Two non-overlapping entities work in parallel to help drive BioCompute, the IEEE 2791-2020 Standard, and a Public Private Partnership. Leadership for the Public Private Partnership consists of an Executive Steering Committee and a Technical Steering Committee. The schema that is referenced by the current draft of the IEEE standard is maintained by an IEEE GitLab repository. BioCompute Objects nf-core/chipseq: nf-core/chipseq v1.2.1 - Platinum Mole https://github.com/biocompute-objects/bco-ro-example-chipseq/archive/main.zip GitHub download of biocompute-objects/bco-ro-example-chipseq bco-ro-example-chipseq GitHub issue tracker https://github.com/biocompute-objects/bco-ro-example-chipseq/issues https://spdx.org/licenses/MIT MIT License MIT License Copyright (c) 2018 nf-core Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. MIT License Stian Soiland-Reyes nfcore/chipseq is a bioinformatics analysis pipeline used for Chromatin ImmunopreciPitation sequencing (ChIP-seq) data nf-core/chipseq Phil Ewels Alexander Peltzer Winni Kretzschmar Harshil Patel Drew Behrens Tiago Chedraoui Silva mashehu Chuan Wang Nextflow 19.10.0 Rotholandus Sofia Haglund Maxime Garcia 2020-09-09T23:00:00.000Z Workflow run of a ChIP-seq peak-calling, QC and differential analysis pipeline Workflow run of nf-core/chipseq 2020-09-10T13:10:50.246Z .nextflow.log 2020-09-10T13:50:02.378Z IEEE 2791 description chipseq_20200910.json 2020-09-10T13:10:50.250Z nextflow.log 2020-09-10T13:20:49.143Z Nextflow outputs from examplar run of nf-core/ pipeline workflow. results 2020-09-10T12:00:09.238Z bwa 2020-09-10T12:02:59.495Z mergedLibrary 2020-09-10T12:04:31.692Z bigwig 2020-09-10T12:04:31.696Z scale 2020-09-10T12:11:43.943Z deepTools 2020-09-10T12:05:17.700Z plotFingerprint 2020-09-10T12:26:12.375Z plotProfile 2020-09-10T12:02:33.471Z macs 2020-09-10T12:04:26.336Z phantompeakqualtools 2020-09-10T12:04:45.952Z picard_metrics 2020-09-10T11:58:56.905Z fastqc 2020-09-10T11:58:56.909Z zips 2020-09-10T11:56:45.292Z genome 2020-09-10T11:56:45.324Z genome.fa 2020-09-10T12:26:50.263Z igv 2020-09-10T12:26:50.267Z broadPeak 2020-09-10T12:26:50.267Z igv_session.xml 2020-09-10T12:26:59.183Z multiqc 2020-09-10T12:26:59.183Z broadPeak 2020-09-10T12:26:59.207Z multiqc_data 2020-09-10T12:26:59.191Z multiqc_report.html 2020-09-10T12:27:01.599Z pipeline_info 2020-09-10T12:27:01.755Z image/svg+xml pipeline_dag.svg 2020-09-10T11:57:13.996Z trim_galore 2020-09-10T11:58:55.705Z fastqc 2020-09-10T11:58:55.705Z zips 2020-09-10T11:58:55.705Z logs Made with Describo: https://uts-eresearch.github.io/describo/ ro-crate-metadata.json https://spdx.org/licenses/CC0-1.0 Creative Commons Zero v1.0 Universal object_id dc308d7c-7949-446a-9c39-511b8ab40caf thien@unimelb.edu.au Thieberger Nick Nick Thieberger University of Melbourne Australia VU Vanuatu 166.427,-22.283 166.467,-22.241 168.159,-17.83 168.594,-17.585 168.217,-17.8235 168.317,-17.7235 collectionIdentifier NT1 doi 10.4225/72/56F94A61DA9EC domain paradisec.org.au hashId 72b3dc1401c8ff06aacba0990a128fc113cf9ad5275f494b05c1142177356561bd7f4c0e8800bade2cbbbed75f6d9d019894735ad7e40762684d243a442d658a id /paradisec.org.au/NT1/98007 itemIdentifier 98007 bis Bislama erk Efate, South item 2012-09-27T10:08:01.000Z 2018-05-17T04:13:04.000Z NT1-98007. Text #047 (speaker is John Maklen. Text title: History of villages before Erakor); Text #048 (speaker is John Maklen. Text title: Mantu the flying fox and Erromango); Text #049. Text title: Asaraf (speaker is John Maklen);Text #050. Text title: Mumu and Kotkot (speaker is John Maklen); Text #051. Text title: Natopu ni Erakor—the spirit who lives at Erakor (speaker is John Maklen);Text #038. Text title: The need for respect (speaker is Iokopeth) Stories can be seen at NT8-TEXT. There are time-aligned transcripts of this item and handwritten transcripts by Manuel Wayane scanned as jpg files. 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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 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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))) service-account-enrichment 272345 https://api.rohub.org/api/ros/d108d8e6-fe96-4823-a63c-e4aab15b6bce/crate/download/ 2021-11-08 19:54:44.999207+00:00 2025-03-05 00:51:28.974019+00:00 2021-11-08 19:54:44.999207+00:00 This Research Object aggregates all the resourced used during the EOSC-Future Demo by RELIANCE, which consisted mostly on use of demonstrate the use of ADAM-API and RoHUB-API from Python Jupyter notebooks. application/ld+json https://w3id.org/ro-id/d108d8e6-fe96-4823-a63c-e4aab15b6bce EOSC-Future Demo by RELIANCE MANUAL notebook reliance use earth sciences ADAM-API Research Object RoHUB-API notebook reliance use geosciences EOSC-Future Demo by reliance Python Jupyter notebook RoHUB-API from Python Jupyter notebook demonstrate the use use of ADAM-API EOSC-Future Demo by RELIANCE. This Research Object aggregates all the resourced used during the EOSC-Future Demo by RELIANCE, which consisted mostly on use of demonstrate the use of ADAM-API and RoHUB-API from Python Jupyter notebooks. https://w3id.org/ro-id/d108d8e6-fe96-4823-a63c-e4aab15b6bce/1d49254e-b4a9-4606-a1f4-9130d7c8a6ad Anne Foilloux. "EOSC-Future Demo by RELIANCE." ROHub. Nov 08 ,2021. https://w3id.org/ro-id/d108d8e6-fe96-4823-a63c-e4aab15b6bce. https://box.pionier.net.pl/f/9f34f702c93e4d65a26d/?dl=1 2021-11-08 20:33:13.025889+00:00 2021-11-08 20:33:13.026410+00:00 This is not the real demo itself; it was recorded separately. Demo video recording 2021-11-08 20:33:13.025889+00:00 10.24424/x6rx-dj19 https://github.com/NordicESMhub/RELIANCE/blob/main/demo-eosc-future/RELIANCE_v0.2-France.ipynb 2021-11-08 20:27:19.941634+00:00 2021-11-08 20:52:39.256476+00:00 This notebook shows how to discover and access the Copernicus Atmosphere Monitoring products available in the RELIANCE datacube resources and how to create an associated research object and publish it in Zenodo Jupyter Notebook for using CAMS European air quality analysis over France from Copernicus Atmosphere Monitoring with RELIANCE services 2021-11-08 20:27:19.941634+00:00 234819 https://api.rohub.org/api/resources/a6cdef08-1e24-4039-ba74-69ba3b0eb209/download/ 2021-11-08 20:12:38.036268+00:00 2021-11-08 20:12:38.036730+00:00 image/png User journeys across RIs via the EOSC 2021-11-08 20:12:38.036268+00:00 https://github.com/NordicESMhub/RELIANCE/blob/main/demo-eosc-future/RELIANCE-Datacube-featuring-EOSC_v0.2.ipynb 2021-11-08 20:24:02.170297+00:00 2021-11-08 20:24:02.170862+00:00 This notebook shows how to discover and access the Copernicus Atmosphere Monitoring products available in the RELIANCE datacube resources Jupyter Notebook for using CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services 2021-11-08 20:24:02.170297+00:00 https://github.com/NordicESMhub/RELIANCE/blob/main/demo-eosc-future/README.md 2021-11-08 20:18:44.130489+00:00 2021-11-08 20:18:44.130946+00:00 text/markdown README 2021-11-08 20:18:44.130489+00:00 https://w3id.org/ro-id-dev/2aec1040-82ee-4107-9394-d72a24c9834a 2021-11-08 20:31:18.830175+00:00 2021-11-08 20:31:18.830716+00:00 Research Object created during the demo Copernicus Atmosphere Monitoring Service Code Research Object - snapshot 2021-11-08 20:31:18.830175+00:00 42997 https://api.rohub.org/api/resources/f0d0aac8-97a6-4521-a936-bd19710845a8/download/ 2021-11-08 20:35:55.504415+00:00 2021-11-08 20:35:55.505100+00:00 application/json France geometry 2021-11-08 20:35:55.504415+00:00 Anne Fouilloux Raul Palma Earth sciences https://github.com/NordicESMhub/RELIANCE/blob/main/MOD_Aqua_ADAM.ipynb 2021-11-08 21:09:59.780719+00:00 2021-11-08 21:09:59.781169+00:00 This notebook shows how to use ADAM API and ROHub API Jupyter Notebook for using ADAM-API to access MODIS Aqua 2021-11-08 21:09:59.780719+00:00 concentration chlorophyll concentration 10.32064128256513 10.3 concentration 13.554216867469878 13.5 analysis 6.526104417670682 6.5 concentration 22.02852614896989 13.9 Research Object 15.863453815261042 15.8 This Research Object aggregates the resources associated with the analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water 65.36536536536536 65.3 Analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water. 34.63463463463463 34.6 geochemistry 100.0 0.7202270030975342 salt water 29.001584786053883 18.3 geosciences 100.0 0.9254304766654968 geophysics 100.0 0.9254304766654968 chlorophyll 37.717908082408876 23.8 109619 https://api.rohub.org/api/ros/9e533d0d-b1de-4b0d-9dd8-14d136aacea5/crate/download/ 2021-11-08 21:06:20.914340+00:00 2025-12-17 10:08:29.660011+00:00 2021-11-08 21:06:20.914340+00:00 This Research Object aggregates the resources associated with the analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water application/ld+json https://w3id.org/ro-id/9e533d0d-b1de-4b0d-9dd8-14d136aacea5 Analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water MANUAL https://w3id.org/ro-id/0db12483-1d72-4ec5-8f43-7244a0ef5cb5 https://w3id.org/ro-id/3e602429-165b-4c38-821b-b185c2d19566 https://w3id.org/ro-id/95d3e2f1-2706-446e-a12f-00e5f22e43aa https://w3id.org/ro-id/a6cca92d-f385-4230-a0e1-881e29db8601 https://w3id.org/ro-id/34fd419d-c014-4140-b4a8-92a58fe19b07 https://w3id.org/ro-id/b925e277-dafb-48da-b69b-443ee492e971 https://w3id.org/ro-id/02d44787-a55c-4dc5-8968-38360eb4712c https://w3id.org/ro-id/033fe89c-7935-4136-94ab-bc6b8e4631fb https://w3id.org/ro-id/2280c53a-ef9b-4fd6-ad25-331b51fd21ce https://w3id.org/ro-id/9f1673cc-ed44-43c0-a513-13baff083467 https://w3id.org/ro-id/bb654c5f-afb3-4284-a3ae-7e0eada02b24 https://w3id.org/ro-id/c9755e58-000b-4ff3-9ac0-e5c8a600f8ee https://w3id.org/ro-id/e5495369-d48b-4536-bee2-676a5ed66a7f https://w3id.org/ro-id/549fc843-cefc-4f70-ae2f-6e4bd6397645 https://w3id.org/ro-id/7cbd7a86-f1b1-4244-8b15-00af8d6c53fe https://w3id.org/ro-id/0140bc8c-4555-4103-acf5-334ab798e827 https://w3id.org/ro-id/d8454f8f-1581-4f2a-b45c-7cef3a7285e8 https://w3id.org/ro-id/f0492ea2-a97b-4c28-837a-82528d4fb014 https://w3id.org/ro-id/ff180a79-e2a8-4156-bdd5-aeaaefc1d8b9 https://w3id.org/ro-id/297b7d28-5cbd-49ba-b01b-ecf37bd32a05 https://w3id.org/ro-id/30408fdd-52d2-41a1-b306-962f6bf9e3c3 Anne Foilloux, and Anne Foilloux. "Analysis of MOD_Aqua mass concentration chlorophyll concentration in sea water." ROHub. Nov 08 ,2021. https://w3id.org/ro-id/9e533d0d-b1de-4b0d-9dd8-14d136aacea5. 106111 https://api.rohub.org/api/resources/1be16907-9d03-456d-a2ec-db11e3a8af2f/download/ 2021-11-08 21:08:02.599866+00:00 2021-11-08 21:08:02.600559+00:00 image/png Mass concentration chlorophyll concentration in sea water Year 2013 over the Mediteranean region 2021-11-08 21:08:02.599866+00:00 MOD_Aqua 14.257028112449799 14.2 resource 11.251980982567353 7.1 2022-03-24 11:53:42.281517+00:00 earth sciences 100.0 0.7202270030975342 resource 7.228915662650602 7.2 chlorophyll 24.196787148594375 24.1 chlorophyll concentration 84.1683366733467 84.0 sea water 18.373493975903614 18.3 aggregate the resource 5.01002004008016 5.0 mass concentration chlorophyll concentration 0.5010020040080161 0.5 Anne Fouilloux Raul Palma service-account-enrichment Earth sciences research object 83.11557788944724 82.7 map 17.05639614855571 12.4 country 11.829436038514443 8.6 False https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 2021-11-09 16:18:39.029666+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl POINT (38.0 38.0) Nov-9 38.0 38.0 POINT (38.0 38.0) 9b071de5-4738-4072-9e66-4822fb20d61a POINT (38.0 38.0) service-account-enrichment https://w3id.org/ro-id/0e5f85c2-45ce-4b79-af5b-a940086cc802 https://w3id.org/ro-id/48eb1f98-3c64-4dd2-95b7-fe7044b08ff1 https://w3id.org/ro-id/4df864f9-4427-4f6d-a11a-b6f1a340eb42 https://w3id.org/ro-id/56840bfe-6946-4cb1-a8a4-e4e3c4927063 False https://w3id.org/ro-id/2755900c-b77c-4a29-ac59-f6f51af20fa7 2021-11-09 16:23:28.991236+00:00 mailto:rpalma@man.poznan.pl 81973 https://api.rohub.org/api/ros/321e3b22-04a7-48f8-a647-7ebc49c19301/crate/download/ 2021-11-09 15:51:17.774513+00:00 2025-03-05 00:45:33.607132+00:00 2021-11-09 15:51:17.774513+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot MANUAL https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301/bc445fcb-5960-4feb-a1ae-5ca50453ad6e https://w3id.org/ro-id/0b1f7680-3fc1-47db-b176-0440853ecde0 https://w3id.org/ro-id/0bcf2515-210c-4717-8b9a-e337adbcef55 https://w3id.org/ro-id/7d9815be-40e5-4718-ac91-8d865d795324 https://w3id.org/ro-id/e5b7d130-4697-4a7b-9a2c-16756071ba04 https://w3id.org/ro-id/8284ac3e-dc7f-4d20-806c-0a94b344af89 https://w3id.org/ro-id/e7c9062c-5cba-473f-bf89-259f6dcaae5d https://w3id.org/ro-id/a7e8d560-a7df-4aa4-9472-3811d8ee43c6 https://w3id.org/ro-id/aa3e2a7e-7135-44ca-8d61-39390c727761 https://w3id.org/ro-id/c303edac-f3f8-470c-be5f-0d776c719869 https://w3id.org/ro-id/e6323df0-add4-4f69-9a0b-b464fbe20b56 https://w3id.org/ro-id/eb46cc83-dbea-468f-9d40-48d383c42557 https://w3id.org/ro-id/ebf1d891-b6d4-4462-9a93-e7c0bea64e81 https://w3id.org/ro-id/da144e0f-548b-4ba9-a351-6fe62c0e6635 https://w3id.org/ro-id/ff422ab5-a055-493a-b016-fb9dec5db6cb https://w3id.org/ro-id/096fd79f-1da7-4130-8560-50bf8860e376 https://w3id.org/ro-id/6d5cd942-1e2f-4661-9460-e31f9cd16732 https://w3id.org/ro-id/db392796-e679-4c0a-ae88-4db03f91ac9c https://w3id.org/ro-id/e21fdf10-81c2-4d5e-ba0e-f27768551e15 https://w3id.org/ro-id/f241bce9-3878-4c85-a937-860380c8cd3e https://w3id.org/ro-id/3f23826e-7037-4a82-84c0-954a1fac2062 https://w3id.org/ro-id/bcc38d7f-6ca4-4809-a13e-3afbdd362efe https://w3id.org/ro-id/2bba2635-0803-4822-bfe2-7c15d2f0bba4 Palma, Raul. "9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 09 ,2021. https://doi.org/10.24424/j2gh-5322. List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 2021-11-09 16:23:26.891779+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 73394 https://api.rohub.org/api/resources/440e3907-011c-4185-936a-16a0a868a444/download/ 2021-11-09 15:51:45.742090+00:00 2021-11-09 16:23:26.956721+00:00 image/png flow-dcro.png 2021-11-09 15:51:45.742090+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 2021-11-09 16:23:26.855020+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G Flow to compute monthly map https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 2021-11-09 16:23:26.816350+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 2021-11-09 16:23:26.923462+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services This Research Object demonstrate how to compute monthly map of PM10 over your country - modified 69.66966966966967 69.6 False https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 2021-11-09 16:19:16.618594+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 2021-11-09 16:06:58.516914+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 2021-11-09 16:15:26.873492+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl monthly map 6.231155778894473 6.2 aim 31.499312242090785 22.9 atmospheric sciences 100.0 0.7866491675376892 object 25.208333333333332 24.2 PM10 13.541666666666666 13.0 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot. 30.330330330330334 30.3 research 31.145833333333332 29.9 astronautics (general) 100.0 0.38756152987480164 data cube research object 1.0050251256281406 1.0 data cube 0.4020100502512563 0.4 research 39.61485557083906 28.8 map 13.333333333333334 12.8 earth sciences 100.0 0.7866491675376892 Copernicus Atmosphere Monitoring Service 8.229166666666666 7.9 country 8.541666666666666 8.2 map of PM10 9.246231155778894 9.2 astronautics 100.0 0.38756152987480164 Raul Palma Earth sciences False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:18:39.029666+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl monthly map 6.231155778894473 6.2 38.0 38.0 POINT (38.0 38.0) ee61e733-5a21-43d3-a8b9-1e7e3cd58df1 POINT (38.0 38.0) service-account-enrichment https://w3id.org/ro-id/0e5f85c2-45ce-4b79-af5b-a940086cc802 https://w3id.org/ro-id/321e3b22-04a7-48f8-a647-7ebc49c19301 https://w3id.org/ro-id/48eb1f98-3c64-4dd2-95b7-fe7044b08ff1 https://w3id.org/ro-id/4df864f9-4427-4f6d-a11a-b6f1a340eb42 https://w3id.org/ro-id/56840bfe-6946-4cb1-a8a4-e4e3c4927063 False https://w3id.org/ro-id/2755900c-b77c-4a29-ac59-f6f51af20fa7 2021-11-09 16:38:47.238379+00:00 mailto:rpalma@man.poznan.pl 82295 https://api.rohub.org/api/ros/164e222b-0bdd-4638-93e7-010bad13d655/crate/download/ 2021-11-09 15:51:17.774513+00:00 2025-03-05 00:45:33.909189+00:00 2021-11-09 15:51:17.774513+00:00 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified application/ld+json https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot MANUAL https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655/bc445fcb-5960-4feb-a1ae-5ca50453ad6e https://w3id.org/ro-id/7dc16fbc-7188-4209-9937-a7a2932d2997 https://w3id.org/ro-id/82a04fbd-62bc-4b45-9d39-7cf5f1034dc8 https://w3id.org/ro-id/8ffcf3e4-af24-4061-a08e-5ed6c080b739 https://w3id.org/ro-id/eb545d52-4143-4b2a-be9d-c074ac089f17 https://w3id.org/ro-id/9b55ecf3-2e9a-4a92-a0fe-484a62c91593 https://w3id.org/ro-id/a8b59a03-2bf8-4736-a216-fbe8f75e6e67 https://w3id.org/ro-id/531895fd-615d-47cd-97ab-61244289921e https://w3id.org/ro-id/84fc9958-604b-4750-a50f-9638ad628bdf https://w3id.org/ro-id/91354b80-10cb-4027-9832-cb9ed4792db6 https://w3id.org/ro-id/913c9817-553d-458a-a319-0ec12c61a2b7 https://w3id.org/ro-id/f4db0903-93b0-4f0f-b25c-904a33dbc608 https://w3id.org/ro-id/f70dd780-e275-4682-8ac2-fcc47d3307f4 https://w3id.org/ro-id/1af0e739-034c-4d44-87b7-0af39b5ad382 https://w3id.org/ro-id/24eec82c-8ece-445a-8e1a-73d98222c0c2 https://w3id.org/ro-id/0f6a7d8f-9ba6-4741-8687-cad255b1516c https://w3id.org/ro-id/6c3b3f18-4625-48c0-8594-bf13fc863dd7 https://w3id.org/ro-id/cb1a64b5-d528-48f4-8151-0c7684cfa128 https://w3id.org/ro-id/d3e198c4-80cb-4099-8414-e97c9798c120 https://w3id.org/ro-id/d9523df2-d927-46e8-8379-02a12a2e92ce https://w3id.org/ro-id/6bf626d2-4ee2-4fed-a8f6-4aed427ef252 https://w3id.org/ro-id/886380ba-096d-4c04-ba1d-ff6f0d57f001 https://w3id.org/ro-id/afec8c8a-2d85-4ef7-9efb-6c5579d4c1bb Palma, Raul. "9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot." ROHub. Nov 09 ,2021. https://doi.org/10.24424/yw22-x266. List of hourly PM10 concentration data for September 1st 2018 over Europe Index of daily PM10 concentration for September 1st 2018 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 2021-11-09 16:38:44.990014+00:00 https://reliance-das.adamplatform.eu/wcs?service=WCS&Request=GetCoverage&CoverageID=EU_CAMS_SURFACE_PM10_G&subset=unix(2018-09-01,2018-09-01)&format=image/tiff 2021-11-09 15:51:56.143768+00:00 73394 https://api.rohub.org/api/resources/0369a2c2-53af-4929-a325-ecaa4f28eb78/download/ 2021-11-09 15:51:45.742090+00:00 2021-11-09 16:38:45.030794+00:00 image/png flow-dcro.png 2021-11-09 15:51:45.742090+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 2021-11-09 16:38:44.952284+00:00 https://zenodo.org/record/5554786#.YYlWo9nMI-Q 2021-11-09 15:52:03.894247+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 2021-11-09 16:38:44.873578+00:00 https://reliance-das.adamplatform.eu/opensearch/datasets?datasetId=EU_CAMS_SURFACE_PM10_G 2021-11-09 15:51:51.850517+00:00 This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of nine air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. EU_CAMS_SURFACE_PM10_G https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 2021-11-09 16:38:44.915292+00:00 https://reliance-das.adamplatform.eu/opensearch/search?datasetId=EU_CAMS_SURFACE_PM10_G&startDate=2018-09-01&endDate=2018-09-01 2021-11-09 15:51:59.534956+00:00 Flow to compute monthly map Daily PM10 concentration for 1st September 2018 over Europe Daily PM10 concentration Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services astronautics (general) 100.0 0.38756152987480164 astronautics 100.0 0.38756152987480164 POINT (38.0 38.0) False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:23:28.979805+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:19:16.618594+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:06:58.516914+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl map 13.333333333333334 12.8 False https://w3id.org/ro-id/164e222b-0bdd-4638-93e7-010bad13d655 2021-11-09 16:15:26.873492+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl 9th November - Copernicus Atmosphere Monitoring Service Data Cube Research Object - snapshot. 