REsearch LIfecycle mAnagemeNt for Earth Science Communities and CopErnicus users in EOSC

Mathematics cohomology of the mod usage of the dataset mathematics minimal resolution algebra map mountain range document cohomology dataset range usage document CohomA2.pdf Steenrod algebra squaring resolution subject service-account-enrichment 7227 https://api.rohub.org/api/ros/ea70fc98-a80f-4758-8ae5-067ae64905ed/crate/download/ 2022-03-22 01:20:46.276139+00:00 2025-03-05 01:26:34.044209+00:00 2022-03-22 01:20:46.276139+00:00 The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail. application/ld+json https://w3id.org/ro-id/ea70fc98-a80f-4758-8ae5-067ae64905ed The cohomology of the mod 2 Steenrod algebra MANUAL

Robert Bruner, and John Rognes. "The cohomology of the mod 2 Steenrod algebra." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/ea70fc98-a80f-4758-8ae5-067ae64905ed.

biblio raw data data metadata https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00078 2022-03-22 01:21:00.644689+00:00 2022-03-22 01:21:05.591782+00:00 The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail. The cohomology of the mod 2 Steenrod algebra 2022-03-22 01:21:00.644689+00:00 Geo H. john.rognes@rohub.com John Rognes robert.bruner@rohub.com Robert Bruner Mathematics cohomology of the mod usage of the dataset mathematics minimal resolution algebra map mountain range document cohomology dataset range usage document CohomA2.pdf Steenrod algebra squaring resolution subject service-account-enrichment 8675 https://api.rohub.org/api/ros/32eba436-e9ef-4ed2-8911-fec14c5a3779/crate/download/ 2022-03-22 01:21:07.680973+00:00 2025-03-05 01:26:33.828703+00:00 2022-03-22 01:21:07.680973+00:00 The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail. application/ld+json https://w3id.org/ro-id/32eba436-e9ef-4ed2-8911-fec14c5a3779 The cohomology of the mod 2 Steenrod algebra MANUAL

Robert Bruner, and John Rognes. "The cohomology of the mod 2 Steenrod algebra." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/32eba436-e9ef-4ed2-8911-fec14c5a3779.

raw data biblio data metadata Bruner, R., Rognes, J. (2022).The cohomology of the mod 2 Steenrod algebra [Data set]. Norstore. https://doi.org/10.11582/2022.00015 Robert Ray Bruner Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00077 None 2022-03-22 01:21:29.913760+00:00 The dataset contains a minimal resolution of the mod 2 Steenrod algebra in the range 0 <= s <= 128, 0 <= t <= 184, together with chain maps for each cocycle in that range and for the squaring operation Sq^0 in the cohomology of the Steenrod algebra. The included document CohomA2.pdf explains the contents and usage of the dataset in detail. The cohomology of the mod 2 Steenrod algebra None Robert Ray Bruner Geo H. john.rognes@rohub.com John Rognes robert.bruner@rohub.com Robert Bruner Environmental research Life sciences Physical sciences Biology Svalbard time series station West Spitsbergen Current West Spitsbergen Current ecology Atlantic Ocean mouth of Adventfjorden Atlantic water hydrography ecosystem time series dataset stream mouth broadcasting station Spitsbergen inflow Atlantic Ocean ecosystem effects of climate change effects of climate change Spitsbergen station variability 15.52992 78.26105 POINT (15.52992 78.26105) c9a6edac-883c-4415-aad5-5270dff5e8fb POINT (15.52992 78.26105) service-account-enrichment 6795 https://api.rohub.org/api/ros/1b7874e4-6c2f-4b84-87cb-74db13d49196/crate/download/ 2022-03-22 01:21:31.715074+00:00 2025-03-05 01:01:11.704641+00:00 2022-03-22 01:21:31.715074+00:00 The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths. application/ld+json https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196 ISA_Svalbard_Chlorophyll_A_2011_2019 MANUAL https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196/870010a7-224a-4b80-8ee6-bcdb220e619e

University Centre in Svalbard (UNIS). "ISA_Svalbard_Chlorophyll_A_2011_2019." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/1b7874e4-6c2f-4b84-87cb-74db13d49196.

POINT (15.52992 78.26105) raw data data metadata biblio https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00069 2022-03-22 01:21:41.637352+00:00 2022-03-22 01:21:46.049432+00:00 The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths. ISA_Svalbard_Chlorophyll_A_2011_2019 2022-03-22 01:21:41.637352+00:00 UNIS@rohub.com University Centre in Svalbard (UNIS) Geo H. Environmental research Life sciences Physical sciences Earth sciences Svalbard temperature logger physics medicine temperature processing observatory fluorescence diagram dataset recovery moor Svalbard mooring diagram fluorescence data observatory layout marine biology logger layout information watercraft and nautical navigation UiT The Arctic University of Norway and The Scottish Association observatories consist occupational overuse syndrome 11.8239 78.9589 POINT (11.8239 78.9589) bece0544-4961-412d-91ad-ecf25c63d637 POINT (11.8239 78.9589) service-account-enrichment 11468 https://api.rohub.org/api/ros/b4960d2f-d2a6-462c-83b1-9b85f4c046ac/crate/download/ 2022-03-22 01:21:47.502650+00:00 2025-03-05 01:24:10.636796+00:00 2022-03-22 01:21:47.502650+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. application/ld+json https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 MANUAL https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac/1815b2b8-8a48-4223-a69b-52dcee7b0fca

Finlo Cottier, Jørgen Berge, Estelle Dumont, Tomasz Piotr Kopec, Emily Joanne Venables, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/b4960d2f-d2a6-462c-83b1-9b85f4c046ac.

POINT (11.8239 78.9589) data biblio metadata raw data Cottier, F., Berge, J., Dumont, E., Kopec, T. P., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 [Data set]. Norstore. https://doi.org/10.11582/2021.00065 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00065 2021-07-16 00:00:00 2022-03-22 01:22:40.368999+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 2021-07-16 00:00:00 Daniel Ludwig Vogedes SAMS@rohub.com Scottish Association for Marine Science (SAMS) UiT@rohub.com UiT The Arctic University of Norway (UiT) daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes emily.joanne.venables@rohub.com Emily Joanne Venables estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. jorgen.berge@rohub.com Jørgen Berge tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences Earth sciences 11.8237 78.9592 POINT (11.8237 78.9592) 6dbca1e0-2173-4f1d-b61e-f4e28bec383e POINT (11.8237 78.9592) service-account-enrichment 11480 https://api.rohub.org/api/ros/04c3f1b2-96ec-4065-98a1-9499762d2405/crate/download/ 2022-03-22 01:22:41.914748+00:00 2025-03-05 01:24:10.453697+00:00 2022-03-22 01:22:41.914748+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. At this deployment, two settlement plates were deployed (25m and 208m). application/ld+json https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405 Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017 MANUAL Svalbard dataset diagram fluorescence information layout mooring observatory occupational overuse syndrome processing recovery temperature earth sciences CTD Kongsfjorden Rijpfjorden Observatory Programme data diagram fluorescence mooring observatory space sciences fluorescence data mooring diagram observatories consist observatory layout recovery processing As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017. 2016-2017 Aug-2016-Aug-2017 https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405/43a67b20-7c3d-4cd5-b299-35283289cf7f armed forces medicine physics Svalbard

Finlo Cottier, Jørgen Berge, Estelle Dumont, Colin Griffith, John Beaton, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/04c3f1b2-96ec-4065-98a1-9499762d2405.

POINT (11.8237 78.9592) metadata raw data biblio data Cottier, F., Berge, J., Dumont, E., Griffith, C., Beaton, J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017 [Data set]. Norstore. https://doi.org/10.11582/2021.00062 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00062 2021-07-12 00:00:00 2022-03-22 01:23:32.132406+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2016-2017. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. At this deployment, two settlement plates were deployed (25m and 208m). Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) August 2016-August 2017 2021-07-12 00:00:00 Daniel Ludwig Vogedes SAMS@rohub.com Scottish Association for Marine Science (SAMS) UiT@rohub.com UiT The Arctic University of Norway (UiT) colin.griffith@rohub.com Colin Griffith daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. john.beaton@rohub.com John Beaton jorgen.berge@rohub.com Jørgen Berge Environmental research Life sciences Physical sciences Earth sciences 40e24f2d-a1b1-42dd-ba2e-eedfdfeea4ec POINT (11.8238 78.9589) 11.8238 78.9589 POINT (11.8238 78.9589) service-account-enrichment 11633 https://api.rohub.org/api/ros/868972b2-e340-4007-8521-8f03b58cb7b9/crate/download/ 2022-03-22 01:23:33.372212+00:00 2025-03-05 01:24:10.793750+00:00 2022-03-22 01:23:33.372212+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. For this deployment a RAS500 water sampler and a SUNA nitrate sensor were deployed for a specific project, data are not part of the long-term monitoring efforts and are available upon request. application/ld+json https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9 Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016 MANUAL Svalbard data dataset diagram fluorescence information mooring nitrate observatory occupational overuse syndrome sampler sensor temperature earth sciences CTD Kongsfjorden Rijpfjorden Observatory Programme data diagram fluorescence mooring observatory space sciences SUNA nitrate sensor fluorescence data mooring diagram observatories consist observatory layout As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016. 2015-2016 Sep-2015-Aug-2016 https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9/f68b0cde-9eb9-4e5e-a13e-f980ee3ccbf5 armed forces marine biology medicine physics Svalbard

Finlo Cottier, Jørgen Berge, Colin Griffith, Estelle Dumont, John Beaton, Daniel Ludwig Vogedes, UiT The Arctic University of Norway (UiT), and Scottish Association for Marine Science (SAMS). "Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/868972b2-e340-4007-8521-8f03b58cb7b9.

POINT (11.8238 78.9589) metadata biblio raw data data Cottier, F., Berge, J., Griffith, C., Dumont, E., Beaton, J., Vogedes, D. L., UiT The Arctic University of Norway, Scottish Association for Marine Science (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016 [Data set]. Norstore. https://doi.org/10.11582/2021.00061 Daniel Ludwig Vogedes Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00061 2021-07-12 00:00:00 2022-03-22 01:24:23.830029+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Kongsfjorden 2015-2016. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. For this deployment a RAS500 water sampler and a SUNA nitrate sensor were deployed for a specific project, data are not part of the long-term monitoring efforts and are available upon request. Temperature, salinity, light and fluorescence (CTD) measurements from the Kongsfjorden (Svalbard) marine observatory (mooring) September 2015-August 2016 2021-07-12 00:00:00 Daniel Ludwig Vogedes SAMS@rohub.com Scottish Association for Marine Science (SAMS) UiT@rohub.com UiT The Arctic University of Norway (UiT) colin.griffith@rohub.com Colin Griffith daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. john.beaton@rohub.com John Beaton jorgen.berge@rohub.com Jørgen Berge Environmental research Life sciences Physical sciences Svalbard National Institute of Standards and Technology Ny-Ålesund Svalbard Australia time-series measurement USSIMO sensor output Norway USSIMO spectroradiometer raw data time series physics United States of America light observatory data observatory Teflon sensor time series spectroradiometer dataset raw data Norway Arctic Zone UiT The Arctic University of Norway Norwegian University of Science and Technology Kings Bay N-NW NTNU UiT The Arctic University Perth UiT 56f9c025-4431-44aa-8c4a-c90ceb56df10 POINT (11.84213 78.94116) 11.84213 78.94116 POINT (11.84213 78.94116) service-account-enrichment 13192 https://api.rohub.org/api/ros/2eed7eb1-bb6d-4336-b4ec-b832d41270af/crate/download/ 2022-03-22 01:31:31.343064+00:00 2025-03-05 02:47:05.411745+00:00 2022-03-22 01:31:31.343064+00:00 UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2019 data. application/ld+json https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) MANUAL https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af/50972f9a-041f-46bd-9d1e-f5c5a55a40db

Jørgen Berge, Stephen Grant, Rune Bjørgum, Jonathan H. Cohen, David McKee, Geir Johnsen, Artur Zolich, Tomasz Piotr Kopec, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2eed7eb1-bb6d-4336-b4ec-b832d41270af.

POINT (11.84213 78.94116) metadata biblio raw data data Berge, J., Grant, S., Bjørgum, R., Cohen, J. H., McKee, D., Johnsen, G., Zolich, A., Kopec, T. P., Vogedes, D. L., UiT The Arctic University of Norway (2021).USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) [Data set]. Norstore. https://doi.org/10.11582/2021.00045 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00045 2021-05-19 00:00:00 2022-03-22 01:32:34.318806+00:00 UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2019 data. USSIMO spectroradiometer raw data time series (2019) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) 2021-05-19 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) artur.zolich@rohub.com Artur Zolich daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes david.mckee@rohub.com David McKee geir.johnsen@rohub.com Geir Johnsen Geo H. jonathan.h.cohen@rohub.com Jonathan H. Cohen jorgen.berge@rohub.com Jørgen Berge rune.bjorgum@rohub.com Rune Bjørgum stephen.grant@rohub.com Stephen Grant tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences 11.84213 78.94116 POINT (11.84213 78.94116) 2b40daef-2152-44ad-9a28-a0d37f288bec POINT (11.84213 78.94116) service-account-enrichment 13206 https://api.rohub.org/api/ros/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f/crate/download/ 2022-03-22 01:32:35.810584+00:00 2025-03-05 02:47:05.631433+00:00 2022-03-22 01:32:35.810584+00:00 UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2020 data. application/ld+json https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) MANUAL Norway Teflon data dataset observatory raw data sensor spectroradiometer time series earth sciences IT-computer sciences Nanotechnology Software Synthetic and plastic chemicals University Norway data dataset observatory sensor spectroradiometer time series engineering USSIMO spectroradiometer raw data time series UiT The Arctic University light observatory sensor output time-series measurement The observatory consists of an array of light sensors including an all sky camera. This dataset contains the data of the hyperspectral radiometer USSIMO In-situ Marine Optics, Perth, WA, Australia) It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund Svalbard, Norway) in January 2017. 2020 30 seconds in Jan-2017 of 6 seconds https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f/83569c44-db68-4953-bb10-2a5bb71c2635 computer science database physics software National Institute of Standards and Technology Arctic Zone Australia Norway Perth Svalbard United States of America

Jørgen Berge, Stephen Grant, Rune Bjørgum, Jonathan H. Cohen, David McKee, Geir Johnsen, Artur Zolich, Tomasz Piotr Kopec, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/e4ed6bdc-327b-4a57-86c0-394bf4a37b3f.

POINT (11.84213 78.94116) metadata raw data data biblio Berge, J., Grant, S., Bjørgum, R., Cohen, J. H., McKee, D., Johnsen, G., Zolich, A., Kopec, T. P., Vogedes, D. L., UiT The Arctic University of Tromsø (2021).USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) [Data set]. Norstore. https://doi.org/10.11582/2021.00046 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00046 2021-05-19 00:00:00 2022-03-22 01:33:42.099209+00:00 UiT The Arctic University of Norway (UiT) and the Norwegian University of Science and Technology (NTNU) established a light observatory at Kings Bay, Ny-Ålesund (Svalbard, Norway) in January 2017. The observatory consists of an array of light sensors including an all sky camera. It is located outside the settlement of Ny-Ålesund, approximately 1 km N-NW of the airport towards Brandalspynten. The array of sensors is mounted on a tripod under a transparent dome. This dataset contains the data of the hyperspectral radiometer USSIMO (In-situ Marine Optics, Perth, WA, Australia). It is equipped with a Zeiss MMS1 UV-VIS NIR detector with National Institute of Standards and Technology, USA traceable radiometric calibration between 380 and 900 nm. This instrument is used for time-series measurement of down-welling spectral irradiance in energy Wm-2 nm-1. Spectral resolution is 10 nm (3.3 nm pixel spacing) and a cosine-corrected polytetrafluoroethylene (PTFE) light diffusor with cosine error: <3% (0 - 60°), <10% (60 - 87.5°), is fitted. The device acquired measurements with a 16 bit analogue to digital converter. It samples continuously internally. Integration time is controlled by the sensor depending on the light intensity, with a maximum of 6 seconds. Actual integration time is stored with the data in each sample. The sensor output is saved on a PC with custom software which records 30 seconds of output data every 29:30 min. Number of samples collected in that period depends on the USSIMO integration time. The sensor is equipped with a pitch and roll sensor which is used to ensure that the spectroradiometer remains in the fixed position throughout the time-series acquisition. This dataset contains the 2020 data. USSIMO spectroradiometer raw data time series (2020) measured under the dome of a light observatory in the Arctic (Ny-Ålesund, Svalbard, Norway) 2021-05-19 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) artur.zolich@rohub.com Artur Zolich daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes david.mckee@rohub.com David McKee geir.johnsen@rohub.com Geir Johnsen Geo H. jonathan.h.cohen@rohub.com Jonathan H. Cohen jorgen.berge@rohub.com Jørgen Berge rune.bjorgum@rohub.com Rune Bjørgum stephen.grant@rohub.com Stephen Grant tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences Earth sciences 22.2991 80.2951 POINT (22.2991 80.2951) 8a93a745-c942-415f-a9d8-def5bede99b1 POINT (22.2991 80.2951) service-account-enrichment 11405 https://api.rohub.org/api/ros/4ff3f101-109b-4e35-babc-46862cd4d330/crate/download/ 2022-03-22 01:39:08.957789+00:00 2025-03-05 01:24:11.330191+00:00 2022-03-22 01:39:08.957789+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. The mooring was deployed for 2 years due to heavy ice cover on Rijpfjorden in 2019 which made recovery impossible. It was equipped with 6 SBE37 to get a good picture of the water mass exchange throughout the water column. All sensor still logging after 2 years, the sediment trap only collected the 2018-19 samples. application/ld+json https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330 Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020 MANUAL Svalbard dataset diagram fluorescence information mooring observatory occupational overuse syndrome recovery sediment temperature trap earth sciences CTD Kongsfjorden Rijpfjorden Observatory Programme Rijpfjorden data fluorescence mooring observatory space sciences fluorescence data mooring diagram observatories consist observatory layout sediment trap As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020. 2018-2020 Aug-2018-Sep-2020 after 2 years for 2 years in 2019 which the 2018-2019 https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330/0bf6a835-85bd-4000-bb1d-2c6077e214bb armed forces medicine physics Svalbard

Jørgen Berge, Finlo Cottier, Tomasz Piotr Kopec, Estelle Dumont, Emily Joanne Venables, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/4ff3f101-109b-4e35-babc-46862cd4d330.

POINT (22.2991 80.2951) biblio data raw data metadata Berge, J., Cottier, F., Kopec, T. P., Dumont, E., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020 [Data set]. Norstore. https://doi.org/10.11582/2021.00031 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00031 2021-04-26 00:00:00 2022-03-22 01:40:00.650124+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2018-2020. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. The mooring was deployed for 2 years due to heavy ice cover on Rijpfjorden in 2019 which made recovery impossible. It was equipped with 6 SBE37 to get a good picture of the water mass exchange throughout the water column. All sensor still logging after 2 years, the sediment trap only collected the 2018-19 samples. Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2018-September 2020 2021-04-26 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes emily.joanne.venables@rohub.com Emily Joanne Venables estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. jorgen.berge@rohub.com Jørgen Berge tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences Earth sciences 5fd6c729-070e-411d-b60c-08a8daa9cce2 POINT (22.3038 80.2943) 22.3038 80.2943 POINT (22.3038 80.2943) service-account-enrichment 11334 https://api.rohub.org/api/ros/65a52384-b1db-4b77-ab42-d61b495ba937/crate/download/ 2022-03-22 01:49:03.345354+00:00 2025-03-05 01:24:12.299726+00:00 2022-03-22 01:49:03.345354+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. Together with the top and bottom SBE37 two plastic settlement plates had been deployed for a settlement experiment for the recruitment of benthic invertebrates. The sediment trap was mounted at 58m instead the usual depth of 100 m because of specific requirements for an experiment. The observatory layout is available in the mooring diagram provided. application/ld+json https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937 Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015 MANUAL Svalbard dataset diagram fluorescence information layout mooring observatory occupational overuse syndrome sediment temperature trap earth sciences Synthetic and plastic chemicals CTD Kongsfjorden Rijpfjorden Observatory Programme Rijpfjorden data fluorescence mooring observatory space sciences fluorescence data mooring diagram observatories consist observatory layout sediment trap As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015. 2014-2015 Sep-2014-Sep-2015 https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937/1280bc3c-1c8e-41f1-be9b-3e605e49280a armed forces marine biology medicine physics Svalbard

Jørgen Berge, Finlo Cottier, Estelle Dumont, John Beaton, Colin Griffith, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/65a52384-b1db-4b77-ab42-d61b495ba937.

POINT (22.3038 80.2943) raw data biblio metadata data Berge, J., Cottier, F., Dumont, E., Beaton, J., Griffith, C., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015 [Data set]. Norstore. https://doi.org/10.11582/2021.00018 Daniel Ludwig Vogedes Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00018 2021-03-22 00:00:00 2022-03-22 01:49:53.563806+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2014-2015. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. Together with the top and bottom SBE37 two plastic settlement plates had been deployed for a settlement experiment for the recruitment of benthic invertebrates. The sediment trap was mounted at 58m instead the usual depth of 100 m because of specific requirements for an experiment. The observatory layout is available in the mooring diagram provided. Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) September 2014-September 2015 2021-03-22 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) colin.griffith@rohub.com Colin Griffith daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. john.beaton@rohub.com John Beaton jorgen.berge@rohub.com Jørgen Berge Environmental research Life sciences Physical sciences Earth sciences 22.29918 80.29443 POINT (22.29918 80.29443) d52a1692-016e-478f-95fc-cc06435a0ee2 POINT (22.29918 80.29443) service-account-enrichment 11088 https://api.rohub.org/api/ros/2939a6bb-c42b-4c16-af6a-a56cc065079b/crate/download/ 2022-03-22 01:51:43.001070+00:00 2025-03-05 01:24:11.147171+00:00 2022-03-22 01:51:43.001070+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. application/ld+json https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 MANUAL Svalbard dataset diagram fluorescence information layout mooring observatory occupational overuse syndrome processing recovery temperature earth sciences CTD Kongsfjorden Rijpfjorden Observatory Programme Rijpfjorden data fluorescence mooring observatory space sciences fluorescence data mooring diagram observatories consist observatory layout recovery processing As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatory layout is available in the mooring diagram provided. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018. 2017-2018 Aug-2017-Aug-2018 https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b/e1bb7d0c-c504-422b-a45e-3dbd5b72448d medicine physics Svalbard

Jørgen Berge, Finlo Cottier, Tomasz Piotr Kopec, Estelle Dumont, Emily Joanne Venables, Daniel Ludwig Vogedes, and UiT The Arctic University of Norway (UiT). "Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2939a6bb-c42b-4c16-af6a-a56cc065079b.

POINT (22.29918 80.29443) biblio metadata raw data data Berge, J., Cottier, F., Kopec, T. P., Dumont, E., Venables, E. J., Vogedes, D. L., UiT The Arctic University of Norway (2021).Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 [Data set]. Norstore. https://doi.org/10.11582/2021.00017 UiT The Arctic University of Norway (UiT) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00017 2021-03-12 00:00:00 2022-03-22 01:52:37.735565+00:00 As part of the "KROP - Kongsfjorden Rijpfjorden Observatory Programme" UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain marine observatories (moorings) in two high-Arctic fjords in Svalbard: Kongsfjorden and Rijpfjorden. The observatories consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This dataset contains the CTD, PAR and fluorescence data from Rijpfjorden 2017-2018. Fluorescence data is given as raw voltage only, due to calibration and fouling issues. It is meant as an indication of the timing of the phytoplankton bloom, not as absolute chlorophyll a concentration. No post-recovery processing of light data (to correct for fouling) has been performed. The observatory layout is available in the mooring diagram provided. Temperature, salinity, light and fluorescence (CTD) measurements from the Rijpfjorden (Svalbard) marine observatory (mooring) August 2017-August 2018 2021-03-12 00:00:00 Daniel Ludwig Vogedes UiT@rohub.com UiT The Arctic University of Norway (UiT) daniel.ludwig.vogedes@rohub.com Daniel Ludwig Vogedes emily.joanne.venables@rohub.com Emily Joanne Venables estelle.dumont@rohub.com Estelle Dumont finlo.cottier@rohub.com Finlo Cottier Geo H. jorgen.berge@rohub.com Jørgen Berge tomasz.piotr.kopec@rohub.com Tomasz Piotr Kopec Environmental research Life sciences Physical sciences Earth sciences data from Norwegian Meteorological Institute meteorology Norway trends in cold spell observational data frequency analysis cold weather dataset reanalysis data data from ERA5 Norway spell re-analysis trend information Norwegian Meteorological Institute POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0)) -20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0 b5d7b903-059c-4f00-9347-ffa7082d698d POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0)) service-account-enrichment 7693 https://api.rohub.org/api/ros/cb7986b3-a55f-4281-af69-7269917b8a02/crate/download/ 2022-03-22 01:52:57.468777+00:00 2025-03-05 00:50:49.038103+00:00 2022-03-22 01:52:57.468777+00:00 This data set contains an analysis of observational data from Norwegian Meteorological Institute and reanalysis data from ERA5. application/ld+json https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02 Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation MANUAL https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02/f7002b1d-65c6-4458-9cf7-c54a1f7585b9

Marek Ratajczak. "Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/cb7986b3-a55f-4281-af69-7269917b8a02.

POLYGON ((-20.0 80.0, 40.0 80.0, 40.0 40.0, -20.0 40.0, -20.0 80.0)) biblio metadata data raw data Ratajczak, M. (2021).Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation [Data set]. Norstore. https://doi.org/10.11582/2021.00016 Marek Grzegorz Ratajczak Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00016 2021-03-11 00:00:00 2022-03-22 01:53:15.007448+00:00 This data set contains an analysis of observational data from Norwegian Meteorological Institute and reanalysis data from ERA5. Frequency and trends in cold and warm spells in Norway in relation to large-scale atmospheric circulation 2021-03-11 00:00:00 Marek Grzegorz Ratajczak Geo H. marek.ratajczak@rohub.com Marek Ratajczak Environmental research Life sciences Physical sciences Biology service-account-enrichment 7126 https://api.rohub.org/api/ros/d6328caa-df3b-4b17-9595-d2e361dfccf1/crate/download/ 2022-03-22 01:55:05.007973+00:00 2025-03-05 01:19:15.480150+00:00 2022-03-22 01:55:05.007973+00:00 Custom sequence database from assembled NCBI SRA reads. Supplementary Data to Undheim and Jenner, Nat. Commun., 2021 application/ld+json https://w3id.org/ro-id/d6328caa-df3b-4b17-9595-d2e361dfccf1 SRA transcriptome assemblies MANUAL custom data database national sequence transcriptome earth sciences Genetics IT-computer sciences Newspaper Periodical Commun NCBI SRA Undheim custom data database transcriptome mathematical and computer sciences Nat. Commun SRA transcriptome assembly custom sequence database data to Undheim supplementary data Custom sequence database from assembled NCBI SRA reads. SRA transcriptome assemblies. Supplementary Data to Undheim and Jenner, Nat. Commun. database

Eivind Undheim. "SRA transcriptome assemblies." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/d6328caa-df3b-4b17-9595-d2e361dfccf1.

data metadata biblio raw data Undheim, E. (2020).SRA transcriptome assemblies [Data set]. Norstore. https://doi.org/10.11582/2020.00067 Eivind Andreas Baste Undheim Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00067 2020-12-29 00:00:00 2022-03-22 01:55:21.374031+00:00 Custom sequence database from assembled NCBI SRA reads. Supplementary Data to Undheim and Jenner, Nat. Commun., 2021 SRA transcriptome assemblies 2020-12-29 00:00:00 Eivind Andreas Baste Undheim eivind.undheim@rohub.com Eivind Undheim Geo H. Environmental research Life sciences Physical sciences Earth sciences ground station report on the gap fill process physics machine learning soil short wave dataset composition system mailto forest radiation stations from GEBA archive total information station aperture mailing machine learning technique UiO trude.storelvmo@geo.uio.no Trude Storelvmo 0000-0002-0068-2430 service-account-enrichment 7618 https://api.rohub.org/api/ros/46f90d7b-1e36-4701-9acd-2681dfe565a6/crate/download/ 2022-03-22 01:55:23.106126+00:00 2025-03-05 00:59:11.026646+00:00 2022-03-22 01:55:23.106126+00:00 Global (diffuse and direct) shortwave downwelling radiation at the surface between year 1961 and 2014. A total of 1847 ground stations from GEBA archive has been selected and been through the machine learning technique "random forests" (Breiman, 2001) to fill gaps in from original GEBA dataset. A report on the gap filling process can be attained by e-mailing Trude Storelvmo (LINK: mailto:truds@uio.no). application/ld+json https://w3id.org/ro-id/46f90d7b-1e36-4701-9acd-2681dfe565a6 Gap filled GEBA data MANUAL

Trude Storelvmo. "Gap filled GEBA data." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/46f90d7b-1e36-4701-9acd-2681dfe565a6.

biblio raw data metadata data Storelvmo, T. (2020).Gap filled GEBA data [Data set]. Norstore. https://doi.org/10.11582/2020.00066 Trude Storelvmo Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00066 2020-12-21 00:00:00 2022-03-22 01:55:40.621748+00:00 Global (diffuse and direct) shortwave downwelling radiation at the surface between year 1961 and 2014. A total of 1847 ground stations from GEBA archive has been selected and been through the machine learning technique "random forests" (Breiman, 2001) to fill gaps in from original GEBA dataset. A report on the gap filling process can be attained by e-mailing Trude Storelvmo (<a href="mailto:truds@uio.no" class="linkified" target="_blank">LINK</a>). Gap filled GEBA data 2020-12-21 00:00:00 Trude Storelvmo Geo H. Environmental research Life sciences Physical sciences Biology 15.52992 78.26105 POINT (15.52992 78.26105) dc5618a8-8b14-4ece-a082-c6a3b691c89b POINT (15.52992 78.26105) service-account-enrichment 8670 https://api.rohub.org/api/ros/271b95da-7537-43a1-b65b-a5d84f2227ab/crate/download/ 2022-03-22 01:55:42.873140+00:00 2025-03-05 01:01:11.472723+00:00 2022-03-22 01:55:42.873140+00:00 The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths. application/ld+json https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab ISA_Svalbard_Chlorophyll_A_2011_2019 MANUAL Atlantic Ocean Spitsbergen broadcasting station dataset ecosystem effects of climate change inflow station stream mouth time series variability earth sciences Climate change Ecosystem IT-computer sciences Weather Atlantic Ocean ISA_Svalbard_Chlorophyll_A_2011_2019 Isfjorden-Adventfjorden University Centre dataset inflow time series geosciences Atlantic water West Spitsbergen Current ecosystem effects of climate change mouth of Adventfjorden time series station IsA has been sampled on a monthly basis since December 2011. The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS) It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. since Dec-2011 https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab/388c2df4-da51-4c59-8696-918f504891a6 ecology hydrography Atlantic Ocean Spitsbergen Svalbard

University Centre in Svalbard (UNIS). "ISA_Svalbard_Chlorophyll_A_2011_2019." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/271b95da-7537-43a1-b65b-a5d84f2227ab.

POINT (15.52992 78.26105) data raw data metadata biblio University Centre in Svalbard (2020).ISA_Svalbard_Chlorophyll_A_2011_2019 [Data set]. Norstore. https://doi.org/10.11582/2020.00063 University Centre in Svalbard (UNIS) Observation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00063 2020-12-09 00:00:00 2022-03-22 01:56:01.214326+00:00 The Isfjorden-Adventfjorden (IsA) time series station is a marine station operated by the University Centre in Svalbard (UNIS). It is located in the mouth of Adventfjorden within Isfjorden on the west coast of Spitsbergen, and is frequently influenced by inflow of warm Atlantic Water from the West Spitsbergen Current. The station is therefore well suited for monitoring seasonal variability and ecosystem effects of climate change. IsA has been sampled on a monthly basis since December 2011. This dataset represents the acid-corrected Chl a values from several depths. ISA_Svalbard_Chlorophyll_A_2011_2019 2020-12-09 00:00:00 Luke Marsden UNIS@rohub.com University Centre in Svalbard (UNIS) Geo H. Environmental research Life sciences Physical sciences Chemistry force field 23.076923076923077 9.9 biochemistry 58.82352941176471 6.0 file 13.51981351981352 5.8 results file 13.58428805237316 8.3 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical betterment 11.616766467065867 9.7 Physics Science and technology/Natural science/Physics raw data 7.18562874251497 6.0 chemistry and materials (general) 100.0 0.38146594166755676 validation 10.955710955710956 4.7 force field 16.047904191616766 13.4 Newspaper Arts, culture and entertainment/Mass media/Newspaper Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. 37.755102040816325 14.8 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. 28.061224489795915 11.0 improvement 16.083916083916083 6.9 earth sciences 100.0 0.620610237121582 result 6.107784431137724 5.1 manuscripts file 8.67430441898527 5.3 comparison 4.6706586826347305 3.9 quartermaster 8.622754491017965 7.2 chemistry and materials 100.0 0.38146594166755676 force field comparison 20.29459901800327 12.4 QM calculation 12.76595744680851 7.8 computer science 18.627450980392158 1.9 service-account-enrichment 9478 https://api.rohub.org/api/ros/b7df46c2-d54e-4cf0-8600-36516706cfbe/crate/download/ 2022-03-22 02:18:37.698329+00:00 2025-03-05 00:45:29.658387+00:00 2022-03-22 02:18:37.698329+00:00 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. If you have trouble navigating, please send email to LINK: mailto:reza.611@gmail.com. ### Please see the file "file_organisations" for the directories and subdirectories. # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test. The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case. For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference. # For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019. ### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories. *** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to LINK: mailto:reza.611@gmail.com. application/ld+json https://w3id.org/ro-id/b7df46c2-d54e-4cf0-8600-36516706cfbe 2016_Khan_etal_JCTC MANUAL https://w3id.org/ro-id/0d57f62d-6c70-40b8-9d8e-e9a566cad649 https://w3id.org/ro-id/ae6ff238-5828-468f-96fb-4ccf675ca305 https://w3id.org/ro-id/f026d752-ef4f-4eb3-bd58-204c343a38a3 https://w3id.org/ro-id/15b367a2-7f08-4111-9f25-e305fe38a0d4 https://w3id.org/ro-id/4a19e75c-d6a3-4d35-b5a2-983f9386e7df https://w3id.org/ro-id/6a5072ec-d759-43ed-bffe-54269bbaec0a https://w3id.org/ro-id/76b25a83-0ac2-47af-ba30-a57d580444ac https://w3id.org/ro-id/7be25221-aa5c-4111-baae-72c2b0a9ea3d https://w3id.org/ro-id/83227a6c-fc8b-4c33-8e0a-e22a7ecbd466 https://w3id.org/ro-id/c93a421e-e398-46dc-b619-e8c2c2d6b754 https://w3id.org/ro-id/cec39022-070a-4186-9a3b-f4afb95e4e04 https://w3id.org/ro-id/d00a2fea-2639-4214-8e89-e7e029ee4cc3 https://w3id.org/ro-id/dfa0f404-0ce4-42e0-9f20-f0a49017bab8 https://w3id.org/ro-id/e5db7894-f353-4242-bd8e-1b9193d7da4e https://w3id.org/ro-id/ef77f5d5-6dbb-49f0-a362-52fd884161bd https://w3id.org/ro-id/76682de5-ba8d-4121-a995-42edee55b9ea https://w3id.org/ro-id/c8330e75-3669-4710-8eae-233dff9ed7db https://w3id.org/ro-id/10c24aae-d46c-48e3-8a62-130cba301ea2 https://w3id.org/ro-id/3d2a408d-b8b1-49b5-9eee-2384a5b9c45c https://w3id.org/ro-id/6ada0939-f2a7-48e3-bf28-ded4054d05c5 https://w3id.org/ro-id/fe92320b-d092-4d17-9002-09f6a1cafac6 https://w3id.org/ro-id/fef725a8-2d68-4745-868b-5f8276d364b5 https://w3id.org/ro-id/0af7beec-53cf-4022-b048-2843ecf01aa4 https://w3id.org/ro-id/0e4e9ab8-d4e9-41ef-b6a1-655dd17d5415 https://w3id.org/ro-id/69eca4e5-31ca-47e2-b7ed-19e4a5d3f053 https://w3id.org/ro-id/75647177-6b16-4ebf-a946-48ff3b600413 https://w3id.org/ro-id/ee1bbe17-772c-46bb-b304-baade63555a8 https://w3id.org/ro-id/f534d7b0-e5e1-4f73-adeb-7478f274586a https://w3id.org/ro-id/fd42eafd-774b-4be5-81db-1fa4019a03e8 https://w3id.org/ro-id/677261f6-b685-4b94-99ef-4cd89dfa629b https://w3id.org/ro-id/83f81bde-e952-47fc-9794-d26d6d19d31f https://w3id.org/ro-id/10343264-da33-4b51-98dd-78bb264b85e4 https://w3id.org/ro-id/7a1ad00d-5694-444f-9d82-88ac60ea68f7 https://w3id.org/ro-id/8a35b2bf-6a3d-4f32-bded-47ebd8d39999 https://w3id.org/ro-id/9c4bb3c1-8f1a-4bd0-a8b7-474606e4312a https://w3id.org/ro-id/c61ae304-ecf9-4e49-b012-0db5e0453981 https://w3id.org/ro-id/fe12d1bc-0348-4616-b250-9382962a23bc https://w3id.org/ro-id/6c07622b-9d77-48b3-a892-f24e34ef7c57 https://w3id.org/ro-id/6f457be9-fb82-4597-80a3-bf0c1a8faa75 https://w3id.org/ro-id/f43a3122-c71d-487c-bf34-0aea1c6a60d1

Nathalie Reuter. "2016_Khan_etal_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/b7df46c2-d54e-4cf0-8600-36516706cfbe.

biblio data metadata raw data Reuter, N. (2021).2016_Khan_etal_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00103 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00103 2021-11-22 00:00:00 2022-03-22 02:18:56.113155+00:00 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. If you have trouble navigating, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>. ### Please see the file "file_organisations" for the directories and subdirectories. # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test. The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case. For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference. # For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019. ### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories. *** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>. 2016_Khan_etal_JCTC 2021-11-22 00:00:00 Nathalie Reuter https://doi.org/10.1021/acs.jctc.6b00654 2022-03-22 02:18:51.634853+00:00 2022-03-22 02:18:51.891040+00:00 https://doi.org/10.1021/acs.jctc.6b00654 2022-03-22 02:18:51.634853+00:00 force field parameter validation 14.238952536824875 8.7 geology 100.0 0.620610237121582 subdirectory 5.389221556886228 4.5 choline 8.502994011976048 7.1 file 9.461077844311378 7.9 directory 8.74251497005988 7.3 validation 7.664670658682635 6.4 directory 12.121212121212121 5.2 calculation 5.9880239520958085 5.0 armed forces 22.549019607843135 2.3 # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. 34.183673469387756 13.4 choline 12.121212121212121 5.2 QM 12.121212121212121 5.2 tryptophan-choline force field improvement 30.441898527004913 18.6 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences Diseases and conditions Health/Diseases and conditions Geo H. nathalie.reuter@rohub.com Nathalie Reuter Environmental research Life sciences Physical sciences Chemistry Physics Science and technology/Natural science/Physics tryptophan-choline force field improvement 30.441898527004913 18.6 manuscripts file 8.67430441898527 5.3 subdirectory 5.389221556886228 4.5 quartermaster 8.622754491017965 7.2 biochemistry 58.82352941176471 6.0 improvement 16.083916083916083 6.9 betterment 11.616766467065867 9.7 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical comparison 4.6706586826347305 3.9 earth sciences 100.0 0.620610237121582 force field 23.076923076923077 9.9 validation 7.664670658682635 6.4 Diseases and conditions Health/Diseases and conditions armed forces 22.549019607843135 2.3 force field 16.047904191616766 13.4 geology 100.0 0.620610237121582 force field parameter validation 14.238952536824875 8.7 calculation 5.9880239520958085 5.0 service-account-enrichment 9458 https://api.rohub.org/api/ros/6d7f7856-f2c8-4172-9a35-ef22b6cc4561/crate/download/ 2022-03-22 02:18:57.862656+00:00 2025-03-05 00:45:31.255755+00:00 2022-03-22 02:18:57.862656+00:00 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. If you have trouble navigating, please send email to LINK: mailto:reza.611@gmail.com. ### Please see the file "file_organisations" for the directories and subdirectories. # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test. The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case. For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference. # For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019. ### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories. *** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to LINK: mailto:reza.611@gmail.com. application/ld+json https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561 2019_Khan_etal_JCTC MANUAL https://w3id.org/ro-id/1eb8b1da-9a9a-4f3d-85dd-f7806d7716ac https://w3id.org/ro-id/45c97f98-4cfc-4aec-a86d-972e5ee7c719 https://w3id.org/ro-id/f94049ca-f965-407c-bbd6-9dec6d0b4f36 https://w3id.org/ro-id/1b8dc5b9-3af9-4db4-91b7-47e4762481cf https://w3id.org/ro-id/1e7f2235-59f3-486d-9c05-8208bc9b6650 https://w3id.org/ro-id/2497f9a5-a789-4c12-87f7-36adaafe0b2f https://w3id.org/ro-id/38d6400b-ef9c-491a-87db-aa0e19241052 https://w3id.org/ro-id/42da6fae-3c5d-402f-a475-e3de433fb9c5 https://w3id.org/ro-id/51678519-ffd4-4990-a2ef-45e8cebea870 https://w3id.org/ro-id/6d49e53b-ee7d-487c-8005-f3d33075c010 https://w3id.org/ro-id/9d724b70-f51e-4801-a33c-0ddacb9f6380 https://w3id.org/ro-id/a373b5e2-9377-4ea5-ac77-3000d7e4ea1d https://w3id.org/ro-id/b4696554-3ad0-482c-a812-64ad881bf2df https://w3id.org/ro-id/bd4b37ee-9510-4d1b-a866-d5ac716d7d6d https://w3id.org/ro-id/f1de38ae-6531-4b97-b7cb-871e09afdb6c https://w3id.org/ro-id/3b59da1c-05ba-4240-9d9c-5fb7b28ad53c https://w3id.org/ro-id/5a3f3091-042f-4b19-9448-202c9bc06e4a https://w3id.org/ro-id/02142eed-e7ad-4ecf-b92c-17535047e45e https://w3id.org/ro-id/2a817a60-bda4-4de6-811b-8b464c6f4fbe https://w3id.org/ro-id/43eecec7-f276-4fa6-a009-b6d74bee5ac4 https://w3id.org/ro-id/8fe39cdd-2592-442a-ab2f-75bce594e8bc https://w3id.org/ro-id/ebb04bc3-1b64-4723-abb1-2bb1e9fb5c41 https://w3id.org/ro-id/1fd4a1b7-7f08-4932-81b9-e3e8219a45bd https://w3id.org/ro-id/42c9b12d-dec9-4e1d-bfa3-e6b20ca574ed https://w3id.org/ro-id/77b8f708-1da5-4937-88c1-bf913536e396 https://w3id.org/ro-id/780df816-5a29-4a1e-ada6-975c80157441 https://w3id.org/ro-id/91a19a15-66ea-4795-b53f-18d843c39303 https://w3id.org/ro-id/dfcc6de7-f307-4a3d-b80f-1ea59b685cea https://w3id.org/ro-id/e68dd0da-25b0-4f21-83a8-2090c91f2290 https://w3id.org/ro-id/931b0404-6d29-470f-bb13-ac082723d322 https://w3id.org/ro-id/dbfc01ff-6296-4efc-bf3a-072f7c2409b6 https://w3id.org/ro-id/08b34e3d-b475-4ece-a0e7-0a6e52e6453e https://w3id.org/ro-id/1a33ec44-1314-4e9b-b0f4-9476e11d0c7b https://w3id.org/ro-id/6510d15b-80ba-43f6-bd4e-bd8cb28ff442 https://w3id.org/ro-id/822a91de-8ad6-49aa-821a-fed4a5f225a0 https://w3id.org/ro-id/c9e71228-0405-4c6b-9183-c94103544c47 https://w3id.org/ro-id/e13cc288-ae77-4cc5-9a27-2223c47c4bd9 https://w3id.org/ro-id/b2bb9f47-e1bf-472d-b70d-b04bbbb8cdd1 https://w3id.org/ro-id/c15316b5-7fc7-4545-a8bb-f8d31e4060d6 https://w3id.org/ro-id/fe8868e4-2180-433d-b349-a8b9f0a2692c

