 Bar plot showing NO2 averaged between March and June for 2019 and 2020. The goal is to compare values before and during the covid-19 lockdown. NO2 NO2 Copernicus Air Quality forecasts for March-June 2019-2020 2023-01-08 20:22:56.960407+00:00 46709 https://api.rohub.org/api/resources/8fb0d0f3-cfc3-4b09-8920-7837cbe3964d/download/ 2023-01-08 19:35:05.569853+00:00 2023-02-19 13:23:04.921504+00:00 Dataset shows monthly values and error bars. image/png Water quality in the Venice Lagoon between 2010 and 2020. 2023-01-08 19:35:05.569853+00:00 object 6.428098078197481 9.7 Russia Tehran Iran Mar Apr TotalShops 2.185430463576159 3.3 Mexico City covid 2.253147779986746 3.4 Japan Australia São Paulo Madrid software 8.673469387755102 1.7 New York Environmental pollution Environment/Environmental pollution covid pandemic lockdown 1.8543046357615893 2.8 Moscow lockdown 2.6507620941020544 4.0 To assess the impact of the COVID pandemiclockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the changein air quality in major cities on six continents. 8.793969849246231 10.5 documentation and information science 68.58537549936264 0.658052384853363 Tehran Germany We are re-using existing Research Objects, and in particular Jupyter Notebooks that were created in previous studies. 19.011725293132326 22.7 Thus, in order to provide a more comprehensive analysis ofthe impact of lockdowns on all critical air pollutants during the entire lockdown period and to assessthe impact of different lockdown strategies on air pollution, AQI in major cities worldwide wasexamined. 4.857621440536013 5.8 water quality 9.602793539938892 22.0 Wuhan Mexico City 2.982107355864811 4.5 data 2.1206096752816435 3.2 geophysics 31.414624500637366 0.3014121949672699 World s air pollution 2.119205298013245 3.2 Mar 5.301524188204109 8.0 earth sciences 54.63797800317049 0.9944986701011658 New York 3.180914512922465 4.8 India Environmental Protection Agency air pollution 4.539502400698385 10.4 Africa United Kingdom lockdown strategy 1.390728476821192 2.1 Berlin water quality 15.904572564612325 24.0 South Korea Paris Turkey air quality 3.7773359840954273 5.7 Johannesburg Changes in air and water quality during the Covid-19 Lockdown in the Venice Lagoon. 21.1892797319933 25.3 aim 3.7101702313400264 8.5 geology 45.36202199682951 0.8256614208221436 France Asia research 8.681245858184228 13.1 Seoul Venice Lagoon 10.603048376408218 16.0 Air pollution Environment/Environmental pollution/Air pollution Venice 7.333042339589699 16.8 Europe Rome AQI 3.7773359840954273 5.7 Los Angeles U.S.A Mar 2.119205298013245 3.2 London Venice Iran Beijing Mexico City 3.3173286774334354 7.6 China Keywords: COVID ; AQI; lockdown policy; major cities; NO ; PM . ; ozone . 2.6800670016750416 3.2 research object 21.52317880794702 32.5 Los Angeles medicine 14.795918367346937 2.9 Spain air quality 5.019642077695329 11.5 major city 3.90987408880053 5.9 Lima ecology 42.3469387755102 8.3 availablefor Los Angeles 3.443708609271523 5.2 study 3.753819292885203 8.6 computer science 19.387755102040813 3.8 Jupyter Notebooks 6.096752816434724 9.2 air pollution 3.5785288270377733 5.4 Mexico big city 5.019642077695329 11.5 Los Angeles 3.186381492797905 7.3 meteorology 14.795918367346937 2.9 The World air quality project 2.384105960264901 3.6 Delhi research 5.063291139240507 11.6 air quality index 3.8847664775207336 8.9 pollutant discharge 1.5894039735099337 2.4 social and information sciences 68.58537549936264 0.658052384853363 understanding 2.5316455696202533 5.8 data 2.793539938891314 6.4 world s major cities 1.7218543046357615 2.6 air quality data 1.9205298013245033 2.9 South America Sao Paulo Brazil Mar 1.6556291390728477 2.5 Antarctica changes in air and water quality 10.927152317880795 16.5 toan air quality index 1.9867549668874172 3.0 May 2.618943692710607 6.0 lockdown data 1.5231788079470197 2.3 lockdown lockdown Policy 2.052980132450331 3.1 Istanbul United States of America geosciences 31.414624500637366 0.3014121949672699 study 5.699138502319417 8.6 earth sciences 45.36202199682951 0.8256614208221436 Madrid Spain Mar May TotalOutdoorphysicalexercise 3.576158940397351 5.4 In this study, we focusing on understanding changes in air and water quality during the Covid-19 lockdown in the Venice Lagoon. 43.46733668341708 51.9 South Africa Brazil Mexico City Mexico Mar 3.1788079470198674 4.8 lockdown 7.289393278044522 16.7 February 2.3134002618943694 5.3 pollution 2.6625927542557837 6.1 New York 3.4482758620689657 7.9 Claremont March 5.674378000872982 13.0 Weather Weather Peru Sydney atmospheric sciences 54.63797800317049 0.9944986701011658 Venice 7.090788601722995 10.7 Venice Venice Lagoon 31.192052980132452 47.1 World Health Organization Los Angeles 2.8495692511597084 4.3 International Agency for Research on Cancer World Meteorological Organization pollutant 3.1146454605699136 4.7 Italy Tokyo https://zenodo.org/record/7513765/files/NO2_EUROPE_ADAMAPI2019-03-01_2021-06-30.nc 2023-01-08 19:38:36.937507+00:00 2023-02-19 13:22:54.308417+00:00 NO2 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube. application/x-netcdf NO2 NO2 CAMS over Europe March-June 2019, 2020 and 2021 2023-01-08 19:38:36.937507+00:00 mantovani@meeo.it Simone Mantovani Raul Palma service-account-enrichment Applied sciences Climatology https://doi.org/10.1525/collabra.35903 2022-10-14 12:43:21.299002+00:00 2023-02-19 13:45:14.242980+00:00 This paper is from Gisela H. Govaart, Simon M. Hofmann, Evelyn Medawar. Ever-increasing anthropogenic greenhouse gas emissions narrow the timeframe for humanity to mitigate the climate crisis. Scientific research activities are resource demanding and, consequently, contribute to climate change; at the same time, scientists have a central role in advancing knowledge, also on climate-related topics. In this opinion piece, we discuss (1) how open science – adopted on an individual as well as on a systemic level – can contribute to making research more environmentally friendly, and (2) how open science practices can make research activities more efficient and thereby foster scientific progress and solutions to the climate crisis. While many building blocks are already at hand, systemic changes are necessary in order to create academic environments that support open science practices and encourage scientists from all fields to become more carbon-conscious, ultimately contributing to a sustainable future. climate crisis open science sustainability The Sustainability Argument for Open Science 2022-10-14 12:43:21.299002+00:00 https://doi.org/10.5281/zenodo.6589624 2022-10-14 13:44:35.094465+00:00 2023-02-19 13:45:14.339103+00:00 Sharan, Malvika In this talk, I discuss open science as a framework to ensure that all our research components can be easily accessed, openly examined and built upon by others. I will introduce The Turing Way - an open source, open collaboration and community-driven guide to reproducible, ethical and inclusive data science and research. Drawing insights from the project, I will share best practices that researchers should integrate to ensure the highest reproducible and ethical standards from the start of their projects so that their research work is easy to reuse and reproduce at all stages of the development. All attendees will leave the talk understanding the many dimensions of openness and how they can participate in an inclusive, kind and inspiring open source ecosystem as they collaboratively seek to improve research culture. All questions and contributions are welcome at the GitHub repository: https://github.com/alan-turing-institute/the-turing-way. Home page: https://malvikasharan.github.io/ This was a closing keynote at Concordia University in Montreal on 27 May 2022. Open science for enabling reproducible, ethical and collaborative research: Insights from The Turing Way 2022-10-14 13:44:35.094465+00:00 https://en.wikipedia.org/wiki/Climate_justice 2022-10-15 08:03:53.413292+00:00 2023-02-19 13:45:22.058293+00:00 Definition of Climate Justice from Wikipedia. wikipedia Climate Justice (Wikipedia) 2022-10-15 08:03:53.413292+00:00 https://en.wikipedia.org/wiki/Environmental_justice 2022-10-15 08:02:56.218937+00:00 2023-02-19 13:45:11.910714+00:00 Definition of Environmental Justice from Wikipedia wikipedia Environmental Justice (Wikipedia) 2022-10-15 08:02:56.218937+00:00 Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 post@simula.no 00vn06n10 Simula Research Laboratory POLYGON ((-170.15625 -81.09321385260837, -170.15625 84.67351256610525, 191.25000000000003 84.67351256610525, 191.25000000000003 -81.09321385260837, -170.15625 -81.09321385260837)) -170.15625 -81.09321385260837, -170.15625 84.67351256610525, 191.25000000000003 84.67351256610525, 191.25000000000003 -81.09321385260837, -170.15625 -81.09321385260837 bf216e0e-aa84-449a-9cc2-dc6454de6d14 POLYGON ((-170.15625 -81.09321385260837, -170.15625 84.67351256610525, 191.25000000000003 84.67351256610525, 191.25000000000003 -81.09321385260837, -170.15625 -81.09321385260837)) https://doi.org/10.24424/pm9w-vq46 False 2023-02-19 13:45:25.668867+00:00 5188278 https://api.rohub.org/api/ros/0e6ee388-ce22-4237-bf54-74336d1215ce/crate/download/ 2022-10-14 12:24:09.298835+00:00 2024-03-05 12:22:11.007454+00:00 2022-10-14 12:24:09.298835+00:00 OpenAIRE OAWeek - Research communities & climate action; being open to drive change Description This session will invite expert researchers to discuss their scientific impact in climate related topics. Why are they embracing open science practices in their current research workflows? How are they using the resources provided in the European Open Science Cloud (EOSC)? Is open access enabling researchers to contribute better to Climate Change solutions? If you’re curious about these innovative initiatives by the research communities, make sure you attend! Speakers: Anne Fouilloux (RELIANCE) Anabela de Oliveira (EGI-ACE) Bjorn Backeberg (C-SCALE) Prof Spyridon Rapsomanikis, Athena RC, NEANIAS application/ld+json https://w3id.org/ro-id/0e6ee388-ce22-4237-bf54-74336d1215ce climate change climate justice open science Presentation OpenAIRE OAWeek: Open for Climate Justice - snapshot OpenAIRE OAWeek: Open for Climate Justice MANUAL Fouilloux, Anne, Jean Iaquinta, and Pangeo Europe. "OpenAIRE OAWeek: Open for Climate Justice." ROHub. Oct 14 ,2022. https://doi.org/10.24424/pm9w-vq46. POLYGON ((-170.15625 -81.09321385260837, -170.15625 84.67351256610525, 191.25000000000003 84.67351256610525, 191.25000000000003 -81.09321385260837, -170.15625 -81.09321385260837)) biblio 1543707 https://api.rohub.org/api/resources/093aba46-6cbf-4187-8b44-a7c8223f742f/download/ 2023-02-23 21:51:14.126358+00:00 2023-02-23 21:51:15.102954+00:00 image/jpeg one_world_markus_spiske.jpg 2023-02-23 21:51:14.126358+00:00 4398668 https://api.rohub.org/api/resources/4e3a662f-08a9-4e2c-93e9-03cafc7e9d6d/download/ 2023-02-23 21:51:15.334939+00:00 2023-02-23 21:51:16.814390+00:00 application/pdf OpenAireWeek-20221025-AnneFouilloux.pdf 2023-02-23 21:51:15.334939+00:00 1543707 https://api.rohub.org/api/resources/55af9559-4bd0-42a3-9ec5-77d834e21c8a/download/ 2022-10-16 07:52:27.418362+00:00 2023-02-19 13:45:24.055864+00:00 Photo by Markus Spiske on Unsplash. image/jpeg unsplash license one_world_markus_spiske.jpg 2022-10-16 07:52:27.418362+00:00 4398668 https://api.rohub.org/api/resources/588ede06-712e-48c6-81bf-8f1403ae8d34/download/ 2022-10-29 17:26:41.955553+00:00 2023-02-19 13:45:24.738142+00:00 Slides used by Anne Fouilloux to present her work on Open Science and the link to Climate Justice. application/pdf slides Open Science & Climate Justice: every little helps 2022-10-29 17:26:41.955553+00:00 4398668 https://api.rohub.org/api/resources/b7425822-3e95-4f71-9461-321ddfcd0410/download/ 2022-10-29 17:27:25.676240+00:00 2023-02-19 13:45:25.555431+00:00 application/pdf OpenAireWeek-20221025-AnneFouilloux.pdf 2022-10-29 17:27:25.676240+00:00 Computational notebooks community focused on Environmental Data Science environmental.ds.book@gmail.com Environmental Data Science Book Community https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose scientific discipline 9.994051160023796 16.8 deposit research work 6.99248120300752 9.3 reproducibility 6.763285024154589 7.0 climate 5.889351576442594 9.9 Search, Find & Access Reproducibility & Reuse Be cited 8.340807174887894 9.3 climate Justice 7.293233082706766 9.7 Research Lifecycle Management technologies for Earth Science Communities and Copernicus users in EOSC 10.672645739910314 11.9 infrastructure 3.1528851873884594 5.3 reuse 5.893719806763285 6.1 social and information sciences 45.285806773764904 0.6795470118522644 http 3.688280785246877 6.2 expert researcher 8.270676691729323 11.0 description 4.927536231884058 5.1 work 2.617489589530042 4.4 European Union research community 10.827067669172932 14.4 meteorology and climatology 54.714193226235096 0.8210269212722778 computer science 23.469387755102037 2.3 C scale 2.498512790005949 4.2 practice 7.4396135265700485 7.7 Lifecycle 5.990338164251208 6.2 How are they using the resources provided in the European Open Science Cloud (EOSC)? Is open access enabling researchers to contribute better to Climate Change solutions? 15.426008968609866 17.2 This project has received funding from the European research infrastructures (including e Infrastructures) under the European Union s Horizon research and innovation programme under grant agreement No 26.18834080717489 29.2 European Open Science Cloud 6.183574879227053 6.4 meteorology 32.6530612244898 3.2 research infrastructure 6.240601503759399 8.3 technology 6.666666666666667 6.9 session 5.024154589371981 5.2 environmental science and management 60.634430589016254 0.9501588940620422 reproducibility 4.521118381915526 7.6 open access 4.586466165413534 6.1 plan 3.1528851873884594 5.3 geosciences 54.714193226235096 0.8210269212722778 Science and technology Science and technology OpenAIRE OAWeek - Research communities & climate action; being open to drive change 22.062780269058297 24.6 session 3.509815585960738 5.9 European Union s Horizon research 5.864661654135339 7.8 climate justice 6.99248120300752 9.3 practice 5.0565139797739445 8.5 Science and technology Science and technology atmospheric sciences 39.365569410983746 0.6168697476387024 reuse Be 14.81203007518797 19.7 reuse 3.9262343842950624 6.6 climate action 24.210526315789476 32.2 science 14.879227053140095 15.399999999999999 research 16.038647342995173 16.6 research 12.433075550267699 20.9 researcher 7.971445568114218 13.4 climate 8.695652173913043 9.0 environmental sciences 60.634430589016254 0.9501588940620422 kind 3.4503271861986913 5.8 management 3.747769185008923 6.3 Weather Weather This session will invite expert researchers to discuss their scientific impact in climate related topics. 17.309417040358746 19.3 earth sciences 39.365569410983746 0.6168697476387024 open access 2.914931588340274 4.9 researcher 11.497584541062801 11.9 objects portal http 3.9097744360902253 5.2 mood 3.8667459845330163 6.5 technology 4.580606781677573 7.7 information technology 43.87755102040816 4.3 beryllium 3.747769185008923 6.3 Climate change Environment/Climate change documentation and information science 45.285806773764904 0.6795470118522644 https://www.earthdata.nasa.gov/learn/backgrounders/environmental-justice 2022-10-25 15:36:55.221574+00:00 2023-02-19 13:45:21.937342+00:00 NASA data are being used to support environmental and climate justice efforts as highlighted in several use cases showing how scientists and decision-makers are applying a wide combination of datasets to assess the vulnerability and exposure of communities to environmental challenges. climate change climate justice Environmental Justice at NASA 2022-10-25 15:36:55.221574+00:00 https://www.openaire.eu/oaweek2022 2022-10-25 19:28:42.371901+00:00 2023-02-19 13:45:22.163052+00:00 OpenAIRE participates in the International Open Access Week - Open for Climate Justice 24 - 30 October 2022 announcement programme OpenAIRE participates in the International Open Access Week - Open for Climate Justice 2022-10-25 19:28:42.371901+00:00 https://youtu.be/oHE0aD2JQ-k 2022-10-14 12:35:41.182975+00:00 2023-02-19 13:45:14.008945+00:00 This session on "Justice and Climate Change" has been held online during the CESM Workshop 2022. Agenda: - Jola Ajibade: "Understanding the complexity of Climate justice and Climate Change"; - Laura Landrum: "SEARCH - Study of Environmental Arctic Change Program"; - Yifan Cheng: "Informing Climate and Land Surface Model Decisions with Indigenous Guidance"; - Panel discussion with speakers. discussion Justice and Climate Change Cross Working Group - 2022 CESM Workshop Day 2 2022-10-14 12:35:41.182975+00:00 pangeo.europe@gmail.com Pangeo Europe service-account-enrichment Applied sciences Climatology Anne Fouilloux University of Freiburg, Freiburg (Germany) bjoern.gruening@gmail.com Björn Grüning 0000-0002-3079-6586 01xtthb56 University of Oslo 04jcwf484 Nordic e-Infrastructure Collaboration Docker for Galaxy Pangeo notebook from official Pangeo image. 27.555110220440877 27.5 container 11.246612466124663 8.3 It is based on Pangeo notebook docker image (https://github.com/pangeo-data/pangeo-docker-images) and contained a few additional packages required for Galaxy (to exchange data, etc. 47.29458917835671 47.2 docker for Galaxy Pangeo 12.179487179487179 11.4 package 11.655011655011656 10.0 container 9.906759906759907 8.5 Wireless technology Economy, business and finance/Economic sector/Computing and information technology/Wireless technology Waterway and maritime transport Economy, business and finance/Economic sector/Transport/Waterway and maritime transport Samsung Galaxy 19.11421911421911 16.4 Galaxy Pangeo 11.538461538461538 9.9 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware computer operations and hardware 100.0 0.3924674689769745 Jupyter Docker container 5.662393162393162 5.3 laptop 21.138211382113823 15.6 Samsung Galaxy 22.086720867208673 16.3 This Jupyter Docker container is used by the Galaxy Project. 