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Jun 15 ,2018. https://doi.org/10.5072/ro-id.BPIH2F2WOA. datasets produced software web services inputs main nested results config used biblio setup workflows components scripts ggg 143 https://api.rohub.org/api/resources/906a758b-2781-462c-8c3b-41cd882f632d/download/ 2018-05-10 08:09:44.546000+00:00 2022-03-24 19:49:43.581239+00:00 .txt Input-Master.txt 2018-05-10 08:09:44.546000+00:00 11 https://api.rohub.org/api/resources/9e39ca91-b2a0-4177-96d4-90d5a32b549a/download/ 2018-05-10 10:50:29.452000+00:00 2022-03-24 19:49:45.325235+00:00 .txt Copyright.txt 2018-05-10 10:50:29.452000+00:00 4 https://api.rohub.org/api/resources/d547a8e8-0d4f-4a73-9a3c-3be2ff00ce67/download/ 2018-05-10 08:19:07.994000+00:00 2022-03-24 19:49:49.392487+00:00 .txt workflow.txt 2018-05-10 08:19:07.994000+00:00 0 https://api.rohub.org/api/resources/f948aa0c-f8c4-45e3-b9a8-456307449a48/download/ 2018-05-10 08:21:25.852000+00:00 2022-03-24 19:49:47.742295+00:00 .txt definition.txt 2018-05-10 08:21:25.852000+00:00 test 25.018764073054793 100.0 geology 26.632741500408624 0.9956269264221191 earth sciences 26.214273292711976 0.9799830913543701 geology 17.014721850579107 0.6360710263252258 earth sciences 18.57463758996624 0.6943862438201904 oceanography 11.563625766334056 0.43228960037231445 master image 50.02501250625313 100.0 image 4.736419587904736 17.7 geosciences 22.1282807437931 0.33686426281929016 Satcen 2018 25.018764073054793 100.0 change Detection over Madrid 0.7003501750875437 1.4 astronautics 15.408499143145692 0.23456737399101257 uniform resource identifier 4.194470924690181 8.8 Madrid 6.208188386406208 23.2 Madrid 11.1534795042898 23.4 geophysics 8.407055595076324 0.12798267602920532 atmospheric sciences 18.57463758996624 0.6943862438201904 spacecraft design, testing and performance 15.408499143145692 0.23456737399101257 Detection over Madrid 31.715857928964482 63.4 image 13.203050524308864 27.7 URI: http://box.everest.psnc.pl:8000/f/aa333acec2/ 4.928696522391794 19.7 expert 2.573879885605338 5.4 test 47.66444232602478 100.0 atmospheric sciences 26.214273292711976 0.9799830913543701 test 26.75943270002676 100.0 data 8.482740165908483 31.7 space sciences (general) 3.3287645856718493 0.05067460238933563 geosciences 8.407055595076324 0.12798267602920532 change Detection 17.55877938969485 35.1 earth resources and remote sensing 50.727399932313034 0.7722356915473938 centric 1.9542421353670159 4.1 http 4.242135367016206 8.9 Madrid S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip 16.135937918116138 60.3 Satcen 26.75943270002676 100.0 Detection 10.917848541610917 40.8 S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip 14.360770577933451 57.4 Change Detection over Madrid 10.28271203402552 41.1 geosciences 50.727399932313034 0.7722356915473938 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 WebProcessingServiceExecution 2 http://box.everest.psnc.pl:8000/f/0c4347ad9d/ 2022-03-25 15:09:39.543771+00:00 2022-03-25 15:09:52.874890+00:00 .png cd.png 2022-03-25 15:09:39.543771+00:00 WebProcessingServiceExecution 2018-05-08T16:22:04.503000+00:00 WebProcessingServiceExecution 2 http://box.everest.psnc.pl:8000/f/6157842c73/ 2022-03-25 15:09:39.543368+00:00 2022-03-25 15:09:53.454536+00:00 .tgz cd.tgz 2022-03-25 15:09:39.543368+00:00 WebProcessingServiceExecution 2018-05-08T16:22:04.503000+00:00 985000 http://box.everest.psnc.pl:8000/f/6a67815420/ 2022-03-25 15:09:39.544187+00:00 2022-03-25 15:09:51.086061+00:00 .zip S1A_IW_GRDH_1SDV_20170820T061754_20170820T061819_018003_01E376_9EC3.zip 2022-03-25 15:09:39.544187+00:00 WebProcessingServiceExecution 2 http://box.everest.psnc.pl:8000/f/a25b04c564/ 2022-03-25 15:09:39.542816+00:00 2022-03-25 15:09:57.372474+00:00 .pngw cd.pngw 2022-03-25 15:09:39.542816+00:00 WebProcessingServiceExecution 2018-05-08T16:22:04.503000+00:00 985000 http://box.everest.psnc.pl:8000/f/aa333acec2/ 2022-03-25 15:09:39.544631+00:00 2022-03-25 15:09:55.954370+00:00 .zip S1A_IW_GRDH_1SDV_20170808T061754_20170808T061819_017828_01DE2C_F086.zip 2022-03-25 15:09:39.544631+00:00 Cartography anca popescu EU SatCen EU SatCen POLYGON((-3.6384382604 40.5622470252, -3.3858490478 40.5622470252, -3.3858490478 40.3847421816, -3.6384382604 40.3847421816, -3.6384382604 40.5622470252)) WebProcessingService AreaofInterest MasterSentinel-1product Polarization SlaveSentinel-1product SatCen Change Detection Workflow execution Result Files Distribution Package com.terradue.wps_oozie.process.OozieAbstractAlgorithm SatCen Change Detection Workflow POLYGON( ( -3.611394871468983 40.53713623661004, -3.5125914250302666 40.53713623661004, -3.4137879785915506 40.53713623661004, -3.4137879785915506 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"Change Detection Data Centric." ROHub. Jun 15 ,2018. https://w3id.org/ro-id/f8fafb66-4349-4d35-a695-0db97605e324. setup components datasets produced web services inputs results biblio main scripts workflows nested used software config 0 https://api.rohub.org/api/resources/0dfaa382-a57d-4f1b-b160-2aed85cb2b36/download/ 2018-05-10 08:21:25.852000+00:00 2022-03-25 15:09:55.345849+00:00 .txt definition.txt 2018-05-10 08:21:25.852000+00:00 4 https://api.rohub.org/api/resources/549abf24-05cc-4ad6-a9f4-1d276eaf0dde/download/ 2018-05-10 08:19:07.994000+00:00 2022-03-25 15:09:57.149262+00:00 .txt workflow.txt 2018-05-10 08:19:07.994000+00:00 ggg 143 https://api.rohub.org/api/resources/5c385343-84f6-4709-8095-e4a2a699cc5f/download/ 2018-05-10 08:09:44.546000+00:00 2022-03-25 15:09:50.104320+00:00 .txt Input-Master.txt 2018-05-10 08:09:44.546000+00:00 11 https://api.rohub.org/api/resources/7d4f074b-cc46-4cad-9b31-79d72d8a1fef/download/ 2018-05-10 10:50:29.452000+00:00 2022-03-25 15:09:52.004094+00:00 .txt Copyright.txt 2018-05-10 10:50:29.452000+00:00 service-account-enrichment service-account-generation-service 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 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https://w3id.org/ro-id/3a5459f8-8145-4062-b51b-86e7edfb8208/annotations/3b2a30cc-ae88-4045-8d2a-91996907ff5c/628e3d90a8a689c5035c5329/coverage 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. 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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. 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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 38.35888785866677, 16.089477539062504 36.94989178681327, 13.815307617187502 36.94989178681327)) POLYGON ((13.815307617187502 36.94989178681327, 13.815307617187502 38.35888785866677, 16.089477539062504 38.35888785866677, 16.089477539062504 36.94989178681327, 13.815307617187502 36.94989178681327)) 13.815307617187502 36.94989178681327, 13.815307617187502 38.35888785866677, 16.089477539062504 38.35888785866677, 16.089477539062504 36.94989178681327, 13.815307617187502 36.94989178681327 63ff07eb-8aae-44a9-8c14-3e4c89b3e267 POLYGON ((-24.5119723 35.4378339, 17.256608 35.4378339, 17.256608 66.405028, -24.5119723 66.405028, -24.5119723 35.4378339)) c50f65c4-3548-4437-985c-c61ed3e6a297 POLYGON ((14.506072998046875 37.37670527881838, 14.506072998046875 37.98100996893789, 15.434417724609377 37.98100996893789, 15.434417724609377 37.37670527881838, 14.506072998046875 37.37670527881838)) POLYGON ((-24.5119723 35.4378339, 17.256608 35.4378339, 17.256608 66.405028, -24.5119723 66.405028, -24.5119723 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Sentinel-1 dataset from LiCSAR over Etna volcano MANUAL https://w3id.org/ro-id/7763bf5f-0979-4c40-9655-41b38435f685 https://w3id.org/ro-id/24a2dfd1-acb1-42e2-b95c-227721bfd689 https://w3id.org/ro-id/68e024d5-caff-4412-97a5-aefcdf2ff97e https://w3id.org/ro-id/83ca820c-c7a1-4e76-861d-79aa98cdded8 https://w3id.org/ro-id/ac8f1761-f6c5-458c-bf52-80cdc5ebb348 https://w3id.org/ro-id/d51f0278-6a90-4c49-9816-ec3d3ee8823f https://w3id.org/ro-id/ea46e248-5bf0-4bcb-b4aa-18dec8b68d2f https://w3id.org/ro-id/03742bf5-ad81-4faf-988c-e1b56811937d https://w3id.org/ro-id/5a5d4885-b423-4376-837b-efab8fa5bf7d https://w3id.org/ro-id/17db76d0-dbc6-4dca-930e-401555497efd https://w3id.org/ro-id/28ce51b5-5a27-429e-9586-910eb4b467de https://w3id.org/ro-id/a2d520f1-e210-4945-a43a-fd8a0e124ce8 https://w3id.org/ro-id/eb7632b9-6b2b-4731-9b4e-f6e5dfffe55f https://w3id.org/ro-id/f9e74312-9816-4cc8-b720-46ff3c43aed0 https://w3id.org/ro-id/1643480d-0c47-4ac4-9325-727d35dda7e1 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"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 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)).  *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:  *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).  *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)).  *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:  *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).  *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. data raw data metadata biblio 10.24424/7nkm-2072 36301 https://api.rohub.org/api/resources/8d871c23-a90b-47a0-8e62-2bbb450f8054/download/ 2024-01-05 14:26:46.457298+00:00 2024-01-09 09:49:06.876306+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/c1d71471-3c76-43a4-a1fa-2cbe6ee7a84a/download/ 2024-01-05 14:33:02.319503+00:00 2024-01-09 09:49:06.533423+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 bandwidth 8.112874779541444 4.6 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 computer science 77.34375 9.9 computer programming and software 100.0 0.6090793609619141 mathematical and computer sciences 100.0 0.6090793609619141 These benchmarks are often used for comparing different Message Passing Inerface (MPI) implementations and the underlying network interconnect. 30.184804928131417 29.4 interconnect 11.28747795414462 6.4 Ohio State University 14.586466165413531 9.7 earth sciences 100.0 0.4361959993839264 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 computer network 9.876543209876543 5.6 network 7.36842105263158 4.9 atmospheric sciences 100.0 0.4361959993839264 network interconnect 11.627906976744185 9.5 Trondheim micro benchmark 21.052631578947366 17.2 Office of Management and Budget Message Passing Inerface 13.68421052631579 9.1 High Performance Computer 8.721804511278195 5.8 University Education/School/Higher education/University Osu micro-benchmark 47.61321909424724 38.9 Steeple chase Sport/Competition discipline/Horse racing/Steeple chase bench mark 24.867724867724867 14.1 interconnect 10.225563909774436 6.8 information technology 22.65625 2.9 Education Education MPI operation 12.607099143206854 10.3 different Message Passing Inerface 7.099143206854345 5.8 benchmark 23.60902255639098 15.7 benchmark 22.045855379188712 12.5 Tromsø micro 21.804511278195488 14.5 microcomputer 23.809523809523807 13.5 Applied sciences https://doi.org/10.5281/zenodo.10307603 2024-01-11 12:55:59.876896+00:00 2024-01-11 12:56:00.628531+00:00 https://doi.org/10.5281/zenodo.10307603 2024-01-11 12:55:59.876896+00:00 https://doi.org/10.5281/zenodo.10316689 2024-01-11 12:57:43.731992+00:00 2024-01-11 12:57:44.544155+00:00 https://doi.org/10.5281/zenodo.10316689 2024-01-11 12:57:43.731992+00:00 6035 https://api.rohub.org/api/ros/13dfbe3b-3132-4089-9ec9-5ae100fb143c/crate/download/ 2024-01-11 12:54:17.949857+00:00 2026-04-13 09:37:49.860760+00:00 2024-01-11 12:54:17.949857+00:00 "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. In this paper we describe a lightweight method for effectively identifying bidirectional links between papers and repositories from both LaTeX and PDF sources. 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. application/ld+json https://w3id.org/ro-id/13dfbe3b-3132-4089-9ec9-5ae100fb143c Bidirectional Paper-Repository Tracing in Software Engineering MANUAL 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. metadata raw data biblio data 13200 https://api.rohub.org/api/resources/3b70d877-b722-4108-9f2c-13ffded4a078/download/ 2024-01-24 18:35:11.506674+00:00 2024-01-24 18:35:14.265417+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 18:35:11.506674+00:00 14598 https://api.rohub.org/api/resources/50315613-3195-4d1a-ac95-7906506466e8/download/ 2024-01-24 18:33:22.989042+00:00 2024-01-24 18:33:25.641151+00:00 This data sheet contains reference data used in the manuscript. The data concerns systems of f.c.c. hard spheres with size C2 [001]-nanochannel filled with hard spheres of diferent diameter (equal to sigma’) application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Data for reference system C2 2024-01-24 18:33:22.989042+00:00 13412 https://api.rohub.org/api/resources/83cf1e74-8269-4e22-ab04-f0e823186300/download/ 2024-01-24 18:36:12.053770+00:00 2024-01-24 18:36:14.478015+00:00 This data sheet contains data used in the manuscript. 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 18:36:12.053770+00:00 12965 https://api.rohub.org/api/resources/a566b59a-255d-4983-ab55-3ae88c3f9356/download/ 2024-01-24 18:31:01.550464+00:00 2024-01-24 18:39:33.611026+00:00 This data sheet contains reference data used in the manuscript. The data concerns systems of f.c.c. hard spheres with size C1 [001]-nanochannel filled with hard spheres of diferent diameter (equal to sigma’) application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Data for reference system C1 2024-01-24 18:31:01.550464+00:00 14705 https://api.rohub.org/api/resources/ac305d86-0cb4-4281-b8ee-5e87946b2fbf/download/ 2024-01-24 18:38:16.914356+00:00 2024-01-24 18:38:19.189690+00:00 This data sheet contains data used in the manuscript. 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 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 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. 