Nathalie Reuter. "2019_Khan_etal_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6d7f7856-f2c8-4172-9a35-ef22b6cc4561.

metadata raw data data biblio https://doi.org/10.1021/acs.jctc.8b00839 2022-03-22 02:19:11.787124+00:00 2022-03-22 02:19:12.026713+00:00 https://doi.org/10.1021/acs.jctc.8b00839 2022-03-22 02:19:11.787124+00:00 Reuter, N. (2021).2019_Khan_etal_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00104 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00104 2021-11-22 00:00:00 2022-03-22 02:19:16.071315+00:00 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. For ease of processing, results from both works are kept together. Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. If you have trouble navigating, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>. ### Please see the file "file_organisations" for the directories and subdirectories. # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. For the QM calculations, they are based on the softwares and methods used. PSI4 directories for energy decompositions and SAPT methods, further divided in subdiectories. NWCHEM is for all other QM methods, and those can be differentiated by the dircetoty names which contains the method (DFT, MP2, CCSD(T)) and basis sets. CHARMM* directories contains the force field comparison vs the improvements test. The Khan_etal_JCTC_2016 paper contains phenol-TMA and benzene-TMA cases. The Khan_etal_JCTC_2019 contains the indole-TMA case. For the QM part, benzene-tma and indole-tma directories have naming explicitly (i.e. NWCHEM_cat_pi_benzene_tma/, PSI4_calc_benzene_tma/, CHARMM_pes_cat_pi_benzene_tma/). For phenol-TMA, look at the *_RESTART directories; these are the production ones for CHARMM and NWCHEM cases. For PSI4, it is PSI4_calc/). Other directories are old tests with different PES conditions (relaxation etc.). I did not remove them for my personal reference. # For the Parameters validations, reboot_piplc-dmpc/ is the test case for Khan_etal_JCTC_2016; all others are for Khan_etal_JCTC_2019. ### Manuscripts files are seperated for both these works in the main directories with submission files and figures. The raw data are also there. You will also find the raw data in the different results subdirectories. *** I am writing this very quickly, so some explanations might not be obvious. If you have doubt, please send email to <a href="mailto:reza.611@gmail.com" class="linkified" target="_blank">LINK</a>. 2019_Khan_etal_JCTC 2021-11-22 00:00:00 Nathalie Reuter directory 12.121212121212121 5.2 QM 12.121212121212121 5.2 force field comparison 20.29459901800327 12.4 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences choline 12.121212121212121 5.2 chemistry and materials (general) 100.0 0.38146594166755676 result 6.107784431137724 5.1 raw data 7.18562874251497 6.0 Results are divided in QM calculations+force field comparison and improvements, and force field parameter validations. 37.755102040816325 14.8 choline 8.502994011976048 7.1 directory 8.74251497005988 7.3 # Results files are organised based on QM calculations and force field comparisons (also improvements), and Parameters validations where you have the test cases. 34.183673469387756 13.4 QM calculation 12.76595744680851 7.8 chemistry and materials 100.0 0.38146594166755676 validation 10.955710955710956 4.7 results file 13.58428805237316 8.3 file 13.51981351981352 5.8 Newspaper Arts, culture and entertainment/Mass media/Newspaper file 9.461077844311378 7.9 computer science 18.627450980392158 1.9 Results for Tyrosine-choline, Phenylalanine-choline and Tryptophan-choline force field improvements. 28.061224489795915 11.0 Geo H. nathalie.reuter@rohub.com Nathalie Reuter Environmental research Life sciences Physical sciences service-account-enrichment 7833 https://api.rohub.org/api/ros/d5486b94-4c58-4cb4-8b1e-6945adf5eba9/crate/download/ 2022-03-22 02:19:17.388294+00:00 2025-03-05 01:19:14.776191+00:00 2022-03-22 02:19:17.388294+00:00 This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions. The enzymes simulated are Li_alphaIA1, St_beta1B1, Ll_alphaIII and R44Y/S60Y St_beta1B1. The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer. The trajectories are in DCD format and the topology file in PSF format.The following simulations are available: 1. Li_alphaIA1 on a pure POPC bilayer; 2. Li_alphaIA1 on a PC:SM:CHOL (70:20:10) bilayer; 3. Li_alphaIA1 on a POPC:POPE (50:50) bilayer; 4. Ll_alphaIII on a pure POPC bilayer; 5. Ll_alphaIII on a PC:SM:CHOL (70:20:10) bilayer; 6. R44Y/S60Y St_beta1B1 on a pure POPC bilayer; 7. St_beta1B1 on a pure POPC bilayer; 8. St_beta1B1 on a PC:SM:CHOL (70:20:10) bilayer; 9. St_beta1B1 on a POPC:POPE (50:50) bilayer; application/ld+json https://w3id.org/ro-id/d5486b94-4c58-4cb4-8b1e-6945adf5eba9 Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage MANUAL bilayer choline clade dataset enzyme information lipid phospholipase simulation earth sciences Hardware IT-computer sciences Pope Religious leader POPC S60Y St_beta1B1 bilayer clade enzyme lipid phospholipase chemistry and materials Loxosceles phospholipase d enzyme POPC bilayer bilayer composition choline-containing lipid pure POPC Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage. The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer. This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions. biochemistry

Emmanuel Moutoussamy. "Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/d5486b94-4c58-4cb4-8b1e-6945adf5eba9.

metadata biblio data raw data Moutoussamy, E. (2021).Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage [Data set]. Norstore. https://doi.org/10.11582/2021.00099 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00099 2021-11-19 00:00:00 2022-03-22 02:19:33.483910+00:00 This dataset contains molecular dynamic simulation data of Loxosceles phospholipase D enzymes, of both clades, in presence of different bilayer compositions. The enzymes simulated are Li_alphaIA1, St_beta1B1, Ll_alphaIII and R44Y/S60Y St_beta1B1. The bilayers are used are a pure POPC, PC:SM:CHOL (70:20:10) and POPC:POPE (50:50) bilayer. The trajectories are in DCD format and the topology file in PSF format.The following simulations are available: 1. Li_alphaIA1 on a pure POPC bilayer; 2. Li_alphaIA1 on a PC:SM:CHOL (70:20:10) bilayer; 3. Li_alphaIA1 on a POPC:POPE (50:50) bilayer; 4. Ll_alphaIII on a pure POPC bilayer; 5. Ll_alphaIII on a PC:SM:CHOL (70:20:10) bilayer; 6. R44Y/S60Y St_beta1B1 on a pure POPC bilayer; 7. St_beta1B1 on a pure POPC bilayer; 8. St_beta1B1 on a PC:SM:CHOL (70:20:10) bilayer; 9. St_beta1B1 on a POPC:POPE (50:50) bilayer; Specificity of Loxosceles alpha clade phospholipase D enzymes for choline-containing lipids: role of a conserved aromatic cage 2021-11-19 00:00:00 Emmanuel Edouard Moutoussamy emmanuel.moutoussamy@rohub.com Emmanuel Moutoussamy Geo H. Environmental research Life sciences Physical sciences Earth sciences Norway debris flow field observation calculations data geology West Norway RAMMS simulation slide deposit measure perimeter data mudslide sediment data type treatise angle of repose domain dataset GNSS measurement profile Department of Geosciences Oslo calculation University of Oslo service-account-enrichment 8150 https://api.rohub.org/api/ros/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707/crate/download/ 2022-03-22 02:21:05.280489+00:00 2025-03-05 00:50:09.588296+00:00 2022-03-22 02:21:05.280489+00:00 Data created for Marius Julian Grønli’s Master thesis at the Department of Geosciences at the University of Oslo fall 2021. Title: Quantitative back calculation of three debris flows in western Norway Dataset includes: GNSS measurements of three debris flows on the west coast of Norway. Logged perimeter with 15 m increments and several profiles across the flow paths. Grain size distribution, angle of repose for several soil samples at each study site. Including samples of slide deposits and Origin material. RAMMS simulations of the three events with varying input parameters. Each data type has a documentation file explaining the workflow of each data set. Event dates: Stamnes: 16th of February 2020 Jordalen: 5th of August 2019 Osdalsvatenet: 21st of January 2020 application/ld+json https://w3id.org/ro-id/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707 Debris flow field observations and RAMMS back calculations MANUAL

Marius Julian Grønli. "Debris flow field observations and RAMMS back calculations." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/fc0d05e2-f5b4-4b47-b520-ea9d05d9f707.

raw data biblio metadata data Grønli, M. J. (2021).Debris flow field observations and RAMMS back calculations [Data set]. Norstore. https://doi.org/10.11582/2021.00092 Marius Julian Grønli Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00092 2021-10-14 00:00:00 2022-03-22 02:21:24.160454+00:00 Data created for Marius Julian Grønli’s Master thesis at the Department of Geosciences at the University of Oslo fall 2021. Title: Quantitative back calculation of three debris flows in western Norway Dataset includes: GNSS measurements of three debris flows on the west coast of Norway. Logged perimeter with 15 m increments and several profiles across the flow paths. Grain size distribution, angle of repose for several soil samples at each study site. Including samples of slide deposits and Origin material. RAMMS simulations of the three events with varying input parameters. Each data type has a documentation file explaining the workflow of each data set. Event dates: Stamnes: 16th of February 2020 Jordalen: 5th of August 2019 Osdalsvatenet: 21st of January 2020 Debris flow field observations and RAMMS back calculations 2021-10-14 00:00:00 Marius Julian Grønli Geo H. marius.julian.gronli@rohub.com Marius Julian Grønli Environmental research Life sciences Physical sciences publication 12.651821862348179 12.5 data for the publication 11.322645290581162 11.3 computer modelling 24.898785425101217 24.6 experiment 29.322548028311424 29.0 data 33.46814964610718 33.1 service-account-enrichment 7174 https://api.rohub.org/api/ros/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9/crate/download/ 2022-03-22 02:21:25.532255+00:00 2025-03-05 00:45:26.451604+00:00 2022-03-22 02:21:25.532255+00:00 raw experiment/simulation data for the publication application/ld+json https://w3id.org/ro-id/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9 2012_Grauffel_etal_PLoSONE MANUAL https://w3id.org/ro-id/df41cd03-d30a-42df-9c90-70a3a34bfe9f https://w3id.org/ro-id/118fb288-3b7c-4e01-8a2d-604ee92fdec6 https://w3id.org/ro-id/24d62ac8-ce28-468c-b126-d51d011d186f https://w3id.org/ro-id/bd9594b0-a34c-4a13-9e05-1f53a8a7cf77 https://w3id.org/ro-id/c3dbab5f-3057-475d-8d95-e81050e0af2c https://w3id.org/ro-id/8e20766c-e0f4-4c8d-9ed1-2be1852aaaa6 https://w3id.org/ro-id/a2bd4f65-14ed-4820-ad2d-3d7c5eb57515 https://w3id.org/ro-id/4179efb5-d3c5-4ec7-90c1-29d0a08fa7c3 https://w3id.org/ro-id/5285a352-03c0-4dac-97f9-7c8a1176b0af https://w3id.org/ro-id/6c5ad61d-9ed9-4975-96f0-72bf193f5d10 https://w3id.org/ro-id/9c06af6c-1ff6-4fd5-acc6-ec57b549da79 https://w3id.org/ro-id/d5d9a091-9e58-4a01-a8ed-728f8ec8dd8d https://w3id.org/ro-id/e6948f96-6520-4b3f-9646-ddb3925dde31 https://w3id.org/ro-id/12fdff6e-f2ec-490d-b64b-7fa11480934b https://w3id.org/ro-id/685b3d26-2957-47d4-bff7-4a355bebf2bb https://w3id.org/ro-id/7d1d290d-bc88-48b2-8895-9ba409d88240 https://w3id.org/ro-id/d8911953-eb97-44fe-8dcd-16b624f19d22

Nathalie Reuter. "2012_Grauffel_etal_PLoSONE." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/5e8cbd3f-7b47-4d75-976a-b3d2c1d207e9.

raw data metadata biblio data Reuter, N. (2021).2012_Grauffel_etal_PLoSONE [Data set]. Norstore. https://doi.org/10.11582/2021.00090 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00090 2021-10-13 00:00:00 2022-03-22 02:21:45.800191+00:00 raw experiment/simulation data for the publication 2012_Grauffel_etal_PLoSONE 2021-10-13 00:00:00 Nathalie Reuter https://doi.org/10.1371/journal.pone.0052642 2022-03-22 02:21:40.999932+00:00 2022-03-22 02:21:41.269967+00:00 https://doi.org/10.1371/journal.pone.0052642 2022-03-22 02:21:40.999932+00:00 simulation data 88.27655310621243 88.1 simulation 25.176946410515672 24.9 simulation data for the publication 0.40080160320641284 0.4 earth sciences 100.0 0.9631021022796631 publication 12.032355915065722 11.9 atmospheric sciences 100.0 0.9631021022796631 data 31.781376518218625 31.4 experiment 30.668016194331983 30.3 life sciences (general) 100.0 0.756223201751709 2012_Grauffel_etal_PLoSONE. raw experiment/simulation data for the publication 100.0 100.0 computer science 100.0 16.0 life sciences 100.0 0.756223201751709 Geo H. nathalie.reuter@rohub.com Nathalie Reuter Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 8544 https://api.rohub.org/api/ros/c35062bf-df50-4667-b876-e5c69570125c/crate/download/ 2022-03-22 02:21:47.331157+00:00 2025-03-05 00:56:57.081384+00:00 2022-03-22 02:21:47.331157+00:00 This dataset contains the model output (atmospheric component only) used in Blichner, S. M., Sporre, M. K., and Berntsen, T. K.: Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos. Chem. Phys., LINK: http://doi.org/10.5194/acp-2021-151, accepted, 2021. See LINK: http://github.com/sarambl/OAS-ERF for analysis code. application/ld+json https://w3id.org/ro-id/c35062bf-df50-4667-b876-e5c69570125c Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" MANUAL aerosol cloud interaction output radiative forcing therapy tumor earth sciences Therapy T. K. Reduced aerosol cloud growth interaction radiative forcing treatment mathematical and computer sciences Chem. Phys aerosol growth cloud-aerosol interaction improved treatment model output Blichner, S. M. Sporre, M. K. and Berntsen, T. K. Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos. Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" This dataset contains the model output (atmospheric component only) used in See LINK: http: github.com/sarambl/OAS-ERF for analysis code. medicine physics

Sara Blichner, and University of Oslo (UiO). "Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model"." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/c35062bf-df50-4667-b876-e5c69570125c.

raw data metadata data biblio https://acp.copernicus.org/preprints/acp-2021-151/ 2022-03-22 02:22:07.384799+00:00 2022-03-22 02:22:07.648299+00:00 https://acp.copernicus.org/preprints/acp-2021-151/ 2022-03-22 02:22:07.384799+00:00 Blichner, S., University of Oslo (2021).Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" [Data set]. Norstore. https://doi.org/10.11582/2021.00087 Sara Marie Blichner Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00087 2021-10-11 00:00:00 2022-03-22 02:22:11.450221+00:00 This dataset contains the model output (atmospheric component only) used in Blichner, S. M., Sporre, M. K., and Berntsen, T. K.: Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model, Atmos. Chem. Phys., <a href="http://doi.org/10.5194/acp-2021-151" class="linkified" target="_blank">LINK</a>, accepted, 2021. See <a href="http://github.com/sarambl/OAS-ERF" class="linkified" target="_blank">LINK</a> for analysis code. Model output for "Reduced effective radiative forcing from cloud-aerosol interactions (ERFaci) with improved treatment of early aerosol growth in an Earth System Model" 2021-10-11 00:00:00 Sara Marie Blichner UiO@rohub.com University of Oslo (UiO) Geo H. sara.blichner@rohub.com Sara Blichner Environmental research Life sciences Physical sciences Chemistry computer modelling 16.75025075225677 16.7 computer science 29.648241206030153 11.8 mathematical and computer sciences 100.0 0.29949691891670227 manuscript 7.321965897693079 7.3 source code 38.581856100104275 37.0 atmospheric sciences 100.0 0.7998380064964294 analysis data 35.03503503503503 35.0 manuscript 7.7163712200208545 7.4 source code 36.91073219658976 36.8 computer programming and software 100.0 0.29949691891670227 source simulation 64.96496496496498 64.9 publication 6.673618352450469 6.4 simulation 18.039624608967674 17.3 data 28.988529718456725 27.8 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences service-account-enrichment 7302 https://api.rohub.org/api/ros/bc9a215f-a0b0-4a5b-85e9-668f59874669/crate/download/ 2022-03-22 02:22:13.239874+00:00 2025-03-05 00:45:27.011181+00:00 2022-03-22 02:22:13.239874+00:00 source simulation files, analysis data, source code, manuscript etc. for the publication application/ld+json https://w3id.org/ro-id/bc9a215f-a0b0-4a5b-85e9-668f59874669 2013_Fuglebakk_Reuter_Hinsen_JCTC MANUAL https://w3id.org/ro-id/21c7efbc-5b32-4252-80de-54332db3c22e https://w3id.org/ro-id/be1f0560-2efb-451a-a480-fbb59273aa12 https://w3id.org/ro-id/0e60f9a3-f509-473d-9a01-daa3b9af0c71 https://w3id.org/ro-id/2f981695-fcdb-4a4d-a62a-a416209dff5f https://w3id.org/ro-id/91ff8e38-d049-4480-8c58-a03a85c6d88d https://w3id.org/ro-id/d44514a5-4fa2-4f20-ba70-9f65dc27e197 https://w3id.org/ro-id/d6888ca3-5ceb-4084-b28b-78afa529f035 https://w3id.org/ro-id/d708cc8d-b7c1-4844-874e-386804236c5d https://w3id.org/ro-id/81b43073-1f12-4ec3-bda0-31871849c11b https://w3id.org/ro-id/e80c508a-eca3-422c-beda-5efab23b0e1f https://w3id.org/ro-id/b1205b18-910f-49c5-b884-3f07f974e3e2 https://w3id.org/ro-id/702af2a6-4a74-491c-98c7-6ff822826ed6 https://w3id.org/ro-id/8b4f92fc-157c-4f04-90b9-1374279a7827 https://w3id.org/ro-id/9ff7e001-1a91-49bc-a62a-64545b49363a https://w3id.org/ro-id/aca95257-9b1b-4273-b9e4-bc9e3cb28849 https://w3id.org/ro-id/b01a20c5-99b9-4550-8b4c-2eed1451cc07 https://w3id.org/ro-id/2a3e1c91-9dd8-4008-a413-921498379217 https://w3id.org/ro-id/9ab04305-2c0f-4079-87dd-2c08edbf499d https://w3id.org/ro-id/844ae3d8-ebe8-46c9-904d-06f0515adb07 https://w3id.org/ro-id/9da1ffd8-f477-445b-88b3-138e0db8ff94 https://w3id.org/ro-id/fb3dea67-85bc-40ab-b9b2-75efecaf7471

Nathalie Reuter. "2013_Fuglebakk_Reuter_Hinsen_JCTC." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/bc9a215f-a0b0-4a5b-85e9-668f59874669.

data metadata biblio raw data Reuter, N. (2021).2013_Fuglebakk_Reuter_Hinsen_JCTC [Data set]. Norstore. https://doi.org/10.11582/2021.00085 Nathalie Reuter Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00085 None 2022-03-22 02:22:36.027755+00:00 source simulation files, analysis data, source code, manuscript etc. for the publication 2013_Fuglebakk_Reuter_Hinsen_JCTC None Nathalie Reuter https://doi.org/10.1021/ct400399x 2022-03-22 02:22:31.146218+00:00 2022-03-22 02:22:31.420467+00:00 https://doi.org/10.1021/ct400399x 2022-03-22 02:22:31.146218+00:00 computer programming 70.35175879396985 28.0 publication 6.41925777331996 6.4 data 28.68605817452357 28.6 analysis 3.9117352056168504 3.9 earth sciences 100.0 0.7998380064964294 2013_Fuglebakk_Reuter_Hinsen_JCTC. source simulation files, analysis data, source code, manuscript etc. for the publication 100.0 100.0 Geo H. nathalie.reuter@rohub.com Nathalie Reuter mailto:nathalie.reuter@rohub.com 7203 https://api.rohub.org/api/ros/359fc143-420a-45a9-8cdb-fc03c7188df5/crate/download/ mailto:georgehadib@gmail.com 2022-03-22 02:22:37.897281+00:00 2025-03-05 00:45:25.879983+00:00 2022-03-22 02:22:37.897281+00:00 source simulation/analysis data for the publication application/ld+json https://w3id.org/ro-id/359fc143-420a-45a9-8cdb-fc03c7188df5 2009_Hajjar_Dejaegere_Reuter_JPCA http://eurovoc.europa.eu/2919 http://eurovoc.europa.eu/3941 http://eurovoc.europa.eu/3946 http://eurovoc.europa.eu/5966 MANUAL https://w3id.org/ro-id/b598ff14-9f40-4dd4-9a51-c6c876d6c4f9 https://w3id.org/ro-id/2d2279a8-e28b-41b5-8d9b-c84d8c56b8cd https://w3id.org/ro-id/8c471337-2d21-4603-af51-8c51424cbc15 https://w3id.org/ro-id/93d0b3c7-5dea-4858-9818-e71de222b644 https://w3id.org/ro-id/b71244db-b6a4-4983-ac77-8aff82f87827 https://w3id.org/ro-id/e87b3e9f-cd16-4868-8c58-cd48d7be649c https://w3id.org/ro-id/4e238fdb-4e45-4543-85f9-56abf3a1c08b https://w3id.org/ro-id/cc72f552-8791-47f5-b30a-26a4a0932ba5 https://w3id.org/ro-id/6829dc3e-b758-42a8-8f96-1accf4a2bca5 https://w3id.org/ro-id/87d5a9cb-bf7e-4817-a12c-95bc4f2a2e67 https://w3id.org/ro-id/8b5e5476-a79e-4512-9427-614a57ffc7e5 https://w3id.org/ro-id/dcb3f7ba-85af-42fb-9f23-bfbc19e7037c https://w3id.org/ro-id/f342e568-22c4-4f61-b78c-511f0e6655c4 https://w3id.org/ro-id/a01cccbe-ac89-4399-b258-c4a5822d7d46 https://w3id.org/ro-id/ebcad90a-8c01-4dee-9547-bb3e6c1f2dcb https://w3id.org/ro-id/d03cfd7d-7cad-411f-9af0-9621c4a2497f https://w3id.org/ro-id/f9191935-e027-403b-9935-032cc2a49be6 https://w3id.org/ro-id/36bd90da-c1d0-4adc-82c4-14adf1a98f01

Nathalie Reuter. "2009_Hajjar_Dejaegere_Reuter_JPCA." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/359fc143-420a-45a9-8cdb-fc03c7188df5.

raw data data metadata biblio Reuter, N. (2021).2009_Hajjar_Dejaegere_Reuter_JPCA [Data set]. Norstore. https://doi.org/10.11582/2021.00086 Published Nathalie Reuter Simulation mailto:nathalie.reuter@rohub.com https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00086 mailto:georgehadib@gmail.com None 2022-03-22 02:22:57.660517+00:00 source simulation/analysis data for the publication CC-BY-4.0 https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00086 None Nathalie Reuter Dandan Xue mailto:nathalie.reuter@rohub.com https://doi.org/10.1021/jp902930u mailto:georgehadib@gmail.com 2022-03-22 02:22:52.717818+00:00 2022-03-22 02:22:52.997316+00:00 https://doi.org/10.1021/jp902930u 2022-03-22 02:22:52.717818+00:00 Geo H. nathalie.reuter@rohub.com Nathalie Reuter mailto:service-account-enrichment Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 8000 https://api.rohub.org/api/ros/e7f5d844-fa99-4407-af00-82fc4ba618f1/crate/download/ 2022-03-22 02:22:59.259226+00:00 2025-03-05 01:23:33.595114+00:00 2022-03-22 02:22:59.259226+00:00 Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations. In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region. All simulations cover the time period 1982-2000. application/ld+json https://w3id.org/ro-id/e7f5d844-fa99-4407-af00-82fc4ba618f1 Subset of the AFES simulations used for doi 10.5194/wcd-2020-50 MANUAL Gulf stream latent heat precipitation pressure sea surface temperature simulation subset supersonic transport aircraft temperature earth sciences Weather AFES Gulf stream SST doi 10.5194 hPa simulation subset geosciences AFES simulation Kuroshio Extension region convective precipitation sensible heat fluxes smoothed SST In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region. Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations. Subset of the AFES simulations used for doi 10.5194/wcd-2020-50. the time period 1982-2000 meteorology physics

Akira Kuwano-Yoshida. "Subset of the AFES simulations used for doi 10.5194/wcd-2020-50." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/e7f5d844-fa99-4407-af00-82fc4ba618f1.

metadata biblio raw data data Kuwano-Yoshida, A. (2021).Subset of the AFES simulations used for doi 10.5194/wcd-2020-50 [Data set]. Norstore. https://doi.org/10.11582/2021.00075 Akira Kuwano-Yoshida Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00075 2021-09-22 00:00:00 2022-03-22 02:23:18.675518+00:00 Sea-level pressure, SST, latent and sensible heat fluxes, large-scale and convective precipitation, specific humidity and temperatures at 850 hPa, and winds at both 925 and 300 hPa from the AFES simulations. In addition to a control simulation with realistic SST, there is one simulation each with strongly smoothed SSTs in either the Gulf Stream or Kuroshio Extension region. All simulations cover the time period 1982-2000. Subset of the AFES simulations used for doi 10.5194/wcd-2020-50 2021-09-22 00:00:00 Clemens Spensberger https://wcd.copernicus.org/preprints/wcd-2020-50/ 2022-03-22 02:23:14.397390+00:00 2022-03-22 02:23:14.649349+00:00 https://wcd.copernicus.org/preprints/wcd-2020-50/ 2022-03-22 02:23:14.397390+00:00 akira.kuwano-yoshida@rohub.com Akira Kuwano-Yoshida Geo H. Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 8136 https://api.rohub.org/api/ros/c0f7a882-6123-477c-aac5-25e8f9e49dcd/crate/download/ 2022-03-22 02:23:20.356464+00:00 2025-03-05 12:49:07.078365+00:00 2022-03-22 02:23:20.356464+00:00 The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea. The user is encourage to read the README file contained within the data set. application/ld+json https://w3id.org/ro-id/c0f7a882-6123-477c-aac5-25e8f9e49dcd Norwegian hindcast archive's wind power data set (NORA3-WP) MANUAL Barents Sea North Sea Norwegian Sea archive file dataset hindcast user variable wind power earth sciences Alternative energy Hardware IT-computer sciences Renewable energy Barents Sea North Sea Norwegian Sea archive data set variable wind power mathematical and computer sciences archive's wind power data set contain wind power hindcast archive's wind power data set parts of the Barents Sea related variable Norwegian hindcast archive's wind power data set (NORA3-WP) The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea. The user is encourage to read the README file contained within the data set. database hydrography software Barents Sea North Sea Norwegian Sea

Ida Marie Solbrekke, Asgeir Sorteberg, and University of Bergen, Institute of biomedicine (UiB). "Norwegian hindcast archive's wind power data set (NORA3-WP)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/c0f7a882-6123-477c-aac5-25e8f9e49dcd.

raw data metadata biblio data Solbrekke, I. M., Sorteberg, A., University of Bergen (2021).Norwegian hindcast archive's wind power data set (NORA3-WP) [Data set]. Norstore. https://doi.org/10.11582/2021.00068 University of Bergen (UiB) Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00068 2021-08-25 00:00:00 2022-03-22 02:23:42.107526+00:00 The data set contains wind power related variables for the North Sea, the Norwegian Sea, and parts of the Barents Sea. The user is encourage to read the README file contained within the data set. Norwegian hindcast archive's wind power data set (NORA3-WP) 2021-08-25 00:00:00 Ida Marie Solbrekke UiB@rohub.com University of Bergen, Institute of biomedicine (UiB) asgeir.sorteberg@rohub.com Asgeir Sorteberg Geo H. ida.marie.solbrekke@rohub.com Ida Marie Solbrekke Environmental research Life sciences Physical sciences Earth sciences WRF model datum South America newspaper understanding fact Future Precipitation Projections for South America South America Understanding Model Diversity in Future Precipitation Projections further detail South America WRF model datum detail newspaper publisher service-account-enrichment 7320 https://api.rohub.org/api/ros/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9/crate/download/ 2022-03-22 02:23:45.142410+00:00 2025-03-05 02:47:01.794301+00:00 2022-03-22 02:23:45.142410+00:00 WRF model data used in the paper "Understanding Model Diversity in Future Precipitation Projections for South America", in review. Further details are given in the paper and in the README file. application/ld+json https://w3id.org/ro-id/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9 Understanding Model Diversity in Future Precipitation Projections for South America MANUAL

Center for International Climate Research (CICERO). "Understanding Model Diversity in Future Precipitation Projections for South America." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6b0cdc2d-2857-44ef-bc5a-c2051ad20ba9.

metadata raw data biblio data Center for International Climate Research (2021).Understanding Model Diversity in Future Precipitation Projections for South America [Data set]. Norstore. https://doi.org/10.11582/2021.00067 Øivind Hodnebrog Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00067 2021-08-12 00:00:00 2022-03-22 02:24:01.479217+00:00 WRF model data used in the paper "Understanding Model Diversity in Future Precipitation Projections for South America", in review. Further details are given in the paper and in the README file. Understanding Model Diversity in Future Precipitation Projections for South America 2021-08-12 00:00:00 Øivind Hodnebrog CICERO@rohub.com Center for International Climate Research (CICERO) Geo H. Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 7111 https://api.rohub.org/api/ros/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c/crate/download/ 2022-03-22 02:24:02.954436+00:00 2025-03-05 01:19:16.225993+00:00 2022-03-22 02:24:02.954436+00:00 Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021. application/ld+json https://w3id.org/ro-id/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c Statistisk prediksjonsmodellering av steinbreer i Norge MANUAL Norway Oslo earth sciences Harald Wathne Hestad Norway Oslo geosciences Bakgrunnsdata for masteroppgaven Institutt for geofag ved Institutt for geofag ved Institutt Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021. Statistisk prediksjonsmodellering av steinbreer i Norge. Norway Oslo

Harald Wathne Hestad. "Statistisk prediksjonsmodellering av steinbreer i Norge." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6a656f34-de1f-4d96-9ce2-a93ebd1f1a4c.

metadata biblio data raw data Hestad, H. W. (2021).Statistisk prediksjonsmodellering av steinbreer i Norge [Data set]. Norstore. https://doi.org/10.11582/2021.00066 Harald Wathne Hestad Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00066 2021-08-05 00:00:00 2022-03-22 02:24:19.675060+00:00 Bakgrunnsdata for masteroppgaven "Statistisk prediksjonsmodellering av steinbreer i Norge" av Harald Wathne Hestad ved Institutt for geofag, Universitetet i Oslo, våren 2021. Statistisk prediksjonsmodellering av steinbreer i Norge 2021-08-05 00:00:00 Harald Wathne Hestad Geo H. harald.wathne.hestad@rohub.com Harald Wathne Hestad Environmental research Life sciences Physical sciences Biology in silico antibody-antigen binding database. 25.165562913907287 22.8 The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction 41.16997792494481 37.3 publication 7.323943661971831 5.2 antigen 27.605633802816904 19.6 life sciences 100.0 0.6574541926383972 antibody 23.253012048192772 19.3 geochemistry 100.0 0.840290367603302 software 9.014084507042254 6.4 51258: antigen 24.698795180722893 20.5 forecast 4.457831325301205 3.7 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences publication 6.506024096385542 5.4 service-account-enrichment 8927 https://api.rohub.org/api/ros/71c90c65-34e6-441e-bb44-d1b31dd1007b/crate/download/ 2022-03-22 02:24:21.217126+00:00 2025-03-05 00:45:34.790548+00:00 2022-03-22 02:24:21.217126+00:00 This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods. The content of the files is explained in Readme.pdf The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction The software used to generate the database is available at LINK: http://github.com/csi-greifflab/Absolut for all explanations. application/ld+json https://w3id.org/ro-id/71c90c65-34e6-441e-bb44-d1b31dd1007b Absolut! in silico antibody-antigen binding database MANUAL https://w3id.org/ro-id/b3fb3d46-5fd8-41e7-a090-58181fbb01f1 https://w3id.org/ro-id/e0ff8858-262a-485b-a8dd-728c838e8a36 https://w3id.org/ro-id/15a39d24-4daa-417e-99ed-4b635b56cae9 https://w3id.org/ro-id/4ad66687-f699-45ff-a561-8aea7c629d2a https://w3id.org/ro-id/5195bb19-e10e-4d1d-90e7-b7a591e2af67 https://w3id.org/ro-id/6aa13e0d-8332-4955-9f68-5e08e7b97b22 https://w3id.org/ro-id/7b70fcab-fd6b-461b-a79b-43cb859348f1 https://w3id.org/ro-id/85946983-f823-4c28-88ad-638fd7745f37 https://w3id.org/ro-id/89a060f4-22d1-43df-8cca-c29ee9213d21 https://w3id.org/ro-id/9d840ee9-e861-4815-ad57-62fc3d3d062b https://w3id.org/ro-id/c1a70a12-6e53-4c98-9b4d-481c69d5a481 https://w3id.org/ro-id/c4ebfa3e-5087-4695-abbd-28cb65a0d1e6 https://w3id.org/ro-id/49e70145-19cf-4ac3-9da9-fb2b49f5471c https://w3id.org/ro-id/286e5fd8-7d69-4aaf-854e-a0921db660a8 https://w3id.org/ro-id/8063038f-6dbd-4148-85a2-afb472b141cd https://w3id.org/ro-id/5782c378-2b05-41eb-b48b-de733932007a https://w3id.org/ro-id/c008e2b7-f43b-4e10-ae2b-e9b7c99d2c24 https://w3id.org/ro-id/0ce573ff-277b-4be8-9a5b-6a9c675e96f5 https://w3id.org/ro-id/0f6898fa-86e3-42c4-8cff-48968d07dbdf https://w3id.org/ro-id/45689fb7-0f9f-4c04-b03e-13e29ca1b3b7 https://w3id.org/ro-id/9021ea62-7039-4cc7-b810-4c89cf540ee5 https://w3id.org/ro-id/a164d3a9-ac2e-4c63-b573-2cc2e497ee5e https://w3id.org/ro-id/c6ab0333-3816-48d6-a0c5-f430a5569eff https://w3id.org/ro-id/e912021a-590a-404b-a2e0-3f2335f06f0a https://w3id.org/ro-id/14804972-fa34-4e28-b8d1-7c1a8c800f2e https://w3id.org/ro-id/fec8824a-34e3-4f6c-be08-b0863b0c242a https://w3id.org/ro-id/7247b893-bbcd-47aa-86dd-b27e9f592c25 https://w3id.org/ro-id/a1c41d03-c971-45df-bfc6-b023608097a5 https://w3id.org/ro-id/c2b8dfdb-c1fc-42e1-841c-7b0e25ca1af3 https://w3id.org/ro-id/d1dc98f7-1b59-477f-b20e-5ce10570a46b https://w3id.org/ro-id/fff4fa92-d823-45bd-8cbb-df9573cbb862 https://w3id.org/ro-id/011545de-72ea-4477-b87e-e4a0e9c8df4b https://w3id.org/ro-id/092bc252-092d-439d-8551-fada7f4d509e https://w3id.org/ro-id/89a51f68-a057-4f1a-9f39-051fe69eefb6

Philippe ROBERT, Victor Greiff, and Rahmad Akbar. "Absolut! in silico antibody-antigen binding database." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/71c90c65-34e6-441e-bb44-d1b31dd1007b.

data raw data metadata biblio https://www.biorxiv.org/content/10.1101/2021.07.06.451258v2 2022-03-22 02:24:46.046298+00:00 2022-03-22 02:24:46.241115+00:00 https://www.biorxiv.org/content/10.1101/2021.07.06.451258v2 2022-03-22 02:24:46.046298+00:00 ROBERT, P., Greiff, V., Akbar, R. (2021).Absolut! in silico antibody-antigen binding database [Data set]. Norstore. https://doi.org/10.11582/2021.00063 University of Oslo (UiO) Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00063 2021-07-12 00:00:00 2022-03-22 02:24:50.074536+00:00 This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods. The content of the files is explained in Readme.pdf The reference publication is available on biorxiv, ID BIORXIV/2021/451258: Robert et al., A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction The software used to generate the database is available at <a href="http://github.com/csi-greifflab/Absolut" class="linkified" target="_blank">LINK</a> for all explanations. Absolut! in silico antibody-antigen binding database 2021-07-12 00:00:00 Philippe Paul Auguste Robert unconstrained machine-learning 7.515923566878981 5.9 database 6.144578313253011 5.1 earth sciences 100.0 0.840290367603302 binding 7.590361445783133 6.3 machine learning 12.891566265060241 10.7 This is a database of in silico generated antibody-antigen bindings (159 antigens times 6.9 million CDRH3 murine sequences), as resource for benchmarking machine learning methods. 33.6644591611479 30.5 antibody 25.774647887323944 18.3 complex 5.783132530120482 4.8 machine learning 14.647887323943662 10.4 silico antibody-antigen 24.331210191082807 19.1 biochemistry 50.0 12.7 Software Economy, business and finance/Economic sector/Computing and information technology/Software specificity 3.6144578313253013 3.0 specificity prediction 22.802547770700635 17.9 investigation 5.0602409638554215 4.2 binding 8.591549295774648 6.1 antigens times 7.388535031847134 5.8 immunology 50.0 12.7 Robert 7.042253521126761 5.0 life sciences (general) 100.0 0.6574541926383972 antibody-antigen binding 37.961783439490446 29.8 Geo H. philippe.robert@rohub.com Philippe ROBERT rahmad.akbar@rohub.com Rahmad Akbar victor.greiff@rohub.com Victor Greiff Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 7096 https://api.rohub.org/api/ros/063f3c98-2306-4388-86b2-4b09976fc16d/crate/download/ 2022-03-22 02:24:51.576414+00:00 2025-03-05 00:52:20.015916+00:00 2022-03-22 02:24:51.576414+00:00 Accumulated preciptiation, 15 min temporal resolution, 3km spatial resolution. Year 1985 to 2005. Covering the inner most part of Oslofjorden in Norway. application/ld+json https://w3id.org/ro-id/063f3c98-2306-4388-86b2-4b09976fc16d HARMONIE-AROME precipitation MANUAL Norway precipitation earth sciences Executive (government) Medicine Ministers (government) Weather HARMONIE-AROME Norway Oslofjorden precipitation general HARMONIE-AROME precipitation part of Oslofjorden Covering the inner most part of Oslofjorden in Norway. HARMONIE-AROME precipitation. Year 1985 to 2005. 15 min Year 1985 to 2005 Norway

Eirik Nordgård. "HARMONIE-AROME precipitation." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/063f3c98-2306-4388-86b2-4b09976fc16d.

biblio metadata data raw data Nordgård, E., Nordgård, E. (2021).HARMONIE-AROME precipitation [Data set]. Norstore. https://doi.org/10.11582/2021.00058 Eirik Nordgård Simulation https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00058 2021-06-21 00:00:00 2022-03-22 02:25:09.852200+00:00 Accumulated preciptiation, 15 min temporal resolution, 3km spatial resolution. Year 1985 to 2005. Covering the inner most part of Oslofjorden in Norway. 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Szymon KupiÅ?ski service-account-enrichment false http://sandbox.rohub.org/rodl/ROs/POPANE/ 2021-03-11T15:18:55.068+01:00 https://orcid.org/0000-0002-4704-6802 http://w3id.org/ro-id/rohub/model#change_specifications/fc6484e8-4439-4613-8d15-829595d4f06b 9986 https://api.rohub.org/api/ros/6a6127bb-03b0-4d0c-a5a9-52cfd0913fd4/crate/download/ 2021-03-11 14:18:55.068000+00:00 2025-03-05 01:17:00.319175+00:00 2021-03-11 14:18:55.068000+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/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.

Biblio Produced Data Metadata Used Raw Data Dataset Documentation https://data.psychosensing.psnc.pl/popane/ 2022-03-24 13:24:35.249281+00:00 2022-03-24 13:24:36.181299+00:00 https://data.psychosensing.psnc.pl/popane/ 2022-03-24 13:24:35.249281+00:00 service-account-generation-service Neurobiology http://sandbox.rohub.org/rodl/ROs/POPANE-1-fork/ http://w3id.org/ro-id/rohub/model#change_specifications/2b6b4a8f-9933-497e-bf11-4137ffdaf516/changes/0c67378d-9501-4a8c-8477-33570a984349 http://w3id.org/ro-id/rohub/model#change_specifications/2b6b4a8f-9933-497e-bf11-4137ffdaf516/changes/2540f165-0e17-4152-944a-67c59a311d5e https://data.psychosensing.psnc.pl/popane/index.html https://data.psychosensing.psnc.pl/popane/index.html 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 http://sandbox.rohub.org/rodl/ROs/POPANE-1/ 2021-03-11T14:07:59.683+01:00 https://orcid.org/0000-0002-2455-4556 http://w3id.org/ro-id/rohub/model#change_specifications/2b6b4a8f-9933-497e-bf11-4137ffdaf516 9094 https://api.rohub.org/api/ros/a3a81739-a5b7-4897-b732-7aba23d6fa5a/crate/download/ 2021-03-11 13:07:59.683000+00:00 2025-03-05 01:14:11.927117+00:00 2021-03-11 13:07:59.683000+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/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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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 Neurobiology https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db 2021-03-11T14:05:41.562+01:00 https://orcid.org/0000-0002-2455-4556 https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db 2021-03-11T14:07:59.683+01:00 https://orcid.org/0000-0002-2455-4556 https://w3id.org/ro-id/9bf840e3-7a39-41fc-be39-7eed9dc294db 2021-03-11T14:04:33.909+01:00 https://orcid.org/0000-0002-2455-4556 http://sandbox.rohub.org/rodl/ROs/POPANE-1-fork/ 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 http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot-1/ http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot-2/ http://sandbox.rohub.org/rodl/ROs/POPANE-1-snapshot/ 8850 https://api.rohub.org/api/ros/9bf840e3-7a39-41fc-be39-7eed9dc294db/crate/download/ 2021-03-11 12:48:21.251000+00:00 2025-03-05 01:14:11.720011+00:00 2021-03-11 12:48:21.251000+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/9bf840e3-7a39-41fc-be39-7eed9dc294db 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/9bf840e3-7a39-41fc-be39-7eed9dc294db.