25.150300601202403 25.1 notebook 17.832167832167833 15.3 Occupations Labour/Employment/Occupations telephony 26.143790849673202 4.0 trade 45.09803921568627 6.9 7.8337097307667145 48.01044395569975 POINT (7.8337097307667145 48.01044395569975) 8cc84709-2433-4b00-847e-1308908d7540 POINT (10.766601562500002 59.921531172441085) 9ae0349f-cdf5-41c5-affe-112558010b6f POINT (7.8337097307667145 48.01044395569975) 10.766601562500002 59.921531172441085 POINT (10.766601562500002 59.921531172441085) service-account-enrichment 10.24424/f0q9-8e35 False https://w3id.org/ro-id/9c3bfd43-7e4f-4073-8735-f280ad4ab419 2023-02-19 13:50:03.527457+00:00 https://orcid.org/0000-0002-1784-2920 30344647 https://api.rohub.org/api/ros/aae72e1c-6d73-4c25-a565-f855cfb434b6/crate/download/ 2022-03-26 09:45:54.364171+00:00 2024-03-05 12:17:38.126280+00:00 2022-03-26 09:45:54.364171+00:00 This Jupyter Docker container is used by the Galaxy Project. It is based on Pangeo notebook docker image (https://github.com/pangeo-data/pangeo-docker-images) and contained a few additional packages required for Galaxy (to exchange data, etc.). application/ld+json https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6 climate docker jupyterlab pangeo Docker for Galaxy Pangeo notebook from official Pangeo image - snapshot Docker for Galaxy Pangeo notebook from official Pangeo image MANUAL https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/86cab475-53ae-4125-83e1-9dd3a547930b https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/f6498540-63ee-43e1-8d91-8e4d97334303 https://w3id.org/ro-id/9fd724fe-0d31-435a-b75e-9cfcf7bbccc2 https://w3id.org/ro-id/a87e33d8-b70b-4cf6-8b72-3dee82615895 https://w3id.org/ro-id/fd35cbe6-e70a-412a-8055-94aaa578dd50 https://w3id.org/ro-id/12ae8cb8-4092-427a-a9ba-8c03e9f0590e https://w3id.org/ro-id/7433fb0b-eb3f-4076-81f3-b0a44708a4ac https://w3id.org/ro-id/7c1460a9-da75-4d3f-a839-63a560aaad51 https://w3id.org/ro-id/d6105912-7106-4535-9457-05df4096f9cb https://w3id.org/ro-id/dcaf0198-96ff-4855-8f6b-1745268706e0 https://w3id.org/ro-id/f25a0333-ddfa-4b10-9c25-f16d15af27ab https://w3id.org/ro-id/f846bc80-92c4-479a-9b9a-127a607d991f https://w3id.org/ro-id/e8963fee-30fb-4350-b1df-f6dd7ebbb912 https://w3id.org/ro-id/f67e34d4-deab-43d1-ab2b-20774975c008 https://w3id.org/ro-id/2196186a-0b35-41b2-9501-f8a6905c3c5a https://w3id.org/ro-id/2bab2488-2a51-457d-aa15-4c96db989619 https://w3id.org/ro-id/4216aa29-660e-488f-b893-da1497bef654 https://w3id.org/ro-id/99ef22eb-89c2-473c-ab03-cdbfa636c3f3 https://w3id.org/ro-id/20a3f70d-6e2a-442f-8dd5-a447ad4f2234 https://w3id.org/ro-id/2115a1fe-16ee-4970-8dc5-74193ff55bcc https://w3id.org/ro-id/2dae6bac-92e0-4a99-b59a-d9d458a9ed0f https://w3id.org/ro-id/3e6ce287-2571-4e9f-b9c6-cd94b5c6c096 https://w3id.org/ro-id/7f39499c-3756-44c3-9333-9d5b5385b9ef https://w3id.org/ro-id/e0e88b93-a9b8-488c-bd68-8e7d51420967 https://w3id.org/ro-id/f902e007-6806-4840-8ed4-a2a551507167 https://w3id.org/ro-id/6294f69c-90ba-498f-978c-dbfc0c4c892b https://w3id.org/ro-id/e6eba9c8-2cb8-4961-9efa-7af29b8976fd https://w3id.org/ro-id/1c1a6e13-cb41-4df0-9433-1c28b1f37465 https://w3id.org/ro-id/63d64882-ce72-4b2f-9757-cdbcf1c59220 https://w3id.org/ro-id/d3e5d393-6031-4fcc-a274-a96434e702f0 https://w3id.org/ro-id/dd8e6ffa-5376-4422-837d-4d7c0e282b28 https://w3id.org/ro-id/f4e2398b-a248-4cfb-b5fb-0b73451ddd5b https://w3id.org/ro-id/02bfef89-7027-4dcc-953b-a4f052f6e20a https://w3id.org/ro-id/15f2cc18-2e58-4e28-a043-b60d6a5807c9 https://w3id.org/ro-id/7e31fcef-0c36-4f11-b7ca-456fd84b7d8d Anne Foilloux, and Björn Grüning. "Docker for Galaxy Pangeo notebook from official Pangeo image." ROHub. Mar 26 ,2022. https://doi.org/10.24424/f0q9-8e35. POINT (7.8337097307667145 48.01044395569975) POINT (10.766601562500002 59.921531172441085) output input biblio tool 448436 https://api.rohub.org/api/resources/039f0e1f-ddb5-4a6b-8047-094aeb37b259/download/ 2022-03-30 16:49:24.424570+00:00 2023-02-19 13:50:03.407487+00:00 Copernicus Atmosphere Monitoring Service PM2.5, 2 day forecasts, 24th December 2021 at 12:00 UTC image/png CAMS PM2.5, 2 day forecasts, 24th December 2021 at 12:00 UTC 2022-03-30 16:49:24.424570+00:00 https://training.galaxyproject.org/training-material/topics/climate/tutorials/pangeo-notebook/tutorial.html 2022-03-30 15:59:56.246391+00:00 2023-02-19 13:49:56.427998+00:00 Training material (hands-on) where Pangeo Notebook is used to learn Xarray. This training is part of the Galaxy Training Network (GTN). In this tutorial, we will learn about Xarray, one of the most used Python library from the Pangeo ecosystem. We will be using data from Copernicus Atmosphere Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. Parallel data analysis with Pangeo is not covered in this tutorial. text/html Pangeo Notebook in Galaxy - Introduction to Xarray (GTN) 2022-03-30 15:59:56.246391+00:00 https://quay.io/repository/nordicesmhub/docker-pangeo-notebook 2022-03-29 11:58:28.213223+00:00 2023-02-19 13:49:55.588074+00:00 These docker images (different tags) correspond to the docker images built for Galaxy Pangeo JupyterLab. The docker images can be used within Galaxy and as standalone docker images. You can use the same images we use in Galaxy on your local computer or any other platform: 1. Pull an existing image locally docker pull quay.io/nordicesmhub/docker-pangeo-notebook 2. Run a pre-build image from docker registry 3. To start your JupyterLab: docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook and you will top open a new terminal and start your favorite web browser. your running Jupyter Notebook instance on http://localhost:7777/ipython/. Remark: for reproducibility purpose, we suggest you use a specific tag e.g. docker pull quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b Then use the same tag when starting your JupyterLab application: docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b Docker images for Galaxy Pangeo JupyterLab (Quay Container Registry) 2022-03-29 11:58:28.213223+00:00 https://doi.org/10.5281/zenodo.5805953 2022-03-30 16:52:19.796786+00:00 2023-02-19 13:49:55.345103+00:00 Dataset used in the Galaxy Pangeo tutorials on Xarray. Data is in netCDF format and is from Copernicus Air Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset. netCDF input file PM2.5 4 days forecast from December, 22 2020 2022-03-30 16:52:19.796786+00:00 10.5281/zenodo.6394185 https://doi.org/10.5281/zenodo.6399102 2022-03-29 17:55:05.034625+00:00 2023-02-19 13:49:56.124648+00:00 This is a tarball for the Docker Galaxy pangeo-JupyterLab image - Version 1c0f66b. To use it: download the image file docker-pangeo-notebook-1c0f66b.tar load it with docker with the command: docker load --input docker-pangeo-notebook-1c0f66b.tar launch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b start your favorite web browser and go to: http://localhost:7777/ipython/ See https://github.com/NordicESMhub/docker-pangeo-notebook for more details Docker Galaxy pangeo-JupyterLab image Version 1c0f66b 2022-03-29 17:55:05.034625+00:00 1729 https://api.rohub.org/api/resources/56c7c29c-badc-45ad-bb41-75ba492064e2/download/ 2022-03-29 12:08:38.781608+00:00 2023-02-19 13:49:59.656812+00:00 Default Jupyter Notebook used when starting Galaxy Climate JupyterLab if no other Jupyter Notebook is passed by the user. Default Jupyter Notebook for Galaxy Climate JupyterLab 2022-03-29 12:08:38.781608+00:00 29899819 https://api.rohub.org/api/resources/9663df26-3adb-40ed-b68e-391c2023ec0b/download/ 2022-03-30 16:44:58.679622+00:00 2023-02-19 13:50:02.589260+00:00 This is a gif animated image showing how to start the Galaxy Pangeo JupyterLab in Galaxy Europe. In this video, we pass an input file (this file will be imported in the Jupyter Notebook /import folder). image/gif How to start Galaxy Pangeo JupyterLab (gif animated) 2022-03-30 16:44:58.679622+00:00 5306 https://api.rohub.org/api/resources/a3f045c3-42e7-4896-a4b7-bef646dade6b/download/ 2022-03-30 16:14:11.257847+00:00 2023-02-19 13:49:59.932314+00:00 This is the Galaxy Pangeo JupyterLab tool wrapper used by Galaxy to start the Galaxy Pangeo JupyterLab on a Galaxy instance. application/xml Galaxy Pangeo JupyterLab Tool wrapper (xml) 2022-03-30 16:14:11.257847+00:00 https://github.com/NordicESMhub/docker-pangeo-notebook 2022-03-29 12:01:31.834492+00:00 2023-02-19 13:49:54.422945+00:00 This github repository contains all the sources required for building the docker containers that are made available in Quay Container Registry. Source code for building the docker container (github repository) 2022-03-29 12:01:31.834492+00:00 https://jupyterlab.readthedocs.io/en/stable/ 2022-03-28 14:14:45.648769+00:00 2023-02-19 13:49:56.297137+00:00 Link to the online JupyterLab documentation. JupyterLab Documentation 2022-03-28 14:14:45.648769+00:00 notebook from official Pangeo image 5.982905982905982 5.6 http 5.826558265582656 4.3 data 8.94308943089431 6.6 additional package 4.914529914529913 4.6 docker 15.034965034965035 12.9 mathematical and computer sciences 100.0 0.3924674689769745 earth sciences 100.0 0.6161516308784485 loader 17.34417344173442 12.8 notebook docker image 71.26068376068375 66.7 atmospheric sciences 100.0 0.6161516308784485 parcel 13.414634146341465 9.9 Pangeo 14.91841491841492 12.8 computer science 28.758169934640524 4.4 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 Meteorology Environmental research Climatology https://doi.org/10.5281/zenodo.5984713 2022-02-06 17:02:59.243925+00:00 2023-03-15 15:29:32.025227+00:00 Contains outputs, (regridded data and figures), generated in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data Outputs 2022-02-06 17:02:59.243925+00:00 https://edsbook.org/gallery/exploration/urban-exploration-climate_ukv/urban-exploration-climate_ukv.html 2022-02-06 17:04:55.919176+00:00 2023-03-15 15:29:08.799869+00:00 Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book text/html Online rendered version of the Jupyter notebook 2022-02-06 17:04:55.919176+00:00 https://github.com/eds-book-gallery/urban-exploration-climate_ukv/blob/main/.lock/conda-linux-64.lock 2022-02-06 17:06:19.473817+00:00 2023-03-15 15:29:11.899466+00:00 Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for linux-64 2022-02-06 17:06:19.473817+00:00 https://github.com/eds-book-gallery/urban-exploration-climate_ukv/blob/main/.lock/conda-osx-64.lock 2022-02-06 17:06:20.621949+00:00 2023-03-15 15:29:13.341003+00:00 Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for osx-64 2022-02-06 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160190 https://api.rohub.org/api/ros/07b95b7b-2492-4077-9c43-23fcbf7003b8/crate/download/ 2022-02-13 19:56:59.395340+00:00 2024-03-05 12:19:28.915589+00:00 2022-02-13 19:56:59.395340+00:00 The research object refers to the MODIS MOD021KM and FIRMS notebook published in the Environmental Data Science book. application/ld+json https://w3id.org/ro-id/07b95b7b-2492-4077-9c43-23fcbf7003b8 Environmental Science Jupyter Notebook MODIS MOD021KM and FIRMS (Jupyter Notebook) published in the Environmental Data Science book - snapshot MODIS MOD021KM and FIRMS (Jupyter Notebook) published in the Environmental Data Science book MANUAL Samuel Jackson, and Alejandro Coca-Castro. 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presented in the Jupyter notebook Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks 2022-02-20 20:24:06.974255+00:00 https://doi.org/10.5281/zenodo.3459802 2022-02-20 20:23:52.295018+00:00 2023-03-20 17:41:45.484203+00:00 Contains input NeonTreeEvaluation RGB images used in the Jupyter notebook of Tree crown detection using DeepForest Input NeonTreeEvaluation RGB images 2022-02-20 20:23:52.295018+00:00 https://doi.org/10.5281/zenodo.6190393 2022-02-20 20:23:58.613588+00:00 2023-03-20 17:41:47.611211+00:00 Contains outputs, (figures), generated in the Jupyter notebook of Tree crown detection using DeepForest Outputs 2022-02-20 20:23:58.613588+00:00 https://edsbook.org/notebooks/gallery/15d986da-2d7c-44fb-af71-700494485def/notebook.html 2022-02-20 20:24:53.810022+00:00 2023-03-20 17:41:49.280327+00:00 Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book text/html Online rendered version of the Jupyter 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https://api.rohub.org/api/ros/574facaf-90e1-488b-9e53-69a08ca03698/crate/download/ 2022-03-27 19:35:37.653424+00:00 2024-03-05 12:24:43.557766+00:00 2022-03-27 19:35:37.653424+00:00 The research object refers to the Tree crown delineation using detectreeRGB notebook published in the Environmental Data Science book. application/ld+json https://w3id.org/ro-id/574facaf-90e1-488b-9e53-69a08ca03698 Environmental Science Jupyter Notebook Tree crown delineation using detectreeRGB (Jupyter Notebook) published in the Environmental Data Science book - snapshot Tree crown delineation using detectreeRGB (Jupyter Notebook) published in the Environmental Data Science book MANUAL Sebastian H. M. Hickman, and Alejandro Coca-Castro. "Tree crown delineation using detectreeRGB (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Mar 27 ,2022. https://doi.org/10.24424/2h9y-jn41. POLYGON ((117.92697350565953 5.848843550463238, 117.92697558937621 5.850108970913337, 117.92571197152036 5.850111056410533, 117.92570989064315 5.848845635506301, 117.92697350565953 5.848843550463238)) input tool output biblio 845896 https://api.rohub.org/api/resources/c6779dc1-968b-4124-b22e-dcc3de3ac43e/download/ 2022-03-27 19:35:54.932203+00:00 2023-03-20 17:57:09.562018+00:00 image/png Image showing interactive plot of detectreeRGB model predictions of tree crown over a sample drone image in Sepilok, Sabah, Malaysia 2022-03-27 19:35:54.932203+00:00 Computational notebooks community focused on Environmental Data Science environmental.ds.book@gmail.com Environmental Data Science Book Community https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose geosciences 100.0 0.418637752532959 Book industry Economy, business and finance/Economic sector/Media/Book industry geophysics 100.0 0.418637752532959 book 15.578635014836797 10.5 notebook 13.501483679525224 9.1 research 14.391691394658753 9.7 Environmental Data Science 15.536374845869299 12.6 trees 14.391691394658753 9.7 The research object refers to the Tree crown delineation using detectreeRGB notebook published in the Environmental Data Science book. 56.25625625625625 56.2 aim 11.12759643916914 7.5 tree 12.577065351418002 10.2 capitulum 8.605341246290802 5.8 characterization 22.40356083086054 15.1 Literature Arts, culture and entertainment/Arts and entertainment/Literature detectreeRGB 16.399506781750926 13.3 tree crown delineation 29.554655870445345 29.2 publishing 54.761904761904766 4.6 Drawing Arts, culture and entertainment/Arts and entertainment/Visual arts/Drawing detectreeRGB notebook 29.251012145748987 28.9 book 11.22071516646116 9.1 crown delineation 1.9230769230769231 1.9 Tree crown delineation using detectreeRGB (Jupyter Notebook) published in the Environmental Data Science book. 43.74374374374374 43.7 atmospheric sciences 100.0 0.7615207433700562 notebook 11.960542540073982 9.7 delineation 19.60542540073983 15.9 research object 26.417004048582996 26.1 Language Arts, culture and entertainment/Culture/Language research 12.700369913686806 10.3 Environmental Data Science book 12.854251012145749 12.7 earth sciences 100.0 0.7615207433700562 botany 45.23809523809525 3.8 environmental.ds.book@gmail.com Environmental Data Science Book Community The Alan Turing Institute Alejandro Coca-Castro University of Cambridge Sebastian H. M. Hickman service-account-enrichment Environmental research Climatology https://doi.org/10.1038/s41467-021-25257-4 2022-04-03 22:38:18.897063+00:00 2023-03-20 18:04:53.028630+00:00 Related publication of the modelling presented in the Jupyter notebook Seasonal Arctic sea ice forecasting with probabilistic deep learning 2022-04-03 22:38:18.897063+00:00 https://doi.org/10.5281/zenodo.5516869 2022-04-03 22:38:16.031702+00:00 2023-03-20 18:04:53.431880+00:00 Contains input Dataset for IceNet's demo notebook used in the Jupyter notebook of Sea ice forecasting using IceNet Input Dataset for IceNet's demo notebook 2022-04-03 22:38:16.031702+00:00 https://doi.org/10.5281/zenodo.6410246 2022-04-03 22:38:17.386248+00:00 2023-03-20 18:04:55.846637+00:00 Contains outputs, (table and figures), generated in the Jupyter notebook of Sea ice forecasting using IceNet Outputs 2022-04-03 22:38:17.386248+00:00 https://doi.org/10.5285/71820e7d-c628-4e32-969f-464b7efb187c 2022-04-03 22:38:14.669821+00:00 2023-03-20 18:04:49.819080+00:00 Contains input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' used in the Jupyter notebook of Sea ice forecasting using IceNet Input Forecasts, neural networks, and results from the paper: 'Seasonal Arctic sea ice forecasting with probabilistic deep learning' 2022-04-03 22:38:14.669821+00:00 https://edsbook.org/notebooks/gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/notebook.html 2022-04-03 22:38:31.388108+00:00 2023-03-20 18:04:56.645098+00:00 Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book text/html Online rendered version of the Jupyter notebook 2022-04-03 22:38:31.388108+00:00 https://github.com/eds-book-gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/blob/main/.lock/conda-linux-64.lock 2022-04-03 22:38:32.938456+00:00 2023-03-20 18:04:52.526142+00:00 Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for linux-64 2022-04-03 22:38:32.938456+00:00 https://github.com/eds-book-gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/blob/main/.lock/conda-osx-64.lock 2022-04-03 22:38:34.714518+00:00 2023-03-20 18:05:06.274230+00:00 Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for osx-64 2022-04-03 22:38:34.714518+00:00 https://raw.githubusercontent.com/Environmental-DS-Book/polar-modelling-icenet/main/.binder/environment.yml 2022-04-03 22:38:36.253117+00:00 2023-03-20 18:04:56.238193+00:00 Conda environment when user want to have the same libraries installed without concerns of package versions Conda environment 2022-04-03 22:38:36.253117+00:00 https://raw.githubusercontent.com/eds-book-gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/main/notebook.ipynb 2022-04-03 22:38:13.405158+00:00 2023-03-20 18:05:06.040137+00:00 Jupyter Notebook hosted by the Environmental Data Science Book Jupyter notebook 2022-04-03 22:38:13.405158+00:00 https://doi.org/10.24424/m8ew-pg51 False 2023-03-20 18:05:06.388049+00:00 357290 https://api.rohub.org/api/ros/a300b52e-f3e2-4d17-8d36-1251c4ade834/crate/download/ 2022-04-03 22:37:45.977506+00:00 2024-03-05 12:23:26.549247+00:00 2022-04-03 22:37:45.977506+00:00 The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. application/ld+json https://w3id.org/ro-id/a300b52e-f3e2-4d17-8d36-1251c4ade834 Environmental Science Jupyter Notebook Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book - snapshot Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book MANUAL Alejandro Coca-Castro, Tom Andersson, and Nick Barlow. "Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Apr 03 ,2022. https://doi.org/10.24424/m8ew-pg51. tool biblio output input 344731 https://api.rohub.org/api/resources/c657b8b8-10f1-4b98-b2be-88914df2e0af/download/ 2022-04-03 22:38:08.092594+00:00 2023-03-20 18:04:55.489566+00:00 image/png Image showing interactive plot of IceNet seasonal forecasts of Artic sea ice according to four lead times and months in 2020 2022-04-03 22:38:08.092594+00:00 Computational notebooks community focused on Environmental Data Science environmental.ds.book@gmail.com Environmental Data Science Book Community https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose forecast 21.405750798722043 13.4 Environmental Data Science book 13.052415210688592 12.7 sea ice forecasting 29.907502569373072 29.1 ice 10.91160220994475 7.9 IceNet notebook 28.571428571428573 27.8 research object 26.721479958890033 26.0 Language Arts, culture and entertainment/Culture/Language IceNet 16.850828729281766 12.2 ice 12.300319488817891 7.7 notebook 13.418530351437699 8.4 sea ice 9.904153354632587 6.2 aim 11.34185303514377 7.1 publishing 100.0 6.1 Environmental Data Science 16.71270718232044 12.1 Book industry Economy, business and finance/Economic sector/Media/Book industry forecasting 19.198895027624307 13.9 earth sciences 100.0 0.779520571231842 Literature Arts, culture and entertainment/Arts and entertainment/Literature geophysics 100.0 0.39970773458480835 Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book. 40.84084084084083 40.8 book 16.61341853035144 10.4 geosciences 100.0 0.39970773458480835 ice forecasting 1.7471736896197327 1.7 research 15.015974440894569 9.4 The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. 