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 53.6 level 11.461988304093568 9.8 inhabitant 6.113989637305699 5.9 Knowledge Sector (EEA) number 7.6683937823834185 7.4 Computer systems storey 10.88082901554404 10.5 Geographical Scope number of floor 9.670781893004115 9.4 computer science 47.48201438848921 6.6 General number 8.187134502923977 7.0 National government agencies ESTEBAN GONZALEZ GUARDIA Earth sciences 10.13039/501100000780 European Commission 10.13039/501100000781 European Commission Elisa Trasatti https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-08 16:30:52.813503+00:00 2021-11-08 17:06:22.193615+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-08 16:30:52.813503+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-08 16:31:25.130170+00:00 2021-11-08 17:06:22.296703+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-08 16:31:25.130170+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-08 16:31:09.076275+00:00 2021-11-08 17:06:22.491861+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-08 16:31:09.076275+00:00 101017501 RELIANCE 101017502 RELIANCE POINT (38.0 38.0) 5926d4c9-986f-42f2-a840-79ae265f653f POINT (38.0 38.0) 38.0 38.0 POINT (38.0 38.0) False 2021-11-08 17:06:28.738078+00:00 79418 https://api.rohub.org/api/ros/bcb5cdba-0605-4602-bd60-b59f2701e05b/crate/download/ 2021-11-08 15:12:22.689370+00:00 2025-10-16 10:35:19.041970+00:00 2021-11-08 15:12:22.689370+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/bcb5cdba-0605-4602-bd60-b59f2701e05b 8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot MANUAL Jose Perez, and Elisa Trasatti. "8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 08 ,2021. https://doi.org/10.24424/1k12-x394. ICHB-PAS Jose Perez PSNC 73394 https://api.rohub.org/api/resources/1f611f7e-a4b7-45de-be8e-d6f0e39d2fde/download/ 2021-11-08 16:30:06.553639+00:00 2021-11-08 17:06:22.592157+00:00 image/png flow-dcro.png 2021-11-08 16:30:06.553639+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G Flow to compute monthly map Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 research object 83.11557788944724 82.7 map 17.05639614855571 12.4 PM10 13.541666666666666 13.0 Copernicus Atmosphere Monitoring Service 8.229166666666666 7.9 object 25.208333333333332 24.2 Nov-8 research 31.145833333333332 29.9 data cube research object 1.0050251256281406 1.0 8th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot. 30.330330330330334 30.3 aim 31.499312242090785 22.9 country 8.541666666666666 8.2 earth sciences 100.0 0.8168788552284241 atmospheric sciences 100.0 0.8168788552284241 research 39.61485557083906 28.8 map 13.333333333333334 12.8 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified 69.66966966966967 69.6 country 11.829436038514443 8.6 monthly map 6.231155778894473 6.2 map of PM10 9.246231155778894 9.2 astronautics 100.0 0.3785407543182373 astronautics (general) 100.0 0.3785407543182373 data cube 0.4020100502512563 0.4 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-08 16:59:14.401521+00:00 2021-11-08 17:06:22.390417+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-08 16:59:14.401521+00:00 Raul Palma service-account-enrichment Earth sciences 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 - 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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 POINT (38.0 38.0) 0a113f7e-5c4d-411e-985e-2d71e8dcbd28 POINT (38.0 38.0) 38.0 38.0 POINT (38.0 38.0) service-account-enrichment False https://w3id.org/ro-id/93ece8d0-3be4-4658-a840-156bda47f612 2021-12-09 15:19:11.307501+00:00 mailto:rpalma@man.poznan.pl 87394 https://api.rohub.org/api/ros/a08ddcb2-ae5f-40ba-b1b6-c64dd2e4d68c/crate/download/ 2021-12-09 15:05:57.255344+00:00 2024-03-05 12:17:25.978567+00:00 2021-12-09 15:05:57.255344+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/a08ddcb2-ae5f-40ba-b1b6-c64dd2e4d68c 8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v1 MANUAL https://w3id.org/ro-id/a08ddcb2-ae5f-40ba-b1b6-c64dd2e4d68c/ea782618-0dff-4cfa-8604-e121ce29d3cf Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v1." ROHub. Dec 09 ,2021. https://doi.org/10.24424/w44h-8089. metadata data biblio raw data https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-09 15:07:51.036076+00:00 2021-12-09 15:19:08.564064+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-09 15:07:51.036076+00:00 List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+00:00 2021-12-09 15:19:08.515865+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+00:00 Flow to compute monthly map https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+00:00 2023-05-16 16:54:04.603729+00:00 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration 73394 https://api.rohub.org/api/resources/7733e68b-7b14-45b8-96ef-b0ff1e3b6a45/download/ 2021-12-09 15:07:22.892363+00:00 2021-12-09 15:19:08.338406+00:00 image/png flow-dcro.png 2021-12-09 15:07:22.892363+00:00 Catch data records sample from 2019 Catch data from Norway This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-09 15:07:47.272247+00:00 2021-12-09 15:19:08.713366+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-09 15:07:47.272247+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+00:00 2021-12-09 15:19:08.607528+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+00:00 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux neworg2@example.org abcd123 Example Org 2 Earth sciences 10.13039/501100000781 European Commission published v2 monthly map of PM10 Copernicus Atmosphere Monitoring Service Data Cube Ro country map Ro monthly map map of PM10 PCSS example4@hotmail.com Pepito Bato 0000-0002-8316-3192 UNO-Recoletos npepito@hotmail.com Nieves Pepito 0000-0003-3784-6651 office@man.poznan.pl 025cj6e44 Poznan Supercomputing and Networking Center 101017502 RELIANCE 38.0 38.0 POINT (38.0 38.0) 86a33d62-4541-495f-a640-2b60e0394266 POINT (38.0 38.0) service-account-enrichment False https://w3id.org/ro-id/93ece8d0-3be4-4658-a840-156bda47f612 2021-12-09 15:20:23.441762+00:00 mailto:rpalma@man.poznan.pl 87383 https://api.rohub.org/api/ros/57cf76e1-2179-4650-b48b-b5990dca86c1/crate/download/ 2021-12-09 15:05:57.255344+00:00 2024-03-05 12:17:26.248043+00:00 2021-12-09 15:05:57.255344+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/57cf76e1-2179-4650-b48b-b5990dca86c1 8th December - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v2 MANUAL https://w3id.org/ro-id/57cf76e1-2179-4650-b48b-b5990dca86c1/ea782618-0dff-4cfa-8604-e121ce29d3cf Anne Foilloux, Nieves Pepito, and Pepito Bato. "Copernicus Atmosphere Monitoring Service Data Cube RO December 9th - published v2." ROHub. Dec 09 ,2021. https://doi.org/10.24424/yptf-km76. biblio metadata raw data data List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-09 15:07:51.036076+00:00 2021-12-09 15:20:20.634446+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-12-09 15:07:51.036076+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-09 15:07:47.272247+00:00 2021-12-09 15:20:20.738000+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-12-09 15:07:47.272247+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+00:00 2021-12-09 15:20:20.597858+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-12-09 15:07:43.448712+00:00 Flow to compute monthly map Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+00:00 2021-12-09 15:20:20.669306+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2021-12-09 15:07:59.055468+00:00 73394 https://api.rohub.org/api/resources/7bfd4974-4bf8-4922-ae40-36a2ca9ef7fe/download/ 2021-12-09 15:07:22.892363+00:00 2021-12-09 15:20:20.444066+00:00 image/png flow-dcro.png 2021-12-09 15:07:22.892363+00:00 Catch data records sample from 2019 Catch data from Norway This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+00:00 2023-05-16 16:54:33.185954+00:00 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-12-09 15:07:55.588569+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services POINT (38.0 38.0) Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux neworg2@example.org abcd123 Example Org 2 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 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 42071453 - 58029 Dragon 5 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 Applied sciences service-account-enrichment 8699 https://api.rohub.org/api/ros/b7601048-d964-4c6f-92ac-f6956817dd44/crate/download/ 2022-01-12 23:11:01.694528+00:00 2025-03-05 00:55:14.849586+00:00 2022-01-12 23:11:01.694528+00:00 The aim of the project was to to understand and map the use of pesticides and fertilizers in the context of home farming and gardening. Simultaneously, it aimed to disseminate information on the topic with the final aim of reducing the use of pesticides and fertilizers. application/ld+json https://w3id.org/ro-id/b7601048-d964-4c6f-92ac-f6956817dd44 IN MY BACKYARD MANUAL backyard context farming fertiliser horticulture information pesticide project purpose subject use earth sciences Agriculture Fertiliser aim farming fertilizer gardening pesticide project topic aeronautics aim of the project fertilizers in the context final aim home farming use of pesticide IN MY BACKYARD. Simultaneously, it aimed to disseminate information on the topic with the final aim of reducing the use of pesticides and fertilizers. The aim of the project was to to understand and map the use of pesticides and fertilizers in the context of home farming and gardening. agriculture project, ACTION. "IN MY BACKYARD." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/b7601048-d964-4c6f-92ac-f6956817dd44. Publications Datasets Presentations https://doi.org/10.5281/zenodo.4081585 2022-01-12 23:13:53.593108+00:00 2022-01-12 23:13:53.593689+00:00 Description of project, process, take aways and impact. Project reflections and take aways 2022-01-12 23:13:53.593108+00:00 https://doi.org/10.5281/zenodo.4081597 2022-01-12 23:14:20.032977+00:00 2022-01-12 23:14:20.033714+00:00 Project Final Report Project Final Report 2022-01-12 23:14:20.032977+00:00 https://doi.org/10.5281/zenodo.4081778 2022-01-12 23:12:03.296651+00:00 2022-01-12 23:12:03.297278+00:00 In My Backyard: On-Site Survey Responses Raw Dataset On-Site Survey Responses Raw Dataset 2022-01-12 23:12:03.296651+00:00 https://doi.org/10.5281/zenodo.4081770 2022-01-12 23:12:48.624112+00:00 2022-01-12 23:12:48.624923+00:00 In My Backyard is a citizen science project promoted by Rio Neiva – Environmental NGO and its partner CEA – Municipal Centre for Environmental Education, both based in Esposende, Portugal. It was funded through the ACTION project. In My Backyard aimed to understand the use of harmful pesticides and fertilizers in home farming and gardening and uncovering sustainable alternatives practiced within domestic backyards. Data Analysis Report 2022-01-12 23:12:48.624112+00:00 https://doi.org/10.5281/zenodo.4081606 2022-01-12 23:13:27.546138+00:00 2022-01-12 23:13:27.546890+00:00 Project key insights - Presentation at EU Week of Regions and Cities, 8th October, Session Citizens safeguarding the environment - https://europa.eu/regions-and-cities/programme/sessions/1451_en Key Insights 2022-01-12 23:13:27.546138+00:00 ACTION project Applied sciences service-account-enrichment 8966 https://api.rohub.org/api/ros/7f2a62c1-21cb-42b1-875f-e7d2d19873c8/crate/download/ 2022-01-12 23:15:44.728087+00:00 2025-03-05 01:27:07.174680+00:00 2022-01-12 23:15:44.728087+00:00 The Po Valley in Northern Italy has one of the worst air qualities in Europe, with many of its cities regularly surpassing the threshold levels for PM concentrations considered safe for human health. Luckily, trees can play a role in tackling this problem: studies all over the world are demonstrating the ability of trees in capturing PM, but evidence is needed at the local level. application/ld+json https://w3id.org/ro-id/7f2a62c1-21cb-42b1-875f-e7d2d19873c8 WOW NATURE MANUAL Europe Northern Italy air quality city evidence hanger health prime minister problem safe study threshold level trees wow earth sciences Air pollution Arrest Executive (government) Government Ministers (government) Europe Po Valley air quality evidence study threshold level tree aeronautics Po Valley in Northern Italy ability of trees demonstrate the ability worst air qualities wow nature Luckily, trees can play a role in tackling this problem: studies all over the world are demonstrating the ability of trees in capturing PM, but evidence is needed at the local level. The Po Valley in Northern Italy has one of the worst air qualities in Europe, with many of its cities regularly surpassing the threshold levels for PM concentrations considered safe for human health. WOW NATURE. Europe Northern Italy project, ACTION. "WOW NATURE." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/7f2a62c1-21cb-42b1-875f-e7d2d19873c8. Publications Presentations Datasets https://doi.org/10.5281/zenodo.5236660 2022-01-12 23:19:02.899248+00:00 2022-01-12 23:19:02.899697+00:00 Air pollution Device Measurements [\"pm1\",\"pm2p5\",\"pm4\",\"pm10\",\"humidity\"] WOW Nature - Bosco del ponte del Quarelo 2022-01-12 23:19:02.899248+00:00 https://zenodo.org/record/5236644 2022-01-12 23:16:42.817433+00:00 2022-01-12 23:16:42.818218+00:00 Air pollution Device Measurements [\"pm1\",\"pm2p5\",\"pm4\",\"pm10\",\"humidity\"] WOW Nature - Bosco di Prasaccon 2022-01-12 23:16:42.817433+00:00 https://doi.org/10.5281/zenodo.5842495 2022-01-12 23:19:37.582385+00:00 2022-01-12 23:19:37.583025+00:00 Final presentation of Wow Nature Wow Nature - Final Presentation 2022-01-12 23:19:37.582385+00:00 https://doi.org/10.5281/zenodo.5842501 2022-01-12 23:20:29.569248+00:00 2022-01-12 23:20:29.569658+00:00 This report summarizes the aims, design, implementation, and results of the WOWNATURE project, a project developed in the context of the first ACTION open call, i.e., a call for citizen science projects related to pollution funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824603. The project aimed to measure the air pollution mitigation capacity of urban and peri-urban forests by using innovative sensors and by engaging citizens throughout the process (i.e., experiment design, safekeeping of sensors, and dissemination of results) with the support of the WOWnature web-based platform, thus strengthening the argument in their favour as an effective policy to tackle air pollution. WOW Nature - Final Report 2022-01-12 23:20:29.569248+00:00 https://doi.org/10.5281/zenodo.5236621 2022-01-12 23:17:49.255133+00:00 2022-01-12 23:17:49.255593+00:00 Air pollution Device Measurements [\"pm1\",\"pm2p5\",\"pm4\",\"pm10\",\"humidity\"] Wow Nature - Bosco Limite 2022-01-12 23:17:49.255133+00:00 ACTION project Earth sciences christian.bignami@ingv.it Christian Bignami 0000-0002-8632-9979 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia soil 9.454545454545455 5.2 The Sentinel-1 SAR data have been processed by using the LiCSBAS method implemented by COMET 4.408817635270542 4.4 Etna Volcano 22.118380062305295 14.2 comet 5.454545454545454 3.0 phase of Etna Volcano 12.760736196319018 10.4 boom 19.09090909090909 10.5 geosciences 100.0 0.5355959534645081 This Research Object reports the results of a fast InSAR multi-temporal analysis to map ground deformation related to the post-eruption phase of Etna Volcano, after the December 2018 event. 72.14428857715431 72.0 ground deformation 35.95092024539877 29.3 eruption phase 19.631901840490798 16.0 Sentinel-1 InSAR ground velocity of Etna Volcano, after the big eruption of December 2018. 