Data Dataset Produced Metadata Used Raw Data Documentation Biblio https://data.psychosensing.psnc.pl/popane/index.html 2021-03-11 12:48:21.251000+00:00 2022-03-24 13:25:15.335097+00:00 text/html https://data.psychosensing.psnc.pl/popane/index.html 2021-03-11 12:48:21.251000+00:00 service-account-generation-service https://w3id.org/ro-id/acfec1a0-48af-48c3-9083-3c1969a31c21 2021-03-11T15:18:55.068+01:00 https://orcid.org/0000-0002-4704-6802 https://w3id.org/ro-id/acfec1a0-48af-48c3-9083-3c1969a31c21 2021-03-11T14:49:03.095+01:00 https://orcid.org/0000-0002-2455-4556 https://data.psychosensing.psnc.pl/popane/ physiology recorded affect dataset impedance cardiography activity International Crisis Group electrocardiography emotions Szymon KupiÅ?ski service-account-enrichment http://sandbox.rohub.org/rodl/ROs/POPANE-snapshot-1/ http://sandbox.rohub.org/rodl/ROs/POPANE-snapshot/ 8763 https://api.rohub.org/api/ros/acfec1a0-48af-48c3-9083-3c1969a31c21/crate/download/ 2021-03-02 08:34:45.016000+00:00 2025-03-05 01:16:59.821378+00:00 2021-03-02 08:34:45.016000+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/acfec1a0-48af-48c3-9083-3c1969a31c21 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 02 ,2021. https://w3id.org/ro-id/acfec1a0-48af-48c3-9083-3c1969a31c21.

Data Documentation Metadata Produced Dataset Biblio Raw Data Used https://data.psychosensing.psnc.pl/popane/ 2021-03-02 08:34:45.016000+00:00 2022-03-24 13:26:12.310038+00:00 https://data.psychosensing.psnc.pl/popane/ 2021-03-02 08:34:45.016000+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 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-3.611394871468983 40.40969285491932, -3.611394871468983 40.53713623661004 ) ) -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 ) POLYGON((-3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252)) 3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252 96bbae64-10f2-4a61-b938-5442c43d10de POLYGON((-3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252)) d4fd55d2-65c2-4a0f-9de7-42f7dc0abba9 POLYGON( ( -3.611394871468983 40.53713623661004, -3.5125914250302666 40.53713623661004, -3.4137879785915506 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EU SatCen. "Change Detection Data Centric." ROHub. Jun 15 ,2018. https://doi.org/10.5072/ro-id.BPIH2F2WOA.

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EU SatCen. "Change Detection Data Centric." ROHub. May 08 ,2018. https://w3id.org/ro-id/9818f4df-2fcb-40d7-9ee9-a8165e49bc24.

software used produced config main nested setup datasets results inputs components web services scripts biblio workflows 11 https://api.rohub.org/api/resources/42062a92-9f2b-46e8-9280-fc339ddc549d/download/ 2018-05-10 10:50:29.452000+00:00 2022-03-24 20:00:49.857303+00:00 .txt Copyright.txt 2018-05-10 10:50:29.452000+00:00 ggg 143 https://api.rohub.org/api/resources/447b7d04-b0b5-45f8-8ec7-d99fb47c7f24/download/ 2018-05-10 08:09:44.546000+00:00 2022-03-24 20:00:48.516688+00:00 .txt Input-Master.txt 2018-05-10 08:09:44.546000+00:00 0 https://api.rohub.org/api/resources/749974f5-0d35-4592-9dd9-075fab6089b9/download/ 2018-05-10 08:21:25.852000+00:00 2022-03-24 20:00:46.169669+00:00 .txt definition.txt 2018-05-10 08:21:25.852000+00:00 4 https://api.rohub.org/api/resources/e49ed5ab-e921-41ff-bda3-4a65a44992ca/download/ 2018-05-10 08:19:07.994000+00:00 2022-03-24 20:00:47.432199+00:00 .txt workflow.txt 2018-05-10 08:19:07.994000+00:00 Madrid image 4.736419587904736 17.7 Detection 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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 centric 1.9542421353670159 4.1 geophysics 8.407055595076324 0.12798267602920532 oceanography 11.563625766334056 0.43228960037231445 change Detection over Madrid 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https://w3id.org/ro-id/c8e682e3-3074-452f-932b-421475d84b6f https://w3id.org/ro-id/e066ae93-85f5-4e78-aeea-90f10bee922d https://w3id.org/ro-id/f2fae24e-8399-4f86-bccd-46aa47425dba https://w3id.org/ro-id/f77bc238-32d3-4534-bfed-3b4e2a377c13 https://w3id.org/ro-id/0efa7763-8e6e-4506-8649-e841fd1742e3 https://w3id.org/ro-id/35aab066-7142-45bd-9816-df855fd2ea64 https://w3id.org/ro-id/b4cca1f7-e903-4d46-ba98-6265edbb0654 https://w3id.org/ro-id/e26db525-e083-4ebb-b031-805926c5b236 https://w3id.org/ro-id/4423743a-0a26-46ec-9d0d-d73bd415a117 https://w3id.org/ro-id/5de19391-22a4-4047-83d5-8f7873ed9894 https://w3id.org/ro-id/6db3021f-1f6b-433f-b3bb-603bf79bd6f7 https://w3id.org/ro-id/78164962-b18f-47a3-b993-f27d73bd32e2 https://w3id.org/ro-id/80526dac-95f1-4327-982c-3650a0f5329b https://w3id.org/ro-id/8a96e877-a037-4c08-935f-ffaadb1c45f9 https://w3id.org/ro-id/a8b8731d-ee4a-4be9-ae77-148ffc9fe995

EU SatCen. "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 Meteorology Applied sciences Ecology https://aqicn.org/map/warsaw/pl/ 2026-01-15 09:56:50.534689+00:00 2026-01-15 10:04:04.245960+00:00 Zanieczyszczenie powietrza w Warszawa Mapa wizualna jakości powietrza w czasie rzeczywistym. Zanieczyszczenie powietrza w Warszawa Mapa wizualna jakości powietrza w czasie rzeczywistym. 2026-01-15 09:56:50.534689+00:00 https://iot.warszawa.pl/ 2026-01-15 10:02:54.788955+00:00 2026-01-15 10:03:23.971993+00:00 Indeks Jakości Powietrza Sprawdź poziom jakości powietrza w swojej okolicy. Wskaż na mapie stację pomiarową, przejrzyj aktualne informacje o poziomie stężenia zanieczyszczeń i zapoznaj się z zaleceniami dotyczącymi ochrony Twojego zdrowia. Warszawska Platforma IoT 2026-01-15 10:02:54.788955+00:00 21.007713326253 52.234864715699715 POINT (21.007713326253 52.234864715699715) 912ad5e5-ed9f-46f5-b4d8-491bd4113270 POINT (21.007713326253 52.234864715699715) 0 https://api.rohub.org/api/ros/d006ed2d-2fa9-438d-b830-a7d4aef81469/crate/download/ 2026-01-15 09:36:30.138049+00:00 2026-04-11 03:22:17.313678+00:00 2026-01-15 09:36:30.138049+00:00 Projekt analizuje jakość powietrza w Warszawie, koncentrując się na wartości stężeń pyłów zawieszonych PM2.5 i PM10 oraz ich wpływie na zdrowie ludzi. W ramach projektu gromadzone są raporty i dane pomiarowe z lokalnych narzędzi monitoringu, a następnie są one porównywane z normami Światowej Organizacji Zdrowia (WHO). Badanie uwzględnia różne dni, pory dnia i miejsca pomiarów na terenie całego miasta Warszawy oraz identyfikuje możliwe przyczyny i skutki przekroczenia dopuszczalnych poziomów zanieczyszczeń. application/ld+json https://w3id.org/ro-id/d006ed2d-2fa9-438d-b830-a7d4aef81469 Air Quality Environment Monitoring PM10 PM2.5 Warsaw Dataset Jakość powietrza w Warszawie — analiza stężeń PM2.5 i PM10 oraz ich przekroczeń MANUAL Janek Gębicki

Gębicki, Janek, and Janek Gębicki. "Jakość powietrza w Warszawie — analiza stężeń PM2.5 i PM10 oraz ich przekroczeń." ROHub. Jan 15 ,2026. https://w3id.org/ro-id/d006ed2d-2fa9-438d-b830-a7d4aef81469.

POINT (21.007713326253 52.234864715699715) biblio data raw data metadata 6205 https://api.rohub.org/api/resources/25295fee-4813-4aaf-ac4b-fb60c693f3a6/download/ 2026-01-15 10:08:30.311996+00:00 2026-01-15 10:08:32.429209+00:00 Dane symulowane, wygenerowane na potrzeby projektu edukacyjnego. Nie przedstawiają rzeczywistych pomiarów, ale odzwierciedlają realistyczne trendy sezonowe i dobowe jakości powietrza w Warszawie. application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Jakość powietrza Warszawa - dane 2026-01-15 10:08:30.311996+00:00 364153 https://api.rohub.org/api/resources/28481e37-3b7d-463e-aa51-da710e432904/download/ 2026-01-15 09:50:01.944724+00:00 2026-01-15 09:50:04.071778+00:00 Ocena jakości powietrza jest bardzo złożonym zagadnieniem, na które wpływa bardzo wiele czynników natury środowiskowej jak i antropogenicznej. Aby określić czy, a jeśli tak to, w jaki sposób pandemia przełożyła się na jakość powietrza w Warszawie, należy wyodrębnić główne czynniki, które przyczyniają się do zmian poziomu zanieczyszczenia powietrza. application/pdf Air Quality PM2.5 Jakość powietrza - raport COVIDOVY 2026-01-15 09:50:01.944724+00:00 36906127 https://api.rohub.org/api/resources/98fe4e3c-4b90-4e25-9752-c314a6cb3938/download/ 2026-01-15 09:51:41.659860+00:00 2026-01-15 09:51:44.977381+00:00 Celem niniejszego raportu jest prezentacja danych z pomiarów stężenia pyłu zawieszonego PM2,5 zebranych w ramach projektu Poszukiwacze Powietrza, zainicjowanego przez Warszawski Alarm Smogowy przy udziale Partnerów w 2019 r. Jego celem jest upowszechnianie wiedzy o skali i źródłach zanieczyszczenia powietrza w Warszawie dzięki rozbudowie sieci obywatelskich czujników smogu Sensor Community (S.C.). Czujniki dokonują pomiarów stężenia pyłu zawieszonego odpowiednio o średnicy nie większej niż 10 i 2,5 mm. Zebrane dane są publicznie dostępne, pozwalając na lepsze zrozumienie problemu zanieczyszczenia powietrza. application/pdf Air Quality PM10 PM2.5 ANALIZA ZANIECZYSZCZENIA POWIETRZA PYŁEM ZAWIESZONYM PM2,5 W WARSZAWIE Z WYKORZYSTANIEM SIECI OBYWATELSKICH CZUJNIKÓW SMOGU 2026-01-15 09:51:41.659860+00:00 43507 https://api.rohub.org/api/resources/e3dd7d26-f36f-4a82-b4f0-f8d1f081502c/download/ 2026-01-15 09:45:53.134501+00:00 2026-01-15 10:04:13.680897+00:00 image/jpeg zanieczyszczenie.jpg 2026-01-15 09:45:53.134501+00:00 Key Type Measures Aerospace medicine Space sciences (General) Geographical Scope Identification of risks Climate Hazard Not reported/ Unknown Astronomy none Physical Sciences Methodology Funding Life sciences Physical and Technological Theoretical and Computational Chemistry Astronautics User Needs (RAST) Stakeholders Physics Mathematical Sciences Systemic Literature Review Portugal Quantum Physics Astronautics (General) Physics (General) none IPCC Local policy Mathematical Physics Policy Scale Structural/physical: Ecosystem-based Knowledge Sector (EEA) Climate-ADAPT Adaptation Sectors Individuals or citizens Chemical Sciences Space sciences Other Physical Sciences Climate change mitigation: reducing emissions j.gebicki@student.uw.edu.pl Janek Gębicki Earth sciences Climatology https://doi.org/10.5281/zenodo.19112545 2026-03-20 13:30:17.698601+00:00 2026-03-20 13:30:20.023982+00:00 Floodlevels in increments of 10 cm ranging from 30 cm to 100cm. Hamburg Floodlevels 2026-03-20 13:30:17.698601+00:00 0 https://api.rohub.org/api/ros/24165a93-ac0d-46ef-98a7-046e6d5a287e/crate/download/ 2026-03-20 13:26:33.130248+00:00 2026-03-27 10:38:55.226794+00:00 2026-03-20 13:26:33.130248+00:00 Floodlevels in increments of 10 cm ranging from 30 cm to 100cm. application/ld+json https://w3id.org/ro-id/24165a93-ac0d-46ef-98a7-046e6d5a287e Hamburg flood levels pluvial flood risk Hamburg Floodlevels MANUAL

GONZALEZ GUARDIA, ESTEBAN. "Hamburg Floodlevels." ROHub. Mar 20 ,2026. https://w3id.org/ro-id/24165a93-ac0d-46ef-98a7-046e6d5a287e.

biblio data metadata raw data Flooding increase 100.0 85.1 Key Type Measures Data on climate-relate hazards Local policy Physics Water management Government/ Public Sector IPCC Extreme weather: floods, droughts, heatwaves Stakeholders increment 87.18718718718719 87.1 Fluid mechanics and thermodynamics Knowledge Sector (EEA) Structural/physical: Engineered and built environments Climate Hazard Climate-ADAPT Adaptation Sectors Engineering National government agencies User Needs (RAST) European Continent Geographical Scope Methodology Mathematical Physics Mathematical Sciences Physical and Technological Scenario Analysis Policy Scale Hamburg Floodlevels Floodlevels in increments of 10 cm ranging from 30 cm to 100cm. 100.0 100.0 Hamburg Floodlevels Floodlevels in increment 100.0 100.0 Funding Physics (General) Other Mathematical Sciences Hamburg Floodlevels Floodlevels 12.812812812812814 12.8 ESTEBAN GONZALEZ GUARDIA Earth sciences https://doi.org/10.5281/zenodo.19125517 2026-03-21 12:45:25.444387+00:00 2026-03-21 12:45:27.225023+00:00 Data of the street outlines in the city of Hamburg. Hamburg Street Data 2026-03-21 12:45:25.444387+00:00 861d719d-fe10-4b8e-a274-c2688593b709 POINT (9.994812011718752 53.57293832648609) 9.994812011718752 53.57293832648609 POINT (9.994812011718752 53.57293832648609) d8f03ff1-8ad1-455a-89d8-8bfe450285e0 POLYGON ((9.819030761718752 53.49294782332984, 9.819030761718752 53.63405351645887, 10.206298828125002 53.63405351645887, 10.206298828125002 53.49294782332984, 9.819030761718752 53.49294782332984)) POLYGON ((9.819030761718752 53.49294782332984, 9.819030761718752 53.63405351645887, 10.206298828125002 53.63405351645887, 10.206298828125002 53.49294782332984, 9.819030761718752 53.49294782332984)) 9.819030761718752 53.49294782332984, 9.819030761718752 53.63405351645887, 10.206298828125002 53.63405351645887, 10.206298828125002 53.49294782332984, 9.819030761718752 53.49294782332984 0 https://api.rohub.org/api/ros/1884b780-507e-4447-975c-87b970c5b503/crate/download/ 2026-03-21 12:43:13.627334+00:00 2026-03-23 09:46:24.296804+00:00 2026-03-21 12:43:13.627334+00:00 Data of the street outlines in the city of Hamburg. application/ld+json https://w3id.org/ro-id/1884b780-507e-4447-975c-87b970c5b503 pluvial flood risk street data Hamburg Street Data MANUAL

GONZALEZ GUARDIA, ESTEBAN. "Hamburg Street Data." ROHub. Mar 21 ,2026. https://w3id.org/ro-id/1884b780-507e-4447-975c-87b970c5b503.

POINT (9.994812011718752 53.57293832648609) POLYGON ((9.819030761718752 53.49294782332984, 9.819030761718752 53.63405351645887, 10.206298828125002 53.63405351645887, 10.206298828125002 53.49294782332984, 9.819030761718752 53.49294782332984)) metadata data raw data biblio city of Hamburg 35.87174348697395 35.8 Geosciences (General) Data on climate street 34.62157809983897 21.5 General Land use planning Key Type Measures Local policy Stakeholders National government agencies Hamburg 26.409017713365536 16.4 Hamburg 17.217217217217218 17.2 Systemic Literature Review street 19.91991991991992 19.9 city 38.969404186795494 24.2 Funding outline in the city of Hamburg 0.10020040080160321 0.1 Land use Methodology none General Climate Hazard User Needs (RAST) Engineering (General) Hamburg Street Data Data of the street 62.324649298597194 62.2 Physical and Technological IPCC Government/ Public Sector Climate-ADAPT Adaptation Sectors Hamburg Street Data Data 39.33933933933934 39.3 Hamburg Street Data Data of the street outlines in the city of Hamburg. 100.0 100.0 Geosciences Knowledge Sector (EEA) Policy Scale Geographical Scope Structural/physical: Engineered and built environments Engineering Hamburg Hamburg Street Data Data outline in the city 1.7034068136272547 1.7 European Continent city 23.523523523523526 23.5 ESTEBAN GONZALEZ GUARDIA Earth sciences https://doi.org/10.5281/zenodo.19113146 2026-03-21 12:52:54.126993+00:00 2026-03-21 12:52:55.351508+00:00 Data on the building level containing the number of inhabitants, the building type and the number of floors for each building. Furthermore, each building is assigned to its Statistical Unit (=Urban District) and the corresponding social vulnerability data. Hamburg: Preprocessed Data on the Building Level 2026-03-21 12:52:54.126993+00:00 POLYGON ((9.830017089843752 53.5117346755535, 9.830017089843752 53.63812471860769, 10.128021240234377 53.63812471860769, 10.128021240234377 53.5117346755535, 9.830017089843752 53.5117346755535)) 9.830017089843752 53.5117346755535, 9.830017089843752 53.63812471860769, 10.128021240234377 53.63812471860769, 10.128021240234377 53.5117346755535, 9.830017089843752 53.5117346755535 e3a4512b-43a9-47aa-ae82-56f6151449ac POLYGON ((9.830017089843752 53.5117346755535, 9.830017089843752 53.63812471860769, 10.128021240234377 53.63812471860769, 10.128021240234377 53.5117346755535, 9.830017089843752 53.5117346755535)) 0 https://api.rohub.org/api/ros/6984727e-5804-4de7-98cf-36068c22c426/crate/download/ 2026-03-21 12:50:10.527429+00:00 2026-04-11 03:16:51.462372+00:00 2026-03-21 12:50:10.527429+00:00 Data on the building level containing the number of inhabitants, the building type and the number of floors for each building. Furthermore, each building is assigned to its Statistical Unit (=Urban District) and the corresponding social vulnerability data. application/ld+json https://w3id.org/ro-id/6984727e-5804-4de7-98cf-36068c22c426 Hamburg building level pluvial flood risk social vulnerability Hamburg: Preprocessed Data on the Building Level MANUAL

GONZALEZ GUARDIA, ESTEBAN. "Hamburg: Preprocessed Data on the Building Level." ROHub. Mar 21 ,2026. https://w3id.org/ro-id/6984727e-5804-4de7-98cf-36068c22c426.

POLYGON ((9.830017089843752 53.5117346755535, 9.830017089843752 53.63812471860769, 10.128021240234377 53.63812471860769, 10.128021240234377 53.5117346755535, 9.830017089843752 53.5117346755535)) biblio data raw data metadata Mathematical and computer sciences Structural/physical: Engineered and built environments Mathematical and computer sciences (general) building 23.508771929824565 20.1 Preparing the ground Methodology data on the Building Level Data 5.864197530864197 5.7 Geosciences Engineering vulnerability 8.304093567251462 7.1 Funding data 23.21243523316062 22.4 Statistics and probability vulnerability data 27.469135802469136 26.7 Engineering (General) Buildings and construction Climate-ADAPT Adaptation Sectors User Needs (RAST) Policy Scale IPCC Climate Hazard Buildings Construction and property Economy, business and finance/Economic sector/Construction and property building level 33.74485596707818 32.8 Environmental Sciences Hamburg 10.409356725146198 8.9 floor 10.673575129533678 10.3 Hamburg Statistics construction industry 52.517985611510795 7.3 Physical and Technological Portugal Stakeholders building 20.621761658031083 19.9 building type 23.25102880658436 22.6 Hamburg 8.290155440414507 8.0 Key Type Measures Geosciences (General) Mathematical Sciences Housing and urban planning policy Politics/Government policy/Interior policy/Housing and urban planning policy exposure 6.943005181347149 6.7 Systemic Literature Review Furthermore, each building is assigned to its Statistical Unit (=Urban District) and the corresponding social vulnerability data. 46.346346346346344 46.3 Regional policy floor 11.345029239766081 9.7 statistical unit 5.595854922279792 5.4 data 26.783625730994153 22.9 none Government/ Public Sector General Other Environmental Sciences Hamburg: Preprocessed Data on the Building Level Data on the building level containing the number of inhabitants, the building type and the number of floors for each building. 53.65365365365365 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EU SatCen. "Change Detection Data Centric." ROHub. Jun 15 ,2018. https://doi.org/10.5072/ro-id.8EOHX9TR1W.

web services biblio software config inputs used datasets results setup produced main workflows nested scripts components ggg 143 https://api.rohub.org/api/resources/23905c1b-ff81-462d-96b7-f84dd0d8f67c/download/ 2018-05-10 08:09:44.546000+00:00 2022-03-24 19:49:05.651383+00:00 .txt Input-Master.txt 2018-05-10 08:09:44.546000+00:00 4 https://api.rohub.org/api/resources/4e5b6eb7-44ae-4a52-b460-d8eb4a0bbc87/download/ 2018-05-10 08:19:07.994000+00:00 2022-03-24 19:49:10.882785+00:00 .txt workflow.txt 2018-05-10 08:19:07.994000+00:00 11 https://api.rohub.org/api/resources/7a54c4a5-90a6-4190-a019-d993bbb78a5d/download/ 2018-05-10 10:50:29.452000+00:00 2022-03-24 19:49:07.459671+00:00 .txt Copyright.txt 2018-05-10 10:50:29.452000+00:00 0 https://api.rohub.org/api/resources/ec8ff65d-ecd9-4571-ab9d-a273b506eb73/download/ 2018-05-10 08:21:25.852000+00:00 2022-03-24 19:49:09.555074+00:00 .txt definition.txt 2018-05-10 08:21:25.852000+00:00 URI: http://box.everest.psnc.pl:8000/f/aa333acec2/ 4.928696522391794 19.7 earth sciences 26.214273292711976 0.9799830913543701 Change Detection over Madrid 10.28271203402552 41.1 geology 17.014721850579107 0.6360710263252258 Madrid 6.208188386406208 23.2 space sciences 3.3287645856718493 0.05067460238933563 Change Detection Data Centric. 14.711033274956218 58.8 earth resources and remote sensing 50.727399932313034 0.7722356915473938 atmospheric sciences 26.214273292711976 0.9799830913543701 S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip 16.135937918116138 60.3 earth sciences 26.632741500408624 0.9956269264221191 Madrid 11.1534795042898 23.4 change Detection 17.55877938969485 35.1 image 13.203050524308864 27.7 atmospheric sciences 18.57463758996624 0.6943862438201904 Detection over Madrid 31.715857928964482 63.4 earth sciences 17.014721850579107 0.6360710263252258 oceanography 11.563625766334056 0.43228960037231445 Satcen 2018 25.018764073054793 100.0 information 15.014299332697806 31.5 Madrid image 4.736419587904736 17.7 expert 2.573879885605338 5.4 http 4.242135367016206 8.9 Satcen 26.75943270002676 100.0 change Detection over Madrid 0.7003501750875437 1.4 earth sciences 18.57463758996624 0.6943862438201904 earth resources and remote sensing 22.1282807437931 0.33686426281929016 S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip 14.360770577933451 57.4 geosciences 8.407055595076324 0.12798267602920532 geology 26.632741500408624 0.9956269264221191 test 26.75943270002676 100.0 geosciences 50.727399932313034 0.7722356915473938 centric 1.9542421353670159 4.1 Detection 10.917848541610917 40.8 service-account-enrichment service-account-generation-service Geophysics Elisa Trasatti Document geodetic data at Campi Flegrei (Italy) IREAINGV_ 2018-02-26T15:49:49.800+01:00 http://everest.psnc.pl/users/elisa.trasatti https://w3id.org/ro-id/57294633-c17e-40d2-a2c4-408f81e4c72e Campi Flegrei ascii file GPS data American Standard Code for Information Interchange data file caldera Ascii dataset png image InSAR data GPS Italy image gamma software telecommunications GPS data from INGV file gamma software Italy SAR interferometry service-account-enrichment http://sandbox.rohub.org/rodl/ROs/InSAR_GPS_Campi_Fregrei_2011_2013-release/ http://rohub.org/performedtasks/26048903/ 1648739 https://api.rohub.org/api/ros/57294633-c17e-40d2-a2c4-408f81e4c72e/crate/download/ http://rohub.org/users/portal/26975763/ 2017-12-13 20:45:37.381000+00:00 2025-03-05 00:55:15.411045+00:00 2017-12-13 20:45:37.381000+00:00 This Research Object contains the InSAR data (COSMO-Skymed ascending and descending orbits) and GPS data from INGV related to the Campi Flegrei caldera during 2011-2013. The dataset was processed with GAMMA software and was subsampled with step 100m-150m. Ascii file and png images are stored. application/ld+json https://w3id.org/ro-id/57294633-c17e-40d2-a2c4-408f81e4c72e InSAR and GPS data of the 2011-2013 unrest at Campi Flegrei (Italy) Open Elisa Trasatti

Elisa Trasatti. "InSAR and GPS data of the 2011-2013 unrest at Campi Flegrei (Italy)." ROHub. Dec 13 ,2017. https://w3id.org/ro-id/57294633-c17e-40d2-a2c4-408f81e4c72e.

Dataset Data Documentation Metadata Biblio Used Raw Data Produced 81 https://api.rohub.org/api/resources/1c9749bd-fcb1-4104-8a8a-541ece5c3e9f/download/ 2017-12-14 14:56:48.154000+00:00 2022-03-24 20:58:05.484914+00:00 PNG SAR_RESULTS_DSC_SAR_RESULTS_OBS_col.pngw 2017-12-14 14:56:48.154000+00:00 http://onlinelibrary.wiley.com/doi/10.1002/2015GL063621/full 2017-12-13 20:45:37.381000+00:00 2022-03-24 20:58:00.348297+00:00 http://onlinelibrary.wiley.com/doi/10.1002/2015GL063621/full 2017-12-13 20:45:37.381000+00:00 661 https://api.rohub.org/api/resources/500f201c-039e-4970-9c7a-66b6ef37aa2e/download/ 2017-12-14 14:49:46.677000+00:00 2022-03-24 20:58:03.676983+00:00 text ASC-DSC-disp.rtf 2017-12-14 14:49:46.677000+00:00 318333 https://api.rohub.org/api/resources/7c707754-def5-452a-b307-c7b43ae56d83/download/ 2017-12-14 14:48:37.059000+00:00 2022-03-24 20:58:01.567160+00:00 ascii obs_sar.dat 2017-12-14 14:48:37.059000+00:00 1966 https://api.rohub.org/api/resources/80af1036-e303-45c5-b173-d659759cc182/download/ 2017-12-14 15:01:16.873000+00:00 2022-03-24 20:58:06.570506+00:00 text Method.rtf 2017-12-14 15:01:16.873000+00:00 1575 https://api.rohub.org/api/resources/8464dc9b-fd91-478c-a86b-393a224fe366/download/ 2017-12-14 14:48:50.965000+00:00 2022-03-24 20:58:02.643427+00:00 ascii obs_gps.dat 2017-12-14 14:48:50.965000+00:00 3382 https://api.rohub.org/api/resources/907e93c1-28bb-4d5e-b87c-6adc4df0754c/download/ 2017-12-14 14:55:38.393000+00:00 2022-03-24 20:57:53.806993+00:00 PNG SAR_RESULTS_ASC_SAR_RESULTS_OBS_col.png 2017-12-14 14:55:38.393000+00:00 6038 https://api.rohub.org/api/resources/ced68f5f-fa02-4e8e-8263-5288b37e1819/download/ 2017-12-14 14:56:33.068000+00:00 2022-03-24 20:57:55.662736+00:00 PNG SAR_RESULTS_DSC_SAR_RESULTS_OBS_col.png 2017-12-14 14:56:33.068000+00:00 81 https://api.rohub.org/api/resources/d1fb025e-acb9-4c7d-bac6-4af94011530c/download/ 2017-12-14 14:56:17.431000+00:00 2022-03-24 20:58:04.577002+00:00 PNG SAR_RESULTS_ASC_SAR_RESULTS_OBS_col.pngw 2017-12-14 14:56:17.431000+00:00 1579356 https://api.rohub.org/api/resources/fe2d8782-3dd0-41bc-85c8-dcd6534ac3e9/download/ 2017-12-14 15:03:17.780000+00:00 2022-03-24 20:58:07.678371+00:00 image/png ASC_DSC_GPS.png 2017-12-14 15:03:17.780000+00:00 service-account-generation-service Geophysics Elisa Trasatti Docoument geodetic data at Campi Flegrei IREAINGV_ 2018-02-26T15:45:15.335+01:00 http://everest.psnc.pl/users/elisa.trasatti https://w3id.org/ro-id/02204bef-b3c3-448e-9d99-9685006f2ded memory descending orbit Campi Flegrei contain the InSAR data ascii file Research Object American Standard Code for Information Interchange data file Ascii dataset png image InSAR data Italy image InSAR data file ferment unrest Italy SAR interferometry service-account-enrichment http://sandbox.rohub.org/rodl/ROs/InSAR_Campi_Flegrei_2004_2006-release/ http://rohub.org/performedtasks/36071950/ 1037570 https://api.rohub.org/api/ros/02204bef-b3c3-448e-9d99-9685006f2ded/crate/download/ http://rohub.org/users/portal/26975763/ 2017-12-08 11:22:53.432000+00:00 2025-03-05 00:55:15.805905+00:00 2017-12-08 11:22:53.432000+00:00 This Research Object contains the InSAR data (ENVISAT ascending and descending orbits) at Campi Flegrei during 2004-2006. The dataset was processed with SBAS and is subsampled with step 100m-150m. Ascii file and png images are stored. application/ld+json https://w3id.org/ro-id/02204bef-b3c3-448e-9d99-9685006f2ded InSAR data of 2004-2006 unrest at Campi Flegrei (Italy) Open Elisa Trasatti

Elisa Trasatti. "InSAR data of 2004-2006 unrest at Campi Flegrei (Italy)." ROHub. Dec 08 ,2017. https://w3id.org/ro-id/02204bef-b3c3-448e-9d99-9685006f2ded.

Documentation Documentation Biblio Used Raw_Data Dataset documentation Dataset Produced ASCII 645 https://api.rohub.org/api/resources/4746a8d8-ff1e-4c14-b46d-7d2bf6057be5/download/ 2017-12-14 14:53:43.276000+00:00 2022-03-24 21:01:13.446478+00:00 text ASC-DSC-disp.rtf 2017-12-14 14:53:43.276000+00:00 81 https://api.rohub.org/api/resources/9244fff7-d17a-429a-b94d-4cb054ccf6d1/download/ 2017-12-08 12:06:26.426000+00:00 2022-03-24 21:01:11.321713+00:00 PNG SAR_RESULTS_ASC_SAR_RESULTS_OBS_col.pngw 2017-12-08 12:06:26.426000+00:00 4209 https://api.rohub.org/api/resources/c27e32c2-4673-489f-86d9-3ce85fc8a423/download/ 2017-12-08 12:06:12.180000+00:00 2022-03-24 21:01:01.262842+00:00 PNG SAR_RESULTS_ASC_SAR_RESULTS_OBS_col.png 2017-12-08 12:06:12.180000+00:00 1539 https://api.rohub.org/api/resources/d0d538e7-fb88-4ad6-8978-89ea608d0e53/download/ 2017-12-08 11:44:13.062000+00:00 2022-03-24 21:01:09.493808+00:00 ASCII Method.rtf 2017-12-08 11:44:13.062000+00:00 http://onlinelibrary.wiley.com/doi/10.1029/1998GL900033/abstract 2017-12-08 11:22:53.432000+00:00 2022-03-24 21:01:07.449755+00:00 http://onlinelibrary.wiley.com/doi/10.1029/1998GL900033/abstract 2017-12-08 11:22:53.432000+00:00 4080 https://api.rohub.org/api/resources/d8f704f1-fa16-4d64-9945-8293c984735c/download/ 2017-12-08 12:06:44.114000+00:00 2022-03-24 21:01:04.042057+00:00 PNG SAR_RESULTS_DSC_SAR_RESULTS_OBS_col.png 2017-12-08 12:06:44.114000+00:00 1013108 https://api.rohub.org/api/resources/df95e927-b75c-4ba2-8f17-e6870dee97d8/download/ 2017-12-08 11:38:47.295000+00:00 2022-03-24 21:01:08.632536+00:00 image/png ASC_DSC.png 2017-12-08 11:38:47.295000+00:00 81 https://api.rohub.org/api/resources/e4b9a950-0112-4f30-9ede-84c489589309/download/ 2017-12-08 12:07:00.217000+00:00 2022-03-24 21:01:12.343097+00:00 PNG SAR_RESULTS_DSC_SAR_RESULTS_OBS_col.pngw 2017-12-08 12:07:00.217000+00:00 http://onlinelibrary.wiley.com/doi/10.1029/2007GL033091/abstract 2017-12-08 11:22:53.432000+00:00 2022-03-24 21:01:07.519588+00:00 http://onlinelibrary.wiley.com/doi/10.1029/2007GL033091/abstract 2017-12-08 11:22:53.432000+00:00 service-account-generation-service Elisa Trasatti LOADING M. Polcari mathematics deformation velocity vector Riccardo Lanari phase pattern Institute of Electrical and Electronics Engineers physics decorrelation phenomena velocity component deformations phase signal pixel signal subsets results phase artifact Baseline norm episode phase Italy Section technique 1992-2010 displacement 9.29705215419501 8.2 Colli Albani 1.0070493454179255 1.0 volcanic area 20.365168539325843 14.5 cumulate displacements in ascending 0.6042296072507553 0.6 cumulate displacement 9.164149043303123 9.1 volcanic area 16.666666666666668 14.7 ERS 11.791383219954648 10.4 displacement 11.516853932584267 8.2 dataset 17.346938775510203 15.3 satellite 15.079365079365079 13.3 Italy https://www.wikidata.org/wiki/Q38 satellite 18.820224719101123 13.4 engineering 100.0 0.6484560966491699 earth sciences 100.0 0.6677627563476562 contain cumulate displacement 0.5035246727089627 0.5 geology 100.0 0.6677627563476562 Satellite technology Economy, business and finance/Economic sector/Computing and information technology/Satellite technology This dataset contains cumulate displacements in Ascending and Descending Line of Sights from ERS/Envisat satellites during 1992-2010 at Colli Albani (Italy), a volcanic area close to Rome. 92.29229229229229 92.2 envisat satellite 88.72104733131924 88.1 Italy 17.977528089887638 12.8 Rome 10.674157303370785 7.6 Italy 14.399092970521542 12.7 during 1992-2010 Rome https://www.wikidata.org/wiki/Q220 ENVISAT 15.419501133786847 13.6 Colli Albani (Italy) InSAR Data 1992-2010. 7.707707707707708 7.7 dataset 20.646067415730336 14.7 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware communications and radar 100.0 0.6484560966491699 astronautics 100.0 2.6 service-account-enrichment 10.5072/ro-id.X2XTIJE5PA 2017-10-23T09:23:26.566+02:00 http://everest.psnc.pl/users/elisa.trasatti http://sandbox.rohub.org/rodl/ROs/Colli_Albani_InSAR_1992_2010/ http://rohub.org/performedtasks/75935672/ 339289 https://api.rohub.org/api/ros/f29e9cb2-2c95-4db8-af48-13d6bb5fe2b5/crate/download/ INGV 2017-10-23 07:23:26.566000+00:00 2025-03-05 00:46:59.559997+00:00 2017-10-23 07:23:26.566000+00:00 This dataset contains cumulate displacements in Ascending and Descending Line of Sights from ERS/Envisat satellites during 1992-2010 at Colli Albani (Italy), a volcanic area close to Rome. application/ld+json https://w3id.org/ro-id/f29e9cb2-2c95-4db8-af48-13d6bb5fe2b5 Colli Albani (Italy) InSAR Data 1992-2010 open access E. Trasatti https://w3id.org/ro-id/eefec9f5-bb20-48ee-974a-e641c683983f https://w3id.org/ro-id/59ad65f2-068a-4130-9367-1041453c0b2a https://w3id.org/ro-id/bc3225f0-68f1-482d-95b3-aa7a905c421c https://w3id.org/ro-id/1de087e1-140b-4478-b01f-206f0d62ec4f https://w3id.org/ro-id/38f0fcc7-19aa-438b-8af3-52bf1f03d1fe https://w3id.org/ro-id/5b68b054-40da-4793-9c97-e55ff246384d https://w3id.org/ro-id/ad99b684-8ee6-4a3a-89b4-d801ee3eb53f https://w3id.org/ro-id/b3af6844-0fa4-4604-90e3-5bad78c891d0 https://w3id.org/ro-id/d4ba00a6-c3b2-42ff-a9a4-6a15ba880641 https://w3id.org/ro-id/68ffdeff-678a-4b96-b838-28b07d59338d https://w3id.org/ro-id/8f77e33d-a7ef-4132-a793-bf81286330e3 https://w3id.org/ro-id/95248884-51fe-4f0e-baa9-c8e9e4e13dab https://w3id.org/ro-id/e8504614-fdd8-43f9-89a4-5bf7dd7f17bd https://w3id.org/ro-id/0b8583f3-050c-43e5-b5ac-48fac44b8e30 https://w3id.org/ro-id/253bc09f-d50e-4f16-9027-6082c1f238ae https://w3id.org/ro-id/344db0bb-2d23-493c-9ee5-7a4139139cf8 https://w3id.org/ro-id/44999bcd-c856-4488-8321-8d3eaf7310cc https://w3id.org/ro-id/4cbb2335-d38f-4bf5-9b73-21689c7bdce6 https://w3id.org/ro-id/b9374aba-7097-4147-bfa4-789e7f52efe8 https://w3id.org/ro-id/cd159223-b3a1-4472-a1f7-307b07146add https://w3id.org/ro-id/649052c9-0027-4318-854b-938b6493476f https://w3id.org/ro-id/eeb0086c-f767-4732-9b80-4b9a94c594ab https://w3id.org/ro-id/1b2f0a0c-eb39-46d4-81bb-e513430c2c8b https://w3id.org/ro-id/208756b4-b33a-46a3-8154-c2644291ea11 https://w3id.org/ro-id/20deb0b8-8195-42ad-80ed-d795d87be8d8 https://w3id.org/ro-id/730b8665-feba-474d-8ff0-24512d846197 https://w3id.org/ro-id/aa145f30-3902-48ba-b982-d2dabd629936 https://w3id.org/ro-id/a3cadfa8-a86b-49ce-9c28-b9b0f0c1b926 https://w3id.org/ro-id/d3f35e50-15da-43ca-b8a0-45e7af617403 https://w3id.org/ro-id/0ae1a3a8-fc54-46a0-b575-da7d053620d6 https://w3id.org/ro-id/b98d7a85-2361-4da4-9f1e-9fa25e102c7d

Elisa Trasatti. "Colli Albani (Italy) InSAR Data 1992-2010." ROHub. Oct 23 ,2017. https://doi.org/10.5072/ro-id.X2XTIJE5PA.

Used Metadata Produced Dataset Biblio Documentation Raw Data Data 78336 https://api.rohub.org/api/resources/07ebd403-715b-45c8-ba07-66a1fd09492e/download/ 2017-10-22 16:21:50.378000+00:00 2022-03-25 15:14:02.732573+00:00 Excel file containing the list of the ERS ENVISAT ascending and descending data used for the time-series analysis. excel file List of ERS ENVISAT data 2017-10-22 16:21:50.378000+00:00 229897 https://api.rohub.org/api/resources/2be5dbbf-ba61-4cfb-81db-089e3ba50caa/download/ 2017-10-22 17:09:12.906000+00:00 2022-03-25 15:13:59.686497+00:00 image/jpeg asc-dsc.jpg 2017-10-22 17:09:12.906000+00:00 https://pdfs.semanticscholar.org/b89d/ad1cc6b319f9d98887902c4a2d58426b3914.pdf 2022-03-25 15:13:40.224034+00:00 2022-03-25 15:13:57.775477+00:00 application/pdf Radar interferogram filtering for geophysical applications 2022-03-25 15:13:40.224034+00:00 420 KB 421932 https://api.rohub.org/api/resources/4199574c-1ddd-44df-ba60-bcb5d2361ca0/download/ 2017-10-22 16:43:52.767000+00:00 2022-03-25 15:13:55.684347+00:00 Ascending component. ASCII ASC-300-disp-R16.dat 2017-10-22 16:43:52.767000+00:00 360 KB 364812 https://api.rohub.org/api/resources/5577efde-dfa4-4a9e-a405-0bd00469a0b5/download/ 2017-10-22 16:46:14.095000+00:00 2022-03-25 15:14:01.870106+00:00 Descending component. ASCII DSC-300-disp-R16.dat 2017-10-22 16:46:14.095000+00:00 2684 https://api.rohub.org/api/resources/64de4fc2-37df-4491-bf30-87898bdaf714/download/ 2017-10-22 16:40:03.563000+00:00 2022-03-25 15:13:53.428529+00:00 text method.rtf 2017-10-22 16:40:03.563000+00:00 http://onlinelibrary.wiley.com/doi/10.1029/1998GL900033/abstract 2022-03-25 15:13:40.224374+00:00 2022-03-25 15:14:00.869323+00:00 SBAS Algorithm 2022-03-25 15:13:40.224374+00:00 701 https://api.rohub.org/api/resources/800d2bfb-8546-4ed0-889d-e69708b50064/download/ 2017-10-22 16:42:43.069000+00:00 2022-03-25 15:13:52.423243+00:00 text Metadata associated to the ASC and DSC data files 2017-10-22 16:42:43.069000+00:00 http://ieeexplore.ieee.org/document/1295516/?reload=true 2022-03-25 15:13:40.223593+00:00 2022-03-25 15:14:02.815395+00:00 Interferometric point target analysis for deformation mapping 2022-03-25 15:13:40.223593+00:00 service-account-generation-service Elisa Trasatti M. Polcari mathematics deformation velocity vector Riccardo Lanari phase pattern Institute of Electrical and Electronics Engineers physics decorrelation phenomena velocity component deformations phase signal pixel signal subsets results phase artifact Baseline norm episode phase Italy Section technique service-account-enrichment 336324 https://api.rohub.org/api/ros/c313affd-5afd-4981-9a70-9d76470ab3ca/crate/download/ INGV 2017-10-18 13:18:18.157000+00:00 2025-03-05 00:46:59.798483+00:00 2017-10-18 13:18:18.157000+00:00 This dataset contains cumulate displacements in Ascending and Descending Line of Sights from ERS/Envisat satellites during 1992-2010 at Colli Albani (Italy), a volcanic area close to Rome. application/ld+json https://w3id.org/ro-id/c313affd-5afd-4981-9a70-9d76470ab3ca Colli Albani (Italy) InSAR Data 1992-2010 Italy Rome dataset displacement satellite volcanic area earth sciences Hardware Satellite technology ENVISAT ERS Italy dataset displacement satellite volcanic area engineering Colli Albani contain cumulate displacement cumulate displacement cumulate displacements in ascending envisat satellite Colli Albani (Italy) InSAR Data 1992-2010. This dataset contains cumulate displacements in Ascending and Descending Line of Sights from ERS/Envisat satellites during 1992-2010 at Colli Albani (Italy) a volcanic area close to Rome. 1992-2010 during 1992-2010 E. Trasatti astronautics Italy Rome

Elisa Trasatti. "Colli Albani (Italy) InSAR Data 1992-2010." ROHub. Oct 18 ,2017. https://w3id.org/ro-id/c313affd-5afd-4981-9a70-9d76470ab3ca.