30.330330330330334 30.3 map of PM10 9.246231155778894 9.2 aim 31.499312242090785 22.9 research 39.61485557083906 28.8 Copernicus Atmosphere Monitoring Service 8.229166666666666 7.9 This Research Object demonstrate how to compute monthly map of PM10 over your country - modified 69.66966966966967 69.6 country 11.829436038514443 8.6 object 25.208333333333332 24.2 country 8.541666666666666 8.2 earth sciences 100.0 0.7866491675376892 atmospheric sciences 100.0 0.7866491675376892 Nov-9 research object 83.11557788944724 82.7 data cube research object 1.0050251256281406 1.0 data cube 0.4020100502512563 0.4 map 17.05639614855571 12.4 research 31.145833333333332 29.9 PM10 13.541666666666666 13.0 Raul Palma Earth sciences related modelling Campi Flegrei Caldera reliance-jupyter of the Adam platform global positioning system deformations from InSAR Italy deformation Italy SAR interferometry Elisa Trasatti service-account-enrichment 2832410 https://api.rohub.org/api/ros/7749bd2b-72f8-4a29-8565-20a04f419b3a/crate/download/ 2021-11-09 21:43:28.116800+00:00 2025-03-05 00:48:32.542896+00:00 2021-11-09 21:43:28.116800+00:00 This Research Object has been created by the reliance-jupyter of the ADAM platform application/ld+json https://w3id.org/ro-id/7749bd2b-72f8-4a29-8565-20a04f419b3a Campi Flegrei Caldera (Italy) 2011-2013 deformations from InSAR and GPS and related modelling MANUAL Trasatti, Elisa. "Campi Flegrei Caldera (Italy) 2011-2013 deformations from InSAR and GPS and related modelling." ROHub. 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"Campi Flegrei Caldera (Italy) 2011-2013 deformations from InSAR and GPS and related modelling." ROHub. Nov 10 ,2021. https://w3id.org/ro-id/cdcd3c0a-f280-40de-93e6-9a4ba2be6f67. 170405 https://api.rohub.org/api/resources/0f949b3c-9768-4673-90fb-7e902c955ce8/download/ 2021-11-10 13:18:39.938042+00:00 2021-11-10 13:18:39.939155+00:00 image/png 1D and 2D probability distributions of the parameters of the volcanic source at Campi Flegrei 2021-11-10 13:18:39.938042+00:00 3726 https://api.rohub.org/api/resources/1d463d07-79d9-42e2-876e-959b9e92a159/download/ 2021-11-10 13:18:56.091407+00:00 2021-11-10 13:18:56.092247+00:00 Log of the run 2021-11-10 13:18:56.091407+00:00 2520946 https://api.rohub.org/api/resources/1e512641-0020-4582-a953-321bc634c46c/download/ 2021-11-10 13:19:09.955332+00:00 2021-11-10 13:19:09.955929+00:00 application/zip Zip with all the products of the run 2021-11-10 13:19:09.955332+00:00 244961 https://api.rohub.org/api/resources/7a4ded13-3cd6-4eeb-ba21-f86e949a81ae/download/ 2021-11-10 13:18:49.400757+00:00 2021-11-10 13:18:49.401941+00:00 image/png Data - Model - Residuals with InSAR data in ascending orbit 2021-11-10 13:18:49.400757+00:00 211879 https://api.rohub.org/api/resources/83dcf926-a02f-4e25-9d7e-b5be1056c293/download/ 2021-11-10 13:18:53.564524+00:00 2021-11-10 13:18:53.565560+00:00 image/png Data - Model - Residuals with InSAR data in descending orbit 2021-11-10 13:18:53.564524+00:00 83769 https://api.rohub.org/api/resources/8915623b-19c6-4272-a0c2-a0a34a0e960e/download/ 2021-11-10 13:18:44.502860+00:00 2021-11-10 13:18:44.503535+00:00 image/png Parameters vs sampling 2021-11-10 13:18:44.502860+00:00 933718 https://api.rohub.org/api/resources/9c7d6f32-a8f2-44e4-98ff-5bc1fd426857/download/ 2021-11-10 13:18:19.047026+00:00 2021-11-10 13:18:19.048282+00:00 Jupyter Notebook for running the VSM code with geodetic data in RELIANCE VSM test with magmatic point-source 2021-11-10 13:18:19.047026+00:00 Earth sciences University of Geneva luca.caricchi@unige.ch Luca Caricchi 0000-0001-9051-2621 Università Roma Tre, Rome, Italy acocella@uniroma3.it Valerio Acocella 0000-0002-1258-9401 Istituto Nazionale di Geofisica e Vulcanologia elisa.trasatti@ingv.it Elisa Trasatti 0000-0002-2983-045X Istituto Nazionale di Geofisica e Vulcanologia mauro.divito@ingv.it Mauro Antonio Di Vito 0000-0002-7913-9149 00qps9a02 Istituto Nazionale di Geofisica e Vulcanologia volcanic island 25.859697386519944 18.8 Modelling of 27 years of subsidence at the volcanic island of Ischia (Italy) detected by in situ data. 70.07007007007006 70.0 27 years volcanic island of Ischia 53.15315315315315 53.1 earth sciences 100.0 0.9417281150817871 year 5.364511691884457 3.9 data 7.547169811320754 7.2 subsidence 13.480055020632738 9.8 Ischia 21.069182389937108 20.1 Italy 13.10272536687631 12.5 Italy https://www.wikidata.org/wiki/Q38 POLYGON ((13.86035406119117 40.696523807270744, 13.956906959160799 40.696523807270744, 13.956906959160799 40.75439703959889, 13.86035406119117 40.75439703959889, 13.86035406119117 40.696523807270744)) POLYGON ((13.86035406119117 40.696523807270744, 13.956906959160799 40.696523807270744, 13.956906959160799 40.75439703959889, 13.86035406119117 40.75439703959889, 13.86035406119117 40.696523807270744)) 13.86035406119117 40.696523807270744, 13.956906959160799 40.696523807270744, 13.956906959160799 40.75439703959889, 13.86035406119117 40.75439703959889, 13.86035406119117 40.696523807270744 ef66b67e-eb6f-4cb4-9c32-ce81cb974890 POLYGON ((13.86035406119117 40.696523807270744, 13.956906959160799 40.696523807270744, 13.956906959160799 40.75439703959889, 13.86035406119117 40.75439703959889, 13.86035406119117 40.696523807270744)) service-account-enrichment https://doi.org/10.24424/t83f-5t97 2827780 https://api.rohub.org/api/ros/6d6e540b-1b52-4ad1-8906-33d3144fce68/crate/download/ 2021-11-10 14:04:01.839381+00:00 2025-03-05 00:56:54.690717+00:00 2021-11-10 14:04:01.839381+00:00 This Research Object has been created by the Reliance-Jupyter of EGI application/ld+json https://w3id.org/ro-id/6d6e540b-1b52-4ad1-8906-33d3144fce68 Volcano leveling Result Modelling of 27 years of subsidence at the volcanic island of Ischia (Italy) detected by in situ data MANUAL https://w3id.org/ro-id/6d6e540b-1b52-4ad1-8906-33d3144fce68/fbefb25a-8715-47bf-b75e-19f621fb2dc4 https://w3id.org/ro-id/6466b720-9ce3-4f7f-b9f8-4956dbf4dc0c https://w3id.org/ro-id/e0330803-73ad-4d49-86ed-a6e6a924eda4 https://w3id.org/ro-id/07bc74ae-e25b-4023-8442-e1b7e73d9261 https://w3id.org/ro-id/3d7e5ba1-5c74-4158-8bd8-e6112258f0d7 https://w3id.org/ro-id/46789d5f-ccdc-4036-bbb4-28424c54fe09 https://w3id.org/ro-id/9e8853db-6b27-449d-805d-845efd6984a5 https://w3id.org/ro-id/adb0ac90-64b1-4d23-b47b-9164392fcbc5 https://w3id.org/ro-id/c5bfcb7f-a5a2-44d1-a3da-391ef32b7b9f https://w3id.org/ro-id/3c31e98b-6dea-4945-89c9-0ae9ae6a90dc https://w3id.org/ro-id/9d3609de-44e5-496e-801a-b35b8f402d5a https://w3id.org/ro-id/3ed77bac-bb01-4caf-97a1-c7b4fd9433c5 https://w3id.org/ro-id/4aea398d-5cf8-43c3-926a-cb8b31a0f962 https://w3id.org/ro-id/5906cf42-b31d-450c-81c6-03ad7ba92564 https://w3id.org/ro-id/74c15335-0416-4428-b959-000c3737852f https://w3id.org/ro-id/7e51563c-7edb-4cd0-bc81-e8bdcb8d8405 https://w3id.org/ro-id/7ebb071c-d228-4e94-a991-8e7578db493c https://w3id.org/ro-id/ddef59e5-6a6d-4b56-9d9d-44008e4b0f39 https://w3id.org/ro-id/c5fcf459-f73f-4469-97cc-0dea4f8bb391 https://w3id.org/ro-id/f5e10a15-3760-48fb-b7f0-13f88f3872ad https://w3id.org/ro-id/2f7bdbd9-87ab-42ae-af43-b9e912b19a93 https://w3id.org/ro-id/b4658004-981c-4c59-b109-d1b22cd2be54 https://w3id.org/ro-id/bb4b34b2-562b-492e-9fa6-2fadaf5d3588 https://w3id.org/ro-id/18b2cfc9-35e1-4e76-86b0-e6a7ab00756f https://w3id.org/ro-id/8a146c00-b8ad-4cb7-afe4-d822c0464815 https://w3id.org/ro-id/2a1be5b0-96fb-4edb-8e21-f857644e4fb0 Trasatti, Elisa, Mauro Antonio Di Vito, Valerio Acocella, Ciro Ricco, Carlo Del Gaudio, and Luca Caricchi. "Modelling of 27 years of subsidence at the volcanic island of Ischia (Italy) detected by in situ data." ROHub. Nov 10 ,2021. https://w3id.org/ro-id/6d6e540b-1b52-4ad1-8906-33d3144fce68. output input tool biblio 3820256 https://api.rohub.org/api/resources/1cafa591-d450-4b5c-8cdf-723411f0c3ca/download/ 2021-11-10 14:05:12.625391+00:00 2021-11-10 14:05:12.626031+00:00 application/zip Zip with all the products of the run 2021-11-10 14:05:12.625391+00:00 135334 https://api.rohub.org/api/resources/3ac62fa4-8746-4bd9-8174-82afeec3ca1f/download/ 2021-11-10 14:04:21.153150+00:00 2021-11-10 14:04:21.153919+00:00 image/png Parameters vs sampling 2021-11-10 14:04:21.153150+00:00 53779 https://api.rohub.org/api/resources/3cb3ca57-dc22-4606-bb35-13787adc9cf2/download/ 2021-11-10 14:04:30.759876+00:00 2021-11-10 14:04:30.760370+00:00 image/png Data - Model - Residuals with levelling data 2021-11-10 14:04:30.759876+00:00 552107 https://api.rohub.org/api/resources/60e61978-1881-46f3-9cd5-d0d529fd1350/download/ 2021-11-10 14:04:15.961877+00:00 2021-11-10 14:04:15.962362+00:00 image/png 1D and 2D probability distributions of the parameters of the volcanic source at Campi Flegrei 2021-11-10 14:04:15.961877+00:00 4361 https://api.rohub.org/api/resources/770ea2d3-2915-4e92-9c37-72548ef92226/download/ 2021-11-10 14:04:51.598381+00:00 2021-11-10 14:04:51.598790+00:00 Log of the run 2021-11-10 14:04:51.598381+00:00 25040 https://api.rohub.org/api/resources/a0ba9343-8e70-47ee-a276-6be0861e4212/download/ 2021-11-10 14:04:12.636681+00:00 2022-06-20 12:54:18.454310+00:00 Jupyter Notebook for running the VSM code with geodetic data in RELIANCE Notebook with the modelling of the subsidence 2021-11-10 14:04:12.636681+00:00 Research Object 12.578616352201257 12.0 volcanic island 19.39203354297694 18.5 subsidence 10.377358490566037 9.9 This Research Object has been created by the Reliance-Jupyter of EGI 29.92992992992993 29.9 geology 100.0 0.9417281150817871 Ischia 27.92297111416781 20.3 information 9.766162310866575 7.1 Reliance-Jupyter of EGI 37.73773773773774 37.7 years of subsidence 9.10910910910911 9.1 Italy 17.606602475928472 12.8 astrophysics 100.0 0.15075749158859253 Reliance-Jupyter 15.932914046121592 15.2 Ischia https://www.wikidata.org/wiki/Q189387 space sciences 100.0 0.15075749158859253 Istituto Nazionale di Geofisica e Vulcanologia carlo.delgaudio@ingv.it Carlo Del Gaudio Istituto Nazionale di Geofisica e Vulcanologia ciro.ricco@ingv.it Ciro Ricco Earth sciences contain result results from the run VSM code inflation remote sensing Italy Reliance-Jupyter inflation phase Etna computer programming computer code outcome information execution Reliance-Jupyter of the Adam platform phase Italy Elisa Trasatti POLYGON ((14.789438285757985 37.5431884117504, 15.2128549247466 37.5431884117504, 15.2128549247466 37.88323514684568, 14.789438285757985 37.88323514684568, 14.789438285757985 37.5431884117504)) POLYGON ((14.789438285757985 37.5431884117504, 15.2128549247466 37.5431884117504, 15.2128549247466 37.88323514684568, 14.789438285757985 37.88323514684568, 14.789438285757985 37.5431884117504)) 14.789438285757985 37.5431884117504, 15.2128549247466 37.5431884117504, 15.2128549247466 37.88323514684568, 14.789438285757985 37.88323514684568, 14.789438285757985 37.5431884117504 80b11621-f3c8-4eb7-8b77-d30322b67abe POLYGON ((14.789438285757985 37.5431884117504, 15.2128549247466 37.5431884117504, 15.2128549247466 37.88323514684568, 14.789438285757985 37.88323514684568, 14.789438285757985 37.5431884117504)) service-account-enrichment 3286201 https://api.rohub.org/api/ros/d86d66ff-c096-4e78-b02a-95cfeba21e35/crate/download/ 2021-11-10 14:08:28.357310+00:00 2025-03-05 00:56:54.894202+00:00 2021-11-10 14:08:28.357310+00:00 This Research Object has been created by the Reliance-Jupyter of the ADAM platform. It contains results from the run of the VSM code, related to the modelling of the inflation phase at Mt Etna during 1993-1997. application/ld+json https://w3id.org/ro-id/d86d66ff-c096-4e78-b02a-95cfeba21e35 Modelling of the 1993-1997 inflation at Mt Etna (Italy) detected by remote sensing and in situ data MANUAL https://w3id.org/ro-id/d86d66ff-c096-4e78-b02a-95cfeba21e35/18a09710-664d-4c6f-99f1-7a15254c5dc9 Trasatti, Elisa. "Modelling of the 1993-1997 inflation at Mt Etna (Italy) detected by remote sensing and in situ data." ROHub. Nov 10 ,2021. https://w3id.org/ro-id/d86d66ff-c096-4e78-b02a-95cfeba21e35. 4139 https://api.rohub.org/api/resources/53f4412b-dd1c-4ced-8dda-8c1e5949e098/download/ 2021-11-10 14:11:18.006154+00:00 2021-11-10 14:11:18.006723+00:00 Log of the run 2021-11-10 14:11:18.006154+00:00 428608 https://api.rohub.org/api/resources/8537022e-8ccf-4b61-824f-4f0a62c5f3e0/download/ 2021-11-10 14:10:33.748214+00:00 2021-11-10 14:10:33.748767+00:00 image/png 1D and 2D probability distributions of the parameters of the volcanic source inverted 2021-11-10 14:10:33.748214+00:00 3166105 https://api.rohub.org/api/resources/922317fa-6809-4ab7-9e61-fb87065fa195/download/ 2021-11-10 14:11:26.907609+00:00 2021-11-10 14:11:26.908029+00:00 application/zip Zip with all the products of the run 2021-11-10 14:11:26.907609+00:00 136869 https://api.rohub.org/api/resources/aeb42f8f-d37d-46a8-a7e2-f5705b56064f/download/ 2021-11-10 14:10:58.826126+00:00 2021-11-10 14:10:58.826667+00:00 image/png Data - Model - Residuals with InSAR ascending data 2021-11-10 14:10:58.826126+00:00 135022 https://api.rohub.org/api/resources/b3ebf83b-498f-4e98-b68f-55d882b0bbce/download/ 2021-11-10 14:11:12.991749+00:00 2021-11-10 14:11:12.992268+00:00 image/png Data - Model - Residuals with InSAR data in descending orbit 2021-11-10 14:11:12.991749+00:00 762325 https://api.rohub.org/api/resources/f6dd8b49-579f-4a2e-9ddb-14644a615353/download/ 2021-11-10 14:10:03.076073+00:00 2021-11-10 14:10:03.076580+00:00 Jupyter Notebook for running the VSM code with geodetic data in RELIANCE Notebook with the modelling of the subsidence 2021-11-10 14:10:03.076073+00:00 127177 https://api.rohub.org/api/resources/f830b192-ccff-46b2-ab45-c8a7f626877d/download/ 2021-11-10 14:10:39.079774+00:00 2021-11-10 14:10:39.080250+00:00 image/png Parameters vs sampling 2021-11-10 14:10:39.079774+00:00 Earth sciences contain result results from the run VSM code inflation remote sensing Greece Reliance-Jupyter inflation phase Greece computer programming computer code outcome execution Reliance-Jupyter of the Adam platform phase Elisa Trasatti POLYGON ((25.322162850585062 36.33140654355215, 25.486383580741393 36.33140654355215, 25.486383580741393 36.48215433130998, 25.322162850585062 36.48215433130998, 25.322162850585062 36.33140654355215)) 1fe02f1f-6771-44d2-a065-a9d766f62992 POLYGON ((25.322162850585062 36.33140654355215, 25.486383580741393 36.33140654355215, 25.486383580741393 36.48215433130998, 25.322162850585062 36.48215433130998, 25.322162850585062 36.33140654355215)) POLYGON ((25.322162850585062 36.33140654355215, 25.486383580741393 36.33140654355215, 25.486383580741393 36.48215433130998, 25.322162850585062 36.48215433130998, 25.322162850585062 36.33140654355215)) 25.322162850585062 36.33140654355215, 25.486383580741393 36.33140654355215, 25.486383580741393 36.48215433130998, 25.322162850585062 36.48215433130998, 25.322162850585062 36.33140654355215 service-account-enrichment 3409581 https://api.rohub.org/api/ros/09d35670-c07e-41f2-94f9-c58119e33b60/crate/download/ 2021-11-10 14:46:19.306390+00:00 2025-03-05 00:56:55.080964+00:00 2021-11-10 14:46:19.306390+00:00 This Research Object has been created by the Reliance-Jupyter of the ADAM platform. It contains results from the run of the VSM code, related to the modelling of the inflation phase at Santorni during 2011-2012. application/ld+json https://w3id.org/ro-id/09d35670-c07e-41f2-94f9-c58119e33b60 Modelling of the 2011-2012 inflation at Santorini (Greece) detected by remote sensing and GPS data MANUAL https://w3id.org/ro-id/09d35670-c07e-41f2-94f9-c58119e33b60/1e8a8a9d-e38a-4c27-9710-128d96850457 Trasatti, Elisa. "Modelling of the 2011-2012 inflation at Santorini (Greece) detected by remote sensing and GPS data." ROHub. Nov 10 ,2021. https://w3id.org/ro-id/09d35670-c07e-41f2-94f9-c58119e33b60. 126802 https://api.rohub.org/api/resources/3e6bed52-f06d-435a-aef4-6da2af43bbcc/download/ 2021-11-10 14:46:57.398712+00:00 2021-11-10 14:46:57.399208+00:00 image/png Parameters vs sampling 2021-11-10 14:46:57.398712+00:00 850211 https://api.rohub.org/api/resources/406f9127-f524-44fb-98d2-a770d674dd0f/download/ 2021-11-10 14:46:31.188381+00:00 2021-11-10 14:46:31.188836+00:00 Jupyter Notebook for running the VSM code with geodetic data in RELIANCE Notebook with the modelling by VSM 2021-11-10 14:46:31.188381+00:00 3940 https://api.rohub.org/api/resources/4c2d9480-d9bf-4c6d-ba47-cb557f9ab983/download/ 2021-11-10 14:47:12.342737+00:00 2021-11-10 14:47:12.343145+00:00 Log of the run 2021-11-10 14:47:12.342737+00:00 132247 https://api.rohub.org/api/resources/67a9c77d-6289-4f00-bb9b-dc42817c713d/download/ 2021-11-10 14:47:07.379566+00:00 2021-11-10 14:47:07.380054+00:00 image/png Data - Model - Residuals with InSAR descending data 2021-11-10 14:47:07.379566+00:00 3376102 https://api.rohub.org/api/resources/7ae57346-bafb-4d9a-86a4-c0074c2a2188/download/ 2021-11-10 14:47:17.424275+00:00 2021-11-10 14:47:17.425094+00:00 application/zip Zip with all the products of the run 2021-11-10 14:47:17.424275+00:00 511548 https://api.rohub.org/api/resources/901a4b7d-cd5a-4fd7-bd6e-a5678eb99caa/download/ 2021-11-10 14:46:34.789073+00:00 2021-11-10 14:46:34.789703+00:00 image/png 1D and 2D probability distributions of the parameters of the volcanic source inverted 2021-11-10 14:46:34.789073+00:00 https://notebooks.egi.eu/user/cf05fc266133bac809a5c002fa8106f884c1bd112be246b2a54bcee6793fe8c6@egi.eu/doc/tree/MYWORK/USECASE/Santorini_EGI/santorini_new.ipynb 2023-05-12 11:51:55.411234+00:00 2023-05-12 11:51:56.960997+00:00 test notebook test 2023-05-12 11:51:55.411234+00:00 Earth sciences Nyiragongo Volcano VSM code remote sensing Reliance-Jupyter remote sensing eruption surface computer code outcome deformation Democrat modelling of the dyke Reliance-Jupyter of the Adam platform surface deformation Elisa Trasatti POLYGON ((29.07168896638874 -1.7627948920611483, 29.43132472659933 -1.7627948920611483, 29.43132472659933 -1.4914514445072093, 29.07168896638874 -1.4914514445072093, 29.07168896638874 -1.7627948920611483)) POLYGON ((29.07168896638874 -1.7627948920611483, 29.43132472659933 -1.7627948920611483, 29.43132472659933 -1.4914514445072093, 29.07168896638874 -1.4914514445072093, 29.07168896638874 -1.7627948920611483)) 29.07168896638874 -1.7627948920611483, 29.43132472659933 -1.7627948920611483, 29.43132472659933 -1.4914514445072093, 29.07168896638874 -1.4914514445072093, 29.07168896638874 -1.7627948920611483 e2fa2315-46f7-4747-9d6f-4927a5464676 POLYGON ((29.07168896638874 -1.7627948920611483, 29.43132472659933 -1.7627948920611483, 29.43132472659933 -1.4914514445072093, 29.07168896638874 -1.4914514445072093, 29.07168896638874 -1.7627948920611483)) service-account-enrichment 4160624 https://api.rohub.org/api/ros/0938a79e-f5a6-4481-b01a-a82e17f7d51a/crate/download/ 2021-11-10 18:55:24.946015+00:00 2025-03-05 01:23:34.827004+00:00 2021-11-10 18:55:24.946015+00:00 This Research Object has been created by the Reliance-Jupyter of the ADAM platform. It contains results from the run of the VSM code, related to the modelling of the dyke feeding the eruption of 22 May 2021 at Nyiragongo Volcano (Dem. Rep. Congo) based on remote sensing data (Sentinel-1). application/ld+json https://w3id.org/ro-id/0938a79e-f5a6-4481-b01a-a82e17f7d51a Surface deformation related to the eruption (22 May 2021) of Nyiragongo Volcano (Dem. Rep. Congo) detected by remote sensing MANUAL https://w3id.org/ro-id/0938a79e-f5a6-4481-b01a-a82e17f7d51a/45e23fea-a031-464f-bb44-49b70d4dfcf0 Trasatti, Elisa. "Surface deformation related to the eruption (22 May 2021) of Nyiragongo Volcano (Dem. Rep. Congo) detected by remote sensing." ROHub. Nov 10 ,2021. https://w3id.org/ro-id/0938a79e-f5a6-4481-b01a-a82e17f7d51a. 126812 https://api.rohub.org/api/resources/4c97c24a-edcd-41ec-9374-6e449d8a6390/download/ 2021-11-10 18:55:44.470254+00:00 2021-11-10 18:55:44.470988+00:00 image/png Data - Model - Residuals with InSAR ascending data 2021-11-10 18:55:44.470254+00:00 364886 https://api.rohub.org/api/resources/57e2fb84-65a4-4d0e-bfa3-cca1ca221ce4/download/ 2021-11-10 18:55:35.925838+00:00 2021-11-10 18:55:35.926284+00:00 image/png 1D and 2D probability distributions of the parameters of the volcanic source inverted 2021-11-10 18:55:35.925838+00:00 112025 https://api.rohub.org/api/resources/6ce9349d-e00d-4294-af9b-54d6b0a7f4c9/download/ 2021-11-10 18:55:51.235851+00:00 2021-11-10 18:55:51.236356+00:00 image/png Data - Model - Residuals with InSAR data in descending orbit 2021-11-10 18:55:51.235851+00:00 626591 https://api.rohub.org/api/resources/80f4e1e9-6ffa-422a-85c7-7cfea5469c07/download/ 