59.15915915915915 59.1 book 10.91160220994475 7.9 research 13.397790055248617 9.7 notebook 12.016574585635357 8.7 physical geography and environmental geoscience 100.0 0.779520571231842 environmental.ds.book@gmail.com Environmental Data Science Book Community The Alan Turing Institute Alejandro Coca-Castro The Alan Turing Institute Nick Barlow he British Antarctic Survey Tom Andersson service-account-enrichment http://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2 2022-07-24 18:44:23.809948+00:00 2023-03-20 18:21:06.782308+00:00 Related publication of the exploration presented in the Jupyter notebook The NMC/NCAR 40-year reanalysis project 2022-07-24 18:44:23.809948+00:00 Environmental research Climatology https://doi.org/10.1175/BAMS-D-20-0117.1 2022-07-24 18:44:26.453549+00:00 2023-03-20 18:21:11.415216+00:00 Related publication of the exploration presented in the Jupyter notebook Quantifying Causal Pathways of Teleconnections 2022-07-24 18:44:26.453549+00:00 https://doi.org/10.5281/zenodo.6824189 2022-07-24 18:44:21.623972+00:00 2023-03-20 18:21:06.510660+00:00 Contains outputs, (figures), generated in the Jupyter notebook of Concatenating a gridded rainfall reanalysis dataset into a time series Outputs 2022-07-24 18:44:21.623972+00:00 https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis.derived/surface_gauss/prate.sfc.mon.mean.nc 2022-07-24 18:44:17.979925+00:00 2023-03-20 18:21:11.297473+00:00 Contains input of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series used in the Jupyter notebook of Concatenating a gridded rainfall reanalysis dataset into a time series application/x-netcdf Input of the Jupyter Notebook - Concatenating a gridded rainfall reanalysis dataset into a time series 2022-07-24 18:44:17.979925+00:00 https://edsbook.org/notebooks/gallery/ea34568e-d86e-4720-be2f-3f826f66a26c/notebook.html 2022-07-25 07:55:35.391910+00:00 2023-03-20 18:21:06.263751+00:00 Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book text/html Online rendered version of the Jupyter notebook 2022-07-25 07:55:35.391910+00:00 https://github.com/eds-book-gallery/ea34568e-d86e-4720-be2f-3f826f66a26c/blob/main/.lock/conda-osx-64.lock 2022-07-25 07:55:37.939360+00:00 2023-03-20 18:21:07.093471+00:00 Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for osx-64 2022-07-25 07:55:37.939360+00:00 https://github.com/eds-book-gallery/ea34568e-d86e-4720-be2f-3f826f66a26c/blob/main/.lock/requirements.txt 2022-07-25 07:56:37.104852+00:00 2023-03-20 18:21:05.514200+00:00 Pip requirements file containing libraries to install after conda lock text/plain Pip requirements for lock conda environments 2022-07-25 07:56:37.104852+00:00 https://raw.githubusercontent.com/eds-book-gallery/ea34568e-d86e-4720-be2f-3f826f66a26c/main/.binder/environment.yml 2022-07-25 07:56:45.244365+00:00 2023-03-20 18:21:06.392124+00:00 Conda environment when user want to have the same libraries installed without concerns of package versions Conda environment 2022-07-25 07:56:45.244365+00:00 https://raw.githubusercontent.com/eds-book-gallery/ea34568e-d86e-4720-be2f-3f826f66a26c/main/notebook.ipynb 2022-07-24 18:44:15.645584+00:00 2023-03-20 18:21:05.626584+00:00 Jupyter Notebook hosted by the Environmental Data Science Book Jupyter notebook 2022-07-24 18:44:15.645584+00:00 0c828382-6170-4787-81eb-be1e6b9e459c POLYGON ((-171.02743148803714 -58.697824821873, -171.02743148803714 74.07635756884055, 213.40530395507815 74.07635756884055, 213.40530395507815 -58.697824821873, -171.02743148803714 -58.697824821873)) POLYGON ((-171.02743148803714 -58.697824821873, -171.02743148803714 74.07635756884055, 213.40530395507815 74.07635756884055, 213.40530395507815 -58.697824821873, -171.02743148803714 -58.697824821873)) -171.02743148803714 -58.697824821873, -171.02743148803714 74.07635756884055, 213.40530395507815 74.07635756884055, 213.40530395507815 -58.697824821873, -171.02743148803714 -58.697824821873 https://doi.org/10.24424/1vw8-6519 False 2023-03-20 18:21:11.603227+00:00 146479 https://api.rohub.org/api/ros/bba00431-cb1c-4fc4-b90b-14cbc5edf0ac/crate/download/ 2022-07-24 18:43:58.657005+00:00 2024-03-05 12:17:23.998720+00:00 2022-07-24 18:43:58.657005+00:00 The research object refers to the Concatenating a gridded rainfall reanalysis dataset into a time series notebook published in the Environmental Data Science book. application/ld+json https://w3id.org/ro-id/bba00431-cb1c-4fc4-b90b-14cbc5edf0ac Environmental Science climate science Jupyter Notebook Concatenating a gridded rainfall reanalysis dataset into a time series (Jupyter Notebook) published in the Environmental Data Science book - snapshot Concatenating a gridded rainfall reanalysis dataset into a time series (Jupyter Notebook) published in the Environmental Data Science book MANUAL Timothy Lam, Marlene Kretschmer, Samantha Adams, Rachel Prudden, Elena Saggioro, Nick Homer, and Alejandro Coca-Castro. "Concatenating a gridded rainfall reanalysis dataset into a time series (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Jul 24 ,2022. https://doi.org/10.24424/1vw8-6519. POLYGON ((-171.02743148803714 -58.697824821873, -171.02743148803714 74.07635756884055, 213.40530395507815 74.07635756884055, 213.40530395507815 -58.697824821873, -171.02743148803714 -58.697824821873)) output input tool biblio 122755 https://api.rohub.org/api/resources/574e5560-0b34-4e4e-a3a8-00fb459c8160/download/ 2022-07-24 18:44:09.437631+00:00 2023-03-20 18:21:07.485193+00:00 image/png Image showing interactive plot of global monthly precipitation mean computed from NCEP/NCAR reanalysis dataset 2022-07-24 18:44:09.437631+00:00 Computational notebooks community focused on Environmental Data Science environmental.ds.book@gmail.com Environmental Data Science Book Community https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences re-analysis 18.248175182481752 15.0 publishing 100.0 4.8 The research object refers to the Concatenating a gridded rainfall reanalysis dataset into a time series notebook published in the Environmental Data Science book. 55.65565565565565 55.6 notebook 8.160779537149818 6.7 Jupyter Notebook 3.178206583427923 2.8 book 7.542579075425791 6.2 Literature Arts, culture and entertainment/Arts and entertainment/Literature time series notebook 12.599318955732123 11.1 reanalysis 18.39220462850183 15.1 notebook 8.150851581508515 6.7 aim 5.7177615571776155 4.7 Weather Weather research 8.880778588807786 7.3 dataset 28.102189781021895 23.1 atmospheric sciences 100.0 0.9929063320159912 Language Arts, culture and entertainment/Culture/Language research 8.891595615103533 7.3 gridded rainfall reanalysis dataset 34.846765039727586 30.7 Book industry Economy, business and finance/Economic sector/Media/Book industry earth sciences 100.0 0.9929063320159912 meteorology and climatology 100.0 0.695183515548706 dataset 28.380024360535934 23.3 Concatenating a gridded rainfall reanalysis dataset into a time series (Jupyter Notebook) published in the Environmental Data Science book. 44.34434434434434 44.3 Environmental Data Science 8.891595615103533 7.3 Environmental Data Science book 15.323496027241772 13.5 time series 19.853836784409257 16.3 geosciences 100.0 0.695183515548706 time series 23.357664233576642 19.2 book 7.429963459196103 6.1 research object 34.0522133938706 30.0 The Alan Turing Institute acoca@turing.ac.uk Alejandro Coca-Castro University of Reading e.saggioro@pgr.reading.ac.uk Elena Saggioro environmental.ds.book@gmail.com Environmental Data Science Book Community University of Reading m.j.a.kretschmer@reading.ac.uk Marlene Kretschmer University of Edinburgh nhomer@turing.ac.uk Nick Homer Met Office Informatics Lab rachel.prudden@informaticslab.co.uk Rachel Prudden Met Office Informatics Lab samantha.adams@metoffice.gov.uk Samantha Adams service-account-enrichment University of Exeter tlam@turing.ac.uk Timothy Lam Hydrology Environmental research Soil science https://doi.org/10.1002/hyp.10929 2022-05-20 22:39:28.244049+00:00 2023-03-20 18:24:06.725779+00:00 Related publication of the exploration presented in the Jupyter notebook Soil water content in southern england derived from a cosmic-ray soil moisture observing system – cosmos-uk 2022-05-20 22:39:28.244049+00:00 https://doi.org/10.5194/hess-16-4079-2012 2022-05-20 22:39:29.949068+00:00 2023-03-20 18:24:10.768453+00:00 Related publication of the exploration presented in the Jupyter notebook Cosmos: the cosmic-ray soil moisture observing system 2022-05-20 22:39:29.949068+00:00 https://doi.org/10.5281/zenodo.6566942 2022-05-20 22:39:24.934972+00:00 2023-03-20 18:24:10.374744+00:00 Contains outputs, (table and figures), generated in the Jupyter notebook of Cosmos-UK soil moisture Outputs 2022-05-20 22:39:24.934972+00:00 https://doi.org/10.5281/zenodo.6567018 2022-05-20 22:39:22.837640+00:00 2023-03-20 18:24:10.245008+00:00 Contains input Inputs of the Jupyter Notebook - Cosmos-UK soil moisture used in the Jupyter notebook of Cosmos-UK soil moisture Input Inputs of the Jupyter Notebook - Cosmos-UK soil moisture 2022-05-20 22:39:22.837640+00:00 https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185 2022-05-20 22:39:26.375450+00:00 2023-03-20 18:24:10.094217+00:00 Related publication of the exploration presented in the Jupyter notebook Daily and sub-daily hydrometeorological and soil data (2013-2019) [cosmos-uk] 2022-05-20 22:39:26.375450+00:00 https://edsbook.org/notebooks/gallery/435f534c-e49b-43c3-9bd6-3393100bef3f/notebook.html 2022-05-20 22:39:36.667715+00:00 2023-03-20 18:24:10.597853+00:00 Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book text/html Online rendered version of the Jupyter notebook 2022-05-20 22:39:36.667715+00:00 https://github.com/eds-book-gallery/435f534c-e49b-43c3-9bd6-3393100bef3f/blob/main/.lock/conda-linux-64.lock 2022-05-20 22:39:38.382479+00:00 2023-03-20 18:24:15.727317+00:00 Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for linux-64 2022-05-20 22:39:38.382479+00:00 https://github.com/eds-book-gallery/435f534c-e49b-43c3-9bd6-3393100bef3f/blob/main/.lock/conda-osx-64.lock 2022-05-20 22:39:39.813698+00:00 2023-03-20 18:24:11.163321+00:00 Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for osx-64 2022-05-20 22:39:39.813698+00:00 https://github.com/eds-book-gallery/435f534c-e49b-43c3-9bd6-3393100bef3f/blob/main/.lock/conda-win-64.lock 2022-05-20 22:39:41.571667+00:00 2023-03-20 18:24:10.893182+00:00 Lock conda file for win-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for win-64 2022-05-20 22:39:41.571667+00:00 https://raw.githubusercontent.com/eds-book-gallery/435f534c-e49b-43c3-9bd6-3393100bef3f/main/.binder/environment.yml 2022-05-20 22:39:43.049202+00:00 2023-03-20 18:24:11.433249+00:00 Conda environment when user want to have the same libraries installed without concerns of package versions Conda environment 2022-05-20 22:39:43.049202+00:00 https://raw.githubusercontent.com/eds-book-gallery/435f534c-e49b-43c3-9bd6-3393100bef3f/main/notebook.ipynb 2022-05-20 22:39:21.464899+00:00 2023-03-20 18:24:07.333101+00:00 Jupyter Notebook hosted by the Environmental Data Science Book Jupyter notebook 2022-05-20 22:39:21.464899+00:00 2023-05-03 14:42:20.277543+00:00 10.24424/y99k-rz74 False 2023-03-20 18:24:16.160631+00:00 273880 https://api.rohub.org/api/ros/39cdb4f8-ac25-42c7-8263-0cab87547992/crate/download/ 2022-05-20 22:38:58.048267+00:00 2025-10-17 20:08:55.319724+00:00 2022-05-20 22:38:58.048267+00:00 The research object refers to the Cosmos-UK soil moisture notebook published in the Environmental Data Science book. application/ld+json https://w3id.org/ro-id/39cdb4f8-ac25-42c7-8263-0cab87547992 Environmental Science Jupyter Notebook Cosmos-UK soil moisture (Jupyter Notebook) published in the Environmental Data Science book MANUAL Alejandro Coca-Castro, Doran Khamis, and Matt Fry. "Cosmos-UK soil moisture (Jupyter Notebook) published in the Environmental Data Science book." ROHub. May 20 ,2022. https://doi.org/10.24424/y99k-rz74. input biblio output tool 265882 https://api.rohub.org/api/resources/7e33725e-7d51-4e78-b7c6-e15cf14c99d4/download/ 2022-05-20 22:39:16.241433+00:00 2023-03-20 18:24:09.087208+00:00 image/png Image showing interactive plot of IceNet seasonal forecasts of Artic sea ice according to four lead times and months in 2020 2022-05-20 22:39:16.241433+00:00 Computational notebooks community focused on Environmental Data Science environmental.ds.book@gmail.com Environmental Data Science Book Community https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose book 18.376722817764165 12.0 soil moisture notebook 36.483739837398375 35.9 soil moisture 16.894018887722982 16.1 Book industry Economy, business and finance/Economic sector/Media/Book industry earth sciences 100.0 0.5232846140861511 notebook 24.19601837672282 15.8 United Kingdom research object 26.321138211382117 25.9 research 11.752360965372509 11.2 book 11.227701993704093 10.7 Language Arts, culture and entertainment/Culture/Language Environmental Data Science 14.690451206715636 14.0 notebook 17.313746065057714 16.5 research 16.84532924961715 11.0 atmospheric sciences 100.0 0.5232846140861511 United Kingdom 27.411944869831544 17.9 Literature Arts, culture and entertainment/Arts and entertainment/Literature cosmos-UK soil moisture 21.84959349593496 21.5 geosciences 100.0 0.630386233329773 The research object refers to the Cosmos-UK soil moisture notebook published in the Environmental Data Science book. 60.36036036036036 60.3 publishing 100.0 6.7 United Kingdom 18.782791185729273 17.9 refer to the cosmos-UK 1.829268292682927 1.8 object 9.338929695697797 8.9 Cosmos-UK soil moisture (Jupyter Notebook) published in the Environmental Data Science book. 39.63963963963964 39.6 geophysics 100.0 0.630386233329773 aim 13.16998468606432 8.6 Environmental Data Science book 13.516260162601627 13.3 environmental.ds.book@gmail.com Environmental Data Science Book Community The Alan Turing Institute Alejandro Coca-Castro UK Centre for Ecology & Hydrology Doran Khamis UK Centre for Ecology & Hydrology Matt Fry service-account-enrichment http://doi.org/10.1109/IGARSS47720.2021.9553499 2022-09-21 22:55:46.631043+00:00 2023-03-20 18:53:25.497782+00:00 Related publication of the exploration presented in the Jupyter notebook Global land use / land cover with Sentinel 2 and deep learning 2022-09-21 22:55:46.631043+00:00 Geography Environmental research https://doi.org/10.5281/zenodo.7101976 2022-09-21 22:55:41.737294+00:00 2023-03-20 18:53:28.931585+00:00 Contains outputs, (figures and tables), generated in the Jupyter notebook of Exploring Land Cover Data (Impact Observatory) Outputs 2022-09-21 22:55:41.737294+00:00 https://edsbook.org/notebooks/gallery/b128b282-dee7-44a7-bc21-f1fd21452a83/notebook.html 2022-09-23 08:45:44.438607+00:00 2023-03-20 18:53:22.793924+00:00 Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book text/html Online rendered version of the Jupyter notebook 2022-09-23 08:45:44.438607+00:00 https://github.com/eds-book-gallery/b128b282-dee7-44a7-bc21-f1fd21452a83/tree/master/.lock/conda-linux-64.lock 2022-09-23 08:45:49.944297+00:00 2023-03-20 18:53:25.613937+00:00 Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for linux-64 2022-09-23 08:45:49.944297+00:00 https://github.com/eds-book-gallery/b128b282-dee7-44a7-bc21-f1fd21452a83/tree/master/.lock/conda-osx-64.lock 2022-09-23 08:45:54.442299+00:00 2023-03-20 18:53:28.810754+00:00 Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for osx-64 2022-09-23 08:45:54.442299+00:00 https://github.com/eds-book-gallery/b128b282-dee7-44a7-bc21-f1fd21452a83/tree/master/.lock/conda-win-64.lock 2022-09-23 08:45:58.830681+00:00 2023-03-20 18:53:23.667456+00:00 Lock conda file for win-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book Lock conda file for win-64 2022-09-23 08:45:58.830681+00:00 https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc 2022-09-21 22:55:36.625617+00:00 2023-03-20 18:53:24.456838+00:00 Contains input of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory) used in the Jupyter notebook of Exploring Land Cover Data (Impact Observatory) Input of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory) 2022-09-21 22:55:36.625617+00:00 https://raw.githubusercontent.com/eds-book-gallery/b128b282-dee7-44a7-bc21-f1fd21452a83/main/.binder/environment.yml 2022-09-23 08:47:58.692539+00:00 2023-03-20 18:53:22.666056+00:00 Conda environment when user want to have the same libraries installed without concerns of package versions Conda environment 2022-09-23 08:47:58.692539+00:00 https://raw.githubusercontent.com/eds-book-gallery/b128b282-dee7-44a7-bc21-f1fd21452a83/main/notebook.ipynb 2022-09-21 22:55:31.029870+00:00 2023-03-20 18:53:24.050360+00:00 Jupyter Notebook hosted by the Environmental Data Science Book Jupyter notebook 2022-09-21 22:55:31.029870+00:00 b6556ecc-2773-4262-8249-ce413537947f POLYGON ((-57.9018018018018 -15.127927927927928, -57.9018018018018 -9.27207207207207, -54.2981981981982 -9.27207207207207, -54.2981981981982 -15.127927927927928, -57.9018018018018 -15.127927927927928)) POLYGON ((-57.9018018018018 -15.127927927927928, -57.9018018018018 -9.27207207207207, -54.2981981981982 -9.27207207207207, -54.2981981981982 -15.127927927927928, -57.9018018018018 -15.127927927927928)) -57.9018018018018 -15.127927927927928, -57.9018018018018 -9.27207207207207, -54.2981981981982 -9.27207207207207, -54.2981981981982 -15.127927927927928, -57.9018018018018 -15.127927927927928 https://doi.org/10.24424/7cde-g605 False 2023-03-20 18:53:30.021775+00:00 614940 https://api.rohub.org/api/ros/16b9c078-a01d-4f14-8268-4c40cfe9d467/crate/download/ 2022-09-21 22:54:53.791364+00:00 2024-03-05 12:18:21.242107+00:00 2022-09-21 22:54:53.791364+00:00 The research object refers to the Exploring Land Cover Data (Impact Observatory) notebook published in the Environmental Data Science book. application/ld+json https://w3id.org/ro-id/16b9c078-a01d-4f14-8268-4c40cfe9d467 Environmental Science Jupyter Notebook Exploring Land Cover Data (Impact Observatory) (Jupyter Notebook) published in the Environmental Data Science book - snapshot Exploring Land Cover Data (Impact Observatory) (Jupyter Notebook) published in the Environmental Data Science book MANUAL James Millington, Amandine Debus, and Anne Foilloux. 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POLYGON ((-57.9018018018018 -15.127927927927928, -57.9018018018018 -9.27207207207207, -54.2981981981982 -9.27207207207207, -54.2981981981982 -15.127927927927928, -57.9018018018018 -15.127927927927928)) input biblio tool output 606454 https://api.rohub.org/api/resources/10211dd1-34c1-4408-8933-1dd40beec9f1/download/ 2022-09-21 22:55:23.004177+00:00 2023-03-20 18:53:29.722402+00:00 image/png Image showing interactive plot of global monthly precipitation mean computed from NCEP/NCAR reanalysis dataset 2022-09-21 22:55:23.004177+00:00 Computational notebooks community focused on Environmental Data Science environmental.ds.book@gmail.com Environmental Data Science Book Community https://github.com/alan-turing-institute/environmental-ds-book/issues/new/choose Literature Arts, culture and entertainment/Arts and entertainment/Literature data 17.53130590339893 9.8 Exploring Land Cover Data (Impact Observatory) (Jupyter Notebook) published in the Environmental Data Science book. 39.03903903903904 39.0 environmental science and management 100.0 0.7133293151855469 Environmental Data Science 10.741687979539641 8.4 environmental sciences 100.0 0.7133293151855469 aim 15.026833631484795 8.4 research 19.141323792486585 10.7 data science book 28.134878819810325 26.7 Plant Human interest/Plant Exploring Land Cover Data 12.78772378516624 10.0 geophysics 100.0 0.7759513258934021 computer science 45.238095238095234 5.7 Language Arts, culture and entertainment/Culture/Language publishing 54.76190476190476 6.9 The research object refers to the Exploring Land Cover Data (Impact Observatory) notebook published in the Environmental Data Science book. 60.96096096096096 60.9 geosciences 100.0 0.7759513258934021 Science and technology Science and technology Impact Observatory 14.066496163682864 11.0 land cover data 2.107481559536354 2.0 environmental data science book 2.3182297154899896 2.2 notebook 17.774936061381073 13.9 object 11.636828644501279 9.1 Environmental Data Science book 18.33508956796628 17.4 research 14.45012787723785 11.3 notebook 23.076923076923077 12.9 research object 49.104320337197045 46.6 book 25.22361359570662 14.1 book 18.542199488491047 14.5 https://www.impactobservatory.com/static/lulc_methodology_accuracy-ee742a0a389a85a0d4e7295941504ac2.pdf 2022-09-21 22:55:53.343618+00:00 2023-03-20 18:53:23.934855+00:00 Related publication of the exploration presented in the Jupyter notebook application/pdf Impact Observatory - Methodology & Accuracy Summary 2022-09-21 22:55:53.343618+00:00 University of Cambridge aed58@cam.ac.uk Amandine Debus Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 environmental.ds.book@gmail.com Environmental Data Science Book Community King's College London james.millington@kcl.ac.uk James Millington service-account-enrichment Applied sciences Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 post@simula.no 00vn06n10 Simula Research Laboratory learning 9.105960264900663 5.5 instructor 8.111533586818757 6.4 general 100.0 0.8254843354225159 training 14.82889733840304 11.7 approach 5.4635761589403975 3.3 overview 10.646387832699618 8.4 general (general) 100.0 0.8254843354225159 artefact 9.759188846641317 7.7 It gives an overview of The Carpentries initiatives, how they operate, how they collaboratively develop and maintain training materials, and how they train their instructors. 25.68922305764411 20.5 environmental sciences 100.0 0.7156451940536499 training 16.887417218543046 10.2 Tips and Approaches for collaboratively developing, maintaining and delivering training: example with The Carpentries approach. 29.69924812030075 23.7 education 100.0 2.1 Carpentries 26.489226869455006 20.9 Teachers Education/Teaching and learning/Teachers initiative 13.245033112582782 8.0 training material 36.58256880733945 31.9 instructor 9.437086092715232 5.7 This Research Object has as a main artefact a presentation (slides) on The Carpentries approach to training. 