Metadata Documentation Used Dataset Produced Data Raw Data Biblio http://onlinelibrary.wiley.com/doi/10.1029/1998GL900033/abstract 2017-10-18 13:18:18.157000+00:00 2022-03-25 15:14:38.810276+00:00 SBAS Algorithm 2017-10-18 13:18:18.157000+00:00 78336 https://api.rohub.org/api/resources/3a2fa6e9-91b5-4212-b4e7-2fbe037ed9b1/download/ 2017-10-22 16:21:50.378000+00:00 2022-03-25 15:14:38.514111+00:00 Excel file containing the list of the ERS ENVISAT ascending and descending data used for the time-series analysis. excel file List of ERS ENVISAT data 2017-10-22 16:21:50.378000+00:00 701 https://api.rohub.org/api/resources/640f5e73-82d8-4138-b34e-a8cebe8e93cc/download/ 2017-10-22 16:42:43.069000+00:00 2022-03-25 15:14:41.487649+00:00 text Metadata associated to the ASC and DSC data files 2017-10-22 16:42:43.069000+00:00 2684 https://api.rohub.org/api/resources/6f0a6796-efd2-414d-8b4d-add3efa5038e/download/ 2017-10-22 16:40:03.563000+00:00 2022-03-25 15:14:39.809544+00:00 text method.rtf 2017-10-22 16:40:03.563000+00:00 https://pdfs.semanticscholar.org/b89d/ad1cc6b319f9d98887902c4a2d58426b3914.pdf 2017-10-18 13:18:18.157000+00:00 2022-03-25 15:14:38.874393+00:00 application/pdf Radar interferogram filtering for geophysical applications 2017-10-18 13:18:18.157000+00:00 360 KB 364812 https://api.rohub.org/api/resources/82e6a484-0c7d-4a91-ad57-a1c829e21125/download/ 2017-10-22 16:46:14.095000+00:00 2022-03-25 15:14:46.213573+00:00 Descending component. ASCII DSC-300-disp-R16.dat 2017-10-22 16:46:14.095000+00:00 420 KB 421932 https://api.rohub.org/api/resources/8a914aa7-906d-4b94-9cab-c088fd7a50ee/download/ 2017-10-22 16:43:52.767000+00:00 2022-03-25 15:14:45.143842+00:00 Ascending component. ASCII ASC-300-disp-R16.dat 2017-10-22 16:43:52.767000+00:00 229897 https://api.rohub.org/api/resources/8b3532ff-6894-44fc-ba49-510c37f3ac71/download/ 2017-10-22 17:09:12.906000+00:00 2022-03-25 15:14:44.223626+00:00 image/jpeg asc-dsc.jpg 2017-10-22 17:09:12.906000+00:00 http://ieeexplore.ieee.org/document/1295516/?reload=true 2017-10-18 13:18:18.157000+00:00 2022-03-25 15:14:38.949804+00:00 Interferometric point target analysis for deformation mapping 2017-10-18 13:18:18.157000+00:00 service-account-generation-service Applied sciences Earth sciences service-account-enrichment 2022-05-12 10:06:01.082494+00:00 https://orcid.org/0000-0002-2983-045X https://w3id.org/ro-id/03c34c87-6a8d-4ef1-a22e-67e96d52b607 True 2032004 https://api.rohub.org/api/ros/261f85b0-1ba1-4d27-8a42-df2816937f1e/crate/download/ 2022-05-12 09:54:45.242361+00:00 2024-03-05 12:24:28.622853+00:00 2022-05-12 09:54:45.242361+00:00 This Research Object contains the raster file of the mean ground velocity at the Changbaishan Volcano (China/North Korea) from ALOS-2 satellite data during 2018-2020. Find more on processing and results in the related paper: 'Upward Magma Migration within the Multi-level Plumbing System of the Changbaishan Volcano (China/North Korea) Revealed by the Modeling of 2018-2020 SAR Data' by E. Trasatti, C. Tolomei, L. Wei, G. Ventura. DOI: 10.3389/feart.2021.741287 . Raw data property of JAXA (Japan). application/ld+json https://w3id.org/ro-id/261f85b0-1ba1-4d27-8a42-df2816937f1e ALOS test DEMO - archive test DEMO MANUAL China North Korea demonstration file plumbing processing raster soil test velocity earth sciences Executive (government) Government department Newspaper Satellite technology Changbaishan Volcano China North Korea Research Object raster satellite data velocity aeronautics ground velocity property of JAXA raster file raw data property test demo Find more on processing and results in the related paper: 'Upward Magma Migration within the Multi-level Plumbing System of the Changbaishan Volcano (China/North Korea) Revealed by the Modeling of 2018-2020 SAR Data' by E. Trasatti, C. Tolomei, L. Wei, G. Ventura. This Research Object contains the raster file of the mean ground velocity at the Changbaishan Volcano (China/North Korea) from ALOS-2 satellite data during 2018-2020. test DEMO. during 2018-2020 aerospace engineering school systems Interior China Japan North Korea

Trasatti, Elisa. "test DEMO." ROHub. May 12 ,2022. https://doi.org/10.24424/0x0k-6772.

data biblio raw data metadata 10222 https://api.rohub.org/api/resources/06712ff7-1be4-4043-b549-3d2fac395d70/download/ 2022-05-12 10:01:50.355873+00:00 2022-05-12 10:05:59.703678+00:00 application/vnd.openxmlformats-officedocument.spreadsheetml.sheet list of images 2022-05-12 10:01:50.355873+00:00 460884 https://api.rohub.org/api/resources/35e407ff-fdeb-4140-babf-345502633787/download/ 2022-05-12 10:02:26.720307+00:00 2022-05-12 10:06:00.868037+00:00 image/png image 2022-05-12 10:02:26.720307+00:00 499011 https://api.rohub.org/api/resources/731dc8af-d55e-440f-b0fc-8c278bc044cb/download/ 2022-05-12 09:58:37.948679+00:00 2022-05-12 10:05:56.827888+00:00 text/csv data 2022-05-12 09:58:37.948679+00:00 https://www.frontiersin.org/articles/10.3389/feart.2021.741287/full 2022-05-12 09:57:38.006648+00:00 2022-05-12 10:05:59.806322+00:00 paper 2022-05-12 09:57:38.006648+00:00 1548320 https://api.rohub.org/api/resources/b397f843-c005-4a5a-9df6-af34664ef107/download/ 2022-05-12 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https://w3id.org/ro-id/3a5459f8-8145-4062-b51b-86e7edfb8208/annotations/3b2a30cc-ae88-4045-8d2a-91996907ff5c/628e3d90a8a689c5035c5329/temporal TROPOMI S5P L2 55406151 https://api.rohub.org/api/ros/3a5459f8-8145-4062-b51b-86e7edfb8208/crate/download/ 2022-06-28 18:46:58.536426+00:00 2025-10-18 11:32:10.096137+00:00 2022-06-28 18:46:58.536426+00:00 Sentinel-5P: SO2 total column (OFFL) - time range: 2022-06-26T19:57:45Z/2018-11-28T12:46:38Z - min/max Value: -10/60 - DataType: Float32 - Resolution: 0 - application/ld+json https://w3id.org/ro-id/3a5459f8-8145-4062-b51b-86e7edfb8208 How to create a Research Object using adamapi and rohub api - 28.06.22a MANUAL

Palma, Raul. "How to create a Research Object using adamapi and rohub api - 28.06.22a." ROHub. Jun 28 ,2022. https://w3id.org/ro-id/3a5459f8-8145-4062-b51b-86e7edfb8208.

tools data raw data metadata 98599 https://api.rohub.org/api/resources/14349e56-1829-41de-b721-f66467217b00/download/ 2022-07-11 08:35:48.879483+00:00 2022-07-11 08:35:54.725674+00:00 desc application/pdf res-new2 2022-07-11 08:35:48.879483+00:00 3995950 https://api.rohub.org/api/resources/23d1da17-c362-4cc6-984b-97e7564b5902/download/ 2022-07-06 14:41:34.377702+00:00 2022-07-06 14:41:38.979078+00:00 desc8 application/pdf res8 2022-07-06 14:41:34.377702+00:00 884558 https://api.rohub.org/api/resources/4fda69d6-1b50-4c96-a1e4-7b6bf06ab12e/download/ 2022-07-11 08:34:14.057431+00:00 2022-07-11 08:34:21.923033+00:00 description1 image/png res-new1 2022-07-11 08:34:14.057431+00:00 2949626 https://api.rohub.org/api/resources/50fa27ec-f07f-4eb5-86d7-225bff89ae86/download/ 2022-07-06 14:34:34.528035+00:00 2022-07-06 14:34:39.254099+00:00 desc3 application/pdf res3 2022-07-06 14:34:34.528035+00:00 1490272 https://api.rohub.org/api/resources/52aa7ec0-cb2d-4e7a-a178-ae42b9a26c9a/download/ 2022-07-06 14:29:31.444767+00:00 2022-07-06 14:29:36.329683+00:00 desc application/pdf resx-title 2022-07-06 14:29:31.444767+00:00 439045 https://api.rohub.org/api/resources/87653312-15fe-4cb8-8e50-e08e357ddc11/download/ 2022-07-06 14:39:59.233484+00:00 2022-07-06 14:40:04.479965+00:00 desc6 application/pdf res6 2022-07-06 14:39:59.233484+00:00 74929 https://api.rohub.org/api/resources/92cce42c-4aea-40fa-9487-e725fed07d27/download/ 2022-07-06 14:39:02.119007+00:00 2022-07-06 14:39:06.449582+00:00 desc5 application/json res5 2022-07-06 14:39:02.119007+00:00 133508 https://api.rohub.org/api/resources/9c9b17a3-2f4f-42a7-aafc-9e27ad9cd362/download/ 2022-07-06 14:40:59.356130+00:00 2022-07-06 14:41:05.005548+00:00 desc7 application/pdf res7 2022-07-06 14:40:59.356130+00:00 2286908 https://api.rohub.org/api/resources/9f67e243-3af0-4233-98a1-474c59c8799e/download/ 2022-07-06 14:32:11.189177+00:00 2022-07-06 14:32:16.726685+00:00 desc2 application/pdf res2 2022-07-06 14:32:11.189177+00:00 825126 https://api.rohub.org/api/resources/bbbdd03e-f69a-4aa5-9d39-84ad633ae821/download/ 2022-07-06 14:37:00.365389+00:00 2022-07-06 14:37:05.897069+00:00 desc3 application/pdf res4 2022-07-06 14:37:00.365389+00:00 43791839 https://api.rohub.org/api/resources/e9c09afd-6f02-4de4-9165-9c6ae5b2c2a3/download/ 2022-07-11 08:38:16.698689+00:00 2022-07-11 08:38:24.464397+00:00 desc application/pdf res-new3 2022-07-11 08:38:16.698689+00:00 ci 2.288135593220339 5.4 Madrid iridium 3.4422198805760456 9.8 A scien fic or computa onal workflow is the descrip on of the sequence of processing steps they use for a par cular data processing task their data analysis pipeline. 5.1063829787234045 10.8 maximum 4.214963119072708 12.0 scrip 2.1426062521952933 6.1 data 7.11864406779661 16.8 earth sciences 24.605547838852317 0.7781310677528381 linguistics 18.316831683168317 14.799999999999999 workflow 5.690200210748156 16.2 environmental sciences 22.02377407201785 0.6964845061302185 scop 2.4152542372881354 5.7 column 5.169491525423729 12.2 user datum 2.63724434876211 4.9 researcher 3.0508474576271185 7.2 Science and technology Science and technology computer 3.020723568668774 8.6 Pompy Logatherm WLW 6.864406779661017 16.2 Rock and roll music Arts, culture and entertainment/Arts and entertainment/Music/Musical style/Rock and roll music information 2.247980330172111 6.4 research 7.1610169491525415 16.9 aim 3.301721109940288 9.4 datum 3.6440677966101696 8.6 system sterowania Logamatic EMS Plus 1.7761033369214208 3.3 European Commission Library and museum Arts, culture and entertainment/Culture/Library and museum European Community meteorology and climatology 43.255104527007774 0.5567314624786377 mathematical and computer sciences 14.52922542616331 0.1870039850473404 dataset 3.0084745762711864 7.1 object 4.279661016949152 10.1 geosciences 43.255104527007774 0.5567314624786377 a. Sentinel-5P 7.3735199138858984 13.7 Poetry Arts, culture and entertainment/Arts and entertainment/Literature/Poetry sentinel-5 precursor 4.576271186440678 10.8 value 7.584745762711863 17.9 data intensive 2.4219590958019372 4.5 max 5.338983050847458 12.6 computer programming and software 14.52922542616331 0.1870039850473404 Ir 3.728813559322034 8.8 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware Wsp czynnik scop si gaj cy warto ci 1.93756727664155 3.6 job market 2.9702970297029703 2.4 research object 24.70398277717976 45.9 workflow part 3.9289558665231428 7.3 computer science 33.29207920792079 26.900000000000002 max value 18.89128094725511 35.1 Science and technology Science and technology minim 2.7046013347383213 7.7 research datum 1.8837459634015068 3.5 dataset 6.252195293291184 17.8 geology 53.37067808912983 1.6878056526184082 dom 3.0084745762711864 7.1 Arkansas 5.169491525423729 12.2 value 6.392694063926941 18.2 research technique 3.4445640473627552 6.4 Genetics Science and technology/Natural science/Biology/Genetics column 4.495960660344222 12.8 document object model 2.669476642079382 7.6 Wf Ever 2.288135593220339 5.4 data management plan information reliance data management plan 2.099031216361679 3.9 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences Such data data include various large Earth Observation datasets and products, in particular from Copernicus programme, as well as other data used for the analysis/simulations, along with the resulting datasets produced by those processes. 2.647754137115839 5.6 metadata 3.090972953986653 8.8 general 42.21567004682892 0.5433530211448669 Comment: There will be two types of data such as research data and user data. 1.5130023640661938 3.2 Jun-28-1922 Language Arts, culture and entertainment/Culture/Language Klasyfikacja efektywno ci energetycznej Logatherm WLW i AR E / WLW i IR E w zestawie z regulatorem Logamatic HMC . 6.997635933806147 14.8 data utility 1.7761033369214208 3.3 general (general) 42.21567004682892 0.5433530211448669 total column 2.7448869752421956 5.1 Interior oceanography 24.605547838852317 0.7781310677528381 OGC EO dataset metadata GeoJSON 2.47578040904198 4.6 earth sciences 53.37067808912983 1.6878056526184082 system 3.4070951879171054 9.7 software 18.81188118811881 15.2 datum 5.690200210748156 16.2 Ir nadaje si 1.7761033369214208 3.3 research 6.498068141903759 18.5 data 9.167544783983141 26.099999999999998 AR Logatherm WLW 3.9289558665231428 7.3 Workflows and Digital Libraries Making a workflow part of the research record is a way of capturing the methods used in a piece of research it makes it easier to interpret the results, and helps repeat and reproduce it. 5.4373522458628845 11.5 SO2 5.805084745762712 13.7 workflow 5.889830508474576 13.9 researcher 2.9504741833508956 8.4 Pompy Logatherm WLW i AR / WLW i IR wykorzystuj powietrze do zapewnienia d ugotrwa ego komfortu w zakresie ogrzewania i ciep ej wody u ytkowej. 7.1867612293144205 15.2 scien fic workflow 3.7674919268030136 7.0 Arkansas computa onal workflow 3.1216361679224973 5.8 Arkansas 4.8823322795925534 13.9 database 26.60891089108911 21.5 Workflows the New Rock and Roll Research in many disciplines is increasingly data intensive, and researchers are using computa onal techniques to manipulate and analyse the data. 11.725768321513002 24.8 How to create a Research Object using adamapi and rohub api - 28.06.22a. Sentinel-5P: SO2 total column (OFFL) - time range: 2022-06-26T19:57:45Z/2018-11-28T12:46:38Z - min/max Value: -10/60 - DataType: Float32 - Resolution: 0 - 47.28132387706856 100.0 sampleor specimen data 1.8299246501614639 3.4 environmental science and management 22.02377407201785 0.6964845061302185 Wherewas user data is concerned with data collected by the RELIANCE services such as ROhub services which collects user data. 1.8439716312056738 3.9 Jeden system do wszystkich zastosowa Niezale nie od tego czy budujesz nowy dom, modernizujesz stary, czy tylko wymieniasz tradycyjn instalacj grzewcz nasza nowa wielofunkcyjna pompa ciep a Logatherm WLW i AR / WLW i IR nadaje si do dom w jednorodzinnych i niewielkich budynk w wielorodzinnych, a tak e budowy nowych i rozbudowy istniej cych system w grzewczych. 10.260047281323876 21.7 Kangar Securities Economy, business and finance/Market and exchange/Securities Raul Palma service-account-enrichment Applied sciences 5429 https://api.rohub.org/api/ros/1be0f190-6a64-4696-89ba-3509748d84fa/crate/download/ 2022-07-18 13:07:15.313202+00:00 2025-10-18 11:31:44.980651+00:00 2022-07-18 13:07:15.313202+00:00 The goal is to generate automatically a RO from a DMP using RDA DMP Common Standard for Machine-actionable DMP. application/ld+json https://w3id.org/ro-id/1be0f190-6a64-4696-89ba-3509748d84fa Data Management Plan using RDA DMP Common Standard for Machine-actionable DMP MANUAL

Anne Foilloux. "Data Management Plan using RDA DMP Common Standard for Machine-actionable DMP." ROHub. Jul 18 ,2022. https://w3id.org/ro-id/1be0f190-6a64-4696-89ba-3509748d84fa.

data raw data biblio metadata data management 22.174840085287848 10.4 Machine-actionable DMP 25.154004106776178 24.5 earth sciences 100.0 0.5071393251419067 goal 20.25586353944563 9.5 plan 9.814612868047982 9.0 mathematical and computer sciences 100.0 0.8756278157234192 DMP Common Standard 5.236139630390142 5.1 standard 10.021321961620469 4.7 Language Arts, culture and entertainment/Culture/Language computer operations and hardware 100.0 0.8756278157234192 Ro from a DMP 4.928131416837782 4.8 Ro 26.865671641791046 12.6 goal 10.032715376226825 9.2 RDA DMP 28.644763860369608 27.9 plan 20.68230277185501 9.7 RDA 19.73827699018539 18.1 data management plan 36.03696098562628 35.1 geophysics 100.0 0.5071393251419067 The goal is to generate automatically a RO from a DMP using RDA DMP Common Standard for Machine-actionable DMP. 53.35335335335335 53.3 Ro 14.394765539803707 13.2 Data Management Plan using RDA DMP Common Standard for Machine-actionable DMP. 46.646646646646644 46.6 Machine 22.57360959651036 20.7 data management 11.995637949836423 11.0 Common Standard 11.450381679389313 10.5 Anne Fouilloux service-account-enrichment Applied sciences 2459 https://api.rohub.org/api/ros/095cd4b8-7027-4b97-bf9d-511fc5351d6d/crate/download/ 2022-07-22 08:45:34.245892+00:00 2025-10-18 11:24:46.077488+00:00 2022-07-22 08:45:34.245892+00:00 Data on beach litter application/ld+json https://w3id.org/ro-id/095cd4b8-7027-4b97-bf9d-511fc5351d6d Marine litter MANUAL

Bocci, Martina. "Marine litter." ROHub. Jul 22 ,2022. https://w3id.org/ro-id/095cd4b8-7027-4b97-bf9d-511fc5351d6d.

life sciences (general) 100.0 0.7818937301635742 marine litter 39.0992835209826 38.2 Data on beach litter 61.56156156156156 61.5 life sciences 100.0 0.7818937301635742 information 51.42857142857143 30.6 Marine litter. 38.43843843843844 38.4 litter 48.57142857142857 28.9 data 31.934493346980553 31.2 beach litter 6.506506506506506 6.5 earth sciences 100.0 0.8758946061134338 litter 28.96622313203685 28.3 oceanography 100.0 0.8758946061134338 data on beach litter 93.49349349349349 93.4 Martina Bocci service-account-enrichment Environmental research Life sciences Applied sciences giorgio.castellan@bo.ismar.cnr.it Giorgio Castellan 0000-0001-6084-1504 federica.foglini@ismar.cnr.it Federica Foglini 0000-0002-2736-0052 POLYGON ((12.275848388671875 45.37080033235662, 12.275848388671875 45.52238536664779, 12.547760009765627 45.52238536664779, 12.547760009765627 45.37080033235662, 12.275848388671875 45.37080033235662)) 12.275848388671875 45.37080033235662, 12.275848388671875 45.52238536664779, 12.547760009765627 45.52238536664779, 12.547760009765627 45.37080033235662, 12.275848388671875 45.37080033235662 3ea7aee0-5ba2-4809-af66-347c88b9e95e POLYGON ((12.275848388671875 45.37080033235662, 12.275848388671875 45.52238536664779, 12.547760009765627 45.52238536664779, 12.547760009765627 45.37080033235662, 12.275848388671875 45.37080033235662)) 33575282 https://api.rohub.org/api/ros/b3dd84e2-9a82-4364-a030-7b8a4269744d/crate/download/ 2022-11-11 11:40:47.634012+00:00 2025-10-18 10:59:29.019769+00:00 2022-11-11 11:40:47.634012+00:00 The RO focuses on the monitoring of the stranded and floating marco-litter pollution in the World Heritage Site of the Venice lagoon. The data also consider the covid-19 lockdown period. application/ld+json https://w3id.org/ro-id/b3dd84e2-9a82-4364-a030-7b8a4269744d Marine Litter Pollution macro and microplastics contaminants Marine Litter Pollution Monitoring in the WHS of Venice and its lagoon MANUAL

Davide Poletto, paolo franceschetti, Manuel Scarpa, Teresa Cecchi, Federica Foglini, and Giorgio Castellan. "Marine Litter Pollution Monitoring in the WHS of Venice and its lagoon." ROHub. Nov 11 ,2022. https://w3id.org/ro-id/b3dd84e2-9a82-4364-a030-7b8a4269744d.

POLYGON ((12.275848388671875 45.37080033235662, 12.275848388671875 45.52238536664779, 12.547760009765627 45.52238536664779, 12.547760009765627 45.37080033235662, 12.275848388671875 45.37080033235662)) data biblio metadata raw data 1628248 https://api.rohub.org/api/resources/0a6fc07f-e7d3-4617-8023-e341ec6a02fb/download/ 2022-11-23 16:50:18.377731+00:00 2022-11-23 16:50:22.257611+00:00 Map of the itinerary image/png itinerary Marine Litter itinerary Legambiente 2016 2022-11-23 16:50:18.377731+00:00 1880693 https://api.rohub.org/api/resources/29b67dc6-a820-4d28-b615-8d9a70bd4b19/download/ 2022-11-23 11:03:14.260829+00:00 2022-11-23 11:03:15.607284+00:00 image/png Carel Chioggia Clean-up_76 002.png 2022-11-23 11:03:14.260829+00:00 757590 https://api.rohub.org/api/resources/488ab8f9-3af6-4838-83e5-e7576b76af12/download/ 2022-11-23 14:18:37.248981+00:00 2022-11-23 16:56:27.945059+00:00 Marine litter data collected by Legambiente in the lagoon of Venice - year 2016 application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Marine Litter Floating ML data Legambiente 2016 2022-11-23 14:18:37.248981+00:00 2326292 https://api.rohub.org/api/resources/5234568d-8d9c-4add-9682-a7632b6307f7/download/ 2022-11-23 11:02:58.866142+00:00 2022-11-23 11:03:00.988043+00:00 image/png Carel Chioggia Clean-up_75 002.png 2022-11-23 11:02:58.866142+00:00 21521898 https://api.rohub.org/api/resources/5d1adedf-6a03-40e9-866c-aeec4c705944/download/ 2022-11-23 14:13:54.524426+00:00 2022-11-23 16:55:51.251093+00:00 Marine litter data collected by Venice Lagoon Plastic Free in the lagoon of Venice - year 2020 image/tiff Marine Litter itinerary VLPF 2020 2022-11-23 14:13:54.524426+00:00 1895716 https://api.rohub.org/api/resources/7c4b805a-9253-430b-9a68-4ded383daef1/download/ 2022-11-23 11:23:35.529943+00:00 2022-11-23 11:23:36.885522+00:00 image/png WWF x VLPF Cleanup April 22_17 002.png 2022-11-23 11:23:35.529943+00:00 25293 https://api.rohub.org/api/resources/7e81b980-c241-485b-aa83-1ce1e721b2c9/download/ 2022-11-16 09:59:18.531335+00:00 2022-11-16 09:59:21.932175+00:00 Stranded litter monitoring campaign in Venice application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Marine Litter ROs-Marine Macro litter pollution monitoring in Venice 04_2021-10_2022 2022-11-16 09:59:18.531335+00:00 1957891 https://api.rohub.org/api/resources/a08c2624-294c-497d-a5be-0e0bba1d640f/download/ 2022-11-23 11:02:47.458114+00:00 2022-11-23 11:02:51.802628+00:00 image/png Carel Chioggia Clean-up_74 002.png 2022-11-23 11:02:47.458114+00:00 25801 https://api.rohub.org/api/resources/ac63b244-1791-42d0-ae64-6b7efcb5cf13/download/ 2022-12-05 14:12:42.184640+00:00 2022-12-05 14:12:44.029936+00:00 This file collects the overall VLPF monitoring campaign of stranded plastics litter in the beach-islands of Venice within the 2021-2022 timeline application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Beach litter monitoring campaign 2021-22 2022-12-05 14:12:42.184640+00:00 2313490 https://api.rohub.org/api/resources/d65e39b9-c0ca-4377-aead-e764bcd9976a/download/ 2022-11-23 11:23:24.993680+00:00 2022-11-23 11:23:26.723844+00:00 image/png WWF x VLPF Cleanup April 22_5 002.png 2022-11-23 11:23:24.993680+00:00 66482 https://api.rohub.org/api/resources/e568c410-1758-4a26-be81-fb7ca53a6fac/download/ 2022-11-23 16:54:32.252778+00:00 2022-11-23 16:54:35.312073+00:00 2020 floating Marine Littere campaign in the historical city center of Venice application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Floating ML data VLPF 2020 2022-11-23 16:54:32.252778+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 UNESCO World Heritage Site 11.764705882352942 10.0 data 8.352941176470589 7.1 Arts, culture and entertainment Arts, culture and entertainment Environmental pollution Environment/Environmental pollution The data also consider the covid-19 lockdown period. 12.912912912912912 12.9 atmospheric sciences 100.0 0.9923840165138245 litter 5.764705882352942 4.9 Language Arts, culture and entertainment/Culture/Language Venice 18.10918774966711 13.6 The RO focuses on the monitoring of the stranded and floating marco-litter pollution in the World Heritage Site of the Venice lagoon. 50.85085085085085 50.8 space sciences (general) 100.0 0.6279668807983398 space sciences 100.0 0.6279668807983398 monitoring 5.999999999999999 5.1 marco-litter pollution 23.375262054507335 22.3 marine litter pollution 11.740041928721173 11.2 Venice Ro 8.705882352941176 7.4 data 9.587217043941413 7.2 pollution 19.04127829560586 14.3 lockdown 13.581890812250332 10.2 Monument and heritage site Arts, culture and entertainment/Culture/Monument and heritage site earth sciences 100.0 0.9923840165138245 lagoon 16.11185086551265 12.1 Venice lagoon 35.53459119496855 33.9 UNESCO World Heritage Site 13.315579227696405 10.0 hydrography 100.0 4.1 pollution 17.058823529411764 14.5 Ro 10.252996005326233 7.7 lockdown period 20.230607966457022 19.3 lagoon 14.352941176470589 12.2 lockdown 11.529411764705884 9.8 whs of Venice 9.11949685534591 8.7 Venice 16.470588235294116 14.0 Marine Litter Pollution Monitoring in the WHS of Venice and its lagoon. 36.23623623623624 36.2 https://www.sciencedirect.com/science/article/abs/pii/S0048969721020210 2022-12-04 15:24:36.666143+00:00 2022-12-04 15:24:37.592719+00:00 The UNESCO World Heritage site “Venice and its Lagoon”, is one of the top tourist destinations in the world. Since there is a dearth in the literature regarding microplastic leachable compounds and overtourism-related pollutants, the project studied the Head Space-Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC–MS) molecular fingerprint of volatile lagoon water pollutants, to gain insight into the extent of this phenomenon in August 2019. The chromatographic analyses enabled the identification of 40 analytes related to the presence of polymers in seawater, water traffic, and tourists habits. In Italy, on the 10th March 2020, the lockdown restrictions were enforced to control the spread of the SARS-CoV-2 infection; the ordinary urban water traffic around Venice came to a halt, and the ever-growing presence of tourists suddenly ceased. This situation provided a unique opportunity to analyze the environmental effects of restrictions on VOCs load in the Lagoon. 17 contaminants became not detectable after the lockdown period. The statistical analysis indicated that the amounts of many other contaminants significantly dropped. The presence of 9 analytes was not statistically influenced by the lockdown restrictions, probably because of their stronger persistence or continuous input in the environment from diverse sources. Results signify a sharp and encouraging pollution decrease at the molecular level, concomitant with the anthropogenic stress release. macro marine litter monitoring outputs Analysis of volatile organic compounds in Venice lagoon water reveals COVID-19 lockdown impact on microplastics and mass tourism-related pollutants 2022-12-04 15:24:36.666143+00:00 d.poletto@plasticfreevenice.org Davide Poletto Davide Poletto info@isdigroup.com ISDI group ISDI Group manuelscarpa75@gmail.com Manuel Scarpa Venice lagoon plastic free p.franceschetti@plasticfreevenice.org paolo franceschetti segreteria@plasticfreevenice.org Venice Lagoon Plastic Free service-account-enrichment teresacecchi@tiscali.it Teresa Cecchi Applied sciences Social sciences Cultural geography Regional geography 12.32638548128307 45.436044651757186 POINT (12.32638548128307 45.436044651757186) 2ab100ec-a6e8-4aa6-86fd-08dcf3ecd66f POINT (12.32638548128307 45.436044651757186) 170673 https://api.rohub.org/api/ros/2f126bfc-d4fb-4d72-914f-bfd121b0cf35/crate/download/ 2022-11-22 19:08:00.583184+00:00 2025-10-18 10:59:05.929705+00:00 2022-11-22 19:08:00.583184+00:00 This dataset represents the monthly level of tourism arrivals and overnight-stays in Venice city centre, Italy. application/ld+json https://w3id.org/ro-id/2f126bfc-d4fb-4d72-914f-bfd121b0cf35 Overtourism Tourism Flows Tourism Venice Dataset Tourism Overnight-stays in Venice Pre and Post Covid19 MANUAL

Bertocchi, Dario, and Lisa ZECCHIN. "Tourism Overnight-stays in Venice Pre and Post Covid19." ROHub. Nov 22 ,2022. https://w3id.org/ro-id/2f126bfc-d4fb-4d72-914f-bfd121b0cf35.

POINT (12.32638548128307 45.436044651757186) data raw data biblio metadata 147710 https://api.rohub.org/api/resources/12ab161c-6a2f-49ad-bdd9-10f19b229635/download/ 2022-11-22 19:17:04.445500+00:00 2022-11-22 19:17:05.342120+00:00 image/jpeg Venice.jpg 2022-11-22 19:17:04.445500+00:00 14784 https://api.rohub.org/api/resources/4c5e8340-5b09-45b8-bbc3-38f3209a4e9b/download/ 2022-11-22 19:19:21.210425+00:00 2022-11-22 19:20:51.482833+00:00 Tourism Arrivals and overnight-stays in Venice - monthly level 2017-2021 application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Tourism Venice Tourism flows in Venice 2022-11-22 19:19:21.210425+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 monthly level 0.20040080160320642 0.2 dataset 13.800424628450106 13.0 monthly level of tourism arrival 0.5010020040080161 0.5 Post Covid19 11.57112526539278 10.9 Tourism and leisure Economy, business and finance/Economic sector/Tourism and leisure general (general) 100.0 0.6069987416267395 level of tourism arrival 4.809619238476954 4.8 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences general 100.0 0.6069987416267395 reaching 5.07343124165554 3.8 Venice 20.026702269692922 15.0 This dataset represents the monthly level of tourism arrivals and overnight-stays in Venice city centre, Italy. 58.65865865865865 58.6 Venice tourism 22.39915074309979 21.1 Tourism Overnight-stays in Venice Pre and Post Covid19. 41.34134134134134 41.3 Italy 11.67728237791932 11.0 atmospheric sciences 100.0 0.468875527381897 Italy 14.686248331108143 11.0 Italy represent the monthly level city centre 16.02136181575434 12.0 Venice 15.817409766454352 14.9 earth sciences 100.0 0.468875527381897 tourism 27.770360480640853 20.8 city centre 12.738853503184712 12.0 tourism arrival 94.48897795591182 94.3 dataset 16.421895861148197 12.3 Pre 11.995753715498939 11.3 Tourism Lifestyle and leisure/Leisure/Travel/Tourism dario.bertocchi@uniud.it Dario Bertocchi dill@postacert.uniud.it Università degli Studi di Udine lisa.zecchin@unive.it Lisa ZECCHIN service-account-enrichment Applied sciences Earth observation https://doi.org/10.5281/zenodo.7413790 2022-12-09 08:44:30.232146+00:00 2022-12-09 08:44:48.523770+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS AIS data prepared by Statsat AS for January 2020 2022-12-09 08:44:30.232146+00:00 https://doi.org/10.5281/zenodo.7415523 2022-12-09 13:10:31.490480+00:00 2022-12-09 13:11:34.416361+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS data prepared by Statsat AS for February 2020 2022-12-09 13:10:31.490480+00:00 https://doi.org/10.5281/zenodo.7415565 2022-12-09 13:13:01.537799+00:00 2022-12-09 13:13:15.608629+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS AIS data prepared by Statsat AS for March 2020 2022-12-09 13:13:01.537799+00:00 https://doi.org/10.5281/zenodo.7415613 2022-12-09 13:14:19.140292+00:00 2022-12-09 13:14:36.495847+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS data prepared by Statsat AS for April 2020 2022-12-09 13:14:19.140292+00:00 https://doi.org/10.5281/zenodo.7415840 2022-12-09 13:15:45.986616+00:00 2022-12-09 13:16:04.964630+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS AIS data prepared by Statsat AS for May 2020 2022-12-09 13:15:45.986616+00:00 https://doi.org/10.5281/zenodo.7415948 2022-12-09 13:17:02.112594+00:00 2022-12-09 13:18:21.412111+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS data prepared by Statsat AS for June 2020 2022-12-09 13:17:02.112594+00:00 https://doi.org/10.5281/zenodo.7416056 2022-12-09 13:18:04.670277+00:00 2022-12-09 13:35:38.264200+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS data prepared by Statsat AS for July 2020 2022-12-09 13:18:04.670277+00:00 https://doi.org/10.5281/zenodo.7416092 2022-12-09 13:23:05.481849+00:00 2022-12-09 13:35:18.583323+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS AIS data prepared by Statsat AS for August 2020 2022-12-09 13:23:05.481849+00:00 https://doi.org/10.5281/zenodo.7416098 2022-12-09 13:37:13.532003+00:00 2022-12-09 13:37:53.422886+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds AIS AIS data prepared by Statsat AS for September 2020 2022-12-09 13:37:13.532003+00:00 https://doi.org/10.5281/zenodo.7416100 2022-12-09 13:38:57.730553+00:00 2022-12-09 13:39:24.193261+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS data prepared by Statsat AS for October 2020 2022-12-09 13:38:57.730553+00:00 https://doi.org/10.5281/zenodo.7416110 2022-12-09 13:40:39.906484+00:00 2022-12-09 13:41:20.070877+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS AIS data prepared by Statsat AS for November 2020 2022-12-09 13:40:39.906484+00:00 https://doi.org/10.5281/zenodo.7416118 2022-12-09 13:32:38.512157+00:00 2022-12-09 13:34:44.654847+00:00 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file. AIS AIS data prepared by Statsat AS for December 2020 2022-12-09 13:32:38.512157+00:00 https://doi.org/10.5281/zenodo.7418694 2022-12-09 14:38:54.357665+00:00 2022-12-09 14:42:12.930004+00:00 AIS raw data (ASCII) provided by Statsat AS in the context of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway). The file contains AIS messages. The documentation is also provided in this archive. The file is a NMEA text file. This file has not been used for training the deep learning method (T-SAR project). Decoded, it may not correspond exactly to what is in the data folder. AIS AIS raw data covering 2020 (nmea format) 2022-12-09 14:38:54.357665+00:00 https://drive.google.com/drive/folders/1gtlJ8WmYpC-O7b0YBQKWzMUTRFRuJy5S?usp=sharing 2023-01-03 08:12:48.873333+00:00 2023-01-03 08:12:53.807507+00:00 Link to google drive folder containing input data (csv format) used for detection of anomalies with AIS satellite data. datasets Statsat-zip (private google drive) 2023-01-03 08:12:48.873333+00:00 Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 post@simula.no 00vn06n10 Simula Research Laboratory The main objective of Simula is to create knowledge about fundamental scientific challenges that are of genuine value for society. post@simula.no Simula https://www.simula.no 56900f0c-4f7e-4321-918e-221d655b73c8 POLYGON ((-170.62501430511477 -80.79816944840414, -170.62501430511477 85.15119983948293, 192.89061069488528 85.15119983948293, 192.89061069488528 -80.79816944840414, -170.62501430511477 -80.79816944840414)) POLYGON ((-170.62501430511477 -80.79816944840414, -170.62501430511477 85.15119983948293, 192.89061069488528 85.15119983948293, 192.89061069488528 -80.79816944840414, -170.62501430511477 -80.79816944840414)) -170.62501430511477 -80.79816944840414, -170.62501430511477 85.15119983948293, 192.89061069488528 85.15119983948293, 192.89061069488528 -80.79816944840414, -170.62501430511477 -80.79816944840414 507021 https://api.rohub.org/api/ros/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe/crate/download/ 2022-12-09 08:34:30.338885+00:00 2025-10-18 10:48:39.155333+00:00 2022-12-09 08:34:30.338885+00:00 AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway). The dataset contains AIS data (satellite + other) on a global coverage for 2020. There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. The csv files have one header line: mmsi;lon;lat;date_time_utc;sog;cog;true_heading;nav_status;rot;message_nr;source where: mmsi (integer): MMSI number of the vessel (AIS identifier). All records belonging to the same vessel will have the same identifier; lon (float): Geographical longitude (WGS84) between -180 to 180; lat (float): Geographical latitude (WGS84) between -90 to 90; date_time_utc (datetime): Date and Time (in UTC) when position was recorded by AIS. It is represented as: YYYY-MM-DD HH:MM:SS (for instance 2020-01-01 00:00:00); sog (float): Speed over ground (knots); cog (float): Course over ground (degrees); true_heading (integer): Heading (degrees) of the vessel's hull. A value of 511 indicates there is no heading data; nav_status (integer): Navigation status according to AIS Specification; rot (integer): rate of turn; message_nr (integer): message number; source (integer): source is the source of AIS data ('g' for ground or 's' for satellite); One row in the CSV file corresponds to one message. application/ld+json https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe AIS NorSat Vessel ship surveillance Dataset AIS 2020 data prepared for the T-SAR project by Statsat AS MANUAL

Pierre Bernabé, Anne Fouilloux, and Dusica Marijan. "AIS 2020 data prepared for the T-SAR project by Statsat AS." ROHub. Dec 09 ,2022. https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe.