2021-11-10 18:55:33.917130+00:00 2021-11-10 18:55:33.917540+00:00 Jupyter Notebook for running the VSM code with geodetic data in RELIANCE Notebook with the modelling by VSM 2021-11-10 18:55:33.917130+00:00 5838139 https://api.rohub.org/api/resources/b886e4a4-30e8-4b0a-915f-badd67a234e4/download/ 2021-11-10 18:56:04.000461+00:00 2021-11-10 18:56:04.000974+00:00 application/zip Zip with all the products of the run 2021-11-10 18:56:04.000461+00:00 4041 https://api.rohub.org/api/resources/f97ad854-a149-46f8-808c-856e6063b403/download/ 2021-11-10 18:55:56.934212+00:00 2021-11-10 18:55:56.934977+00:00 Log of the run 2021-11-10 18:55:56.934212+00:00 104628 https://api.rohub.org/api/resources/fb405f3c-f2ed-4439-8e97-14e297de7caa/download/ 2021-11-10 18:55:37.649634+00:00 2021-11-10 18:55:37.650055+00:00 image/png Parameters vs sampling 2021-11-10 18:55:37.649634+00:00 Earth sciences 10.13039/501100000781 European Commission 101017502 RELIANCE data cube research object 1.0050251256281406 1.0 False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:18:39.029666+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:38:47.222796+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl earth sciences 100.0 0.7866491675376892 map 17.05639614855571 12.4 research 39.61485557083906 28.8 research 31.145833333333332 29.9 POINT (38.0 38.0) POINT (38.0 38.0) POLYGON ((14.049395 40.779358, 14.240586 40.779358, 14.240586 40.912968, 14.049395 40.912968, 14.049395 40.779358)) POLYGON ((14.049395 40.779358, 14.240586 40.779358, 14.240586 40.912968, 14.049395 40.912968, 14.049395 40.779358)) False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:23:28.979805+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl astronautics (general) 100.0 0.38756152987480164 astronautics 100.0 0.38756152987480164 False https://w3id.org/ro-id/7740459a-b9fc-411b-88af-763a0de9d9b1 2021-11-09 16:19:16.618594+00:00 https://w3id.org/ro-id/users/rpalma%40man.poznan.pl This Research Object demonstrate how to compute monthly map of PM10 over your country - 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 Earth sciences api Ocean Color Data max value research maximum value resolution aim minim rohub api Ocean Color Data service-account-enrichment 2382 https://api.rohub.org/api/ros/da5df5a7-697d-4d79-991e-c3a7960ae6bc/crate/download/ 2021-11-15 12:59:05.887860+00:00 2025-03-05 00:53:44.167018+00:00 2021-11-15 12:59:05.887860+00:00 Ocean Color Data: Modis-aqua_chl-a (monthly) - time range: 2002-07-04T00:40:05Z/2018-12-01T00:10:01Z - min/max Value: 0/99 - DataType: Float32 - Resolution: 0 - application/ld+json https://w3id.org/ro-id/da5df5a7-697d-4d79-991e-c3a7960ae6bc How to create a Research Object using adamapi and rohub api MANUAL Mantovani, Simone. "How to create a Research Object using adamapi and rohub api." ROHub. Nov 15 ,2021. https://w3id.org/ro-id/da5df5a7-697d-4d79-991e-c3a7960ae6bc. Simone Mantovani Earth sciences api Ocean Color Data max value research maximum value resolution aim minim rohub api Ocean Color Data service-account-enrichment 2241416 https://api.rohub.org/api/ros/bc628c46-d12a-469d-be4b-81769889d1c8/crate/download/ 2021-11-15 13:33:12.823393+00:00 2025-03-05 00:52:28.430114+00:00 2021-11-15 13:33:12.823393+00:00 Ocean Color Data: Modis-aqua_chl-a (yearly) - time range: 2002-07-04T00:40:05Z/2018-01-01T00:00:01Z - min/max Value: 0/20 - DataType: Float32 - Resolution: 0 - application/ld+json https://w3id.org/ro-id/bc628c46-d12a-469d-be4b-81769889d1c8 How to create a Research Object using adamapi and rohub api MANUAL Mantovani, Simone. "How to create a Research Object using adamapi and rohub api." ROHub. Nov 15 ,2021. https://w3id.org/ro-id/bc628c46-d12a-469d-be4b-81769889d1c8. 80809825 https://api.rohub.org/api/resources/0d656fc1-1336-4bbf-80a0-84a4e2748e70/download/ 2021-11-15 13:38:48.882003+00:00 2021-11-15 13:38:48.882669+00:00 60e861576dfebc0806d14bcd 2021-11-15 13:38:48.882003+00:00 1114131 https://api.rohub.org/api/resources/11d1f574-05ca-4725-8738-d698516fbe0c/download/ 2021-11-15 13:36:57.625498+00:00 2021-11-15 13:36:57.625931+00:00 6174373a961c231550d5dd30 2021-11-15 13:36:57.625498+00:00 1158805 https://api.rohub.org/api/resources/161dcecf-7ccb-4615-8b37-824b8bf7a2f6/download/ 2021-11-15 13:36:53.864095+00:00 2021-11-15 13:36:53.865068+00:00 6174373a961c231550d5dd21 2021-11-15 13:36:53.864095+00:00 1161817 https://api.rohub.org/api/resources/28a8ca52-16df-4778-a691-83052f443507/download/ 2021-11-15 13:36:47.922391+00:00 2021-11-15 13:36:47.923137+00:00 6174373a961c231550d5dcdf 2021-11-15 13:36:47.922391+00:00 75004601 https://api.rohub.org/api/resources/2e7a4609-d4a6-4659-a54f-351da2fac836/download/ 2021-11-15 13:37:42.049650+00:00 2021-11-15 13:37:42.050212+00:00 60e8610f6dfebc0806d13375 2021-11-15 13:37:42.049650+00:00 1149911 https://api.rohub.org/api/resources/460991e4-0b7a-4dfb-b9b0-5d8b80fc69fc/download/ 2021-11-15 13:37:10.445527+00:00 2021-11-15 13:37:10.446177+00:00 6174373a961c231550d5dd81 2021-11-15 13:37:10.445527+00:00 1149456 https://api.rohub.org/api/resources/46134ac8-09fb-40ba-968a-021d032b4e63/download/ 2021-11-15 13:36:44.009644+00:00 2021-11-15 13:36:44.010617+00:00 6174373a961c231550d5dcc8 2021-11-15 13:36:44.009644+00:00 1162143 https://api.rohub.org/api/resources/4852c36d-b5e9-4850-aef4-01999d8e9c1b/download/ 2021-11-15 13:36:55.740296+00:00 2021-11-15 13:36:55.740828+00:00 6174373a961c231550d5dd2e 2021-11-15 13:36:55.740296+00:00 72245695 https://api.rohub.org/api/resources/58249660-a21c-4784-8505-70c1ffc143ee/download/ 2021-11-15 13:39:13.697139+00:00 2021-11-15 13:39:13.697629+00:00 60e861706dfebc0806d152a4 2021-11-15 13:39:13.697139+00:00 1102873 https://api.rohub.org/api/resources/62eba05f-201c-433c-bfca-29532cadbd26/download/ 2021-11-15 13:37:12.574607+00:00 2021-11-15 13:37:12.575362+00:00 6174373a961c231550d5dd8d 2021-11-15 13:37:12.574607+00:00 1156261 https://api.rohub.org/api/resources/6c061fca-d909-413c-a860-18425590cda3/download/ 2021-11-15 13:36:49.870144+00:00 2021-11-15 13:36:49.871105+00:00 6174373a961c231550d5dce3 2021-11-15 13:36:49.870144+00:00 1145690 https://api.rohub.org/api/resources/727672ef-60e2-4642-83ee-59fa055ec512/download/ 2021-11-15 13:37:04.128617+00:00 2021-11-15 13:37:04.129434+00:00 6174373a961c231550d5dd5d 2021-11-15 13:37:04.128617+00:00 1160790 https://api.rohub.org/api/resources/82efb7c3-2865-4056-8501-a291636cd786/download/ 2021-11-15 13:36:45.955791+00:00 2021-11-15 13:36:45.956517+00:00 6174373a961c231550d5dccb 2021-11-15 13:36:45.955791+00:00 1169460 https://api.rohub.org/api/resources/86d5fdc1-cc86-4244-91b9-c54104bbf712/download/ 2021-11-15 13:36:51.895018+00:00 2021-11-15 13:36:51.895670+00:00 6174373a961c231550d5dd16 2021-11-15 13:36:51.895018+00:00 1162712 https://api.rohub.org/api/resources/93f42cb5-979d-44dc-8ba7-cd5beef5f6ca/download/ 2021-11-15 13:36:41.983263+00:00 2021-11-15 13:36:41.983876+00:00 6172e597961c23155097d053 2021-11-15 13:36:41.983263+00:00 1164756 https://api.rohub.org/api/resources/a3f35032-42ac-4ab6-ac98-80e4ac356ce5/download/ 2021-11-15 13:37:26.091547+00:00 2021-11-15 13:37:26.092099+00:00 6174373b961c231550d5ddf9 2021-11-15 13:37:26.091547+00:00 1128330 https://api.rohub.org/api/resources/a9b3f213-7e07-48cc-a7e1-fb3b8f4819ce/download/ 2021-11-15 13:37:19.811718+00:00 2021-11-15 13:37:19.812232+00:00 6174373b961c231550d5ddbe 2021-11-15 13:37:19.811718+00:00 1161353 https://api.rohub.org/api/resources/ae8b3654-b53e-4443-9918-9abddb090b1a/download/ 2021-11-15 13:36:59.606969+00:00 2021-11-15 13:36:59.607554+00:00 6174373a961c231550d5dd46 2021-11-15 13:36:59.606969+00:00 97399037 https://api.rohub.org/api/resources/b6135108-95c8-4861-8051-a24b4842bacb/download/ 2021-11-15 13:40:29.340568+00:00 2021-11-15 13:40:29.341360+00:00 60e844116dfebc0806c6b2de 2021-11-15 13:40:29.340568+00:00 1128583 https://api.rohub.org/api/resources/c3880e18-1d48-4e9f-ac08-a5936e427064/download/ 2021-11-15 13:37:18.007258+00:00 2021-11-15 13:37:18.007784+00:00 6174373b961c231550d5ddbb 2021-11-15 13:37:18.007258+00:00 65489938 https://api.rohub.org/api/resources/caf6ecf6-87f7-42bd-af0b-541989402f74/download/ 2021-11-15 13:39:32.241620+00:00 2021-11-15 13:39:32.242205+00:00 60e861886dfebc0806d15cc3 2021-11-15 13:39:32.241620+00:00 1153542 https://api.rohub.org/api/resources/cd0e0f28-9596-4985-844e-5e00ffc150e3/download/ 2021-11-15 13:37:27.967924+00:00 2021-11-15 13:37:27.968424+00:00 6174373b961c231550d5ddfb 2021-11-15 13:37:27.967924+00:00 1118554 https://api.rohub.org/api/resources/cf838e40-412f-4c09-b21b-725d06ec1a5c/download/ 2021-11-15 13:37:08.365000+00:00 2021-11-15 13:37:08.365426+00:00 6174373a961c231550d5dd76 2021-11-15 13:37:08.365000+00:00 97622562 https://api.rohub.org/api/resources/dc1c5eba-67b5-487c-9709-bee093d56dc6/download/ 2021-11-15 13:40:00.744334+00:00 2021-11-15 13:40:00.744729+00:00 60e842726dfebc0806c5e3ae 2021-11-15 13:40:00.744334+00:00 1145892 https://api.rohub.org/api/resources/dce9876e-00dd-43dc-8f57-9e4a76d3e393/download/ 2021-11-15 13:37:01.813730+00:00 2021-11-15 13:37:01.814459+00:00 6174373a961c231550d5dd51 2021-11-15 13:37:01.813730+00:00 1115974 https://api.rohub.org/api/resources/dfa2d334-1ecf-4413-9199-55c35f2a9031/download/ 2021-11-15 13:37:14.411826+00:00 2021-11-15 13:37:14.412324+00:00 6174373a961c231550d5dd90 2021-11-15 13:37:14.411826+00:00 74787510 https://api.rohub.org/api/resources/dfd985cd-5d53-4d76-8c14-bbcefdda9ad9/download/ 2021-11-15 13:38:02.466519+00:00 2021-11-15 13:38:02.467052+00:00 60e861266dfebc0806d13b21 2021-11-15 13:38:02.466519+00:00 1133276 https://api.rohub.org/api/resources/e096b2ab-48ff-428a-beae-a76fe5579eed/download/ 2021-11-15 13:37:22.183715+00:00 2021-11-15 13:37:22.184240+00:00 6174373b961c231550d5ddc3 2021-11-15 13:37:22.183715+00:00 1127176 https://api.rohub.org/api/resources/eedf9e42-587c-4c6e-9ff2-2e4313949fab/download/ 2021-11-15 13:37:23.973241+00:00 2021-11-15 13:37:23.973805+00:00 6174373b961c231550d5dde3 2021-11-15 13:37:23.973241+00:00 79288242 https://api.rohub.org/api/resources/f1f88426-3f5d-4745-96a6-b8b597020e16/download/ 2021-11-15 13:38:25.523959+00:00 2021-11-15 13:38:25.524386+00:00 60e8613f6dfebc0806d142cf 2021-11-15 13:38:25.523959+00:00 1152112 https://api.rohub.org/api/resources/f58a8974-e9e7-44d3-a6f6-5cce55de0ea4/download/ 2021-11-15 13:37:06.068025+00:00 2021-11-15 13:37:06.068680+00:00 6174373a961c231550d5dd5f 2021-11-15 13:37:06.068025+00:00 93834153 https://api.rohub.org/api/resources/fc7dcee4-23ff-478c-bd47-6338cca474b7/download/ 2021-11-15 13:41:02.902924+00:00 2021-11-15 13:41:02.903837+00:00 60e8459f6dfebc0806c783be 2021-11-15 13:41:02.902924+00:00 1128890 https://api.rohub.org/api/resources/fea3b14f-5b3c-4b6e-8096-4c12d086d069/download/ 2021-11-15 13:37:16.245804+00:00 2021-11-15 13:37:16.246255+00:00 6174373a961c231550d5ddb0 2021-11-15 13:37:16.245804+00:00 Simone Mantovani Applied sciences service-account-enrichment 87175059 https://api.rohub.org/api/ros/49f52ad4-bf2c-4f50-9242-a26962d5a7a2/crate/download/ 2021-11-17 14:41:26.637340+00:00 2025-03-05 00:56:08.393332+00:00 2021-11-17 14:41:26.637340+00:00 The pandemic influenced our way to live and our way to make science. It also gave to the scientific community the biggest experimental pool ever recorded to study the impact on the natural environment of reduced human activities. Studies reported wildfires diminishing, fisheries pausing, transport and commerce shrinking; people witnessed more wildlife sighting close to inhabited areas. Within a case study that is part of the EU H2020 project RELIANCE (www.reliance-project.eu), we built an inventory of all existing monitoring efforts of the marine environment that have been put in place in the seas and oceans, to assess the impact (or de-impact) of the Covid-19 - related lockdowns. application/ld+json https://w3id.org/ro-id/49f52ad4-bf2c-4f50-9242-a26962d5a7a2 anthropause covid lockdown marine mediterranean monitoring Marine Monitoring during the Anthropause MANUAL European Union case study impact marine environment monitoring natural environment pandemic scientific discipline sighting stock try wildfire wildlife environmental sciences Science and technology effort impact inventory marine environment monitoring sighting wildlife life sciences assess the impact case study efforts of the marine environment monitoring effort part of the EU It also gave to the scientific community the biggest experimental pool ever recorded to study the impact on the natural environment of reduced human activities. Studies reported wildfires diminishing, fisheries pausing, transport and commerce shrinking; people witnessed more wildlife sighting close to inhabited areas. Within a case study that is part of the EU H2020 project RELIANCE (www.reliance-project.eu) we built an inventory of all existing monitoring efforts of the marine environment that have been put in place in the seas and oceans, to assess the impact (or de-impact) of the Covid-19 - related lockdowns. hydrography medicine trade European Union Aracri, Simona, and KATRIN SCHROEDER. "Marine Monitoring during the Anthropause." ROHub. Nov 17 ,2021. https://w3id.org/ro-id/49f52ad4-bf2c-4f50-9242-a26962d5a7a2. Relevant publications and resources Bibliography 1894698 https://api.rohub.org/api/resources/0271a2c4-05d5-439f-94c0-28d5a15b7702/download/ 2022-07-15 15:06:49.016559+00:00 2022-07-15 15:06:51.083333+00:00 application/pdf manenti_2020_covid_wildlife.pdf 2022-07-15 15:06:49.016559+00:00 https://view.genial.ly/6183ac2f9e12570db4ed5c86/guide-reliance-questionnaire 2021-12-17 14:58:45.335836+00:00 2021-12-17 14:58:45.336269+00:00 Questionnaire Presentation 2021-12-17 14:58:45.335836+00:00 229321 https://api.rohub.org/api/resources/0cadf0b4-a2ad-45ec-a9ef-4eb35ac820b4/download/ 2021-12-20 11:03:55.116088+00:00 2021-12-20 11:03:55.117473+00:00 Parameters measured during the pandemic image/png Parameters 2021-12-20 11:03:55.116088+00:00 3887718 https://api.rohub.org/api/resources/238b12ab-cb62-4762-84b9-e11f5360ccce/download/ 2022-07-15 15:11:04.852945+00:00 2022-07-15 15:11:07.375750+00:00 application/pdf menut_2020_airquality.pdf 2022-07-15 15:11:04.852945+00:00 https://docs.google.com/forms/d/e/1FAIpQLSe9yaL-lV4bLW41sc482YxxV-TKYP6WtLK7iTVehgWORtTqrg/viewform?usp=sf_link 2021-12-17 14:59:59.823942+00:00 2021-12-17 14:59:59.824912+00:00 Questionnaire: Marine Monitoring during the Anthropause 2021-12-17 14:59:59.823942+00:00 133417 https://api.rohub.org/api/resources/38855e2a-7bf7-4e36-98c3-fc5f2bb44059/download/ 2021-12-17 14:56:44.479304+00:00 2021-12-17 14:56:44.480398+00:00 Graph showing under which category marine data collected during the pandemic fall under image/png Category of Data 2021-12-17 14:56:44.479304+00:00 2741137 https://api.rohub.org/api/resources/39912a0b-5e77-48ca-be44-fd79d4fee23a/download/ 2022-07-15 15:26:56.997151+00:00 2022-07-15 15:26:59.120532+00:00 application/pdf guardian_2020_covid19 fishery impact.pdf 2022-07-15 15:26:56.997151+00:00 53618744 https://api.rohub.org/api/resources/40407545-e56e-4f90-b316-f31ef7a21134/download/ 2022-07-15 15:05:18.018942+00:00 2022-07-15 15:05:21.461849+00:00 application/pdf swain_2022_lockdown_impact.PDF 2022-07-15 15:05:18.018942+00:00 7645354 https://api.rohub.org/api/resources/4d762cf1-6cb1-40eb-b5d1-c52b4d8a9476/download/ 2022-07-15 15:07:33.468813+00:00 2022-07-15 15:07:36.544867+00:00 application/pdf staffetta_2020_distribution_covid_seasonal.pdf 2022-07-15 15:07:33.468813+00:00 4484642 https://api.rohub.org/api/resources/68ccaaf1-803d-497b-a9bb-d4fa53a7b366/download/ 2022-07-15 15:07:12.509960+00:00 2022-07-15 15:07:14.809606+00:00 application/pdf poli_2020_seismic_covid.pdf 2022-07-15 15:07:12.509960+00:00 1990124 https://api.rohub.org/api/resources/69389f04-f5b2-48d7-b2bb-23ba3c7dfc0e/download/ 2022-07-15 15:21:54.703343+00:00 2022-07-15 15:21:56.938343+00:00 application/pdf hochman_2021_seasonal_influenza.pdf 2022-07-15 15:21:54.703343+00:00 163504 https://api.rohub.org/api/resources/7ea2af73-c122-4ad8-a8eb-32973c2a27cc/download/ 2022-07-15 15:22:58.682472+00:00 2022-07-15 15:23:00.718605+00:00 application/pdf ICES_2020_impact covid19 fishery.pdf 2022-07-15 15:22:58.682472+00:00 2640313 https://api.rohub.org/api/resources/8fd14751-bdb5-4981-84c1-a6c526a72f83/download/ 2022-07-15 15:22:37.526834+00:00 2022-07-15 15:22:39.840197+00:00 application/pdf piccinini_2020_northern_italy.pdf 2022-07-15 15:22:37.526834+00:00 13091837 https://api.rohub.org/api/resources/92fc88d3-1ead-4953-9384-ed3af9467bea/download/ 2022-07-15 15:27:27.227509+00:00 2022-07-15 15:27:30.323166+00:00 application/pdf braga_2020_venice.pdf 2022-07-15 15:27:27.227509+00:00 771236 https://api.rohub.org/api/resources/9aeafa95-d61a-4436-bf2f-9cdbc06a17d6/download/ 2022-07-15 15:10:31.345581+00:00 2022-07-15 15:10:33.392081+00:00 application/pdf giannakis_2020_eastern_mediterranean_fisheries.pdf 2022-07-15 15:10:31.345581+00:00 2906682 https://api.rohub.org/api/resources/b3fa9cbb-bfbe-4024-98c3-af433033f034/download/ 2022-07-15 15:21:16.990559+00:00 2022-07-15 15:21:19.451830+00:00 application/pdf luterbacher_2020_pandemics_climate_variability.pdf 2022-07-15 15:21:16.990559+00:00 847711 https://api.rohub.org/api/resources/f9039923-05dc-46eb-8c07-1ed5c4f03608/download/ 2022-07-15 15:11:27.176012+00:00 2022-07-15 15:11:29.084106+00:00 application/pdf rodriguez_2020_wildfires.pdf 2022-07-15 15:11:27.176012+00:00 2736179 https://api.rohub.org/api/resources/f9fb0b8c-e864-457a-9011-73c7300c63d2/download/ 2022-07-15 15:09:56.918011+00:00 2022-07-15 15:09:59.639561+00:00 application/pdf roviello_2020_immunoprotection_forests.pdf 2022-07-15 15:09:56.918011+00:00 katrin.schroeder@cnr.it KATRIN SCHROEDER Simona Aracri 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 Earth sciences analysis on lockdown unprecedented lockdown number of paper earth scientific discipline newspaper bibliography analysis composition consequence bibliography analysis scientific paper lockdown beginning issue marine environment Ro giorgio.castellan@bo.ismar.cnr.it Giorgio Castellan 0000-0001-6084-1504 Valentina Grande service-account-enrichment 102401 https://api.rohub.org/api/ros/7f025f6e-9835-4af8-aa48-0152474eda37/crate/download/ 2021-12-07 09:16:43.892982+00:00 2025-03-05 12:47:13.249944+00:00 2021-12-07 09:16:43.892982+00:00 Since the beginning of March 2020 many European (and non-European) countries went into an unprecedented lockdown. As a consequence a big number of papers trying to analyse the effect of the anthropause on the marine environment has been published around the world. This RO contains a list of scientific papers, dealing with the COVID19 lockdown and Earth Sciences, downloaded through Web of Science. application/ld+json https://w3id.org/ro-id/7f025f6e-9835-4af8-aa48-0152474eda37 Bibliography analysis on lockdown related papers within Earth science MANUAL Grande, Valentina, and Giorgio Castellan. "Bibliography analysis on lockdown related papers within Earth science." ROHub. Dec 07 ,2021. https://w3id.org/ro-id/7f025f6e-9835-4af8-aa48-0152474eda37. 297984 https://api.rohub.org/api/resources/9b4c4b3e-ea24-4ae5-ab10-40d45dfe4ea4/download/ 2021-12-15 11:53:00.791962+00:00 2021-12-15 11:53:00.792798+00:00 List of scientific papers, dealing with the COVID19 lockdown and Earth Sciences (DS1-GC0-SC1) and downloaded through Web of Science (https://www.webofscience.com/wos/woscc/basic-search) application/vnd.ms-excel List of papers 2021-12-15 11:53:00.791962+00:00 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 Thule Station 8.841463414634145 8.7 MODIS data 94.38877755511022 94.2 POINT (-68.74 76.51) -68.74 76.51 POINT (-68.74 76.51) bfbf6f5a-683e-46cb-813a-a93bae06b6f2 POINT (-68.74 76.51) service-account-enrichment 32999 https://api.rohub.org/api/ros/36113393-cbf6-4bac-8698-d69cf6cd0329/crate/download/ 2021-12-08 21:52:28.639768+00:00 2025-03-05 00:45:31.765683+00:00 2021-12-08 21:52:28.639768+00:00 This Research Object get MODIS data on ADAM Platform. application/ld+json https://w3id.org/ro-id/36113393-cbf6-4bac-8698-d69cf6cd0329 (2021) Integrated Water_Vapour on the Thule Station. MODIS data _(TEST) MANUAL https://w3id.org/ro-id/36113393-cbf6-4bac-8698-d69cf6cd0329/c89a7887-a3c0-420b-ad52-49aef857c4c2 https://w3id.org/ro-id/925adff2-02dc-42b5-bfc0-5e581b8ae6a8 https://w3id.org/ro-id/c50688a6-c71a-4d97-a197-34fa433bec83 https://w3id.org/ro-id/b745b77d-7587-4162-8785-5abc4bf2d952 https://w3id.org/ro-id/f128e7ce-0601-4f0b-9f50-538a6f6a1f54 https://w3id.org/ro-id/0a3c0ad0-cd98-42f0-a524-60188dd61bf7 https://w3id.org/ro-id/5fcae548-3b21-4a6c-ba68-e71d81dda4e0 https://w3id.org/ro-id/7e8efc9b-98e9-44eb-9c35-8708db32392b https://w3id.org/ro-id/d8f17cb2-81c3-439e-ba82-b87ad5f0dc94 https://w3id.org/ro-id/da6d652f-222a-4daf-94f3-63881de7e5b9 https://w3id.org/ro-id/f98454df-ec68-4416-9c42-9c6c305ef5e7 https://w3id.org/ro-id/3e5759d1-24b3-4d14-b86b-955e45f889af https://w3id.org/ro-id/5f536cb2-9302-494a-a669-716d6d1d5e6e https://w3id.org/ro-id/2501b09c-2e95-400b-a60a-f0e027315f8b https://w3id.org/ro-id/5a41bca0-a02e-41d8-af37-ef5fd7b01682 https://w3id.org/ro-id/7a47a70e-cfd3-43c8-a6b3-2ea4a301e197 https://w3id.org/ro-id/bed4c2d2-b1da-4e77-b8ae-d58a835eb99e https://w3id.org/ro-id/d5937bcd-b20d-4318-8f6c-230e48ef76a9 https://w3id.org/ro-id/fdc6bfea-a041-4ee3-ba82-23e3047fb67e https://w3id.org/ro-id/d2e1f6d2-03e7-40c9-b677-fa163e7353df Stelitano, Dario. "(2021) Integrated Water_Vapour on the Thule Station. MODIS data _(TEST)." ROHub. Dec 08 ,2021. https://w3id.org/ro-id/36113393-cbf6-4bac-8698-d69cf6cd0329. 