44.61152882205514 35.6 material of interest 8.830275229357797 7.7 Education Education learning 7.984790874524714 6.3 Research Object 22.179974651457538 17.5 environmental science and management 100.0 0.7156451940536499 artifact 10.927152317880795 6.6 material 16.225165562913908 9.8 overview 12.08609271523179 7.3 service-account-enrichment https://doi.org/10.24424/7gt2-h852 False https://w3id.org/ro-id/a66bbb17-5bfa-4ba1-9199-712bdfbd6b2a 2023-07-27 12:22:18.039123+00:00 https://orcid.org/0000-0002-1784-2920 18319 https://api.rohub.org/api/ros/d8fae2d3-7277-454b-b663-f8cd5d82b001/crate/download/ 2023-07-27 11:55:15.799692+00:00 2024-03-05 12:24:41.384891+00:00 2023-07-27 11:55:15.799692+00:00 This Research Object has as a main artefact a presentation (slides) on The Carpentries approach to training. It gives an overview of The Carpentries initiatives, how they operate, how they collaboratively develop and maintain training materials, and how they train their instructors. The Research Object also contains additional links to other presentations and material of interest for learning more about The Carpentries or other similar initiatives. application/ld+json https://w3id.org/ro-id/d8fae2d3-7277-454b-b663-f8cd5d82b001 backward design community training Presentation Tips and Approaches for collaboratively developing, maintaining and delivering training: example with The Carpentries approach - snapshot Tips and Approaches for collaboratively developing, maintaining and delivering training: example with The Carpentries approach MANUAL https://w3id.org/ro-id/62aac0fe-14d0-40b9-b4f0-8e4832d1d6c1 https://w3id.org/ro-id/05f29410-c6e0-4863-a7d9-5ab879f706ec https://w3id.org/ro-id/2b97f774-9732-457f-8b40-96215669a0dd https://w3id.org/ro-id/5a20df60-6319-411c-bc07-f4386c2f07a1 https://w3id.org/ro-id/7b661f50-c197-46ee-bcf5-5c26a11756ea https://w3id.org/ro-id/8d3640f7-8ccb-4a6a-a9f8-f27ac47ee363 https://w3id.org/ro-id/c548d815-d4e9-420d-a2fa-e4dd48ca6c01 https://w3id.org/ro-id/c5b0a3c5-ec24-4440-8a8d-734b12a07eeb https://w3id.org/ro-id/d8e62c28-001b-4a26-8940-1ba0330cb2c6 https://w3id.org/ro-id/f39e18c5-ab76-4950-b03b-52632e00ab72 https://w3id.org/ro-id/531eb693-802e-4fb8-a220-0b9cb0b0a7a3 https://w3id.org/ro-id/b19b7486-90a1-402a-94bc-cd64eaffba31 https://w3id.org/ro-id/70d2eba2-4910-4919-b5cc-811b73e21bf9 https://w3id.org/ro-id/a89015ed-9ebd-4289-9745-a827c0bfed51 https://w3id.org/ro-id/08dcc895-9a99-4402-9d46-981d27433556 https://w3id.org/ro-id/2b46b708-c103-44f2-9a97-7adb5487879b https://w3id.org/ro-id/3486f643-f98a-4500-9d29-fdd5dafd30bf https://w3id.org/ro-id/45d3de80-3374-4770-9cf4-132205fa0daf https://w3id.org/ro-id/62e380e3-b78f-4980-9dff-1800e34535e4 https://w3id.org/ro-id/aa2b78be-91f0-4101-b317-d997744c8cab https://w3id.org/ro-id/ae6f92a4-add5-46a8-8201-b02d707bf3ec https://w3id.org/ro-id/1b2876e6-7492-413b-9191-94129bd4f74e https://w3id.org/ro-id/407f2be0-75d9-44db-83fe-2a0fb68b48d0 https://w3id.org/ro-id/8b4be447-da3c-4bb3-8fe4-d4d51c3f1473 https://w3id.org/ro-id/a1fc4ebd-bb2c-4fce-a816-61e53311ff35 https://w3id.org/ro-id/da46652b-4c69-4dd8-881a-4d0dd6b05865 https://w3id.org/ro-id/dbe1c1f0-1f01-4048-bfae-1c001c6e9b7e https://w3id.org/ro-id/f393af0a-5664-40ea-91e7-ec3211bbdd5e https://w3id.org/ro-id/49e131e7-2e2c-4d62-b3a4-e64d05436ee5 https://w3id.org/ro-id/61e26731-91d0-4339-9247-14acd620c970 https://w3id.org/ro-id/9a37dd2e-4f9a-49fd-a45d-af1955ea7ed1 Fouilloux, Anne. "Tips and Approaches for collaboratively developing, maintaining and delivering training: example with The Carpentries approach." ROHub. Jul 27 ,2023. https://doi.org/10.24424/7gt2-h852. biblio https://docs.google.com/presentation/d/1Zf94N8sm-oVo5ypOXiuIQp9dMGOV_MvOkLKdbyX89o0/edit#slide=id.gcd7e7a0697_0_0 2023-07-27 12:09:06.279463+00:00 2023-07-27 12:22:17.741960+00:00 Presentation given by Toby Hodges on 29 April 2021 to reflect on the First round of Lesson Development Study Groups. Toby explains what the training material on "Lesson Development Study Group" is about and how it helps The Carpentries community to co-develop training material. Lesson Development Study Groups: Reflecting on Round 1 & Planning for the Future 2023-07-27 12:09:06.279463+00:00 https://coderefinery.org 2023-07-27 12:13:39.430228+00:00 2023-07-27 12:22:10.948474+00:00 CodeRefinery is a community project where you can find Training and e-Infrastructure for Research Software Development. The CodeRefinery website 2023-07-27 12:13:39.430228+00:00 https://galaxyproject.org 2023-07-27 12:18:17.787123+00:00 2023-07-27 12:22:13.425735+00:00 Galaxy is an open-source platform for data analysis that enables users to: 1) Use tools from various domains (that can be plugged into workflows) through its graphical web interface. Run code in interactive environments (RStudio, Jupyter...) along with other tools or workflows; 2) Manage data by sharing and publishing results, workflows, and visualizations; 3) Ensure reproducibility by capturing the necessary information to repeat and understand data analyses; 4) The Galaxy Community is actively involved in helping the ecosystem improve and sharing scientific discoveries. Project The Galaxy Project website 2023-07-27 12:18:17.787123+00:00 https://doi.org/10.5281/zenodo.8189268 2023-07-27 12:15:32.339298+00:00 2023-07-27 12:22:12.823369+00:00 A short overview of The Carpentries initiative, how they operate and collaboratively develop, maintain and deliver training on foundational coding and data science skills to researchers worldwide for researchers. Informal presentation given for the GO FAIR Foundation Fellow on July 27th 2023. Galaxy Project The Carpentries approach to training 2023-07-27 12:15:32.339298+00:00 https://carpentries.org 2023-07-27 12:12:23.447445+00:00 2023-07-27 12:22:13.228406+00:00 The Carpentries website is the main page where one can find about The Carpentries initiative. You can find many other links from there, including the Carpentries training material. The Carpentries website 2023-07-27 12:12:23.447445+00:00 https://training.galaxyproject.org 2023-07-27 12:16:39.376538+00:00 2023-07-27 12:22:13.640548+00:00 Website where you can find all the training material for The Galaxy Project with many different topics. Galaxy project Galaxy Training website 2023-07-27 12:16:39.376538+00:00 https://docs.google.com/presentation/d/1MlZ5FWXc6pAOhioBlIOKj3RdAzQyk-1U1uTg6G9q1vM/edit#slide=id.g3b8317a2f2_1_29 2023-07-27 12:00:19.351898+00:00 2023-07-27 12:22:12.201561+00:00 Presentation from The Carpentries Community on "The Carpentries Instructor Training" and on how to build skills in a community of practice. Carpentries Instructor Training: Building skills in a community of practice 2023-07-27 12:00:19.351898+00:00 example with The Carpentries 12.041284403669724 10.5 overview of The Carpentries initiative 22.706422018348622 19.8 approach to training 19.839449541284402 17.3 breeding 6.622516556291391 4.0 Applied sciences jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 01xtthb56 University of Oslo 10.24424/zcq6-9r81 False 2024-01-05 15:11:39.987851+00:00 45133 https://api.rohub.org/api/ros/97b0167c-0cb4-457d-abe8-41d1a9d1b981/crate/download/ 2024-01-05 14:14:55.022211+00:00 2024-03-05 12:22:12.345762+00:00 2024-01-05 14:14:55.022211+00:00 The Ohio State University (OSU) Micro Benchmarks (OMB) are a widely used suite of benchmarks for measuring and evaluating the performance of MPI operations for point-to-point, multi-pair, and collective communications. These benchmarks are often used for comparing different Message Passing Inerface (MPI) implementations and the underlying network interconnect. Here we use the OSU micro-benchmark (version 7.2) to assess the performance in terms of bandwidth achieved with an Apptainer container between 2 processors on different nodes with OpenMPI (version 4.1.6) on the Norwegian academic High Performance Computers (HPC) located in Tromsø (Fram) and Trondheim (Betzy). application/ld+json https://w3id.org/ro-id/97b0167c-0cb4-457d-abe8-41d1a9d1b981 Apptainer HPC MPI OSU Performance bandwidth container interconnect Dataset OSU MPI Get Bandwidth Test v7.2 with OpenMPI 4.1.6 on Fram & Betzy MANUAL Iaquinta, Jean. "OSU MPI Get Bandwidth Test v7.2 with OpenMPI 4.1.6 on Fram & Betzy." ROHub. Jan 05 ,2024. https://doi.org/10.24424/zcq6-9r81. data raw data biblio metadata 10.24424/7nkm-2072 36301 https://api.rohub.org/api/resources/55d5e2f5-c395-4da5-a7d1-9621c480d0ef/download/ 2024-01-05 14:26:46.457298+00:00 2024-01-05 15:11:39.077450+00:00 Plot showing the bandwidth as a function of the message size on Fram and Betzy image/png OSU-2023Dec.png 2024-01-05 14:26:46.457298+00:00 10.24424/pv5n-vq62 1338 https://api.rohub.org/api/resources/abbe45f6-a4c4-4ec4-af82-79bb0a95440e/download/ 2024-01-05 14:33:02.319503+00:00 2024-01-05 15:11:39.583041+00:00 Output of the OSU MPI Get Bandwidth Test with openMPI 4.1.6 on Fram and Betzy text/csv Apptainer OpenMPI OSU7.2-Fram-Betzy 2024-01-05 14:33:02.319503+00:00 NICEST-2 - the second phase of the Nordic Collaboration on e-Infrastructures for Earth System Modeling focuses on strengthening the Nordic position within climate modeling by leveraging, reinforcing and complementing ongoing initiatives. Nordic Collaboration on e-Infrastructures for Earth System Modeling Tools https://w3id.org/ro-id/ed4e6aa2-9db8-452d-9301-ba1606361034 2024-01-09 09:49:11.365811+00:00 bandwidth 8.112874779541444 4.6 Message Passing Inerface 13.68421052631579 9.1 Trondheim Osu micro-benchmark 47.61321909424724 38.9 Ohio State University 14.586466165413531 9.7 High Performance Computer 8.721804511278195 5.8 network interconnect 11.627906976744185 9.5 benchmark 22.045855379188712 12.5 The Ohio State University (OSU) Micro Benchmarks (OMB) are a widely used suite of benchmarks for measuring and evaluating the performance of MPI operations for point-to-point, multi-pair, and collective communications. 47.12525667351129 45.9 bench mark 24.867724867724867 14.1 Education Education micro benchmark 21.052631578947366 17.2 University Education/School/Higher education/University MPI operation 12.607099143206854 10.3 computer network 9.876543209876543 5.6 benchmark 23.60902255639098 15.7 interconnect 10.225563909774436 6.8 earth sciences 100.0 0.4361959993839264 computer programming and software 100.0 0.6090793609619141 mathematical and computer sciences 100.0 0.6090793609619141 information technology 22.65625 2.9 interconnect 11.28747795414462 6.4 different Message Passing Inerface 7.099143206854345 5.8 Steeple chase Sport/Competition discipline/Horse racing/Steeple chase micro 21.804511278195488 14.5 Here we use the OSU micro-benchmark (version 7.2) to assess the performance in terms of bandwidth achieved with an Apptainer container between 2 processors on different nodes with OpenMPI (version 4.1.6) on the Norwegian academic High Performance Computers (HPC) located in Tromsø (Fram) and Trondheim (Betzy) 22.689938398357288 22.1 atmospheric sciences 100.0 0.4361959993839264 computer science 77.34375 9.9 Tromsø microcomputer 23.809523809523807 13.5 Office of Management and Budget network 7.36842105263158 4.9 These benchmarks are often used for comparing different Message Passing Inerface (MPI) implementations and the underlying network interconnect. 30.184804928131417 29.4 https://www.osti.gov/servlets/purl/1997634 2024-01-17 10:54:09.114061+00:00 2024-01-17 10:54:10.405249+00:00 Abstract—Open MPI is an open-source implementation of the MPI-3 standard that is developed and maintained by collaborators from academia, industry, and national laboratories. Oak Ridge National Laboratory (ORNL) and Los Alamos National Laboratory (LANL) are collaborating on porting and optimizing Open MPI and related components for use on HPE Cray EX systems, with a focus on the DOE Frontier and Aurora exa-scale systems. A key component of this effort involves development of a new LinkX Open Fabrics Interface (OFI) provider. In this paper, we describe enhancements to Open MPI, OpenPMIx runtime components, and the LinkX OFI provider. Performance results are presented for point to point and collective communication operations using both the vendor CXI provider and the LinkX provider, including results obtained using GPU accelerators. Recommended deployment options for EX systems will be discussed, along with future work. Slingshot 11 libfabric Open MPI for HPE Cray EX Systems 2024-01-17 10:54:09.114061+00:00 Applied sciences Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 forecasting sea ice 25.628140703517587 35.7 Climate change Environment/Climate change Arctic Zone https://www.wikidata.org/wiki/Q25322 Einet Galaxy 5.8225508317929755 6.3 motivation involvement 5.455850681981334 7.6 work 6.284658040665434 6.8 motivation 5.545286506469501 6.0 EGU 7.024029574861368 7.6 impact 6.800286327845384 9.5 environment 5.511811023622048 7.7 geophysics 63.38268230536455 0.8625994324684143 job market 15.053763440860214 1.4 Wireless technology Economy, business and finance/Economic sector/Computing and information technology/Wireless technology forecast 5.730129390018484 6.2 work 5.368647100930566 7.5 pipeline 6.657122405153903 9.3 Science and technology Science and technology motivation 5.440229062276307 7.6 Research Object 6.3770794824399255 6.9 motivation impact 10.768126346015793 15.0 sociology 15.053763440860214 1.4 IceNet 7.116451016635859 7.7 software 17.204301075268816 1.6 pipeline 8.13308687615527 8.8 oceanography 36.61731769463545 0.4983392357826233 forecast 13.958482462419472 19.5 Unsplash 4.621072088724584 5.0 Galaxy http 10.409188801148598 14.5 deep learning probabilistic sea ice forecasting outperforms dynamical models 7.884097035040431 11.7 implementation 6.1922365988909425 6.7 Earth Modeling 7.753050969131371 10.8 approach PANGEO 14.142139267767407 19.7 photo 3.3643521832498213 4.7 sea ice 18.396564065855404 25.7 Motivation impacts exceed local environments, populations and economies need for climate change research accurate seasonal Arctic sea ice forecasts with IceNet: 18.059299191374663 26.8 http 11.52469577666428 16.099999999999998 sea ice forecasting 8.542713567839195 11.9 research 3.937007874015748 5.5 Internet 31.18279569892473 2.9 Einet Galaxy 5.440229062276307 7.6 physical geography and environmental geoscience 100.0 1.9435470700263977 sea ice 20.794824399260627 22.5 abstraction 3.937007874015748 5.5 10.24424/vpkn-k902 False https://w3id.org/ro-id/aab53e25-a351-46b0-bcfe-a0e0bf02f881 2024-04-19 13:34:23.698072+00:00 https://orcid.org/0000-0002-1784-2920 71504702 https://api.rohub.org/api/ros/a57b6bcf-b4da-4dc3-b76e-5fed2bd180b5/crate/download/ 2024-04-09 18:50:15.660054+00:00 2024-04-19 13:35:30.077318+00:00 2024-04-09 18:50:15.660054+00:00 This Research Object corresponds to the work done by Vanessa Stoeckl, and presented as a poster at EGU 2024, ESSI 2.9 "Seamless transitioning between HPC and cloud in support of Earth Observation, Earth Modeling and community-driven Geoscience approach PANGEO". - Abstract submitted and accepted at EGU: [https://doi.org/10.5194/egusphere-egu24-8343](https://doi.org/10.5194/egusphere-egu24-8343) - [Rendered Jupyter notebook](https://edsbook.org/notebooks/gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/notebook) - [Galaxy workflow showcasing the pipeline for forecasting sea ice](https://usegalaxy.eu/u/vstoeckl/w/icenet) application/ld+json https://w3id.org/ro-id/a57b6bcf-b4da-4dc3-b76e-5fed2bd180b5 Implementation of a reproducible pipeline for forecasting sea ice - snapshot Implementation of a reproducible pipeline for forecasting sea ice MANUAL https://w3id.org/ro-id/2c11f2e6-c1f8-4f9e-8026-37c52575ddb5 https://w3id.org/ro-id/4746da2c-eed3-40e1-a0df-25317208f149 https://w3id.org/ro-id/5b814e94-ab7c-4e0d-b6e3-611d5ce75811 https://w3id.org/ro-id/998925e3-109d-420f-8b7c-f879f8bf14e3 https://w3id.org/ro-id/ce65afeb-fd62-4023-b4ee-9ddc129fc24d https://w3id.org/ro-id/f1ce5d1e-ffe9-4c4a-abc2-60d6ab2d3ea4 https://w3id.org/ro-id/031ab65d-cc0b-4ca6-90a3-2b86dedf36c7 https://w3id.org/ro-id/1cb6ed20-5088-4ab2-8e4f-97a6329dd603 https://w3id.org/ro-id/1ffaafa3-c608-4846-a3c7-2ccaa036f56e https://w3id.org/ro-id/330ef136-3065-4c0f-9b0d-fab1345a33ea https://w3id.org/ro-id/3794e23e-84b3-4658-a633-c0ebec7de401 https://w3id.org/ro-id/3a0f0984-b457-4298-96b6-ad2a4ae97c2c https://w3id.org/ro-id/692d5f1d-c7f0-430b-b639-b8671282405b https://w3id.org/ro-id/82b6fa52-29ba-4e6f-ae63-82d51640ce85 https://w3id.org/ro-id/8347d60f-f073-4f40-b5fe-125e2b727fbf https://w3id.org/ro-id/8fe40097-9a74-4e19-99ad-31c339b60b18 https://w3id.org/ro-id/98d1bdea-00d9-491f-8709-6f1f2ae21880 https://w3id.org/ro-id/99a7fdab-81cb-4642-b578-2629aa44cae5 https://w3id.org/ro-id/a02bd6c9-29f4-4153-9b51-04adfba279e8 https://w3id.org/ro-id/bba83f37-be81-48fc-94ab-591c60c9e868 https://w3id.org/ro-id/e760c923-153c-49b9-8941-5b678030acbe https://w3id.org/ro-id/ec36c1b1-b69c-4ecb-843b-b5e9c4fecb86 https://w3id.org/ro-id/9b8baf42-f413-4330-b2e5-a1b9f298a8c6 https://w3id.org/ro-id/dafe4de4-5e24-48f3-9d58-5773eae8564a https://w3id.org/ro-id/01949d0b-9a4d-4d5a-9d58-c26a490671da https://w3id.org/ro-id/2d91e0bb-54dd-4621-b8d3-9248e9d72b0e https://w3id.org/ro-id/37a33987-54dd-4d39-9f9f-810764e90a07 https://w3id.org/ro-id/aec0da5a-aab9-44de-8a06-ef15ca569090 https://w3id.org/ro-id/c6b0d5f1-007f-4b72-b81c-d5368afc70e1 https://w3id.org/ro-id/d6fd6cf4-2d36-4110-ba91-1767beef2a0f https://w3id.org/ro-id/06795639-1c3b-4250-8475-4704697b1502 https://w3id.org/ro-id/17129289-2c07-4822-9a5a-e120b09ddbc8 https://w3id.org/ro-id/180e5cff-abed-4a07-9c5a-612fccc288cf https://w3id.org/ro-id/1c32b8e8-2dc8-4cab-90fc-8d00bfb43b9f https://w3id.org/ro-id/2f4dcfea-aaf1-4f7a-9244-d5421fc79085 https://w3id.org/ro-id/3ea5990f-5b53-4a67-8562-f271a3c7db6c https://w3id.org/ro-id/4ae96008-c232-47d1-8476-95aed9a42f25 https://w3id.org/ro-id/5be31081-1517-4ef2-b189-3f39024fb49c https://w3id.org/ro-id/6a0d4695-c61e-4e99-a622-0d0035635840 https://w3id.org/ro-id/7494daff-78c9-4ed7-b13c-71d8aabcdb01 https://w3id.org/ro-id/9fd88fff-872c-46bd-ac5b-00ddd546c2aa https://w3id.org/ro-id/b9b315c5-2c94-4168-b37a-5132c4433989 https://w3id.org/ro-id/f704e9d7-3934-4d59-ab95-a5cae3d37c8e https://w3id.org/ro-id/289c2b90-e049-4524-87e6-c381df1ed362 https://w3id.org/ro-id/61e29de9-d74b-4234-8484-7f98c9edc9b7 https://w3id.org/ro-id/ddc6f5eb-8f69-4033-9af9-a914504ec8f6 https://w3id.org/ro-id/f0e0c429-0918-4f12-959f-7ce918250a62 https://w3id.org/ro-id/014eea59-5bcd-40d3-8643-4dd781d176cd https://w3id.org/ro-id/1633279d-6b92-4f81-b42d-f6bfb1c38ebe https://w3id.org/ro-id/3f54a781-eb8a-46fb-96a1-a16ea9625173 https://w3id.org/ro-id/6e4e00c6-a12f-4edb-9fbc-335f75191294 https://w3id.org/ro-id/76f3e8ed-3db0-4133-afc2-1090cc82b84a https://w3id.org/ro-id/8262af5b-77dd-4d59-9ba4-d741b4d10d69 https://w3id.org/ro-id/919b07eb-1ee3-43a3-9f08-b9f7bcb1392f https://w3id.org/ro-id/c1edeba3-3148-4e17-91a8-9de20ad30a89 https://w3id.org/ro-id/df8ba451-5528-40e3-a780-c6de579a0de3 https://w3id.org/ro-id/74691455-a770-4cec-b980-c4c82a4f8ee1 https://w3id.org/ro-id/8532d638-bd04-4074-9569-4da28ee4f26e https://w3id.org/ro-id/d712f5e0-72db-4188-abdd-93e225d20eee https://w3id.org/ro-id/e1da4802-0052-457a-8eba-c9407c31c37b https://w3id.org/ro-id/ebc9dc8b-2cab-49c1-84ad-7db71b8e5250 Stoeckl, Vanessa, Alejandro Coca-Castro, Anne Fouilloux, Björn Grüning, and Jean Iaquinta. "Implementation of a reproducible pipeline for forecasting sea ice." ROHub. Apr 09 ,2024. https://doi.org/10.24424/vpkn-k902. tool biblio output input 772547 https://api.rohub.org/api/resources/0ad18b1b-594f-4b8a-937e-eff3460be9dd/download/ 2024-04-09 19:02:15.319953+00:00 2024-04-19 13:34:19.301131+00:00 Poster EGU 2024 (pdf) Implementation of a reproducible pipeline for producing seasonal Arctic sea ice forecasts application/pdf Poster Poster EGU 2024 (pdf) 2024-04-09 19:02:15.319953+00:00 https://usegalaxy.eu/u/vstoeckl/w/icenet 2024-04-09 18:52:16.320626+00:00 2024-04-19 13:34:22.939052+00:00 Galaxy workflow on the Galaxy Europe instance. To execute it, you would need first to get an account on Galaxy Europe (free of charge) and prepare the input dataset. galaxy Galaxy Workflow IceNet sea-ice forecasting 2024-04-09 18:52:16.320626+00:00 https://doi.org/10.1093/nar/gkac247 2024-04-09 18:59:25.010332+00:00 2024-04-19 13:34:23.594734+00:00 Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations. galaxy-platform The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update 2024-04-09 18:59:25.010332+00:00 10.24424/tckn-et23 323417298 https://api.rohub.org/api/resources/79406cf5-4e66-44dd-97ae-b996a17f2ec6/download/ 2024-04-12 19:22:32.134693+00:00 2024-04-19 13:34:23.287670+00:00 video/mp4 Presentation 2024-04-12 19:22:32.134693+00:00 1260982 https://api.rohub.org/api/resources/8105ba74-e8df-435b-ac88-b2fb762365a5/download/ 2024-04-11 11:31:09.064376+00:00 2024-04-19 13:34:22.725438+00:00 Sketch used in RoHub to illustrate the Research Object created for the poster at EGU 2024. image/png sketch (based on the poster) 2024-04-11 11:31:09.064376+00:00 1473306 https://api.rohub.org/api/resources/bccd43db-8caf-4734-93d7-c864fb8139c3/download/ 2024-04-12 19:24:37.138056+00:00 2024-04-19 13:34:20.251203+00:00 application/pdf Presentation slides 2024-04-12 19:24:37.138056+00:00 https://doi.org/10.5194/egusphere-egu24-8343 2024-04-09 18:57:09.049029+00:00 2024-04-19 13:34:20.491838+00:00 EGU abstract submitted. Abstract EGU24-8343 (poster) 2024-04-09 18:57:09.049029+00:00 https://w3id.org/ro-id/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef 2024-04-09 18:54:45.057065+00:00 2024-04-19 13:34:22.131841+00:00 Research Object with the Jupyter Notebook showcasing Sea ice forecasting in the Environmental Data Science book [https://edsbook.org/welcome.html](https://edsbook.org/welcome.html) Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book 2024-04-09 18:54:45.057065+00:00 Oil and gas - upstream activities Economy, business and finance/Economic sector/Energy and resource/Oil and gas - upstream activities http 10.720887245841034 11.6 implementation 5.082319255547603 7.1 reproducible pipeline 10.050251256281406 14.0 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware computer science 21.50537634408602 2.0 Weather Weather Implementation of a reproducible pipeline for forecasting sea ice. 19.20485175202156 28.5 earth sciences 100.0 1.9435470700263977 geosciences 36.61731769463545 0.4983392357826233 Galaxy workflow 7.2505384063173 10.1 Implementation of a reproducible pipeline for forecasting sea ice 6.738544474393531 10.0 workflow 2.43378668575519 3.4 This Research Object corresponds to the work done by Vanessa Stoeckl, and presented as a poster at EGU 2024, ESSI 2.9 "Seamless transitioning between HPC and cloud in support of Earth Observation, Earth Modeling and community-driven