23.44689378757515 23.4 after the Dec-2018 POLYGON ((14.7 37.95, 15.3 37.95, 15.3 37.44, 14.7 37.44, 14.7 37.95)) 24368e97-b7e8-47a0-a677-7610777dc6cc POLYGON ((14.7 37.95, 15.3 37.95, 15.3 37.44, 14.7 37.44, 14.7 37.95)) POLYGON ((14.7 37.95, 15.3 37.95, 15.3 37.44, 14.7 37.44, 14.7 37.95)) 14.7 37.95, 15.3 37.95, 15.3 37.44, 14.7 37.44, 14.7 37.95 service-account-enrichment . https://w3id.org/ro-id/1c9bfc94-dbb9-475e-af50-601bff9f6c0c 49515117 https://api.rohub.org/api/ros/8b715b0d-b5bb-4d6a-9228-704ec87652f2/crate/download/ 2022-02-16 16:10:01.055517+00:00 2025-03-05 01:21:29.885983+00:00 2022-02-16 16:10:01.055517+00:00 This Research Object reports the results of a fast InSAR multi-temporal analysis to map ground deformation related to the post-eruption phase of Etna Volcano, after the December 2018 event. The Sentinel-1 SAR data have been processed by using the LiCSBAS method implemented by COMET application/ld+json https://w3id.org/ro-id/8b715b0d-b5bb-4d6a-9228-704ec87652f2 Ground Motion Ground Velocity InSAR SAR Sentinel-1 Sentinel-1 InSAR ground velocity of Etna Volcano, after the big eruption of December 2018 MANUAL https://w3id.org/ro-id/8b715b0d-b5bb-4d6a-9228-704ec87652f2/6453fc74-be94-4832-9757-d228b557ea6d https://w3id.org/ro-id/13597655-8c57-4280-9765-76e5e9a6278e https://w3id.org/ro-id/48cef009-12f2-452f-89b6-fc05d6f2938d https://w3id.org/ro-id/54566b31-3489-43c4-abcd-53f59eaefa9c https://w3id.org/ro-id/9a6cbb90-8725-4377-994f-2b8367fccb6b https://w3id.org/ro-id/b73b8141-bc82-4ad9-abbd-79c89a88c558 https://w3id.org/ro-id/eae3c482-5579-418f-b98d-20d693f845b5 https://w3id.org/ro-id/f101ae40-b251-42fb-9a50-515f91e970ce https://w3id.org/ro-id/f37fcac5-5fb4-4d37-941c-9cdec9e6c892 https://w3id.org/ro-id/f3b79827-3acf-4530-8fe2-9c81b345d563 https://w3id.org/ro-id/9bd87173-eb18-4afe-9c70-dd33d7b67127 https://w3id.org/ro-id/cfbdf5cf-2eef-4f65-abeb-76c16db92cc7 https://w3id.org/ro-id/db7665de-1070-46f0-8d4f-f037027cb28a https://w3id.org/ro-id/dff9b071-8fd3-477f-9875-00c787161fae https://w3id.org/ro-id/3e532cd3-916b-42c1-8583-ab7774e19db5 https://w3id.org/ro-id/b1ed85de-043f-4561-84b1-fddaea4f8aea https://w3id.org/ro-id/bae57bdb-f352-4983-a4ce-611db59e115d https://w3id.org/ro-id/d4528387-10e5-406f-8f95-368e0c813c7e https://w3id.org/ro-id/f26805af-fc16-4d47-8453-1c507f01c903 https://w3id.org/ro-id/f5873a88-4af3-408d-9cf1-49bd5e638a1c https://w3id.org/ro-id/5e1e9100-2c79-48d1-93c9-7a821a7b7b1e https://w3id.org/ro-id/e1abba21-4272-4eb6-abd1-dc51a57939c3 https://w3id.org/ro-id/49c29a0b-0d21-4f05-afa0-3261f9b56842 https://w3id.org/ro-id/63ace803-4197-48f3-8bec-44fddd903a59 https://w3id.org/ro-id/725a9d35-d8bc-4420-9fce-5d58ef77981d https://w3id.org/ro-id/939164aa-8730-4b35-8e0b-5946cc1ccef8 https://w3id.org/ro-id/b1416fc5-5abd-48f5-a6e0-130423b0ac93 https://w3id.org/ro-id/248d2117-7bbf-4a80-8954-66a9d38348ad https://w3id.org/ro-id/624c51d5-e2e3-4eab-bb95-5f2cfa84f464 https://w3id.org/ro-id/771c087c-8823-4614-b47b-ddf602aa9bff https://w3id.org/ro-id/857591e6-7e6e-455a-8113-071f69804f05 https://w3id.org/ro-id/b5dd7f41-defe-4614-9121-7bb50e0ee2f0 Bignami, Christian, and INGV GeoSAR Laboratory. "Sentinel-1 InSAR ground velocity of Etna Volcano, after the big eruption of December 2018." ROHub. Feb 16 ,2022. https://w3id.org/ro-id/8b715b0d-b5bb-4d6a-9228-704ec87652f2. script metadata biblio data 213 https://api.rohub.org/api/resources/07e25d65-eb01-48d6-a874-502f4b7fbecb/download/ 2022-03-19 08:21:49.029862+00:00 2023-06-09 13:05:01.450514+00:00 application/vnd.google-earth.kml+xml Reference point in KML format 2022-03-19 08:21:49.029862+00:00 1413 https://api.rohub.org/api/resources/141f0775-4b87-439d-87d5-47c0c3cfae92/download/ 2022-03-19 08:21:40.300883+00:00 2023-06-09 13:04:10.928930+00:00 text/plain List of the used images 2022-03-19 08:21:40.300883+00:00 13162 https://api.rohub.org/api/resources/5df10037-1895-43db-84b4-f629af6982d3/download/ 2022-03-18 19:53:24.339423+00:00 2022-03-18 19:53:29.252273+00:00 Jupyter Notebook used to process SAR data based on LiCSBAS method 2022-03-18 19:53:24.339423+00:00 6702901 https://api.rohub.org/api/resources/7f36b3c5-de72-490d-bdd6-1f8b0208627a/download/ 2023-06-09 12:05:26.685797+00:00 2023-06-09 12:06:17.299846+00:00 application/pdf One Reference paper of LiCSBAS method 2023-06-09 12:05:26.685797+00:00 4049604 https://api.rohub.org/api/resources/81cecfa4-7222-4862-b53c-5ace30066ab8/download/ 2023-06-09 12:06:02.187826+00:00 2023-06-09 12:06:03.584840+00:00 application/pdf A second Reference paper of LiCSBAS method 2023-06-09 12:06:02.187826+00:00 388733 https://api.rohub.org/api/resources/99224e8f-d02c-41b7-868f-179a3f4838a3/download/ 2022-03-19 08:21:54.744273+00:00 2023-06-09 13:04:51.128359+00:00 image/png Final connection graph 2022-03-19 08:21:54.744273+00:00 154390 https://api.rohub.org/api/resources/9d318340-e188-4727-8f0c-c90287696f6e/download/ 2022-03-18 21:04:11.292887+00:00 2022-03-18 21:04:14.294836+00:00 image/tiff Final mean ground velocity map by LiCSBAS method in GEOTiff 2022-03-18 21:04:11.292887+00:00 39967814 https://api.rohub.org/api/resources/ab7bd32e-5ac9-426f-ac49-7f46990694f7/download/ 2022-03-18 19:48:02.704188+00:00 2022-03-18 19:48:10.780688+00:00 This file is a structured h5 file, containing all the results of the MT-InSAR processing application/x-tar LiCSBAS output 2022-03-18 19:48:02.704188+00:00 101757 https://api.rohub.org/api/resources/dcb3913c-0ae2-438a-879a-ccff1fd3752d/download/ 2022-03-18 19:52:27.521128+00:00 2023-06-09 13:04:42.382632+00:00 image/png Mean ground Velocity map from LiCSBAS processing 2022-03-18 19:52:27.521128+00:00 ground velocity 25.39877300613497 20.7 ground 10.545454545454545 5.8 earth sciences 100.0 0.5819281339645386 big eruption 6.257668711656441 5.1 deformation 15.887850467289717 10.2 of Dec-2018 result 7.818181818181818 4.3 Research Object 19.937694704049843 12.8 atmospheric sciences 100.0 0.5819281339645386 SAR interferometry 11.370716510903426 7.3 Sports facilities Lifestyle and leisure/Leisure/Leisure venue/Sports facilities Sport venue Sport/Sport venue earth resources and remote sensing 100.0 0.5355959534645081 velocity 14.0 7.7 case 7.636363636363637 4.2 velocity 13.551401869158877 8.7 deformation 16.545454545454547 9.1 phase 9.454545454545455 5.2 eruption 17.133956386292834 11.0 labgeosar@ingv.it INGV GeoSAR Laboratory Earth sciences published v1 monthly map of PM10 Copernicus Atmosphere Monitoring Service Data Cube Ro country map Ro monthly map map of PM10 federica.foglini@ismar.cnr.it Federica Foglini PCSS example5@hotmail.com Pepito Baston 0000-0002-8316-3195 UNO-Recoletos npepito@hotmail.com Nieves Pepito 0000-0003-3784-6651 office@man.poznan.pl 025cj6e44 Poznan Supercomputing and Networking Center neworg3@example.org abcd56789 Example Org 3 17ac6881-a240-4b44-974c-a83463aca07a MULTIPOLYGON (((9.4858320000001 42.615273, 9.49472 42.603607, 9.4827770000001 42.613052, 9.47778 42.617218, 9.465277 42.630829, 9.457777 42.643326, 9.4858320000001 42.615273)), ((9.446665 42.67889, 9.4480550000001 42.64944, 9.452221 42.630272, 9.473888 42.582222, 9.47805 42.576111, 9.50555 42.563889, 9.509998 42.563606, 9.51139 42.56721, 9.511665 42.571663, 9.509443 42.578049, 9.503054 42.59166, 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1.598333 50.37055, 1.603851 50.36879, 1.562778 50.39611, 1.560278 50.399162, 1.558055 50.4061, 1.577222 50.528053, 1.564167 50.68471, 1.564444 50.705826, 1.5986110000001 50.809166, 1.625 50.877777, 1.733333 50.942497, 1.7458330000001 50.948051, 1.768889 50.95583, 1.792778 50.962776, 1.9433330000001 50.995277, 2.23528 51.03805, 2.3594440000001 51.054443, 2.38472 51.051941, 2.407222 51.054993, 2.42305 51.058052, 2.492222 51.07611, 2.5166660000001 51.082771, 2.5416670000001 51.09111) 38.0 38.0 POINT (38.0 38.0) a384dfb8-5a8b-4ded-86fd-3ac17ecab1d6 POINT (38.0 38.0) service-account-enrichment False https://w3id.org/ro-id/88374f34-bdb3-4449-97b3-8e0a8483aa66 2022-02-17 21:13:07.580080+00:00 mailto:rpalma@man.poznan.pl 103398 https://api.rohub.org/api/ros/4e7a0712-e600-4645-a375-5a9b0d4ef122/crate/download/ 2022-02-17 14:58:39.070868+00:00 2024-03-05 12:17:26.373675+00:00 2022-02-17 14:58:39.070868+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/4e7a0712-e600-4645-a375-5a9b0d4ef122 17th Feb - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot Copernicus Atmosphere Monitoring Service Data Cube RO Feb 17th - published v1 MANUAL https://w3id.org/ro-id/4e7a0712-e600-4645-a375-5a9b0d4ef122/0675dd94-c052-4526-9e7f-3274e2a20d63 https://w3id.org/ro-id/4e7a0712-e600-4645-a375-5a9b0d4ef122/a14e28c2-9990-45f1-a7f1-194103406268 https://w3id.org/ro-id/4e7a0712-e600-4645-a375-5a9b0d4ef122/f3ebe1ed-9c2d-4da4-8e05-55df51e8bcbb Foglini, Federica, Nieves Pepito, and Pepito Baston. "Copernicus Atmosphere Monitoring Service Data Cube RO Feb 17th - published v1." ROHub. Feb 17 ,2022. https://doi.org/10.24424/kh1w-th55. biblio data myfolder mysubfolder raw data metadata https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2022-02-17 15:22:30.059240+00:00 2023-05-16 17:11:30.443960+00:00 https://zenodo.org/record/5554786/files/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2022-02-17 15:22:30.059240+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 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://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 2022-02-17 15:22:03.118296+00:00 2022-02-17 21:13:02.711907+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 2022-02-17 15:22:03.118296+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2022-02-17 15:22:15.915421+00:00 2022-02-17 21:13:02.585683+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2022-02-17 15:22:15.915421+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration This resource has this description Flow to compute monthly map - updated List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 73394 https://api.rohub.org/api/resources/a4a570d8-f83c-47e8-bf0f-50c2a66810b1/download/ 2022-02-17 15:19:04.098429+00:00 2022-02-17 21:13:02.115316+00:00 image/png flow-dcro.png 2022-02-17 15:19:04.098429+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2022-02-17 15:22:47.629469+00:00 2022-02-17 21:13:02.616681+00:00 https://box.psnc.pl/f/d90a0e1e0d/?raw=1 2022-02-17 15:22:47.629469+00:00 Catch data records sample from 2019 Catch data from Norway https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2022-02-17 15:21:26.221676+00:00 2022-02-17 21:13:02.517346+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2022-02-17 15:21:26.221676+00:00 POINT (38.0 38.0) MULTIPOLYGON (((9.4858320000001 42.615273, 9.49472 42.603607, 9.4827770000001 42.613052, 9.47778 42.617218, 9.465277 42.630829, 9.457777 42.643326, 9.4858320000001 42.615273)), ((9.446665 42.67889, 9.4480550000001 42.64944, 9.452221 42.630272, 9.473888 42.582222, 9.47805 42.576111, 9.50555 42.563889, 9.509998 42.563606, 9.51139 42.56721, 9.511665 42.571663, 9.509443 42.578049, 9.503054 42.59166, 9.497221 42.60083, 9.50028 42.59861, 9.5202770000001 42.572495, 9.531666 42.54916, 9.5338880000001 42.541939, 9.562222 42.272774, 9.5599990000001 42.19221, 9.5555550000001 42.127777, 9.5533330000001 42.115555, 9.54583 42.102219, 9.4480550000001 41.999443, 9.42555 41.975, 9.41111 41.954163, 9.405554 41.934998, 9.397192 41.875931, 9.396666 41.862778, 9.398611 41.85083, 9.402498 41.840271, 9.404444 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51.054443, 2.38472 51.051941, 2.407222 51.054993, 2.42305 51.058052, 2.492222 51.07611, 2.5166660000001 51.082771, 2.5416670000001 51.09111))) POINT (-155.499 19.661) Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) 78c886f0-071a-4c18-b4dd-ebe86a23d74b POINT (15.0 37.75) service-account-enrichment 1850761 https://api.rohub.org/api/ros/114d6770-78b5-4f7a-a357-360cc8095bf1/crate/download/ 2022-03-10 00:49:43.815847+00:00 2025-03-05 00:51:30.903940+00:00 2022-03-10 00:49:43.815847+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/114d6770-78b5-4f7a-a357-360cc8095bf1 Etna Eruption 2021 02 19 Research Object MANUAL https://w3id.org/ro-id/114d6770-78b5-4f7a-a357-360cc8095bf1/a112a689-d0f4-44f9-9c8c-15425f4329ad Stelitano, Dario. "Etna Eruption 2021 02 19 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/114d6770-78b5-4f7a-a357-360cc8095bf1. raw data data metadata biblio https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 00:50:16.244407+00:00 2022-03-10 00:50:16.459624+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 00:50:16.244407+00:00 532684 https://api.rohub.org/api/resources/5de3508c-4dd6-41dc-bd95-1f40c90df054/download/ 2022-03-10 00:50:22.276715+00:00 2022-03-10 00:50:25.390021+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210219_0800 2022-03-10 00:50:22.276715+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 00:50:18.849951+00:00 2022-03-10 00:50:19.065264+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 00:50:18.849951+00:00 1788885 https://api.rohub.org/api/resources/65419b11-ffa1-49e5-88f9-7d6ec2de6f24/download/ 2022-03-10 00:50:03.945479+00:00 2022-03-10 00:50:07.182951+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210219_1000 2022-03-10 00:50:03.945479+00:00 https://youtu.be/MRZUVJRi2xI 2022-03-10 00:50:10.563424+00:00 2022-03-10 00:50:10.804040+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210219_1000 2022-03-10 00:50:10.563424+00:00 https://youtu.be/5bUT8Sih4mo 2022-03-10 00:50:13.121438+00:00 2022-03-10 00:50:13.338019+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210219_1000 2022-03-10 00:50:13.121438+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 1f74d3d6-f6bd-4f4b-bfe9-e52cebccd7a6 POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) service-account-enrichment 1957096 https://api.rohub.org/api/ros/00635a9e-6a0b-4280-84a3-0a622bf27482/crate/download/ 2022-03-10 11:41:06.217407+00:00 2025-03-05 00:51:30.475196+00:00 2022-03-10 11:41:06.217407+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/00635a9e-6a0b-4280-84a3-0a622bf27482 Etna Eruption 2021 02 16 Research Object MANUAL https://w3id.org/ro-id/00635a9e-6a0b-4280-84a3-0a622bf27482/f7036a22-ab6d-47ab-956b-bfa1fbb138b3 Stelitano, Dario. "Etna Eruption 2021 02 16 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/00635a9e-6a0b-4280-84a3-0a622bf27482. biblio raw data data metadata https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 11:41:38.688525+00:00 2022-03-10 11:41:38.898711+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 11:41:38.688525+00:00 215657 https://api.rohub.org/api/resources/3bd13b28-46cd-4bd4-8ddb-475756c34707/download/ 2022-03-10 11:41:45.381170+00:00 2022-03-10 11:41:48.306873+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210216_1600 2022-03-10 11:41:45.381170+00:00 https://youtu.be/BiqGc6hBQAU 2022-03-10 11:41:36.168259+00:00 2022-03-10 11:41:36.436387+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210216_1800 2022-03-10 11:41:36.168259+00:00 1913984 https://api.rohub.org/api/resources/6780a15b-874e-4ce7-a543-1ef33e13ea5c/download/ 2022-03-10 11:41:26.839167+00:00 2022-03-10 11:41:29.713120+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210216_1800 2022-03-10 11:41:26.839167+00:00 https://youtu.be/5ReEp8UbRmU 2022-03-10 11:41:33.506223+00:00 2022-03-10 11:41:33.728852+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210216_1800 2022-03-10 11:41:33.506223+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 11:41:41.137273+00:00 2022-03-10 11:41:41.357790+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 11:41:41.137273+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 13de5422-0db1-4a9d-99b9-f12a06e5931f POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) service-account-enrichment 1833419 https://api.rohub.org/api/ros/258c5e83-ce3b-4ea2-a845-23d0aebdae7d/crate/download/ 2022-03-10 12:01:08.866449+00:00 2025-03-05 00:51:30.689261+00:00 2022-03-10 12:01:08.866449+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/258c5e83-ce3b-4ea2-a845-23d0aebdae7d Etna Eruption 2021 02 17 Research Object MANUAL https://w3id.org/ro-id/258c5e83-ce3b-4ea2-a845-23d0aebdae7d/5384f20e-5fef-4062-ba36-8ae2d10b7312 Stelitano, Dario. "Etna Eruption 2021 02 17 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/258c5e83-ce3b-4ea2-a845-23d0aebdae7d. metadata biblio data raw data http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 12:02:51.163660+00:00 2022-03-10 12:02:51.420130+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 12:02:51.163660+00:00 https://youtu.be/t5YcOViEU_E 2022-03-10 12:02:43.977530+00:00 2022-03-10 12:02:44.222223+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210218_0100 2022-03-10 12:02:43.977530+00:00 https://youtu.be/-7lSBNfnToM 2022-03-10 12:02:41.047055+00:00 2022-03-10 12:02:41.271096+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210218_0100 2022-03-10 12:02:41.047055+00:00 1780706 https://api.rohub.org/api/resources/650a6881-3ef1-43af-89cd-3dcfce374983/download/ 2022-03-10 12:02:26.301770+00:00 2022-03-10 12:02:29.717878+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210217_2300 2022-03-10 12:02:26.301770+00:00 471319 https://api.rohub.org/api/resources/d64df5a0-9bca-44ff-9f9b-d4b023efdfc2/download/ 2022-03-10 12:03:50.586593+00:00 2022-03-10 12:03:53.727687+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210217_2300 2022-03-10 12:03:50.586593+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 12:02:47.173805+00:00 2022-03-10 12:02:47.404197+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 12:02:47.173805+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 44174c5e-4f9c-4192-b342-62a5013f8db0 POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) service-account-enrichment 