POLYGON ((-170.62501430511477 -80.79816944840414, -170.62501430511477 85.15119983948293, 192.89061069488528 85.15119983948293, 192.89061069488528 -80.79816944840414, -170.62501430511477 -80.79816944840414)) raw data metadata biblio data 452439 https://api.rohub.org/api/resources/211a9b8f-569a-452b-a2e5-a4c23c784a45/download/ 2023-01-17 14:37:47.220088+00:00 2023-01-17 14:37:48.377642+00:00 image/png AIS-satellite-surveillance.png 2023-01-17 14:37:47.220088+00:00 1984 https://api.rohub.org/api/resources/32057316-113a-48de-bede-927c605d3e58/download/ 2022-12-12 08:42:15.989000+00:00 2022-12-12 08:42:20.938998+00:00 This document explains where to find input data on Simula's computing resources (EX3) text/plain EX3 Input data information for usage at Simula Research Laboratory (EX3) 2022-12-12 08:42:15.989000+00:00 Jan-1-2020 00:00:00 student 7.154605263157894 8.7 file 13.487584650112865 23.900000000000002 geosciences 100.0 0.6201595067977905 32 Oct-24 17:41 AIS 10.608552631578949 12.9 Oct-31-1924 17:56 computer science 87.53246753246755 33.7 Students Education/Teaching and learning/Students The dataset contains AIS data (satellite + other) on a global coverage for 2020. 13.86748844375963 18.0 Oct-31-1924 17:55 zip file 4.853273137697516 8.6 INT 2.4830699774266365 4.4 difficulty 2.878103837471783 5.1 heading data 5.279698302954117 8.4 Oct-24 17:44 raw data 10.197368421052632 12.4 dataset 5.427631578947369 6.6 information 2.765237020316027 4.9 PhD student 27.718416090509116 44.1 .zip 6.167763157894737 7.5 Oct-31-1924 17:51 month 2.0316027088036117 3.6 32 Oct-24 17:48 data 16.139954853273135 28.6 Oct-24 17:56 New set of data was provided (zipped csv) 9.861325115562403 12.8 32 Oct-24 17:45 32 Oct-24 17:58 Oct-24 17:52 earth resources and remote sensing 100.0 0.6201595067977905 set of data 7.479572595851666 11.9 data 21.957236842105264 26.7 comma separated values 6.743421052631579 8.2 geology 55.360552794702386 0.7127519845962524 the Jan-1-2020 Oct-24 17:49 comma separated values 5.417607223476297 9.6 raw data 6.772009029345372 12.0 Oct-24 17:54 There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. 9.09090909090909 11.8 software 12.467532467532468 4.8 Geography Science and technology/Social sciences/Geography Norway 2.765237020316027 4.9 Oct-24 17:43 Oct-30-24 17:46 original raw data 3.016970458830924 4.8 earth sciences 44.639447205297614 0.5747206807136536 folder 2.9345372460496613 5.2 file 14.226973684210527 17.3 Oct-24 17:42 student 5.079006772009029 9.0 32 Oct-24 17:49 atmospheric sciences 44.639447205297614 0.5747206807136536 PhD 7.8125 9.5 Data used by the PhD student was provided to me as zipped csv files: - one folder per month - in each folder, one file per day - file is zipped csv 17.103235747303543 22.2 csv file 12.69641734758014 20.2 AIS identifier 7.416719044626022 11.8 Doctor of Philosophy 5.079006772009029 9.0 32 Oct-24 17:53 earth sciences 55.360552794702386 0.7127519845962524 Oct-24 17:47 year of data 3.3312382149591455 5.3 ID number 2.821670428893905 5.0 AIS data 17.15901948460088 27.3 2020 dataset 4.514672686230248 8.0 T-SAR 3.536184210526316 4.3 AIS 2020 data prepared for the T-SAR project by Statsat AS. AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) 16.101694915254235 20.9 zipped comma separated values 9.490886235072281 15.1 identifier 1.8623024830699773 3.3 Oct-24 17:57 zip format 3.7810383747178324 6.7 Schutzstaffel Oct-24 17:58 CSV file 6.411062225015713 10.2 artificial immune system 8.521444695259593 15.1 Norway .zip 3.9503386004514667 7.0 Oct-31-1924 17:50 zip file 6.167763157894737 7.5 The original raw data was not used (no code provided to read) because PhD student had too much difficulties with raw data. 33.97534668721109 44.1 https://www.nmea.org 2022-12-09 14:28:47.664937+00:00 2023-03-18 16:44:36.652468+00:00 The National Marine Electronics Association, is a worldwide, member based trade organization revolving around marine electronics interface standards, marine electronics installer training, and its annual marine electronics conference & expo. The NMEA and its members are committed to enhancing the technology and safety of marine electronics through installer training and interface standards. NMEA members promote professionalism within the marine electronics industry. NMEA installer trainings and certifications are recognized by many major electronics manufacturers for installation, support and warranty. AIS National Marine Electronics Association website 2022-12-09 14:28:47.664937+00:00 Anne Fouilloux Simula Research Laboratory dusica@simula.no Dusica Marijan Simula Research Laboratory pierbernabe@simula.no Pierre Bernabé service-account-enrichment Applied sciences https://doi.org/10.5061/dryad.5qg30sd 2023-03-18 15:48:46.831143+00:00 2025-10-14 08:32:33.717964+00:00 Estimating impacts of offshore windfarm construction on marine mammals requires data on displacement in relation to different noise levels and sources. Using echolocation detectors and noise recorders, we investigated harbour porpoise behavioural responses to piling noise during the 10-month foundation installation of a North Sea windfarm. Current UK guidance assumes total displacement within 26 km of pile driving. In contrast, we recorded a 50 % probability of response within 7.4 km (95 % CI = 5.7 – 9.4) at the first location piled, decreasing to 1.3 km (95 % CI = 0.2 – 2.8) by the final location; representing 28 % (95 % CI = 21 – 35) and 18 % (95 % CI = 13 – 23) displacement of individuals within 26 km. Distance proved as good a predictor of responses as audiogram weighted received levels, presenting a more practicable variable for environmental assessments. Critically, acoustic deterrent device (ADD) use and vessel activity increased response levels. Policy and management to minimise impacts of renewables on cetaceans have concentrated on pile-driving noise. Our results highlight the need to consider trade-offs between efforts to reduce far-field behavioural disturbance and near-field injury through ADD use. Data from: Harbour porpoise responses to pile-driving diminish over time 2023-03-18 15:48:46.831143+00:00 https://doi.org/10.5281/zenodo.3754481 2023-03-18 15:45:43.141299+00:00 2023-03-18 15:46:52.776393+00:00 The schema of the dataset is provided below: · t: the time at which the message was received (UTC) · shipid: the anonymized id of the ship · lon: the longitude of the current ship position · lat: the latitude of the current ship position · heading: (see: https://en.wikipedia.org/wiki/Course_(navigation)) · course: the direction in which the ship moves (see: https://en.wikipedia.org/wiki/Course_(navigation)) · speed: the speed of the ship (measured in knots) · shiptype: AIS reported ship-type · destination: AIS reported destination Single Ground Based AIS Receiver Vessel Tracking Dataset 2023-03-18 15:45:43.141299+00:00 https://doi.org/10.5281/zenodo.5718284 2023-03-18 15:41:41.912053+00:00 2023-03-18 15:43:25.836869+00:00 Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2) 2023-03-18 15:41:41.912053+00:00 https://doi.org/10.5281/zenodo.6323416 2023-03-18 15:53:28.368823+00:00 2023-03-18 15:54:45.934342+00:00 The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9th, 2017 to December 26th, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes. The Piraeus AIS Dataset for Large-scale Maritime Data Analytics 2023-03-18 15:53:28.368823+00:00 https://doi.org/10.5281/zenodo.6402160 2023-03-18 15:50:58.144079+00:00 2023-03-18 15:52:08.005277+00:00 With an ever-increasing number of vessels at sea, the modelling, analysis and visualisation of maritime traffic are of paramount importance to support the monitoring tasks of maritime stakeholders. Sensors have been developed in this respect to track vessels and capture the maritime traffic at the global scale. The Automatic Identification System (AIS) is transmitting maritime positional and nominative information at highest frequency rate, making it a valuable source for maritime traffic modelling. From an original AIS dataset covering the area of Brest, France, we extracted a set of 17 maritime routes, connecting ports in this area. Two different representations for the routes are provided: (1) clusters of AIS contacts, and (2) route prototypes, representing the nominal trajectory of the vessels following the route. Additionally, a set of tracklets (built by five consecutive AIS contacts from the same vessel trajectory) has been extracted from the set of routes and the original dataset, and labelled either with the route name to which they belong or as off-route tracklets. This dataset provides thus some ground truth on the routes followed by vessels and is aimed at testing and validating vessel-to-route or track-to-route association algorithms. Maritime routes and vessel tracklet dataset for vessel-to-route association 2023-03-18 15:50:58.144079+00:00 https://en.wikipedia.org/wiki/Automatic_identification_system 2023-03-18 15:36:32.417130+00:00 2023-03-18 15:36:33.751392+00:00 Definition of Automatic Identification System (AIS) given by Wikipedia. Automatic Identification System (AIS) wikipedia 2023-03-18 15:36:32.417130+00:00 https://kartkatalog.geonorge.no/metadata/automatisk-identifikasjonssystem-ais-shipsposisjoner-nedlasting-12nm-fra-grunnlinja/7997fd76-83f9-4e94-bfe7-f4677a6cd787 2023-03-18 16:04:04.650396+00:00 2023-03-18 19:40:25.709890+00:00 Automatic Identification System (AIS) - Ships positions - download - 12nm from baseline Data set from the Norwegian Coastal Administration Data can be downloaded directly from https://ais-public.kystverket.no/ais-download/ (free registration). Automatic Identification System (AIS) - Ships positions - download - 12nm from baseline 2023-03-18 16:04:04.650396+00:00 Simula Research Laboratory dokken@simula.no Jørgen Schartum Dokken 0000-0001-6489-8858 Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 Simula Research Laboratory roehr@simula.no Thomas Roehr 0000-0002-7715-7052 post@simula.no 00vn06n10 Simula Research Laboratory 22052 https://api.rohub.org/api/ros/0b1fcfe6-ab98-4df7-be19-e952de9776c6/crate/download/ 2023-01-19 20:52:56.511892+00:00 2025-10-18 10:06:05.462344+00:00 2023-01-19 20:52:56.511892+00:00 This Research Object gathers information about publicly available AIS datasets. application/ld+json https://w3id.org/ro-id/0b1fcfe6-ab98-4df7-be19-e952de9776c6 Vessel ais surveillance AIS public datasets MANUAL

Fouilloux, Anne, Jørgen Schartum Dokken, and Thomas Roehr. "AIS public datasets." ROHub. Jan 19 ,2023. https://w3id.org/ro-id/0b1fcfe6-ab98-4df7-be19-e952de9776c6.

biblio data https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a 2023-01-19 20:59:22.716386+00:00 2023-03-18 15:39:42.998669+00:00 This dataset contains ships' information collected though the Automatic Identification System, integrated with a set of complementary data having spatial and temporal dimensions aligned. The dataset contains four categories of data: Navigation data, vessel-oriented data, geographic data, and environmental data. It covers a time span of six months, from October 1st, 2015 to March 31st, 2016 and provides ships positions within Celtic sea, the Channel and Bay of Biscay (France). The dataset is proposed with predefined integration and querying principles for relational databases. These rely on the widespread and free relational database management system PostgreSQL, with the adjunction of the PostGIS extension, for the treatment of all spatial features proposed in the dataset. AIS Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance 2023-01-19 20:59:22.716386+00:00 The main objective of Simula is to create knowledge about fundamental scientific challenges that are of genuine value for society. post@simula.no Simula https://www.simula.no Research Object 17.203219315895375 17.1 artificial immune system 40.745192307692314 33.9 AIS dataset 59.118236472945895 59.0 dataset 50.24038461538462 41.8 information 7.444668008048289 7.4 public dataset 5.410821643286574 5.4 AIS public datasets. 32.53253253253253 32.5 dataset 42.354124748490946 42.1 This Research Object gathers information about publicly available AIS datasets. 67.46746746746747 67.4 mathematical and computer sciences 100.0 0.4841751158237457 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware information 9.014423076923078 7.5 gather information 2.80561122244489 2.8 AIS public dataset 27.45490981963928 27.4 available AIS dataset 5.210420841683367 5.2 other earth sciences 100.0 0.46449342370033264 earth sciences 100.0 0.46449342370033264 computer operations and hardware 100.0 0.4841751158237457 AIS 32.99798792756538 32.8 service-account-enrichment Applied sciences http://gismarcloud.myqnapcloud.com:8080/share.cgi?ssid=a52d73f5f2fb42b38e0064e936c1718a 2023-02-14 15:36:15.032982+00:00 2023-02-14 15:36:34.354936+00:00 Microplastic assesment 2023-02-14 15:36:15.032982+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/ita/catalog.search#/search?resultType=manager&sortBy=relevance&any=Maelstrom&from=1&to=20 2023-02-13 14:36:17.112558+00:00 2023-02-13 14:36:45.015925+00:00 Description of microplastic metadata 2023-02-13 14:36:17.112558+00:00 08fb9a65-3505-47ad-949f-f91e853689f1 POINT (12.53219609381631 45.43427794991445) 12.53219609381631 45.43427794991445 POINT (12.53219609381631 45.43427794991445) 12.309050560215837 45.425182522859224 POINT (12.309050560215837 45.425182522859224) e159320e-c127-413b-b768-1ae43db58920 POINT (12.309050560215837 45.425182522859224) 10.24424/cyxa-2645 2024-02-14 14:15:15.142704+00:00 True 247986 https://api.rohub.org/api/ros/9df7b289-2ac1-4c50-aa42-184df24dcafa/crate/download/ 2023-02-10 14:40:56.036090+00:00 2025-10-18 10:05:01.209888+00:00 2023-02-10 14:40:56.036090+00:00 Marine litter, in particular plastics, is a significant and growing marine contaminant that has become a global problem. Macro-litter is subject to fragmentation and degradation due to physical, chemical and biological processes, leading to the formation of micro-litter, the so-called microplastics. The purpose of this research is to evaluate the concentrations of microplastics in different environmental matrices: water, sediment and biota (i.e. mussels and fish) and to contribute to the European project MAELSTROM (Smart technology for MArinE Litter SusTainable RemOval and Management). The aim is to monitor the presence of micro-litter at two sites of the Venice coastal area: an abandoned mussel farm at sea and a lagoon site near the artificial Island of Sacca Fisola; both sites are subject to strong anthropogenic pressure. The results showed that both study areas are characterised by the presence of microplastics in all the analysed matrices and in both sites. Generally, higher microplastics concentrations were found in the Lagoon site (i.e. in surface waters, mussels and fish). Moreover, some differences were also observed in shapes and colours comparing the two sites. These differences are related to the different types of macro-litter that characterised the two areas. The distribution of marine litter is therefore related to the main anthropogenic activities of the two areas such as fishery, aquaculture, tourism and waste management. application/ld+json https://w3id.org/ro-id/9df7b289-2ac1-4c50-aa42-184df24dcafa Adriatic sea Manta Microplastics Venice Lagoon Research Object MAELSTROM: 2022 February Microplastics assessment MANUAL Antonio Petrizzo

Susanna Mesghez, ANTONIO PETRIZZO, Fantina Madricardo, Nicoletta Nesto, Tihana Marceta, and Vanessa Moschino. "MAELSTROM: 2022 February Microplastics assessment." ROHub. Feb 10 ,2023. https://doi.org/10.24424/cyxa-2645.

POINT (12.53219609381631 45.43427794991445) POINT (12.309050560215837 45.425182522859224) data biblio 230113 https://api.rohub.org/api/resources/a233cca6-fba9-46af-9ea7-571a5b3fbd1f/download/ 2023-02-14 15:29:55.562368+00:00 2023-02-14 15:29:56.762339+00:00 image/png Sketch RoHub.png 2023-02-14 15:29:55.562368+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 oceanography 100.0 0.9957613348960876 marine litter 14.403292181069958 7.0 mussel 6.628571428571429 5.8 micro 5.257142857142856 4.6 macro 9.485714285714288 8.3 biological process 10.905349794238683 5.3 macro 15.637860082304526 7.6 Environmental pollution Environment/Environmental pollution mussel 10.905349794238683 5.3 Animal Human interest/Animal Venice Marine litter, in particular plastics, is a significant and growing marine contaminant that has become a global problem. 40.031397174254316 25.5 microprocessor 5.142857142857143 4.5 biological process 6.742857142857143 5.9 Aquaculture Economy, business and finance/Economic sector/Agriculture/Aquaculture diversity 4.914285714285715 4.3 earth sciences 100.0 0.9957613348960876 aim 8.457142857142857 7.4 car litter 9.485714285714288 8.3 Generally, higher microplastics concentrations were found in the Lagoon site (i.e. in surface waters, mussels and fish). Moreover, some differences were also observed in shapes and colours comparing the two sites. 24.33281004709576 15.5 plastics concentration 37.753222836095766 20.5 distribution 4.914285714285715 4.3 types of macro-litter 15.101289134438304 8.2 chemistry 40.67796610169491 2.4 plastics 17.078189300411523 8.3 lagoon site 20.626151012891345 11.2 contaminant 9.82857142857143 8.6 geophysics 100.0 0.9447683095932007 litter 15.22633744855967 7.4 project Maelstrom 12.707182320441989 6.9 formation of micro-litter 13.812154696132598 7.5 Macro-litter is subject to fragmentation and degradation due to physical, chemical and biological processes, leading to the formation of micro-litter, the so-called microplastics. 35.63579277864992 22.7 litter 7.428571428571429 6.5 lagoon 6.4 5.6 geosciences 100.0 0.9447683095932007 hydrography 59.32203389830508 3.5 Synthetic and plastic chemicals Economy, business and finance/Economic sector/Chemicals/Synthetic and plastic chemicals Maelstrom 4.8 4.2 plastics 10.514285714285712 9.2 contaminant 15.843621399176953 7.7 Università Ca' Foscari 956784@stud.unive.it Susanna Mesghez antonio.petrizzo@cnr.it ANTONIO PETRIZZO direttore@ismar.cnr.it CNR-ISMAR CNR ISMAR fantina.madricardo@ve.ismar.cnr.it Fantina Madricardo CNR - ISMAR nicoletta.nesto@ve.ismar.cnr.it Nicoletta Nesto service-account-enrichment Taha Lahami tihana.marceta@ve.ismar.cnr.it Tihana Marceta CNR ISMAR Venice vanessa.moschino@ve.ismar.cnr.it Vanessa Moschino Earth sciences https://reliance.adamplatform.eu/?dataset=69628:EU_CAMS_SURFACE_REC_G&feature=61ad007aeb51328b642656c9 2023-03-21 00:03:32.040501+00:00 2025-10-14 08:32:41.016422+00:00 CAMS Data Cube Product 20211204150000 2023-03-21 00:03:32.040501+00:00 Online https://reliance.adamplatform.eu/?dataset=69628:EU_CAMS_SURFACE_REC_G&feature=61ad007aeb51328b642656dc 2023-03-21 00:02:04.233505+00:00 2023-06-27 16:26:31.450751+00:00 Data Cube Product 20211204160000 2023-03-21 00:02:04.233505+00:00 Online 10.24424/qma4-mr07 https://reliance.adamplatform.eu/?dataset=69628:EU_CAMS_SURFACE_REC_G 2023-03-20 23:59:16.741374+00:00 2023-06-27 16:26:31.369109+00:00 2021-12-04T23:00:00Z CAMS https://reliance.adamplatform.eu/?dataset=69628:EU_CAMS_SURFACE_REC_G 2023-03-20 23:59:16.741374+00:00 2021-06-08T00:00:00Z Float32 mailto:govoni@meeo.it [6.103110017363633e-09] [0.0] 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 31dfa2dc-9257-4800-a66d-b2f497b8bbc8 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)) -25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997 57af8e5d-c16f-4729-b576-30d19f9a6cb2 POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) 8c8e4259-6507-44b9-a7e4-3ef27cce74d7 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)) -25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997 665215 https://api.rohub.org/api/ros/3939a208-64fc-4800-8b29-6a97676c7508/crate/download/ 2023-03-20 23:50:28.686399+00:00 2025-12-17 10:08:45.551373+00:00 2023-03-20 23:50:28.686399+00:00 Data cube Research Object for CAMS European air quality forecasts application/ld+json https://w3id.org/ro-id/3939a208-64fc-4800-8b29-6a97676c7508 CAMS CAMS European air quality forecasts MANUAL

Palma, Raul. "CAMS European air quality forecasts." ROHub. Mar 20 ,2023. https://w3id.org/ro-id/3939a208-64fc-4800-8b29-6a97676c7508.

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)) 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)) 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)) raw data biblio metadata data 640813 https://api.rohub.org/api/resources/1c42782a-8669-4959-a5a1-72f66972615c/download/ 2023-03-21 00:35:58.849167+00:00 2023-06-27 16:32:32.650118+00:00 image/png Data Cube Collection - ADAM screenshot 2023-03-21 00:35:58.849167+00:00 Air pollution Environment/Environmental pollution/Air pollution forecast 27.092511013215855 12.3 Weather Weather CAMS European air quality forecasts. 29.129129129129126 29.1 Research Object for cam 0.7056451612903225 0.7 meteorology and climatology 100.0 0.48560017347335815 geosciences 100.0 0.48560017347335815 cam 13.951120162932792 13.7 Weather forecast Weather/Weather forecast air quality forecast 56.149193548387096 55.7 forecast 12.118126272912425 11.9 Data cube Research Object for CAMS European air quality forecasts 70.87087087087086 70.8 atmospheric sciences 100.0 0.993925154209137 data cube Research Object 21.471774193548388 21.3 data cube 14.358452138492874 14.1 air quality 34.92871690427698 34.3 Research Object 24.64358452138493 24.2 air quality 72.90748898678413 33.1 earth sciences 100.0 0.993925154209137 data cube Research Object for cam 1.9153225806451613 1.9 European air quality 19.758064516129036 19.6 Raul Palma service-account-enrichment Hydrology Environmental research Applied sciences Climatology WUNDER 17.76061776061776 9.2 monitoring 9.652509652509654 5.0 Science and technology Science and technology geosciences 100.0 0.8694591522216797 agriculture 30.85106382978724 2.9 extreme drought 10.666666666666666 6.4 Weather phenomena Weather/Weather phenomena nature system 14.166666666666666 8.5 meteorology 54.255319148936174 5.1 production system 10.424710424710426 5.4 climate change 10.96774193548387 8.5 scenario 4.387096774193548 3.4 research 5.935483870967741 4.6 The WUNDER project will develop an integrated modeling system for understanding the behavior of soil and vegetation during prolonged drought events. 29.79310344827586 21.6 WUNDER project 37.833333333333336 22.7 manager 6.709677419354839 5.2 soil sciences 100.0 0.9632349610328674 environmental sciences 100.0 0.9632349610328674 production 8.129032258064516 6.3 behavior 4.516129032258065 3.5 drought 24.258064516129032 18.8 Netherlands project 11.583011583011583 6.0 hurt 4.645161290322581 3.6 use case 8.687258687258687 4.5 the economy 14.893617021276597 1.4 climate change 13.127413127413128 6.8 Climate change Environment/Climate change drought 28.764478764478767 14.9 WUNDER research project 26.0 15.6 Weather Weather The project will actively engage with farmers, water managers and other decision makers and develop practical use cases for daily drought monitoring and prediction, thereby supporting climate-robust production systems and water management. 32.13793103448276 23.3 Drought Disaster, accident and emergency incident/Disaster/Natural disasters/Drought strategy 6.580645161290322 5.1 project 9.935483870967742 7.7 farming 6.193548387096774 4.8 water manager 11.333333333333334 6.8 geophysics 100.0 0.8694591522216797 monitoring 7.741935483870968 6.0 As a result of climate change, extreme droughts are expected to occur more often in the Netherlands, potentially causing social distress and huge economic damages. 38.06896551724138 27.6 53283316-d077-4992-9342-6e4e0c3cbe9e POINT (5.972120761871339 52.250662924742485) 5.972120761871339 52.250662924742485 POINT (5.972120761871339 52.250662924742485) 71399 https://api.rohub.org/api/ros/f02dc7aa-2824-4adf-8711-16dcb28ecaa1/crate/download/ 2023-03-30 08:08:58.014568+00:00 2025-10-17 20:05:26.398813+00:00 2023-03-30 08:08:58.014568+00:00 As a result of climate change, extreme droughts are expected to occur more often in the Netherlands, potentially causing social distress and huge economic damages. The WUNDER project will develop an integrated modeling system for understanding the behavior of soil and vegetation during prolonged drought events. The system will enable to explore scenarios and evaluate strategies for managing, planning and adapting agriculture and nature systems to extreme droughts. The project will actively engage with farmers, water managers and other decision makers and develop practical use cases for daily drought monitoring and prediction, thereby supporting climate-robust production systems and water management. application/ld+json https://w3id.org/ro-id/f02dc7aa-2824-4adf-8711-16dcb28ecaa1 Climate robust Drought Netherlands agriculture production systems watermanagement WUNDER research project MANUAL

Bos, Liduin. "WUNDER research project." ROHub. Mar 30 ,2023. https://w3id.org/ro-id/f02dc7aa-2824-4adf-8711-16dcb28ecaa1.

POINT (5.972120761871339 52.250662924742485) raw data metadata biblio data 64896 https://api.rohub.org/api/resources/d61369c9-3c29-439e-9d88-ec3a5cc70038/download/ 2023-03-30 08:13:55.983508+00:00 2023-03-30 08:13:58.122530+00:00 image/png Logo_with_text.png 2023-03-30 08:13:55.983508+00:00 Liduin Bos service-account-enrichment Earth sciences Istituto Nazionale di Geofisica e Vulcanologia elisa.trasatti@ingv.it Elisa Trasatti 0000-0002-2983-045X christian.bignami@ingv.it Christian Bignami 0000-0002-8632-9979 https://reliance.adamplatform.eu/?dataset=87613:S1AB_interferograms_diff_unfiltered_pha 2023-05-12 08:01:10.862259+00:00 2023-05-12 08:06:49.762391+00:00 Stack of Sentinel-1 interferograms used to generate time series of deformation by LiCSBAS method 2022-06-14T00:00:00Z LiCSAR https://reliance.adamplatform.eu/?dataset=87613:S1AB_interferograms_diff_unfiltered_pha 2023-05-12 08:01:10.862259+00:00 2014-10-19T00:00:00Z Float32 mailto:mantovani@meeo.it [3.141592025756836] [-3.1415774822235107] 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 8a063575-480c-465a-8c9d-9e240373663d POLYGON ((-24.5114722 35.4383341, 17.2561084 35.4383341, 17.2561084 66.4045278, -24.5114722 66.4045278, -24.5114722 35.4383341)) POLYGON ((-17.9 65.4, -15.3 65.4, -15.3 64.68, -17.9 64.68, -17.9 65.4)) -17.9 65.4, -15.3 65.4, -15.3 64.68, -17.9 64.68, -17.9 65.4 POLYGON ((-24.5114722 35.4383341, 17.2561084 35.4383341, 17.2561084 66.4045278, -24.5114722 66.4045278, -24.5114722 35.4383341)) -24.5114722 35.4383341, 17.2561084 35.4383341, 17.2561084 66.4045278, -24.5114722 66.4045278, -24.5114722 35.4383341 e13e32ef-ecbb-4404-8c69-e40c7cc3625b POLYGON ((-17.9 65.4, -15.3 65.4, -15.3 64.68, -17.9 64.68, -17.9 65.4)) 16180731 https://api.rohub.org/api/ros/293a5412-d1de-4c2e-9d51-6e467d08e493/crate/download/ 2023-03-30 13:42:54.684948+00:00 2025-10-17 20:05:19.325455+00:00 2023-03-30 13:42:54.684948+00:00 This is a preliminary output of multi-temporal InSAR application based on LiCSBAS method and Sentinel-1 data application/ld+json https://w3id.org/ro-id/293a5412-d1de-4c2e-9d51-6e467d08e493 Interferometry SAR Sentinel-1 Volcano InSAR ground velocity map and deformation time series of Askja Volcano - Iceland MANUAL

Bignami, Christian, and Elisa Trasatti. "InSAR ground velocity map and deformation time series of Askja Volcano - Iceland." ROHub. Mar 30 ,2023. https://w3id.org/ro-id/293a5412-d1de-4c2e-9d51-6e467d08e493.

POLYGON ((-17.9 65.4, -15.3 65.4, -15.3 64.68, -17.9 64.68, -17.9 65.4)) POLYGON ((-24.5114722 35.4383341, 17.2561084 35.4383341, 17.2561084 66.4045278, -24.5114722 66.4045278, -24.5114722 35.4383341)) POLYGON ((-24.5114722 35.4383341, 17.2561084 35.4383341, 17.2561084 66.4045278, -24.5114722 66.4045278, -24.5114722 35.4383341)) POLYGON ((-24.5114722 35.4383341, 17.2561084 35.4383341, 17.2561084 66.4045278, -24.5114722 66.4045278, -24.5114722 35.4383341)) POLYGON ((-24.5114722 35.4383341, 17.2561084 35.4383341, 17.2561084 66.4045278, -24.5114722 66.4045278, -24.5114722 35.4383341)) POLYGON ((-24.5114722 35.4383341, 17.2561084 35.4383341, 17.2561084 66.4045278, -24.5114722 66.4045278, -24.5114722 35.4383341)) biblio raw data metadata data 6264209 https://api.rohub.org/api/resources/11dc008f-b711-40a0-8e82-eaa697e1f823/download/ 2023-03-30 14:03:05.990607+00:00 2023-03-30 14:03:08.331213+00:00 Jupyter Notebook used to process SAR data based on LiCSBAS method 2023-03-30 14:03:05.990607+00:00 4049604 https://api.rohub.org/api/resources/5151f236-1eeb-406d-b2ba-4e29d56a66dc/download/ 2023-03-30 14:01:54.140394+00:00 2023-03-30 14:01:57.000178+00:00 application/pdf Additional reference paper of LiCSBAS method 2023-03-30 14:01:54.140394+00:00 217 https://api.rohub.org/api/resources/670bca4c-404f-4a9a-97bd-303806de1af0/download/ 2023-03-30 13:58:57.335504+00:00 2023-03-30 13:58:59.536364+00:00 application/vnd.google-earth.kml+xml Reference point for LiCSBAS in KML format 2023-03-30 13:58:57.335504+00:00 48531 https://api.rohub.org/api/resources/712c463e-2f2f-475b-967f-2e561b2eee28/download/ 2023-03-30 14:08:11.436199+00:00 2023-03-30 14:08:13.252025+00:00 image/png Askja mean ground velocity map from LiCSBAS processing 2023-03-30 14:08:11.436199+00:00 6702901 https://api.rohub.org/api/resources/9374549b-0117-4045-9588-6f483e9856bc/download/ 2023-03-30 14:01:48.200622+00:00 2023-03-30 14:01:50.637234+00:00 application/pdf Reference paper of LiCSBAS method 2023-03-30 14:01:48.200622+00:00 2475 https://api.rohub.org/api/resources/c02e5cfe-8c6d-4aee-b8c0-a4766840bda5/download/ 2023-03-30 13:58:52.543824+00:00 2023-03-30 13:58:54.691796+00:00 text/plain List of the used images 2023-03-30 13:58:52.543824+00:00 1192839 https://api.rohub.org/api/resources/e5fab799-28f6-4183-afa8-9b330c19f422/download/ 2023-03-30 13:59:02.903186+00:00 2023-03-30 13:59:06.133136+00:00 image/png Final connection graph 2023-03-30 13:59:02.903186+00:00 131 https://api.rohub.org/api/resources/e6b17268-f2db-4e36-9fbe-b626f17e8c42/download/ 2023-03-30 14:12:40.289166+00:00 2023-03-30 14:12:45.130812+00:00 This file is a structured h5 file, containing all the results obtained by InSAR processing text/plain LiCSBAS output 2023-03-30 14:12:40.289166+00:00 data 10.277324632952691 6.3 Askja Volcano 12.293853073463266 8.2 Iceland velocity 13.70309951060359 8.4 application 7.504078303425774 4.6 Sentinel-1 11.394302848575713 7.6 earth sciences 100.0 0.7591474056243896 map 14.518760195758565 8.9 Iceland 14.02936378466558 8.6 LiCSBAS method 7.427341227125941 6.9 velocity 12.893553223388306 8.6 Iceland 12.593703148425787 8.4 SAR interferometry 20.83958020989505 13.9 InSAR ground velocity map and deformation time series of Askja Volcano - Iceland. 50.25025025025025 50.2 This is a preliminary output of multi-temporal InSAR application based on LiCSBAS method and Sentinel-1 data 49.749749749749746 49.7 ground 11.745513866231649 7.2 Computer crime Crime, law and justice/Crime/Computer crime time series 16.49175412293853 11.0 deformation 10.766721044045678 6.6 time series 17.45513866231648 10.7 geophysics 100.0 0.7591474056243896 InSAR ground velocity map 50.16146393972013 46.6 geosciences 100.0 0.8135037422180176 earth resources and remote sensing 100.0 0.8135037422180176 InSAR application 23.896663078579117 22.2 deformation time series 15.823466092572659 14.7 map 13.493253373313342 9.0 ground velocity map 2.6910656620021527 2.5 service-account-enrichment Applied sciences Earth sciences Earth observation Istituto Nazionale di Geofisica e Vulcanologia elisa.trasatti@ingv.it Elisa Trasatti 0000-0002-2983-045X christian.bignami@ingv.it Christian Bignami 0000-0002-8632-9979 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia earth sciences 100.0 0.8714317679405212 earth resources and remote sensing 100.0 0.6567071676254272 Adam 12.130801687763713 11.5 Sentinel-1 dataset from LiCSAR over Iceland. 29.429429429429426 29.4 geosciences 100.0 0.6567071676254272 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences Ro 19.646017699115045 11.1 collection 6.751054852320675 6.4 Language Arts, culture and entertainment/Culture/Language catalog 8.495575221238939 4.8 dataset from LiCSAR 32.16432865731463 32.1 Ro 12.236286919831224 11.6 LiCSAR catalogue 39.478957915831664 39.4 Iceland 26.371681415929203 14.9 This RO provides the ADAM collection of the Sentinel-1 dataset over Iceland based on the LiCSAR catalogue. 70.57057057057057 70.5 LiCSAR 17.19409282700422 16.3 Sentinel-1 16.350210970464136 15.5 collection of the Sentinel-1 dataset 28.35671342685371 28.3 collection 12.212389380530974 6.9 Iceland 15.611814345991561 14.8 dataset 33.27433628318584 18.8 atmospheric sciences 100.0 0.8714317679405212 dataset 19.72573839662447 18.7 Iceland https://www.wikidata.org/wiki/Q189 POLYGON ((-24.5114722 62.9130833, -18.2004722 62.9130833, -18.2004722 65.6250833, -24.5114722 65.6250833, -24.5114722 62.9130833)) -24.5114722 62.9130833, -18.2004722 62.9130833, -18.2004722 65.6250833, -24.5114722 65.6250833, -24.5114722 62.9130833 service-account-enrichment f6815677-f0f3-4adf-b41c-3651b46dcd98 POLYGON ((-24.5114722 62.9130833, -18.2004722 62.9130833, -18.2004722 65.6250833, -24.5114722 65.6250833, -24.5114722 62.9130833)) 3171585 https://api.rohub.org/api/ros/fb3a8b1f-7132-4c0e-80c8-33ff294808da/crate/download/ 2023-05-11 13:56:45.603551+00:00 2025-03-05 01:21:29.663824+00:00 2023-05-11 13:56:45.603551+00:00 This RO provides the ADAM collection of the Sentinel-1 dataset over Iceland based on the LiCSAR catalogue. application/ld+json https://w3id.org/ro-id/fb3a8b1f-7132-4c0e-80c8-33ff294808da Volcano deformation remote sensing Data Cube Product Sentinel-1 dataset from LiCSAR over Iceland MANUAL https://w3id.org/ro-id/edb66ea9-473d-4c06-ab3f-2d19c2c1324b https://w3id.org/ro-id/402c40d4-158f-4ce4-96dd-c68830ea532c https://w3id.org/ro-id/57b95ef9-d464-4a29-b821-261368682ee4 https://w3id.org/ro-id/9e53d711-9fb6-4f41-86bc-f23e836c020e https://w3id.org/ro-id/bdc4f6fb-0827-4b40-9cb5-42cbcbd380b1 https://w3id.org/ro-id/c59c2dcd-3f67-48e2-b254-850ca2f37180 https://w3id.org/ro-id/0c494b28-e519-491c-96c4-bae3d7ce3dea https://w3id.org/ro-id/c5f4a19e-8630-4121-a916-0c34026e6b85 https://w3id.org/ro-id/3e66252e-95da-47dd-b4aa-6f5e79f7fe4d https://w3id.org/ro-id/45982317-df71-49ea-945f-b3f0af0bb375 https://w3id.org/ro-id/1290a558-3d81-4cc5-b89e-824ef6fb2542 https://w3id.org/ro-id/449cd8ed-30cd-4efc-bb48-f9bc4c311a27 https://w3id.org/ro-id/5fd43eaf-e74c-4105-86c6-4f300fdc646b https://w3id.org/ro-id/b0878bec-a168-480a-b191-826f71b7431c https://w3id.org/ro-id/b92ba176-1b66-4ccd-ab10-6bd5815a5d17 https://w3id.org/ro-id/c07a54ea-e182-4b4c-bd54-dba02bc7dac8 https://w3id.org/ro-id/c78f114e-30f4-48d3-81b8-b50ab305bdcc https://w3id.org/ro-id/0e4008cc-f8f0-4667-b0f7-8d88f8e00be1 https://w3id.org/ro-id/310fe117-8367-4ca4-b2ec-bf88a05f5f34 https://w3id.org/ro-id/5c40da1c-58f1-4795-add1-596257276a1a https://w3id.org/ro-id/8c101cd3-be24-4d7d-b87b-ec1152e62fb3 https://w3id.org/ro-id/bc49f557-72b1-47ff-9725-e195bd9b9c20 https://w3id.org/ro-id/30336621-dc3e-482d-b63b-11f259060da8 https://w3id.org/ro-id/a1fd7c1b-0998-4fac-a1cb-129aac343b9b

Bignami, Christian, INGV GeoSAR Laboratory, and Elisa Trasatti. "Sentinel-1 dataset from LiCSAR over Iceland." ROHub. May 11 ,2023. https://w3id.org/ro-id/fb3a8b1f-7132-4c0e-80c8-33ff294808da.

POLYGON ((-24.5114722 62.9130833, -18.2004722 62.9130833, -18.2004722 65.6250833, -24.5114722 65.6250833, -24.5114722 62.9130833)) POLYGON ((-24.5114722 62.9130833, -18.2004722 62.9130833, -18.2004722 65.6250833, -24.5114722 65.6250833, -24.5114722 62.9130833)) POLYGON ((-24.5114722 62.9130833, -18.2004722 62.9130833, -18.2004722 65.6250833, -24.5114722 65.6250833, -24.5114722 62.9130833)) data metadata raw data biblio 3158394 https://api.rohub.org/api/resources/384da3d5-c835-478a-b8bc-ea4c3512394e/download/ 2023-05-12 11:59:25.373609+00:00 2023-05-12 11:59:26.557539+00:00 image/png Screenshot 2023-05-12 alle 13.59.08.png 2023-05-12 11:59:25.373609+00:00 https://reliance.adamplatform.eu/?dataset=87613:S1AB_interferograms_diff_unfiltered_pha&feature=62c5b5dd8b687b8b40641282 2023-05-11 17:51:52.274623+00:00 2023-05-12 11:57:48.040740+00:00 LICSAR interferograms dataset based on Sentinel-1 SAR data since 2016 over Iceland LICSAR interferograms dataset in Adam 2023-05-11 17:51:52.274623+00:00 https://w3id.org/ro-id/fb3a8b1f-7132-4c0e-80c8-33ff294808da/3daef89c-f0cf-4d92-9dc1-045ae1442625/1cc1f343-644b-47ad-b037-33e288492ae6 Online labgeosar@ingv.it INGV GeoSAR Laboratory Applied sciences Istituto Nazionale di Geofisica e Vulcanologia luca.merucci@ingv.it Luca MERUCCI 0000-0001-6910-8800 ocean 3.807390817469205 3.4 In this research object a test to extract the plume pixel using AOT retrieval at 0.55 micron for both ocean and land is performed. 18.31831831831832 18.3 geosciences 100.0 0.6860049962997437 partition 4.367301231802911 3.9 Etna https://www.wikidata.org/wiki/Q16990 pixel 5.03919372900336 4.5 Satellite technology Economy, business and finance/Economic sector/Computing and information technology/Satellite technology Etna 13.406940063091481 8.5 National Aeronautics and Space Administration https://www.wikidata.org/wiki/Q23548 MODIS 15.772870662460566 10.0 Mountains Environment/Natural resources/Land resources/Mountains atmospheric sciences 100.0 0.5951478481292725 Etna 9.518477043673013 8.5 National Aeronautics and Space Administration 18.769716088328074 11.9 earth sciences 100.0 0.5951478481292725 Etna plume segmentation using MODIS retrieval. 34.73473473473474 34.7 Feb-28-2021 Etna plume segmentation 16.10810810810811 14.9 satellite 11.356466876971608 7.2 plume 7.3908174692049275 6.6 terra 7.838745800671893 7.0 sensor 13.091482649842272 8.3 eruption of Etna 12.0 11.1 National Aeronautics and Space Administration 12.989921612541993 11.6 retrieval 11.534154535274357 10.3 plume pixel 7.891891891891892 7.3 research 6.9428891377379625 6.2 3cc83fb8-8712-4862-958a-64a8332cd1c2 POINT (14.993591308593752 37.73705525336632) 14.993591308593752 37.73705525336632 POINT (14.993591308593752 37.73705525336632) service-account-enrichment 593028 https://api.rohub.org/api/ros/9a0df9ca-1970-4edf-9815-4a2f15702046/crate/download/ 2023-06-05 11:09:57.449463+00:00 2025-03-05 00:51:33.278883+00:00 2023-06-05 11:09:57.449463+00:00 The eruption of Etna 28 February 2021 was seen by the MODIS sensor during the passage of the satellite NASA-Terra alle 09:40 UTC. In this research object a test to extract the plume pixel using AOT retrieval at 0.55 micron for both ocean and land is performed. application/ld+json https://w3id.org/ro-id/9a0df9ca-1970-4edf-9815-4a2f15702046 Etna plume segmentation using MODIS retrieval MANUAL https://w3id.org/ro-id/9a0df9ca-1970-4edf-9815-4a2f15702046/7685a7a0-0d58-4366-80bc-9036f5369fc1 https://w3id.org/ro-id/cb78c0a3-972f-4162-8f84-65604e513145 https://w3id.org/ro-id/2340d9e9-56c6-42bc-bf73-cb4cb4a74ee0 https://w3id.org/ro-id/1382a7b2-856c-4fa8-886d-c706a6003b28 https://w3id.org/ro-id/07b265f2-1c9e-4a34-886d-ae5f09527db8 https://w3id.org/ro-id/0bcd94e5-a41f-4b79-849c-6ad946f0819a https://w3id.org/ro-id/161890b8-bebc-4be5-aa2b-29b88ebeab58 https://w3id.org/ro-id/261c0989-1e44-4e1c-bcf5-4cdfcc3489d8 https://w3id.org/ro-id/544da7f4-7bd1-42cc-bb21-4d68da85ce2a https://w3id.org/ro-id/6e87e897-65a4-49d3-a47b-40387a62c3cf https://w3id.org/ro-id/8d6fb7e3-2d58-4d19-b785-2880a620a66a https://w3id.org/ro-id/93015785-ebaf-49eb-94d5-1c8dfe174615 https://w3id.org/ro-id/99117f8e-9331-46f7-b5bd-b33ffcdf1908 https://w3id.org/ro-id/9e26ea0b-0c58-49d4-bdda-e4e266cc5afa https://w3id.org/ro-id/9fcb78e5-b57b-494e-97f1-03da405a37a0 https://w3id.org/ro-id/ae4df87d-f749-409b-8c9a-865d8f815314 https://w3id.org/ro-id/bd0d5017-7f6b-4a08-9c33-afc4ba06afc4 https://w3id.org/ro-id/bd8d6d94-088b-4528-8aa6-95ed1827e5a1 https://w3id.org/ro-id/25911737-dd4a-4af9-bcfc-45acab10ed98 https://w3id.org/ro-id/31714453-8941-43e4-860a-7c30ebdfee36 https://w3id.org/ro-id/1b999346-95e0-4890-8ffc-d1ad64888260 https://w3id.org/ro-id/24e78d13-58b7-421b-a39d-b8cc2fe4ccd9 https://w3id.org/ro-id/9c03ceda-aaa1-4cc2-ba81-c6f51c4764a5 https://w3id.org/ro-id/b6ea3e05-ed2e-4168-8a9e-df2d5c2e6026 https://w3id.org/ro-id/d1970226-f9c6-4c6f-a137-85a6530d65dd https://w3id.org/ro-id/2267a922-f042-40de-a3d7-32798f152a04 https://w3id.org/ro-id/23fe4e72-0779-44c9-b950-0e0785bf1cb5 https://w3id.org/ro-id/3125fa22-e031-4180-a162-f8ead692c437 https://w3id.org/ro-id/4360193a-931d-4bba-bb57-7ab053f6012f https://w3id.org/ro-id/7a0715e5-30b3-4fd3-87c8-146c6ad9b690 https://w3id.org/ro-id/bffbbe94-d40c-4a88-b67b-63f1770689ea https://w3id.org/ro-id/f3e4abb9-f1a6-46fb-9a69-148dd735b7f9 https://w3id.org/ro-id/09142028-49b9-46b1-a96d-e26348e6150c https://w3id.org/ro-id/c6c75d22-23a8-4932-98d3-43a4c09ca2db https://w3id.org/ro-id/38f7182e-10af-4b18-aaf0-821bcc642854 https://w3id.org/ro-id/88bc2796-e188-4b86-adf4-ca80a0bed2ae https://w3id.org/ro-id/968fa1cf-e9ec-4fa8-95a2-2f7f8ba83662 https://w3id.org/ro-id/a52a78c3-0157-48a9-a6eb-36f6deaceeba https://w3id.org/ro-id/e35f12d1-13bf-4d60-af04-2b0ab1c23c75 https://w3id.org/ro-id/07e4a9be-752e-47fb-9a7f-1f13f2b0ec20 https://w3id.org/ro-id/34b14943-b21c-43ed-a896-adf42a24e307 https://w3id.org/ro-id/cd5439d8-e2d8-48a9-83da-361014dcb3f6 https://w3id.org/ro-id/374d4281-d6bb-4a65-88d1-e7f526571618 https://w3id.org/ro-id/d7bc00db-b65e-4133-a7b7-ccaf410f866c

Stelitano, Dario, and Luca MERUCCI. "Etna plume segmentation using MODIS retrieval." ROHub. Jun 05 ,2023. https://w3id.org/ro-id/9a0df9ca-1970-4edf-9815-4a2f15702046.