Geoscience approach PANGEO". - Abstract submitted and accepted at EGU: [https://doi.org/10.5194/egusphere-egu24-8343](https://doi.org/10.5194/egusphere-egu24-8343) - [Rendered Jupyter notebook](https://edsbook.org/notebooks/gallery/ac327c3a-5264-40a2-8c6e-1e8d7c4b37ef/notebook) - [Galaxy workflow showcasing the pipeline for forecasting sea ice](https://usegalaxy.eu/u/vstoeckl/w/icenet) 48.113207547169814 71.4 poster 2.1474588403722263 3.0 geosciences 63.38268230536455 0.8625994324684143 National Oceanic and Atmospheric Administration https://www.wikidata.org/wiki/Q214700 environment 5.6377079482439925 6.1 The Alan Turing Institute acoca@turing.ac.uk Alejandro Coca-Castro bjoern.gruening@gmail.com Björn Grüning vanessa-tamara@web.de Vanessa Stoeckl Oceanography Environmental research Applied sciences https://destination-earth.eu/use-cases/global-fish-tracking-system-gfts 2024-03-13 08:41:04.316157+00:00 2024-08-12 19:50:27.276584+00:00 Link to the official GFTS DESP use case. WebSite DestinE Use Case official website: Global Fish Tracking System (GFTS) DESP Use Case 2024-03-13 08:41:04.316157+00:00 https://destination-earth.github.io/DestinE_ESA_GFTS 2024-03-13 08:45:35.825465+00:00 2024-08-12 19:50:16.380874+00:00 These webpages are rendered from GitHub repository and contain all the information about the GFTS project. This includes internal description of the use case, technical documentation, progress, presentations, etc. WebSite GFTS Use case project website 2024-03-13 08:45:35.825465+00:00 https://doi.org/10.5281/zenodo.10372387 2023-12-13 20:50:53.907411+00:00 2024-08-12 19:50:17.291963+00:00 Slides presented by Mathieu Woillez at the Roadshow Webinar: DestinE in action – meet the first DESP use cases (13 December 2023) Global Fish Tracking System - DESP Use Case 2023-12-13 20:50:53.907411+00:00 https://doi.org/10.5281/zenodo.10809819 2024-03-12 15:34:59.645366+00:00 2024-08-12 19:50:18.034736+00:00 Poster presented at the 8th InternationalBio-logging Science Symposium by Tina Odaka, March 2024. BSL8 Leveraging Pangeo to Geolocate Fish Using Biologging Data: The Pangeo-Fish Initiative 2024-03-12 15:34:59.645366+00:00 https://doi.org/10.5281/zenodo.11185948 2024-05-13 14:46:37.545543+00:00 2024-08-12 19:50:27.882827+00:00 Project Management Plan for the Global fish Tracking System Use Case on the DestinE Platform. Deliverable 5.1 - Project Management Plan for GFTS Use Case Application 2024-05-13 14:46:37.545543+00:00 https://doi.org/10.5281/zenodo.11186084 2024-05-13 14:52:05.526290+00:00 2024-08-12 19:50:21.743081+00:00 Deliverable 5.2 - Use Case Descriptor for the Global fish Tracking System Use Case on the DestinE Platform. Deliverable 5.2 - Use Case Descriptor for GFTS Use Case Application 2024-05-13 14:52:05.526290+00:00 https://doi.org/10.5281/zenodo.11186123 2024-05-13 14:53:36.574750+00:00 2024-08-12 19:50:20.705135+00:00 The Gobal Fish track system Use case Application on the DestinE Platform Deliverable 5.3 - GFTS Use case Application 2024-05-13 14:53:36.574750+00:00 https://doi.org/10.5281/zenodo.11186179 2024-05-13 14:54:51.494324+00:00 2024-08-12 19:50:17.564291+00:00 Deliverable 5.5 corresponding to the Global Fish tracking System Use Case Promotion Package Deliverable 5.5 - GFTS Use Case Promotion Package 2024-05-13 14:54:51.494324+00:00 https://doi.org/10.5281/zenodo.11186191 2024-05-13 14:56:18.168828+00:00 2024-08-12 19:50:26.999845+00:00 This report corresponds to the Software Reuse File for the GFTS DestinE Platform Use Case. New version will be uploaded regularly. Software Reuse File for the GFTS DestinE Platform Use Case 2024-05-13 14:56:18.168828+00:00 https://doi.org/10.5281/zenodo.11186227 2024-05-13 14:57:17.755650+00:00 2024-08-12 19:50:15.236431+00:00 The Software Release Plan for the Global Fish Tracking System DestinE Use Case. GFTS Software Release Plan 2024-05-13 14:57:17.755650+00:00 https://doi.org/10.5281/zenodo.11186257 2024-05-13 14:58:42.259068+00:00 2024-08-12 19:50:18.592163+00:00 The Software Requirement Specifications for the Global fish Tracking System DestinE Use Case. GFTS Software Requirement Specifications 2024-05-13 14:58:42.259068+00:00 https://doi.org/10.5281/zenodo.11186288 2024-05-13 15:02:41.488702+00:00 2024-08-12 19:50:12.780989+00:00 The Software Verification and Validation Plan for the Global fish Tracking System DestinE Use Case. GFTS Software Verification and Validation Plan 2024-05-13 15:02:41.488702+00:00 https://doi.org/10.5281/zenodo.11186318 2024-05-13 15:04:39.257557+00:00 2024-08-12 19:50:19.449409+00:00 The Software Verification and Validation Report from the Global Fish Tracking System DestinE Use Case. GFTS Software Verification and Validation Report 2024-05-13 15:04:39.257557+00:00 https://gfts.minrk.net/ 2024-04-03 08:56:53.930425+00:00 2024-08-12 19:50:18.317893+00:00 Link to the Pangeo JupyterHub we are using for developing Pangeo Fish. Only users from GFTS can register and authenticate to this JupyterHub jupyterhub Pangeo JupyterHub (OVH) 2024-04-03 08:56:53.930425+00:00 https://jupyter.central.data.destination-earth.eu/ 2024-04-03 08:59:43.770409+00:00 2024-08-12 19:50:19.196703+00:00 JupyterHub on Destination Earth Data Lake jupyterhub JupyterHub on Destination Earth Data Lake 2024-04-03 08:59:43.770409+00:00 IFREMER Emmanuelle Autret 0000-0002-0979-9192 IFREMER Mathieu Woillez 0000-0002-1032-2105 Ifremer tina.odaka@ifremer.fr Tina Odaka 0000-0002-1500-0156 Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 Development Seed danielwiesmann@developmentseed.org Daniel Wiesmann 0000-0002-3190-4278 Development Seed olaf@developmentseed.org Olaf Veerman 0000-0002-5408-9923 Development Seed Daniel da Silva 0009-0002-4476-7927 Development Seed Ricardo Mestre 0009-0008-7946-8568 post@simula.no 00vn06n10 Simula Research Laboratory dpo@ifremer.fr 044jxhp58 IFREMER https://w3id.org/np/RAsFv4Wt5R_8zdUBoBBHAfqyDYNbfnrMEoJ4t6iDfBUUY 2024-03-13 08:29:17.573606+00:00 2024-08-12 19:50:27.586876+00:00 FAIR Implementation Profile (FIP) for the GFTS project. FIP FIP for GFTS project 2024-03-13 08:29:17.573606+00:00 -4.483552708405557 48.396968528918855 POINT (-4.483552708405557 48.396968528918855) -9.156135762570598 38.705400547590436 POINT (-9.156135762570598 38.705400547590436) 5430859f-f450-4baa-a173-5e81c7daa881 POINT (-4.483552708405557 48.396968528918855) 6d368449-5709-4c1a-a381-88d5e206aaff POINT (-9.156135762570598 38.705400547590436) 7d448ca4-aaea-4492-a11c-b75fe0bf7b77 POINT (10.748991231319016 59.91003939873761) 10.748991231319016 59.91003939873761 POINT (10.748991231319016 59.91003939873761) 10.24424/sjfs-sn41 False 2024-08-12 19:50:28.397218+00:00 0 https://api.rohub.org/api/ros/9b361a58-e5ba-4683-a004-08a489be9df4/crate/download/ 2023-11-28 14:53:38.668993+00:00 2024-08-12 19:50:47.316403+00:00 2023-11-28 14:53:38.668993+00:00 **Use Case topic**: The goal of this use case is the development and implementation of the Global Fish Tracking System (GFTS) to enhance understanding and management of wild fish stocks **Scale of the Use Case (Global/Regional/National)**: Local to Global (various locations worldwide) **Policy addressed**: Fisheries Management Policy **Data Sources used**: Climate Change Adaptation (Climate DT: Routine and On-Demand for some higher resolution tracking), Sea Temperature observation (Satelite, in-situ) Copernicus Marine services (Sea temperature and associated value), Bathymetry (Gebco), biologging fish data **Github Repository**: [https://github.com/destination-earth/DestinE_ESA_GFTS.git](https://github.com/destination-earth/DestinE_ESA_GFTS.git) application/ld+json https://w3id.org/ro-id/9b361a58-e5ba-4683-a004-08a489be9df4 fish fish-tracking Global Fish Tracking System (GFTS): a Destination Earth Service Platform Use Case - snapshot Global Fish Tracking System (GFTS): a Destination Earth Service Platform Use Case MANUAL Fouilloux, Anne, Benjamin Ragan-Kelley, Mathieu Woillez, Tina Odaka, Daniel Wiesmann, Emmanuelle Autret, Olaf Veerman, Daniel da Silva, and Ricardo Mestre. "Global Fish Tracking System (GFTS): a Destination Earth Service Platform Use Case." ROHub. Nov 28 ,2023. https://doi.org/10.24424/sjfs-sn41. POINT (10.748991231319016 59.91003939873761) POINT (-9.156135762570598 38.705400547590436) POINT (-4.483552708405557 48.396968528918855) This folder contains presentations or other kind of materials (such as training material) developed and presented during events. events This folder contains project documents such as DMP, link to website and github repository, etc. documents tool output input reports_and_deliverables 2081827 https://api.rohub.org/api/resources/1997c5cc-9799-4c11-a43b-08ea1440d62f/download/ 2024-03-13 07:40:26.715784+00:00 2024-08-12 19:50:21.505925+00:00 This pitcure shows Tina Odaka presenting the Global Fish Tracking System (GFTS) DestinE DESP Use Case at the 8th International Bio-logging Science Symposium, Tokyo, Japan (4-8 March 2024). image/png Photo of Tina Odaka at BSL8 2024-03-13 07:40:26.715784+00:00 258569 https://api.rohub.org/api/resources/91115265-0c34-4681-bdbf-d3d2683b1ed6/download/ 2023-11-28 14:55:18.760223+00:00 2024-08-12 19:50:21.125141+00:00 image/png GFTS.png 2023-11-28 14:55:18.760223+00:00 A community platform for Big Data geoscience pangeo-europe@gmail.com Pangeo https://pangeo.io/ **Use Case topic**: The goal of this use case is the development and implementation of the Global Fish Tracking System (GFTS) to enhance understanding and management of wild fish stocks **Scale of the Use Case (Global/Regional/National)**: Local to Global (various locations worldwide) **Policy addressed**: Fisheries Management Policy **Data Sources used**: Climate Change Adaptation (Climate DT: Routine and On-Demand for some higher resolution tracking), Sea Temperature observation (Satelite, in-situ) Copernicus Marine services (Sea temperature and associated value), Bathymetry (Gebco), biologging fish data **Github Repository**: [https://github.com/destination-earth/DestinE_ESA_GFTS.git](https://github.com/destination-earth/DestinE_ESA_GFTS.git) 66.36636636636636 66.3 fish 12.665406427221173 6.7 earth sciences 100.0 0.8193894028663635 implementation of the Global Fish Tracking System 10.472972972972972 6.2 temperature 17.391304347826086 9.2 data source 7.503828483920369 4.9 http 11.1531190926276 5.9 value 8.728943338437979 5.7 Weather Weather sea 14.93383742911153 7.9 value 8.695652173913043 4.6 destination Earth service platform use case 28.885135135135137 17.1 http 10.719754977029098 7.0 temperature 17.151607963246555 11.2 Global Fish Tracking System (GFTS): a Destination Earth Service Platform Use Case. 33.63363363363363 33.6 oceanography 100.0 0.8193894028663635 subject 5.819295558958653 3.8 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware tracking 4.900459418070445 3.2 Global Fish Tracking System 12.098298676748582 6.4 earth resources and remote sensing 100.0 0.43638113141059875 fish data 18.75 11.1 sea 15.007656967840738 9.8 geosciences 100.0 0.43638113141059875 fish 12.404287901990813 8.1 use case 23.062381852551987 12.2 meteorology 25.35211267605634 1.8 sea temperature observation 27.702702702702698 16.4 data 12.404287901990813 8.1 use case topic 14.189189189189188 8.4 Climate change Environment/Climate change logging 5.359877488514549 3.5 information technology 74.64788732394366 5.3 Simula, Department of Numerical Analysis and Scientific Computing (Norway) benjaminrk@simula.no Benjamin Ragan-Kelley info@developmentseed.org Development Seed File ts_cities.csv 2025-05-23T17:37:48.953747 Climate Stripes datasets/stripes.png_31e7840b5aedca43c0a4f330c3d24460.png 2025-05-23T17:37:48.953745 workflows/f1ada1d68e850ab0.gxwf.yml Galaxy workflow engine File stripes.png 2025-05-24T11:15:49.407239 Run of Galaxy workflow engine 2025-05-24T11:15:41.585048 #df271e0b-a648-4bff-95d4-739be6c1c6b4 Galaxy CWL Common Workflow Language 10.24424/9cee-cz89 False 2025-05-24 13:39:23.468184+00:00 0 https://api.rohub.org/api/ros/d5430aa5-7a8b-44fe-8d21-6a7c80ac36d4/crate/download/ 2025-05-24 11:31:11+00:00 2025-10-16 11:38:20.704323+00:00 2025-05-24 11:31:11+00:00 # Galaxy Workflow Rerun Information **Workflow:** Climate Stripes **Execution Status:** scheduled **Executed:** 2025-05-24 11:15:41.585048 ## Workflow Inputs ### Formal Input Definitions - **ts_cities.csv** (File) ### Actual Input Files Used - **ts_cities.csv** - Format: `text/plain` - Path: `datasets/ts_cities.csv_31e7840b5aedca433fb349714141a239.tabular` ## Workflow Parameters - **input:** - __class__: `NoReplacement` - **adv:** - colormap: `RdBu_r` - format_date: `` - format_plot: `` - nxsplit: `None` - xname: `` - **ifilename:** - __class__: `ConnectedValue` - **title:** `My ScienceLive Stripes` - **variable:** `tg_anomalies_freiburg` ## Workflow Outputs ### Formal Output Definitions - **stripes.png** (File) ### Actual Output Files Generated - **stripes.png** - Format: `application/octet-stream` - Path: `datasets/stripes.png_31e7840b5aedca43c0a4f330c3d24460.png` ## Rerun Template To rerun this workflow: 1. **Workflow:** Climate Stripes 2. **Required inputs:** - ts_cities.csv (type: `File`) 3. **Parameters to set:** - input: - __class__: `NoReplacement` - adv: - colormap: `RdBu_r` - format_date: `` - format_plot: `` - nxsplit: `None` - xname: `` - ifilename: - __class__: `ConnectedValue` - title: `My ScienceLive Stripes` - variable: `tg_anomalies_freiburg` 4. **Expected outputs:** - stripes.png (type: `File`) application/ld+json https://w3id.org/ro-id/d5430aa5-7a8b-44fe-8d21-6a7c80ac36d4 workflows/f1ada1d68e850ab0.gxwf.yml #d31ef107-881d-4cf7-8f96-c91af5a2a368 74b68f2f-6fec-4a9e-85fd-c83574046358__climate.rocrate.zip Fouilloux, Anne. "74b68f2f-6fec-4a9e-85fd-c83574046358__climate.rocrate.zip." ROHub. May 24 ,2025. https://doi.org/10.24424/9cee-cz89. datasets workflows tmp3jnevr5o tmp 4528 https://api.rohub.org/api/resources/3a3aae77-4357-49e4-82ac-b3bc8a081158/download/ 2025-05-24 11:40:44.800306+00:00 2025-05-24 13:39:06.901275+00:00 text/html workflows/f1ada1d68e850ab0 2025-05-24 11:40:44.800306+00:00 1460 https://api.rohub.org/api/resources/3d1c14ef-a393-4f71-99ab-9f065c9e07a4/download/ 2025-05-24 11:40:44.798821+00:00 2025-05-24 13:39:05.161767+00:00 Climate Stripes 2025-05-24 11:40:44.798821+00:00 workflows/f1ada1d68e850ab0.abstract.cwl #b3cb61d2-2f69-478a-8d99-b015043391d4

#f358985c-9db4-42f0-b063-48d0d154953a

2 https://api.rohub.org/api/resources/3f513099-1a08-46a1-877e-3686194e9a25/download/ 2025-05-24 11:40:44.790250+00:00 2025-05-24 13:39:10.463351+00:00 library folders properties application/json library_folders_attrs.txt 2025-05-24 11:40:44.790250+00:00 2.0 30 https://api.rohub.org/api/resources/7086ff93-15c5-4c42-9189-7d84bdcc8518/download/ 2025-05-24 11:40:44.795199+00:00 2025-05-24 13:39:17.701983+00:00 export properties application/json export_attrs.txt 2025-05-24 11:40:44.795199+00:00 2.0 2945 https://api.rohub.org/api/resources/71ee97dc-10b8-4fd6-8187-ff6c9da64f90/download/ 2025-05-24 11:40:44.794466+00:00 2025-05-24 13:39:16.881976+00:00 invocation properties application/json invocation_attrs.txt 2025-05-24 11:40:44.794466+00:00 2.0 45985 https://api.rohub.org/api/resources/8d4cb501-4697-45ed-be43-65e001e07e8f/download/ 2025-05-24 11:40:44.796433+00:00 2025-05-24 13:39:12.105178+00:00 text/plain #b3cb61d2-2f69-478a-8d99-b015043391d4 ts_cities.csv 2025-05-24 11:40:44.796433+00:00 12736 https://api.rohub.org/api/resources/940c8545-32e6-4043-bbfa-1b14170ab168/download/ 2025-05-24 11:40:44.797284+00:00 2025-05-24 13:39:18.918458+00:00 application/octet-stream #f358985c-9db4-42f0-b063-48d0d154953a stripes.png_31e7840b5aedca43c0a4f330c3d24460.png 2025-05-24 11:40:44.797284+00:00 2 https://api.rohub.org/api/resources/a868f3bc-ec7a-4f58-9856-9c9afe7bc70e/download/ 2025-05-24 11:40:44.788812+00:00 2025-05-24 13:39:09.617299+00:00 datasets provenance properties application/json datasets_attrs.txt.provenance 2025-05-24 11:40:44.788812+00:00 2.0 2134 https://api.rohub.org/api/resources/aac2fe5c-5d6d-4ec5-8675-f864c5a4f36e/download/ 2025-05-24 11:40:44.787974+00:00 2025-05-24 13:39:11.683497+00:00 datasets properties application/json datasets_attrs.txt 2025-05-24 11:40:44.787974+00:00 2.0 764 https://api.rohub.org/api/resources/b5f6c475-b588-4581-8ec9-ff5d36a90f09/download/ 2025-05-24 11:40:44.799549+00:00 2025-05-24 13:39:08.432963+00:00 workflows/f1ada1d68e850ab0.abstract 2025-05-24 11:40:44.799549+00:00 2 https://api.rohub.org/api/resources/c7f1c1fa-caa1-4d3f-8b81-9adf18090c74/download/ 2025-05-24 11:40:44.793765+00:00 2025-05-24 13:39:22.602451+00:00 implicit collection jobs properties application/json implicit_collection_jobs_attrs.txt 2025-05-24 11:40:44.793765+00:00 2.0 2 https://api.rohub.org/api/resources/c891c902-f3d5-4ea8-b682-8553ea7ab434/download/ 2025-05-24 11:40:44.789539+00:00 2025-05-24 13:39:14.036041+00:00 libraries properties application/json libraries_attrs.txt 2025-05-24 11:40:44.789539+00:00 2.0 2 https://api.rohub.org/api/resources/de997230-e253-4ea1-8ef3-367cad87f52e/download/ 2025-05-24 11:40:44.791155+00:00 2025-05-24 13:39:23.412146+00:00 collections properties application/json collections_attrs.txt 2025-05-24 11:40:44.791155+00:00 2.0 2 https://api.rohub.org/api/resources/ee371b29-df2f-4f66-b301-1a25f5937d96/download/ 2025-05-24 11:40:44.791921+00:00 2025-05-24 13:39:20.927491+00:00 text/plain implicit_dataset_conversions.txt 2025-05-24 11:40:44.791921+00:00 3647 https://api.rohub.org/api/resources/ef444f9a-3d41-4f8a-8a62-b792e981a17a/download/ 2025-05-24 11:40:44.798061+00:00 2025-05-24 13:39:20.496823+00:00 workflows/f1ada1d68e850ab0 2025-05-24 11:40:44.798061+00:00 job 3.710862002319289 9.6 other earth sciences 28.140164323698208 0.8604831099510193 earth sciences 15.5005337397516 0.473982572555542 galaxy 6.500646830530402 20.1 info 4.020100502512563 10.4 server 2.6843467011642956 8.3 rerun 6.841901816776189 17.7 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences oceanography 14.137058879162895 0.43228960037231445 designation ofilename 3.7470725995316156 12.8 metadata 6.015523932729626 18.6 ScienceLive 2.8604561267877853 7.4 geology 42.222243057387296 1.291091501712799 May-24-2025 11:18:13 life sciences (general) 48.48964280977599 0.691840410232544 atmospheric sciences 15.5005337397516 0.473982572555542 template 3.285659064553537 8.5 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware param 3.33117723156533 10.3 database 39.473684210526315 19.5 computer programming 5.870445344129555 2.9 To rerun this workflow: 1. 2.6981450252951094 9.6 earth sciences 42.222243057387296 1.291091501712799 space sciences 3.5516765615937134 0.05067460238933563 life sciences 48.48964280977599 0.691840410232544 Capital punishment Crime, law and justice/Law enforcement/Punishment (criminal)/Capital punishment My ScienceLive Stripes 28.015222482435597 95.7 May-24-2025 11:15:49 name 4.915912031047865 15.2 dataset 8.796895213454075 27.2 [{"command_line": "python '/opt/galaxy/server/lib/galaxy/tools/data_fetch.py' --galaxy-root '/opt/galaxy/server' --datatypes-registry '/data/jwd05e/main/083/891/83891872/registry.xml' --request-version '1' --request '/data/jwd05e/main/083/891/83891872/configs/tmp9ijk_gw5'", "create_time": "2025-05-24T11:15:37.910670", "encoded_id": "9fa5573dd746a59a5204daf0450df025", "exit_code": 0, "galaxy_version": "24.2", "implicit_output_dataset_collection_mapping": {}, "info": null, "input_dataset_collection_element_mapping": {}, "input_dataset_collection_mapping": {}, "input_dataset_mapping": {}, "job_messages": [], "job_stderr": "", "job_stdout": "", "model_class": "Job", "output_dataset_collection_mapping": {}, "output_dataset_mapping": {"output0": ["31e7840b5aedca433fb349714141a239"]}, "params": {"file_count": "1", "files": [{"__index__": 0, "file_data": "/data/misc07/tus_upload/main/4c0201ed-0ac2-4bc9-bb7b-ee629ef6738b"}], "paramfile": null, "request_json": "{\"targets\": [{\"destination\": {\"type\": \"hdas\"}, \"elements\": [{\"name\": \"ts_cities.csv\", \"dbkey\": \"?