12605 https://api.rohub.org/api/resources/171055c3-c92a-48d7-8add-bf781e8074f6/download/ 2021-12-08 21:54:05.171272+00:00 2021-12-08 21:54:05.172442+00:00 text/html Interactive_html 2021-12-08 21:54:05.171272+00:00 25892 https://api.rohub.org/api/resources/570b4730-7ab4-44ae-a0ea-c3b5167a8d61/download/ 2021-12-08 21:54:35.637718+00:00 2021-12-08 21:54:35.639366+00:00 image/png IWV_Thule_Sept2021.png 2021-12-08 21:54:35.637718+00:00 2822 https://api.rohub.org/api/resources/f3912336-c918-4f34-bff1-b9522273458a/download/ 2021-12-08 21:53:19.741390+00:00 2021-12-08 21:53:19.744069+00:00 text/csv IWV_Data.csv 2021-12-08 21:53:19.741390+00:00 life sciences (general) 100.0 0.9445598721504211 get MODIS data 1.7034068136272547 1.7 life sciences 100.0 0.9445598721504211 data 27.540650406504064 27.1 get MODIS data on ADAM Platform ADAM Platform 14.837398373983739 14.6 test 23.249299719887958 8.3 earth sciences 100.0 0.6300640106201172 data on ADAM Platform 3.907815631262525 3.9 information 76.75070028011204 27.4 2021 (2021) Integrated Water_Vapour on the Thule Station. 4.3043043043043046 4.3 MODIS 25.101626016260163 24.7 test 7.621951219512194 7.5 atmospheric sciences 100.0 0.6300640106201172 This Research Object 16.05691056910569 15.8 MODIS data _(TEST). This Research Object get MODIS data on ADAM Platform. 95.6956956956957 95.6 Dario Stelitano 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 Adriatic Sea naval forces Soundscape WAV data physics sound pressure levels from wav file http AND sound pressure resource Adriatic Sea Levels It file sound pressure level Develogic SonoVault hydrophone computer code soundscape information acoustics impact North Adriatic sea POLYGON ((17.369384765625 43.068887774169625, 17.193603515625 43.15710884095329, 17.017822265625 43.27720532212024, 16.8310546875 43.35713822211053, 16.63330078125 43.45291889355465, 16.446533203125 43.5326204268101, 16.23779296875 43.48481212891603, 15.99609375 43.50075243569041, 15.919189453124998 43.59630591596548, 15.963134765625 43.644025847699496, 15.6005859375 43.8028187190472, 15.49072265625 43.9058083561574, 15.227050781249998 44.071800467511565, 15.1171875 44.19795903948531, 15.468749999999998 44.26093725039923, 14.94140625 44.66865287227321, 14.94140625 44.91035917458495, 14.820556640625 45.09679146394738, 14.326171874999998 45.336701909968134, 14.150390625 45.0657615477031, 13.963623046874998 44.84029065139799, 13.853759765625 44.85586880735725, 13.645019531249998 45.089035564831036, 13.51318359375 45.44471679159555, 13.721923828124998 45.56021795715051, 13.765869140624998 45.62940492064501, 13.5791015625 45.75985868785574, 13.524169921874998 45.71385093029221, 13.205566406249998 45.744526980468436, 13.095703125 45.69850658738846, 13.128662109375 45.62172169252446, 12.41455078125 45.398449976304086, 12.568359375 45.55252525134013, 12.513427734375 45.56021795715051, 12.3046875 45.460130637921004, 12.1728515625 45.398449976304086, 12.117919921874998 45.26715476332791, 12.337646484375 45.1742925240767, 12.3046875 45.07352060670971, 12.513427734375 44.95702412512118, 12.447509765625 44.88701247981298, 12.3046875 44.78573392716592, 12.315673828125 44.42593442145313, 12.359619140624998 44.24519901522129, 12.6123046875 44.000717834282774, 12.908935546875 43.874138181474734, 13.128662109375 43.74728909225908, 13.304443359375 43.636075155965784, 13.53515625 43.58039085560784, 13.634033203125 43.51668853502906, 13.634033203125 43.42100882994726, 13.68896484375 43.30119623257966, 13.82080078125 43.13306116240612, 13.875732421875 42.99661231842139, 13.9306640625 42.84375132629021, 13.99658203125 42.73894375124377, 17.369384765625 43.068887774169625)) 3fcc5848-b2eb-412f-abc8-34d813de8c86 POLYGON ((17.369384765625 43.068887774169625, 17.193603515625 43.15710884095329, 17.017822265625 43.27720532212024, 16.8310546875 43.35713822211053, 16.63330078125 43.45291889355465, 16.446533203125 43.5326204268101, 16.23779296875 43.48481212891603, 15.99609375 43.50075243569041, 15.919189453124998 43.59630591596548, 15.963134765625 43.644025847699496, 15.6005859375 43.8028187190472, 15.49072265625 43.9058083561574, 15.227050781249998 44.071800467511565, 15.1171875 44.19795903948531, 15.468749999999998 44.26093725039923, 14.94140625 44.66865287227321, 14.94140625 44.91035917458495, 14.820556640625 45.09679146394738, 14.326171874999998 45.336701909968134, 14.150390625 45.0657615477031, 13.963623046874998 44.84029065139799, 13.853759765625 44.85586880735725, 13.645019531249998 45.089035564831036, 13.51318359375 45.44471679159555, 13.721923828124998 45.56021795715051, 13.765869140624998 45.62940492064501, 13.5791015625 45.75985868785574, 13.524169921874998 45.71385093029221, 13.205566406249998 45.744526980468436, 13.095703125 45.69850658738846, 13.128662109375 45.62172169252446, 12.41455078125 45.398449976304086, 12.568359375 45.55252525134013, 12.513427734375 45.56021795715051, 12.3046875 45.460130637921004, 12.1728515625 45.398449976304086, 12.117919921874998 45.26715476332791, 12.337646484375 45.1742925240767, 12.3046875 45.07352060670971, 12.513427734375 44.95702412512118, 12.447509765625 44.88701247981298, 12.3046875 44.78573392716592, 12.315673828125 44.42593442145313, 12.359619140624998 44.24519901522129, 12.6123046875 44.000717834282774, 12.908935546875 43.874138181474734, 13.128662109375 43.74728909225908, 13.304443359375 43.636075155965784, 13.53515625 43.58039085560784, 13.634033203125 43.51668853502906, 13.634033203125 43.42100882994726, 13.68896484375 43.30119623257966, 13.82080078125 43.13306116240612, 13.875732421875 42.99661231842139, 13.9306640625 42.84375132629021, 13.99658203125 42.73894375124377, 17.369384765625 43.068887774169625)) POLYGON ((17.369384765625 43.068887774169625, 17.193603515625 43.15710884095329, 17.017822265625 43.27720532212024, 16.8310546875 43.35713822211053, 16.63330078125 43.45291889355465, 16.446533203125 43.5326204268101, 16.23779296875 43.48481212891603, 15.99609375 43.50075243569041, 15.919189453124998 43.59630591596548, 15.963134765625 43.644025847699496, 15.6005859375 43.8028187190472, 15.49072265625 43.9058083561574, 15.227050781249998 44.071800467511565, 15.1171875 44.19795903948531, 15.468749999999998 44.26093725039923, 14.94140625 44.66865287227321, 14.94140625 44.91035917458495, 14.820556640625 45.09679146394738, 14.326171874999998 45.336701909968134, 14.150390625 45.0657615477031, 13.963623046874998 44.84029065139799, 13.853759765625 44.85586880735725, 13.645019531249998 45.089035564831036, 13.51318359375 45.44471679159555, 13.721923828124998 45.56021795715051, 13.765869140624998 45.62940492064501, 13.5791015625 45.75985868785574, 13.524169921874998 45.71385093029221, 13.205566406249998 45.744526980468436, 13.095703125 45.69850658738846, 13.128662109375 45.62172169252446, 12.41455078125 45.398449976304086, 12.568359375 45.55252525134013, 12.513427734375 45.56021795715051, 12.3046875 45.460130637921004, 12.1728515625 45.398449976304086, 12.117919921874998 45.26715476332791, 12.337646484375 45.1742925240767, 12.3046875 45.07352060670971, 12.513427734375 44.95702412512118, 12.447509765625 44.88701247981298, 12.3046875 44.78573392716592, 12.315673828125 44.42593442145313, 12.359619140624998 44.24519901522129, 12.6123046875 44.000717834282774, 12.908935546875 43.874138181474734, 13.128662109375 43.74728909225908, 13.304443359375 43.636075155965784, 13.53515625 43.58039085560784, 13.634033203125 43.51668853502906, 13.634033203125 43.42100882994726, 13.68896484375 43.30119623257966, 13.82080078125 43.13306116240612, 13.875732421875 42.99661231842139, 13.9306640625 42.84375132629021, 13.99658203125 42.73894375124377, 17.369384765625 43.068887774169625)) 17.369384765625 43.068887774169625, 17.193603515625 43.15710884095329, 17.017822265625 43.27720532212024, 16.8310546875 43.35713822211053, 16.63330078125 43.45291889355465, 16.446533203125 43.5326204268101, 16.23779296875 43.48481212891603, 15.99609375 43.50075243569041, 15.919189453124998 43.59630591596548, 15.963134765625 43.644025847699496, 15.6005859375 43.8028187190472, 15.49072265625 43.9058083561574, 15.227050781249998 44.071800467511565, 15.1171875 44.19795903948531, 15.468749999999998 44.26093725039923, 14.94140625 44.66865287227321, 14.94140625 44.91035917458495, 14.820556640625 45.09679146394738, 14.326171874999998 45.336701909968134, 14.150390625 45.0657615477031, 13.963623046874998 44.84029065139799, 13.853759765625 44.85586880735725, 13.645019531249998 45.089035564831036, 13.51318359375 45.44471679159555, 13.721923828124998 45.56021795715051, 13.765869140624998 45.62940492064501, 13.5791015625 45.75985868785574, 13.524169921874998 45.71385093029221, 13.205566406249998 45.744526980468436, 13.095703125 45.69850658738846, 13.128662109375 45.62172169252446, 12.41455078125 45.398449976304086, 12.568359375 45.55252525134013, 12.513427734375 45.56021795715051, 12.3046875 45.460130637921004, 12.1728515625 45.398449976304086, 12.117919921874998 45.26715476332791, 12.337646484375 45.1742925240767, 12.3046875 45.07352060670971, 12.513427734375 44.95702412512118, 12.447509765625 44.88701247981298, 12.3046875 44.78573392716592, 12.315673828125 44.42593442145313, 12.359619140624998 44.24519901522129, 12.6123046875 44.000717834282774, 12.908935546875 43.874138181474734, 13.128662109375 43.74728909225908, 13.304443359375 43.636075155965784, 13.53515625 43.58039085560784, 13.634033203125 43.51668853502906, 13.634033203125 43.42100882994726, 13.68896484375 43.30119623257966, 13.82080078125 43.13306116240612, 13.875732421875 42.99661231842139, 13.9306640625 42.84375132629021, 13.99658203125 42.73894375124377, 17.369384765625 43.068887774169625 service-account-enrichment 3145561 https://api.rohub.org/api/ros/6640422d-57ed-4814-b0d0-8eb4ee85f501/crate/download/ 2021-12-09 15:16:54.820238+00:00 2025-03-05 01:19:12.595351+00:00 2021-12-09 15:16:54.820238+00:00 It allows to calculate Sound Pressure Levels from wav files. This is the code used within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape). It works with wav files recorded by Develogic SonoVault Hydrophones application/ld+json https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501 Underwater noise, SPLs, Soundscape Soundscape WAV data to Sound Pressure Levels MANUAL https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501/95dadbae-eb9b-4d26-9ab3-86d7f22f3004 Petrizzo, Antonio, Zdroik Jakub, Mihanović Hrvoje, and Vukadin Predrag. "Soundscape WAV data to Sound Pressure Levels." ROHub. Dec 09 ,2021. https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501. wav file input input Jupyter notebooks here notebook some results results some information metadata 1224673 https://api.rohub.org/api/resources/00a6cb9f-40ff-40bc-b89e-f407df279b47/download/ 2021-12-14 12:49:11.532828+00:00 2021-12-14 12:49:11.533765+00:00 Monitoring Stations of Soundscape Project image/png Maps of the stations 2021-12-14 12:49:11.532828+00:00 https://owncloud.ve.ismar.cnr.it/owncloud/index.php/s/U3hjD3ETPL9B2JF 2021-12-14 12:28:01.996205+00:00 2021-12-14 12:42:47.357174+00:00 SPL dataset obtained in the SoundScape Project (available soon) SPL dataset 2021-12-14 12:28:01.996205+00:00 1908773 https://api.rohub.org/api/resources/669996c7-7c4c-42f1-b368-12943683f5f5/download/ 2021-12-09 15:17:40.578025+00:00 2021-12-09 15:17:40.579536+00:00 image/png Processing WAV data recorded with Develogic SonoVault hydrophone 2021-12-09 15:17:40.578025+00:00 https://notebooks.egi.eu/user/da47d3640f619a02cb075c15d288fc09e053bf46b90d26ec335392acdfae866b@egi.eu/doc/tree/datahub/Reliance/Soundscape/SoundscapeWAVProcessing.ipynb 2021-12-09 15:17:37.254527+00:00 2021-12-14 12:40:21.862063+00:00 This Notebook processes wav files recorded by Develogic SonoVault hydrophone and calculates Sound Pressure Levels Wav to SPL processing 2021-12-09 15:17:37.254527+00:00 67407 https://api.rohub.org/api/resources/b17ec859-5532-4395-807a-c928bdc60f75/download/ 2021-12-14 12:23:57.268734+00:00 2021-12-14 12:41:56.005557+00:00 Example of SPL file 20 seconds averaged text/csv SPL output file example 2021-12-14 12:23:57.268734+00:00 https://owncloud.ve.ismar.cnr.it/owncloud/index.php/s/QyDCi5EOPkFDuUs 2021-12-14 12:18:53.138358+00:00 2021-12-14 12:40:46.379226+00:00 Example of 1 hour wav file recorded within the Soundscape Project Wav File example 2021-12-14 12:18:53.138358+00:00 https://w3id.org/ro-id/3f68f32e-1b6d-4689-acee-b797ba2c8429 2021-12-14 12:46:56.272172+00:00 2021-12-14 12:46:56.272740+00:00 This RO process SPL data obtained within the Soundscape Project RO to process SPL data 2021-12-14 12:46:56.272172+00:00 https://owncloud.ve.ismar.cnr.it/owncloud/index.php/s/1kY77cVcA4rxxYt 2021-12-14 12:10:14.497389+00:00 2021-12-14 12:28:32.196671+00:00 Dataset of wav files recorded within the Soundscape Project (available soon) Soundscape WAV file Dataset 2021-12-14 12:10:14.497389+00:00 CNR ISMAR Venice antonio.petrizzo@ve.ismar.cnr.it Antonio Petrizzo IZOR hrvoje.mihanovic@izor.hr Mihanović Hrvoje University of Gdańsk jakub.zdroik@gmail.com Zdroik Jakub IZOR predrag.vukadin@hrbi.hr Vukadin Predrag 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 Acoustics Asdrubali meteorology disease nan map special issue microbiology medicine virus noise naan climate disease noise climate special issue federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 3798 https://api.rohub.org/api/ros/92f6334f-26fc-4181-bc0c-ef2725ba3ebc/crate/download/ 2021-12-10 09:56:44.923361+00:00 2025-03-05 02:45:34.918805+00:00 2021-12-10 09:56:44.923361+00:00 nan application/ld+json https://w3id.org/ro-id/92f6334f-26fc-4181-bc0c-ef2725ba3ebc Noise Mapping Special Issue: The noise climate at the time of SARS-CoV-2 Virus/COVID-19 Disease MANUAL Foglini, Federica. "Noise Mapping Special Issue: The noise climate at the time of SARS-CoV-2 Virus/COVID-19 Disease." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/92f6334f-26fc-4181-bc0c-ef2725ba3ebc. 178 https://api.rohub.org/api/resources/51ed4ce5-1bb0-43bb-bf96-a924faf972ce/download/ 2021-12-10 09:56:48.725077+00:00 2021-12-10 09:56:48.726109+00:00 nan text/plain Noise Mapping Special Issue: The noise climate at the time of SARS-CoV-2 Virus/COVID-19 Disease 2021-12-10 09:56:48.725077+00:00 service-account-generation-service Biology Ecology biology Biol. Conserv zoology botany Manenti ecology countrywide lockdown Mercurio medicine social media information taxa data zoonosis mean solar day social media wildlife field protected area conservation result Mori Italy crisis good field data wildlife conservation lockdown effect lockdown Mori bad insight information European impact Italy federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5896 https://api.rohub.org/api/ros/29aa2c18-e5b1-4c19-9273-cfe40ab9c42a/crate/download/ 2021-12-10 09:56:51.962215+00:00 2025-03-05 01:26:36.933640+00:00 2021-12-10 09:56:51.962215+00:00 The COVID-19 pandemic zoonosis has determined extensive lockdowns worldwide that provide an unprecedented opportunity to understand how large-scale shifts of human activities can impact wildlife. We addressed the impacts of the COVID-19 lockdown on wildlife in Italy, the first European country that performed a countrywide lockdown, and identified potentially beneficial and negative consequences for wildlife conservation and management. We combined a qualitative analysis of social media information with field data from multiple taxa, data from citizen science projects, and questionnaires addressed to managers of protected areas. Both social media information and field data suggest that a reduction of human disturbance allowed wildlife to exploit new habitats and increase daily activity. The field data confirmed some positive effects on wildlife conservation, such as an increase in species richness in temporarily less-disturbed habitats, a higher breeding success of an aerial insectivorous bird, and reduction of road-killing of both amphibians and reptiles. Despite some positive effects, our data also highlighted several negative impacts of the COVID-19 crisis on wildlife. The lower human disturbance linked to lockdown was in fact beneficial for invasive alien species. Results from questionnaires addressed to managers of protected areas highlighted that the COVID-19 lockdown interrupted actions for the control of invasive alien species, and hampered conservation activities targeting threatened taxa. Furthermore, the reduction of enforcement could cause a surge of illegal killing of wildlife. The COVID-19 crisis, besides having deep socio-economic impacts, might profoundly affect wildlife conservation, with potentially long-lasting effects. application/ld+json https://w3id.org/ro-id/29aa2c18-e5b1-4c19-9273-cfe40ab9c42a The good, the bad and the ugly of COVID-19 lockdown effects on wildlife conservation: Insights from the first European locked down country MANUAL Foglini, Federica. "The good, the bad and the ugly of COVID-19 lockdown effects on wildlife conservation: Insights from the first European locked down country." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/29aa2c18-e5b1-4c19-9273-cfe40ab9c42a. 282 https://api.rohub.org/api/resources/7a8963f4-9cb4-464c-b34e-fdf45cd02627/download/ 2021-12-10 09:56:54.931117+00:00 2021-12-10 09:56:54.932157+00:00 The COVID-19 pandemic zoonosis has determined extensive lockdowns worldwide that provide an unprecedented opportunity to understand how large-scale shifts of human activities can impact wildlife. We addressed the impacts of the COVID-19 lockdown on wildlife in Italy, the first European country that performed a countrywide lockdown, and identified potentially beneficial and negative consequences for wildlife conservation and management. We combined a qualitative analysis of social media information with field data from multiple taxa, data from citizen science projects, and questionnaires addressed to managers of protected areas. Both social media information and field data suggest that a reduction of human disturbance allowed wildlife to exploit new habitats and increase daily activity. The field data confirmed some positive effects on wildlife conservation, such as an increase in species richness in temporarily less-disturbed habitats, a higher breeding success of an aerial insectivorous bird, and reduction of road-killing of both amphibians and reptiles. Despite some positive effects, our data also highlighted several negative impacts of the COVID-19 crisis on wildlife. The lower human disturbance linked to lockdown was in fact beneficial for invasive alien species. Results from questionnaires addressed to managers of protected areas highlighted that the COVID-19 lockdown interrupted actions for the control of invasive alien species, and hampered conservation activities targeting threatened taxa. Furthermore, the reduction of enforcement could cause a surge of illegal killing of wildlife. The COVID-19 crisis, besides having deep socio-economic impacts, might profoundly affect wildlife conservation, with potentially long-lasting effects. text/plain The good, the bad and the ugly of COVID-19 lockdown effects on wildlife conservation: Insights from the first European locked down country 2021-12-10 09:56:54.931117+00:00 service-account-generation-service Medical science Picano wind nan myocardial infarction naan answer wind Europe contaminate federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 3169 https://api.rohub.org/api/ros/7cb18233-aaf5-4986-9a79-199d1e539d3c/crate/download/ 2021-12-10 09:56:57.569230+00:00 2025-03-05 01:28:03.080321+00:00 2021-12-10 09:56:57.569230+00:00 nan application/ld+json https://w3id.org/ro-id/7cb18233-aaf5-4986-9a79-199d1e539d3c Where have all the myocardial infarctions gone during lockdown? The answer is blowing in the less-polluted wind MANUAL Foglini, Federica. "Where have all the myocardial infarctions gone during lockdown? The answer is blowing in the less-polluted wind." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/7cb18233-aaf5-4986-9a79-199d1e539d3c. 183 https://api.rohub.org/api/resources/a96415f7-cab2-4f50-a858-51a13b0a437b/download/ 2021-12-10 09:57:00.079271+00:00 2021-12-10 09:57:00.080447+00:00 nan text/plain Where have all the myocardial infarctions gone during lockdown? The answer is blowing in the less-polluted wind 2021-12-10 09:57:00.079271+00:00 service-account-generation-service Medical science candidate target gene based drug Stolfi Proximity- repurpose strategy Network Proximity-based drug volume 8 anatomy medicine gene cell drug drug pandemic gene expression tissue epidemiology therapy strategy standardisation candidate pharmacology network medicine approach genetics Network aggregation gene expression data part aim genes candidate information Dev. Biol. 2020 off-the-shelf drug federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5737 https://api.rohub.org/api/ros/d8e686b6-199a-4296-88fa-3c03cdb5a4c1/crate/download/ 2021-12-10 09:57:03.314406+00:00 2025-03-05 00:50:11.962750+00:00 2021-12-10 09:57:03.314406+00:00 The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle. application/ld+json https://w3id.org/ro-id/d8e686b6-199a-4296-88fa-3c03cdb5a4c1 Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19 MANUAL Foglini, Federica. "Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/d8e686b6-199a-4296-88fa-3c03cdb5a4c1. 169 https://api.rohub.org/api/resources/424605bc-3aaa-4d60-b516-8c57721e8c90/download/ 2021-12-10 09:57:05.938504+00:00 2021-12-10 09:57:05.939802+00:00 The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle. text/plain Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19 2021-12-10 09:57:05.938504+00:00 service-account-generation-service Biology federica.foglini@ismar.cnr.it Federica Foglini Giusiano, S; Laura, P; Iazzolino, B; Mastro, E; Arcari, M; Palumbo, F; Torrieri, MC; Bombaci, A; Grassano, M; Cabras, S; Di Pede, F; DeMattei, F; Matteoni, E; Solero, L; Daviddi, M; Salamone, P; Fuda, G; Manera, U; Canosa, A; Chio, A; Calvo, A; Moglia, C; Vasta, R. Amyotrophic lateral sclerosis caregiver burden and patients' quality of life during COVID-19 pandemic. 55.873402312842366 91.8 caregiver 5.911330049261084 4.8 burden 18.96551724137931 15.4 patient 15.394088669950738 12.5 lateral Scher 4.583602324080052 7.1 covid-QoL questionnaire 4.777275661717237 7.4 Laura 6.686046511627906 6.9 quality of life 8.497536945812808 6.9 lockdown 6.650246305418719 5.4 patients quality of life 10.974822466107165 17.0 Torrieri 6.395348837209302 6.6 Objective: To assess patients Quality of life (QoL) and the burden of their caregivers during Covid-19 pandemic and specifically the impact of two-month lockdown period. 20.9373097991479 34.4 life sciences 86.23743515548426 0.9878090620040894 Amyotrophic lateral sclerosis caregiver burden and patients' quality of life during COVID-19 pandemic. 8.88618381010347 14.6 burden 13.75968992248062 14.2 Giusiano 6.492248062015504 6.7 patient 9.496124031007753 9.8 Matteoni 6.492248062015504 6.7 Palumbo 6.87984496124031 7.1 Mastro 7.170542635658914 7.4 Medical staff Health/Medical profession/Medical staff caregiver burden 3.55067785668173 5.5 In Apr-2020 Music Arts, culture and entertainment/Arts and entertainment/Music Health Health health status 4.712717882504841 7.3 geology 100.0 1.4761965870857239 medicine 51.09717868338558 16.3 quality of life 0.45190445448676564 0.7 Musical instrument Arts, culture and entertainment/Arts and entertainment/Music/Musical instrument Lateral Scher. 4.930006086427268 8.1 According to the COVID-QoL questionnaire, caregivers perceived lower family help compared to patients (p < 0.001). Conclusions: Restricted measures of lockdown period during COVID-19 pandemic did not result in a significant reduction of QoL in our cohort of ALS patients, while caregiver burden significantly increased. 9.373097991479003 15.4 service-account-enrichment 5787 https://api.rohub.org/api/ros/976aabb0-7b38-441a-96b3-49844ba773c5/crate/download/ 2021-12-10 09:57:08.786289+00:00 2025-03-05 00:46:17.227887+00:00 2021-12-10 09:57:08.786289+00:00 Objective: To assess patients Quality of life (QoL) and the burden of their caregivers during Covid-19 pandemic and specifically the impact of two-month lockdown period. Methods: In April 2020, a total of 60 patients and 59 caregivers were administered by phone scales assessing patients' QoL (McGill QoL Questionnaire), general health status (EQ-5D-5L), and caregiver burden (Zarit Burden Interview). The administration was repeated one month after the end of lockdown measures, with the addition of a qualitative questionnaire (COVID-QoL Questionnaire) exploring family reorganization and personal perception of lock down. Results: QoL and perceived health status did not worsen during lockdown, while caregiver burden increased (p = 0.01). Patient's QoL and caregiver burden were inversely correlated at T1 (ZBI total score mildly correlated with Mc Gill existential subscore, p = 0.02, rho = 0.30 and with Mc Gill total score, p = 0.05, rho = 0.265). No significant correlations were found at T2. According to the COVID-QoL questionnaire, caregivers perceived lower family help compared to patients (p < 0.001). Conclusions: Restricted measures of lockdown period during COVID-19 pandemic did not result in a significant reduction of QoL in our cohort of ALS patients, while caregiver burden significantly increased. ALS motor impairment may have played a role in the unchanged life conditions of patients. Instead, the restriction of family help for primary caregivers could be responsible of their increased burden, reflecting the importance of a wide social support in the management of this clinical condition. application/ld+json https://w3id.org/ro-id/976aabb0-7b38-441a-96b3-49844ba773c5 Amyotrophic lateral sclerosis caregiver burden and patients' quality of life during COVID-19 pandemic MANUAL https://w3id.org/ro-id/6d3f4b98-f443-4ac9-a315-f507af7fef8f https://w3id.org/ro-id/d6d92bba-7253-480d-8865-9febf47dd360 https://w3id.org/ro-id/03d607ad-a893-4182-803e-267d1c4055f9 https://w3id.org/ro-id/064f631e-148d-4b96-9f7e-6c2a443fb3e6 https://w3id.org/ro-id/0c2c448b-6c1e-4389-ba4e-ef1c09698d5c https://w3id.org/ro-id/2abfa715-c6d2-4c1b-a5b7-984f4808fa6b https://w3id.org/ro-id/2b9531f4-4665-4788-8d56-905be134505b https://w3id.org/ro-id/b14f5c48-ce65-4004-9b28-a05867c2ce69 https://w3id.org/ro-id/bd878af8-e610-4e24-91bd-fffad7a49745 https://w3id.org/ro-id/c112f497-0e9a-4b36-9bfd-a5eba7a65ffd https://w3id.org/ro-id/d5a486df-f0c9-403b-b432-eaa4f07ec76e https://w3id.org/ro-id/e86d68c5-9de1-478f-a71b-12a4e77e56d7 https://w3id.org/ro-id/e90a81b0-d1ba-4c7e-ab47-e1f0becfc3f0 https://w3id.org/ro-id/6a181bdd-a41d-4607-855b-f66cac92d918 https://w3id.org/ro-id/f90f8d44-ec37-41a7-a333-98460c6f3749 https://w3id.org/ro-id/5706c642-6990-423e-b4e8-48b09f23c877 https://w3id.org/ro-id/5f9352ea-a53e-49b1-9e24-29c130e26058 https://w3id.org/ro-id/61fb2862-f952-42cd-82b8-54b6ee5c405f https://w3id.org/ro-id/6f6d5a05-2473-4c4e-a8f1-5ba182196f62 https://w3id.org/ro-id/b6e8f5b0-bc52-45f1-b610-3b4e415c1de6 https://w3id.org/ro-id/e37bebbc-22b8-4c1f-9bda-92a7b7f8d082 https://w3id.org/ro-id/228129df-3a19-4887-ac5e-d2171766317c https://w3id.org/ro-id/36596768-2dbe-48ce-b242-07fa1ed16420 https://w3id.org/ro-id/401897c0-b558-46c4-8f86-a12a9c6832e6 https://w3id.org/ro-id/4073766e-d49e-4ea8-9b80-ba1592abf1f5 https://w3id.org/ro-id/491e5b6e-6627-443c-9dac-7d9e883f6d8a https://w3id.org/ro-id/4e0274ed-b67e-43cf-8dbd-c9178035dc94 https://w3id.org/ro-id/528973ff-cba4-4ad9-a50b-8e40d7160856 https://w3id.org/ro-id/54ddf13c-be80-4e80-9659-a5bfcb6e1388 https://w3id.org/ro-id/9c7d18bf-2ee7-4d88-9850-bfa743d8b18d https://w3id.org/ro-id/b5ff016e-41d6-4107-aad4-7e37669e4240 https://w3id.org/ro-id/cb38c001-1cdd-4371-a83b-f8db95d813f2 https://w3id.org/ro-id/f041449e-cee6-4256-9ab4-c08638710c11 https://w3id.org/ro-id/f5c58b71-5861-43c4-aa29-a691a79c71b5 https://w3id.org/ro-id/3c297c11-9d1b-4d79-be6a-669b6bcd7b10 https://w3id.org/ro-id/a383a2d6-5c86-4908-8281-e05e3d8dc9cf https://w3id.org/ro-id/a4ce82da-af3d-467d-b069-3bedad6a70da https://w3id.org/ro-id/cbe23dbd-61ca-4f1f-b0a2-3a81a0fd7778 https://w3id.org/ro-id/12946d2a-d04c-4174-ab10-93aa7ba6fa75 https://w3id.org/ro-id/1eb27e46-99fb-4ec4-b4d6-d3e577cd5ee2 https://w3id.org/ro-id/2cebcd12-f022-4bbd-b94e-ac27e6a92a11 https://w3id.org/ro-id/59839531-a007-4a10-97ea-6b380ba0378f https://w3id.org/ro-id/67d5d9c6-552e-43ae-9edd-e982277aabc3 https://w3id.org/ro-id/6dcc0d8b-390f-4ac2-81f0-31a9eb40dd60 https://w3id.org/ro-id/a45e45d5-c012-4e9b-ab2f-7438c8ca8573 https://w3id.org/ro-id/cce7f9b5-40df-4186-8eb8-0c0d59e765b6 https://w3id.org/ro-id/d097b90d-0cb9-497e-ba91-6c51dbdfd038 https://w3id.org/ro-id/dc791ff0-52f3-4f53-9f18-3d8a289c449d https://w3id.org/ro-id/01068300-9d4b-4ed6-a3d9-aae5f9136d2f https://w3id.org/ro-id/38960fff-d8a0-4219-8142-ac6b3151cf05 https://w3id.org/ro-id/3dcfab9b-0a83-4596-860e-cbebae31bbfb https://w3id.org/ro-id/8611b3b8-61ce-464d-8b21-01922e2c3ea8 https://w3id.org/ro-id/8de398b6-2b7c-4418-baf6-0845724bbb97 https://w3id.org/ro-id/5e1ea08c-4799-4d97-8c22-d1737f2db8a2 Foglini, Federica. "Amyotrophic lateral sclerosis caregiver burden and patients' quality of life during COVID-19 pandemic." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/976aabb0-7b38-441a-96b3-49844ba773c5. 419 https://api.rohub.org/api/resources/e8d6574a-b552-475b-8d7f-04a8dcf5b90b/download/ 2021-12-10 09:57:11.268079+00:00 2021-12-10 09:57:11.269490+00:00 Objective: To assess patients Quality of life (QoL) and the burden of their caregivers during Covid-19 pandemic and specifically the impact of two-month lockdown period. Methods: In April 2020, a total of 60 patients and 59 caregivers were administered by phone scales assessing patients' QoL (McGill QoL Questionnaire), general health status (EQ-5D-5L), and caregiver burden (Zarit Burden Interview). The administration was repeated one month after the end of lockdown measures, with the addition of a qualitative questionnaire (COVID-QoL Questionnaire) exploring family reorganization and personal perception of lock down. Results: QoL and perceived health status did not worsen during lockdown, while caregiver burden increased (p = 0.01). Patient's QoL and caregiver burden were inversely correlated at T1 (ZBI total score mildly correlated with Mc Gill existential subscore, p = 0.02, rho = 0.30 and with Mc Gill total score, p = 0.05, rho = 0.265). No significant correlations were found at T2. According to the COVID-QoL questionnaire, caregivers perceived lower family help compared to patients (p < 0.001). Conclusions: Restricted measures of lockdown period during COVID-19 pandemic did not result in a significant reduction of QoL in our cohort of ALS patients, while caregiver burden significantly increased. ALS motor impairment may have played a role in the unchanged life conditions of patients. Instead, the restriction of family help for primary caregivers could be responsible of their increased burden, reflecting the importance of a wide social support in the management of this clinical condition. text/plain Amyotrophic lateral sclerosis caregiver burden and patients' quality of life during COVID-19 pandemic 2021-12-10 09:57:11.268079+00:00 Lou Gehrig's disease 9.496124031007753 9.8 life sciences (general) 86.23743515548426 0.9878090620040894 ALS patient 7.4241446094254355 11.5 space sciences 13.76256484451574 0.15764367580413818 questionnaire 4.1871921182266005 3.4 health 6.007751937984496 6.2 Medical profession Health/Medical profession Lou Gehrig's disease 12.68472906403941 10.3 total 4.064039408866995 3.3 status 5.910852713178294 6.1 space sciences (general) 13.76256484451574 0.15764367580413818 amyotrophic lateral sclerosis caregiver burden 8.005164622336991 12.4 sclerosis caregiver burden 44.480309877340225 68.9 sclerosis 7.0197044334975365 5.7 linguistics 48.90282131661442 15.6 patients' quality of life 11.039380245319562 17.1 Epidemic Health/Diseases and conditions/Communicable disease/Epidemic health 8.374384236453201 6.8 status 8.251231527093596 6.7 quality of life 6.2984496124031 6.5 QoL 8.91472868217054 9.2 earth sciences 100.0 1.4761965870857239 service-account-generation-service Information science epidemic modeling epidemic scenario agent-based framework software library medicine public intervention pandemic epidemic system code library restriction researcher Inf. Syst. theoretical account infectious disease specialization Rossetti epidemic agent central bank intervention epidemic phenomena scenario information agent intervention agent-based framework for modeling framework for modeling federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 4338 https://api.rohub.org/api/ros/699fb8ae-4815-48d4-a082-c2a6eee6f7cf/crate/download/ 2021-12-10 09:57:13.978205+00:00 2025-03-05 02:49:06.314579+00:00 2021-12-10 09:57:13.978205+00:00 Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate what if epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns horizontal ellipsis ). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena. application/ld+json https://w3id.org/ro-id/699fb8ae-4815-48d4-a082-c2a6eee6f7cf UTLDR: an agent-based framework for modeling infectious diseases and public interventions MANUAL Foglini, Federica. "UTLDR: an agent-based framework for modeling infectious diseases and public interventions." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/699fb8ae-4815-48d4-a082-c2a6eee6f7cf. 160 https://api.rohub.org/api/resources/8aaf39d2-a3c4-4b21-9e4b-6c61716d4a41/download/ 2021-12-10 09:57:16.352435+00:00 2021-12-10 09:57:16.353576+00:00 Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate what if epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns horizontal ellipsis ). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena. text/plain UTLDR: an agent-based framework for modeling infectious diseases and public interventions 2021-12-10 09:57:16.352435+00:00 service-account-generation-service Information science time frame Sweden human activities impact of the COVID-19 pandemic medicine pandemic times candela packet loss act impact local government Sweden Germany Italy Germany Europe Europe France whole of Europe large-scale study study whole Internet latency impact Italy Spain federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5113 https://api.rohub.org/api/ros/4e5fe683-1b85-4457-8f22-5eccf858c728/crate/download/ 2021-12-10 09:57:18.925998+00:00 2025-03-05 00:55:11.439101+00:00 2021-12-10 09:57:18.925998+00:00 The COVID-19 pandemic dramatically changed the way of living of billions of people in a very short time frame. In this paper, we evaluate the impact on the Internet latency caused by the increased amount of human activities that are carried out on-line. The study focuses on Italy, which experienced significant restrictions imposed by local authorities, but results about Spain, France, Germany, Sweden, and the whole of Europe are also included. The analysis of a large set of measurements shows that the impact on the network can be significant, especially in terms of increased variability of latency. In Italy we observed that the standard deviation of the average additional delay - the additional time with respect to the minimum delay of the paths in the region - during lockdown is similar to 3 - 4 times as much as the value before the pandemic. Similarly, in Italy, packet loss is similar to 2 - 3 times as much as before the pandemic. The impact is not negligible also for the other countries and for the whole of Europe, but with different levels and distinct patterns. application/ld+json https://w3id.org/ro-id/4e5fe683-1b85-4457-8f22-5eccf858c728 Impact of the COVID-19 pandemic on the Internet latency: A large-scale study MANUAL Foglini, Federica. "Impact of the COVID-19 pandemic on the Internet latency: A large-scale study." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/4e5fe683-1b85-4457-8f22-5eccf858c728. 145 https://api.rohub.org/api/resources/d747417a-4572-48c5-ac2e-4adb0d8e042b/download/ 2021-12-10 09:57:21.206682+00:00 2021-12-10 09:57:21.208536+00:00 The COVID-19 pandemic dramatically changed the way of living of billions of people in a very short time frame. In this paper, we evaluate the impact on the Internet latency caused by the increased amount of human activities that are carried out on-line. The study focuses on Italy, which experienced significant restrictions imposed by local authorities, but results about Spain, France, Germany, Sweden, and the whole of Europe are also included. The analysis of a large set of measurements shows that the impact on the network can be significant, especially in terms of increased variability of latency. In Italy we observed that the standard deviation of the average additional delay - the additional time with respect to the minimum delay of the paths in the region - during lockdown is similar to 3 - 4 times as much as the value before the pandemic. Similarly, in Italy, packet loss is similar to 2 - 3 times as much as before the pandemic. The impact is not negligible also for the other countries and for the whole of Europe, but with different levels and distinct patterns. text/plain Impact of the COVID-19 pandemic on the Internet latency: A large-scale study 2021-12-10 09:57:21.206682+00:00 service-account-generation-service Information science federica.foglini@ismar.cnr.it Federica Foglini alerting 12.487804878048781 12.8 alerting system 56.83506686478455 76.5 psychology 3.409090909090909 0.9 Government health care Health/Healthcare policy/Government health care health 5.073170731707317 5.2 55 weeks geology 51.04037970026865 0.9339817762374878 warning 12.48900615655233 14.2 care 4.661389621811785 5.3 equipment 6.156552330694811 7.0 sleep 5.853658536585366 6.0 service 8.70712401055409 9.9 earth sciences 51.04037970026865 0.9339817762374878 pattern 3.869832893579596 4.4 An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care. 7.986348122866894 11.7 sociology 12.878787878787879 3.4 artificial intelligence 6.420404573438874 7.3 Emilia Romagna https://www.wikidata.org/wiki/Q1263 service 4.48780487804878 4.6 health care 9.410729991204924 10.7 Italy https://www.wikidata.org/wiki/Q38 Music Arts, culture and entertainment/Arts and entertainment/Music life sciences 100.0 1.399156928062439 sleep 6.772207563764292 7.7 health-status trajectory 6.909361069836554 9.3 service system 7.6523031203566125 10.3 Its aim is to keep the caregivers informed of subjects' health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. 