POINT (14.993591308593752 37.73705525336632) raw data biblio metadata data https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007EF17F736861726547756964233635386633376136306464366439636263306433323262343164323164653766636836656363233732356634616233366362323664306662666330633132346337373565666565636865653439236362303263643363373038366639396331313065363635623935336633663364636836623762/content 2023-06-05 11:16:42.168642+00:00 2023-06-05 11:27:50.073280+00:00 Static JN with embedded output image 2023-06-05 11:16:42.168642+00:00 https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007E76CE736861726547756964233661653535626535623262646230333833613535303139653362346238633664636836656363233732356634616233366362323664306662666330633132346337373565666565636865653439233030303365313462646165316637376335656636633639353639336232666163636837376132/content 2023-06-05 11:19:03.184328+00:00 2023-06-05 11:19:04.040439+00:00 MODIS pixel search and extractJupyter notebook 2023-06-05 11:19:03.184328+00:00 587903 https://api.rohub.org/api/resources/d3d41a85-6473-4e7a-ae57-860b69f7119f/download/ 2023-06-05 11:12:59.686792+00:00 2023-06-05 11:21:57.629406+00:00 image/png MODIS_ROI.png 2023-06-05 11:12:59.686792+00:00 Space programme Science and technology/Research/Scientific exploration/Space programme ground 4.143337066069429 3.7 test 3.5834266517357225 3.2 MODIS sensor 26.7027027027027 24.7 sensor 9.182530795072788 8.2 Oceans Environment/Natural resources/Water/Oceans eruption 4.591265397536394 4.1 satellite 9.070548712206048 8.1 retrieval 16.246056782334385 10.3 earth resources and remote sensing 100.0 0.6860049962997437 astronautics 100.0 1.2 The eruption of Etna 28 February 2021 was seen by the MODIS sensor during the passage of the satellite NASA-Terra alle 09:40 UTC. 46.946946946946944 46.9 Science and technology Science and technology 09:40 UTC MODIS retrieval 37.2972972972973 34.5 terra 11.356466876971608 7.2 INGV dario.stelitano@ingv.it Dario Stelitano Earth observation Istituto Nazionale di Geofisica e Vulcanologia elisa.trasatti@ingv.it Elisa Trasatti 0000-0002-2983-045X christian.bignami@ingv.it Christian Bignami 0000-0002-8632-9979 https://reliance.adamplatform.eu/?dataset=87614:S1AB_interferograms_unw 2023-06-12 11:09:11.380098+00:00 2023-06-12 11:10:19.663967+00:00 2022-06-14T00:00:00Z SAR https://reliance.adamplatform.eu/?dataset=87614:S1AB_interferograms_unw 2023-06-12 11:09:11.380098+00:00 2014-10-19T00:00:00Z Float32 mailto:mantovani@meeo.it [157.0796356201172] [-201.0619354248047] 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia geology 100.0 0.8757247924804688 dataset from LiCSAR over Etna 2.705410821643287 2.7 Ro 11.371237458193978 10.2 Sentinel-1 dataset from LiCSAR over Etna volcano. 30.43043043043043 30.4 Mountains Environment/Natural resources/Land resources/Mountains 012547af-a1a2-445e-a1be-ad7dd3497791 POLYGON ((13.815307617187502 36.94989178681327, 13.815307617187502 38.35888785866677, 16.089477539062504 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Bignami, Christian, Elisa Trasatti, and INGV GeoSAR Laboratory. "Sentinel-1 dataset from LiCSAR over Etna volcano." ROHub. Jun 09 ,2023. https://w3id.org/ro-id/1c9bfc94-dbb9-475e-af50-601bff9f6c0c.

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CAMS monitors and forecasts European air quality and worldwide long-range transport of pollutants. CAMS air quality Copernicus- Air quality 2023-09-12 07:25:07.996127+00:00 Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 https://reliance.adamplatform.eu/?dataset=69624:EU_CAMS_SURFACE_NO_G&feature=61acffa0eb51328b64265197 2023-09-27 09:48:55.471034+00:00 2023-09-27 09:48:58.624211+00:00 Nitrogen Oxide CAMS CAMS Surface No 2023-09-27 09:48:55.471034+00:00 Online https://reliance.adamplatform.eu/?dataset=69630:EU_CAMS_SURFACE_SO2_G&feature=64cbc2a1c97dc8f411d9fbe8 2023-09-11 08:46:27.967031+00:00 2023-09-26 08:22:35.382855+00:00 Sulphur dioxide (SO2) from Copernicus Atmosphere Monitoring Service SO2 CAMS Surface SO2 2023-09-11 08:46:27.967031+00:00 Online post@simula.no 00vn06n10 Simula Research Laboratory 01xtthb56 University of Oslo 10.13039/501100000780::101017501 REsearch LIfecycle mAnagemeNt for Earth Science Communities and CopErnicus users in EOSC REsearch LIfecycle mAnagemeNt for Earth Science Communities and CopErnicus users in EOSC 19943f05-b786-49a5-956c-83ee4018ff5c POLYGON ((-5.185546875000001 35.7928652277101, -5.185546875000001 36.325747502046006, -2.2192382812500004 36.325747502046006, -2.2192382812500004 35.7928652277101, -5.185546875000001 35.7928652277101)) 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 ((-5.185546875000001 35.7928652277101, -5.185546875000001 36.325747502046006, -2.2192382812500004 36.325747502046006, -2.2192382812500004 35.7928652277101, -5.185546875000001 35.7928652277101)) -5.185546875000001 35.7928652277101, -5.185546875000001 36.325747502046006, -2.2192382812500004 36.325747502046006, -2.2192382812500004 35.7928652277101, -5.185546875000001 35.7928652277101 6bd79248-01ce-4172-9cad-6be8ea28c0c6 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)) -25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997 e3367937-9499-4651-9418-60dd5cd26c1c POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) 1304843 https://api.rohub.org/api/ros/075e9430-5949-4a09-8622-d20916994eaa/crate/download/ 2023-09-11 08:38:38.270595+00:00 2025-03-05 00:52:20.209305+00:00 2023-09-11 08:38:38.270595+00:00 ## Rationale From 1st January 2020 the global upper limit on the sulphur content of ships' fuel oil was reduced from 3.50% to 0.50%, which represents an ~86% cut (from [https://www.imo.org/en/MediaCentre/PressBriefings/pages/34-IMO-2020-sulphur-limit-.aspx](https://www.imo.org/en/MediaCentre/PressBriefings/pages/34-IMO-2020-sulphur-limit-.aspx)). ![Ship image](https://wwwcdn.imo.org/localresources/en/MediaCentre/PressBriefings/PublishingImages/2019 images pb/Sulphur 2020 inside pic.jpg) *Image from [IMO 2020 - cleaner shipping for cleaner air, 20 December 2019](https://www.imo.org/en/MediaCentre/PressBriefings/pages/34-IMO-2020-sulphur-limit-.aspx)* According to the [International Maritime Organization (IMO)](https://www.imo.org/) the new limit should lead to a 77% drop in overall SOx emissions from ships. The Figure below shows the 5 key beneficial changes from IMO's **Sulphur Limit** for Ships' fuel oil: ![Sulphur key changes](https://wwwcdn.imo.org/localresources/en/MediaCentre/PressBriefings/PublishingImages/2019 images pb/5 changes - Sulphur 2020 - infographic web.jpg) *Five beneficial changes from IMO’s Sulphur Limit for ships’ fuel oil* ## This Research Object's purpose In this work we look as the **actual impact of these measures on air pollution along shipping routes in Europe** based on Copernicus air quality data. [Copernicus Atmosphere Monitoring Service (CAMS)](https://ads.atmosphere.copernicus.eu/cdsapp#!/home) uses satellite data and other observations, together with computer models, to track the accumulation and movement of air pollutants around the planet (see [https://atmosphere.copernicus.eu/air-quality](https://atmosphere.copernicus.eu/air-quality)). ### Rohub - Adam plateform integration Some of this CAMS data is available from the [Adam platform](https://adamplatform.eu) and can be imported into a [Research Object](https://www.researchobject.org). ![ADAM Platform](https://adamplatform.eu/wp-content/uploads/2021/03/adam-home-platfomr.png) *Example of data (here daily temperatures) displayed on the Adam plateform* It is also possible, from the Research Object, to open the resource in the Adam platform, then interactively zoom into a particular geographical area (say to the right of the Strait of Gibraltar, along the track presumably followed by cargo ships to/from the Suez Canal) and change the date (for example between 2018-07-19 and 2023-07-19) to appreciate the change. #### To go further Obviously **a more detailed statistical analysis** would be required to minimize the effect of external factors (meteorological condition, level of cargo traffic, etc.) over a longer period of time to derive meaningful conclusions, however it does not seem that the high level of sulfur dioxide concentration from the pre-IMO regulation was reached after. ##### Looking at other pollutants Besides SOx the new regulation also contributed to decrease atmospheric concentrations in nitric oxides (NOx) as well as particulate matter (PM). ### References - [IMO 2020 - cleaner shipping for cleaner air, 20 December 2019](https://www.imo.org/en/MediaCentre/PressBriefings/pages/34-IMO-2020-sulphur-limit-.aspx) ## Rationale From 1st January 2020 the global upper limit on the sulphur content of ships' fuel oil was reduced from 3.50% to 0.50%, which represents an ~86% cut (from [https://www.imo.org/en/MediaCentre/PressBriefings/pages/34-IMO-2020-sulphur-limit-.aspx](https://www.imo.org/en/MediaCentre/PressBriefings/pages/34-IMO-2020-sulphur-limit-.aspx)). ![Ship image](https://wwwcdn.imo.org/localresources/en/MediaCentre/PressBriefings/PublishingImages/2019%20images%20pb/Sulphur%202020%20inside%20pic.jpg) *Image from [IMO 2020 - cleaner shipping for cleaner air, 20 December 2019](https://www.imo.org/en/MediaCentre/PressBriefings/pages/34-IMO-2020-sulphur-limit-.aspx)* According to the [International Maritime Organization (IMO)](https://www.imo.org/) the new limit should lead to a 77% drop in overall SOx emissions from ships. The Figure below shows the 5 key beneficial changes from IMO's **Sulphur Limit** for Ships' fuel oil: ![Sulphur key changes](https://wwwcdn.imo.org/localresources/en/MediaCentre/PressBriefings/PublishingImages/2019%20images%20pb/5%20changes%20-%20Sulphur%202020%20-%20infographic%20web.jpg) *Five beneficial changes from IMO’s Sulphur Limit for ships’ fuel oil* ## This Research Object's purpose In this work we look as the **actual impact of these measures on air pollution along shipping routes in Europe** based on Copernicus air quality data. [Copernicus Atmosphere Monitoring Service (CAMS)](https://ads.atmosphere.copernicus.eu/cdsapp#!/home) uses satellite data and other observations, together with computer models, to track the accumulation and movement of air pollutants around the planet (see [https://atmosphere.copernicus.eu/air-quality](https://atmosphere.copernicus.eu/air-quality)). ### Rohub - Adam plateform integration Some of this CAMS data is available from the [Adam platform](https://adamplatform.eu) and can be imported into a [Research Object](https://www.researchobject.org). ![ADAM Platform](https://adamplatform.eu/wp-content/uploads/2021/03/adam-home-platfomr.png) *Example of data (here daily temperatures) displayed on the Adam plateform* It is also possible, from the Research Object, to open the resource in the Adam platform, then interactively zoom into a particular geographical area (say to the right of the Strait of Gibraltar, along the track presumably followed by cargo ships to/from the Suez Canal) and change the date (for example between 2018-07-19 and 2023-07-19) to appreciate the change. #### To go further Obviously **a more detailed statistical analysis** would be required to minimize the effect of external factors (meteorological condition, level of cargo traffic, etc.) over a longer period of time to derive meaningful conclusions, however it does not seem that the high level of sulfur dioxide concentration from the pre-IMO regulation was reached after. ##### Looking at other pollutants Besides SOx the new regulation also contributed to decrease atmospheric concentrations in nitric oxides (NOx) as well as particulate matter (PM). ### References - [IMO 2020 - cleaner shipping for cleaner air, 20 December 2019](https://www.imo.org/en/MediaCentre/PressBriefings/pages/34-IMO-2020-sulphur-limit-.aspx) application/ld+json https://w3id.org/ro-id/075e9430-5949-4a09-8622-d20916994eaa Air pollution sulphur Data Cube Product Has the 2020 IMO fuel regulation had any noticeable impact on air pollution from shipping? MANUAL

Iaquinta, Jean, Anne Fouilloux, and Raul Palma. "Has the 2020 IMO fuel regulation had any noticeable impact on air pollution from shipping?." ROHub. Sep 11 ,2023. https://w3id.org/ro-id/075e9430-5949-4a09-8622-d20916994eaa.

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IMI news: Global limit on sulphur in ships' fuel oil reduced from 01 January 2020. 2023-09-11 11:52:22.814459+00:00 https://www.itf-oecd.org/sites/default/files/docs/potential-efuels-decarbonise-ships-aircraft-v2.pdf 2023-09-19 10:42:29.889446+00:00 2023-09-19 10:42:30.688864+00:00 This report examines the potential of electrofuels (e-fuels) to decarbonise long-haul aviation and maritime shipping. E-fuels like hydrogen, ammonia, e-methanol or e-kerosene can be produced from renewable energy and feedstocks and are more economical to deploy in these two modes than direct electrification. The analysis evaluates the challenges and opportunities related to e-fuel production technologies and feedstock options to identify priorities for making e-fuels cheaper and maximising emissions cuts. The research also explores operational requirements for the two sectors to deploy e-fuels and how governments can assist in adopting low-carbon fuels application/pdf e-fuels The Potential of E-fuels to Decarbonise Ships and Aircraft 2023-09-19 10:42:29.889446+00:00 https://wwwcdn.imo.org/localresources/en/MediaCentre/PressBriefings/PublishingImages/2019%20images%20pb/5%20changes%20-%20Sulphur%202020%20-%20infographic%20web.jpg 2023-09-12 07:52:45.389481+00:00 2023-09-12 07:52:46.306392+00:00 Five beneficial changes from IMO's Sulphur Limit for ships' fuel oil image/jpeg IMO benefits IMO 2020 - five key changes 2023-09-12 07:52:45.389481+00:00 PSNC rpalma@man.poznan.pl Raul Palma service-account-enrichment Social sciences psychometric test 31.72242874845105 25.6 test 31.72242874845105 25.6 test project 58.25825825825825 58.2 20.802612304687504 41.054501963290505 POINT (20.802612304687504 41.054501963290505) ef6298c6-a8d1-453c-82d4-ac64b589902e POINT (20.802612304687504 41.054501963290505) service-account-enrichment 5570 https://api.rohub.org/api/ros/38051187-5ecf-4bcd-86a2-2110bda04a83/crate/download/ 2023-09-12 13:33:11.284627+00:00 2025-03-05 01:24:17.300410+00:00 2023-09-12 13:33:11.284627+00:00 Description for this test project application/ld+json https://w3id.org/ro-id/38051187-5ecf-4bcd-86a2-2110bda04a83 terminology translation Test for MANU MANUAL https://w3id.org/ro-id/38051187-5ecf-4bcd-86a2-2110bda04a83/8e8db799-e445-4a8c-ab48-7bf0c9b869b1 https://w3id.org/ro-id/056b42f0-8ae7-4b4c-b74f-aec8cd96869e https://w3id.org/ro-id/0d5c07ce-b6d1-4090-a340-90cd3f792167 https://w3id.org/ro-id/49cad995-6901-445e-81aa-b14f86f1b13b https://w3id.org/ro-id/e7400201-46bb-4150-b0af-e46c74da61cb https://w3id.org/ro-id/47c04cf8-161f-4639-9cb0-49b09a72d276 https://w3id.org/ro-id/6bf2a0de-229e-4320-9921-e7d2bbf3cc15 https://w3id.org/ro-id/47755dea-9ffd-4e53-a7df-7d5338fdb661 https://w3id.org/ro-id/745153e0-edf8-4a67-9123-a27d5edcd17c https://w3id.org/ro-id/d1181dfd-cefb-4a91-b5f8-31cfefe73282 https://w3id.org/ro-id/f25f98be-cbac-42e4-ae1d-5cfca98d9fb2 https://w3id.org/ro-id/9910bcde-4b9d-48a0-ba06-d3b4a0aad5b9 https://w3id.org/ro-id/e191868c-1c50-4dd5-8b23-cbc1b3779c4d https://w3id.org/ro-id/18decb54-f980-4829-a6a8-74cc37f6db08 https://w3id.org/ro-id/e90b9796-ec48-4c6c-b68f-47b805e30c12 https://w3id.org/ro-id/cd65835f-6a54-4c00-a28d-8bfe87e81762 https://w3id.org/ro-id/d603055e-8848-4564-a9ff-b7fcaaf33087

Mickoski, Nikolche. "Test for MANU." ROHub. Sep 12 ,2023. https://w3id.org/ro-id/38051187-5ecf-4bcd-86a2-2110bda04a83.

POINT (20.802612304687504 41.054501963290505) metadata biblio data raw data description 23.223223223223222 23.2 earth sciences 100.0 0.9974162578582764 project 14.993804213135068 12.1 geology 100.0 0.9974162578582764 project 15.415415415415415 15.4 engineering 100.0 0.710332453250885 Description for this test project 55.45545545545545 55.4 test 35.73573573573574 35.7 Test for MANU. 44.54454454454454 44.5 electronics and electrical engineering 100.0 0.710332453250885 description 21.561338289962823 17.4 test for Manu 41.74174174174174 41.7 Manu 25.625625625625624 25.6 neworg1@example.org abcd123 Example Org 1 Nikolche Mickoski Medical science life sciences (general) 100.0 0.9461730122566223 immunology 70.85714285714286 12.4 vaccine 27.764976958525345 24.1 approach 4.86284289276808 3.9 vaccination with Covid-19 vaccine 8.913934426229508 8.7 vaccination 23.041474654377883 20.0 approaches of parents 13.831967213114755 13.5 Children Society/Mankind/Children vaccination 23.44139650872818 18.8 geochemistry 100.0 0.9932084679603577 children's vaccination 19.672131147540984 19.2 service-account-enrichment 4603 https://api.rohub.org/api/ros/58507a37-ee00-4173-9c7d-f0bd8effa41d/crate/download/ 2023-09-12 13:51:43.099319+00:00 2025-03-05 01:14:06.201707+00:00 2023-09-12 13:51:43.099319+00:00 The aim of this project is to evaluate the perceptions and approaches of parents toward childhood vaccines and especially children's vaccination with covid-19 vaccines. application/ld+json https://w3id.org/ro-id/58507a37-ee00-4173-9c7d-f0bd8effa41d Parent's hesitation toward children's vaccination with Covid-19 vaccine MANUAL https://w3id.org/ro-id/112e1778-8aa1-47f2-9c5f-396538366e85 https://w3id.org/ro-id/7dbfc356-29c8-4155-90fd-ac348e68f2a4 https://w3id.org/ro-id/1b9f9b27-d55f-46a0-b0da-f3557bab3d54 https://w3id.org/ro-id/2ee2200d-bfc3-4f2b-ab13-6e00980b276c https://w3id.org/ro-id/6611e58b-05d6-419a-a1cf-6e5b63ddd58a https://w3id.org/ro-id/7417d9b3-a1cd-434a-8912-36017cf5f773 https://w3id.org/ro-id/adfd78bd-7095-4394-9a69-3e7526f31e45 https://w3id.org/ro-id/ba3a8506-9cc0-44d3-acc9-e2ad73e9e16f https://w3id.org/ro-id/e208b14e-a72d-43ad-9309-b00c175c2b0b https://w3id.org/ro-id/eeb75df2-4302-4bef-995f-be708190febe https://w3id.org/ro-id/efa14479-832b-4795-85bf-db09af3e0a33 https://w3id.org/ro-id/4858ce40-f2f9-485a-a052-5b4446884e69 https://w3id.org/ro-id/8adcb243-5ec4-413e-9813-4cbcb44dc18f https://w3id.org/ro-id/220d1263-b6a9-4c9e-a7e1-c8479fdf8e16 https://w3id.org/ro-id/5ce3ac7a-2c28-455e-b98e-b156305836e8 https://w3id.org/ro-id/d3e0f74d-a10f-4f17-8095-a8d20b54f1c5 https://w3id.org/ro-id/155fa4a8-d0d1-4a84-99c6-d37901dc63b5 https://w3id.org/ro-id/20518f29-4a3d-4c45-85df-871d0eccdf1d https://w3id.org/ro-id/813b67f3-47be-416e-814b-dd677f90a547 https://w3id.org/ro-id/afed8da2-f0b8-4ea2-9e23-027bcea2f446 https://w3id.org/ro-id/d97256ec-18aa-4774-90a0-04e8920ea64e https://w3id.org/ro-id/f44e2816-6519-406d-9145-54f9038a33d6 https://w3id.org/ro-id/f8000b11-cb32-4a62-bdc1-3f7550c207d3 https://w3id.org/ro-id/11007b44-0506-4a9d-abf4-37f240a04257 https://w3id.org/ro-id/a5ad2357-c8f1-4522-88f0-8e2767f5fe94 https://w3id.org/ro-id/1c8f7d50-c588-4b29-b265-56db3eb0d44d https://w3id.org/ro-id/2114b0c9-a6fa-4318-9a31-4656ffa91e29 https://w3id.org/ro-id/4d736908-7861-46ed-b730-350be987555a https://w3id.org/ro-id/de136d4f-8320-45fe-b700-4ceba40e05c9 https://w3id.org/ro-id/f65fdfe2-dcc0-4734-b91a-5fe0013568fb https://w3id.org/ro-id/9bb36a1e-385e-46f0-bf8c-2ca1a81eff53 https://w3id.org/ro-id/ec4f5246-c818-4ebf-b36e-f60f406d2eeb

Mehmeti, Irsida. "Parent's hesitation toward children's vaccination with Covid-19 vaccine." ROHub. Sep 12 ,2023. https://w3id.org/ro-id/58507a37-ee00-4173-9c7d-f0bd8effa41d.

raw data data biblio metadata Parent and child Society/Family/Parent and child parent 5.486284289276808 4.4 parents 7.855361596009975 6.3 medicine 29.14285714285714 5.1 hesitation 8.064516129032258 7.0 earth sciences 100.0 0.9932084679603577 The aim of this project is to evaluate the perceptions and approaches of parents toward childhood vaccines and especially children's vaccination with covid-19 vaccines. 71.87187187187187 71.8 life sciences 100.0 0.9461730122566223 reluctance 7.730673316708229 6.2 covid 19 17.741935483870968 15.4 vaccine 28.428927680798004 22.8 Vaccines Health/Health treatment/Preventative medicine/Vaccines aim 8.179723502304148 7.1 childhood vaccine 42.21311475409836 41.2 project 7.605985037406484 6.1 Parent's hesitation toward children's vaccination with Covid-19 vaccine. 28.128128128128125 28.1 purpose 7.98004987531172 6.4 perception 6.6084788029925186 5.3 parent 8.064516129032258 7.0 parent's hesitation 15.368852459016395 15.0 project 7.142857142857143 6.2 Irsida Mehmeti Physical sciences Fundamental particle Electronics Science and technology Science and technology virtual environment 19.719827586206897 18.3 code on the field 20.387359836901123 20.0 virtual reality 18.25993555316864 17.0 physics 20.193340494092375 18.8 Fortran 6.015037593984963 5.6 theoretical solid state physics 2.344546381243629 2.3 engineering 100.0 0.4695174992084503 Language Arts, culture and entertainment/Culture/Language Creating and streamlining virtual environments with **OpenMP** to use parallelization with **Fortran** code on the field of theoretical solid state physics. 86.58658658658658 86.5 HPC clusters 20.591233435270134 20.2 other earth sciences 100.0 0.5775578618049622 code 13.10418904403867 12.2 earth sciences 100.0 0.5775578618049622 Fortran 5.926724137931035 5.5 solid state 16.433941997851772 15.3 physics 100.0 6.2 communications and radar 100.0 0.4695174992084503 Using OpenMP with HPC clusters. 13.413413413413412 13.4 code 12.931034482758621 12.0 physics 20.258620689655174 18.8 solid state 16.810344827586206 15.6 Food Economy, business and finance/Economic sector/Consumer goods/Food HPC 9.267241379310345 8.6 domain 16.86358754027927 15.7 service-account-enrichment 4913 https://api.rohub.org/api/ros/da3e1263-e472-48be-9f0e-a287ad4ca28b/crate/download/ 2023-09-12 13:52:04.402153+00:00 2025-03-05 02:47:04.534534+00:00 2023-09-12 13:52:04.402153+00:00 Creating and streamlining virtual environments with **OpenMP** to use parallelization with **Fortran** code on the field of theoretical solid state physics. application/ld+json https://w3id.org/ro-id/da3e1263-e472-48be-9f0e-a287ad4ca28b HPC Dataset Using OpenMP with HPC clusters MANUAL https://w3id.org/ro-id/7336f54e-2ac1-4767-9964-6c495fbe3e05 https://w3id.org/ro-id/216d4ee3-aec0-4485-8623-31241c293c62 https://w3id.org/ro-id/2e80b20f-9c8d-43b8-b8c2-b53e6bccca0f https://w3id.org/ro-id/3688f0ab-1274-4c55-a0e7-e5f7f8633cef https://w3id.org/ro-id/5b84c855-7c55-4bc2-b96b-80c162538a20 https://w3id.org/ro-id/69deff9a-1377-43a3-aafd-0e0dc66c5f42 https://w3id.org/ro-id/d4cdecd7-d40e-442e-bc1a-7f018033545d https://w3id.org/ro-id/f4581024-4dda-41c4-b961-0ede329f5024 https://w3id.org/ro-id/550a9663-401c-421a-af69-07d277f6979f https://w3id.org/ro-id/60f102ef-f7bf-42a1-880c-06a33f33192d https://w3id.org/ro-id/00c553a0-9512-49a8-bf2b-e52a0317f8b9 https://w3id.org/ro-id/4328152e-b9a3-48ac-adf0-f367904a4ee8 https://w3id.org/ro-id/b7444973-df06-4952-99cb-087af65f81b0 https://w3id.org/ro-id/e46a1809-a04b-4a16-b4e1-8d2e64ba8c75 https://w3id.org/ro-id/0182391d-efab-4963-afe7-8c58bf1b040a https://w3id.org/ro-id/657b12f0-af3a-45bf-a97e-fe02dc8cae21 https://w3id.org/ro-id/94037e2e-8a00-41b6-a363-c337543235b6 https://w3id.org/ro-id/94356294-3ed4-478a-93d1-ff22523697d8 https://w3id.org/ro-id/9eeb1954-8110-4ba3-a4b1-b58556eab879 https://w3id.org/ro-id/d0591043-bb2b-45ed-9425-c298ffdb2ac8 https://w3id.org/ro-id/f4f4cfe9-9e73-494d-b334-4ef52a0cb390 https://w3id.org/ro-id/41bdb7b5-614a-4dee-94b1-3578703c73aa https://w3id.org/ro-id/8827f75d-faac-4afc-b909-8448f400ec8a https://w3id.org/ro-id/0efec195-496f-40f2-ae39-81842ca2e0b4 https://w3id.org/ro-id/39b098b8-37e1-47b3-a501-9849aabf5305 https://w3id.org/ro-id/47b5b13d-02dd-47ee-8661-76dea4ddab2a https://w3id.org/ro-id/dce3a0c5-3a08-4e7c-9ff0-a5eb0f693873 https://w3id.org/ro-id/f8865cac-2161-4985-a360-3df2b0e1b87c https://w3id.org/ro-id/44a27f08-8807-4636-82f6-c0f14dce687e https://w3id.org/ro-id/8dab0d81-aefa-4e49-b9d0-f2d47a7bcd57

Rrustemi, Zgjim. "Using OpenMP with HPC clusters." ROHub. Sep 12 ,2023. https://w3id.org/ro-id/da3e1263-e472-48be-9f0e-a287ad4ca28b.

data raw data biblio metadata streamline virtual environments 22.93577981651376 22.5 Design (visual arts) Arts, culture and entertainment/Arts and entertainment/Visual arts/Design (visual arts) handheld 9.12996777658432 8.5 field 15.086206896551724 14.0 solid state physics 33.74108053007136 33.1 Zgjim Rrustemi Applied sciences https://docs.google.com/spreadsheets/d/16cYvtoMfpW2uw2U5gkO8bPQ4hnXJ2PsZsZIKoOwkDfw/edit?usp=sharing 2023-10-06 07:32:35.525329+00:00 2023-10-06 07:33:16.902294+00:00 This file uses new_atrix.csv as a starting point and filtered by dates e.g. from 1st January 2022 to 31 December 2022. Then a pivot table is created where sort, q and reslabel were selected for column and c for row. pivot filtered matrix 2022 (Google Sheet) 2023-10-06 07:32:35.525329+00:00 https://docs.google.com/spreadsheets/d/1zDfCTEyoAbD8-w-Yg9eSbSGkkcGJEFDsOAsY6k8wE3U/edit?usp=sharing 2023-10-06 07:35:30.774035+00:00 2023-10-06 07:52:03.130233+00:00 This google sheet contains the FIP convergence matrix for year 2022. The tab "matrix" is the main tab while FERs are unique list of FERs and Communities tab the list of unique names for communities. csv FIP convergence matrix Google Sheet (2022) 2023-10-06 07:35:30.774035+00:00 Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 GO FAIR Foundation barbara@gofair.foundation Barbara Magagna 0000-0003-2195-3997 https://osf.io/de6su/ 2023-10-06 07:38:16.652599+00:00 2023-10-06 07:38:17.859174+00:00 FIP.21 FIP Facilitator training by Barbara Magagna. This presentation is part of the GO FAIR Foundation series of training for facilitators. FIP FIP.21 FIP Facilitator training 2023-10-06 07:38:16.652599+00:00 post@simula.no 00vn06n10 Simula Research Laboratory 0 https://api.rohub.org/api/ros/073ab8fc-67b3-4ec7-915e-17ffb47f09c5/crate/download/ 2023-10-06 07:11:17.444701+00:00 2025-10-16 13:12:54.720948+00:00 2023-10-06 07:11:17.444701+00:00 This Research Object contains all the data used for producing a FIP convergence matrix for the year 2022. The raw data has been fetch from [https://github.com/peta-pico/dsw-nanopub-api](https://github.com/peta-pico/dsw-nanopub-api) on Thursday 5 October 2023. The original matrix called new_matrix.csv ([https://github.com/peta-pico/dsw-nanopub-api/blob/main/tables/new_matrix.csv](https://github.com/peta-pico/dsw-nanopub-api/blob/main/tables/new_matrix.csv)) is stored in the raw data folder for reference. The methodology used to create the FIP convergence Matrix is detailed in the presentation from Barbara Magagna [https://osf.io/de6su/](https://osf.io/de6su/). application/ld+json https://w3id.org/ro-id/073ab8fc-67b3-4ec7-915e-17ffb47f09c5 FAIR FIP Dataset FIP convergence Matrix Year 2022 MANUAL

Fouilloux, Anne, and Barbara Magagna. "FIP convergence Matrix Year 2022." ROHub. Oct 06 ,2023. https://w3id.org/ro-id/073ab8fc-67b3-4ec7-915e-17ffb47f09c5.

biblio data raw data metadata 598174 https://api.rohub.org/api/resources/7683f508-3363-4c9a-8eb2-12d31d7e5a4a/download/ 2023-10-06 07:19:07.196369+00:00 2023-10-06 07:40:30.380591+00:00 Partial view of the FIP convergence matrix for illustration purposes only. image/png FIP_convergenceMatrix_AF.png 2023-10-06 07:19:07.196369+00:00 2686114 https://api.rohub.org/api/resources/a55b4924-4d0b-4a17-8a5c-22dce26fcf6c/download/ 2023-10-06 07:26:35.220079+00:00 2023-10-06 07:26:37.702648+00:00 This CSV file is the result of a SPARQL query executed by a GitHub action. text/csv csv new_matrix.csv 2023-10-06 07:26:35.220079+00:00 262192 https://api.rohub.org/api/resources/bc3f8893-19ec-4d72-a1fb-20fc92244634/download/ 2023-10-06 07:50:28.741225+00:00 2023-10-06 07:51:29.248486+00:00 PDF file generated from the FIP convergence Matrix google sheet. application/pdf FIP FIP Convergence Matrix 2022 (PDF) 2023-10-06 07:50:28.741225+00:00 folder 5.031446540880504 9.6 Tut The methodology used to create the FIP convergence Matrix is detailed in the presentation from Barbara Magagna [https://osf.io/de6su/](https://osf.io/de6su/ 20.43859649122807 23.3 search 2.777777777777778 5.3 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences http 19.18238993710692 36.599999999999994 Language Arts, culture and entertainment/Culture/Language ICOS 3.2494758909853254 6.2 data folder for reference 0.7907979870596694 1.1 F D B HANDLE http://purl.org/np/RAyNaDj ru RUNoBacACVvIqIZvBGgOZ TL BsBasOvHM B HANDLE DEIMS.ID http://purl.org/np/RAT egvL VArd XMPJXkOR gelUy M wgDu Ax P gM DEIMS ID DOI Digital Object Identifierhttp://purl.org/np/RAnAWGdeI GGmDAqv vZjby XqbL ZujNz vgwK cRI DOI ePIC Persistent Identifier Consortium for eResearchhttp://purl.org/np/RAkc x DEBnK XXpw uh ZzZcfEF Kc zV m xUw j ePIC Handle System http://purl.org/np/RAvSpggcYeB tEfjF SwIkM R LmxuOvK FqJqIDIkxt Handle System PURL Persistent Uniform Resource Locatorhttp://p l.org/np/RA G fg dqE NhP QdDYI ELM nlQKILdbm m nW AdM PURL URI Uniform Resource Identifierhttp://purl.org/np/RA OsT sjRbcoFEGfOzkrcFtExipMRmoLErzg QWL c URI 12.017543859649123 13.7 version http 1.9410496046010066 2.7 Research Object 3.2494758909853254 6.2 Interior account http 1.9410496046010066 2.7 data 13.102725366876312 25.0 catalog 2.555066079295154 5.8 gold 2.6872246696035242 6.1 convergence matrix 3.1631919482386777 4.4 application profile 0.9345794392523364 1.3 Lightweight Directory Access Protocol 4.713656387665198 10.7 computer programming and software 60.702858639094615 0.71315598487854 convergence 3.19706498951782 6.1 data folder 0.9345794392523364 1.3 EML GBIF profile EML 0.8626887131560029 1.2 portal site 3.612334801762114 8.2 AP data catalog vocabulary application profile 3.8820992092020132 5.4 European Community vocabulary 2.5681341719077575 4.9 Google 3.4801762114537445 7.9 Argo 4.4493392070484585 10.1 The original matrix called new_matrix.csv ([https://github.com/peta-pico/dsw-nanopub-api/blob/main/tables/new_matrix.csv](https://github.com/peta-pico/dsw-nanopub-api/blob/main/tables/new_matrix.csv)) is stored in the raw data folder for reference. 22.280701754385966 25.4 environmental science and management 58.52139388999399 0.9911122918128967 on Thu, Oct-5-2023 Department for Education k lsQ RlxK af jos Gma PalQ EML GBIF profile EML 1.2221423436376708 1.7 Google account 2.777777777777778 5.3 aOyOilMUWEgfhamM ERDDAP Argo API Argo GDAC Argo 0.9345794392523364 1.3 Year 2022 http 14.405286343612337 32.7 linguistics 68.25396825396825 47.3 earth resources and remote sensing 39.297141360905385 0.4616749882698059 earth sciences 41.47860611000601 0.7024773955345154 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences New York This Research Object contains all the data used for producing a FIP convergence matrix for the year 2022. 22.280701754385966 25.4 metadata 2.1585903083700444 4.9 data 13.127753303964758 29.8 LDAP 4.035639412997904 7.7 for the year 2022 TA eduGAIN EGI Checkin http 5.463695183321351 7.6 raw data 6.563876651982379 14.9 portal 2.515723270440252 4.8 vocabulary 3.171806167400881 7.2 oLKoyxgj DzkS q OAuth Open data http 2.875629043853343 4.0 fip convergence matrix 50.97052480230051 70.9 Library and museum Arts, culture and entertainment/Culture/Library and museum in the raw data folder 13.587347232207044 18.9 account InnovAuth http 2.4442846872753416 3.4 lookup 4.0528634361233475 9.2 computer science 22.22222222222222 15.4 profile 4.350104821802936 8.3 atmospheric sciences 41.47860611000601 0.7024773955345154 Associated Press sixpence 5.506607929515418 12.5 directory access Protocolhttp 3.1631919482386777 4.4 terminal adapter 2.8634361233480177 6.5 CZQ PhiHlM gDrksdM EML EML GBIF profile metadata 1.1502516175413373 1.6 eduGAIN 3.406708595387841 6.5 folder 4.317180616740089 9.8 Lod F CKAN Comprehensive Knowledge Archive Networkhttp://purl.org/np/RAw cpti Df z MLB mWkB R clObaPsyklAlLhwbOCw CKAN DataCite Metadata Schemehttp://purl.org/np/RAko U Q boW drM t DcbX Ixu mcSQf BrM geIQ datacite metadata scheme DCAT Data Catalog Vocabulary Version http://purl.org/ p/RAi pnoXjWoZ RjEd WVLDIyp oJsHMUx Au mPsNEdyo DCAT DCAT AP Data Catalog Vocabulary Application Profile for Data Portals in Europehttp://purl.o g/n /RAGhCnj zKSZURRm MgkkeLAnGfGYp Bw he pYS k o DCAT AP EML Ecological Metadata Languagehttp://purl.org/np/RAUOTQKnMjCWdbbaEXfelgKYEK CZQ PhiHlM gDrksdM EML EML GBIF Profile Metadata Ecological Metadata Language Global Biodiversity Information Facility Profile Metadatahttp://purl. rg/np/RA xklqUA c i K lsQ RlxK aF jos Gma PalQ EML GBIF Profile EML . . Ecological Metadata Language . . http://purl.org/np/RAwNJdzIvrCDLAzKDbVz ZpGwQzjnXI m OFm hGeDG EML . . 11.491228070175438 13.1 TA 2.725366876310273 5.2 convergence 2.7753303964757707 6.3 Associated Press 2.511013215859031 5.7 user account 5.594713656387666 12.7 European Union information technology 9.523809523809524 6.6 Argo 3.1446540880503147 6.0 jRuQCPFqt APN SVD vmG CDFKjog iso NetCDF CF. http 0.9345794392523364 1.3 search 2.3788546255506606 5.4 environmental sciences 58.52139388999399 0.9911122918128967 DCAT 3.878406708595388 7.4 World Meteorological Organization mathematical and computer sciences 60.702858639094615 0.71315598487854 INSPIRE EMF Infrastructure for Spatial Information in the European Community Environmental Monitoring Facilitieshttp://purl.org/np/RAlRm dQCh ziWs T VCJhvZHuCDLtCOsmylHSiegd k INSPIRE EMF ISO Geographic information Metadatahttp://purl.org/np/RAw X gfKkN Df Wws Axou vMrejVEFumgga D i nY ISO ISO Geographic information Metadata XML schema implementationhttp://purl.org/np/RAnLN EMEQoZ A jRuQCPFqt APN SVD vmG CDFKjog ISO NetCDF CF . http://purl.org/np/RAm N Essj oekn Qd KgooUYAk Szk bBK R jcGnlM NetCDF CF . 11.491228070175438 13.1 geosciences 39.297141360905385 0.4616749882698059 raw data 7.4423480083857445 14.2 Sextant SeaDataNet Sextant search engineshttp 0.7907979870596694 1.1 fip 6.656184486373166 12.7 Checkin 2.515723270440252 4.8 service-account-enrichment Applied sciences jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 01xtthb56 University of Oslo 0 https://api.rohub.org/api/ros/3125b7be-03f9-447e-806f-20beb66f7949/crate/download/ 2024-01-05 14:14:55.022211+00:00 2025-10-16 13:11:52.799935+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/3125b7be-03f9-447e-806f-20beb66f7949 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://w3id.org/ro-id/3125b7be-03f9-447e-806f-20beb66f7949.

biblio raw data data metadata 10.24424/pv5n-vq62 1338 https://api.rohub.org/api/resources/bdf5c934-6836-4f3c-a2f1-3438b7cd91ae/download/ 2024-01-05 14:33:02.319503+00:00 2024-01-05 14:42:23.278595+00:00 Output of the OSU MPI Get Bandwidth Test with openMPI 4.1.6 on Fram and Betzy text/csv Apptainer OSU OSU7.2-Fram-Betzy 2024-01-05 14:33:02.319503+00:00 10.24424/7nkm-2072 36301 https://api.rohub.org/api/resources/ecc14c39-87d8-4cda-8e54-2e5b5a6bd9cc/download/ 2024-01-05 14:26:46.457298+00:00 2024-01-05 14:43:41.523852+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 False 2024-01-05 15:11:39.987851+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 bench mark 24.867724867724867 14.1 earth sciences 100.0 0.4361959993839264 High Performance Computer 8.721804511278195 5.8 benchmark 22.045855379188712 12.5 MPI operation 12.607099143206854 10.3 Office of Management and Budget information technology 22.65625 2.9 Steeple chase Sport/Competition discipline/Horse racing/Steeple chase benchmark 23.60902255639098 15.7 mathematical and computer sciences 100.0 0.6090793609619141 interconnect 10.225563909774436 6.8 Education Education atmospheric sciences 100.0 0.4361959993839264 microcomputer 23.809523809523807 13.5 different Message Passing Inerface 7.099143206854345 5.8 Message Passing Inerface 13.68421052631579 9.1 computer programming and software 100.0 0.6090793609619141 interconnect 11.28747795414462 6.4 micro 21.804511278195488 14.5 University Education/School/Higher education/University Osu micro-benchmark 47.61321909424724 38.9 Trondheim micro benchmark 21.052631578947366 17.2 computer network 9.876543209876543 5.6 network interconnect 11.627906976744185 9.5 network 7.36842105263158 4.9 Ohio State University 14.586466165413531 9.7 These benchmarks are often used for comparing different Message Passing Inerface (MPI) implementations and the underlying network interconnect. 30.184804928131417 29.4 computer science 77.34375 9.9 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 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 Tromsø bandwidth 8.112874779541444 4.6 https://www.sciopen.com/article/10.1007/s11390-023-2907-5 2024-01-16 16:05:31.945926+00:00 2024-01-16 16:05:33.287119+00:00 Abstract The Slingshot interconnect designed by HPE/Cray is becoming more relevant in high-performance computing with its deployment on the upcoming exascale systems. In particular, it is the interconnect empowering the first exascale and highest-ranked supercomputer in the world, Frontier. It offers various features such as adaptive routing, congestion control, and isolated workloads. The deployment of newer interconnects sparks interest related to performance, scalability, and any potential bottlenecks as they are critical elements contributing to the scalability across nodes on these systems. In this paper, we delve into the challenges the Slingshot interconnect poses with current state-of-the-art MPI (message passing interface) libraries. In particular, we look at the scalability performance when using Slingshot across nodes. We present a comprehensive evaluation using various MPI and communication libraries including Cray MPICH, OpenMPI + UCX, RCCL, and MVAPICH2 on CPUs and GPUs on the Spock system, an early access cluster deployed with Slingshot-10, AMD MI100 GPUs and AMD Epyc Rome CPUs to emulate the Frontier system. We also evaluate preliminary CPU-b:ed support of MPI libraries on the Slingshot-11 interconnect. Slingshot interconnect High Performance MPI over the Slingshot Interconnect 2024-01-16 16:05:31.945926+00:00 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 jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 01xtthb56 University of Oslo 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 10.24424/zcq6-9r81 2024-01-09 09:49:11.365811+00:00 0 https://api.rohub.org/api/ros/ee8895fd-fe5a-46d7-9228-9d98a2d3a205/crate/download/ 2024-01-05 14:14:55.022211+00:00 2024-03-05 12:22:12.231024+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/ee8895fd-fe5a-46d7-9228-9d98a2d3a205 Apptainer HPC MPI OSU Performance bandwidth container interconnect Dataset OSU MPI Get Bandwidth Test v7.2 with OpenMPI 4.1.6 on Fram & Betzy - fork 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://w3id.org/ro-id/ee8895fd-fe5a-46d7-9228-9d98a2d3a205.