\", \"ext\": \"auto\", \"space_to_tab\": false, \"to_posix_lines\": true, \"src\": \"path\", \"hashes\": [], \"in_place\": false, \"purge_source\": true, \"path\": \"/data/misc07/tus_upload/main/4c0201ed-0ac2-4bc9-bb7b-ee629ef6738b\", \"object_id\": 198375399}]}], \"auto_decompress\": false, \"check_content\": true}", "request_version": "1"}, "state": "ok", "tool_id": "__DATA_FETCH__", "tool_stderr": "", "tool_stdout": "", "tool_version": "0.1.0", "traceback": null, "update_time": "2025-05-24T11:16:35.950780"}, {"command_line": "python3 '/opt/galaxy/shed_tools/toolshed.g2.bx.psu.edu/repos/climate/climate_stripes/abdc27e01dca/climate_stripes/climate_stripes.py' '/data/dnb11/galaxy_db/files/b/3/c/dataset_b3cb61d2-2f69-478a-8d99-b015043391d4.dat' 'tg_anomalies_freiburg' --cmap 'RdBu_r' --title 'My ScienceLive Stripes' --output image.png", "create_time": "2025-05-24T11:15:49.359197", "encoded_id": "9fa5573dd746a59acc6f0e1eb8574ba7", "exit_code": 0, "galaxy_version": "24.2", "implicit_output_dataset_collection_mapping": {}, "info": null, "input_dataset_collection_element_mapping": {}, "input_dataset_collection_mapping": {}, "input_dataset_mapping": {"ifilename": ["31e7840b5aedca433fb349714141a239"]}, "job_messages": [], "job_stderr": "", "job_stdout": "", "model_class": "Job", "output_dataset_collection_mapping": {}, "output_dataset_mapping": {"ofilename": ["31e7840b5aedca43c0a4f330c3d24460"]}, "params": {"__input_ext": "auto", "__workflow_invocation_uuid__": "6db993da389011f08d47001e67d2ec02", "adv": {"colormap": "RdBu_r", "format_date": "", "format_plot": "", "nxsplit": null, "xname": ""}, "chromInfo": "/opt/galaxy/tool-data/shared/ucsc/chrom/?.len", "dbkey": "?", "ifilename": {"values": [{"id": "31e7840b5aedca433fb349714141a239", "src": "hda"}]}, "title": "My ScienceLive Stripes", "variable": "tg_anomalies_freiburg"}, "state": "ok", "tool_id": "toolshed.g2.bx.psu.edu/repos/climate/climate_stripes/climate_stripes/1.0.2", "tool_stderr": "", "tool_stdout": "", "tool_version": "1.0.2", "traceback": null, "update_time": "2025-05-24T11:18:13.429890" 28.10567734682406 100.0 b3cb61d2-2f69-478a-8d99-b015043391d4 2.8604561267877853 7.4 software 7.08502024291498 3.5 climate 3.4016235021260153 8.8 datum 3.0150753768844223 7.8 jwd05e 2.7831465017394668 7.2 name stripes.png 4.947306791569086 16.9 workflow input 2.781030444964871 9.5 data 9.184993531694696 28.4 metadata 3.8268264398917666 9.9 workflow output 2.839578454332552 9.7 WorkflowRequestInputParameter 4.290684190181678 11.1 earth sciences 14.137058879162895 0.43228960037231445 May-24-2025 11:15:41 output 5.450328565906456 14.1 earth sciences 28.140164323698208 0.8604831099510193 adverb 2.9754204398447603 9.2 May-24-2025 11:16:35 climate 2.652005174644243 8.2 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences galaxy 5.914186316196367 15.3 ScienceLive stripe 4.156908665105386 14.2 input 3.0150753768844223 7.8 information 4.406648627754156 11.4 # Galaxy Workflow Rerun Information **Workflow:** Climate Stripes **Execution Status:** scheduled **Executed:** 2025-05-24 11:15:41.585048 10.286677908937605 36.6 dataset 9.277155005798223 24.0 computer programming and software 47.9586806286303 0.684264749288559 WorkflowInvocationStep 3.092385001932741 8.0 galaxy workflow rerun information 8.899297423887587 30.4 WorkflowInvocationOutputDatasetAssociation dataset 4.976580796252927 17.0 rerun template 3.0444964871194378 10.4 input 3.719275549805951 11.5 hda title 1.2295081967213113 4.2 delimiter t 4.332552693208431 14.8 workflow 6.841901816776189 17.7 fact 2.8137128072445017 8.7 space sciences (general) 3.5516765615937134 0.05067460238933563 [{"model_class": "WorkflowInvocation", "state": "scheduled", "create_time": "2025-05-24 11:15:41.585048", "update_time": "2025-05-24 11:15:49.407239", "steps": [{"model_class": "WorkflowInvocationStep", "state": "scheduled", "create_time": "2025-05-24 11:15:49.372141", "update_time": "2025-05-24 11:15:49.372142", "order_index": 0, "action": null, "outputs": [{"output_name": "output", "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca433fb349714141a239"}}], "output_collections": []}, {"model_class": "WorkflowInvocationStep", "state": "scheduled", "create_time": "2025-05-24 11:15:49.372143", "update_time": "2025-05-24 11:18:13.433603", "order_index": 1, "action": null, "job": {"model_class": "Job", "encoded_id": "9fa5573dd746a59acc6f0e1eb8574ba7"}, "outputs": [{"output_name": "ofilename", "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca43c0a4f330c3d24460"}}, {"output_name": "ofilename", "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca43c0a4f330c3d24460"}}], "output_collections": []}], "input_parameters": [{"model_class": "WorkflowRequestInputParameter", "name": "5453637", "value": "{\"title\": \"My ScienceLive Stripes\", \"variable\": \"tg_anomalies_freiburg\", \"adv|colormap\": \"RdBu_r\"}", "type": "step"}, {"model_class": "WorkflowRequestInputParameter", "name": "copy_inputs_to_history", "value": "false", "type": "meta"}, {"model_class": "WorkflowRequestInputParameter", "name": "use_cached_job", "value": "false", "type": "meta"}], "step_states": [{"model_class": "WorkflowRequestStepState", "value": {"__page__": 0, "__rerun_remap_job_id__": null, "input": "{\"__class__\": \"NoReplacement\"}"}, "order_index": 0}, {"model_class": "WorkflowRequestStepState", "value": {"__STEP_META_STATE__": "{\"__POST_JOB_ACTIONS__\": {}}", "__page__": 0, "__rerun_remap_job_id__": null, "adv": "{\"colormap\": \"RdBu_r\", \"format_date\": \"\", \"format_plot\": \"\", \"nxsplit\": null, \"xname\": \"\"}", "ifilename": "{\"__class__\": \"ConnectedValue\"}", "title": "\"My ScienceLive Stripes\"", "variable": "\"tg_anomalies_freiburg\""}, "order_index": 1}], "input_step_parameters": [], "input_datasets": [{"model_class": "WorkflowRequestToInputDatasetAssociation", "name": null, "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca433fb349714141a239"}, "order_index": 0}], "input_dataset_collections": [], "output_dataset_collections": [], "output_datasets": [{"model_class": "WorkflowInvocationOutputDatasetAssociation", "dataset": {"model_class": "HistoryDatasetAssociation", "encoded_id": "31e7840b5aedca43c0a4f330c3d24460"}, "order_index": 1, "workflow_output": {"model_class": "WorkflowOutput", "output_name": "ofilename", "label": "stripes.png", "uuid": "8413b0d2-5eb1-419f-a1ed-a329fcf7366b"}}], "output_values": [], "encoded_id": "37738047cb7b8ee8", "workflow": "f1ada1d68e850ab0" 28.10567734682406 100.0 tool 4.172056921086676 12.9 rerun 5.271668822768435 16.3 output dataset 3.366510538641686 11.5 end product 2.5226390685640365 7.8 HistoryDatasetAssociation 3.0150753768844223 7.8 file 2.1345407503234153 6.6 tabular file 8.19672131147541 28.0 workflow 5.401034928848642 16.7 title 2.296248382923674 7.1 [{"annotation": "", "blurb": "836 lines 7 columns", "copied_from_history_dataset_association_id_chain": [], "create_time": "2025-05-24 11:15:37.899869", "dataset_uuid": "b3cb61d2-2f69-478a-8d99-b015043391d4", "deleted": false, "designation": null, "encoded_id": "31e7840b5aedca433fb349714141a239", "extension": "tabular", "file_metadata": {}, "file_name": "datasets/ts_cities.csv_31e7840b5aedca433fb349714141a239.tabular", "hid": 1, "history_encoded_id": "081c8f9306e90852", "info": "uploaded tabular file", "metadata": {"column_names": [], "column_types": ["str", "float", "float", "float", "float", "float", "float"], "columns": 7, "comment_lines": 0, "data_lines": 836, "dbkey": "?", "delimiter": "\t"}, "model_class": "HistoryDatasetAssociation", "name": "ts_cities.csv", "peek": "Year\ttg_avg_paris\ttg_anomalies_paris\ttg_avg_freiburg\ttg_anomalies_freiburg\ttg_avg_oslo\ttg_anomalies_oslo\n1950-01-16\t2.85\t-1.62\t-0.65999997\t-1.11\t-5.5299997\t-1.61\n1950-02-14\t7.5899997\t2.32\t3.77\t2.44\t-2.3999999\t1.24\n1950-03-16\t8.63\t0.35999998\t5.02\t0.28\t1.3199999\t1.3199999\n1950-04-15\t9.679999\t-1.37\t6.17\t-2.06\t5.5099998\t0.66999996\n", "state": "ok", "tags": [], "tool_version": null, "update_time": "2025-05-24 11:16:35.918742", "validated_state": "unknown", "validated_state_message": null, "visible": true}, {"annotation": "", "blurb": "12.4 KB", "copied_from_history_dataset_association_id_chain": [], "create_time": "2025-05-24 11:15:49.377150", "dataset_uuid": "f358985c-9db4-42f0-b063-48d0d154953a", "deleted": false, "designation": "ofilename", "encoded_id": "31e7840b5aedca43c0a4f330c3d24460", "extension": "png", "file_metadata": {"created_from_basename": "image.png"}, "file_name": "datasets/stripes.png_31e7840b5aedca43c0a4f330c3d24460.png", "hid": 2, "history_encoded_id": "081c8f9306e90852", "info": "", "metadata": {"dbkey": "?"}, "model_class": "HistoryDatasetAssociation", "name": "stripes.png", "peek": "Image in png format", "state": "ok", "tags": [], "tool_version": null, "update_time": "2025-05-24 11:18:13.393458", "validated_state": "unknown", "validated_state_message": null, "visible": true} 28.10567734682406 100.0 peek image 6.26463700234192 21.4 WorkflowInvocationStep state 4.566744730679156 15.6 computer science 47.57085020242915 23.5 mathematical and computer sciences 47.9586806286303 0.684264749288559 template 2.4902975420439843 7.7 WorkflowRequestInputParameter name 6.411007025761124 21.9 work 3.007761966364813 9.3 ## Workflow Parameters - **input:** - __class__: `NoReplacement` - **adv:** - colormap: `RdBu_r` - format_date: `` - format_plot: `` - nxsplit: `None` - xname: `` - **ifilename:** - __class__: `ConnectedValue` - **title:** `My ScienceLive Stripes` - **variable:** `tg_anomalies_freiburg` 2.6981450252951094 9.6 May-24-2025 11:15:37 climate stripe 2.2248243559718968 7.6 Process Run Crate 0.1 Workflow Run Crate 0.1 Workflow RO-Crate 1.0 Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64 2025-05-27T10:25:53+00:00 COMPSs matmul_files.py execution at MacBook-Pro-Raul-2025.local file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.0 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.1 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.0 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.1 2025-05-27T10:25:48+00:00 application_sources/matmul_files.py #compss_home #compss_python_version #localhost.matmul_tasks.multiply.avgTime #localhost.matmul_tasks.multiply.executions #localhost.matmul_tasks.multiply.maxTime #localhost.matmul_tasks.multiply.minTime #overall.matmul_files.py.executionTime #overall.matmul_tasks.multiply.avgTime #overall.matmul_tasks.multiply.executions #overall.matmul_tasks.multiply.maxTime #overall.matmul_tasks.multiply.minTime COMPSs COMPSs Programming Model 3.3.3 COMPSS_HOME /Users/rsirvent/opt/COMPSs/ COMPSS_PYTHON_VERSION 3.10.16 avgTime 68 executions 8 maxTime 106 minTime 34 executionTime 5781 avgTime 68 executions 8 maxTime 106 minTime 34 Lezzi Daniele Daniele Lezzi Vázquez Novoa Fernando Fernando Vázquez Novoa Amela Milian Ramon Ramon Amela Milian Conejero Javier Javier Conejero Iraola de Acevedo Eduardo Eduardo Iraola de Acevedo Vergés Pere Pere Vergés Puigdemunt-Schmolling Gabriel Gabriel Puigdemunt-Schmolling Bertran Marta Marta Bertran Álvarez Vecino Pol Pol Álvarez Vecino francesc.lordan@bsc.es Lordan Francesc Francesc Lordan Foyer Clément Clément Foyer Sirvent Raül Raül Sirvent Mammadli Nihad Nihad Mammadli Badia Rosa M Rosa M Badia Ramon-Cortes Vilarrodona Cristian Cristian Ramon-Cortes Vilarrodona Ejarque Jorge Jorge Ejarque Tatu Cristian Cătălin Cristian Cătălin Tatu Giacomini Nicolò Nicolò Giacomini Dabral Archit Archit Dabral Indian Institute of Technology BHU Universitat Politècnica de Catalunya Association for Computing Machinery Baku State University Barcelona Supercomputing Center Author francesc.lordan@bsc.es francesc.lordan@bsc.es size 10.867052023121387 9.4 out-of-core using file 30.78470824949698 30.6 using 19.64085297418631 17.5 size 2x2 0.5030181086519114 0.5 other earth sciences 61.94550662720011 0.7036855816841125 using file 1.6096579476861166 1.6 computer operations and hardware 91.3961987340549 0.5383046269416809 block size 2x2 element 45.774647887323944 45.5 COMPSs Matrix Multiplication, out-of-core using files. 16.216216216216214 16.2 hyper 11.791907514450866 10.2 mathematical and computer sciences 91.3961987340549 0.5383046269416809 hyper 13.131313131313131 11.7 earth sciences 61.94550662720011 0.7036855816841125 element 45.79124579124579 40.8 earth sciences 38.05449337279989 0.43228960037231445 block size 21.436588103254774 19.1 element 40.80924855491329 35.3 Disabled Society/Mankind/Disabled using 17.22543352601156 14.9 oceanography 38.05449337279989 0.43228960037231445 block size 19.30635838150289 16.7 space sciences (general) 8.603801265945089 0.05067460238933563 Hypermatrix size 2x2 blocks, block size 2x2 elements 83.78378378378378 83.7 space sciences 8.603801265945089 0.05067460238933563 matrix size 2x2 21.327967806841045 21.2 10.24424/rwf8-yj04 False 2025-05-27 10:46:53.984042+00:00 0 https://api.rohub.org/api/ros/f8958193-a08c-4f9f-a26c-7bf3f640d76e/crate/download/ 2025-05-27 10:25:54+00:00 2025-10-16 11:37:30.481824+00:00 2025-05-27 10:25:54+00:00 Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64 Hypermatrix size 2x2 blocks, block size 2x2 elements application/ld+json https://w3id.org/ro-id/f8958193-a08c-4f9f-a26c-7bf3f640d76e application_sources/matmul_files.py #COMPSs_Workflow_Run_Crate_MacBook-Pro-Raul-2025.local_7defb487-c7d1-4c81-b77e-e886b9c7cbdf COMPSs Matrix Multiplication, out-of-core using files - snapshot COMPSs Matrix Multiplication, out-of-core using files Cristian Ramon-Cortes Vilarrodona, https://orcid.org/0009-0003-8848-9436, Nihad Mammadli, Jorge Ejarque, Pol Álvarez Vecino, Gabriel Puigdemunt-Schmolling, Pere Vergés, et al. "COMPSs Matrix Multiplication, out-of-core using files." ROHub. May 27 ,2025. https://doi.org/10.24424/rwf8-yj04. 16 A.0.0 16 A.0.1 16 A.1.0 16 A.1.1 16 B.0.0 16 B.0.1 16 B.1.0 16 B.1.1 20 C.0.0 20 C.0.1 20 C.1.0 20 C.1.1 application_sources 6313 https://api.rohub.org/api/resources/5b331be1-dea5-4582-9bab-897dbe4cbd93/download/ 2025-05-27 10:27:54.083294+00:00 2025-05-27 10:46:53.888452+00:00 The graph diagram of the workflow, automatically generated by COMPSs runtime https://www.nationalarchives.gov.uk/PRONOM/fmt/92 complete_graph.svg 2025-05-27 10:27:54.083294+00:00 03fc6c911f447c2465e0d418fce444fdb574a6534fb66e086ff131ea23df414e 242 https://api.rohub.org/api/resources/7e3ef2d9-73b9-41d0-b445-5aac6912eb2e/download/ 2025-05-27 10:27:54.086528+00:00 2025-05-27 10:46:48.145820+00:00 COMPSs application Tasks profile https://www.nationalarchives.gov.uk/PRONOM/fmt/817 App_Profile.json 2025-05-27 10:27:54.086528+00:00 6fdc527f609cad0e6b5ff9dd7f7a7bdfc05fac884d54747fc0d5f5da627e52c7 1549 https://api.rohub.org/api/resources/b49bdb09-698f-4bf6-93ef-44f232720598/download/ 2025-05-27 10:27:54.085110+00:00 2025-05-27 10:46:49.576367+00:00 Auxiliary File text/plain matmul_tasks.py 2025-05-27 10:27:54.085110+00:00 154 https://api.rohub.org/api/resources/e0f59e06-9bee-4ab6-96d5-97e709bfdad6/download/ 2025-05-27 10:27:54.087244+00:00 2025-05-27 10:46:50.643764+00:00 COMPSs submission command line (runcompss / enqueue_compss), including flags and parameters passed to the application text/plain compss_submission_command_line.txt 2025-05-27 10:27:54.087244+00:00 26cfe40aee0664efe823e349544073530f24b8e63852167d255fcc5082dd93ba 4076 https://api.rohub.org/api/resources/ea56f243-9655-42a5-82d6-05cd7e9a5541/download/ 2025-05-27 10:27:54.082178+00:00 2025-05-27 10:46:51.473973+00:00 COMPSs Workflow Provenance YAML configuration file AUTHORS_COMPSS_COMPLETE.yaml 2025-05-27 10:27:54.082178+00:00 46ec0e3f267505f663c4b36d8ef4a0f123bccac284ba75941640dbd27456e66c 2212 https://api.rohub.org/api/resources/fbaadabd-93d9-4035-b944-5fab2d7ae783/download/ 2025-05-27 10:27:54.085813+00:00 2025-05-27 10:46:47.327353+00:00 Main file of the COMPSs workflow source files text/plain complete_graph.svg matmul_files.py #compss 2025-05-27 10:27:54.085813+00:00 Process Run Crate 0.5 Provenance Run Crate 0.5 Workflow Run Crate 0.5 Workflow RO-Crate 1.0 JSON Data Interchange Format YAML Scalable Vector Graphics Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64 2025-05-27T10:25:53+00:00 COMPSs matmul_files.py execution at MacBook-Pro-Raul-2025.local file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.0 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.1 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.0 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.1 2025-05-27T10:25:48+00:00 application_sources/matmul_files.py #compss_home #compss_python_version #localhost.matmul_tasks.multiply.avgTime #localhost.matmul_tasks.multiply.executions #localhost.matmul_tasks.multiply.maxTime #localhost.matmul_tasks.multiply.minTime #overall.matmul_files.py.executionTime #overall.matmul_tasks.multiply.avgTime #overall.matmul_tasks.multiply.executions #overall.matmul_tasks.multiply.maxTime #overall.matmul_tasks.multiply.minTime COMPSs COMPSs Programming Model 3.3.3 COMPSS_HOME /Users/rsirvent/opt/COMPSs/ COMPSS_PYTHON_VERSION 3.10.16 avgTime 68 executions 8 maxTime 106 minTime 34 executionTime 5781 avgTime 68 executions 8 maxTime 106 minTime 34 Lezzi Daniele Daniele Lezzi Vázquez Novoa Fernando Fernando Vázquez Novoa Amela Milian Ramon Ramon Amela Milian Conejero Javier Javier Conejero Iraola de Acevedo Eduardo Eduardo Iraola de Acevedo Vergés Pere Pere Vergés Puigdemunt-Schmolling Gabriel Gabriel Puigdemunt-Schmolling Bertran Marta Marta Bertran Álvarez Vecino Pol Pol Álvarez Vecino francesc.lordan@bsc.es Lordan Francesc Francesc Lordan Foyer Clément Clément Foyer Sirvent Raül Raül Sirvent Mammadli Nihad Nihad Mammadli Badia Rosa M Rosa M Badia Ramon-Cortes Vilarrodona Cristian Cristian Ramon-Cortes Vilarrodona Ejarque Jorge Jorge Ejarque Tatu Cristian Cătălin Cristian Cătălin Tatu Giacomini Nicolò Nicolò Giacomini Dabral Archit Archit Dabral Indian Institute of Technology BHU Universitat Politècnica de Catalunya Association for Computing Machinery Baku State University Barcelona Supercomputing Center Author francesc.lordan@bsc.es francesc.lordan@bsc.es 10.24424/28a3-r044 False 2025-05-27 17:56:14.628047+00:00 0 https://api.rohub.org/api/ros/9bfe8543-c088-4745-95c9-1f582c516dc6/crate/download/ 2025-05-27 10:25:54+00:00 2025-10-16 11:37:17.188174+00:00 2025-05-27 10:25:54+00:00 Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64 Hypermatrix size 2x2 blocks, block size 2x2 elements application/ld+json https://w3id.org/ro-id/9bfe8543-c088-4745-95c9-1f582c516dc6 application_sources/matmul_files.py #COMPSs_Workflow_Run_Crate_MacBook-Pro-Raul-2025.local_7defb487-c7d1-4c81-b77e-e886b9c7cbdf COMPSs Matrix Multiplication, out-of-core using files - snapshot COMPSs Matrix Multiplication, out-of-core using files Cristian Ramon-Cortes Vilarrodona, https://orcid.org/0009-0003-8848-9436, Nihad Mammadli, Jorge Ejarque, Pol Álvarez Vecino, Gabriel Puigdemunt-Schmolling, Pere Vergés, et al. "COMPSs Matrix Multiplication, out-of-core using files." ROHub. May 27 ,2025. https://doi.org/10.24424/28a3-r044. 16 A.0.0 16 A.0.1 16 A.1.0 16 A.1.1 16 B.0.0 16 B.0.1 16 B.1.0 16 B.1.1 20 C.0.0 20 C.0.1 20 C.1.0 20 C.1.1 application_sources 4076 https://api.rohub.org/api/resources/21921476-d31f-49fa-b4f0-abdc582278f8/download/ 2025-05-27 10:27:54.082178+00:00 2025-05-27 17:56:05.768985+00:00 COMPSs Workflow Provenance YAML configuration file https://www.nationalarchives.gov.uk/PRONOM/fmt/818 AUTHORS_COMPSS_COMPLETE.yaml 2025-05-27 10:27:54.082178+00:00 46ec0e3f267505f663c4b36d8ef4a0f123bccac284ba75941640dbd27456e66c 1549 https://api.rohub.org/api/resources/7b8b4e93-106a-44fe-b0e3-1f947ef1cac6/download/ 2025-05-27 10:27:54.085110+00:00 2025-05-27 17:56:02.782844+00:00 Auxiliary File text/plain matmul_tasks.py 2025-05-27 10:27:54.085110+00:00 242 https://api.rohub.org/api/resources/9fbd94eb-f8b7-4e2f-af79-641114ea8d32/download/ 2025-05-27 10:27:54.086528+00:00 2025-05-27 17:56:14.539842+00:00 COMPSs application Tasks profile https://www.nationalarchives.gov.uk/PRONOM/fmt/817 App_Profile.json 2025-05-27 10:27:54.086528+00:00 6fdc527f609cad0e6b5ff9dd7f7a7bdfc05fac884d54747fc0d5f5da627e52c7 154 https://api.rohub.org/api/resources/a5bed260-5529-40d5-b754-336c2dea98f0/download/ 2025-05-27 10:27:54.087244+00:00 2025-05-27 17:56:06.824982+00:00 COMPSs submission command line (runcompss / enqueue_compss), including flags and parameters passed to the application text/plain compss_submission_command_line.txt 2025-05-27 10:27:54.087244+00:00 26cfe40aee0664efe823e349544073530f24b8e63852167d255fcc5082dd93ba 2212 https://api.rohub.org/api/resources/c483c9d5-fe79-46d5-83ae-1f0396f27469/download/ 2025-05-27 10:27:54.085813+00:00 2025-05-27 17:56:04.607316+00:00 Main file of the COMPSs workflow source files text/plain complete_graph.svg matmul_files.py #compss 2025-05-27 10:27:54.085813+00:00 6313 https://api.rohub.org/api/resources/d4088ff7-ced1-4596-ba85-a02a0aca4eb1/download/ 2025-05-27 10:27:54.083294+00:00 2025-05-27 17:56:01.479452+00:00 The graph diagram of the workflow, automatically generated by COMPSs runtime https://www.nationalarchives.gov.uk/PRONOM/fmt/92 complete_graph.svg 2025-05-27 10:27:54.083294+00:00 03fc6c911f447c2465e0d418fce444fdb574a6534fb66e086ff131ea23df414e oceanography 38.05449337279989 0.43228960037231445 size 10.867052023121387 9.4 block size 2x2 element 45.774647887323944 45.5 element 40.80924855491329 35.3 element 45.79124579124579 40.8 using 17.22543352601156 14.9 matrix size 2x2 21.327967806841045 21.2 COMPSs Matrix Multiplication, out-of-core using files. 