10.648464163822526 15.6 delivery 3.5180299032541775 4.0 environmental sciences 48.95962029973135 0.8959062099456787 IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences system 13.463414634146341 13.8 detection 6.420404573438874 7.3 unsupervised behavioral modeling 1.263001485884101 1.7 trajectory 4.661389621811785 5.3 healthcare 8.097560975609758 8.3 Senior citizens Society/Mankind/Senior citizens IT-computer sciences Science and technology/Technology and engineering/IT-computer sciences passive sensing 4.0861812778603275 5.5 Health Health life sciences (general) 100.0 1.399156928062439 computer science 12.878787878787879 3.4 anomaly 6.596306068601583 7.5 patient 4.925241864555849 5.6 11 days in advance artificial intelligence 5.560975609756097 5.7 Hu, R; Michel, B; Russo, D; Mora, N; Matrella, G; Ciampolini, P; Cocchi, F; Montanari, E; Nunziata, S; Brunschwiler, T. An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care. 68.25938566552901 100.0 Michel 7.609756097560975 7.8 behavioral modeling 13.00148588410104 17.5 Montanari 7.609756097560975 7.8 service-account-enrichment 6391 https://api.rohub.org/api/ros/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/crate/download/ 2021-12-10 09:57:24.261137+00:00 2025-03-05 00:46:22.398733+00:00 2021-12-10 09:57:24.261137+00:00 Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects' health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia-Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects' daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient's behavior as a 'Bag of Words', based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects' daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort. application/ld+json https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16 An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care MANUAL https://w3id.org/ro-id/1337aa66-38de-4d06-beb4-92e71c7583ac https://w3id.org/ro-id/445bca3d-efe8-4366-935a-cf6de557d0dd https://w3id.org/ro-id/97799977-efc6-4589-861c-9de48bfeb3e7 https://w3id.org/ro-id/df72c2ed-caad-4863-94b9-a947bdde5db3 https://w3id.org/ro-id/dfd154e3-0707-4690-876c-8bbc4216661c https://w3id.org/ro-id/eb4a5ae3-f379-45b4-97ab-9629194b4fad https://w3id.org/ro-id/48195f2c-471d-491d-8641-acf769afe4aa https://w3id.org/ro-id/5400c508-5066-498d-b48e-87e0d5cfbcaf https://w3id.org/ro-id/2f95e4b4-d498-42e3-9700-f38a40d5f2dc https://w3id.org/ro-id/34d3b70a-659c-460a-8093-ace4e9ac4208 https://w3id.org/ro-id/38739332-e761-4023-8fa7-ed9aeca9a878 https://w3id.org/ro-id/3d34b04c-ac12-45a4-a834-e727d76b49ab https://w3id.org/ro-id/3f4fb3d5-2707-483f-97b1-1f6c04cb5e60 https://w3id.org/ro-id/47b44765-b240-4c3d-9305-74c3271b20ff https://w3id.org/ro-id/498c9d3d-f6d0-41cd-a76f-7347e879f4ba https://w3id.org/ro-id/6044d4b8-00ea-4dd5-95c9-2dd4997a279f https://w3id.org/ro-id/6246f417-f8c9-4a61-a8bc-78ed193cd71f https://w3id.org/ro-id/71b398f4-b4bd-47f2-ac63-1c881cbd06af https://w3id.org/ro-id/76555952-0a7a-48ac-9f2f-e853544aaf78 https://w3id.org/ro-id/9828191a-7398-4a1e-b32a-701a1fd1f5df https://w3id.org/ro-id/985905e4-a26a-46da-bf2e-888372de9b1b https://w3id.org/ro-id/d86f3590-ff12-467d-b95a-6810f9669fda https://w3id.org/ro-id/e35dac02-2142-485d-8b77-c06e415fc9af https://w3id.org/ro-id/ec73fb56-7025-4707-b6b1-7064052e4c59 https://w3id.org/ro-id/1e906297-2a99-4e15-bd17-7bfd1fbca935 https://w3id.org/ro-id/3d8980bc-b5d8-461f-a00a-36a943209dc4 https://w3id.org/ro-id/6296a98f-13e2-41a9-bfea-77b808eb4169 https://w3id.org/ro-id/c1b63d44-e7b6-41bb-ba2b-ae58ba5656d0 https://w3id.org/ro-id/15074093-af13-43db-af49-bbebd0c65f41 https://w3id.org/ro-id/59e8a1ee-03d2-422f-a089-822ddc873395 https://w3id.org/ro-id/67c6a2b3-1684-46ed-9871-6f829d210475 https://w3id.org/ro-id/8561c20f-5e1b-45a6-bb14-c78deda397eb https://w3id.org/ro-id/85a6a283-546a-462b-a9ec-cbaa3f5b7f51 https://w3id.org/ro-id/86eaf06d-595a-4ad7-a924-1fe25254460b https://w3id.org/ro-id/cfda43be-04e5-4366-b3d7-f3adf2c80bbf https://w3id.org/ro-id/08a05435-d8c4-49d4-b69d-4d696ef224ef https://w3id.org/ro-id/170ff614-9b1b-4fa6-91c6-34c940398f49 https://w3id.org/ro-id/3a86f8f6-cfba-4a5b-ae7c-0f5fc7d8a0b1 https://w3id.org/ro-id/48462d1c-f706-4a8e-b66e-d89b750dd4dc https://w3id.org/ro-id/6c993b77-71c4-4f88-b235-d1f8c2085358 https://w3id.org/ro-id/82ae38cb-3105-408d-b064-607e28637479 https://w3id.org/ro-id/a3dd6660-7096-4800-bdf4-182c900aef6e https://w3id.org/ro-id/b94ed5cf-7bb1-4431-803b-6e5be67be843 https://w3id.org/ro-id/bf565e1c-4d3d-4219-87ce-a2e37aa0abdd https://w3id.org/ro-id/c7707710-6ce0-438e-9eca-c75ec289e130 https://w3id.org/ro-id/f1340319-0dbd-409e-bcb2-41090e3cb6c1 https://w3id.org/ro-id/ff2a58ef-2b66-4d4c-b85e-d9ac953328aa https://w3id.org/ro-id/5cdf7768-9375-4650-b493-070ec8bc0e5c https://w3id.org/ro-id/969f7b4b-d8bc-46f8-ada3-715d770d632b https://w3id.org/ro-id/0cf3ba2d-6359-4670-b31b-1334347e6e8d https://w3id.org/ro-id/60d0d7c5-1ddf-40c5-b388-2a3f211d508b https://w3id.org/ro-id/60dbc21c-886b-459c-a0df-f68fdc42cc83 https://w3id.org/ro-id/75535675-a42e-48c4-973e-336f47690fd0 https://w3id.org/ro-id/864dbc7a-7334-4ca4-8ec4-d26685af7e69 https://w3id.org/ro-id/ba9dc9a6-0d23-41ed-8c48-50d5e9fbf4e5 https://w3id.org/ro-id/c6aa47f9-e7f2-4cb8-98cf-20c849b0c858 https://w3id.org/ro-id/f01fca29-d470-4958-9b5c-615ce34bc830 https://w3id.org/ro-id/f5eab9c0-479d-4bf4-b586-26d5f48b7f3d https://w3id.org/ro-id/400c5a36-d55b-4c1e-804f-edf22973a268 https://w3id.org/ro-id/622525d4-a74e-46bf-a1da-bbfb31b8fc3a https://w3id.org/ro-id/ad674471-d3b0-4b24-9818-4af2f4d51390 https://w3id.org/ro-id/fc18f000-3e2b-4361-a1f3-8487bbea1072 https://w3id.org/ro-id/1e419192-b4e1-4f72-a1fa-d34b972bf736 https://w3id.org/ro-id/a24ce1ab-a7ef-405c-9768-28f6c18338fd Foglini, Federica. "An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16. 261 https://api.rohub.org/api/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4/download/ 2021-12-10 09:57:27.302384+00:00 2021-12-10 09:57:27.303841+00:00 Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects' health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia-Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects' daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient's behavior as a 'Bag of Words', based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects' daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort. text/plain An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care 2021-12-10 09:57:27.302384+00:00 environmental science and management 48.95962029973135 0.8959062099456787 sleep quality 2.6745913818722142 3.6 Cocchi 8.097560975609758 8.3 Healthcare provider Economy, business and finance/Economic sector/Financial and business service/Healthcare provider health 7.036059806508355 8.0 medicine 14.393939393939394 3.8 linguistics 51.8939393939394 13.7 sensor 4.133685136323659 4.7 technology 4.545454545454546 1.2 standing 4.221635883905013 4.8 sleep profile 4.383358098068351 5.9 Ciampolini 7.121951219512195 7.3 sensing for elderly care 3.1946508172362558 4.3 Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. 13.10580204778157 19.2 Brunschwiler 14.536585365853659 14.9 service-account-generation-service Information science contact-tracing app Andrienko data Priv den Hoven Comande avoiding location tracking Journal of geophysical research. Biogeosciences location data medicine personal data collection dynamics application software chain drive store vision help COVID-19 containment collection tracking containment privacy Lehmann van den Hoven tracing personal data information rebirth citizen detection locating Hoven Nanni control to individual citizen federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 6557 https://api.rohub.org/api/ros/56b65356-9a6b-4e59-b830-0b3c4ce82b81/crate/download/ 2021-12-10 09:57:31.095148+00:00 2025-03-05 00:59:13.365278+00:00 2021-12-10 09:57:31.095148+00:00 The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' personal data stores, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates - if and when they want and for specific aims - with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. application/ld+json https://w3id.org/ro-id/56b65356-9a6b-4e59-b830-0b3c4ce82b81 Give more data, awareness and control to individual citizens, and they will help COVID-19 containment MANUAL Foglini, Federica. "Give more data, awareness and control to individual citizens, and they will help COVID-19 containment." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/56b65356-9a6b-4e59-b830-0b3c4ce82b81. 666 https://api.rohub.org/api/resources/d67668a2-d6b7-4e6f-ab8b-a6de7f684a78/download/ 2021-12-10 09:57:34.657315+00:00 2021-12-10 09:57:34.658502+00:00 The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' personal data stores, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates - if and when they want and for specific aims - with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. text/plain Give more data, awareness and control to individual citizens, and they will help COVID-19 containment 2021-12-10 09:57:34.657315+00:00 service-account-generation-service Social sciences university teacher education medicine university pandemic instructor ecosystem dynamics manual activity result preconception lens lens of the Italian university teacher Italian university teacher school systems Giovannella education curricula study learning working load education learning ecosystem school teacher refuge comparison activity teaching activity effects of the Covid-19 pandemic teachers category federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5693 https://api.rohub.org/api/ros/e1d32110-086e-4de3-80b2-21dfe6ae068a/crate/download/ 2021-12-10 09:57:38.379197+00:00 2025-03-05 01:26:35.622066+00:00 2021-12-10 09:57:38.379197+00:00 In this paper, we report one of the first investigations conducted at the National level with university teachers, with the aim to capture their perceptions about the capability of the learning ecosystems to react to the lockdown imposed by the pandemic and the recourse to on-line learning. The study, conducted about two months after the beginning of the lock-down, shows that: a) learning ecosystems reacted promptly and in a satisfactory manner to assure the didactic continuity at both the systemic and individual level; b) the teaching activities were mainly confined to transmissive excathedra lectures in the attempt to reproduce standard university dynamics; c) the working load increased with respect to face-to-face activities; d) the intention to use on-line learning in the future is driven by preconceptions rather than experiences and by the capability to manage one's own time. The comparison with the outcomes of a similar study conducted with school teachers shows that the latter adopt a broader spectrum of didactic activities (although they still tend to remain in their comfort zone), experienced a heavier increase of the working load, and were more influenced by the situation they experienced. Although both teachers categories recognized the relevance of digital pedagogy, in the case of school teachers - as shown by the causal structure of the variables considered in our studies - it should be urgently included in teacher education curricula, while in the case of the university teachers it appears to be a possible route to support integration of on-line activities with standard face-to-face ones. application/ld+json https://w3id.org/ro-id/e1d32110-086e-4de3-80b2-21dfe6ae068a The effects of the Covid-19 pandemic seen through the lens of the Italian university teachers and the comparison with school teachers' perspective MANUAL Foglini, Federica. "The effects of the Covid-19 pandemic seen through the lens of the Italian university teachers and the comparison with school teachers' perspective." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/e1d32110-086e-4de3-80b2-21dfe6ae068a. 234 https://api.rohub.org/api/resources/a598492b-37fb-4f3f-8b8f-f637f75f9f67/download/ 2021-12-10 09:57:42.635524+00:00 2021-12-10 09:57:42.636510+00:00 In this paper, we report one of the first investigations conducted at the National level with university teachers, with the aim to capture their perceptions about the capability of the learning ecosystems to react to the lockdown imposed by the pandemic and the recourse to on-line learning. The study, conducted about two months after the beginning of the lock-down, shows that: a) learning ecosystems reacted promptly and in a satisfactory manner to assure the didactic continuity at both the systemic and individual level; b) the teaching activities were mainly confined to transmissive excathedra lectures in the attempt to reproduce standard university dynamics; c) the working load increased with respect to face-to-face activities; d) the intention to use on-line learning in the future is driven by preconceptions rather than experiences and by the capability to manage one's own time. The comparison with the outcomes of a similar study conducted with school teachers shows that the latter adopt a broader spectrum of didactic activities (although they still tend to remain in their comfort zone), experienced a heavier increase of the working load, and were more influenced by the situation they experienced. Although both teachers categories recognized the relevance of digital pedagogy, in the case of school teachers - as shown by the causal structure of the variables considered in our studies - it should be urgently included in teacher education curricula, while in the case of the university teachers it appears to be a possible route to support integration of on-line activities with standard face-to-face ones. text/plain The effects of the Covid-19 pandemic seen through the lens of the Italian university teachers and the comparison with school teachers' perspective 2021-12-10 09:57:42.635524+00:00 service-account-generation-service Social sciences school teachers education Italian learning ecosystem teachers' perspective medicine school pandemic diameter instructor ecosystem dynamics experience strategy education system steady state workload increase school systems university teaching strategy Giovannella learning ecosystem investigation search perception learning descriptive analysis classroom dynamics education respondent school teacher effects of the Covid-19 pandemic school teachers' perspective federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5737 https://api.rohub.org/api/ros/072efa39-ca8c-4670-8e26-8b46b56eafb3/crate/download/ 2021-12-10 09:57:46.025661+00:00 2025-03-05 01:26:35.411692+00:00 2021-12-10 09:57:46.025661+00:00 This study is one of the first investigations conducted within the Italian school system to capture teachers' perspective, experiences and perceptions about the impact of the COVID-19 pandemic on school education. It was performed two months after the beginning of lockdown, when online teaching and learning processes were fully in place and had reached a steady state. The paper reports a descriptive analysis together with a network analysis, and the search for causal relationships among the variables that have been investigated. Generally, respondents reported that the reactions of educational institutions and individual teachers were satisfactory, preventing the collapse of the education system in spite of loss of contact with 6-10% of the student population and a significant teacher workload increase that posed individual time management challenges. Although teachers tended to adopt teaching strategies that reproduced standard classroom dynamics, the possibility of operating in this comfort zone generated a positive feeling about using technologies, a perception of increased digital skills mastery and a change in mindset about educational processes. In turn, this led to an increase in the perceived sustainability of online education, with about a third of the teachers expressing the wish to adopt a blended configuration for future teaching activities. Almost all participants recognized the significance of a digital pedagogy and the need to include it in the training curricula to prepare future teachers. application/ld+json https://w3id.org/ro-id/072efa39-ca8c-4670-8e26-8b46b56eafb3 The Effects of the Covid-19 Pandemic on Italian Learning Ecosystems: the School Teachers' Perspective at the steady state MANUAL Foglini, Federica. "The Effects of the Covid-19 Pandemic on Italian Learning Ecosystems: the School Teachers' Perspective at the steady state." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/072efa39-ca8c-4670-8e26-8b46b56eafb3. 221 https://api.rohub.org/api/resources/065c98c9-2e21-4f57-83ef-ecce692e4efe/download/ 2021-12-10 09:57:50.482354+00:00 2021-12-10 09:57:50.483502+00:00 This study is one of the first investigations conducted within the Italian school system to capture teachers' perspective, experiences and perceptions about the impact of the COVID-19 pandemic on school education. It was performed two months after the beginning of lockdown, when online teaching and learning processes were fully in place and had reached a steady state. The paper reports a descriptive analysis together with a network analysis, and the search for causal relationships among the variables that have been investigated. Generally, respondents reported that the reactions of educational institutions and individual teachers were satisfactory, preventing the collapse of the education system in spite of loss of contact with 6-10% of the student population and a significant teacher workload increase that posed individual time management challenges. Although teachers tended to adopt teaching strategies that reproduced standard classroom dynamics, the possibility of operating in this comfort zone generated a positive feeling about using technologies, a perception of increased digital skills mastery and a change in mindset about educational processes. In turn, this led to an increase in the perceived sustainability of online education, with about a third of the teachers expressing the wish to adopt a blended configuration for future teaching activities. Almost all participants recognized the significance of a digital pedagogy and the need to include it in the training curricula to prepare future teachers. text/plain The Effects of the Covid-19 Pandemic on Italian Learning Ecosystems: the School Teachers' Perspective at the steady state 2021-12-10 09:57:50.482354+00:00 service-account-generation-service Ecology the economy Independent Samples Test duration of lockdown Sweden economic growth of country lessons learned medicine death investment man health pandemic gross domestic product mortality rate economic growth economy contraction Austria country deterioration of economic system deaths of covid 19 relationship Total Environ growth level of gross domestic product health sector Coccia France lockdown lockdown duration sector people deterioration Italy pandemic crisis Total Environ Portugal Spain federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5592 https://api.rohub.org/api/ros/4124aa67-10cb-4105-9da4-034a777911ac/crate/download/ 2021-12-10 09:57:53.690965+00:00 2025-03-05 02:47:41.945223+00:00 2021-12-10 09:57:53.690965+00:00 How is the relation between duration of lockdown and numbers of infected people and deaths of Coronavirus disease 2019 (COVID-19), and growth level of Gross Domestic Product (GDP) in countries? Results here suggest that, during the first wave of COVID-19 pandemic, countries with a shorter period of lockdown (about 15 days: Austria, Portugal and Sweden) have average confirmed cases divided by population higher than countries with a longer period of lockdown (about 60 days, i.e., 2 months: France, Italy and Spain); moreover, countries with a shorter period of lockdown have average fatality rate (5.45%) lower than countries with a longer length of lockdown (12.70%), whereas average variation of fatality rate from March to August 2020 (first pandemic wave of COVID-19) suggests a higher reduction in countries with a longer period of lockdown than countries with a shorter duration (-1.9% vs. -0.72%). Independent Samples Test reveals that average fatality rate of countries with a shorter period of lockdown was significantly lower than countries with a longer period of lockdown (5.4% vs. 12.7%, p-value<.05). The Mann-Whitney Test confirms that average fatality rate of countries with a shorter period of lockdown is significantly lower than countries having a longer period of lockdown (U = 0, p-value =.005). In addition, results show that lockdowns of longer duration have generated negative effects on GDP growth: average contraction of GDP (index 2010 = 100) from second quarter 2019 to second quarter of 2020 in countries applying a longer period of lockdown (i.e., about two months) is about -21%, whereas it is -13% in countries applying a shorter period of lockdown of about 15 days (significant difference with Independent Samples Test: t(4) = -2.274, p-value <.085). This finding shows a systematic deterioration of economic system because of containment policies based on a longer duration of lockdown in society. Another novel finding here reveals that countries with higher investments in healthcare (as percentage of GDP) have alleviated fatality rate of COVID-19 and simultaneously have applied a shorter period of lockdown, reducing negative effects on economic system in terms of contraction of economic growth. Overall, then, using lessons learned of the first wave of COVID-19 pandemic crisis, this study must conclude that a strategy to reduce the negative impact of future epidemics similar to COVID-19 has to be based on a reinforcement of healthcare sector to have efficient health organizations to cope with pandemics of new viral agents by minimizing fatality rates; finally, high investments in health sector create the social conditions to apply lockdowns of short run with lower negative effects on socioeconomic systems. (c) 2021 Elsevier B.V. All rights reserved. application/ld+json https://w3id.org/ro-id/4124aa67-10cb-4105-9da4-034a777911ac The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to Covid-19 and to constrain the deterioration of economic system MANUAL Foglini, Federica. "The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to Covid-19 and to constrain the deterioration of economic system." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/4124aa67-10cb-4105-9da4-034a777911ac. 296 https://api.rohub.org/api/resources/cc67e177-291a-473f-bc90-cc674b6bdb14/download/ 2021-12-10 09:57:57.350896+00:00 2021-12-10 09:57:57.352165+00:00 How is the relation between duration of lockdown and numbers of infected people and deaths of Coronavirus disease 2019 (COVID-19), and growth level of Gross Domestic Product (GDP) in countries? Results here suggest that, during the first wave of COVID-19 pandemic, countries with a shorter period of lockdown (about 15 days: Austria, Portugal and Sweden) have average confirmed cases divided by population higher than countries with a longer period of lockdown (about 60 days, i.e., 2 months: France, Italy and Spain); moreover, countries with a shorter period of lockdown have average fatality rate (5.45%) lower than countries with a longer length of lockdown (12.70%), whereas average variation of fatality rate from March to August 2020 (first pandemic wave of COVID-19) suggests a higher reduction in countries with a longer period of lockdown than countries with a shorter duration (-1.9% vs. -0.72%). Independent Samples Test reveals that average fatality rate of countries with a shorter period of lockdown was significantly lower than countries with a longer period of lockdown (5.4% vs. 12.7%, p-value<.05). The Mann-Whitney Test confirms that average fatality rate of countries with a shorter period of lockdown is significantly lower than countries having a longer period of lockdown (U = 0, p-value =.005). In addition, results show that lockdowns of longer duration have generated negative effects on GDP growth: average contraction of GDP (index 2010 = 100) from second quarter 2019 to second quarter of 2020 in countries applying a longer period of lockdown (i.e., about two months) is about -21%, whereas it is -13% in countries applying a shorter period of lockdown of about 15 days (significant difference with Independent Samples Test: t(4) = -2.274, p-value <.085). This finding shows a systematic deterioration of economic system because of containment policies based on a longer duration of lockdown in society. Another novel finding here reveals that countries with higher investments in healthcare (as percentage of GDP) have alleviated fatality rate of COVID-19 and simultaneously have applied a shorter period of lockdown, reducing negative effects on economic system in terms of contraction of economic growth. Overall, then, using lessons learned of the first wave of COVID-19 pandemic crisis, this study must conclude that a strategy to reduce the negative impact of future epidemics similar to COVID-19 has to be based on a reinforcement of healthcare sector to have efficient health organizations to cope with pandemics of new viral agents by minimizing fatality rates; finally, high investments in health sector create the social conditions to apply lockdowns of short run with lower negative effects on socioeconomic systems. (c) 2021 Elsevier B.V. All rights reserved. text/plain The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to Covid-19 and to constrain the deterioration of economic system 2021-12-10 09:57:57.350896+00:00 service-account-generation-service Ecology Pollut results of cardiac functional stress Union Territory of Delhi test in coronary artery disease anatomy medicine heart failure ischaemia clean atherosclerosis air cleaning result air quality indicator cardiology 4-site lockdown heart failure patient roadblock aim air quality re dysfunction stress echocardiography change hurt patient Sci. Pollut air quality change barrier distress federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 6001 https://api.rohub.org/api/ros/a6a75c2b-d935-4b65-be9f-4c8d6870461c/crate/download/ 2021-12-10 09:58:01.204965+00:00 2025-03-05 01:26:35.194872+00:00 2021-12-10 09:58:01.204965+00:00 In vulnerable subjects, the increase in air pollution worsens the signs of myocardial ischemia. Lockdown during COVID-19 pandemics substantially cleaned the air. The objective of this is to assess the effects of air cleaning due to lockdown on stress echocardiography (SE) results. We enrolled 19 patients with chronic coronary artery disease and/or heart failure referred to SE (semi-supine bicycle exercise, n = 8, or dipyridamole, n = 11). Before and soon after lockdown, we assessed regional wall motion abnormalities (abnormal value: worsening of >= 2 segments), B-lines (a sign of pulmonary congestion, 4-site simplified scan, abnormal value >= 2), and coronary flow velocity reserve in left anterior descending artery (CFVR, abnormal value < 2.0). Local air quality indicators (same day of SE) of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were obtained from publicly available data sets of the regional authority of environmental protection. After lockdown, NO2 concentration decreased from 19 +/- 10 to 10 +/- 4 mu g/m(3) (p = 0.006). After lockdown, abnormal responses remained unchanged for ischemia (21% vs 16%, p = ns) and decreased for B-lines (42% vs 5%, p = 0.008) and CFVR (84 vs 42%, p = 0.007). Changes in coronary flow velocity reserve (CFVR) were correlated to same-day variations in NO2 (r = -0.578, p = 0.010) and preceding 30-day changes in PM2.5 (r = -0.518, p = 0.023). After lockdown, air cleaning was associated with a beneficial effect on coronary small vessel dysfunction and alveolar-capillary barrier distress mirrored by improvement of CFVR and B-lines during SE in vulnerable patients. Identifier: NCT 030.49995 application/ld+json https://w3id.org/ro-id/a6a75c2b-d935-4b65-be9f-4c8d6870461c The effects of lockdown-induced air quality changes on the results of cardiac functional stress testing in coronary artery disease and heart failure patients MANUAL Foglini, Federica. "The effects of lockdown-induced air quality changes on the results of cardiac functional stress testing in coronary artery disease and heart failure patients." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/a6a75c2b-d935-4b65-be9f-4c8d6870461c. 253 https://api.rohub.org/api/resources/fa5ddea5-9e02-4869-9a66-97c0a6cbc412/download/ 2021-12-10 09:58:05.576420+00:00 2021-12-10 09:58:05.578099+00:00 In vulnerable subjects, the increase in air pollution worsens the signs of myocardial ischemia. Lockdown during COVID-19 pandemics substantially cleaned the air. The objective of this is to assess the effects of air cleaning due to lockdown on stress echocardiography (SE) results. We enrolled 19 patients with chronic coronary artery disease and/or heart failure referred to SE (semi-supine bicycle exercise, n = 8, or dipyridamole, n = 11). Before and soon after lockdown, we assessed regional wall motion abnormalities (abnormal value: worsening of >= 2 segments), B-lines (a sign of pulmonary congestion, 4-site simplified scan, abnormal value >= 2), and coronary flow velocity reserve in left anterior descending artery (CFVR, abnormal value < 2.0). Local air quality indicators (same day of SE) of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were obtained from publicly available data sets of the regional authority of environmental protection. After lockdown, NO2 concentration decreased from 19 +/- 10 to 10 +/- 4 mu g/m(3) (p = 0.006). After lockdown, abnormal responses remained unchanged for ischemia (21% vs 16%, p = ns) and decreased for B-lines (42% vs 5%, p = 0.008) and CFVR (84 vs 42%, p = 0.007). Changes in coronary flow velocity reserve (CFVR) were correlated to same-day variations in NO2 (r = -0.578, p = 0.010) and preceding 30-day changes in PM2.5 (r = -0.518, p = 0.023). After lockdown, air cleaning was associated with a beneficial effect on coronary small vessel dysfunction and alveolar-capillary barrier distress mirrored by improvement of CFVR and B-lines during SE in vulnerable patients. Identifier: NCT 030.49995 text/plain The effects of lockdown-induced air quality changes on the results of cardiac functional stress testing in coronary artery disease and heart failure patients 2021-12-10 09:58:05.576420+00:00 service-account-generation-service Ecology federica.foglini@ismar.cnr.it Federica Foglini Analysis of Air Quality during the COVID-19 Pandemic Lockdown in Naples (Italy). Lockdown measures applied in the aftermath of the COVID-19 pandemic spread to Italy in the period March 13th-May 4th strongly limited the social and industrial activities with consequent effects on the air pollution. 32.92053663570692 63.8 ecology 58.10055865921788 10.4 service-account-enrichment 5687 https://api.rohub.org/api/ros/0ca44684-25d3-4893-9009-283903561da0/crate/download/ 2021-12-10 09:58:09.104389+00:00 2025-03-05 00:46:18.551908+00:00 2021-12-10 09:58:09.104389+00:00 Lockdown measures applied in the aftermath of the COVID-19 pandemic spread to Italy in the period March 13th-May 4th strongly limited the social and industrial activities with consequent effects on the air pollution. Here we report a study on the influence of the lockdown measures on the air quality in the city of Naples (Italy). The comparison of the levels of various gaseous pollutants (C6H6, CO, NO2 and SO2) and particulate matter (PM10, PM2.5, PM1) at ground level as well as of atmospheric aerosol properties registered by remote sensing techniques during the lockdown period with the values observed in the earlier months and during the same period of the previous year is used to gain interesting information on the environmental impact of the human activities. Our findings show a rather significant reduction of the pollution due to NO2 (49-62%) in urban as well as in green suburban area, while CO and SO2 showed a more important reduction in urban or industrial districts of the city (50-58% and 70%, respectively). Particulate matter at ground level is also affected but to a more limited extent (29-49%). Nevertheless, characterization of atmospheric aerosol columnar properties suggests an interesting variation of its composition. The observed features have been associated to the strong meteorological interference from Saharan Dust in the Mediterranean area also affecting the city of Naples. application/ld+json https://w3id.org/ro-id/0ca44684-25d3-4893-9009-283903561da0 Analysis of Air Quality during the COVID-19 Pandemic Lockdown in Naples (Italy) MANUAL https://w3id.org/ro-id/0c6669d9-18ca-4443-8f19-6f5ac0425361 https://w3id.org/ro-id/4958b44a-924b-4a21-8a66-490691f66823 https://w3id.org/ro-id/7b95eee4-fa24-4bc2-bcb8-294dbf8c9a64 https://w3id.org/ro-id/b5c117bc-09b5-47a3-bf2d-f3c41b091260 https://w3id.org/ro-id/0d133ca6-2e4b-4680-a8a3-d3ac4addea07 https://w3id.org/ro-id/3b364b23-d0e1-430b-92d2-dea7432b4383 https://w3id.org/ro-id/6145b5d9-c1c7-4f6c-afde-25df41d391e3 https://w3id.org/ro-id/93f19b1d-e095-4ceb-a33f-a0eb204e9fb8 https://w3id.org/ro-id/1841891c-9b24-4f39-b917-681281762ebc https://w3id.org/ro-id/1f66750d-4884-4031-b564-ecacebfa9a19 https://w3id.org/ro-id/2488fd74-3de8-4e29-b8df-6642738460e7 https://w3id.org/ro-id/33747683-132c-4fbb-bbdc-3ff16cd6c76c https://w3id.org/ro-id/4d0384dd-354e-49dd-9710-3f8f2ced99e0 https://w3id.org/ro-id/584abf4d-9783-4ec0-b752-dfb0ed240e7d https://w3id.org/ro-id/6de9fbf0-bf63-4544-aa3b-909337717138 https://w3id.org/ro-id/8f62f708-9578-4067-80d6-f13525ffba25 https://w3id.org/ro-id/8fbdb600-2c5e-496a-a119-37d1b7db27e9 https://w3id.org/ro-id/a25aa39a-078e-40d1-8b92-b2448be82764 https://w3id.org/ro-id/aa579488-c73d-4018-a99e-4b8bfd75a69c https://w3id.org/ro-id/c7fdfd3d-aa27-4ceb-81ed-d303b0a8e937 https://w3id.org/ro-id/ca660383-5b3e-4e2d-a68b-a7970dfe32a2 https://w3id.org/ro-id/d6968f9b-ff87-4cb2-b736-2c86c830afc6 https://w3id.org/ro-id/dca7f083-4f7a-43b6-b581-18e44a316d2e https://w3id.org/ro-id/ed36df39-5478-4785-b54b-8da3801e6838 https://w3id.org/ro-id/85f3f94b-dfdd-4167-9f89-63af38cf86af https://w3id.org/ro-id/9b639c90-15d8-4a95-8820-ddc3753c887d https://w3id.org/ro-id/bf2f1e27-b1ff-4b9b-a4ac-80b691fbb156 https://w3id.org/ro-id/e99b68e7-e487-4dc8-9345-461c1aa22d33 https://w3id.org/ro-id/23b97def-5fcf-4bb2-b37a-739b52bb8775 https://w3id.org/ro-id/5135ccab-8bbc-4e28-b3fc-c120784b9942 https://w3id.org/ro-id/6634c633-a309-4bd2-b53a-05f89139b956 https://w3id.org/ro-id/bed68a71-7623-4921-b153-1c10f83d2157 https://w3id.org/ro-id/cf8a99db-bc7d-4fa5-b559-9d4bc8bcc8be https://w3id.org/ro-id/e429e1e5-4f71-4278-98e2-1ba36ba902cf https://w3id.org/ro-id/f7f141de-038c-4ad7-a82f-d0e17c5fb64f https://w3id.org/ro-id/273ff22b-b3b5-43e8-9204-42dde6a1b3d1 https://w3id.org/ro-id/3294f936-b7e5-442b-a3f3-3518adcac7fc https://w3id.org/ro-id/33e9b4d3-4fb8-4d9d-8ef0-a231bde47f6f https://w3id.org/ro-id/4c822dfc-7daf-4bb1-b4c0-c9c552b2b321 https://w3id.org/ro-id/4cb934a9-7500-4bdd-a897-54a8ec1ae349 https://w3id.org/ro-id/7accd650-02ad-4f86-ae76-320d1e0381a0 https://w3id.org/ro-id/8edb9944-887d-4a42-a248-ced2b438c731 https://w3id.org/ro-id/c72c5c1e-3d0d-4374-aece-dee03f2c2b69 https://w3id.org/ro-id/d2201653-4ea5-40dd-95b4-98c6b54ec9e2 https://w3id.org/ro-id/dd1840b1-4603-4c43-a30e-c62df2555d1a https://w3id.org/ro-id/f9146dd8-4ce2-49bf-bb99-89d0f2897797 https://w3id.org/ro-id/fcda45c1-515a-4d2a-8132-86a049ef6921 https://w3id.org/ro-id/571d4afd-6cac-45ee-a51f-ce82df164ad9 https://w3id.org/ro-id/7cd3f7d2-9e02-43a2-b691-abf1a1fdf747 https://w3id.org/ro-id/e3e48359-e5a7-4772-8459-5c6e74b065b5 https://w3id.org/ro-id/e8bca9e5-0c8c-4592-b72e-713334571b36 https://w3id.org/ro-id/21127140-8a6f-41d4-a281-d8f9d67d4f9f https://w3id.org/ro-id/3222330c-2dce-412b-a864-9d53ad276260 https://w3id.org/ro-id/3fd98286-bf03-4740-848d-7238bf6fe0ca https://w3id.org/ro-id/46514bb8-6d46-4097-b2c4-a565b060e01c https://w3id.org/ro-id/55164e73-ff77-4c77-9ac5-0ed540dac3e2 https://w3id.org/ro-id/6aadd265-f195-4cc8-82ee-90c53a42c146 https://w3id.org/ro-id/7bf66f65-3563-4796-8bf4-ef03e4ea1f58 https://w3id.org/ro-id/8539742b-3a68-42b9-bbbd-5be53d7f6664 https://w3id.org/ro-id/8ad04647-333e-4ccc-a6a2-7c1737362179 https://w3id.org/ro-id/a4e2d82d-0f38-47be-9d39-6d8e95c14a2c https://w3id.org/ro-id/01d03ab4-db65-4cc5-836f-b8a702c9a8d4 https://w3id.org/ro-id/774544ea-e3cb-492c-8d38-6047eaa402b5 https://w3id.org/ro-id/a660b2ad-1b28-40f1-8c75-32bb2577e16f https://w3id.org/ro-id/abecb522-1725-4257-9e65-bdb095f47863 https://w3id.org/ro-id/c1429ce9-4e39-4468-b3e2-9e954fe96294 https://w3id.org/ro-id/f04168c8-4865-4a99-b88b-a35466d5d8a2 Foglini, Federica. "Analysis of Air Quality during the COVID-19 Pandemic Lockdown in Naples (Italy)." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/0ca44684-25d3-4893-9009-283903561da0. 194 https://api.rohub.org/api/resources/5c6c5fcc-678d-4e14-8d4c-a663868d98e0/download/ 2021-12-10 09:58:14.689767+00:00 2021-12-10 09:58:14.690715+00:00 Lockdown measures applied in the aftermath of the COVID-19 pandemic spread to Italy in the period March 13th-May 4th strongly limited the social and industrial activities with consequent effects on the air pollution. Here we report a study on the influence of the lockdown measures on the air quality in the city of Naples (Italy). The comparison of the levels of various gaseous pollutants (C6H6, CO, NO2 and SO2) and particulate matter (PM10, PM2.5, PM1) at ground level as well as of atmospheric aerosol properties registered by remote sensing techniques during the lockdown period with the values observed in the earlier months and during the same period of the previous year is used to gain interesting information on the environmental impact of the human activities. Our findings show a rather significant reduction of the pollution due to NO2 (49-62%) in urban as well as in green suburban area, while CO and SO2 showed a more important reduction in urban or industrial districts of the city (50-58% and 70%, respectively). Particulate matter at ground level is also affected but to a more limited extent (29-49%). Nevertheless, characterization of atmospheric aerosol columnar properties suggests an interesting variation of its composition. The observed features have been associated to the strong meteorological interference from Saharan Dust in the Mediterranean area also affecting the city of Naples. text/plain Analysis of Air Quality during the COVID-19 Pandemic Lockdown in Naples (Italy) 2021-12-10 09:58:14.689767+00:00 Naples https://www.wikidata.org/wiki/Q2634 Naples 11.800766283524904 15.4 quality 5.670498084291188 7.4 aerosol air Qual 34.989648033126294 50.7 Housing and urban planning policy Politics/Government policy/Interior policy/Housing and urban planning policy lockdown 6.130268199233717 8.0 Boselli 10.318076027928626 13.3 aftermath of the COVID-19 pandemic 4.278812974465148 6.2 Naples 11.249030256012412 14.5 pandemic 3.908045977011494 5.1 aerosol 5.3529868114817685 6.9 Italy https://www.wikidata.org/wiki/Q38 Res. 2021 3.4506556245686677 5.0 city of Naples 7.79848171152519 11.3 chemistry 15.64245810055866 2.8 Italy 17.455391776570984 22.5 Castellano 8.068269976726144 10.4 Italy 18.773946360153257 24.5 Weather Weather volume 21 0.966183574879227 1.4 life sciences (general) 29.13677626694442 0.34515276551246643 ground state 3.908045977011494 5.1 Naples https://www.wikidata.org/wiki/Q2634 Environment Environment columnar properties 5.590062111801242 8.1 social 3.3716475095785445 4.4 Here we report a study on the influence of the lockdown measures on the air quality in the city of Naples (Italy). The comparison of the levels of various gaseous pollutants (C6H6, CO, NO2 and SO2) and particulate matter (PM10, PM2.5, PM1) at ground level as well as of atmospheric aerosol properties registered by remote sensing techniques during the lockdown period with the values observed in the earlier months and during the same period of the previous year is used to gain interesting information on the environmental impact of the human activities. 10.577915376676987 20.5 Sannino 8.999224204809929 11.6 medicine 16.201117318435756 2.9 Italy in the period 3.7957211870255345 5.5 geophysics 70.86322373305558 0.8394421339035034 A. Analysis of air quality 26.639061421670117 38.6 geology 49.8607190060513 0.9912623167037964 air Qual 2.070393374741201 3.0 lockdown 4.7323506594259115 6.1 character 2.6053639846743293 3.4 activity 2.9118773946360155 3.8 Italy https://www.wikidata.org/wiki/Q38 earth sciences 50.1392809939487 0.9968003034591675 particulate 8.352490421455938 10.9 lockdown measure 10.420979986197377 15.1 Res. 2021, Volume 21, issue 2) 1.7543859649122808 3.4 aerosol 10.344827586206897 13.5 Sannino, A; D'Emilio, M; Castellano, P; Amoruso, S; Boselli, A. Analysis of Air Quality during the COVID-19 Pandemic Lockdown in Naples (Italy). (Aerosol Air Qual. 49.7936016511868 96.5 publishing 10.05586592178771 1.8 Epidemic Health/Diseases and conditions/Communicable disease/Epidemic atmospheric sciences 50.1392809939487 0.9968003034591675 Our findings show a rather significant reduction of the pollution due to NO2 (49-62%) in urban as well as in green suburban area, while CO and SO2 showed a more important reduction in urban or industrial districts of the city (50-58% and 70%, respectively). Particulate matter at ground level is also affected but to a more limited extent (29-49%). Nevertheless, characterization of atmospheric aerosol columnar properties suggests an interesting variation of its composition. 