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GONZALEZ GUARDIA, ESTEBAN. "Bidirectional Paper-Repository Tracing in Software Engineering." ROHub. Jan 11 ,2024. https://w3id.org/ro-id/13dfbe3b-3132-4089-9ec9-5ae100fb143c.

raw data biblio metadata data software engineering category 19.25820256776034 13.5 none none Methodology Key Type Measures Institutional: Economic dataset 10.147991543340382 9.6 paper-code implementation 11.982881597717546 8.4 none software engineering 15.942028985507246 11.0 code repository 33.38088445078459 23.4 Knowledge Sector (EEA) Funding software engineering 10.465116279069768 9.9 Not reported/ Unknown Physical and Technological Bidirectional Paper-Repository Tracing in Software Engineering "While computer science papers frequently include their associated code repositories, establishing a clear link between papers and their corresponding implementations can be challenging due to the number of code repositories used by research publications. 42.08416833667334 42.0 research publication 13.552068473609129 9.5 Engineering none repository 18.49894291754757 17.5 Climate-ADAPT Adaptation Sectors paper 5.073995771670191 4.8 repository 28.405797101449277 19.6 dataset 11.594202898550725 8.0 Academia/ Research Institutions Policy Scale computer programming 28.94736842105263 5.5 composition 8.879492600422834 8.4 category 4.2283298097251585 4.0 IPCC publication 7.826086956521739 5.4 Manufacturing and engineering Economy, business and finance/Economic sector/Manufacturing and engineering computer science 13.333333333333332 9.2 computer science 8.879492600422834 8.4 In this paper we describe a lightweight method for effectively identifying bidirectional links between papers and repositories from both LaTeX and PDF sources. 34.969939879759515 34.9 newspaper 5.602536997885835 5.3 No policy or regulation none LaTeX 3.1712473572938693 3.0 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences implementation 9.565217391304348 6.6 Geographical Scope Climate Hazard publication 5.2854122621564485 5.0 computer science 47.89473684210526 9.1 implementation 6.342494714587739 6.0 User Needs (RAST) research 4.7568710359408035 4.5 citation 3.594080338266385 3.4 computer science paper 21.825962910128386 15.3 none Stakeholders We have used our approach to analyze more than 14000 PDF and Latex files in the Software Engineering category of Arxiv, generating a dataset of more than 1400 paper-code implementations and assessing current citation practices on it. 22.94589178356713 22.9 paper 13.333333333333332 9.2 Engineering (General) database 23.15789473684211 4.4 ESTEBAN GONZALEZ GUARDIA Physical sciences 89561 https://api.rohub.org/api/ros/3ec0b51b-0346-4ed8-b731-de744dd3bee2/crate/download/ 2024-01-24 18:20:54.288838+00:00 2025-10-16 12:45:34.548024+00:00 2024-01-24 18:20:54.288838+00:00 Datasets used in the manuscript CMST 29(1-4) 37-44 (2023) DOI:10.12921/cmst.2023.0000023 application/ld+json https://w3id.org/ro-id/3ec0b51b-0346-4ed8-b731-de744dd3bee2 Monte Carlo method auxetics hard sphere system Data management plan The f.c.c. Crystals of Hard Spheres with an Array of [001]-Nanochannel Inclusions Filled by the Simplest Hard Sphere Molecules MANUAL

Narojczyk, Jakub. "The f.c.c. Crystals of Hard Spheres with an Array of [001]-Nanochannel Inclusions Filled by the Simplest Hard Sphere Molecules." ROHub. Jan 24 ,2024. https://w3id.org/ro-id/3ec0b51b-0346-4ed8-b731-de744dd3bee2.

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The data concerns systems of f.c.c. hard spheres with size C2 [001]-nanochannel filled with hard dimers formed by spheres of diferent diameter (equal to sigma’). The length of the dimers is equal to sigma’ (L=sigma’). application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Data for systems with C2 dimer nanochannel L=sigma' 2024-01-24 18:38:16.914356+00:00 13249 https://api.rohub.org/api/resources/cd6cbce7-1d8e-49ea-b91e-b4047ddcbeec/download/ 2024-01-24 18:37:07.279013+00:00 2024-01-24 18:37:09.712664+00:00 This data sheet contains data used in the manuscript. The data concerns systems of f.c.c. hard spheres with size C1 [001]-nanochannel filled with hard dimers formed by spheres of diferent diameter (equal to sigma’). The length of the dimers is equal to sigma’ (L=sigma’). application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Data for systems with C1 dimer nanochannel L=sigma' 2024-01-24 18:37:07.279013+00:00 Interior sphere molecule 37.17171717171717 36.8 Interior 12.347354138398915 9.1 Executive (government) Politics/Government/Executive (government) dataset 22.116689280868385 16.3 physics 100.0 0.34250643849372864 CMST 29 2.2222222222222223 2.2 earth sciences 100.0 0.8104575872421265 Interior 10.942956926658905 9.4 geology 100.0 0.8104575872421265 domain 18.724559023066487 13.8 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences selection 4.74898236092266 3.5 manuscript 16.689280868385346 12.3 Datasets used in the manuscript CMST 29(1-4) 37-44 (2023) DOI:10.12921/cmst.2023.0000023 60.66066066066066 60.6 Nanochannel Inclusions Filled 13.504074505238648 11.6 molecule 12.107101280558789 10.4 sphere 13.853317811408614 11.9 Government department Politics/Government/Government department manuscript 14.202561117578579 12.2 2023 molecule 13.568521031207597 10.0 dataset 20.023282887077997 17.2 The f.c.c. Crystals of Hard Spheres with an Array of [001]-Nanochannel Inclusions Filled by the Simplest Hard Sphere Molecules. 39.33933933933933 39.3 solid-state physics 100.0 0.34250643849372864 manuscript CMST 29 36.86868686868687 36.5 CMST 15.366705471478463 13.2 f.c.c. crystal 22.02020202020202 21.8 hard sphere molecule 1.7171717171717171 1.7 crystal 11.804613297150608 8.7 narojczyk@ifmpan.poznan.pl Jakub Narojczyk Geology Environmental research Applied sciences http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/1988bdeb-d639-41f7-a825-5b2ba641ebfa 2024-02-14 15:26:35.103297+00:00 2024-02-14 15:31:03.185864+00:00 Data collected in May 2022 by CNR- ISMAR VE within the MAELSTROM project MAELSTROM: Sacca Fisola 2022 May Bathymetry 2024-02-14 15:26:35.103297+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/c1dff77c-6c59-46c8-8f5e-025155da31e9 2024-02-14 15:24:34.394902+00:00 2024-02-14 15:24:36.057895+00:00 Data collected in November 2022 by CNR- ISMAR VE within the MAELSTROM project MAELSTROM: Sacca Fisola 2022 November Bathymetry 2024-02-14 15:24:34.394902+00:00 http://libeccio.bo.ismar.cnr.it:8080/geonetwork/srv/eng/catalog.search#/metadata/dd465b46-0217-426a-ba81-4acadf0d12b9 2024-02-14 15:25:42.937203+00:00 2024-02-14 15:25:44.694591+00:00 Data collected in October 2021 by CNR- ISMAR VE within the MAELSTROM project MAELSTROM: Sacca Fisola 2021 Bathymetry 2024-02-14 15:25:42.937203+00:00 12.319541876832256 45.42951679948882 POINT (12.319541876832256 45.42951679948882) f47b6762-749a-454b-a3c7-506b2784649a POINT (12.319541876832256 45.42951679948882) 10.24424/k5aw-ay23 2024-02-14 15:58:59.314936+00:00 True 4405849 https://api.rohub.org/api/ros/7f467b48-95e1-4c0f-93c6-2cfda36a8600/crate/download/ 2024-02-14 15:00:57.275020+00:00 2025-10-16 12:39:42.461928+00:00 2024-02-14 15:00:57.275020+00:00 Bathymetric data collected in three diffrent surveys (October 2021, May 2022 and November 2022) in Sacca Fisola, Venice, Italy, by CNR- ISMAR VE within the MAELSTROM project. application/ld+json https://w3id.org/ro-id/7f467b48-95e1-4c0f-93c6-2cfda36a8600 Marine Litter bathymetry Dataset MAELSTROM Project - Sacca Fisola Bathymetry data 2021 - 2022 MANUAL

Susanna Mesghez, ANTONIO PETRIZZO, Fantina Madricardo, Taha Lahami, Alberto Santi, Nicoletta Nesto, Tihana Marceta, and Vanessa Moschino. "MAELSTROM Project - Sacca Fisola Bathymetry data 2021 - 2022." ROHub. Feb 14 ,2024. https://doi.org/10.24424/k5aw-ay23.

POINT (12.319541876832256 45.42951679948882) data metadata raw data biblio 4389047 https://api.rohub.org/api/resources/4d3dd911-9ee4-41bb-88d9-8f3dc8ad361f/download/ 2024-02-14 15:20:49.383634+00:00 2024-02-14 15:20:50.370538+00:00 image/png bathy_SF_11_2022_overview.png 2024-02-14 15:20:49.383634+00:00 Smart technology for MArinE Litter SusTainable RemOval and Management info@maelstrom-h2020.eu MAELSTROM Project https://www.maelstrom-h2020.eu/ project 7.619047619047619 7.2 hydrography 100.0 10.5 Geography Science and technology/Social sciences/Geography May-2022 and Nov-2022 MAELSTROM Project - Sacca Fisola Bathymetry data 2021 - 2022. 22.322322322322325 22.3 bathymetry 4.613095238095238 3.1 data 22.116402116402117 20.9 study 21.13095238095238 14.2 astronautics 100.0 0.3748457133769989 Venice 11.851851851851851 11.2 ISMAR 11.322751322751323 10.7 Venice Maelstrom project 1.606425702811245 1.6 Venice 15.625 10.5 survey 14.497354497354497 13.7 Sacca Fisola 18.73015873015873 17.7 geology 100.0 0.9953389167785645 bathymetric data 57.329317269076306 57.1 astronautics (general) 100.0 0.3748457133769989 information 30.059523809523807 20.2 ISMAR VE 0.10040160642570281 0.1 Bathymetric data collected in three diffrent surveys (October 2021, May 2022 and November 2022) in Sacca Fisola, Venice, Italy, by CNR- ISMAR VE within the MAELSTROM project. 77.67767767767768 77.6 CNR- ISMAR VE 15.160642570281125 15.1 Maelstrom 18.00595238095238 12.1 2021 - 2022 Oct-2021 project 10.56547619047619 7.1 Maelstrom 13.862433862433862 13.1 diffrent survey 25.803212851405625 25.7 earth sciences 100.0 0.9953389167785645 https://www.myqnapcloud.com/smartshare/7407i2444l6p705u84397342_3980ij4j02pp2589r087x3wb9887cd5f 2024-02-14 15:54:00.818871+00:00 2024-02-14 15:54:30.737845+00:00 Data collecteted in 2021 and 2022 by CNR- ISMAR VE within the MAELSTROM project (ask author for password) MAELSTROM: Sacca Fisola 2021 - 2022 Bathymetry 2024-02-14 15:54:00.818871+00:00 Università Ca' Foscari 956784@stud.unive.it Susanna Mesghez 956785@stud.unive.it Alberto Santi antonio.petrizzo@cnr.it ANTONIO PETRIZZO Antonio Petrizzo direttore@ismar.cnr.it CNR-ISMAR CNR ISMAR fantina.madricardo@ve.ismar.cnr.it Fantina Madricardo CNR - ISMAR nicoletta.nesto@ve.ismar.cnr.it Nicoletta Nesto CNR ISMAR taha.lahami@ve.ismar.cnr.it Taha Lahami tihana.marceta@ve.ismar.cnr.it Tihana Marceta CNR ISMAR Venice vanessa.moschino@ve.ismar.cnr.it Vanessa Moschino Environmental research Simula Research Laboratory annef@simula.no Anne Fouilloux 0000-0002-1784-2920 barbara@gofair.foundation Barbara Magagna data cube 21.858500527983104 20.7 Weather Weather eLTER Standard Observation variables for biosphere 0.802407221664995 0.8 environmental science and management 100.0 0.9788098335266113 B-Cubed Hackathon Project 23.125659978880677 21.9 Research Object 25.131995776135163 23.8 environmental sciences 100.0 0.9788098335266113 eLTER Standard Observation 10.876451953537487 10.3 code 50.28248587570621 8.9 documentation and information science 100.0 0.23940381407737732 code 9.609292502639915 9.1 variable 49.717514124293785 8.8 variables for biosphere 30.391173520561683 30.3 interoperable eLTER Standard Observation variable 5.516549648946841 5.5 2024-04-05 14:27:01.095793+00:00 https://orcid.org/0000-0002-1784-2920 https://github.com/b-cubed-eu/hackathon-project-7 9370 https://api.rohub.org/api/ros/bf1e9074-2d18-42dc-9545-c016c4d0d1b4/crate/download/ 2024-04-03 00:00:00+00:00 2024-04-05 14:27:05.268961+00:00 2024-04-03 00:00:00+00:00 This Research Object contains the developments made during the *B-Cubed Hackathon Project 7 - Interoperable eLTER Standard Observation variables for Biosphere*, including the data cubes and the code used to generate them. application/ld+json https://w3id.org/ro-id/bf1e9074-2d18-42dc-9545-c016c4d0d1b4 B-Cubed Hackathon Project 7 Data Cubes (forked) B-Cubed Hackathon Project 7 Data Cubes - fork https://w3id.org/ro-id/2c0b1898-141d-47a4-acc9-ad8ce70c2cac https://w3id.org/ro-id/64c4d35d-4c7c-4920-9023-539871f64e62 https://w3id.org/ro-id/a15788e6-fe58-4a34-bca5-9f5a82b5de75 https://w3id.org/ro-id/41a2669b-3bfa-48af-9783-aedcc4e5b1c1 https://w3id.org/ro-id/4791689b-84df-4c9b-a8e3-93eb3ed173a2 https://w3id.org/ro-id/25979e29-d259-433f-94a9-aaddb5c57234 https://w3id.org/ro-id/06bdbbd4-a347-4c13-b815-509435a7e085 https://w3id.org/ro-id/42165adf-83d7-4bc6-95fa-bcbc37bfe338 https://w3id.org/ro-id/421fa61d-9bc5-46c5-bfe4-8886af2fb398 https://w3id.org/ro-id/4d16e86c-ceb5-4986-a293-286329cd80fc https://w3id.org/ro-id/9e3964b4-f79f-428d-8fd5-feaba1423107 https://w3id.org/ro-id/c3b7ccd0-2696-4e45-bc31-e885852f7d2e https://w3id.org/ro-id/8cf09932-c616-4f88-a854-ea176c5ff5d4 https://w3id.org/ro-id/da7ab578-3c0f-4782-aabf-f90ac99175c0 https://w3id.org/ro-id/26787314-4a74-45a8-9bcb-a16de57f3f89 https://w3id.org/ro-id/a1702a2d-95be-4619-bfb6-b93dab90e1a6 https://w3id.org/ro-id/a268a7f9-f779-4a9f-b02e-f9a2ab4b10bd https://w3id.org/ro-id/c108f354-1ebc-4069-b153-5f9c7093b09b https://w3id.org/ro-id/db1d7df3-2b62-4a70-97b4-763954324580 https://w3id.org/ro-id/fc93c2d2-7c87-46d8-b14a-76a7f7ad2aa8

Lopez Gordillo, Julian, Anne Fouilloux, and Barbara Magagna. "B-Cubed Hackathon Project 7 Data Cubes (forked)." ROHub. Apr 03 ,2024. https://w3id.org/ro-id/bf1e9074-2d18-42dc-9545-c016c4d0d1b4.

metadata scripts data https://github.com/b-cubed-eu/hackathon-project-7/blob/main/metadata/metadata-model.json 2024-04-05 11:09:34.268377+00:00 2024-04-05 14:27:00.240297+00:00 application/json JSON Schema for Data cube metadata 2024-04-05 11:09:34.268377+00:00 https://raw.githubusercontent.com/b-cubed-eu/hackathon-project-7/main/scripts/birds.ipynb 2024-04-05 11:07:45.780445+00:00 2024-04-05 14:31:26.557075+00:00 Birds data cube Jupyter notebook 2024-04-05 11:07:45.780445+00:00 https://github.com/b-cubed-eu/hackathon-project-7/blob/main/metadata/sample-metadata.json 2024-04-05 11:10:13.759619+00:00 2024-04-05 14:27:00.060033+00:00 application/json Example JSON file of Data cube metadata 2024-04-05 11:10:13.759619+00:00 https://raw.githubusercontent.com/b-cubed-eu/hackathon-project-7/main/scripts/vegetatation_wrangle.ipynb 2024-04-05 11:07:08.421011+00:00 2024-04-05 14:31:53.744923+00:00 Vegetation data cube Jupyter notebook 2024-04-05 11:07:08.421011+00:00 https://raw.githubusercontent.com/b-cubed-eu/hackathon-project-7/main/scripts/weather.ipynb 2024-04-05 11:05:56.102689+00:00 2024-04-05 14:32:27.876029+00:00 Climate data cube Jupyter noteboook 2024-04-05 11:05:56.102689+00:00 eLTER Standard Observation variable 41.72517552657974 41.6 variable 9.398099260823653 8.9 social and information sciences 100.0 0.23940381407737732 include the data cubes 21.56469408224674 21.5 B-Cubed Hackathon Project 7 Data Cubes (forked). This Research Object contains the developments made during the *B-Cubed Hackathon Project 7 - Interoperable eLTER Standard Observation variables for Biosphere*, including the data cubes and the code used to generate them. 100.0 100.0 Environmental research 48fdb5a0-835c-4162-82a5-93b29bed2ae8 POINT (16.589355468750004 49.167338606291075) 16.589355468750004 49.167338606291075 POINT (16.589355468750004 49.167338606291075) 5388 https://api.rohub.org/api/ros/9293f0f7-aad2-422f-ad6f-dfa4c87f5bd2/crate/download/ 2024-06-01 17:22:27.561002+00:00 2025-10-16 12:30:17.971463+00:00 2024-06-01 17:22:27.561002+00:00 DDD application/ld+json https://w3id.org/ro-id/9293f0f7-aad2-422f-ad6f-dfa4c87f5bd2 TTTitle MANUAL

Hůlek, Richard, and Richard Hůlek. "TTTitle." ROHub. Jun 01 ,2024. https://w3id.org/ro-id/9293f0f7-aad2-422f-ad6f-dfa4c87f5bd2.

POINT (16.589355468750004 49.167338606291075) data raw data metadata biblio 172775@mail.muni.cz Richard Hůlek Applied sciences Zoology Biology 0 https://api.rohub.org/api/ros/9ed8167f-1789-4eb4-ba9e-3a74f25c53e8/crate/download/ 2024-11-07 08:14:30.138941+00:00 2025-10-16 12:24:25.442992+00:00 2024-11-07 08:14:30.138941+00:00 This study tested whether human body orientation can influence the behavior of bull sharks by examining sharks’ approach distances from a person positioned vertically or horizontally in the water. Results showed that bull sharks, Carcharhinus leucas, kept a significantly greater distance when the test subject was standing in chest-deep water with his head above water versus lying on the ocean floor. Furthermore, larger bull sharks in the immediate area withdrew when the subject entered the water application/ld+json https://w3id.org/ro-id/9ed8167f-1789-4eb4-ba9e-3a74f25c53e8 OceanBodyOfWater biology shark Journal article Effect of Human Body Position on the Swimming Behavior of Bull Sharks, Carcharhinus leucas MANUAL

Niewiedziala, Wiktoria. "Effect of Human Body Position on the Swimming Behavior of Bull Sharks, Carcharhinus leucas." ROHub. Nov 07 ,2024. https://w3id.org/ro-id/9ed8167f-1789-4eb4-ba9e-3a74f25c53e8.

metadata raw data biblio data 29593 https://api.rohub.org/api/resources/56766766-6725-46be-9258-b01501cd6889/download/ 2024-11-07 08:17:09.204183+00:00 2024-11-07 08:17:09.784319+00:00 image/jpeg OIP.jpg 2024-11-07 08:17:09.204183+00:00 testing 5.361050328227571 4.9 approach distance 14.542483660130719 8.9 swim 3.50109409190372 3.2 orientation 4.704595185995624 4.3 chest 6.345733041575492 5.8 study 7.596685082872928 5.5 life sciences (general) 100.0 0.8189504146575928 bull shark 37.016574585635354 26.8 test 6.6298342541436455 4.8 sharks 16.574585635359114 12.0 behavior 13.23851203501094 12.1 result 7.877461706783369 7.2 from a person positioned vertically or horizo 28.515625 21.9 geology 100.0 0.9295690059661865 life sciences 100.0 0.8189504146575928 result 7.596685082872928 5.5 approach 3.610503282275711 3.3 shark 13.129102844638949 12.0 test subject 18.790849673202615 11.5 paleozoology 100.0 14.2 behavior of bull sharks 36.27450980392157 22.2 behavior 16.574585635359114 12.0 larger bull sharks 13.071895424836601 8.0 study 6.345733041575492 5.8 bull shark 27.899343544857768 25.5 Carcharhinus leucas 17.320261437908496 10.6 Food Economy, business and finance/Economic sector/Consumer goods/Food tally in the water. Results showed that bull sharks, Carcharhinus leucas, kept a significantly greater distance 32.161458333333336 24.7 chest 8.011049723756905 5.8 distance 7.98687089715536 7.3 us lying on the ocean floor. Furthermore, larger bull sharks in the immediate area withdrew when the subject entered the water 39.32291666666667 30.2 Oceans Environment/Natural resources/Water/Oceans Animal Human interest/Animal earth sciences 100.0 0.9295690059661865 Wiktoria Niewiedziala Applied sciences Social sciences xyz 0 https://api.rohub.org/api/ros/5930c9e6-8f23-4c8c-9b1b-84d55deaaf00/crate/download/ 2024-11-14 08:02:26.064775+00:00 2025-10-16 12:17:13.898108+00:00 2024-11-14 08:02:26.064775+00:00 Metadata-enriched Polish Novel Corpus from the 19th and 20th centuries The corpus consists of 1,000 novels originally written in Polish and initially published as books between 1864 and 1939, with the plot timeframe set after 1815. The current version is v1.0.1. Following Linked Open Data (LOD) standards, we do not publish the corpus texts in .txt format. Instead, the entire corpus is accessible through a knowledge graph in Turtle (.ttl) format, with each text being linked separately. The repository contains code to download all corpus texts independently. An explanation of the code can be found in the Data section. application/ld+json https://w3id.org/ro-id/5930c9e6-8f23-4c8c-9b1b-84d55deaaf00 Corpora LinkedOpenData LiteraryStudies Ontology Annotation Collection Korpus 19-20MetaPNC MANUAL

Hubar-Kołodziejczyk, Patryk. "Korpus 19-20MetaPNC." ROHub. Nov 14 ,2024. https://w3id.org/ro-id/5930c9e6-8f23-4c8c-9b1b-84d55deaaf00.

raw data metadata data biblio 86599 https://api.rohub.org/api/resources/86143851-8706-4b88-b70e-41c184211080/download/ 2024-11-14 08:15:28.902820+00:00 2024-11-14 08:15:29.775352+00:00 image/jpeg TCO_ontology.jpg 2024-11-14 08:15:28.902820+00:00 43938 https://api.rohub.org/api/resources/961c69a7-79d1-49ac-8ad2-d5a2e7801d65/download/ 2024-11-14 08:15:39.635599+00:00 2024-11-14 08:15:40.563302+00:00 image/png Meta_tree.png 2024-11-14 08:15:39.635599+00:00 text 11.520737327188941 7.5 corpus consist 20.66905615292712 17.3 model 3.4285714285714284 3.0 consist 4.457142857142857 3.9 geophysics 100.0 0.3627100884914398 text 9.6 8.4 computer code 5.371428571428571 4.7 literature 100.0 10.3 computer operations and hardware 100.0 0.9055352807044983 Linked Open Datum 9.216589861751153 6.0 Book industry Economy, business and finance/Economic sector/Media/Book industry after 1815 corpus 35.63748079877112 23.2 mathematical and computer sciences 100.0 0.9055352807044983 metadata 9.37019969278034 6.1 v1.0.1 8.141321044546851 5.3 corpus 27.2 23.8 novel 4.0 3.5 Polish 3.7714285714285714 3.3 Fiction Arts, culture and entertainment/Arts and entertainment/Literature/Fiction graph 4.457142857142857 3.9 code 6.912442396313365 4.5 earth sciences 100.0 0.3627100884914398 The repository contains code to download all corpus texts independently. 27.053140096618357 16.8 Instead, the entire corpus is accessible through a knowledge graph in Turtle (.ttl) format, with each text being linked separately. 30.756843800322063 19.1 between 1864 and 1939 time frame 4.228571428571429 3.7 Following Linked Open Data (LOD) standards, we do not publish the corpus texts in .txt format. 42.19001610305958 26.2 Literature Arts, culture and entertainment/Arts and entertainment/Literature format 19.201228878648234 12.5 corpus texts in.txt format 44.32497013142174 37.1 format 22.285714285714285 19.5 plot timeframe 5.017921146953404 4.2 metadata 7.085714285714285 6.2 repository 4.114285714285714 3.6 novel corpus 24.731182795698924 20.7 knowledge graph 5.256869772998805 4.4 Patryk Hubar-Kołodziejczyk Meteorology Applied sciences Ecology https://aqicn.org/map/warsaw/pl/ 2026-01-15 09:56:50.534689+00:00 2026-01-15 10:04:04.245960+00:00 Zanieczyszczenie powietrza w Warszawa Mapa wizualna jakości powietrza w czasie rzeczywistym. Zanieczyszczenie powietrza w Warszawa Mapa wizualna jakości powietrza w czasie rzeczywistym. 2026-01-15 09:56:50.534689+00:00 https://iot.warszawa.pl/ 2026-01-15 10:02:54.788955+00:00 2026-01-15 10:03:23.971993+00:00 Indeks Jakości Powietrza Sprawdź poziom jakości powietrza w swojej okolicy. Wskaż na mapie stację pomiarową, przejrzyj aktualne informacje o poziomie stężenia zanieczyszczeń i zapoznaj się z zaleceniami dotyczącymi ochrony Twojego zdrowia. Warszawska Platforma IoT 2026-01-15 10:02:54.788955+00:00 ba147b47-b1df-406d-a3cc-bfd9af963ed5 POINT (21.007713326253 52.234864715699715) 21.007713326253 52.234864715699715 POINT (21.007713326253 52.234864715699715) 0 https://api.rohub.org/api/ros/d006ed2d-2fa9-438d-b830-a7d4aef81469/crate/download/ 2026-01-15 09:36:30.138049+00:00 2026-04-11 03:22:17.313678+00:00 2026-01-15 09:36:30.138049+00:00 Projekt analizuje jakość powietrza w Warszawie, koncentrując się na wartości stężeń pyłów zawieszonych PM2.5 i PM10 oraz ich wpływie na zdrowie ludzi. W ramach projektu gromadzone są raporty i dane pomiarowe z lokalnych narzędzi monitoringu, a następnie są one porównywane z normami Światowej Organizacji Zdrowia (WHO). Badanie uwzględnia różne dni, pory dnia i miejsca pomiarów na terenie całego miasta Warszawy oraz identyfikuje możliwe przyczyny i skutki przekroczenia dopuszczalnych poziomów zanieczyszczeń. application/ld+json https://w3id.org/ro-id/d006ed2d-2fa9-438d-b830-a7d4aef81469 Air Quality Environment Monitoring PM10 PM2.5 Warsaw Dataset Jakość powietrza w Warszawie — analiza stężeń PM2.5 i PM10 oraz ich przekroczeń MANUAL Janek Gębicki

GONZALEZ GUARDIA, ESTEBAN. "Hamburg Floodlevels." ROHub. Mar 20 ,2026. https://w3id.org/ro-id/24165a93-ac0d-46ef-98a7-046e6d5a287e.

biblio data metadata raw data Flooding increase 100.0 85.1 Key Type Measures Data on climate-relate hazards Local policy Physics Water management Government/ Public Sector IPCC Extreme weather: floods, droughts, heatwaves Stakeholders increment 87.18718718718719 87.1 Fluid mechanics and thermodynamics Knowledge Sector (EEA) Structural/physical: Engineered and built environments Climate Hazard Climate-ADAPT Adaptation Sectors Engineering National government agencies User Needs (RAST) European Continent Geographical Scope Methodology Mathematical Physics Mathematical Sciences Physical and Technological Scenario Analysis Policy Scale Hamburg Floodlevels Floodlevels in increments of 10 cm ranging from 30 cm to 100cm. 100.0 100.0 Hamburg Floodlevels Floodlevels in increment 100.0 100.0 Funding Physics (General) Other Mathematical Sciences Hamburg Floodlevels Floodlevels 12.812812812812814 12.8 ESTEBAN GONZALEZ GUARDIA Earth sciences https://doi.org/10.5281/zenodo.19125517 2026-03-21 12:45:25.444387+00:00 2026-03-21 12:45:27.225023+00:00 Data of the street outlines in the city of Hamburg. Hamburg Street Data 2026-03-21 12:45:25.444387+00:00 8d301d44-989d-4023-99b0-901f53435bb7 POLYGON ((9.819030761718752 53.49294782332984, 9.819030761718752 53.63405351645887, 10.206298828125002 53.63405351645887, 10.206298828125002 53.49294782332984, 9.819030761718752 53.49294782332984)) 9.994812011718752 53.57293832648609 POINT (9.994812011718752 53.57293832648609) POLYGON ((9.819030761718752 53.49294782332984, 9.819030761718752 53.63405351645887, 10.206298828125002 53.63405351645887, 10.206298828125002 53.49294782332984, 9.819030761718752 53.49294782332984)) 9.819030761718752 53.49294782332984, 9.819030761718752 53.63405351645887, 10.206298828125002 53.63405351645887, 10.206298828125002 53.49294782332984, 9.819030761718752 53.49294782332984 e02b49a8-d982-476e-8087-ffeb61d4b57e POINT (9.994812011718752 53.57293832648609) 0 https://api.rohub.org/api/ros/1884b780-507e-4447-975c-87b970c5b503/crate/download/ 2026-03-21 12:43:13.627334+00:00 2026-03-23 09:46:24.296804+00:00 2026-03-21 12:43:13.627334+00:00 Data of the street outlines in the city of Hamburg. application/ld+json https://w3id.org/ro-id/1884b780-507e-4447-975c-87b970c5b503 pluvial flood risk street data Hamburg Street Data MANUAL

GONZALEZ GUARDIA, ESTEBAN. "Hamburg Street Data." ROHub. Mar 21 ,2026. https://w3id.org/ro-id/1884b780-507e-4447-975c-87b970c5b503.

POINT (9.994812011718752 53.57293832648609) POLYGON ((9.819030761718752 53.49294782332984, 9.819030761718752 53.63405351645887, 10.206298828125002 53.63405351645887, 10.206298828125002 53.49294782332984, 9.819030761718752 53.49294782332984)) metadata data raw data biblio city of Hamburg 35.87174348697395 35.8 Geosciences (General) Data on climate street 34.62157809983897 21.5 General Land use planning Key Type Measures Local policy Stakeholders National government agencies Hamburg 26.409017713365536 16.4 Hamburg 17.217217217217218 17.2 Systemic Literature Review street 19.91991991991992 19.9 city 38.969404186795494 24.2 Funding outline in the city of Hamburg 0.10020040080160321 0.1 Land use Methodology none General Climate Hazard User Needs (RAST) Engineering (General) Hamburg Street Data Data of the street 62.324649298597194 62.2 Physical and Technological IPCC Government/ Public Sector Climate-ADAPT Adaptation Sectors Hamburg Street Data Data 39.33933933933934 39.3 Hamburg Street Data Data of the street outlines in the city of Hamburg. 100.0 100.0 Geosciences Knowledge Sector (EEA) Policy Scale Geographical Scope Structural/physical: Engineered and built environments Engineering Hamburg Hamburg Street Data Data outline in the city 1.7034068136272547 1.7 European Continent city 23.523523523523526 23.5 ESTEBAN GONZALEZ GUARDIA Earth sciences https://doi.org/10.5281/zenodo.19113146 2026-03-21 12:52:54.126993+00:00 2026-03-21 12:52:55.351508+00:00 Data on the building level containing the number of inhabitants, the building type and the number of floors for each building. Furthermore, each building is assigned to its Statistical Unit (=Urban District) and the corresponding social vulnerability data. Hamburg: Preprocessed Data on the Building Level 2026-03-21 12:52:54.126993+00:00 POLYGON ((9.830017089843752 53.5117346755535, 9.830017089843752 53.63812471860769, 10.128021240234377 53.63812471860769, 10.128021240234377 53.5117346755535, 9.830017089843752 53.5117346755535)) 9.830017089843752 53.5117346755535, 9.830017089843752 53.63812471860769, 10.128021240234377 53.63812471860769, 10.128021240234377 53.5117346755535, 9.830017089843752 53.5117346755535 792d2d0b-9dfa-4af6-855e-10166fb260b5 POLYGON ((9.830017089843752 53.5117346755535, 9.830017089843752 53.63812471860769, 10.128021240234377 53.63812471860769, 10.128021240234377 53.5117346755535, 9.830017089843752 53.5117346755535)) 0 https://api.rohub.org/api/ros/6984727e-5804-4de7-98cf-36068c22c426/crate/download/ 2026-03-21 12:50:10.527429+00:00 2026-04-11 03:16:51.462372+00:00 2026-03-21 12:50:10.527429+00:00 Data on the building level containing the number of inhabitants, the building type and the number of floors for each building. Furthermore, each building is assigned to its Statistical Unit (=Urban District) and the corresponding social vulnerability data. application/ld+json https://w3id.org/ro-id/6984727e-5804-4de7-98cf-36068c22c426 Hamburg building level pluvial flood risk social vulnerability Hamburg: Preprocessed Data on the Building Level MANUAL

GONZALEZ GUARDIA, ESTEBAN. "Hamburg: Preprocessed Data on the Building Level." ROHub. Mar 21 ,2026. https://w3id.org/ro-id/6984727e-5804-4de7-98cf-36068c22c426.

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 https://github.com/NordicESMhub/RELIANCE/blob/main/MOD_Aqua_ADAM.ipynb 2021-11-08 21:09:59.780719+00:00 2021-11-08 21:09:59.781169+00:00 This notebook shows how to use ADAM API and ROHub API Jupyter Notebook for using ADAM-API to access MODIS Aqua 2021-11-08 21:09:59.780719+00:00 concentration chlorophyll concentration 10.32064128256513 10.3 concentration 13.554216867469878 13.5 analysis 6.526104417670682 6.5 concentration 22.02852614896989 13.9 Research Object 15.863453815261042 15.8 This Research Object aggregates the resources associated with the analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water 65.36536536536536 65.3 Analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water. 34.63463463463463 34.6 geochemistry 100.0 0.7202270030975342 salt water 29.001584786053883 18.3 geosciences 100.0 0.9254304766654968 geophysics 100.0 0.9254304766654968 chlorophyll 37.717908082408876 23.8 109619 https://api.rohub.org/api/ros/9e533d0d-b1de-4b0d-9dd8-14d136aacea5/crate/download/ 2021-11-08 21:06:20.914340+00:00 2025-12-17 10:08:29.660011+00:00 2021-11-08 21:06:20.914340+00:00 This Research Object aggregates the resources associated with the analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water application/ld+json https://w3id.org/ro-id/9e533d0d-b1de-4b0d-9dd8-14d136aacea5 Analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water MANUAL https://w3id.org/ro-id/0db12483-1d72-4ec5-8f43-7244a0ef5cb5 https://w3id.org/ro-id/3e602429-165b-4c38-821b-b185c2d19566 https://w3id.org/ro-id/95d3e2f1-2706-446e-a12f-00e5f22e43aa https://w3id.org/ro-id/a6cca92d-f385-4230-a0e1-881e29db8601 https://w3id.org/ro-id/34fd419d-c014-4140-b4a8-92a58fe19b07 https://w3id.org/ro-id/b925e277-dafb-48da-b69b-443ee492e971 https://w3id.org/ro-id/02d44787-a55c-4dc5-8968-38360eb4712c https://w3id.org/ro-id/033fe89c-7935-4136-94ab-bc6b8e4631fb https://w3id.org/ro-id/2280c53a-ef9b-4fd6-ad25-331b51fd21ce https://w3id.org/ro-id/9f1673cc-ed44-43c0-a513-13baff083467 https://w3id.org/ro-id/bb654c5f-afb3-4284-a3ae-7e0eada02b24 https://w3id.org/ro-id/c9755e58-000b-4ff3-9ac0-e5c8a600f8ee https://w3id.org/ro-id/e5495369-d48b-4536-bee2-676a5ed66a7f https://w3id.org/ro-id/549fc843-cefc-4f70-ae2f-6e4bd6397645 https://w3id.org/ro-id/7cbd7a86-f1b1-4244-8b15-00af8d6c53fe https://w3id.org/ro-id/0140bc8c-4555-4103-acf5-334ab798e827 https://w3id.org/ro-id/d8454f8f-1581-4f2a-b45c-7cef3a7285e8 https://w3id.org/ro-id/f0492ea2-a97b-4c28-837a-82528d4fb014 https://w3id.org/ro-id/ff180a79-e2a8-4156-bdd5-aeaaefc1d8b9 https://w3id.org/ro-id/297b7d28-5cbd-49ba-b01b-ecf37bd32a05 https://w3id.org/ro-id/30408fdd-52d2-41a1-b306-962f6bf9e3c3

Anne Foilloux, and Anne Foilloux. "Analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water." ROHub. Nov 08 ,2021. https://w3id.org/ro-id/9e533d0d-b1de-4b0d-9dd8-14d136aacea5.

106111 https://api.rohub.org/api/resources/1be16907-9d03-456d-a2ec-db11e3a8af2f/download/ 2021-11-08 21:08:02.599866+00:00 2021-11-08 21:08:02.600559+00:00 image/png Mass concentration chlorophyll concentration in sea water Year 2013 over the Mediteranean region 2021-11-08 21:08:02.599866+00:00 MOD_Aqua 14.257028112449799 14.2 resource 11.251980982567353 7.1 2022-03-24 11:53:42.281517+00:00 earth sciences 100.0 0.7202270030975342 resource 7.228915662650602 7.2 chlorophyll 24.196787148594375 24.1 chlorophyll concentration 84.1683366733467 84.0 sea water 18.373493975903614 18.3 aggregate the resource 5.01002004008016 5.0 mass concentration chlorophyll concentration 0.5010020040080161 0.5 Anne Fouilloux 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 Research Lifecycle Management for Earth Science Communities and Copernicus Users 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 - modified 69.66966966966967 69.6 False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 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/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:15:26.873492+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl map of PM10 9.246231155778894 9.2 atmospheric sciences 100.0 0.7866491675376892 map 13.333333333333334 12.8 country 8.541666666666666 8.2 country 11.829436038514443 8.6 4faa9adb-0eb7-402e-903d-120affa6ab89 POLYGON ((14.049395 40.779358, 14.240586 40.779358, 14.240586 40.912968, 14.049395 40.912968, 14.049395 40.779358)) 38.0 38.0 POINT (38.0 38.0) c6da8692-3f04-4fe7-a9bc-2e4e13362649 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)) 14.049395 40.779358, 14.240586 40.779358, 14.240586 40.912968, 14.049395 40.912968, 14.049395 40.779358 service-account-enrichment https://w3id.org/ro-id/0e5f85c2-45ce-4b79-af5b-a940086cc802 https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 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 https://w3id.org/ro-id/ad8a8265-109b-4979-b78a-15b205d71029 False https://w3id.org/ro-id/2755900c-b77c-4a29-ac59-f6f51af20fa7 2021-11-10 19:38:10.173024+00:00 mailto:rpalma@man.poznan.pl 83923 https://api.rohub.org/api/ros/7740459a-b9fc-411b-88af-763a0de9d9b1/crate/download/ 2021-11-09 15:51:17.774513+00:00 2025-03-05 00:45:34.213972+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/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 Oceanography Earth sciences Biochemistry https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007E46B7736861726547756964236161643239616133666234633734356464393231356539663536613733616366636836643138233732356634616233366362323664306662666330633132346337373565666565636865653439236361386634383464346533366532646439643230336131383431616362656563636834393661/content 2022-11-29 15:26:53.739562+00:00 2023-06-22 10:59:50.791762+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 2022-11-29 15:26:53.739562+00:00 https://datahub.egi.eu/api/v3/onezone/shares/data/00000000007ECE4C736861726547756964236337653135323330333033383136356532663365646530343262646537343038636836643138233732356634616233366362323664306662666330633132346337373565666565636865653439233836663339353466636461353034663331326637636464363962333037383234636864343237/content 2021-12-22 14:38:45.256955+00:00 2022-11-29 16:15:06.244156+00:00 Jupyter notebook using R 2021-12-22 14:38:45.256955+00:00 https://doi.org/10.5194/essd-12-215-2020 2022-11-29 16:05:17.920104+00:00 2022-11-29 16:06:05.302894+00:00 In this paper, we describe a 50-year (1965–2015) ecological database containing data on plankton communities and related abiotic parameters collected in the northern Adriatic Sea (NAS). Plankton communities, which are at the base of aquatic ecosystem functioning, have a broad and diversified range of seasonal patterns, multi-annual trends, and shifts across different marine ecosystems: making long-term series of plankton and oceanographic observations available provides unique and precious tools for depicting reliable patterns of average annual cycles and for detecting significant changes and trends in response to global or local pressures and impacts. Dataset description 2022-11-29 16:05:17.920104+00:00 CNR-ISMAR malek.belgacem@ve.ismar.cnr.it Malek Belgacem 0000-0003-0745-4155 case study 4.890738813735692 4.7 Adriatic Sea POLYGON ((11.762694865465166 44.67038775819365, 11.762694865465166 45.60037251154824, 13.618163913488388 45.60037251154824, 13.618163913488388 44.67038775819365, 11.762694865465166 44.67038775819365)) 11.762694865465166 44.67038775819365, 11.762694865465166 45.60037251154824, 13.618163913488388 45.60037251154824, 13.618163913488388 44.67038775819365, 11.762694865465166 44.67038775819365 9babe6b2-4629-4111-a574-f1511da18104 POLYGON ((11.762694865465166 44.67038775819365, 11.762694865465166 45.60037251154824, 13.618163913488388 45.60037251154824, 13.618163913488388 44.67038775819365, 11.762694865465166 44.67038775819365)) 1785382 https://api.rohub.org/api/ros/0869e396-3733-4aff-8fb2-94c8937b28aa/crate/download/ 2021-11-29 14:45:39.803487+00:00 2025-03-05 01:19:07.795226+00:00 2021-11-29 14:45:39.803487+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. application/ld+json https://w3id.org/ro-id/0869e396-3733-4aff-8fb2-94c8937b28aa Adriatic Sea Biogeochemistry inorganic nutrients lockdown impact marine platform Research Object Snapshot 2021 study case: Lockdown impacts on the Northern Adriatic Sea at selected site: AcquaAlta Platform Water quality MANUAL https://w3id.org/ro-id/32364e73-ae45-4c01-a14a-bc51e70320d5 https://w3id.org/ro-id/de6cb8e6-8634-4db9-9d89-3de77159038a https://w3id.org/ro-id/081c77a2-2454-4a9d-bf06-ba5ee57ac30f https://w3id.org/ro-id/5cdf50db-4c51-4498-9100-209eebb8fc94 https://w3id.org/ro-id/04c5003a-5357-4cbd-81d2-2027c870062c https://w3id.org/ro-id/186ce286-667e-4048-afd6-37115e55a749 https://w3id.org/ro-id/21ee4901-d22c-47d6-99d7-26db44dae53d https://w3id.org/ro-id/3a8bd7a3-3845-4f8d-9702-9c021a451812 https://w3id.org/ro-id/4fb2baef-3923-4609-9fc8-4de57885bf4b https://w3id.org/ro-id/7b02f0ce-4765-43ab-aa20-41d165264a86 https://w3id.org/ro-id/7c6c4ba8-875e-4403-93e3-4154b66d97b3 https://w3id.org/ro-id/87c08759-3c26-4ff0-8327-d2842c4bb5ef https://w3id.org/ro-id/ac088099-0316-403c-bbe8-271e9cec491e https://w3id.org/ro-id/c6c2224d-2b1d-434e-bcf0-ea556d3dcb50 https://w3id.org/ro-id/dbca260c-e9f1-4cd4-b5da-3d4f74708ce1 https://w3id.org/ro-id/0d32d2db-ea94-4733-aa43-fb079fb997d1 https://w3id.org/ro-id/7f13e266-3c8e-4bbb-a4d0-4576f222927d https://w3id.org/ro-id/35020f27-6e26-4a1b-84e4-ef25c652158c https://w3id.org/ro-id/50b46e80-e71d-4772-83b7-8a5ea277b052 https://w3id.org/ro-id/185f2270-805a-4669-bb44-830bee256947 https://w3id.org/ro-id/75a11d78-b8e5-41f0-a8d8-de167380dff9 https://w3id.org/ro-id/7e3d3c52-6921-4ab1-b486-ae86b5d9cf05 https://w3id.org/ro-id/9d236df1-a6ef-4bd1-85a3-0f4a88675bf6 https://w3id.org/ro-id/b3e44687-2358-497d-8fb4-478467ea19a8 https://w3id.org/ro-id/e11ad585-3fb3-4399-9d94-839faf7fb8a0 https://w3id.org/ro-id/f771803a-7ae9-43f8-8c15-41b214fec39c https://w3id.org/ro-id/4684e0f2-d68e-4ca9-97d8-fb71e6ffb984 https://w3id.org/ro-id/919b433d-808c-4b6d-a635-2e4197524edc https://w3id.org/ro-id/3294b71f-7f2d-41c1-a42a-61a33dd0ed98 https://w3id.org/ro-id/6be9833b-436d-4998-adf3-541da8dd9c03 https://w3id.org/ro-id/99970636-75e9-4088-a200-6ff7de907159 https://w3id.org/ro-id/a131b587-56fe-48ea-a938-d9009236b975 https://w3id.org/ro-id/c23b36ca-c91c-4a5f-8a8c-759d21d9cc67 https://w3id.org/ro-id/1cd3ffb4-ad25-4b0e-ac58-66c6d300234c https://w3id.org/ro-id/a1cdf83b-c6cf-43e8-8ebd-0dbef6a88205 https://w3id.org/ro-id/638d85ed-0513-4a62-9dd8-a8e5ce7ba5eb

Belgacem, Malek, Mauro Bastianini, and Jacopo Chiggiato. "Snapshot 2021 study case: Lockdown impacts on the Northern Adriatic Sea at selected site: AcquaAlta Platform Water quality." ROHub. Nov 29 ,2021. https://w3id.org/ro-id/0869e396-3733-4aff-8fb2-94c8937b28aa.