16.216216216216214 16.2 earth sciences 38.05449337279989 0.43228960037231445 block size 21.436588103254774 19.1 hyper 11.791907514450866 10.2 Disabled Society/Mankind/Disabled using file 1.6096579476861166 1.6 size 2x2 0.5030181086519114 0.5 out-of-core using file 30.78470824949698 30.6 space sciences (general) 8.603801265945089 0.05067460238933563 space sciences 8.603801265945089 0.05067460238933563 block size 19.30635838150289 16.7 earth sciences 61.94550662720011 0.7036855816841125 computer operations and hardware 91.3961987340549 0.5383046269416809 other earth sciences 61.94550662720011 0.7036855816841125 mathematical and computer sciences 91.3961987340549 0.5383046269416809 Hypermatrix size 2x2 blocks, block size 2x2 elements 83.78378378378378 83.7 hyper 13.131313131313131 11.7 using 19.64085297418631 17.5 Process Run Crate 0.5 Provenance Run Crate 0.5 Workflow Run Crate 0.5 Workflow RO-Crate 1.0 JSON Data Interchange Format YAML Scalable Vector Graphics Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64 2025-05-27T10:25:53+00:00 COMPSs matmul_files.py execution at MacBook-Pro-Raul-2025.local file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.0 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.0.1 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.0 file://MacBook-Pro-Raul-2025.local/Users/rsirvent/COMPSs-DP/matmul_files/C.1.1 2025-05-27T10:25:48+00:00 application_sources/matmul_files.py #compss_home #compss_python_version #localhost.matmul_tasks.multiply.avgTime #localhost.matmul_tasks.multiply.executions #localhost.matmul_tasks.multiply.maxTime #localhost.matmul_tasks.multiply.minTime #overall.matmul_files.py.executionTime #overall.matmul_tasks.multiply.avgTime #overall.matmul_tasks.multiply.executions #overall.matmul_tasks.multiply.maxTime #overall.matmul_tasks.multiply.minTime COMPSs COMPSs Programming Model 3.3.3 COMPSS_HOME /Users/rsirvent/opt/COMPSs/ COMPSS_PYTHON_VERSION 3.10.16 avgTime 68 executions 8 maxTime 106 minTime 34 executionTime 5781 avgTime 68 executions 8 maxTime 106 minTime 34 Lezzi Daniele Daniele Lezzi Vázquez Novoa Fernando Fernando Vázquez Novoa Amela Milian Ramon Ramon Amela Milian Conejero Javier Javier Conejero Iraola de Acevedo Eduardo Eduardo Iraola de Acevedo Vergés Pere Pere Vergés Puigdemunt-Schmolling Gabriel Gabriel Puigdemunt-Schmolling Bertran Marta Marta Bertran Álvarez Vecino Pol Pol Álvarez Vecino francesc.lordan@bsc.es Lordan Francesc Francesc Lordan Foyer Clément Clément Foyer Sirvent Raül Raül Sirvent Mammadli Nihad Nihad Mammadli Badia Rosa M Rosa M Badia Ramon-Cortes Vilarrodona Cristian Cristian Ramon-Cortes Vilarrodona Ejarque Jorge Jorge Ejarque Tatu Cristian Cătălin Cristian Cătălin Tatu Giacomini Nicolò Nicolò Giacomini Dabral Archit Archit Dabral Indian Institute of Technology BHU Universitat Politècnica de Catalunya Association for Computing Machinery Baku State University Barcelona Supercomputing Center Author francesc.lordan@bsc.es francesc.lordan@bsc.es 10.24424/m037-s338 False 2025-05-29 12:43:56.884943+00:00 0 https://api.rohub.org/api/ros/d2838de8-72fc-4d17-83c3-fed943ac78f0/crate/download/ 2025-05-27 10:25:54+00:00 2025-10-16 11:36:45.604258+00:00 2025-05-27 10:25:54+00:00 Darwin MacBook-Pro-Raul-2025.local 24.5.0 Darwin Kernel Version 24.5.0: Tue Apr 22 19:53:27 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6041 arm64 Hypermatrix size 2x2 blocks, block size 2x2 elements application/ld+json https://w3id.org/ro-id/d2838de8-72fc-4d17-83c3-fed943ac78f0 application_sources/matmul_files.py #COMPSs_Workflow_Run_Crate_MacBook-Pro-Raul-2025.local_7defb487-c7d1-4c81-b77e-e886b9c7cbdf COMPSs Matrix Multiplication, out-of-core using files - snapshot COMPSs Matrix Multiplication, out-of-core using files Cristian Ramon-Cortes Vilarrodona, https://orcid.org/0009-0003-8848-9436, Nihad Mammadli, Jorge Ejarque, Pol Álvarez Vecino, Gabriel Puigdemunt-Schmolling, Pere Vergés, et al. "COMPSs Matrix Multiplication, out-of-core using files." ROHub. May 27 ,2025. https://doi.org/10.24424/m037-s338. 16 A.0.0 16 A.0.1 16 A.1.0 16 A.1.1 16 B.0.0 16 B.0.1 16 B.1.0 16 B.1.1 20 C.0.0 20 C.0.1 20 C.1.0 20 C.1.1 application_sources 1549 https://api.rohub.org/api/resources/2bf76bb0-6372-4405-9fcd-f234b29bbde7/download/ 2025-05-27 10:27:54.085110+00:00 2025-05-29 12:43:51.084163+00:00 Auxiliary File text/plain matmul_tasks.py 2025-05-27 10:27:54.085110+00:00 242 https://api.rohub.org/api/resources/5d13adf7-6c35-4853-9863-5ed0de3ecc20/download/ 2025-05-27 10:27:54.086528+00:00 2025-05-29 12:43:49.830103+00:00 COMPSs application Tasks profile https://www.nationalarchives.gov.uk/PRONOM/fmt/817 App_Profile.json 2025-05-27 10:27:54.086528+00:00 6fdc527f609cad0e6b5ff9dd7f7a7bdfc05fac884d54747fc0d5f5da627e52c7 4076 https://api.rohub.org/api/resources/613717b6-a0dc-45df-88c5-71c7b4122b3d/download/ 2025-05-27 10:27:54.082178+00:00 2025-05-29 12:43:52.839172+00:00 COMPSs Workflow Provenance YAML configuration file https://www.nationalarchives.gov.uk/PRONOM/fmt/818 AUTHORS_COMPSS_COMPLETE.yaml 2025-05-27 10:27:54.082178+00:00 46ec0e3f267505f663c4b36d8ef4a0f123bccac284ba75941640dbd27456e66c 154 https://api.rohub.org/api/resources/900f6b13-e6e0-42f0-914a-e0c767828c40/download/ 2025-05-27 10:27:54.087244+00:00 2025-05-29 12:43:51.868361+00:00 COMPSs submission command line (runcompss / enqueue_compss), including flags and parameters passed to the application text/plain compss_submission_command_line.txt 2025-05-27 10:27:54.087244+00:00 26cfe40aee0664efe823e349544073530f24b8e63852167d255fcc5082dd93ba 2212 https://api.rohub.org/api/resources/995655f1-39da-415e-bd65-3ee2c08aa027/download/ 2025-05-27 10:27:54.085813+00:00 2025-05-29 12:43:48.857963+00:00 Main file of the COMPSs workflow source files text/plain complete_graph.svg matmul_files.py #compss 2025-05-27 10:27:54.085813+00:00 6313 https://api.rohub.org/api/resources/f5cf6566-7ce3-40a3-a8ec-b1d96689b850/download/ 2025-05-27 10:27:54.083294+00:00 2025-05-29 12:43:54.435390+00:00 The graph diagram of the workflow, automatically generated by COMPSs runtime https://www.nationalarchives.gov.uk/PRONOM/fmt/92 complete_graph.svg 2025-05-27 10:27:54.083294+00:00 03fc6c911f447c2465e0d418fce444fdb574a6534fb66e086ff131ea23df414e earth sciences 61.94550662720011 0.7036855816841125 element 40.80924855491329 35.3 computer operations and hardware 91.3961987340549 0.5383046269416809 size 10.867052023121387 9.4 Hypermatrix size 2x2 blocks, block size 2x2 elements 83.78378378378378 83.7 space sciences 8.603801265945089 0.05067460238933563 block size 2x2 element 45.774647887323944 45.5 using 17.22543352601156 14.9 using 19.64085297418631 17.5 space sciences (general) 8.603801265945089 0.05067460238933563 element 45.79124579124579 40.8 out-of-core using file 30.78470824949698 30.6 oceanography 38.05449337279989 0.43228960037231445 using file 1.6096579476861166 1.6 matrix size 2x2 21.327967806841045 21.2 block size 19.30635838150289 16.7 mathematical and computer sciences 91.3961987340549 0.5383046269416809 hyper 13.131313131313131 11.7 COMPSs Matrix Multiplication, out-of-core using files. 16.216216216216214 16.2 block size 21.436588103254774 19.1 size 2x2 0.5030181086519114 0.5 Disabled Society/Mankind/Disabled other earth sciences 61.94550662720011 0.7036855816841125 earth sciences 38.05449337279989 0.43228960037231445 hyper 11.791907514450866 10.2 Process Run Crate 0.5 Provenance Run Crate 0.5 Workflow Run Crate 0.5 Workflow RO-Crate 1.0 JSON Data Interchange Format YAML Scalable Vector Graphics Chemistry 10.24424/jxpj-vv36 False 2025-07-04 09:08:44.261623+00:00 0 https://api.rohub.org/api/ros/de0b3951-0fa7-4b03-a1fa-d5c4da93a476/crate/download/ 2022-01-12 16:34:39.917729+00:00 2025-10-16 11:15:06.613810+00:00 2022-01-12 16:34:39.917729+00:00 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom. application/ld+json https://w3id.org/ro-id/de0b3951-0fa7-4b03-a1fa-d5c4da93a476 Aromatic compounds - snapshot Aromatic compounds MANUAL Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/jxpj-vv36. arene 7.304347826086956 4.2 aliphatic compound 4.737903225806451 4.7 The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. 21.401515151515152 11.3 Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery oxygen atom 4.032258064516129 4.0 nitrogen 3.9314516129032255 3.9 organic chemistry 65.91639871382637 41.0 oxygen atom 17.794486215538846 7.1 benzene 9.274193548387096 9.2 geochemistry 100.0 0.4569866955280304 heterocyclic compound 9.73913043478261 5.6 chemistry and materials 100.0 0.8506659269332886 aromatic 19.657258064516128 19.5 benzene 12.695652173913043 7.3 monocyclic ring 14.285714285714286 5.7 chemistry and materials (general) 100.0 0.8506659269332886 arene 4.939516129032259 4.9 electron 4.435483870967742 4.4 chemistry 34.08360128617363 21.2 scent 4.536290322580645 4.5 aromatic hydrocarbon 5.94758064516129 5.9 chemical compound 15.826086956521738 9.1 nitrogen atom 29.573934837092732 11.8 aromatic hydrocarbon 8.695652173913043 5.0 ring 3.125 3.1 The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. 39.015151515151516 20.6 organic compound 3.8306451612903225 3.8 chemical compound 10.786290322580644 10.7 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. 39.583333333333336 20.9 carbon atom 15.999999999999998 9.2 aromatic compound benzene 24.81203007518797 9.9 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical heterocyclic compound 6.451612903225806 6.4 aromatic compound 29.739130434782613 17.1 larger compound 13.533834586466165 5.4 earth sciences 100.0 0.4569866955280304 carbon atom 10.786290322580644 10.7 benzene ring 3.528225806451613 3.5 Chemistry 10.24424/070n-rr14 False 2025-07-05 18:47:59.392957+00:00 0 https://api.rohub.org/api/ros/ba53e480-17bb-466f-b789-3533246d7b43/crate/download/ 2022-01-12 16:34:39.917729+00:00 2025-10-16 11:14:31.884055+00:00 2022-01-12 16:34:39.917729+00:00 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom. application/ld+json https://w3id.org/ro-id/ba53e480-17bb-466f-b789-3533246d7b43 Aromatic compounds - snapshot Aromatic compounds MANUAL Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/070n-rr14. chemistry 34.08360128617363 21.2 scent 4.536290322580645 4.5 aromatic 19.657258064516128 19.5 The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. 21.401515151515152 11.3 benzene 9.274193548387096 9.2 carbon atom 10.786290322580644 10.7 geochemistry 100.0 0.4569866955280304 aromatic compound benzene 24.81203007518797 9.9 aromatic compound 29.739130434782613 17.1 larger compound 13.533834586466165 5.4 arene 4.939516129032259 4.9 Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. 39.015151515151516 20.6 arene 7.304347826086956 4.2 oxygen atom 17.794486215538846 7.1 carbon atom 15.999999999999998 9.2 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical electron 4.435483870967742 4.4 aromatic hydrocarbon 8.695652173913043 5.0 chemical compound 15.826086956521738 9.1 benzene ring 3.528225806451613 3.5 heterocyclic compound 6.451612903225806 6.4 aromatic hydrocarbon 5.94758064516129 5.9 nitrogen 3.9314516129032255 3.9 organic compound 3.8306451612903225 3.8 chemistry and materials (general) 100.0 0.8506659269332886 earth sciences 100.0 0.4569866955280304 monocyclic ring 14.285714285714286 5.7 aliphatic compound 4.737903225806451 4.7 benzene 12.695652173913043 7.3 chemical compound 10.786290322580644 10.7 organic chemistry 65.91639871382637 41.0 chemistry and materials 100.0 0.8506659269332886 heterocyclic compound 9.73913043478261 5.6 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. 39.583333333333336 20.9 oxygen atom 4.032258064516129 4.0 nitrogen atom 29.573934837092732 11.8 ring 3.125 3.1 Chemistry https://doi.org/10.24424/x0cn-va37 False 2025-07-05 19:04:55.078129+00:00 0 https://api.rohub.org/api/ros/54c22dc5-ace3-4aaa-be62-b5b4dab97be6/crate/download/ 2022-01-12 16:34:39.917729+00:00 2025-10-16 11:14:13.082777+00:00 2022-01-12 16:34:39.917729+00:00 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom. application/ld+json https://w3id.org/ro-id/54c22dc5-ace3-4aaa-be62-b5b4dab97be6 Aromatic compounds - snapshot Aromatic compounds MANUAL Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://doi.org/10.24424/x0cn-va37. chemical compound 15.826086956521738 9.1 The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. 39.015151515151516 20.6 aromatic hydrocarbon 5.94758064516129 5.9 benzene 9.274193548387096 9.2 carbon atom 15.999999999999998 9.2 chemical compound 10.786290322580644 10.7 electron 4.435483870967742 4.4 oxygen atom 4.032258064516129 4.0 arene 4.939516129032259 4.9 chemistry 34.08360128617363 21.2 organic chemistry 65.91639871382637 41.0 chemistry and materials 100.0 0.8506659269332886 scent 4.536290322580645 4.5 heterocyclic compound 9.73913043478261 5.6 benzene 12.695652173913043 7.3 earth sciences 100.0 0.4569866955280304 geochemistry 100.0 0.4569866955280304 The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. 21.401515151515152 11.3 nitrogen atom 29.573934837092732 11.8 aromatic compound 29.739130434782613 17.1 arene 7.304347826086956 4.2 Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery aliphatic compound 4.737903225806451 4.7 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical benzene ring 3.528225806451613 3.5 larger compound 13.533834586466165 5.4 nitrogen 3.9314516129032255 3.9 heterocyclic compound 6.451612903225806 6.4 aromatic hydrocarbon 8.695652173913043 5.0 aromatic 19.657258064516128 19.5 organic compound 3.8306451612903225 3.8 carbon atom 10.786290322580644 10.7 monocyclic ring 14.285714285714286 5.7 chemistry and materials (general) 100.0 0.8506659269332886 aromatic compound benzene 24.81203007518797 9.9 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. 39.583333333333336 20.9 ring 3.125 3.1 oxygen atom 17.794486215538846 7.1 Biology 10.24424/20ms-v465 False 2025-08-12 08:02:25.321821+00:00 0 https://api.rohub.org/api/ros/07b99b7b-a209-44cc-86fd-327339b2599c/crate/download/ 2022-01-19 13:47:59.181939+00:00 2025-10-16 11:12:08.755267+00:00 2022-01-19 13:47:59.181939+00:00 Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate.Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function. For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance. Additionally, it is associated with other mental disorders and substance use disorders. Although it causes impairment, particularly in modern society, many people with ADHD have sustained attention for tasks they find interesting or rewarding, known as hyperfocus. application/ld+json https://w3id.org/ro-id/07b99b7b-a209-44cc-86fd-327339b2599c Attention deficit hyperactivity disorder - snapshot Attention deficit hyperactivity disorder MANUAL Wolniewicz, Małgorzata. "Attention deficit hyperactivity disorder." ROHub. Jan 19 ,2022. https://doi.org/10.24424/20ms-v465. life sciences 100.0 0.989045262336731 distraction 5.919003115264798 5.7 neurodevelopmental disorder 62.65984654731457 49.0 environmental science and management 100.0 0.6445436477661133 behavioural disorder 7.4766355140186915 7.2 environmental sciences 100.0 0.6445436477661133 inattention 9.515260323159785 5.3 substance use disorder 19.565217391304348 15.3 life sciences (general) 100.0 0.989045262336731 diagnosis 6.645898234683282 6.4 medicine 100.0 12.8 individual 4.7767393561786085 4.6 behavioral disorder 12.208258527827647 6.8 individuals with ADHD 7.416879795396419 5.8 mental disorder 3.426791277258567 3.3 problem 9.345794392523365 9.0 attention 5.815160955347872 5.6 impulsiveness 5.815160955347872 5.6 diagnosis 10.23339317773788 5.7 disorder 10.951526032315979 6.1 symptom 4.569055036344757 4.4 attention deficit hyperactivity disorder 21.599169262720665 20.8 Mental and behavioural disorder Health/Diseases and conditions/Mental and behavioural disorder mental disorders 4.731457800511508 3.7 Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate. 59.31758530183727 45.2 difficulty 10.412926391382404 5.8 disorder 12.772585669781932 12.3 For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance. 18.11023622047244 13.8 emotions 4.984423676012462 4.8 School Education/School Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function. 22.572178477690287 17.2 difficulty 6.853582554517134 6.6 attention deficit hyperactivity disorder 32.85457809694793 18.3 school performance 5.626598465473147 4.4 problem 13.824057450628365 7.7 Earth sciences 10.13039/501100000780 European Commission 10.13039/501100000781 European Commission Elisa Trasatti https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-08 16:30:52.813503+00:00 2021-11-08 17:06:22.193615+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-08 16:30:52.813503+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-08 16:31:25.130170+00:00 2021-11-08 17:06:22.296703+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-08 16:31:25.130170+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-08 16:31:09.076275+00:00 2021-11-08 17:06:22.491861+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-08 16:31:09.076275+00:00 101017501 RELIANCE 101017502 RELIANCE POINT (38.0 38.0) 5926d4c9-986f-42f2-a840-79ae265f653f POINT (38.0 38.0) 38.0 38.0 POINT (38.0 38.0) False 2021-11-08 17:06:28.738078+00:00 79418 https://api.rohub.org/api/ros/bcb5cdba-0605-4602-bd60-b59f2701e05b/crate/download/ 2021-11-08 15:12:22.689370+00:00 2025-10-16 10:35:19.041970+00:00 2021-11-08 15:12:22.689370+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/bcb5cdba-0605-4602-bd60-b59f2701e05b 8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot MANUAL Jose Perez, and Elisa Trasatti. "8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 08 ,2021. https://doi.org/10.24424/1k12-x394. ICHB-PAS Jose Perez PSNC 73394 https://api.rohub.org/api/resources/1f611f7e-a4b7-45de-be8e-d6f0e39d2fde/download/ 2021-11-08 16:30:06.553639+00:00 2021-11-08 17:06:22.592157+00:00 image/png flow-dcro.png 2021-11-08 16:30:06.553639+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G Flow to compute monthly map Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 research object 83.11557788944724 82.7 map 17.05639614855571 12.4 PM10 13.541666666666666 13.0 Copernicus Atmosphere Monitoring Service 8.229166666666666 7.9 object 25.208333333333332 24.2 Nov-8 research 31.145833333333332 29.9 data cube research object 1.0050251256281406 1.0 8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot. 30.330330330330334 30.3 aim 31.499312242090785 22.9 country 8.541666666666666 8.2 earth sciences 100.0 0.8168788552284241 atmospheric sciences 100.0 0.8168788552284241 research 39.61485557083906 28.8 map 13.333333333333334 12.8 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified 69.66966966966967 69.6 country 11.829436038514443 8.6 monthly map 6.231155778894473 6.2 map of PM10 9.246231155778894 9.2 astronautics 100.0 0.3785407543182373 astronautics (general) 100.0 0.3785407543182373 data cube 0.4020100502512563 0.4 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-08 16:59:14.401521+00:00 2021-11-08 17:06:22.390417+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-08 16:59:14.401521+00:00 Raul Palma service-account-enrichment Earth sciences research object 83.11557788944724 82.7 map 17.05639614855571 12.4 country 11.829436038514443 8.6 False https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 2021-11-09 16:18:39.029666+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl POINT (38.0 38.0) Nov-9 38.0 38.0 POINT (38.0 38.0) 9b071de5-4738-4072-9e66-4822fb20d61a POINT (38.0 38.0) service-account-enrichment https://w3id.org/ro-id/0e5f85c2-45ce-4b79-af5b-a940086cc802 https://w3id.org/ro-id/48eb1f98-3c64-4dd2-95b7-fe7044b08ff1 https://w3id.org/ro-id/4df864f9-4427-4f6d-a11a-b6f1a340eb42 https://w3id.org/ro-id/56840bfe-6946-4cb1-a8a4-e4e3c4927063 False https://w3id.org/ro-id/2755900c-b77c-4a29-ac59-f6f51af20fa7 2021-11-09 16:23:28.991236+00:00 mailto:rpalma@man.poznan.pl 81973 https://api.rohub.org/api/ros/321e3b22-04a7-48f8-a647-7ebc49c19301/crate/download/ 2021-11-09 15:51:17.774513+00:00 2025-03-05 00:45:33.607132+00:00 2021-11-09 15:51:17.774513+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot MANUAL https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301/bc445fcb-5960-4feb-a1ae-5ca50453ad6e https://w3id.org/ro-id/0b1f7680-3fc1-47db-b176-0440853ecde0 https://w3id.org/ro-id/0bcf2515-210c-4717-8b9a-e337adbcef55 https://w3id.org/ro-id/7d9815be-40e5-4718-ac91-8d865d795324 https://w3id.org/ro-id/e5b7d130-4697-4a7b-9a2c-16756071ba04 https://w3id.org/ro-id/8284ac3e-dc7f-4d20-806c-0a94b344af89 https://w3id.org/ro-id/e7c9062c-5cba-473f-bf89-259f6dcaae5d https://w3id.org/ro-id/a7e8d560-a7df-4aa4-9472-3811d8ee43c6 https://w3id.org/ro-id/aa3e2a7e-7135-44ca-8d61-39390c727761 https://w3id.org/ro-id/c303edac-f3f8-470c-be5f-0d776c719869 https://w3id.org/ro-id/e6323df0-add4-4f69-9a0b-b464fbe20b56 https://w3id.org/ro-id/eb46cc83-dbea-468f-9d40-48d383c42557 https://w3id.org/ro-id/ebf1d891-b6d4-4462-9a93-e7c0bea64e81 https://w3id.org/ro-id/da144e0f-548b-4ba9-a351-6fe62c0e6635 https://w3id.org/ro-id/ff422ab5-a055-493a-b016-fb9dec5db6cb https://w3id.org/ro-id/096fd79f-1da7-4130-8560-50bf8860e376 https://w3id.org/ro-id/6d5cd942-1e2f-4661-9460-e31f9cd16732 https://w3id.org/ro-id/db392796-e679-4c0a-ae88-4db03f91ac9c https://w3id.org/ro-id/e21fdf10-81c2-4d5e-ba0e-f27768551e15 https://w3id.org/ro-id/f241bce9-3878-4c85-a937-860380c8cd3e https://w3id.org/ro-id/3f23826e-7037-4a82-84c0-954a1fac2062 