1875608 https://api.rohub.org/api/ros/9b2a8c25-30c1-4dc2-9520-96d26cb29e72/crate/download/ 2022-03-10 12:07:01.432625+00:00 2025-03-05 00:51:31.555271+00:00 2022-03-10 12:07:01.432625+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/9b2a8c25-30c1-4dc2-9520-96d26cb29e72 Etna Eruption 2021 02 24 Research Object MANUAL https://w3id.org/ro-id/9b2a8c25-30c1-4dc2-9520-96d26cb29e72/2cf0de84-e9ff-4fa3-a6bf-f904113287e5 Stelitano, Dario. "Etna Eruption 2021 02 24 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/9b2a8c25-30c1-4dc2-9520-96d26cb29e72. biblio metadata data raw data https://youtu.be/Vae8zZzd1wM 2022-03-10 12:07:50.486721+00:00 2022-03-10 12:07:50.691137+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210224_2100 2022-03-10 12:07:50.486721+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 12:07:57.317995+00:00 2022-03-10 12:07:57.722945+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 12:07:57.317995+00:00 1742146 https://api.rohub.org/api/resources/46729143-cc60-4b36-9a0b-0eab61986027/download/ 2022-03-10 12:07:43.761064+00:00 2022-03-10 12:07:46.899849+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210224_2100 2022-03-10 12:07:43.761064+00:00 1844188 https://api.rohub.org/api/resources/6881dbcc-88a0-4652-828d-513503fae5b8/download/ 2022-03-10 12:08:34.279360+00:00 2022-03-10 12:08:37.553317+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210224_1900 2022-03-10 12:08:34.279360+00:00 https://youtu.be/vMbhkmb6b7c 2022-03-10 12:07:53.340556+00:00 2022-03-10 12:07:53.539665+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210224_2100 2022-03-10 12:07:53.340556+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 12:08:00.675099+00:00 2022-03-10 12:08:00.875748+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 12:08:00.675099+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) 95da0fb9-53e0-49f6-b3dd-60986e962211 POINT (15.0 37.75) service-account-enrichment 2021983 https://api.rohub.org/api/ros/687e0266-fe8a-408c-9fb4-57c48640d700/crate/download/ 2022-03-10 13:32:31.782234+00:00 2025-03-05 00:51:31.122075+00:00 2022-03-10 13:32:31.782234+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/687e0266-fe8a-408c-9fb4-57c48640d700 Etna Eruption 2021 02 20 Research Object MANUAL https://w3id.org/ro-id/687e0266-fe8a-408c-9fb4-57c48640d700/ac0d9208-5ba5-4a8b-9bb7-d27c57cb18b4 Stelitano, Dario. "Etna Eruption 2021 02 20 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/687e0266-fe8a-408c-9fb4-57c48640d700. raw data data biblio metadata 2719750 https://api.rohub.org/api/resources/1ea25789-64f2-452d-bc6d-eff5d78c6865/download/ 2022-03-10 13:35:14.562546+00:00 2022-03-10 13:35:17.942422+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210220_2200 2022-03-10 13:35:14.562546+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 13:35:05.988220+00:00 2022-03-10 13:35:06.241023+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 13:35:05.988220+00:00 https://youtu.be/tDdBgXmJt4M 2022-03-10 13:35:02.986303+00:00 2022-03-10 13:35:03.222781+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210221_0000 2022-03-10 13:35:02.986303+00:00 https://youtu.be/VWs20raLgRc 2022-03-10 13:34:59.852231+00:00 2022-03-10 13:35:00.059491+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210221_0000 2022-03-10 13:34:59.852231+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 13:35:08.482633+00:00 2022-03-10 13:35:08.709097+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 13:35:08.482633+00:00 1861342 https://api.rohub.org/api/resources/97ef253b-58f6-4d4a-910e-1d460a0b0f72/download/ 2022-03-10 13:34:52.428826+00:00 2022-03-10 13:34:55.449453+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210220_2300 2022-03-10 13:34:52.428826+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 62b7c249-053f-4258-9f5e-cb85fcd0be34 POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) service-account-enrichment 1921725 https://api.rohub.org/api/ros/5922be96-b69d-429c-ab13-5852cdcd6d09/crate/download/ 2022-03-10 13:48:33.957435+00:00 2025-03-05 00:51:31.341425+00:00 2022-03-10 13:48:33.957435+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/5922be96-b69d-429c-ab13-5852cdcd6d09 Etna Eruption 2021 02 22 Research Object MANUAL https://w3id.org/ro-id/5922be96-b69d-429c-ab13-5852cdcd6d09/5844aec6-f61c-413c-b212-f4c4921a9757 Stelitano, Dario. "Etna Eruption 2021 02 22 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/5922be96-b69d-429c-ab13-5852cdcd6d09. raw data biblio metadata data https://youtu.be/CGhxdxCx0Lc 2022-03-10 13:49:06.861725+00:00 2022-03-10 13:49:07.114032+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210222_2300 2022-03-10 13:49:06.861725+00:00 3077583 https://api.rohub.org/api/resources/a3ee93ea-7075-48db-a19c-8b0ce156254e/download/ 2022-03-10 13:50:19.792902+00:00 2022-03-10 13:50:23.335281+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210222_2100 2022-03-10 13:50:19.792902+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 13:49:15.216661+00:00 2022-03-10 13:49:15.528144+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 13:49:15.216661+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 13:49:12.377848+00:00 2022-03-10 13:49:12.663978+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 13:49:12.377848+00:00 1785546 https://api.rohub.org/api/resources/f8d3c693-b90e-4e3e-9369-7b13bf7c6d1c/download/ 2022-03-10 13:48:59.684382+00:00 2022-03-10 13:49:02.630764+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210222_2200 2022-03-10 13:48:59.684382+00:00 https://youtu.be/IdcjWZluTTI 2022-03-10 13:49:09.507134+00:00 2022-03-10 13:49:09.814444+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210222_2300 2022-03-10 13:49:09.507134+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 9a215572-9f9f-4fca-89bb-8107ad97b063 POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) service-account-enrichment 1819506 https://api.rohub.org/api/ros/7b712a64-9f59-4e6b-a209-b36b88feeee7/crate/download/ 2022-03-10 15:09:15.176399+00:00 2025-03-05 00:51:31.769094+00:00 2022-03-10 15:09:15.176399+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/7b712a64-9f59-4e6b-a209-b36b88feeee7 Etna Eruption 2021 02 28 Research Object MANUAL https://w3id.org/ro-id/7b712a64-9f59-4e6b-a209-b36b88feeee7/bb0be0c2-64c8-4445-8e84-0bd91545bdc0 Stelitano, Dario. "Etna Eruption 2021 02 28 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/7b712a64-9f59-4e6b-a209-b36b88feeee7. raw data biblio metadata data https://youtu.be/d0zuReUfZMg 2022-03-10 15:10:20.426283+00:00 2022-03-10 15:10:20.645938+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210228_0800 2022-03-10 15:10:20.426283+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 15:10:25.484604+00:00 2022-03-10 15:10:25.724746+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 15:10:25.484604+00:00 1739362 https://api.rohub.org/api/resources/36389c6d-5695-43e5-9410-aae2643e9701/download/ 2022-03-10 15:10:12.486663+00:00 2022-03-10 15:10:16.018742+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210228_0700 2022-03-10 15:10:12.486663+00:00 https://youtu.be/ypZ9Pou2LvY 2022-03-10 15:10:22.988881+00:00 2022-03-10 15:10:23.219447+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210228_0800 2022-03-10 15:10:22.988881+00:00 816177 https://api.rohub.org/api/resources/d6824cd3-1eb7-4a89-9a33-0a1497c427c2/download/ 2022-03-10 15:10:45.399136+00:00 2022-03-10 15:10:48.413538+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210228_0600 2022-03-10 15:10:45.399136+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 15:10:28.326455+00:00 2022-03-10 15:10:28.559970+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 15:10:28.326455+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) 3db1f448-809f-4516-8d85-a0617967bde3 POINT (15.0 37.75) service-account-enrichment 2034024 https://api.rohub.org/api/ros/1d9c7bde-4991-4da3-a73f-7f0e2e7d551b/crate/download/ 2022-03-10 15:33:29.483179+00:00 2025-03-05 00:51:31.980275+00:00 2022-03-10 15:33:29.483179+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/1d9c7bde-4991-4da3-a73f-7f0e2e7d551b Etna Eruption 2021 03 02 Research Object MANUAL https://w3id.org/ro-id/1d9c7bde-4991-4da3-a73f-7f0e2e7d551b/80e9d1ee-1cf4-4f18-ba10-3ac60e1a0dcd Stelitano, Dario. "Etna Eruption 2021 03 02 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/1d9c7bde-4991-4da3-a73f-7f0e2e7d551b. raw data data biblio metadata 1344981 https://api.rohub.org/api/resources/6feffb93-325b-4ea0-8ee8-680ed5fd32aa/download/ 2022-03-10 15:34:13.741615+00:00 2022-03-10 15:34:16.835851+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210302_1100 2022-03-10 15:34:13.741615+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 15:34:08.798076+00:00 2022-03-10 15:34:09.028387+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 15:34:08.798076+00:00 1934541 https://api.rohub.org/api/resources/a20c4d43-7be5-4af4-8497-46e724c6bc13/download/ 2022-03-10 15:33:52.992948+00:00 2022-03-10 15:33:56.054384+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210302_1200 2022-03-10 15:33:52.992948+00:00 https://youtu.be/Jkt2_mpgQRI 2022-03-10 15:34:00.337224+00:00 2022-03-10 15:34:00.556596+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210302_1300 2022-03-10 15:34:00.337224+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 15:34:05.847511+00:00 2022-03-10 15:34:06.069957+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 15:34:05.847511+00:00 https://youtu.be/e8LDeJ_-IYM 2022-03-10 15:34:03.174398+00:00 2022-03-10 15:34:03.378438+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210302_1300 2022-03-10 15:34:03.174398+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE service-account-enrichment 1752221 https://api.rohub.org/api/ros/f695db70-62da-434b-a366-5f175494894e/crate/download/ 2022-03-10 15:41:19.270763+00:00 2025-03-05 00:51:32.201032+00:00 2022-03-10 15:41:19.270763+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/f695db70-62da-434b-a366-5f175494894e Etna Eruption 2021 03 04 Research Object MANUAL Stelitano, Dario. "Etna Eruption 2021 03 04 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/f695db70-62da-434b-a366-5f175494894e. metadata data raw data biblio 1741629 https://api.rohub.org/api/resources/2d84d2af-4a9f-4e3b-9522-7ed56c606f6b/download/ 2022-03-10 15:42:08.467725+00:00 2022-03-10 15:42:11.489521+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210304_0700 2022-03-10 15:42:08.467725+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 15:42:21.352340+00:00 2022-03-10 15:42:21.550970+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 15:42:21.352340+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 15:42:24.337846+00:00 2022-03-10 15:42:24.526196+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 15:42:24.337846+00:00 https://youtu.be/blUCdl15HM4 2022-03-10 15:42:18.121380+00:00 2022-03-10 15:42:18.350521+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210304_0800 2022-03-10 15:42:18.121380+00:00 https://youtu.be/Jkt2_mpgQRI 2022-03-10 15:42:15.327095+00:00 2022-03-10 15:42:15.509398+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210304_0800 2022-03-10 15:42:15.327095+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 4b4b4e98-b179-4a20-9d1a-81a93750c620 POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) service-account-enrichment 1728901 https://api.rohub.org/api/ros/90170231-63d3-49ed-b136-4f06c562ab35/crate/download/ 2022-03-10 15:52:27.726412+00:00 2025-03-05 00:51:32.420984+00:00 2022-03-10 15:52:27.726412+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/90170231-63d3-49ed-b136-4f06c562ab35 Etna Eruption 2021 03 07 Research Object MANUAL https://w3id.org/ro-id/90170231-63d3-49ed-b136-4f06c562ab35/f5419588-0987-4c62-a2cf-23f25a891745 Stelitano, Dario. "Etna Eruption 2021 03 07 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/90170231-63d3-49ed-b136-4f06c562ab35. metadata raw data data biblio https://youtu.be/st-PxspAtv0 2022-03-10 15:52:56.121253+00:00 2022-03-10 15:52:56.337231+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210307_0700 2022-03-10 15:52:56.121253+00:00 https://youtu.be/1HBw30dCsQU 2022-03-10 15:52:59.047954+00:00 2022-03-10 15:52:59.264964+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210307_0700 2022-03-10 15:52:59.047954+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 15:53:01.678269+00:00 2022-03-10 15:53:01.903560+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 15:53:01.678269+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 15:53:04.976085+00:00 2022-03-10 15:53:05.150242+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 15:53:04.976085+00:00 1596932 https://api.rohub.org/api/resources/e6e7f136-894e-4378-a101-3ae76db6d729/download/ 2022-03-10 15:52:48.979802+00:00 2022-03-10 15:52:52.039964+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210307_0600 2022-03-10 15:52:48.979802+00:00 2278065 https://api.rohub.org/api/resources/f68827fd-d246-4143-80bc-5e2a90c62581/download/ 2022-03-10 15:53:12.873946+00:00 2022-03-10 15:53:15.945906+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210307_0500 2022-03-10 15:53:12.873946+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission height calculation SEVIRI box volcanic column column research World Meteorological Organization height World Meteorological Organization Trapani Etna Etna eruption Etna aim volcanology calculation EUMETSAT sounding Trapani 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE POINT (15.0 37.75) 1eba86a7-8bd5-405f-83f8-872bb06e6436 POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) service-account-enrichment 1986300 https://api.rohub.org/api/ros/9dda85b5-db3b-4167-8afb-034615cc16c8/crate/download/ 2022-03-10 16:18:25.419673+00:00 2025-03-05 00:51:32.629921+00:00 2022-03-10 16:18:25.419673+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/9dda85b5-db3b-4167-8afb-034615cc16c8 Etna Eruption 2021 03 09 Research Object MANUAL https://w3id.org/ro-id/9dda85b5-db3b-4167-8afb-034615cc16c8/eed3bad3-c43b-4f54-b391-b745e5da654d Stelitano, Dario. "Etna Eruption 2021 03 09 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/9dda85b5-db3b-4167-8afb-034615cc16c8. data biblio metadata raw data 1674681 https://api.rohub.org/api/resources/555862ce-f0a6-42f5-9dd0-9f3143a1caec/download/ 2022-03-10 16:18:46.047715+00:00 2022-03-10 16:18:49.582095+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210309_2300 2022-03-10 16:18:46.047715+00:00 https://youtu.be/AMRKswnAi7Q 2022-03-10 16:18:58.374760+00:00 2022-03-10 16:18:58.618087+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210310_0000 2022-03-10 16:18:58.374760+00:00 https://youtu.be/caFW5gzfVhI 2022-03-10 16:18:55.108176+00:00 2022-03-10 16:18:55.339265+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210310_0000 2022-03-10 16:18:55.108176+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 16:19:01.415958+00:00 2022-03-10 16:19:01.639454+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 16:19:01.415958+00:00 13399939 https://api.rohub.org/api/resources/f7cd6f5a-8c54-440b-86d1-b92a513a8c39/download/ 2022-03-10 16:19:26.308248+00:00 2022-03-10 16:19:30.660126+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210309_2200 2022-03-10 16:19:26.308248+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 16:19:04.290216+00:00 2022-03-10 16:19:04.525751+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 16:19:04.290216+00:00 Dario Stelitano Earth sciences 10.13039/501100000781 European Commission 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia 101017502 RELIANCE Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. 