POLYGON ((11.762694865465166 44.67038775819365, 11.762694865465166 45.60037251154824, 13.618163913488388 45.60037251154824, 13.618163913488388 44.67038775819365, 11.762694865465166 44.67038775819365)) Output Dataset Jupyter_tool 26131 https://api.rohub.org/api/resources/1b950828-28e0-4725-abcb-73f33d0bf32e/download/ 2021-12-22 14:05:23.636780+00:00 2021-12-22 14:05:23.638961+00:00 image/png NO3_change_obsvspred2020.png 2021-12-22 14:05:23.636780+00:00 1049814 https://api.rohub.org/api/resources/28208669-c1ef-475e-85ac-8114691c154d/download/ 2023-06-09 11:33:24.938835+00:00 2023-06-09 11:33:25.669288+00:00 image/png reliance deliv dec2021.png 2023-06-09 11:33:24.938835+00:00 41569 https://api.rohub.org/api/resources/32f66214-fac2-44b8-af57-559916593747/download/ 2021-12-22 14:06:14.381079+00:00 2021-12-22 14:06:44.434429+00:00 image/png NO3_ts_ptf_decompose.png 2021-12-22 14:06:14.381079+00:00 55841 https://api.rohub.org/api/resources/548ed54f-2d96-4e0c-9633-cbb22a04fd20/download/ 2021-12-22 14:06:01.775038+00:00 2021-12-22 14:06:01.777430+00:00 image/png NO3_predict_obsvspred2020vs20092019.png 2021-12-22 14:06:01.775038+00:00 49353 https://api.rohub.org/api/resources/aa6894c2-5c4d-4c39-817b-d63fb155141d/download/ 2021-12-22 14:05:43.603856+00:00 2021-12-22 14:05:43.605820+00:00 image/png NO3_predict_obsvspred2020.png 2021-12-22 14:05:43.603856+00:00 296189 https://api.rohub.org/api/resources/d87d7c73-70c4-4243-9f8b-1b22ad5f4338/download/ 2021-12-22 12:23:51.989470+00:00 2021-12-22 12:23:51.990626+00:00 image/jpeg Study area 2021-12-22 12:23:51.989470+00:00 88174 https://api.rohub.org/api/resources/eaee63ca-aaa5-46eb-8b8a-0d696d1340e9/download/ 2022-07-15 16:29:07.183540+00:00 2023-06-22 10:56:15.115165+00:00 image.jfif 2022-07-15 16:29:07.183540+00:00 444613 https://api.rohub.org/api/resources/fad0f0b1-a385-40df-8698-a1e61df13161/download/ 2021-12-15 15:16:09.372184+00:00 2021-12-15 15:16:09.373777+00:00 image/png RO workflow 2021-12-15 15:16:09.372184+00:00 environmental sciences 100.0 0.5102767944335938 Gulf of Venice 8.92018779342723 7.6 Gulf of Venice 7.075962539021853 6.8 Snapshot 2021 study case: Lockdown impacts on the Northern Adriatic Sea at selected site: AcquaAlta Platform Water quality. 33.83383383383383 33.8 snapshot 4.474505723204995 4.3 hydrography 39.42307692307692 4.1 northern Adriatic Sea 17.122473246135556 14.4 Crime Crime, law and justice/Crime machine learning 6.76378772112383 6.5 geophysics 100.0 0.33035168051719666 Adriatic Sea 25.390218522372532 24.4 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences Gulf of Venice 2021 case of the Gulf of Venice 7.134363852556481 6.0 machine learning 8.685446009389672 7.4 lockdown 23.621227887617067 22.7 impact 6.139438085327784 5.9 project 7.15962441314554 6.1 environmental science and management 100.0 0.5102767944335938 project 6.243496357960458 6.0 geosciences 100.0 0.33035168051719666 machine learning model 17.954815695600477 15.1 impact 7.511737089201878 6.4 snapshot project http 39.00118906064209 32.8 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. 66.16616616616616 66.1 site 3.121748178980229 3.0 Adriatic Sea 28.990610328638496 24.7 lockdown impact 18.7871581450654 15.8 http 8.844953173777316 8.5 Synoptic Assessment of Human Pressures on key Mediterranean Hot Spots The SNAPSHOT project contributes to the informed public debate trying to answer the above questions through an observation campaign involving scientists and citizens with the commo jacopo.chiggiato@ismar.cnr.it SNAPSHOT: the pandemic and post-pandemic marine environment at a glance http://www.bluemed-initiative.eu/snapshot/ snapshot 3.433922996878252 3.3 computer science 60.57692307692309 6.3 http 10.798122065727698 9.2 lockdown 27.934272300469484 23.8 https://zenodo.org/record/3516717#.YboDGWjMI2x 2022-11-29 16:06:59.179645+00:00 2022-11-29 16:07:01.609968+00:00 The present database contains observations for 22 parameters of abiotic, phyto and zooplankton data collected in the Northern Adriatic Sea region (Italy). It relies on a Comma Separated Values file and it is composed by 108687 records. Due to its long temporal coverage, it is classifiable as Long Term Ecological data. Due to the long temporal coverage, the great part of parameters changed collection and analysis method in time. These variations are reported in the database. A long term database can be useful for multiple purposes. This database has been released under a research project focused on Open Science principles application to marine ecology. Dataset source 2022-11-29 16:06:59.179645+00:00 direttore@ismar.cnr.it CNR-ISMAR CNR-ISMAR jacopo.chiggiato@ismar.cnr.it Jacopo Chiggiato CNR-ISMAR mauro.bastianini@ismar.cnr.it Mauro Bastianini service-account-enrichment Geology Applied sciences Earth sciences Ecology giorgio.castellan@bo.ismar.cnr.it Giorgio Castellan 0000-0001-6084-1504 CNR-ISMAR malek.belgacem@ve.ismar.cnr.it Malek Belgacem 0000-0003-0745-4155 geosciences 100.0 0.4974074065685272 concentrations in seawater 22.285067873303166 19.7 Spatial and temporal distribution of Cold Water Corals (CWC) in the Mediterranean Sea - Data. 42.24224224224224 42.2 distribution 10.340314136125654 7.9 7ce41391-7238-4cff-811b-cbd4c074e2d8 POLYGON ((-10.265629291534426 29.22888417844566, -10.265629291534426 46.12198587773459, 38.812497854232795 46.12198587773459, 38.812497854232795 29.22888417844566, -10.265629291534426 29.22888417844566)) POLYGON ((-10.265629291534426 29.22888417844566, -10.265629291534426 46.12198587773459, 38.812497854232795 46.12198587773459, 38.812497854232795 29.22888417844566, -10.265629291534426 29.22888417844566)) -10.265629291534426 29.22888417844566, -10.265629291534426 46.12198587773459, 38.812497854232795 46.12198587773459, 38.812497854232795 29.22888417844566, -10.265629291534426 29.22888417844566 service-account-enrichment 163620 https://api.rohub.org/api/ros/1c87c3d6-46f0-4bfe-bc73-88282fb8c3c5/crate/download/ 2021-12-07 15:20:52.205188+00:00 2025-03-05 01:19:13.509426+00:00 2021-12-07 15:20:52.205188+00:00 Data on temperature, salinity, dissolved oxygen, pH, and nutrient concentrations in seawater used to explore how environmental variables influence the distribution of CWC in the Mediterranean Sea application/ld+json https://w3id.org/ro-id/1c87c3d6-46f0-4bfe-bc73-88282fb8c3c5 MediterraneanSea ResearchProject SeaMonitoring Spatial and temporal distribution of Cold Water Corals (CWC) in the Mediterranean Sea - Data MANUAL https://w3id.org/ro-id/1c87c3d6-46f0-4bfe-bc73-88282fb8c3c5/3ebad49a-f8d8-4dce-9b9a-9f6e27cd5106 https://w3id.org/ro-id/f9cd2ada-3259-4b76-b78f-27d117798018 https://w3id.org/ro-id/eb8c6e5f-abba-4bd0-adb2-7b68c6fe21d9 https://w3id.org/ro-id/1b203cf0-20a3-4696-9581-d6c43d88bcf7 https://w3id.org/ro-id/2484625e-7f31-48c9-97b2-d26dc38f75aa https://w3id.org/ro-id/2c06d311-7204-4595-bf4a-6b56897adf72 https://w3id.org/ro-id/2e99157b-d1de-4959-ba0b-2e549e045edf https://w3id.org/ro-id/432f04ac-dd43-4c14-abbf-c9ae57c765b6 https://w3id.org/ro-id/a9538610-68ae-451c-bc7d-560f17078a3d https://w3id.org/ro-id/b58da8e5-7398-4627-9119-97124ac0e93a https://w3id.org/ro-id/c178a52b-5691-4812-a757-783016e57ab2 https://w3id.org/ro-id/fec59027-5488-4d8b-abbd-3ee2c526e1c9 https://w3id.org/ro-id/851af502-a5ea-4aaf-bca8-85c20b70a773 https://w3id.org/ro-id/a6bbb46e-3b81-4b38-8ca8-d0bb1ce8c772 https://w3id.org/ro-id/437a80c8-c002-4cbf-8e41-a2b675c3c47c https://w3id.org/ro-id/9d533f05-d7e2-4dbb-b4d6-33983ac155d3 https://w3id.org/ro-id/1d1c0aef-740f-4ba4-bf7e-0cff00e317ea https://w3id.org/ro-id/3fa367c5-3f3e-4ba8-bee4-d3fe054485b0 https://w3id.org/ro-id/45c39300-6165-4ae7-8a22-c73be6cad966 https://w3id.org/ro-id/5f5968b3-478b-420b-a827-a3c953c3ca57 https://w3id.org/ro-id/6690fbf2-b62b-4ad9-96b0-73382001b959 https://w3id.org/ro-id/a01f9ba2-684a-4133-8870-fc9c756538f0 https://w3id.org/ro-id/ae3a4178-a3fa-48c1-83b3-b5b0f67752c6 https://w3id.org/ro-id/01fb6ffe-0f0e-49c4-b575-2c1f86aeb1ff https://w3id.org/ro-id/a74077ed-f9f0-4410-95c5-5629cac76b3f https://w3id.org/ro-id/0678163f-e370-4fc2-a839-7142301e3b6c https://w3id.org/ro-id/3356e64a-99ee-4770-a248-3cf3d24adeb4 https://w3id.org/ro-id/38ab2e3f-9e8e-41b8-9cd0-9360880b5ce0 https://w3id.org/ro-id/9824b34e-5944-4bd6-9bae-abac9dc046ca https://w3id.org/ro-id/be906dbc-5d30-4da1-b93b-6a5c63d51730 https://w3id.org/ro-id/1a06e5e7-3b5a-46d8-bd1f-0fc6d0659384 https://w3id.org/ro-id/20867b26-5388-4fe9-88ab-2a5b6c9335c5

Paolo Montagna, Jacopo Chiggiato, Giorgio Castellan, and Malek Belgacem. "Spatial and temporal distribution of Cold Water Corals (CWC) in the Mediterranean Sea - Data." ROHub. Dec 07 ,2021. https://w3id.org/ro-id/1c87c3d6-46f0-4bfe-bc73-88282fb8c3c5.

POLYGON ((-10.265629291534426 29.22888417844566, -10.265629291534426 46.12198587773459, 38.812497854232795 46.12198587773459, 38.812497854232795 29.22888417844566, -10.265629291534426 29.22888417844566)) data input data 30306 https://api.rohub.org/api/resources/3dedb472-6ac3-4ca9-9530-7e7c745a10b8/download/ 2023-06-22 07:48:36.686799+00:00 2023-06-22 07:58:20.937783+00:00 Location and description of living Mediterranean CWC ecosystems application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Living location of Mediterranean CWC 2023-06-22 07:48:36.686799+00:00 145169 https://api.rohub.org/api/resources/f2d36cb2-2662-499e-9130-171e4b904c8f/download/ 2023-06-22 07:40:08.037639+00:00 2023-06-22 07:40:08.549408+00:00 image/jpeg cwc_med.jpg 2023-06-22 07:40:08.037639+00:00 data 14.558823529411764 9.9 Data on temperature, salinity, dissolved oxygen, pH, and nutrient concentrations in seawater used to explore how environmental variables influence the distribution of CWC in the Mediterranean Sea 57.75775775775775 57.7 pH 8.507853403141361 6.5 variable 10.471204188481675 8.0 Mediterranean Sea 16.8848167539267 12.9 environmental variable 11.877828054298641 10.5 distribution of Cold Water Corals 39.59276018099547 35.0 variable 12.205882352941178 8.3 information 13.089005235602093 10.0 Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery Cold Water Coral 17.352941176470587 11.8 Mediterranean Sea 19.11764705882353 13.0 concentration 13.52941176470588 9.2 earth sciences 100.0 0.9809685945510864 data on temperature 7.466063348416289 6.6 Animal Human interest/Animal salinity 11.470588235294118 7.8 oceanography 100.0 0.9809685945510864 geophysics 100.0 0.4974074065685272 salinity 10.078534031413612 7.7 distribution 11.764705882352942 8.0 temperature 8.900523560209423 6.8 nutrient concentration 18.778280542986426 16.6 salt water 9.947643979057592 7.6 Mediterranean Sea https://www.wikidata.org/wiki/Q4918 chemistry 100.0 15.9 concentration 11.780104712041885 9.0 direttore@ismar.cnr.it CNR-ISMAR CNR-ISMAR jacopo.chiggiato@ismar.cnr.it Jacopo Chiggiato paolo.montagna@cnr.it Paolo Montagna Optics Physical sciences Applied sciences Earth sciences Giorgio Castellan POLYGON ((12.002563476562502 45.54098421805078, 12.123413085937502 44.146739625584985, 14.221801757812502 45.174292524076726, 13.9306640625 45.80199916666154, 12.952880859375002 45.84410779560204, 12.002563476562502 45.54098421805078)) d5e5335a-ae75-40ba-8f43-ac9aca05c92d POLYGON ((12.002563476562502 45.54098421805078, 12.123413085937502 44.146739625584985, 14.221801757812502 45.174292524076726, 13.9306640625 45.80199916666154, 12.952880859375002 45.84410779560204, 12.002563476562502 45.54098421805078)) POLYGON ((12.002563476562502 45.54098421805078, 12.123413085937502 44.146739625584985, 14.221801757812502 45.174292524076726, 13.9306640625 45.80199916666154, 12.952880859375002 45.84410779560204, 12.002563476562502 45.54098421805078)) 12.002563476562502 45.54098421805078, 12.123413085937502 44.146739625584985, 14.221801757812502 45.174292524076726, 13.9306640625 45.80199916666154, 12.952880859375002 45.84410779560204, 12.002563476562502 45.54098421805078 service-account-enrichment 91591 https://api.rohub.org/api/ros/894d3a33-8340-497d-beaf-5b9d85c9bfc7/crate/download/ 2021-12-07 15:44:04.866966+00:00 2025-03-05 01:21:25.016018+00:00 2021-12-07 15:44:04.866966+00:00 Satellite Data on Chlorophyll-a and diffuse attenuation coefficient at 490 nm (Kd490) for the Venice Lagoon application/ld+json https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7 Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown MANUAL Venice absorption coefficient chlorophyll a clarity water earth sciences Nanotechnology Satellite technology Venice Lagoon Venice attenuation coefficient chlorophyll a clarity satellite data water geosciences Venice Lagoon during the COVID 19 lockdown Venice Venice Lagoon diffuse attenuation coefficient satellite data on chlorophyll a satellite data on water clarity Satellite Data on Chlorophyll-a and diffuse attenuation coefficient at 490 nm (Kd490) for the Venice Lagoon Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown. https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7/01ac8fa1-202d-44dc-aeca-189b3b2603cb Venice

Castellan, Giorgio. "Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown." ROHub. Dec 07 ,2021. https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7.

29487 https://api.rohub.org/api/resources/4735e2cd-a746-455e-bf0d-02022be56eca/download/ 2021-12-13 15:48:13.309224+00:00 2021-12-13 15:48:13.310234+00:00 Satelite data on Chl-a for the Venice Lagoon application/zip Satelite data on Chl-a for the Venice Lagoon 2021-12-13 15:48:13.309224+00:00 70005 https://api.rohub.org/api/resources/629e9125-130e-4742-b32b-6eaf05fec072/download/ 2021-12-14 08:57:52.941298+00:00 2021-12-14 08:57:52.942495+00:00 image/png Diffuse attenuation coefficient at 490 nm (Kd490) for north Adriatic Sea in 2018 2021-12-14 08:57:52.941298+00:00 23937 https://api.rohub.org/api/resources/982e29d3-e27c-4a2d-ba63-d0edeade9a48/download/ 2021-12-13 15:47:41.772362+00:00 2021-12-13 15:47:41.774296+00:00 Satellite data on Kd490for the Venice Lagoon application/zip Satellite data on Kd490 for the Venice Lagoon 2021-12-13 15:47:41.772362+00:00 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 Research Lifecycle Management for Earth Science Communities and Copernicus Users 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 Research Lifecycle Management for Earth Science Communities and Copernicus Users 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 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 Research Lifecycle Management for Earth Science Communities and Copernicus Users 56289eeb-73b2-4076-852c-6bf6fee8f381 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 15:24:39.649872+00:00 mailto:rpalma@man.poznan.pl 87396 https://api.rohub.org/api/ros/6440c36b-44c8-48c5-9a2a-a3c47de70c8a/crate/download/ 2021-12-09 15:05:57.255344+00:00 2024-03-05 12:17:26.121572+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/6440c36b-44c8-48c5-9a2a-a3c47de70c8a 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/6440c36b-44c8-48c5-9a2a-a3c47de70c8a/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/10.24424/80ze-vx74.

biblio data raw data metadata 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 2021-12-09 15:07:55.588569+00:00 2023-05-16 16:55:20.098335+00:00 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+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 15:24:36.452503+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+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/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 15:24:36.409139+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 15:24:36.536458+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 15:24:36.359834+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+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 73394 https://api.rohub.org/api/resources/fe10d6ac-bc5f-4f26-a4ff-2b617fd1b443/download/ 2021-12-09 15:07:22.892363+00:00 2021-12-09 15:24:36.183105+00:00 image/png flow-dcro.png 2021-12-09 15:07:22.892363+00:00 POINT (38.0 38.0) Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux neworg2@example.org abcd123 Example Org 2 Earth sciences Fundamental Research Funds for Central Universities European Space Agency (ESA) and Ministry of Science and Technology (MOST), China Natural Science Foundation of China Italian Ministry of University aerospace engineering data at Changbaishan Changbaishan Volcano property of JAXA raw data property soil China North Korea velocity ground velocity file raster file raster Changbaishan JAXA Magma Migration North Korea Interior China Japan INGV cristiano.tolomei@ingv.it Tolomei, Cristiano 0000-0001-7378-0712 - Pianeta Dinamico Working Earth 42071453 - - 58029 Dragon 5 Cooperation project N2001027 - - POLYGON ((127.82938662 41.702706825, 128.35894877 41.702706825, 128.35894877 42.1858125, 127.82938662 42.1858125, 127.82938662 41.702706825)) 127.82938662 41.702706825, 128.35894877 41.702706825, 128.35894877 42.1858125, 127.82938662 42.1858125, 127.82938662 41.702706825 dea23d92-11ac-4e7b-87c3-8465437d0bfa POLYGON ((127.82938662 41.702706825, 128.35894877 41.702706825, 128.35894877 42.1858125, 127.82938662 42.1858125, 127.82938662 41.702706825)) service-account-enrichment False https://w3id.org/ro-id/677cf91e-880d-485a-b027-30ba523dac73 2021-12-13 17:51:45.412526+00:00 https://orcid.org/0000-0002-2983-045X 5016613 https://api.rohub.org/api/ros/61bceafe-5b48-4548-8caf-4142153b1b1b/crate/download/ 2021-12-13 17:49:07.069454+00:00 2024-03-05 12:19:21.893221+00:00 2021-12-13 17:49:07.069454+00:00 This Research Object contains the raster file of the mean ground velocity at the Changbaishan Volcano (China/North Korea) from ALOS-2 satellite data during 2018-2020. Find more on processing and results in the related paper: 'Upward Magma Migration within the Multi-level Plumbing System of the Changbaishan Volcano (China/North Korea) Revealed by the Modeling of 2018-2020 SAR Data' by E. Trasatti, C. Tolomei, L. Wei, G. Ventura. DOI: 10.3389/feart.2021.741287 . Raw data property of JAXA (Japan). application/ld+json https://w3id.org/ro-id/61bceafe-5b48-4548-8caf-4142153b1b1b Ground Velocities from ALOS-2 Data of the Changbaishan Volcanic Area (China/North Korea) - snapshot Mean ground velocities from ALOS-2 data at Changbaishan volcano (China/North Korea) during 2018-2020 MANUAL https://w3id.org/ro-id/61bceafe-5b48-4548-8caf-4142153b1b1b/3a69827c-fd1c-4765-a147-5d25c8b8cd38

Trasatti, Elisa, and Tolomei, Cristiano. "Mean ground velocities from ALOS-2 data at Changbaishan volcano (China/North Korea) during 2018-2020." ROHub. Dec 13 ,2021. https://doi.org/10.24424/vfp6-r230.

metadata raw data biblio data 978596 https://api.rohub.org/api/resources/17d678c6-4274-4475-9fb0-bc6fc00199ae/download/ 2021-12-13 17:49:37.806878+00:00 2021-12-13 17:51:43.786630+00:00 image/png sketch.png 2021-12-13 17:49:37.806878+00:00 Mean ground velocities data 10222 https://api.rohub.org/api/resources/2ca3451c-643c-40de-b793-0280cd331831/download/ 2021-12-13 17:49:41.744694+00:00 2021-12-13 17:51:41.042882+00:00 application/vnd.openxmlformats-officedocument.spreadsheetml.sheet List_of_images.xlsx 2021-12-13 17:49:41.744694+00:00 460884 https://api.rohub.org/api/resources/3e9f5ea7-ec5b-4f90-b40e-8d7a6335855b/download/ 2021-12-13 17:49:49.252182+00:00 2021-12-13 17:51:42.921270+00:00 image/png connection_graph.png 2021-12-13 17:49:49.252182+00:00 23598522 https://api.rohub.org/api/resources/6931dcee-ff02-47a4-bb3c-ac38444d73b3/download/ 2021-12-13 17:49:28.816730+00:00 2021-12-13 17:51:40.107321+00:00 image/tiff Changbaishan_ALOS2_asc_poly1.tif 2021-12-13 17:49:28.816730+00:00 https://www.frontiersin.org/articles/10.3389/feart.2021.741287/abstract 2021-12-13 17:49:53.455227+00:00 2021-12-13 17:51:39.306605+00:00 https://www.frontiersin.org/articles/10.3389/feart.2021.741287/abstract 2021-12-13 17:49:53.455227+00:00 List of the ALOS-2 images used in the processing. Paper published in Frontiers Earth Science with data and modelling link to paper 4891 https://api.rohub.org/api/resources/cfa05a53-9836-4c05-8bd5-b05a3a1ffe03/download/ 2021-12-13 17:49:45.522927+00:00 2021-12-13 17:51:41.997249+00:00 application/rtf readme.rtf 2021-12-13 17:49:45.522927+00:00 Details on the data Details on the data Map of the mean ground velocities POLYGON ((127.82938662 41.702706825, 128.35894877 41.702706825, 128.35894877 42.1858125, 127.82938662 42.1858125, 127.82938662 41.702706825)) Applied sciences service-account-enrichment 61063 https://api.rohub.org/api/ros/b5b86f8e-5c07-4b43-9b21-6df3bb6ebe72/crate/download/ 2022-01-11 10:37:21.504328+00:00 2025-03-05 01:19:47.813154+00:00 2022-01-11 10:37:21.504328+00:00 Street Spectra is a citizen science project to map and characterize public lighting sources. Volunteers use a low cost diffraction grating on top of their smartphones’ camera to take pictures of the street lamps and their emission spectra. application/ld+json https://w3id.org/ro-id/b5b86f8e-5c07-4b43-9b21-6df3bb6ebe72 Street Spectra MANUAL cost diffraction grating digital emission spectrum image lamppost lighting smartphone spectrum volunteer earth sciences Photography Wireless technology citizen science cost diffraction grating emission spectrum lighting smartphone volunteer engineering citizen science project cost diffraction grating lighting source pictures of the street lamps street spectra Camera to take pictures of the street lamps and their emission spectra. Street Spectra is a citizen science project to map and characterize public lighting sources. Volunteers use a low cost diffraction grating on top of their smartphones? photography

project, ACTION. "Street Spectra." ROHub. Jan 11 ,2022. https://w3id.org/ro-id/b5b86f8e-5c07-4b43-9b21-6df3bb6ebe72.

Datasets Presentations Publications Software Documents https://five.epicollect.net/project/action-street-spectra 2022-01-11 10:51:00.637210+00:00 2022-01-11 10:51:00.637736+00:00 Application in Epicollect to collect data Data Collector in Epicollect5 2022-01-11 10:51:00.637210+00:00 https://doi.org/10.5281/zenodo.3885566 2022-01-11 10:47:28.165693+00:00 2022-01-11 10:47:28.166269+00:00 This document describes the different templates that are going to be developed in ACTION for helping pilots to export/use external platforms. Also, a new tool to create Data Management Plan documents based on a questionnaire will be described. Finally, a mini guide has been included to help users to create a CS project using the external platforms Epicollect and Zooniverse. D4.2 Lifecycle-aware citizen science templates 2022-01-11 10:47:28.165693+00:00 51542 https://api.rohub.org/api/resources/24952a29-d009-4c32-a16c-48ef780d8d5b/download/ 2022-01-11 10:38:11.122782+00:00 2022-01-11 10:38:11.124960+00:00 image/png ro-street.png 2022-01-11 10:38:11.122782+00:00 https://www.zooniverse.org/projects/actionprojecteu/street-spectra 2022-01-11 10:56:05.208335+00:00 2022-01-11 10:56:05.209024+00:00 Street Spectra - Zooniverse 2022-01-11 10:56:05.208335+00:00 https://doi.org/10.5281/zenodo.4041469 2022-01-11 10:45:36.637238+00:00 2022-01-11 10:45:36.637879+00:00 This lesson plan is to be used in the classroom of 12 and 13 years old students and aims to educate its users on the topic of light pollution. Aside from gaining awareness, the students will be introduced to the Street Spectra citizen science project through which they will learn how to analyze and classify sources of light pollution contributing to science as a citizen scientist. https://streetspectra.actionproject.eu/ These pages will discuss: artificial light at night in general, different types of light pollution, their negative effects as well as the most efficient way to install lighting sources in such a way that any negative impact is minimized. The Street Spectra project with its objectives as well as its relationship to citizen science are explained during the course. Theory is accompanied with suggested activities adapted to the level of the students. With this unit the authors intend to gather contents that can be implemented in the classroom, and which can serve as a guide so that both students and teachers can participate in this citizen science project. In order for a citizen science project to grow the input of researchers, disseminators and a wide range of volunteers are needed. The participation of the students and teachers will directly help the study of light pollution. Street Spectra - Teaching Materials 2022-01-11 10:45:36.637238+00:00 https://doi.org/10.5281/zenodo.3696492 2022-01-11 10:48:49.624858+00:00 2022-01-11 10:48:49.625481+00:00 This document explains all the steps to obtain the spectra of street lights, how to determine their nature, and also how to contribute with this information to the StreetSpectra citizen science project. The first sections are devoted to introduce the StreetSpectra project, and also the light pollution (LP) problem. We have also included some of the science basics (LP and simple physics of spectra). Tutorial: to identify the spectra of common street lamps 2022-01-11 10:48:49.624858+00:00 ACTION project Earth sciences service-account-enrichment 12545 https://api.rohub.org/api/ros/959fa202-b251-4fcd-8d5f-8ed83740fe43/crate/download/ 2022-01-12 19:56:50.046268+00:00 2025-03-05 01:23:32.908133+00:00 2022-01-12 19:56:50.046268+00:00 Norway is the land of fjords, trolls and – electric cars. By actively promoting the purchase of electric cars, the Norwegian government is aiming at protecting the environment and not least improving air quality, especially in urban areas. Air quality is still a reason for concern in many European countries, including the Nordic countries. Not many people are aware of this fact, and this is where the Norwegian pilot of the ACTION project comes in. The pilot gives high school students in Oslo and the larger Oslo area the opportunity to design and carry out their own air quality projects, using an off-the-shelf air quality sensor platform. The aim is to create awareness about the sources of air pollution, make the students think of ways to reduce both emission and exposure and teach them scientific working methods. We use the Nova SDS011 sensor for measuring PM2.5 and PM10 that is transmitting data to an Arduino board. The data can be obtained through an SD card. application/ld+json https://w3id.org/ro-id/959fa202-b251-4fcd-8d5f-8ed83740fe43 STUDENTS, AIR POLLUTION AND DIY SENSING MANUAL Oslo PM10 air pollution air quality awareness electric car emission high school information opportunity pilot project sensor student environmental sciences Air pollution High schools Students Oslo air pollution air quality electric car high school sensor student geosciences action project air quality project high school student purchase of electric cars sensor platform By actively promoting the purchase of electric cars, the Norwegian government is aiming at protecting the environment and not least improving air quality, especially in urban areas. The aim is to create awareness about the sources of air pollution, make the students think of ways to reduce both emission and exposure and teach them scientific working methods. The pilot gives high school students in Oslo and the larger Oslo area the opportunity to design and carry out their own air quality projects, using an off-the-shelf air quality sensor platform. ecology Norway Oslo

project, ACTION. "STUDENTS, AIR POLLUTION AND DIY SENSING." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/959fa202-b251-4fcd-8d5f-8ed83740fe43.

Presentations Publications Datasets Software https://doi.org/10.5281/zenodo.3730478 2022-01-12 20:54:02.708585+00:00 2022-01-12 20:54:02.709862+00:00 Forskningsprosjekt luftforurensning Forskningsprosjekt luftforurensning 2022-01-12 20:54:02.708585+00:00 https://doi.org/10.5281/zenodo.3737608 2022-01-12 20:52:27.138627+00:00 2022-01-12 20:52:27.139413+00:00 This poster has been created by students of the Ullern Upper Secondary School, located in Oslo, Norway. Systematiske målinger: Vi ønsker å finne ut om det akustiske miljøet har effekt på luftkvalitetens endringer 2022-01-12 20:52:27.138627+00:00 https://doi.org/10.5281/zenodo.3737759 2022-01-12 20:48:05.309120+00:00 2022-01-12 20:48:05.309858+00:00 Measurements taken by a DIY sensor designed by the project air:bit (http://airbit.uit.no/#english). Measurements were taken by students of the school Lambertseter VGS, located in the district of Nordstrand in Oslo, Norway. Lambertseter VGS 2022-01-12 20:48:05.309120+00:00 https://doi.org/10.5281/zenodo.3737635 2022-01-12 20:51:50.182916+00:00 2022-01-12 20:51:50.183388+00:00 This poster has been created by students of the Ullern Upper Secondary School, located in Oslo, Norway. Trafikkforurensing: Trafikkerte områder er mer utsatt for forurensing 2022-01-12 20:51:50.182916+00:00 https://doi.org/10.5281/zenodo.3956481 2022-01-12 20:50:39.286485+00:00 2022-01-12 20:50:39.287215+00:00 Firmware of an Arduino board integrated with a Nova SDS011 sensor for measuring PM2.5 and PM10. The data can be obtained through an SD card. ARDUINO_UNO_WITH_NOVASDS011_Firmware 2022-01-12 20:50:39.286485+00:00 https://doi.org/10.5281/zenodo.3730457 2022-01-12 20:54:42.876507+00:00 2022-01-12 20:54:42.877542+00:00 This deliverable serves as a handbook for air quality projects in high schools. It contains information about the ACTION air quality pilot in high schools, tips and lessons learned as well as material that has been used and created within the high school projects. Tutorial for air quality projects in high schools 2022-01-12 20:54:42.876507+00:00 https://doi.org/10.5281/zenodo.3737799 2022-01-12 20:49:30.350970+00:00 2022-01-12 20:49:30.352122+00:00 Measurements taken by a DIY sensor (Sensor 2) designed by the project air:bit (http://airbit.uit.no/#english). Measurements were taken by students of the school Lambertseter VGS, located in the district of Nordstrand in Oslo, Norway. Lambertseter VGS 2022-01-12 20:49:30.350970+00:00 ACTION project Applied sciences service-account-enrichment 13797 https://api.rohub.org/api/ros/370d93ab-df01-46de-982e-0ef74b3acf8a/crate/download/ 2022-01-12 20:56:44.324225+00:00 2025-03-05 02:45:35.385678+00:00 2022-01-12 20:56:44.324225+00:00 The Noise Maps project focused on deploying a citizen science process in the neighborhoods of Sagrada Familia and the Raval (Barcelona) to address the challenge of noise pollution, a serious problem related to health problems (lack of sleep, psychological ailments, cardiovascular disease, risk of higher stroke) and negative social effects (weakness of social cohesion and coexistence, reduced quality of life, loss of cultural diversity). Noise pollution was an urgent problem in the pilot areas, with active community groups on the lookout for a solution to help improve their living conditions. application/ld+json https://w3id.org/ro-id/370d93ab-df01-46de-982e-0ef74b3acf8a NOISE MAPS MANUAL Sagrada Família ailment cardiovascular disease challenge coexistence community health problem noise pollution problem project quality of life scout earth sciences Church Environmental pollution Psychology Science and technology Social condition Noise Maps cardiovascular disease challenge health problem noise pollution problem quality of life geosciences Noise Maps project challenge of noise pollution problem in the pilot area psychological ailment urgent problem NOISE MAPS. The Noise Maps project focused on deploying a citizen science process in the neighborhoods of Sagrada Familia and the Raval (Barcelona) to address the challenge of noise pollution, a serious problem related to health problems (lack of sleep, psychological ailments, cardiovascular disease, risk of higher stroke) and negative social effects (weakness of social cohesion and coexistence, reduced quality of life, loss of cultural diversity) Noise pollution was an urgent problem in the pilot areas, with active community groups on the lookout for a solution to help improve their living conditions. medicine Barcelona

project, ACTION. "NOISE MAPS." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/370d93ab-df01-46de-982e-0ef74b3acf8a.

data Audios Datasets raw data Software Additional_Information Presentations http://www.bitlab.cat/en/projectes/noise-maps/ 2022-01-12 21:10:54.457704+00:00 2022-01-12 21:10:54.458641+00:00 NoiseMaps website 2022-01-12 21:10:54.457704+00:00 https://www.instamaps.cat/visor.html?businessid=1975f976dff9d780c23a1db01eb37ec3 2022-01-12 21:10:30.483090+00:00 2022-01-12 21:10:30.483591+00:00 Platform of the Geographical Institute of Catalunya text/html Instmaps website 2022-01-12 21:10:30.483090+00:00 https://dashboards.dataportal.actionproject.eu/ 2022-01-12 21:09:07.283838+00:00 2022-01-12 21:09:07.284429+00:00 Dashboards created in Grafana to visualze data Noise Maps Dashboards 2022-01-12 21:09:07.283838+00:00 https://freesound.org/people/bitlab_coop/packs/30131/ 2022-01-12 21:04:23.581144+00:00 2022-01-12 21:04:23.581715+00:00 Collection of ambient urban ourdoors audios from Raval Raval May2020 2022-01-12 21:04:23.581144+00:00 https://doi.org/10.5281/zenodo.4059533 2022-01-12 21:05:06.768056+00:00 2022-01-12 21:05:06.769024+00:00 The Noise Maps project focused on deploying a citizen science process in the Barcelona neighborhoods of Sagrada Familia and the Raval to address the challenge of noise pollution. The sound data was generated between May and September 2020. Noise Maps ACTION pilot data 2020 2022-01-12 21:05:06.768056+00:00 https://github.com/pzinemanas/AudioMoth-Firmware-SPL 2022-01-12 21:01:13.650377+00:00 2022-01-12 21:01:13.650871+00:00 This repository contains an AudioMoth firmware adaptation to calculate the Sound Pressure Level (SPL). This is based on the 1.3.0 version of AudioMoth firmware (published on AudioMoth-Project and AudioMoth-Firmware-Basic). We include the SPL library (src/spl.c and inc/spl.h) that implement all the functions related to the SPL estimation. AudioMoth-Firmware-SPL 2022-01-12 21:01:13.650377+00:00 https://doi.org/10.5281/zenodo.4068095 2022-01-12 21:11:57.606752+00:00 2022-01-12 21:11:57.607269+00:00 This presentation will help ACTION pilots to create their own dashboards Data visualization with Grafana 2022-01-12 21:11:57.606752+00:00 https://ars.electronica.art/keplersgardens/en/sonic-heritage/ 2022-01-12 21:13:10.635968+00:00 2022-01-12 21:13:10.636470+00:00 Results were presented in the ArsElectronica 2020 congress ArsElectronica 2020 2022-01-12 21:13:10.635968+00:00 ACTION project Applied sciences service-account-enrichment 13653 https://api.rohub.org/api/ros/4776fc21-01a3-4806-b248-70a577cbc6b0/crate/download/ 2022-01-12 21:39:57.720721+00:00 2025-03-05 01:19:11.360337+00:00 2022-01-12 21:39:57.720721+00:00 The Sonic Kayak system is a low cost open hardware for gathering and mapping fine-scale marine environmental data, which has not been previously possible to obtain. Data is sonified through an onboard speaker allowing paddlers to seek out areas of interest and gain real time feedback of the data. At the beginning of the project, the system included underwater temperature sensors and a hydrophone for measuring underwater sound, each recording data every second with GPS, time and date. Working with ACTION, two new environmental sensors have been designed and integrated into the existing system (turbidity and air quality). New data have been gathered and citizens have been engaged in two online citizen science style surveys. In the first one people could try out 4 different data sonification approaches and see which was the most straightforward for understanding the underlying environmental data, and also give their preferences on which sounds they liked the best. In the second one, feedback on the pilot activities were gathered. application/ld+json https://w3id.org/ro-id/4776fc21-01a3-4806-b248-70a577cbc6b0 SONIC KAYAKS MANUAL canoeist citizen data feedback hydrophone information preference real time sensor sonification study temperature earth sciences Canoeing Kayaking data feedback hydrophone real time sensor sonification survey life sciences real time feedback recording data sonification approach style survey temperature sensor Data is sonified through an onboard speaker allowing paddlers to seek out areas of interest and gain real time feedback of the data. In the first one people could try out 4 different data sonification approaches and see which was the most straightforward for understanding the underlying environmental data, and also give their preferences on which sounds they liked the best. The Sonic Kayak system is a low cost open hardware for gathering and mapping fine-scale marine environmental data, which has not been previously possible to obtain. computer science scientific terms technical terminology

project, ACTION. "SONIC KAYAKS." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/4776fc21-01a3-4806-b248-70a577cbc6b0.

Additional_Information Publications Software Video Datasets https://fo.am/blog/2020/08/17/sonic-kayak-update-new-sensors-sonifications-and-visualisations/ 2022-01-12 21:45:23.695160+00:00 2022-01-12 21:45:23.695860+00:00 This post is to let you know about the changes we've made and new things available within the project, and to call for your feedback and thoughts via the survey at the end. Sonic Kayak update - new sensors, sonifications, and visualisations 2022-01-12 21:45:23.695160+00:00 https://github.com/fo-am/sonic-kayaks 2022-01-12 21:41:03.488190+00:00 2022-01-12 21:41:03.488763+00:00 Originally based on the Sonic Bikes system, a Raspberry Pi based citizen science project where kayaks become musical & scientific instruments for investigating the marine world. Device Firmware 2022-01-12 21:41:03.488190+00:00 https://fo.am/blog/2020/06/30/sonic-kayak-environmental-data-sonification/ 2022-01-12 21:44:54.129696+00:00 2022-01-12 21:44:54.130248+00:00 Sonic Kayaks are rigged with sensors, both underwater (temperature, sound, and turbidity) and above water (air pollution). As the kayaker paddles around, the sensors pick up changes in the environment, and these are played to the kayaker in real time through an on-board speaker. Environmental Data Sonification 2022-01-12 21:44:54.129696+00:00 https://doi.org/10.5281/zenodo.4041588 2022-01-12 21:43:21.850780+00:00 2022-01-12 21:43:21.851256+00:00 These data sets are the result of five trips using Sonic Kayaks to collect data as part of the ACTION Project. The sampling was carried out in the Penryn river, around Falmouth docks and the Helford estuary. A variety of sensors were used: Sonic Kayaks geolocated air pollution, water turbidity, temperature and hydrophone analysis 2022-01-12 21:43:21.850780+00:00 https://fo.am/blog/2020/05/05/sonic-kayak-progress-new-pollution-sensors-for-citizen-science/ 2022-01-12 21:49:05.158199+00:00 2022-01-12 21:49:05.158808+00:00 Post that describes the device Sonic Kayak progress – new pollution sensors for citizen science 2022-01-12 21:49:05.158199+00:00 https://www.flickr.com/photos/foam/albums/72157715979200366 2022-01-12 21:44:11.524385+00:00 2022-01-12 21:44:11.525013+00:00 Collection of maps based on the measurements taken by devices Observations taken by citizens represented on a Map 2022-01-12 21:44:11.524385+00:00 https://magpi.raspberrypi.com/issues/97/pdf 2022-01-12 21:42:35.214419+00:00 2022-01-12 21:42:35.215144+00:00 Magizine of Rasberry Pi projects. It includes an article about Sonic Kayacs The MagPi - Issue 97 2022-01-12 21:42:35.214419+00:00 https://www.youtube.com/watch?v=puLXKj1AVAk 2022-01-12 21:49:38.295745+00:00 2022-01-12 21:49:38.296142+00:00 Sonic Kayaks - citizen science in the marine environment for the ACTION project 2022-01-12 21:49:38.295745+00:00 ACTION project