https://w3id.org/ro-id/bcc38d7f-6ca4-4809-a13e-3afbdd362efe https://w3id.org/ro-id/2bba2635-0803-4822-bfe2-7c15d2f0bba4 Palma, Raul. "9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 09 ,2021. https://doi.org/10.24424/j2gh-5322. List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 2021-11-09 16:23:26.891779+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 73394 https://api.rohub.org/api/resources/440e3907-011c-4185-936a-16a0a868a444/download/ 2021-11-09 15:51:45.742090+00:00 2021-11-09 16:23:26.956721+00:00 image/png flow-dcro.png 2021-11-09 15:51:45.742090+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 2021-11-09 16:23:26.855020+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G Flow to compute monthly map https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 2021-11-09 16:23:26.816350+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 2021-11-09 16:23:26.923462+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services This Research Object demonstrate how to compute monthly map of PM10 over your country - modified 69.66966966966967 69.6 False https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 2021-11-09 16:19:16.618594+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 2021-11-09 16:06:58.516914+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 2021-11-09 16:15:26.873492+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl monthly map 6.231155778894473 6.2 aim 31.499312242090785 22.9 atmospheric sciences 100.0 0.7866491675376892 object 25.208333333333332 24.2 PM10 13.541666666666666 13.0 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot. 30.330330330330334 30.3 research 31.145833333333332 29.9 astronautics (general) 100.0 0.38756152987480164 data cube research object 1.0050251256281406 1.0 data cube 0.4020100502512563 0.4 research 39.61485557083906 28.8 map 13.333333333333334 12.8 earth sciences 100.0 0.7866491675376892 Copernicus Atmosphere Monitoring Service 8.229166666666666 7.9 country 8.541666666666666 8.2 map of PM10 9.246231155778894 9.2 astronautics 100.0 0.38756152987480164 Raul Palma Earth sciences False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:18:39.029666+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl monthly map 6.231155778894473 6.2 38.0 38.0 POINT (38.0 38.0) ee61e733-5a21-43d3-a8b9-1e7e3cd58df1 POINT (38.0 38.0) service-account-enrichment https://w3id.org/ro-id/0e5f85c2-45ce-4b79-af5b-a940086cc802 https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 https://w3id.org/ro-id/48eb1f98-3c64-4dd2-95b7-fe7044b08ff1 https://w3id.org/ro-id/4df864f9-4427-4f6d-a11a-b6f1a340eb42 https://w3id.org/ro-id/56840bfe-6946-4cb1-a8a4-e4e3c4927063 False https://w3id.org/ro-id/2755900c-b77c-4a29-ac59-f6f51af20fa7 2021-11-09 16:38:47.238379+00:00 mailto:rpalma@man.poznan.pl 82295 https://api.rohub.org/api/ros/164e222b-0bdd-4638-93e7-010bad13d655/crate/download/ 2021-11-09 15:51:17.774513+00:00 2025-03-05 00:45:33.909189+00:00 2021-11-09 15:51:17.774513+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot MANUAL https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655/bc445fcb-5960-4feb-a1ae-5ca50453ad6e https://w3id.org/ro-id/7dc16fbc-7188-4209-9937-a7a2932d2997 https://w3id.org/ro-id/82a04fbd-62bc-4b45-9d39-7cf5f1034dc8 https://w3id.org/ro-id/8ffcf3e4-af24-4061-a08e-5ed6c080b739 https://w3id.org/ro-id/eb545d52-4143-4b2a-be9d-c074ac089f17 https://w3id.org/ro-id/9b55ecf3-2e9a-4a92-a0fe-484a62c91593 https://w3id.org/ro-id/a8b59a03-2bf8-4736-a216-fbe8f75e6e67 https://w3id.org/ro-id/531895fd-615d-47cd-97ab-61244289921e https://w3id.org/ro-id/84fc9958-604b-4750-a50f-9638ad628bdf https://w3id.org/ro-id/91354b80-10cb-4027-9832-cb9ed4792db6 https://w3id.org/ro-id/913c9817-553d-458a-a319-0ec12c61a2b7 https://w3id.org/ro-id/f4db0903-93b0-4f0f-b25c-904a33dbc608 https://w3id.org/ro-id/f70dd780-e275-4682-8ac2-fcc47d3307f4 https://w3id.org/ro-id/1af0e739-034c-4d44-87b7-0af39b5ad382 https://w3id.org/ro-id/24eec82c-8ece-445a-8e1a-73d98222c0c2 https://w3id.org/ro-id/0f6a7d8f-9ba6-4741-8687-cad255b1516c https://w3id.org/ro-id/6c3b3f18-4625-48c0-8594-bf13fc863dd7 https://w3id.org/ro-id/cb1a64b5-d528-48f4-8151-0c7684cfa128 https://w3id.org/ro-id/d3e198c4-80cb-4099-8414-e97c9798c120 https://w3id.org/ro-id/d9523df2-d927-46e8-8379-02a12a2e92ce https://w3id.org/ro-id/6bf626d2-4ee2-4fed-a8f6-4aed427ef252 https://w3id.org/ro-id/886380ba-096d-4c04-ba1d-ff6f0d57f001 https://w3id.org/ro-id/afec8c8a-2d85-4ef7-9efb-6c5579d4c1bb Palma, Raul. "9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 09 ,2021. https://doi.org/10.24424/yw22-x266. List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 2021-11-09 16:38:44.990014+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 73394 https://api.rohub.org/api/resources/0369a2c2-53af-4929-a325-ecaa4f28eb78/download/ 2021-11-09 15:51:45.742090+00:00 2021-11-09 16:38:45.030794+00:00 image/png flow-dcro.png 2021-11-09 15:51:45.742090+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 2021-11-09 16:38:44.952284+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 2021-11-09 16:38:44.873578+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 2021-11-09 16:38:44.915292+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 Flow to compute monthly map Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services astronautics (general) 100.0 0.38756152987480164 astronautics 100.0 0.38756152987480164 POINT (38.0 38.0) False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:23:28.979805+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:19:16.618594+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:06:58.516914+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl map 13.333333333333334 12.8 False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:15:26.873492+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot. 30.330330330330334 30.3 map of PM10 9.246231155778894 9.2 aim 31.499312242090785 22.9 research 39.61485557083906 28.8 Copernicus Atmosphere Monitoring Service 8.229166666666666 7.9 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified 69.66966966966967 69.6 country 11.829436038514443 8.6 object 25.208333333333332 24.2 country 8.541666666666666 8.2 earth sciences 100.0 0.7866491675376892 atmospheric sciences 100.0 0.7866491675376892 Nov-9 research object 83.11557788944724 82.7 data cube research object 1.0050251256281406 1.0 data cube 0.4020100502512563 0.4 map 17.05639614855571 12.4 research 31.145833333333332 29.9 PM10 13.541666666666666 13.0 Raul Palma Earth sciences 10.13039/501100000781 European Commission 101017502 RELIANCE data cube research object 1.0050251256281406 1.0 False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:18:39.029666+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:38:47.222796+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl earth sciences 100.0 0.7866491675376892 map 17.05639614855571 12.4 research 39.61485557083906 28.8 research 31.145833333333332 29.9 POINT (38.0 38.0) POINT (38.0 38.0) POLYGON ((14.049395 40.779358, 14.240586 40.779358, 14.240586 40.912968, 14.049395 40.912968, 14.049395 40.779358)) POLYGON ((14.049395 40.779358, 14.240586 40.779358, 14.240586 40.912968, 14.049395 40.912968, 14.049395 40.779358)) False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:23:28.979805+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl astronautics (general) 100.0 0.38756152987480164 astronautics 100.0 0.38756152987480164 False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:19:16.618594+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl This Research Object demonstrate how to compute monthly map of PM10 over your country - 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modified application/ld+json https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot MANUAL https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1/67781900-3d58-4580-83ff-ffe019453c87 https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1/bc445fcb-5960-4feb-a1ae-5ca50453ad6e https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1/c5a0801c-994d-4e19-bf26-ff781f3f6e36 https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1/f6717a94-7781-4efa-9ee0-8fd556e40e99 https://w3id.org/ro-id/1d77df20-e490-49c8-9251-9bedde3ecbfd https://w3id.org/ro-id/1e65d495-bf36-4cca-a348-1a65e28faa72 https://w3id.org/ro-id/6c12088b-4028-40a1-9b17-d7b44398d83a https://w3id.org/ro-id/c6cf3921-2183-47f1-8c3e-8c1b2e142daf https://w3id.org/ro-id/1a5d3a2b-9d57-4992-bdea-f8967834dfea https://w3id.org/ro-id/5fcc2bc3-9f18-4b0e-aa5a-0b95da2b65cd https://w3id.org/ro-id/24b9443b-552e-4446-969a-50cf57263083 https://w3id.org/ro-id/60683ed5-1558-4679-9c87-1ea1e483e7aa https://w3id.org/ro-id/63bccedb-7934-4485-b9c2-f6eaebde1d89 https://w3id.org/ro-id/8659b679-e36f-4037-9895-1ac4108abb4e https://w3id.org/ro-id/af51d342-c1aa-44d2-b29c-7543440d5cd4 https://w3id.org/ro-id/e79319cb-ebfc-44a1-8c41-c4273808b87a https://w3id.org/ro-id/38cf7bac-6c3e-4fed-b621-c8e830d0e8f9 https://w3id.org/ro-id/423b1fd4-a43a-4d06-9f0c-b2f52ca3445e https://w3id.org/ro-id/0914de84-5bc1-48f3-94d2-68ccf5582581 https://w3id.org/ro-id/5828c608-ea04-4b9b-b4d6-63e085ee9af5 https://w3id.org/ro-id/8d4a3c33-d433-4ba4-a51e-2b741cba348b https://w3id.org/ro-id/a29cb4cb-f1a2-4732-8e1a-707045d6ebda https://w3id.org/ro-id/cebe33f2-566b-4310-bb05-f040eaf81892 https://w3id.org/ro-id/4b19d903-158f-45a4-8f8a-80cf55d3d997 https://w3id.org/ro-id/b0c0763c-f99f-4e9a-b32c-0dd7de567ccd https://w3id.org/ro-id/b7592ce2-424e-435f-b9e7-036738c1f17e Palma, Raul. "9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 09 ,2021. https://doi.org/10.24424/zt8j-c157. List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 2021-11-10 19:38:07.510465+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G 73394 https://api.rohub.org/api/resources/7f087685-b1b1-42dc-90b0-ee6b56b2ab75/download/ 2021-11-09 15:51:45.742090+00:00 2021-11-10 19:38:07.580119+00:00 image/png flow-dcro.png 2021-11-09 15:51:45.742090+00:00 Flow to compute monthly map https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 2021-11-10 19:38:07.439709+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 2021-11-10 19:38:07.476563+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 2021-11-10 19:38:07.545500+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services Copernicus Atmosphere Monitoring Service 8.229166666666666 7.9 data cube 0.4020100502512563 0.4 monthly map 6.231155778894473 6.2 False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-10 12:04:39.530811+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl object 25.208333333333332 24.2 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot. 30.330330330330334 30.3 Nov-9 aim 31.499312242090785 22.9 research object 83.11557788944724 82.7 PM10 13.541666666666666 13.0 Raul Palma Earth sciences published v1 monthly map of PM10 Copernicus Atmosphere Monitoring Service Data Cube Ro country map Ro monthly map map of PM10 PCSS example3@hotmail.com Pepito Bato 0000-0002-8316-3192 UNO-Recoletos npepito@hotmail.com Nieves Pepito 0000-0003-3784-6651 office@man.poznan.pl 025cj6e44 Poznan Supercomputing and Networking Center POINT (38.0 38.0) 38.0 38.0 POINT (38.0 38.0) eb1c7b49-7116-4587-aced-c1a1210cbb1d POINT (38.0 38.0) service-account-enrichment False https://w3id.org/ro-id/9177a694-e747-4d7f-ae7e-87672850e0ec 2021-12-08 22:01:26.136904+00:00 mailto:rpalma@man.poznan.pl 86656 https://api.rohub.org/api/ros/abebc0e7-87b6-4ed5-8a0e-9b71dc30e333/crate/download/ 2021-12-08 21:40:02.447472+00:00 2024-03-05 12:17:25.502621+00:00 2021-12-08 21:40:02.447472+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/abebc0e7-87b6-4ed5-8a0e-9b71dc30e333 8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot Copernicus Atmosphere Monitoring Service Data Cube RO December 8th - published v1 MANUAL https://w3id.org/ro-id/abebc0e7-87b6-4ed5-8a0e-9b71dc30e333/df4db37c-7304-430d-b08e-ba41cdc33e9e Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 8th - published v1." ROHub. Dec 08 ,2021. https://doi.org/10.24424/fehe-jb26. metadata data biblio raw data https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-08 21:44:49.477592+00:00 2021-12-08 22:01:19.894769+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-08 21:44:49.477592+00:00 Flow to compute monthly map 73394 https://api.rohub.org/api/resources/25e31ee1-9f77-40d0-a4c3-5bef88b9adc3/download/ 2021-12-08 21:44:36.949407+00:00 2021-12-08 22:01:19.428175+00:00 image/png flow-dcro.png 2021-12-08 21:44:36.949407+00:00 This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-08 21:44:42.801819+00:00 2021-12-08 22:01:19.788776+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-08 21:44:42.801819+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-08 21:44:46.533341+00:00 2021-12-08 22:01:20.217111+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-08 21:44:46.533341+00:00 List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 Catch data records sample from 2019 Catch data from Norway https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-08 21:44:52.711669+00:00 2023-05-16 16:52:12.400121+00:00 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-08 21:44:52.711669+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-08 21:44:55.989277+00:00 2021-12-08 22:01:19.992473+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-08 21:44:55.989277+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux neworg1@example.org abcd123 Example Org 1 Earth sciences published v2 monthly map of PM10 Copernicus Atmosphere Monitoring Service Data Cube Ro country map Ro monthly map map of PM10 PCSS example3@hotmail.com Pepito Bato 0000-0002-8316-3192 UNO-Recoletos npepito@hotmail.com Nieves Pepito 0000-0003-3784-6651 office@man.poznan.pl 025cj6e44 Poznan Supercomputing and Networking Center POINT (38.0 38.0) 38.0 38.0 POINT (38.0 38.0) 6aa2b88b-ca50-4d9b-81fb-b18cf3b25d74 POINT (38.0 38.0) service-account-enrichment False https://w3id.org/ro-id/9177a694-e747-4d7f-ae7e-87672850e0ec 2021-12-08 22:04:49.342182+00:00 mailto:rpalma@man.poznan.pl 86622 https://api.rohub.org/api/ros/c737f695-6715-4916-8bef-8fc0ce879760/crate/download/ 2021-12-08 21:40:02.447472+00:00 2024-03-05 12:17:25.629746+00:00 2021-12-08 21:40:02.447472+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/c737f695-6715-4916-8bef-8fc0ce879760 8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot Copernicus Atmosphere Monitoring Service Data Cube RO December 8th - published v2 MANUAL https://w3id.org/ro-id/c737f695-6715-4916-8bef-8fc0ce879760/df4db37c-7304-430d-b08e-ba41cdc33e9e Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 8th - published v2." ROHub. Dec 08 ,2021. http://doi.org/10.23728/b2share.3c82435c669b49fcaa5541b465e055fa. biblio data raw data metadata https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-08 21:44:55.989277+00:00 2021-12-08 22:04:44.732746+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-08 21:44:55.989277+00:00 Flow to compute monthly map https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-08 21:44:42.801819+00:00 2021-12-08 22:04:44.543287+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-08 21:44:42.801819+00:00 73394 https://api.rohub.org/api/resources/287efd15-0bd1-474d-88c2-4542e1393d8d/download/ 2021-12-08 21:44:36.949407+00:00 2021-12-08 22:04:44.160524+00:00 image/png flow-dcro.png 2021-12-08 21:44:36.949407+00:00 This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-08 21:44:52.711669+00:00 2023-05-16 16:53:21.645987+00:00 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-08 21:44:52.711669+00:00 Catch data records sample from 2019 Catch data from Norway Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-08 21:44:46.533341+00:00 2021-12-08 22:04:44.869071+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-08 21:44:46.533341+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-08 21:44:49.477592+00:00 2021-12-08 22:04:44.654574+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-08 21:44:49.477592+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux neworg1@example.org abcd123 Example Org 1 Earth sciences 10.13039/501100000781 European Commission published v1 monthly map of PM10 Copernicus Atmosphere Monitoring Service Data Cube Ro country map Ro monthly map map of PM10 PCSS example4@hotmail.com Pepito Bato 0000-0002-8316-3192 UNO-Recoletos npepito@hotmail.com Nieves Pepito 0000-0003-3784-6651 office@man.poznan.pl 025cj6e44 Poznan Supercomputing and Networking Center 101017502 RELIANCE POINT (38.0 38.0) 0a113f7e-5c4d-411e-985e-2d71e8dcbd28 POINT (38.0 38.0) 38.0 38.0 POINT (38.0 38.0) service-account-enrichment False https://w3id.org/ro-id/93ece8d0-3be4-4658-a840-156bda47f612 2021-12-09 15:19:11.307501+00:00 mailto:rpalma@man.poznan.pl 87394 https://api.rohub.org/api/ros/a08ddcb2-ae5f-40ba-b1b6-c64dd2e4d68c/crate/download/ 2021-12-09 15:05:57.255344+00:00 2024-03-05 12:17:25.978567+00:00 2021-12-09 15:05:57.255344+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/a08ddcb2-ae5f-40ba-b1b6-c64dd2e4d68c 8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v1 MANUAL https://w3id.org/ro-id/a08ddcb2-ae5f-40ba-b1b6-c64dd2e4d68c/ea782618-0dff-4cfa-8604-e121ce29d3cf Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v1." ROHub. Dec 09 ,2021. https://doi.org/10.24424/w44h-8089. metadata data biblio raw data https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-09 15:07:51.036076+00:00 2021-12-09 15:19:08.564064+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-09 15:07:51.036076+00:00 List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+00:00 2021-12-09 15:19:08.515865+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+00:00 Flow to compute monthly map https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+00:00 2023-05-16 16:54:04.603729+00:00 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration 73394 https://api.rohub.org/api/resources/7733e68b-7b14-45b8-96ef-b0ff1e3b6a45/download/ 2021-12-09 15:07:22.892363+00:00 2021-12-09 15:19:08.338406+00:00 image/png flow-dcro.png 2021-12-09 15:07:22.892363+00:00 Catch data records sample from 2019 Catch data from Norway This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-09 15:07:47.272247+00:00 2021-12-09 15:19:08.713366+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-09 15:07:47.272247+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+00:00 2021-12-09 15:19:08.607528+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+00:00 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux neworg2@example.org abcd123 Example Org 2 Earth sciences 10.13039/501100000781 European Commission published v2 monthly map of PM10 Copernicus Atmosphere Monitoring Service Data Cube Ro country map Ro monthly map map of PM10 PCSS example4@hotmail.com Pepito Bato 0000-0002-8316-3192 UNO-Recoletos npepito@hotmail.com Nieves Pepito 0000-0003-3784-6651 office@man.poznan.pl 025cj6e44 Poznan Supercomputing and Networking Center 101017502 RELIANCE 38.0 38.0 POINT (38.0 38.0) 86a33d62-4541-495f-a640-2b60e0394266 POINT (38.0 38.0) service-account-enrichment False https://w3id.org/ro-id/93ece8d0-3be4-4658-a840-156bda47f612 2021-12-09 15:20:23.441762+00:00 mailto:rpalma@man.poznan.pl 87383 https://api.rohub.org/api/ros/57cf76e1-2179-4650-b48b-b5990dca86c1/crate/download/ 2021-12-09 15:05:57.255344+00:00 2024-03-05 12:17:26.248043+00:00 2021-12-09 15:05:57.255344+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/57cf76e1-2179-4650-b48b-b5990dca86c1 8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v2 MANUAL https://w3id.org/ro-id/57cf76e1-2179-4650-b48b-b5990dca86c1/ea782618-0dff-4cfa-8604-e121ce29d3cf Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v2." ROHub. Dec 09 ,2021. https://doi.org/10.24424/yptf-km76. biblio metadata raw data data List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-09 15:07:51.036076+00:00 2021-12-09 15:20:20.634446+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-09 15:07:51.036076+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-09 15:07:47.272247+00:00 2021-12-09 15:20:20.738000+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-09 15:07:47.272247+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+00:00 2021-12-09 15:20:20.597858+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+00:00 Flow to compute monthly map Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+00:00 2021-12-09 15:20:20.669306+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+00:00 73394 https://api.rohub.org/api/resources/7bfd4974-4bf8-4922-ae40-36a2ca9ef7fe/download/ 2021-12-09 15:07:22.892363+00:00 2021-12-09 15:20:20.444066+00:00 image/png flow-dcro.png 2021-12-09 15:07:22.892363+00:00 Catch data records sample from 2019 Catch data from Norway This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+00:00 2023-05-16 16:54:33.185954+00:00 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services POINT (38.0 38.0) Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux neworg2@example.org abcd123 Example Org 2