65.93186372745491 65.8 Etna eruption 29.656607700312172 28.5 Etna Eruption 2021 03 24 Research Object. 14.228456913827655 14.2 Trapani https://www.wikidata.org/wiki/Q13664 Natural disasters Disaster, accident and emergency incident/Disaster/Natural disasters Etna https://www.wikidata.org/wiki/Q16990 Radiosounding provided by WMO in Trapani station. 19.839679358717436 19.8 Trapani 11.755233494363928 7.3 system 4.99194847020934 3.1 Mountains Environment/Natural resources/Land resources/Mountains calculation 15.780998389694043 9.8 Etna 21.90016103059581 13.6 geology 100.0 0.9904175400733948 Weather Weather Column Top Height 12.709497206703912 9.1 World Meteorological Organization 16.480446927374302 11.8 earth sciences 100.0 0.9904175400733948 Column Top Height calculation 11.446409989594171 11.0 box around Etna 4.578563995837669 4.4 sounding 14.331723027375201 8.9 Etna 18.854748603351958 13.5 EUMETSAT 15.502793296089386 11.1 space sciences 100.0 0.5544100999832153 research object 11.966701352757543 11.5 SEVIRI 10.75418994413408 7.7 space sciences (general) 100.0 0.5544100999832153 volcanology 100.0 7.4 calculation 13.268156424581006 9.5 SEVIRI box 42.35171696149844 40.7 POINT (15.0 37.75) 5d51dbbd-e4c2-46a7-a739-400925445bd8 POINT (15.0 37.75) 15.0 37.75 POINT (15.0 37.75) service-account-enrichment 1968912 https://api.rohub.org/api/ros/ae86fae3-f7a9-4582-9582-d3e5cf27881e/crate/download/ 2022-03-10 16:38:38.600063+00:00 2025-03-05 00:51:33.059059+00:00 2022-03-10 16:38:38.600063+00:00 Volcanic Column Top Height calculation using Dark Pixel method in a 19x19 EUMETSAT/SEVIRI box around Etna volcano. Radiosounding provided by WMO in Trapani station. application/ld+json https://w3id.org/ro-id/ae86fae3-f7a9-4582-9582-d3e5cf27881e Etna Eruption 2021 03 24 Research Object MANUAL https://w3id.org/ro-id/ae86fae3-f7a9-4582-9582-d3e5cf27881e/fb66cf2c-3ca7-4a97-b60c-65726271178b https://w3id.org/ro-id/a9736593-7c13-4a2b-b777-0b5504053f27 https://w3id.org/ro-id/ed7f50a9-cfcd-4940-b04d-d6f3cfc03dff https://w3id.org/ro-id/06cf592a-e81f-4e4a-9d98-61a09f319020 https://w3id.org/ro-id/1d8d44ae-9669-4616-8297-951627ddfa3b https://w3id.org/ro-id/24f87165-d038-49ed-ad6c-e296a320f7d4 https://w3id.org/ro-id/2aa17299-d447-4430-b414-c871412073ee https://w3id.org/ro-id/379da2bc-6db3-47c9-8e0f-40687f593787 https://w3id.org/ro-id/3cd84c6d-efe8-4fec-8b72-fc4034f83538 https://w3id.org/ro-id/732c5667-081d-471e-b979-f4ff6bcfb0b5 https://w3id.org/ro-id/d41373b4-ea29-4305-b3a7-fbb4269c856f https://w3id.org/ro-id/d8358bc4-4724-4263-bdb0-28b7aea66be0 https://w3id.org/ro-id/e54df1fe-bbf4-48d2-9ab5-5d71e5dc9da0 https://w3id.org/ro-id/47f82b88-216e-4647-a275-0dc1ecebad46 https://w3id.org/ro-id/58822377-23cf-4379-ae7a-7f8009bdbc8f https://w3id.org/ro-id/0b92119f-dde0-42a7-b739-8b325443cbfb https://w3id.org/ro-id/37287334-8d4b-41d5-b693-ac173095f2e1 https://w3id.org/ro-id/4e1c922d-c835-45b2-8f60-e6ec8d418beb https://w3id.org/ro-id/aff716b8-bbd7-4e6e-add9-07f87def6f7b https://w3id.org/ro-id/51359fcb-1453-4462-b031-9d8cd1502134 https://w3id.org/ro-id/53b2f5e0-2af6-4d0e-b2d5-4f09d0428269 https://w3id.org/ro-id/7c1ed93d-0aff-4908-9015-b2a28c282e44 https://w3id.org/ro-id/7d916713-3981-4067-8435-a5606218daf5 https://w3id.org/ro-id/952c7fba-8ba2-4bc9-b81f-80fd0bc62874 https://w3id.org/ro-id/ae05a6d9-da60-4f0f-bd8d-a768b072a290 https://w3id.org/ro-id/e208f01c-adaf-41af-83f6-b58dce58fec2 https://w3id.org/ro-id/87ba6515-1def-47be-99ba-60dd1ac67de8 https://w3id.org/ro-id/95898c5e-442d-4f8e-808b-c849597e0da7 https://w3id.org/ro-id/045c1529-667c-4b9e-8edd-32da2b93d405 https://w3id.org/ro-id/6c26254d-121b-42e8-bbf5-c04af6ae4850 https://w3id.org/ro-id/6ee9f3ce-5a1c-4a12-ba39-c052981a8dee https://w3id.org/ro-id/93a250ce-e1bf-42a9-ac45-b268575d2f9a https://w3id.org/ro-id/ae505dac-25bc-46b6-a211-9aa2575c13fe https://w3id.org/ro-id/02d19845-bfa8-4201-9341-3abede5f5675 https://w3id.org/ro-id/04f36e24-dc5d-49fd-9062-39546e677c44 https://w3id.org/ro-id/1d954682-0ef4-4a89-b9b2-fc050437fd21 https://w3id.org/ro-id/b39e208d-fc9c-4ca4-80e1-75d68a829ede Stelitano, Dario. "Etna Eruption 2021 03 24 Research Object." ROHub. Mar 10 ,2022. https://w3id.org/ro-id/ae86fae3-f7a9-4582-9582-d3e5cf27881e. data metadata biblio raw data 8146124 https://api.rohub.org/api/resources/02e0da2d-470d-4fdf-9727-a4b2b746dd17/download/ 2022-03-10 16:39:28.841611+00:00 2023-06-05 14:19:47.335090+00:00 text/html (interactive html) Volcanic Column Top Height using Dark Pixel Etna 20210324_0000 2022-03-10 16:39:28.841611+00:00 https://youtu.be/8gyQjXzLKB0 2022-03-10 16:39:06.078555+00:00 2022-03-10 16:39:06.293621+00:00 EUMETSAT/Meteosat RGB Ash composite video - Central Mediterranean Sea 20210324_0200 2022-03-10 16:39:06.078555+00:00 https://www.mdpi.com/2076-3263/8/4/140/htm 2022-03-10 16:39:12.254646+00:00 2022-03-10 16:39:12.469857+00:00 Dark Pixel procedure description. Chapter 3.3 2022-03-10 16:39:12.254646+00:00 http://editoria.rm.ingv.it/miscellanea/2020/miscellanea57/?page=114 2022-03-10 16:39:14.803356+00:00 2022-03-10 16:39:15.041898+00:00 Multimission Acquisition SysTem (MAST) description 2022-03-10 16:39:14.803356+00:00 1702779 https://api.rohub.org/api/resources/3b80da8f-373e-4de2-8c12-30594b699609/download/ 2022-03-10 16:38:58.411536+00:00 2022-03-10 16:39:01.548185+00:00 image/png EUMETSAT/Meteosat RGB Ash composite - Central Mediterranean Sea 20210324_0100 2022-03-10 16:38:58.411536+00:00 https://youtu.be/QncRb0ZOv0E 2022-03-10 16:39:08.838121+00:00 2022-03-10 16:39:09.080271+00:00 EUMETSAT/Meteosat Brightness Temperature Difference 11-12um video - Central Mediterranean Sea 20210324_0200 2022-03-10 16:39:08.838121+00:00 Volcanic eruption Disaster, accident and emergency incident/Disaster/Natural disasters/Volcanic eruption Mar-24-2021 volcano 6.9243156199677935 4.3 World Meteorological Organization 18.357487922705314 11.4 sounding 12.430167597765363 8.9 research 5.958132045088567 3.7 World Meteorological Organization https://www.wikidata.org/wiki/Q170424 Dario Stelitano Applied sciences Decatur The Illinois Basin Illinois Archer Daniels Midland mechanical data subsurface data Decatur Project Dataset Illinois Illinois Basin United States of America carbon dioxide data injection Archer Daniels Midland dataset Decatur Project Dataset The Illinois Basin Decatur Illinois report United States of America assessment injection at the Archer Daniels Midland Illinois Basin -88.89 39.87 POINT (-88.89 39.87) e459c565-3c19-460e-b703-75af8237a82c POINT (-88.89 39.87) service-account-enrichment 6642 https://api.rohub.org/api/ros/0e64ec05-65dc-4cd1-b652-1c7bf3be0639/crate/download/ 2022-03-22 00:38:18.628199+00:00 2025-03-05 00:53:53.667089+00:00 2022-03-22 00:38:18.628199+00:00 The Illinois Basin - Decatur Project (IBDP) dataset is a reference dataset containing subsurface data, monitoring data, geomodels, geomechanical data, and reports related to assessment and CO2 injection at the Archer Daniels Midland site in Decatur, Illinois, United States. application/ld+json https://w3id.org/ro-id/0e64ec05-65dc-4cd1-b652-1c7bf3be0639 Illinois Basin - Decatur Project Dataset MANUAL https://w3id.org/ro-id/0e64ec05-65dc-4cd1-b652-1c7bf3be0639/75f05324-9f7b-4622-9b10-c4142930e532 Illinois State Geological Survey (Illinois State Geological Survey). "Illinois Basin - Decatur Project Dataset." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/0e64ec05-65dc-4cd1-b652-1c7bf3be0639. POINT (-88.89 39.87) data biblio metadata raw data Illinois State Geological Survey (2022).Illinois Basin - Decatur Project Dataset [Data set]. Norstore. https://doi.org/10.11582/2022.00017 Illinois State Geological Survey (Illinois State Geological Survey) Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2022.00017 2022-03-09 00:00:00 2022-03-22 00:38:32.699107+00:00 The Illinois Basin - Decatur Project (IBDP) dataset is a reference dataset containing subsurface data, monitoring data, geomodels, geomechanical data, and reports related to assessment and CO2 injection at the Archer Daniels Midland site in Decatur, Illinois, United States. Illinois Basin - Decatur Project Dataset 2022-03-09 00:00:00 Illinois State Geological Survey (Illinois State Geological Survey) Illinois.State.Geological.Survey@rohub.com Illinois State Geological Survey (Illinois State Geological Survey) Geo H. Environmental research Life sciences Physical sciences Earth sciences sandstone 19.490254872563717 13.0 Science and technology Science and technology Hades apparatus 15.771812080536913 4.7 deformation 16.34182908545727 10.9 synchrotron 11.094452773613193 7.4 reservoir 7.3085846867749416 6.3 core sample 8.095952023988005 5.4 geophysics 100.0 0.6389099359512329 image 9.628770301624131 8.3 service-account-enrichment 7892 https://api.rohub.org/api/ros/41a50d36-8102-4f0c-a227-3fc44ae00a71/crate/download/ 2022-03-22 00:38:34.979065+00:00 2025-03-05 00:45:32.544737+00:00 2022-03-22 00:38:34.979065+00:00 In-situ X-ray tomography images of the deformation of a core sample of Groningen sandstone (Hades apparatus, beamline ID19, ESRF) application/ld+json https://w3id.org/ro-id/41a50d36-8102-4f0c-a227-3fc44ae00a71 4D synchrotron X-ray imaging of grain scale deformation mechanisms in a seismogenic gas reservoir sandstone during axial compaction MANUAL https://w3id.org/ro-id/f7333542-cfef-4a27-bfb2-c210b53b88b8 https://w3id.org/ro-id/8be0da68-0374-41d6-bc69-fd02b445d89c https://w3id.org/ro-id/2470d20e-b941-4257-8074-1dc69fc89180 https://w3id.org/ro-id/3c686357-c966-43a4-88e3-0a6b2fe6fe7b https://w3id.org/ro-id/66882826-aacd-44f0-b3fb-cb0daef582ea https://w3id.org/ro-id/7224d420-58e9-4d57-bd0c-915bdbaaba95 https://w3id.org/ro-id/882ad179-6f50-4b91-afd6-bb950cb700c0 https://w3id.org/ro-id/982d6bc0-24b0-418f-bf08-69b0bc4a963a https://w3id.org/ro-id/98809bba-63b0-4b80-8059-edd5cbb9000f https://w3id.org/ro-id/a8874812-e332-4236-a389-db2f7d35421f https://w3id.org/ro-id/e22ee2a0-7e6c-453c-aaee-f48c5f0653d8 https://w3id.org/ro-id/f6b78c91-930e-4380-a254-a6bd62b9ba38 https://w3id.org/ro-id/ffcb3f9f-781a-4eee-8cb7-4f8507d4cf67 https://w3id.org/ro-id/44fa2f04-f417-4356-bd8f-c160ef10981e https://w3id.org/ro-id/dbdc745e-aa7f-476b-980d-420cbb230d8a https://w3id.org/ro-id/0d524bd8-b347-4a5e-b0a8-f4dbeace0aff https://w3id.org/ro-id/7d58605c-b081-4b96-8770-b7cf400c7388 https://w3id.org/ro-id/d40de7fd-c426-4c11-9018-83c788192c59 https://w3id.org/ro-id/fa77f78e-fc12-45f0-8be4-de9545c2c893 https://w3id.org/ro-id/06647fc1-5df8-4592-b4a4-dd63c45d1e00 https://w3id.org/ro-id/1a53393e-e47b-467c-acb0-44c8c45e0a91 https://w3id.org/ro-id/1e495c9d-6fdb-43b9-8962-7306bf69e6ff https://w3id.org/ro-id/28e00251-f8a2-4589-8732-fef6a9d67ad3 https://w3id.org/ro-id/6a8a7e5e-6ff8-49da-85f8-4ec1edaae6b5 https://w3id.org/ro-id/86596d1c-e4bb-4fba-95e6-2c19a2657126 https://w3id.org/ro-id/ebd41fb7-29ba-4525-aac1-35b95d919a5e https://w3id.org/ro-id/38cb87ad-c379-407b-9240-934061ab2ec5 https://w3id.org/ro-id/87b442e6-935e-4262-a9a7-e09eabf8cfb6 https://w3id.org/ro-id/0f6e1d80-8bc3-4d1d-a700-c3bb186e8a28 https://w3id.org/ro-id/515472c2-8f88-42b0-8497-99a54f00eac9 https://w3id.org/ro-id/51b25536-7fe9-42f1-bda6-cb44ebe42ec7 https://w3id.org/ro-id/71890ef5-b2d8-4243-ba31-c13d21e8e8c8 https://w3id.org/ro-id/96bbbf2b-dfb4-4aa6-adf2-8340575e46d3 https://w3id.org/ro-id/49099124-4ecb-4d63-9516-6c14b3787521 https://w3id.org/ro-id/561d75de-a583-441c-bb7b-fbcb20e23c27 Francois Renard. "4D synchrotron X-ray imaging of grain scale deformation mechanisms in a seismogenic gas reservoir sandstone during axial compaction." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/41a50d36-8102-4f0c-a227-3fc44ae00a71. data raw data biblio metadata Renard, F. (2022).4D synchrotron X-ray imaging of grain scale deformation mechanisms in a seismogenic gas reservoir sandstone during axial compaction [Data set]. Norstore. https://doi.org/10.11582/2022.00016 Francois Renard Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2022.00016 2022-03-07 00:00:00 2022-03-22 00:38:56.175879+00:00 In-situ X-ray tomography images of the deformation of a core sample of Groningen sandstone (Hades apparatus, beamline ID19, ESRF) 4D synchrotron X-ray imaging of grain scale deformation mechanisms in a seismogenic gas reservoir sandstone during axial compaction 2022-03-07 00:00:00 Francois Renard earth sciences 100.0 0.6915496587753296 In-situ X-ray tomography images of the deformation of a core sample of Groningen sandstone (Hades apparatus, beamline ID19, ESRF) 51.75175175175175 51.7 X-ray tomography image 29.865771812080535 8.9 seismogenic gas reservoir sandstone 10.738255033557047 3.2 4D synchrotron X-ray imaging of grain scale deformation mechanisms in a seismogenic gas reservoir sandstone during axial compaction. 48.248248248248245 48.2 deformation 12.645011600928074 10.9 image 11.994002998500749 8.0 grain scale deformation mechanism 32.88590604026846 9.8 apparatus 4.060324825986079 3.5 Genetics Science and technology/Natural science/Biology/Genetics imaging 23.38830584707646 15.6 geosciences 100.0 0.6389099359512329 core sample 6.0324825986078885 5.2 Groningen https://www.wikidata.org/wiki/Q749 deformation of a core sample of Groningen sandstone 10.738255033557047 3.2 natural gas 5.220417633410673 4.5 imaging 18.329466357308583 15.8 x-ray 9.628770301624131 8.3 Oil and gas - upstream activities Economy, business and finance/Economic sector/Energy and resource/Oil and gas - upstream activities geology 100.0 0.6915496587753296 synchrotron 8.584686774941995 7.4 reservoir 9.5952023988006 6.4 Groningen 3.596287703016241 3.1 geology 100.0 16.7 Medical procedure-test Health/Health treatment/Medical procedure-test sandstone 14.96519721577726 12.9 francois.renard@rohub.com Francois Renard Geo H. Environmental research Neurobiology Life sciences Physical sciences calcium photon http analysis dataset mouse two-photon calcium imaging imaging move mouse calcium imaging computer code information analysis code service-account-enrichment 10761 https://api.rohub.org/api/ros/0c774ee2-aef9-4f41-bc8f-15c244f99ec1/crate/download/ 2022-03-22 00:38:57.172485+00:00 2025-03-05 01:27:08.516180+00:00 2022-03-22 00:38:57.172485+00:00 This dataset contains data presented in the paper "Large-scale two-photon calcium imaging in freely moving mice" Weijian Zong,Horst A. Obenhaus, Emilie R. Skytøen, Hanna Eneqvist, Nienke L. de Jong, Marina R. Jorge, May-Britt Moser, Edvard I. Moser (2022). It is complementary to the analysis code stored at LINK: http://github.com/kavli-ntnu/MINI2P_toolbox application/ld+json https://w3id.org/ro-id/0c774ee2-aef9-4f41-bc8f-15c244f99ec1 Zong 2022 MANUAL Weijian Zong, Horst A. Obenhaus, Emilie Skytøen, Hanna Eneqvist, Nienke de Jong, Marina Jorge, May-Britt Moser, and Edvard Moser. "Zong 2022." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/0c774ee2-aef9-4f41-bc8f-15c244f99ec1. metadata raw data data biblio Zong, W., Obenhaus, H., Skytøen, E., Eneqvist, H., de Jong, N., Jorge, M., Moser, M., Moser, E. (2022).Zong 2022 [Data set]. Norstore. https://doi.org/10.11582/2022.00008 Edvard Ingjald Moser Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2022.00008 2022-02-10 00:00:00 2022-03-22 00:39:38.959576+00:00 This dataset contains data presented in the paper "Large-scale two-photon calcium imaging in freely moving mice" Weijian Zong,Horst A. Obenhaus, Emilie R. Skytøen, Hanna Eneqvist, Nienke L. de Jong, Marina R. Jorge, May-Britt Moser, Edvard I. Moser (2022). It is complementary to the analysis code stored at <a href="http://github.com/kavli-ntnu/MINI2P_toolbox" class="linkified" target="_blank">LINK</a> Zong 2022 2022-02-10 00:00:00 Edvard Ingjald Moser https://doi.org/10.1101/2021.09.20.461015 2022-03-22 00:39:36.559662+00:00 2022-03-22 00:39:36.674275+00:00 https://doi.org/10.1101/2021.09.20.461015 2022-03-22 00:39:36.559662+00:00 HorstA.Obenhaus@rohub.com Horst A. Obenhaus Weijian@hotmail.com Weijian Zong edvard.moser@rohub.com Edvard Moser emilie.skytoen@rohub.com Emilie Skytøen Geo H. hanna.eneqvist@rohub.com Hanna Eneqvist marina.jorge@rohub.com Marina Jorge may-britt.moser@rohub.com May-Britt Moser nienke.de.jong@rohub.com Nienke de Jong Applied sciences service-account-enrichment 7960 https://api.rohub.org/api/ros/a64cc611-d619-492b-9ceb-8bfa66f392d0/crate/download/ 2022-03-22 00:39:39.965760+00:00 2025-03-05 12:49:07.220293+00:00 2022-03-22 00:39:39.965760+00:00 This Excel spreadsheet contains data on the Nutrient optimised beef enhances blood levels of vitamin D and Selenium amount young women. application/ld+json https://w3id.org/ro-id/a64cc611-d619-492b-9ceb-8bfa66f392d0 Nutrient optimised beef enhances blood levels of vitamin D and Selenium amount young women MANUAL Excel beef data lineage miss quantity spreadsheet vitamin D earth sciences Food and drink Food Excel beef blood data spreadsheet vitamin D young woman life sciences Excel spreadsheet blood levels of vitamin D contain data optimised beef selenium amount young women Nutrient optimised beef enhances blood levels of vitamin D and Selenium amount young women. This Excel spreadsheet contains data on the Nutrient optimised beef enhances blood levels of vitamin D and Selenium amount young women. office productivity software Anna Haug. "Nutrient optimised beef enhances blood levels of vitamin D and Selenium amount young women." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/a64cc611-d619-492b-9ceb-8bfa66f392d0. data raw data metadata biblio Haug, A. (2022).Nutrient optimised beef enhances blood levels of vitamin D and Selenium amount young women [Data set]. Norstore. https://doi.org/10.11582/2022.00007 Bjørg Egelandsdal Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2022.00007 2022-02-08 00:00:00 2022-03-22 00:39:51.183359+00:00 This Excel spreadsheet contains data on the Nutrient optimised beef enhances blood levels of vitamin D and Selenium amount young women. Nutrient optimised beef enhances blood levels of vitamin D and Selenium amount young women 2022-02-08 00:00:00 Bjørg Egelandsdal anna.haug@rohub.com Anna Haug Geo H. Environmental research Neurobiology Life sciences Physical sciences service-account-enrichment 11738 https://api.rohub.org/api/ros/55007a51-6a9a-45a9-841d-bbf0d589abf7/crate/download/ 2022-03-22 00:39:52.337240+00:00 2025-03-05 12:49:07.354571+00:00 2022-03-22 00:39:52.337240+00:00 This dataset contains data presented in the paper "Functional network topography of the medial entorhinal cortex" by Horst A. Obenhaus, Weijian Zong, R. Irene Jacobsen, Tobias Rose, Flavio Donato, Liangyi Chen, Heping Cheng, Tobias Bonhoeffer, May-Britt Moser, Edvard I. Moser (2022) We refer to the readme file uploaded with this data for descriptions on how to use the data. LINK: http://github.com/kavli-ntnu/mini2p_topography application/ld+json https://w3id.org/ro-id/55007a51-6a9a-45a9-841d-bbf0d589abf7 Obenhaus2022 MANUAL README file communications network cortices data dataset information paper topography earth sciences Animal Geography IT-computer sciences Horst A. Obenhaus Weijian Zong data dataset network readme file topography geosciences contain data data for description entorhinal cortex network topography refer to the readme file LINK: http: github.com/kavli-ntnu/mini2p_topography This dataset contains data presented in the paper "Functional network topography of the medial entorhinal cortex" by Horst A. Obenhaus, Weijian Zong, R. Irene Jacobsen, Tobias Rose, Flavio Donato, Liangyi Chen, We refer to the readme file uploaded with this data for descriptions on how to use the data. 2022 computer science database information technology Horst A. Obenhaus, Weijian Zong, Ragnhild Irene Jacobsen, Tobias Rose, Flavio Donato, Liangyi Chen, Heping Cheng, Tobias Bonhoeffer, May-Britt Moser, and Edvard Ingjald Moser. "Obenhaus2022." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/55007a51-6a9a-45a9-841d-bbf0d589abf7. biblio data raw data metadata Obenhaus, H. A., Zong, W., Jacobsen, R. I., Rose, T., Donato, F., Chen, L., Cheng, H., Bonhoeffer, T., Moser, M., Moser, E. I. (2022).Obenhaus2022 [Data set]. Norstore. https://doi.org/10.11582/2022.00005 Edvard Ingjald Moser Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2022.00005 2022-01-27 00:00:00 2022-03-22 00:40:42.910252+00:00 This dataset contains data presented in the paper "Functional network topography of the medial entorhinal cortex" by Horst A. Obenhaus, Weijian Zong, R. Irene Jacobsen, Tobias Rose, Flavio Donato, Liangyi Chen, Heping Cheng, Tobias Bonhoeffer, May-Britt Moser, Edvard I. Moser (2022) We refer to the readme file uploaded with this data for descriptions on how to use the data. <a href="http://github.com/kavli-ntnu/mini2p_topography" class="linkified" target="_blank">LINK</a> Obenhaus2022 2022-01-27 00:00:00 Edvard Ingjald Moser Weijian@hotmail.com Weijian Zong edvard.ingjald.moser@rohub.com Edvard Ingjald Moser flavio.donato@rohub.com Flavio Donato Geo H. heping.cheng@rohub.com Heping Cheng horst.a.obenhaus@rohub.com Horst A. Obenhaus liangyi.chen@rohub.com Liangyi Chen may-britt.moser@rohub.com May-Britt Moser ragnhild.irene.jacobsen@rohub.com Ragnhild Irene Jacobsen tobias.bonhoeffer@rohub.com Tobias Bonhoeffer tobias.rose@rohub.com Tobias Rose Environmental research Neurobiology Life sciences Physical sciences recordings from 2-photon microscopy Begonia imaging library animal photon Alzheimer's data microscopy dataset raw data data library upload mouse transgenic mouse imaging pupil movement data recording datum conduct tg-ArcSwe Alzheimer's disease service-account-enrichment 8165 https://api.rohub.org/api/ros/590c074e-60c7-4096-bdb5-5d018b25538f/crate/download/ 2022-03-22 00:40:44.005873+00:00 2025-03-05 00:50:08.665153+00:00 2022-03-22 00:40:44.005873+00:00 (Note: Publication in review at time of upload) Datatypes: Recordings from 2-photon microscopy, behaviour and pupil movement data. HDF5 (see note on compatibility), CSV, MP4 and others. Subjects: tg-ArcSwe Alzheimer's disease model mice at 15-18 months. Animals awake during experiments. State: Derived and raw data. Data is prepared and processed with the Begonia imaging library. application/ld+json https://w3id.org/ro-id/590c074e-60c7-4096-bdb5-5d018b25538f Dataset for "Impaired astrocytic Ca2+ signalling in awake Alzheimer's disease transgenic mice" MANUAL GliaLab (GliaLab). "Dataset for "Impaired astrocytic Ca2+ signalling in awake Alzheimer's disease transgenic mice"." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/590c074e-60c7-4096-bdb5-5d018b25538f. data metadata biblio raw data GliaLab (2021).Dataset for "Impaired astrocytic Ca2+ signalling in awake Alzheimer's disease transgenic mice" [Data set]. Norstore. https://doi.org/10.11582/2021.00100 Rune Enger Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00100 2021-11-20 00:00:00 2022-03-22 00:40:55.789993+00:00 (Note: Publication in review at time of upload) Datatypes: Recordings from 2-photon microscopy, behaviour and pupil movement data. HDF5 (see note on compatibility), CSV, MP4 and others. Subjects: tg-ArcSwe Alzheimer's disease model mice at 15-18 months. Animals awake during experiments. State: Derived and raw data. Data is prepared and processed with the Begonia imaging library. Dataset for "Impaired astrocytic Ca2+ signalling in awake Alzheimer's disease transgenic mice" 2021-11-20 00:00:00 Rune Enger GliaLab@rohub.com GliaLab (GliaLab) Geo H. Environmental research Life sciences Physical sciences Earth sciences vette fault gouge data on laboratory Direct Shear Testing thesis document geology carbon dioxide http data geological fault dataset fault slip implication gouge thesis bet fault implication thesis storage slip potential CO2 storage service-account-enrichment 7440 https://api.rohub.org/api/ros/9cb2a7a6-8dca-4520-b7df-7e5234d2c194/crate/download/ 2022-03-22 00:40:56.716512+00:00 2025-03-05 00:50:42.658583+00:00 2022-03-22 00:40:56.716512+00:00 This dataset contains data on laboratory Direct Shear Testing performed in 2020-2021 on material analogous to the Vette fault gouge (Smeaheia), with applications for Carbon Storage. The resulting thesis can be found here: LINK: http://urn.nb.no/URN:NBN:no-89352 For more details please refer to the thesis document and README file contained in the dataset. application/ld+json https://w3id.org/ro-id/9cb2a7a6-8dca-4520-b7df-7e5234d2c194 Experimental study addressing fault slip Implications for derisking of the Smeaheia potential CO2 storage site MANUAL Diana Carolina Alves Da Silva. "Experimental study addressing fault slip Implications for derisking of the Smeaheia potential CO2 storage site." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/9cb2a7a6-8dca-4520-b7df-7e5234d2c194. raw data metadata biblio data Alves Da Silva, D. C. (2021).Experimental study addressing fault slip Implications for derisking of the Smeaheia potential CO2 storage site [Data set]. Norstore. https://doi.org/10.11582/2021.00093 Diana Carolina Alves Da Silva Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00093 2021-10-27 00:00:00 2022-03-22 00:41:08.944218+00:00 This dataset contains data on laboratory Direct Shear Testing performed in 2020-2021 on material analogous to the Vette fault gouge (Smeaheia), with applications for Carbon Storage. The resulting thesis can be found here: <a href="http://urn.nb.no/URN:NBN:no-89352" class="linkified" target="_blank">LINK</a> For more details please refer to the thesis document and README file contained in the dataset. Experimental study addressing fault slip Implications for derisking of the Smeaheia potential CO2 storage site 2021-10-27 00:00:00 Michael Heeremans diana.carolina.alves.da.silva@rohub.com Diana Carolina Alves Da Silva Geo H. Environmental research Life sciences Physical sciences Earth sciences profiles of the talus Norway talus LG Corporation Norway rockfall rockfall talus measurement Davegaarden measurements measurement talus measurements measurement measure profile Lærdal Gård service-account-enrichment 7281 https://api.rohub.org/api/ros/80092230-08cd-4164-97d3-74a747d7606e/crate/download/ 2022-03-22 00:41:10.367174+00:00 2025-03-05 01:24:09.917952+00:00 2022-03-22 00:41:10.367174+00:00 Measurements from four rockfall taluses in Lærdal and Aurland, Norway. Bo: Bø, DG: Davegaarden, LG: Lærdal Gård. Measurements were collected along profiles of the talus. application/ld+json https://w3id.org/ro-id/80092230-08cd-4164-97d3-74a747d7606e Talus measurements MANUAL Elise Morken. "Talus measurements." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/80092230-08cd-4164-97d3-74a747d7606e. raw data data metadata biblio Morken, E. (2021).Talus measurements [Data set]. Norstore. https://doi.org/10.11582/2021.00080 Elise Morken Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00080 2021-09-29 00:00:00 2022-03-22 00:41:19.737272+00:00 Measurements from four rockfall taluses in Lærdal and Aurland, Norway. Bo: Bø, DG: Davegaarden, LG: Lærdal Gård. Measurements were collected along profiles of the talus. Talus measurements 2021-09-29 00:00:00 Elise Morken elise.morken@rohub.com Elise Morken Geo H. Environmental research Life sciences Physical sciences service-account-enrichment 7713 https://api.rohub.org/api/ros/0d8bd106-97fd-43c2-86ed-1e8243f432bd/crate/download/ 2022-03-22 00:41:21.899171+00:00 2025-03-05 00:45:29.161091+00:00 2022-03-22 00:41:21.899171+00:00 original experiment data, documents, manuscript for the publication application/ld+json https://w3id.org/ro-id/0d8bd106-97fd-43c2-86ed-1e8243f432bd 2014_Tiwari_Fuglebakk_etal_BMC_Bioinfo MANUAL https://w3id.org/ro-id/1aa2c46d-be80-4298-a0a5-5bda68e7063f https://w3id.org/ro-id/628d0049-0560-4516-9822-4cec1e7350f7 https://w3id.org/ro-id/7cbf4a8a-c0e1-412e-b92c-d70be1c8064e https://w3id.org/ro-id/adf67e8e-8e1d-4ac7-b6bd-e734c9465e8b https://w3id.org/ro-id/cc052c5e-089d-488f-a641-810d8f7966be https://w3id.org/ro-id/533a140f-dde4-444e-91bf-b0399c83081f https://w3id.org/ro-id/f2351ab7-1d10-4135-8c66-1f36ba31a829 https://w3id.org/ro-id/d7a0608d-160c-4a78-bdc6-fa2caf6e6ded https://w3id.org/ro-id/0fdf9212-d8ca-4a37-a787-d9ff88d56e2d https://w3id.org/ro-id/1e0d4893-caed-4e69-af48-c4c980a4ec20 https://w3id.org/ro-id/970ec352-4b67-40ca-a5c9-0ee7cfdc2e53 https://w3id.org/ro-id/f240e3bc-3fe7-4b79-81ba-890997962d20 https://w3id.org/ro-id/f7d8e2be-55bb-4a2d-bd0a-311502b63056 https://w3id.org/ro-id/8abe483d-8e39-40be-825b-8da92cae89c4 https://w3id.org/ro-id/8d586b11-8917-4d6e-98b8-fb9eace53b77 https://w3id.org/ro-id/9c14bc16-297a-4135-a6cc-91570d71723f https://w3id.org/ro-id/ab7e274a-43d7-4b9f-84bb-bfefbaed0681 https://w3id.org/ro-id/c839f19e-55b7-4ecc-91c0-e57fbdb91d70 https://w3id.org/ro-id/3ad239ef-93bf-469d-bb67-c39e7d1941a3 Nathalie Reuter. "2014_Tiwari_Fuglebakk_etal_BMC_Bioinfo." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/0d8bd106-97fd-43c2-86ed-1e8243f432bd. metadata biblio raw data data https://doi.org/10.1186/s12859-014-0427-6 2022-03-22 00:41:32.578667+00:00 2022-03-22 00:41:32.697372+00:00 https://doi.org/10.1186/s12859-014-0427-6 2022-03-22 00:41:32.578667+00:00 Reuter, N. (2021).2014_Tiwari_Fuglebakk_etal_BMC_Bioinfo [Data set]. Norstore. https://doi.org/10.11582/2021.00074 Nathalie Reuter Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00074 2021-09-21 00:00:00 2022-03-22 00:41:35.599728+00:00 original experiment data, documents, manuscript for the publication 2014_Tiwari_Fuglebakk_etal_BMC_Bioinfo 2021-09-21 00:00:00 Nathalie Reuter experiment 14.844533600802407 14.8 manuscript 21.450151057401815 21.3 publication 18.956870611835505 18.9 2014_Tiwari_Fuglebakk_etal_BMC_Bioinfo. original experiment data, documents, manuscript for the publication 100.0 100.0 earth sciences 100.0 0.9590817093849182 datum 27.089627391742198 26.9 experiment 14.702920443101712 14.6 life sciences 100.0 0.7785815596580505 life sciences (general) 100.0 0.7785815596580505 document 16.75025075225677 16.7 experiment datum 77.27727727727728 77.2 manuscript for the publication 18.21821821821822 18.2 publication 19.738167170191343 19.6 original experiment datum 4.504504504504505 4.5 document 17.019133937562938 16.9 Book industry Economy, business and finance/Economic sector/Media/Book industry geology 100.0 0.9590817093849182 manuscript 21.063189568706118 21.0 datum 28.385155466399198 28.3 Geo H. nathalie.reuter@rohub.com Nathalie Reuter Environmental research Life sciences Physical sciences Earth sciences service-account-enrichment 7623 https://api.rohub.org/api/ros/c9365b2c-cc7a-48b2-bb8c-5189fefbff94/crate/download/ 2022-03-22 00:41:36.704625+00:00 2025-03-05 00:47:49.207732+00:00 2022-03-22 00:41:36.704625+00:00 Results of direct shear tests on samples of plaster and sandstone. application/ld+json https://w3id.org/ro-id/c9365b2c-cc7a-48b2-bb8c-5189fefbff94 Direct Shear Data MANUAL information plaster result sample sandstone shearing machine testing earth sciences Textile and clothing data plaster result sample sandstone shear test engineering direct shear data direct shear test samples of plaster and sandstone shear data shear test Direct Shear Data. Results of direct shear tests on samples of plaster and sandstone. construction industry Tord Alexander Buvik. "Direct Shear Data." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/c9365b2c-cc7a-48b2-bb8c-5189fefbff94. data metadata raw data biblio Buvik, T. A. (2021).Direct Shear Data [Data set]. Norstore. https://doi.org/10.11582/2021.00073 Tord Alexander Buvik Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00073 2021-09-16 00:00:00 2022-03-22 00:41:52.326175+00:00 Results of direct shear tests on samples of plaster and sandstone. Direct Shear Data 2021-09-16 00:00:00 Tord Alexander Buvik Geo H. tord.alexander.buvik@rohub.com Tord Alexander Buvik Environmental research Life sciences Physical sciences Medical science images from confocal microscopy ultrasound technique ultrasound image medicine radiology ultrasound image imaging microscopy system bubble pulse in vivo image PLOS technique for imaging thick service-account-enrichment 7882 https://api.rohub.org/api/ros/8fafb8df-d1d5-467b-b8f7-6e36b3de25d0/crate/download/ 2022-03-22 00:41:53.590185+00:00 2025-03-05 00:46:17.038419+00:00 2022-03-22 00:41:53.590185+00:00 Images from confocal microscopy and ultrasound images which is used in the paper "A multi-pulse ultrasound technique for imaging of thick-shelled microbubbles demonstrated in vitro and in vivo" which is to be submitted to PLOS One. application/ld+json https://w3id.org/ro-id/8fafb8df-d1d5-467b-b8f7-6e36b3de25d0 A multi-pulse ultrasound technique for imaging of thick-shelled microbubbles demonstrated in vitro and in vivo MANUAL Sigrid Berg. "A multi-pulse ultrasound technique for imaging of thick-shelled microbubbles demonstrated in vitro and in vivo." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/8fafb8df-d1d5-467b-b8f7-6e36b3de25d0. raw data metadata biblio data Berg, S. (2021).A multi-pulse ultrasound technique for imaging of thick-shelled microbubbles demonstrated in vitro and in vivo [Data set]. Norstore. https://doi.org/10.11582/2021.00072 Sigrid Berg Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00072 2021-09-06 00:00:00 2022-03-22 00:42:05.636673+00:00 Images from confocal microscopy and ultrasound images which is used in the paper "A multi-pulse ultrasound technique for imaging of thick-shelled microbubbles demonstrated in vitro and in vivo" which is to be submitted to PLOS One. A multi-pulse ultrasound technique for imaging of thick-shelled microbubbles demonstrated in vitro and in vivo 2021-09-06 00:00:00 Sigrid Berg Geo H. sigrid.berg@rohub.com Sigrid Berg Environmental research Life sciences Physical sciences information technology magnetic disturbance evaluation transfer learning accompanying publication data image preprint persona dataset processed file version of the article method valuation learning model explanation solution magnetic disturbance peer publication service-account-enrichment 7623 https://api.rohub.org/api/ros/e3dfa3c8-4303-4bfb-af43-5eed9d51bdab/crate/download/ 2022-03-22 00:42:06.855630+00:00 2025-03-05 02:46:56.033478+00:00 2022-03-22 00:42:06.855630+00:00 We show that transfer learning can be easily applied to all sky images for the purpose of classifying images, filtering images and predicting magnetic disturbance from auroral images. In the accompanying publication we describe our methods and show the results that we obtained. Version 1 of the dataset corresponds to the preprint, Version 2 to the peer reviewed version of the article. This dataset contains the processed files that can be used to replicate our results. Instructions how to apply the data and on the accompanying code can be found here: LINK: http://tid.uio.no/TAME/ application/ld+json https://w3id.org/ro-id/e3dfa3c8-4303-4bfb-af43-5eed9d51bdab Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation (TAME) MANUAL Pascal Sado. "Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation (TAME)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/e3dfa3c8-4303-4bfb-af43-5eed9d51bdab. raw data data metadata biblio https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00071 2022-03-22 00:42:15.866064+00:00 2022-03-22 00:42:21.085814+00:00 We show that transfer learning can be easily applied to all sky images for the purpose of classifying images, filtering images and predicting magnetic disturbance from auroral images. In the accompanying publication we describe our methods and show the results that we obtained. Version 1 of the dataset corresponds to the preprint, Version 2 to the peer reviewed version of the article. This dataset contains the processed files that can be used to replicate our results. Instructions how to apply the data and on the accompanying code can be found here: <a href="http://tid.uio.no/TAME/" class="linkified" target="_blank">LINK</a> Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation (TAME) 2022-03-22 00:42:15.866064+00:00 https://doi.org/10.1029/2021JA029683 2022-03-22 00:42:18.373699+00:00 2022-03-22 00:42:18.491506+00:00 https://doi.org/10.1029/2021JA029683 2022-03-22 00:42:18.373699+00:00 Geo H. pascal.sado@rohub.com Pascal Sado Environmental research Life sciences Physical sciences plasma density electron frequency rocket data plasma file article editorial dataset electron density tailspin launch density density dataset consist referenced article rocket spin frequency service-account-enrichment 7927 https://api.rohub.org/api/ros/647beba0-6d23-4fad-a0a3-3087817e7407/crate/download/ 2022-03-22 00:42:23.106410+00:00 2025-03-05 00:53:51.453498+00:00 2022-03-22 00:42:23.106410+00:00 This dataset consists of a single ASCII formatted file containing the electron densities derived from the multi-Needle Langmuir Probe (m-NLP) measurements on board the ICI-3 rocket. More information about how the densities were calculated can be found in the referenced article. The first column contains the time after launch in units of s, the second column contains the plasma density in m^{-3}. It should be noted that the data was filtered with a notch filter in order to remove the rocket spin frequency and its first 3 harmonics. application/ld+json https://w3id.org/ro-id/647beba0-6d23-4fad-a0a3-3087817e7407 ICI-3 electron density (spin filtered) MANUAL Lasse Clausen. "ICI-3 electron density (spin filtered)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/647beba0-6d23-4fad-a0a3-3087817e7407. biblio raw data data metadata Clausen, L. (2021).ICI-3 electron density (spin filtered) [Data set]. Norstore. https://doi.org/10.11582/2021.00060 Lasse Clausen Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00060 2021-07-02 00:00:00 2022-03-22 00:42:36.018313+00:00 This dataset consists of a single ASCII formatted file containing the electron densities derived from the multi-Needle Langmuir Probe (m-NLP) measurements on board the ICI-3 rocket. More information about how the densities were calculated can be found in the referenced article. The first column contains the time after launch in units of s, the second column contains the plasma density in m^{-3}. It should be noted that the data was filtered with a notch filter in order to remove the rocket spin frequency and its first 3 harmonics. ICI-3 electron density (spin filtered) 2021-07-02 00:00:00 Lasse Clausen https://doi.org/10.1002/2016JA022999 2022-03-22 00:42:33.088884+00:00 2022-03-22 00:42:33.191876+00:00 https://doi.org/10.1002/2016JA022999 2022-03-22 00:42:33.088884+00:00 Geo H. lasse.clausen@rohub.com Lasse Clausen Environmental research Life sciences Physical sciences plasma density refereced article electron frequency rocket data plasma file article editorial dataset electron density tailspin launch density density dataset consist rocket spin frequency service-account-enrichment 7932 https://api.rohub.org/api/ros/4bdaf37c-16e6-412d-899e-d651e7981e60/crate/download/ 2022-03-22 00:42:37.442231+00:00 2025-03-05 00:53:51.220248+00:00 2022-03-22 00:42:37.442231+00:00 This dataset consists of a single ASCII formatted file containing the electron densities derived from the multi-Needle Langmuir Probe (m-NLP) measurements on board the ICI-2 rocket. More information about how the densities were calculated can be found in the refereced article. The first column contains the time after launch in units of s, the second column contains the plasma density in m^{-3}. It should be noted that the data was filtered with a notch filter in order to remove the rocket spin frequency and its first 3 harmonics. application/ld+json https://w3id.org/ro-id/4bdaf37c-16e6-412d-899e-d651e7981e60 ICI-2 electron density (spin filtered) MANUAL Lasse Clausen. "ICI-2 electron density (spin filtered)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/4bdaf37c-16e6-412d-899e-d651e7981e60. biblio raw data metadata data https://doi.org/10.1029/2012GL051407 2022-03-22 00:42:48.082310+00:00 2022-03-22 00:42:48.192043+00:00 https://doi.org/10.1029/2012GL051407 2022-03-22 00:42:48.082310+00:00 Clausen, L. (2021).ICI-2 electron density (spin filtered) [Data set]. Norstore. https://doi.org/10.11582/2021.00059 Lasse Clausen Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00059 2021-07-02 00:00:00 2022-03-22 00:42:52.544077+00:00 This dataset consists of a single ASCII formatted file containing the electron densities derived from the multi-Needle Langmuir Probe (m-NLP) measurements on board the ICI-2 rocket. More information about how the densities were calculated can be found in the refereced article. The first column contains the time after launch in units of s, the second column contains the plasma density in m^{-3}. It should be noted that the data was filtered with a notch filter in order to remove the rocket spin frequency and its first 3 harmonics. ICI-2 electron density (spin filtered) 2021-07-02 00:00:00 Lasse Clausen Geo H. lasse.clausen@rohub.com Lasse Clausen Environmental research Life sciences Physical sciences information technology magnetic disturbance evaluation transfer learning accompanying publication data image preprint persona dataset processed file version of the article method valuation learning model explanation solution magnetic disturbance peer publication service-account-enrichment 9210 https://api.rohub.org/api/ros/2cc7cda2-a3db-4646-ad31-2580a4a45f85/crate/download/ 2022-03-22 00:42:53.625315+00:00 2025-03-05 02:46:56.257392+00:00 2022-03-22 00:42:53.625315+00:00 We show that transfer learning can be easily applied to all sky images for the purpose of classifying images, filtering images and predicting magnetic disturbance from auroral images. In the accompanying publication we describe our methods and show the results that we obtained. Version 1 of the dataset corresponds to the preprint, Version 2 to the peer reviewed version of the article. This dataset contains the processed files that can be used to replicate our results. Instructions how to apply the data and on the accompanying code can be found here: LINK: http://tid.uio.no/TAME/ application/ld+json https://w3id.org/ro-id/2cc7cda2-a3db-4646-ad31-2580a4a45f85 Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation (TAME) MANUAL Pascal Sado. "Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation (TAME)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2cc7cda2-a3db-4646-ad31-2580a4a45f85. raw data biblio metadata data Sado, P. (2021).Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation (TAME) [Data set]. Norstore. https://doi.org/10.11582/2021.00071 Pascal Sado Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00057 2021-06-16 00:00:00 2022-03-22 00:43:05.148572+00:00 We show that transfer learning can be easily applied to all sky images for the purpose of classifying images, filtering images and predicting magnetic disturbance from auroral images. In the accompanying publication we describe our methods and show the results that we obtained. Version 1 of the dataset corresponds to the preprint, Version 2 to the peer reviewed version of the article. This dataset contains the processed files that can be used to replicate our results. Instructions how to apply the data and on the accompanying code can be found here: <a href="http://tid.uio.no/TAME/" class="linkified" target="_blank">LINK</a> Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation (TAME) 2021-06-16 00:00:00 Pascal Sado Geo H. pascal.sado@rohub.com Pascal Sado Environmental research Life sciences Physical sciences Earth sciences Pinatubo forcing Mt Pinatubo eruption meteorology NorESM output agriculture mean field simulation ensemble experiment field computer modelling eruption uncertainty dataset output proxy end product forcing climate prediction analogy ensemble service-account-enrichment 8135 https://api.rohub.org/api/ros/657fb2f6-a440-4887-81e3-7613f4af8517/crate/download/ 2022-03-22 00:43:06.401309+00:00 2025-03-05 12:49:05.212928+00:00 2022-03-22 00:43:06.401309+00:00 This dataset contains post-processed output from a study with the Norwegian Earth System Model (NorESM) that uses proxy-based stochastic volcanic forcing to assess the effects of volcanic uncertainty on probabilistic 21st century climate projections (Bethke et al. 2017). This particular dataset contains output from a supporting experiment that comprises two historical 60-member simulation ensembles with and without 1991-Mt Pinatubo forcing and temporal coverage 1990-2005. The output contains a range of standard monthly mean fields and selected daily mean fields for addressing monsoon related aspects. application/ld+json https://w3id.org/ro-id/657fb2f6-a440-4887-81e3-7613f4af8517 NorESM output from simulations with and without Mt Pinatubo eruption MANUAL Ingo Bethke. "NorESM output from simulations with and without Mt Pinatubo eruption." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/657fb2f6-a440-4887-81e3-7613f4af8517. biblio data raw data metadata https://doi.org/10.1038/nclimate3394 2022-03-22 00:43:16.261976+00:00 2022-03-22 00:43:16.370207+00:00 https://doi.org/10.1038/nclimate3394 2022-03-22 00:43:16.261976+00:00 Bethke, I. (2021).NorESM output from simulations with and without Mt Pinatubo eruption [Data set]. Norstore. https://doi.org/10.11582/2021.00051 Ingo Bethke Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00051 2021-05-28 00:00:00 2022-03-22 00:43:19.392575+00:00 This dataset contains post-processed output from a study with the Norwegian Earth System Model (NorESM) that uses proxy-based stochastic volcanic forcing to assess the effects of volcanic uncertainty on probabilistic 21st century climate projections (Bethke et al. 2017). This particular dataset contains output from a supporting experiment that comprises two historical 60-member simulation ensembles with and without 1991-Mt Pinatubo forcing and temporal coverage 1990-2005. The output contains a range of standard monthly mean fields and selected daily mean fields for addressing monsoon related aspects. NorESM output from simulations with and without Mt Pinatubo eruption 2021-05-28 00:00:00 Ingo Bethke Geo H. ingo.bethke@rohub.com Ingo Bethke Environmental research Life sciences Physical sciences Biology Medical science receptor data specification file file antigen fact input/output dataset antigen specificity guideline receptor specificity input/output output file learning output file information immuneML use case 2 service-account-enrichment 8013 https://api.rohub.org/api/ros/dfece22d-4a22-4a58-81e6-b42fd4c66728/crate/download/ 2022-03-22 00:43:20.656579+00:00 2025-03-05 00:53:54.291837+00:00 2022-03-22 00:43:20.656579+00:00 This dataset contains the data, original specification files, GLIPH2 input/output files and complete results for immuneML use case 2: Extending immuneML with a deep learning component for predicting antigen specificity of paired receptor data. To use the dataset you should follow the guidelines in the README.txt file contained within the dataset. application/ld+json https://w3id.org/ro-id/dfece22d-4a22-4a58-81e6-b42fd4c66728 immuneML use case 2: Extending immuneML with a deep learning component for predicting antigen specificity of paired receptor data MANUAL Milena Pavlović. "immuneML use case 2: Extending immuneML with a deep learning component for predicting antigen specificity of paired receptor data." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/dfece22d-4a22-4a58-81e6-b42fd4c66728. metadata raw data data biblio Pavlović, M. (2021).immuneML use case 2: Extending immuneML with a deep learning component for predicting antigen specificity of paired receptor data [Data set]. Norstore. https://doi.org/10.11582/2021.00009 Lonneke Scheffer Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00009 2021-02-16 00:00:00 2022-03-22 00:43:31.859965+00:00 This dataset contains the data, original specification files, GLIPH2 input/output files and complete results for immuneML use case 2: Extending immuneML with a deep learning component for predicting antigen specificity of paired receptor data. To use the dataset you should follow the guidelines in the README.txt file contained within the dataset. immuneML use case 2: Extending immuneML with a deep learning component for predicting antigen specificity of paired receptor data 2021-02-16 00:00:00 Lonneke Scheffer Geo H. milena.pavlovic@rohub.com Milena Pavlović Environmental research Life sciences Physical sciences Biology Medical science replication of a published study specification file immuneML file dataset guideline dataset use the dataset README.txt file study reproduction immuneML use case 1 service-account-enrichment 7754 https://api.rohub.org/api/ros/2243f9c9-5c9d-4ad1-8219-a6daed0a7d4f/crate/download/ 2022-03-22 00:43:32.837367+00:00 2025-03-05 00:53:54.083290+00:00 2022-03-22 00:43:32.837367+00:00 This dataset contains the original specification files and complete results for immuneML use case 1: Replication of a published study inside immuneML. To use the dataset you should follow the guidelines in the README.txt file contained within the dataset. application/ld+json https://w3id.org/ro-id/2243f9c9-5c9d-4ad1-8219-a6daed0a7d4f immuneML use case 1: Replication of a published study inside immuneML. MANUAL Milena Pavlović. "immuneML use case 1: Replication of a published study inside immuneML.." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2243f9c9-5c9d-4ad1-8219-a6daed0a7d4f. biblio raw data metadata data Pavlović, M. (2021).immuneML use case 1: Replication of a published study inside immuneML. [Data set]. Norstore. https://doi.org/10.11582/2021.00008 Lonneke Scheffer Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00008 2021-02-16 00:00:00 2022-03-22 00:43:48.054345+00:00 This dataset contains the original specification files and complete results for immuneML use case 1: Replication of a published study inside immuneML. To use the dataset you should follow the guidelines in the README.txt file contained within the dataset. immuneML use case 1: Replication of a published study inside immuneML. 2021-02-16 00:00:00 Lonneke Scheffer Geo H. milena.pavlovic@rohub.com Milena Pavlović Environmental research Life sciences Physical sciences Earth sciences tomography dataset X-ray tomography data x-ray granite sample voxel synchrotron imaging dataset resolution synchrotron X-ray tomography data datasets of a westerly granite sample information micrometers voxel size spatial service-account-enrichment 7825 https://api.rohub.org/api/ros/5c79fe26-7465-4493-875a-9bb44ddcf2ad/crate/download/ 2022-03-22 00:43:49.117619+00:00 2025-03-05 01:24:09.547783+00:00 2022-03-22 00:43:49.117619+00:00 Synchrotron X-ray tomography data of a Westerly granite acquired at two spatial resolutions on beamline ID19 at ESRF application/ld+json https://w3id.org/ro-id/5c79fe26-7465-4493-875a-9bb44ddcf2ad Synchrotron X-ray microtomography datasets of a Westerly granite sample acquired at two spatial resolutions (6.5 and 0.65 micrometers voxel size) MANUAL Francois Renard. "Synchrotron X-ray microtomography datasets of a Westerly granite sample acquired at two spatial resolutions (6.5 and 0.65 micrometers voxel size)." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/5c79fe26-7465-4493-875a-9bb44ddcf2ad. raw data metadata biblio data Renard, F. (2021).Synchrotron X-ray microtomography datasets of a Westerly granite sample acquired at two spatial resolutions (6.5 and 0.65 micrometers voxel size) [Data set]. Norstore. https://doi.org/10.11582/2021.00007 Francois Renard Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00007 2021-02-15 00:00:00 2022-03-22 00:44:00.827454+00:00 Synchrotron X-ray tomography data of a Westerly granite acquired at two spatial resolutions on beamline ID19 at ESRF Synchrotron X-ray microtomography datasets of a Westerly granite sample acquired at two spatial resolutions (6.5 and 0.65 micrometers voxel size) 2021-02-15 00:00:00 Francois Renard francois.renard@rohub.com Francois Renard Geo H. Environmental research Life sciences Physical sciences Earth sciences X-ray tomography data radiology x-ray granite data sample time series imaging time series of tomogram tomography data tomogram Dynamic X-ray tomography experiment experiment samples of Westerley Granite service-account-enrichment 7551 https://api.rohub.org/api/ros/1e9c8d62-dc10-4228-acbb-686d3f48058f/crate/download/ 2022-03-22 00:44:04.365360+00:00 2025-03-05 01:27:07.731759+00:00 2022-03-22 00:44:04.365360+00:00 Dynamic X-ray tomography experiments on three samples of Westerley Granite (WG01, WG02, WG04). Data correspond to time series of tomograms application/ld+json https://w3id.org/ro-id/1e9c8d62-dc10-4228-acbb-686d3f48058f X-ray tomography data of Westerley granite MANUAL Francois Renard. "X-ray tomography data of Westerley granite." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/1e9c8d62-dc10-4228-acbb-686d3f48058f. biblio data raw data metadata Renard, F. (2021).X-ray tomography data of Westerley granite [Data set]. Norstore. https://doi.org/10.11582/2021.00002 Francois Renard Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2021.00002 2021-01-13 00:00:00 2022-03-22 00:44:16.262608+00:00 Dynamic X-ray tomography experiments on three samples of Westerley Granite (WG01, WG02, WG04). Data correspond to time series of tomograms X-ray tomography data of Westerley granite 2021-01-13 00:00:00 Francois Renard francois.renard@rohub.com Francois Renard Geo H. Environmental research Life sciences Physical sciences Earth sciences Anstrude limestone geology rock music sandstone limestone experiment time series physics imaging reservoir rock imaging archive rock samples of Adamswiller sandstone onset mineralogy strain localization locating rock physics experiment Bentheim sandstone service-account-enrichment 7443 https://api.rohub.org/api/ros/556f8bc4-2d71-4b30-bed4-4c2d2e48527f/crate/download/ 2022-03-22 00:44:17.587055+00:00 2025-03-05 01:24:09.161613+00:00 2022-03-22 00:44:17.587055+00:00 The archive contains time series of X-ray tomography rock physics experiments on 7 rock samples of Adamswiller sandstone (ADAM01), Bentheim sandstone (BEN1), Anstrude limestone (ANS2, ANS3, ANS4, ANS5, ANS6) application/ld+json https://w3id.org/ro-id/556f8bc4-2d71-4b30-bed4-4c2d2e48527f Synchrotron 4D X-ray imaging reveals strain localization at the onset of system-size failure of porous reservoir rocks MANUAL Francois Renard. "Synchrotron 4D X-ray imaging reveals strain localization at the onset of system-size failure of porous reservoir rocks." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/556f8bc4-2d71-4b30-bed4-4c2d2e48527f. biblio raw data data metadata Renard, F. (2020).Synchrotron 4D X-ray imaging reveals strain localization at the onset of system-size failure of porous reservoir rocks [Data set]. Norstore. https://doi.org/10.11582/2020.00058 Francois Renard Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00058 2020-11-18 00:00:00 2022-03-22 00:44:28.148566+00:00 The archive contains time series of X-ray tomography rock physics experiments on 7 rock samples of Adamswiller sandstone (ADAM01), Bentheim sandstone (BEN1), Anstrude limestone (ANS2, ANS3, ANS4, ANS5, ANS6) Synchrotron 4D X-ray imaging reveals strain localization at the onset of system-size failure of porous reservoir rocks 2020-11-18 00:00:00 Francois Renard francois.renard@rohub.com Francois Renard Geo H. Environmental research Life sciences Physical sciences service-account-enrichment 7492 https://api.rohub.org/api/ros/2d1255b8-c744-4022-a2f6-fdaf88c6b011/crate/download/ 2022-03-22 00:44:29.447195+00:00 2025-03-05 00:56:02.954057+00:00 2022-03-22 00:44:29.447195+00:00 Summary statistics for multivariate association (MOSTest) of genetic variants and regional brain morphology application/ld+json https://w3id.org/ro-id/2d1255b8-c744-4022-a2f6-fdaf88c6b011 Making the MOSTest of imaging genetics MANUAL associating brain genetics imagination morphology statistic stochastic variable summary earth sciences Genetics brain genetics imaging morphology statistic summary variant life sciences MOSTest of imaging genetics brain morphology genetic variant imaging genetics summary statistic Making the MOSTest of imaging genetics. Summary statistics for multivariate association (MOSTest) of genetic variants and regional brain morphology statistics Oleksandr Frei. "Making the MOSTest of imaging genetics." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/2d1255b8-c744-4022-a2f6-fdaf88c6b011. raw data data biblio metadata Frei, O. (2020).Making the MOSTest of imaging genetics [Data set]. Norstore. https://doi.org/10.11582/2020.00031 Oleksandr Frei Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00031 2020-05-29 00:00:00 2022-03-22 00:44:42.852674+00:00 Summary statistics for multivariate association (MOSTest) of genetic variants and regional brain morphology Making the MOSTest of imaging genetics 2020-05-29 00:00:00 Oleksandr Frei https://www.biorxiv.org/content/10.1101/767905v2 2022-03-22 00:44:39.712345+00:00 2022-03-22 00:44:39.824466+00:00 https://www.biorxiv.org/content/10.1101/767905v2 2022-03-22 00:44:39.712345+00:00 Geo H. oleksandr.frei@rohub.com Oleksandr Frei Environmental research Life sciences Physical sciences Earth sciences data of creep deformation X-ray imaging geology medicine radiology x-ray Carrara marble marble data sample experiment time series deformation voxel synchrotron imaging creep triaxial compression experiments time series compression tomography data faulting voxel size service-account-enrichment 7890 https://api.rohub.org/api/ros/6492da85-5bd1-4c56-82f4-03e8f8c90c69/crate/download/ 2022-03-22 00:44:43.924370+00:00 2025-03-05 00:50:03.633793+00:00 2022-03-22 00:44:43.924370+00:00 Time series of synchrotron X-ray microtomography data of creep deformation of two samples of Carrara marble. Voxel size: 6.5 micrometers application/ld+json https://w3id.org/ro-id/6492da85-5bd1-4c56-82f4-03e8f8c90c69 Creep burst coincident with faulting in marble observed in 4D synchrotron X-ray imaging triaxial compression experiments MANUAL Francois Renard. "Creep burst coincident with faulting in marble observed in 4D synchrotron X-ray imaging triaxial compression experiments." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/6492da85-5bd1-4c56-82f4-03e8f8c90c69. data raw data biblio metadata Renard, F. (2020).Creep burst coincident with faulting in marble observed in 4D synchrotron X-ray imaging triaxial compression experiments [Data set]. Norstore. https://doi.org/10.11582/2020.00022 Francois Renard Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00022 2020-04-07 00:00:00 2022-03-22 00:44:55.209043+00:00 Time series of synchrotron X-ray microtomography data of creep deformation of two samples of Carrara marble. Voxel size: 6.5 micrometers Creep burst coincident with faulting in marble observed in 4D synchrotron X-ray imaging triaxial compression experiments 2020-04-07 00:00:00 Francois Renard francois.renard@rohub.com Francois Renard Geo H. Environmental research Life sciences Physical sciences Earth sciences sample of monzonite synchrotron imaging experiments time series geology medicine radiology error x-ray experiment synchrotron imaging monzonite weirdo damage tomography data saw saw-cut fault fault information sample competition imaging experiments time series service-account-enrichment 8160 https://api.rohub.org/api/ros/b8b9d3f9-ebfc-4c66-a6b6-0c89cdc2f8d2/crate/download/ 2022-03-22 00:44:56.443123+00:00 2025-03-05 00:47:01.088389+00:00 2022-03-22 00:44:56.443123+00:00 Time series of X-ray microtomography data acquired on beamlin ID19 at the ESRF. Four sample of monzonite rocks with a saw-cut fault. application/ld+json https://w3id.org/ro-id/b8b9d3f9-ebfc-4c66-a6b6-0c89cdc2f8d2 Competition between creep and damage on faults revealed in 4D synchrotron imaging experiments MANUAL Francois Renard. "Competition between creep and damage on faults revealed in 4D synchrotron imaging experiments." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/b8b9d3f9-ebfc-4c66-a6b6-0c89cdc2f8d2. biblio data raw data metadata Renard, F. (2020).Competition between creep and damage on faults revealed in 4D synchrotron imaging experiments [Data set]. Norstore. https://doi.org/10.11582/2020.00003 Francois Renard Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00003 2020-01-20 00:00:00 2022-03-22 00:45:10.279284+00:00 Time series of X-ray microtomography data acquired on beamlin ID19 at the ESRF. Four sample of monzonite rocks with a saw-cut fault. Competition between creep and damage on faults revealed in 4D synchrotron imaging experiments 2020-01-20 00:00:00 Francois Renard https://doi.org/10.1016/j.tecto.2020.228437. 2022-03-22 00:45:07.504181+00:00 2022-03-22 00:45:07.628514+00:00 https://doi.org/10.1016/j.tecto.2020.228437. 2022-03-22 00:45:07.504181+00:00 francois.renard@rohub.com Francois Renard Geo H. Environmental research Life sciences Physical sciences Earth sciences physics experiment strain partitioning physics rock and roll rock x-ray data experiment physics disk partitioning imaging dataset mixology non-starter X-ray microtomography rock physics experiment Digital Volume Correlation data service-account-enrichment 7576 https://api.rohub.org/api/ros/642bed30-8205-4c7d-a0e7-cc808430b031/crate/download/ 2022-03-22 00:45:11.603831+00:00 2025-03-05 02:47:40.727346+00:00 2022-03-22 00:45:11.603831+00:00 The dataset contains Digital Volume Correlation data of rock physics experiments performed using X-ray microtomography. application/ld+json https://w3id.org/ro-id/642bed30-8205-4c7d-a0e7-cc808430b031 The mixology of precursory strain partitioning approaching brittle failure in rocks MANUAL Jess McBeck. "The mixology of precursory strain partitioning approaching brittle failure in rocks." ROHub. Mar 22 ,2022. https://w3id.org/ro-id/642bed30-8205-4c7d-a0e7-cc808430b031. metadata biblio data raw data https://doi.org/10.1093/gji/ggaa121 2022-03-22 00:45:31.238021+00:00 2022-03-22 00:45:31.350511+00:00 https://doi.org/10.1093/gji/ggaa121 2022-03-22 00:45:31.238021+00:00 McBeck, J. (2020).The mixology of precursory strain partitioning approaching brittle failure in rocks [Data set]. Norstore. https://doi.org/10.11582/2020.00002 Francois Renard Experiment https://archive.sigma2.no/pages/public/datasetDetail.jsf?id=10.11582/2020.00002 2020-01-08 00:00:00 2022-03-22 00:45:36.140605+00:00 The dataset contains Digital Volume Correlation data of rock physics experiments performed using X-ray microtomography. The mixology of precursory strain partitioning approaching brittle failure in rocks 2020-01-08 00:00:00 Francois Renard Geo H. jess.mcbeck@rohub.com Jess McBeck