4.953560371517028 9.6 A. Analysis 9.309542280837858 12.0 air 3.8314176245210727 5.0 air quality 3.9846743295019156 5.2 Air pollution Environment/Environmental pollution/Air pollution Emilio 9.309542280837858 12.0 result 5.823754789272031 7.6 pollution 5.287356321839081 6.9 properties 4.4996121024049645 5.8 life sciences 29.13677626694442 0.34515276551246643 Epidemic Health/Diseases and conditions/Communicable disease/Epidemic geosciences 70.86322373305558 0.8394421339035034 earth sciences 49.8607190060513 0.9912623167037964 research 3.2950191570881224 4.3 in the period Mar-13-May-4 Environmental pollution Environment/Environmental pollution particulate matter 6.594259115593483 8.5 pollution 4.1117145073700545 5.3 service-account-generation-service Ecology surface observation background station geophysics mitigation in Rome ecology surface station lockdown restriction dioxide reductions from satellite air pollution change medicine pandemic measure satellite air pollution nitrogen dioxide restriction traffic reductions from satellite Rome Italy Bassani reduction observation station surface background Rome dioxide nitrogen dioxide reduction Italy NO2 dioxide reduction federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 4731 https://api.rohub.org/api/ros/d6c968ae-1259-4720-9d4a-7e2eafaefa8b/crate/download/ 2021-12-10 09:58:19.137198+00:00 2025-03-05 02:45:32.636820+00:00 2021-12-10 09:58:19.137198+00:00 Lockdown restrictions were implemented in Italy from 10 March 2020 to contain the COVID-19 pandemic. Our study aims to evaluate air pollution changes, with focus on nitrogen dioxide (NO2), before and during the lockdown in Rome and in the surroundings. Significant NO2 declines were observed during the COVID-19 pandemic with reductions of - 50%, - 34%, and - 20% at urban traffic, urban background, and rural background stations, respectively. Tropospheric NO2 vertical column density (VCD) from the TROPOspheric Monitoring Instrument (TROPOMI) was used to evaluate the spatial-temporal variations of the NO2 before and during the lockdown for the entire area where the surface stations are located. The evaluation is concerned with the pixels including one or more air quality stations to explore the capability of the unprecedented high spatial resolution to monitor urban and rural sites from space with relation to the surface measurements. Good agreement between surface concentration and TROPOMI VCD was obtained in Rome (R = 0.64 in 2019, R = 0.77 in 2020) and in rural sites (R = 0.71 in 2019). Inversely, a slight correlation (R = 0.20) was observed in rural areas during the lockdown due to very low levels of NO2. Finally, the TROPOMI VCD showed a sharp decline in NO2, larger in urban (- 43%) than in rural sites (- 17%) as retrieved with the concurrent surface measurements averaging all the traffic and urban background (- 44%) and all the rural background stations (- 20%). These results suggest air pollution improvement in Rome gained from implementing lockdown restrictions. application/ld+json https://w3id.org/ro-id/d6c968ae-1259-4720-9d4a-7e2eafaefa8b Nitrogen dioxide reductions from satellite and surface observations during COVID-19 mitigation in Rome (Italy) MANUAL Foglini, Federica. "Nitrogen dioxide reductions from satellite and surface observations during COVID-19 mitigation in Rome (Italy)." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/d6c968ae-1259-4720-9d4a-7e2eafaefa8b. 261 https://api.rohub.org/api/resources/87eca91a-1123-4df7-81b7-6b6605cefd15/download/ 2021-12-10 09:58:25.751246+00:00 2021-12-10 09:58:25.752238+00:00 Lockdown restrictions were implemented in Italy from 10 March 2020 to contain the COVID-19 pandemic. Our study aims to evaluate air pollution changes, with focus on nitrogen dioxide (NO2), before and during the lockdown in Rome and in the surroundings. Significant NO2 declines were observed during the COVID-19 pandemic with reductions of - 50%, - 34%, and - 20% at urban traffic, urban background, and rural background stations, respectively. Tropospheric NO2 vertical column density (VCD) from the TROPOspheric Monitoring Instrument (TROPOMI) was used to evaluate the spatial-temporal variations of the NO2 before and during the lockdown for the entire area where the surface stations are located. The evaluation is concerned with the pixels including one or more air quality stations to explore the capability of the unprecedented high spatial resolution to monitor urban and rural sites from space with relation to the surface measurements. Good agreement between surface concentration and TROPOMI VCD was obtained in Rome (R = 0.64 in 2019, R = 0.77 in 2020) and in rural sites (R = 0.71 in 2019). Inversely, a slight correlation (R = 0.20) was observed in rural areas during the lockdown due to very low levels of NO2. Finally, the TROPOMI VCD showed a sharp decline in NO2, larger in urban (- 43%) than in rural sites (- 17%) as retrieved with the concurrent surface measurements averaging all the traffic and urban background (- 44%) and all the rural background stations (- 20%). These results suggest air pollution improvement in Rome gained from implementing lockdown restrictions. text/plain Nitrogen dioxide reductions from satellite and surface observations during COVID-19 mitigation in Rome (Italy) 2021-12-10 09:58:25.751246+00:00 service-account-generation-service Ecology PM emissions increase lockdown analysis analysis in Italy Italy Covid19 ecology Naples VOCS emissions reduction weather greenhouse gas analysis volatile organic compound urban area Palermo Rome Naples Milan Italy PM10 air quality in urban areas Gualtieri Bologna road traffic impact Florence lockdown emission sector Rome emission source air quality road traffic impact growth lockdown analysis in Italy emission Italy Milan Palermo Elsevier Ltd. All federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 6509 https://api.rohub.org/api/ros/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8/crate/download/ 2021-12-10 09:58:29.615992+00:00 2025-03-05 01:17:00.964241+00:00 2021-12-10 09:58:29.615992+00:00 Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48-60% average reduction in Italy. This offered an unprecedented opportunity to assess how a prolonged (similar to 2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020-30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019-02/05/2019). NO2, O-3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data. NO2 levels remarkably dropped over all urban areas (from-24.9% in Milan to-59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O-3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O-3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOC slimited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of -13-17% counterbalanced by increases up to -9%. Higher household heating usage (+16-19% in March), also driven by colder weather conditions than 2019 (-0.2 to -0.8 degrees C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health. (C) 2020 Elsevier Ltd. All rights reserved. application/ld+json https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8 Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy MANUAL Foglini, Federica. "Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/f857a3cd-4a0a-404a-8fe5-c5625d9d6da8. 217 https://api.rohub.org/api/resources/f48883c7-9c0a-41e5-adc5-06936c535f0f/download/ 2021-12-10 09:58:35.222002+00:00 2021-12-10 09:58:35.222847+00:00 Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48-60% average reduction in Italy. This offered an unprecedented opportunity to assess how a prolonged (similar to 2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020-30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019-02/05/2019). NO2, O-3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data. NO2 levels remarkably dropped over all urban areas (from-24.9% in Milan to-59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O-3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O-3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOC slimited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of -13-17% counterbalanced by increases up to -9%. Higher household heating usage (+16-19% in March), also driven by colder weather conditions than 2019 (-0.2 to -0.8 degrees C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health. (C) 2020 Elsevier Ltd. All rights reserved. text/plain Quantifying road traffic impact on air quality in urban areas: A Covid19-induced lockdown analysis in Italy 2021-12-10 09:58:35.222002+00:00 service-account-generation-service Ecology maritime settings of a coastal region Aqua Alta Oceanographic Tower Sci. total fishing activity Depellegrin fishing research coastline policy activity result implementation fishing boat consequence Elsevier B.V. effects of covid 19 reboot North-Eastern Adriatic Sea place setting transportation lockdown measure Venetia Europe lockdown effect total lockdown policy watercraft and nautical navigation activity data from fishing vessels Italy settings of a coastal region scientific maritime federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5623 https://api.rohub.org/api/ros/ff341157-8430-4ce9-857d-c0cb88121baf/crate/download/ 2021-12-10 09:58:39.548364+00:00 2025-03-05 01:26:34.980605+00:00 2021-12-10 09:58:39.548364+00:00 The spread of coronavirus (COVID-19) caused an unprecedented implementation of lockdown measures across world's nations. Veneto Region, located in North-Eastern Adriatic Sea was one of the first maritime regions in Italy and Europe subjected to progressive lockdown restrictions. We systematically analyse the effects of national lockdown policies on maritime settings of the region using Automated Identification System (AIS) data from fishing vessels, passenger ships, tanker and cargo vessels collected through the Aqua Alta Oceanographic Tower (AAOT). We derive consequences on vessel activities during the March-April 2020 lockdown, by using a data-driven, comparative spatio-temporal analysis of vessel trajectories. Results show that compared to the same period of 2017, vessel activity were reduced by 69% during the lockdown, fishing activities reduced by 84% and passenger traffic by 78%. We register a restart of fishing activity in the third week of April 2020. We suggest that the presented conceptual and spatial assessment protocol can guide future research on environmental and socioeconomic effects of COVID-19 on marine realms and contribute to further interdisciplinary research with other marine scientific fields. (C) 2020 Published by Elsevier B.V. application/ld+json https://w3id.org/ro-id/ff341157-8430-4ce9-857d-c0cb88121baf The effects of COVID-19 induced lockdown measures on maritime settings of a coastal region MANUAL Foglini, Federica. "The effects of COVID-19 induced lockdown measures on maritime settings of a coastal region." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/ff341157-8430-4ce9-857d-c0cb88121baf. 184 https://api.rohub.org/api/resources/00af6e20-1132-4e5b-b2c9-9d2eac522116/download/ 2021-12-10 09:58:43.646874+00:00 2021-12-10 09:58:43.648084+00:00 The spread of coronavirus (COVID-19) caused an unprecedented implementation of lockdown measures across world's nations. Veneto Region, located in North-Eastern Adriatic Sea was one of the first maritime regions in Italy and Europe subjected to progressive lockdown restrictions. We systematically analyse the effects of national lockdown policies on maritime settings of the region using Automated Identification System (AIS) data from fishing vessels, passenger ships, tanker and cargo vessels collected through the Aqua Alta Oceanographic Tower (AAOT). We derive consequences on vessel activities during the March-April 2020 lockdown, by using a data-driven, comparative spatio-temporal analysis of vessel trajectories. Results show that compared to the same period of 2017, vessel activity were reduced by 69% during the lockdown, fishing activities reduced by 84% and passenger traffic by 78%. We register a restart of fishing activity in the third week of April 2020. We suggest that the presented conceptual and spatial assessment protocol can guide future research on environmental and socioeconomic effects of COVID-19 on marine realms and contribute to further interdisciplinary research with other marine scientific fields. (C) 2020 Published by Elsevier B.V. text/plain The effects of COVID-19 induced lockdown measures on maritime settings of a coastal region 2021-12-10 09:58:43.646874+00:00 service-account-generation-service Ecology geophysics Sci. total Venice legislation Journal of geophysical research. Biogeosciences physics high water transparency finance Venice Lagoon water transparency lagoon transparency bill restriction stressor impact traffic Braga Venice composite L. COVID-19 lockdown measure lockdown measure quantitative analysis Venice Venice Lagoon vessel traffic lagoon of Venice lockdown lockdown total aftermath Braga Italy Manfe federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5613 https://api.rohub.org/api/ros/f648a0a7-ad3f-471c-a377-21cc65e7ef67/crate/download/ 2021-12-10 09:58:48.285075+00:00 2025-03-05 00:50:03.161437+00:00 2021-12-10 09:58:48.285075+00:00 The lagoon of Venice has always been affected by the regional geomorphological evolution, anthropogenic stressors and global changes. Different morphological settings and variable biogeophysical conditions characterize this continuously evolving system that rapidly responds to the anthropic impacts. When the lockdown measures were enforced in Italy to control the spread of the SARS-CoV-2 infection on March 10th 2020, the ordinary urban water traffic around Venice, one of the major pressures in the lagoon, came to a halt. This provided a unique opportunity to analyse the environmental effects of restrictions to mobility on water transparency. Pseudo true-colour composites Sentinel-2 satellite imagery proved useful for qualitative visual interpretation, showing the reduction of the vessel traffic and their wakes from the periods before and during the SARS-CoV-2 outbreak. A quantitative analysis of suspended matter patterns, based on satellite-derived turbidity, in the absence of traffic perturbations, allowed to focus on natural processes and the residual stress from human activities that continued throughout the lockdown. We conclude that the high water transparency can be considered as a transient condition determined by a combination of natural seasonal factors and the effects of COVID-19 restrictions. application/ld+json https://w3id.org/ro-id/f648a0a7-ad3f-471c-a377-21cc65e7ef67 COVID-19 lockdown measures reveal human impact on water transparency in the Venice Lagoon MANUAL Foglini, Federica. "COVID-19 lockdown measures reveal human impact on water transparency in the Venice Lagoon." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/f648a0a7-ad3f-471c-a377-21cc65e7ef67. 184 https://api.rohub.org/api/resources/43f634c1-11b0-4b4a-861b-5661573b4658/download/ 2021-12-10 09:58:52.938054+00:00 2021-12-10 09:58:52.938898+00:00 The lagoon of Venice has always been affected by the regional geomorphological evolution, anthropogenic stressors and global changes. Different morphological settings and variable biogeophysical conditions characterize this continuously evolving system that rapidly responds to the anthropic impacts. When the lockdown measures were enforced in Italy to control the spread of the SARS-CoV-2 infection on March 10th 2020, the ordinary urban water traffic around Venice, one of the major pressures in the lagoon, came to a halt. This provided a unique opportunity to analyse the environmental effects of restrictions to mobility on water transparency. Pseudo true-colour composites Sentinel-2 satellite imagery proved useful for qualitative visual interpretation, showing the reduction of the vessel traffic and their wakes from the periods before and during the SARS-CoV-2 outbreak. A quantitative analysis of suspended matter patterns, based on satellite-derived turbidity, in the absence of traffic perturbations, allowed to focus on natural processes and the residual stress from human activities that continued throughout the lockdown. We conclude that the high water transparency can be considered as a transient condition determined by a combination of natural seasonal factors and the effects of COVID-19 restrictions. text/plain COVID-19 lockdown measures reveal human impact on water transparency in the Venice Lagoon 2021-12-10 09:58:52.938054+00:00 service-account-generation-service Ecology Valencia Sci. total pollutant emissions Sicard ecology mean concentration inorganic chemistry medicine Ampli fied ozone pollution city ozone pollution effect of lockdown Wuhan China pollution xu Valencia Nice Turin Rome institutions fied ozone pollution in city Europe lockdown effect lockdown Nice Rome concentration total mean pollutant emission Ampli fied ozone pollution in city China NO2 South Europe Turin Wuhan scientific fied ozone pollution federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5965 https://api.rohub.org/api/ros/49f56f03-89b6-4956-880b-182aa3d41992/crate/download/ 2021-12-10 09:58:55.698529+00:00 2025-03-05 00:46:16.654410+00:00 2021-12-10 09:58:55.698529+00:00 The effect of lockdown due to coronavirus disease (COVID-19) pandemic on air pollution in four Southern European cities (Nice, Rome, Valencia and Turin) and Wuhan (China) was quantified, with a focus on ozone (O-3). Compared to the same period in 20172019, the daily O-3 mean concentrations increased at urban stations by 24% in Nice, 14% in Rome, 27% in Turin, 2.4% in Valencia and 36% in Wuhan during the lockdown in 2020. This increase in O-3 concentrations is mainly explained by an unprecedented reduction in NOx emissions leading to a lower O-3 titration by NO. Strong reductions in NO2 mean concentrations were observed in all European cities, similar to 53% at urban stations, comparable to Wuhan (57%), and similar to 65% at traffic stations. NO declined even further, similar to 63% at urban stations and similar to 78% at traffic stations in Europe. Reductions in PM2.5 and PM10 at urban stations were overall much smaller both in magnitude and relative change in Europe (similar to 8%) than in Wuhan (similar to 42%). The PM reductions due to limiting transportation and fuel combustion in institutional and commercial buildings were partly offset by increases of PM emissions from the activities at home in some of the cities. The NOx concentrations during the lockdown were on average 49% lower than those at weekends of the previous years in all cities. The lockdown effect on O-3 production was similar to 10% higher than the weekend effect in Southern Europe and 38% higher in Wuhan, while for PM the lockdown had the same effect as weekends in Southern Europe (similar to 6% of difference). This study highlights the challenge of reducing the formation of secondary pollutants such as O-3 even with strict measures to control primary pollutant emissions. These results are relevant for designing abatement policies of urban pollution. application/ld+json https://w3id.org/ro-id/49f56f03-89b6-4956-880b-182aa3d41992 Ampli fied ozone pollution in cities during the COVID-19 lockdown MANUAL Foglini, Federica. "Ampli fied ozone pollution in cities during the COVID-19 lockdown." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/49f56f03-89b6-4956-880b-182aa3d41992. 207 https://api.rohub.org/api/resources/7c888cef-1ba0-4783-a3a8-f8774d35b3cb/download/ 2021-12-10 09:58:58.521856+00:00 2021-12-10 09:58:58.522896+00:00 The effect of lockdown due to coronavirus disease (COVID-19) pandemic on air pollution in four Southern European cities (Nice, Rome, Valencia and Turin) and Wuhan (China) was quantified, with a focus on ozone (O-3). Compared to the same period in 20172019, the daily O-3 mean concentrations increased at urban stations by 24% in Nice, 14% in Rome, 27% in Turin, 2.4% in Valencia and 36% in Wuhan during the lockdown in 2020. This increase in O-3 concentrations is mainly explained by an unprecedented reduction in NOx emissions leading to a lower O-3 titration by NO. Strong reductions in NO2 mean concentrations were observed in all European cities, similar to 53% at urban stations, comparable to Wuhan (57%), and similar to 65% at traffic stations. NO declined even further, similar to 63% at urban stations and similar to 78% at traffic stations in Europe. Reductions in PM2.5 and PM10 at urban stations were overall much smaller both in magnitude and relative change in Europe (similar to 8%) than in Wuhan (similar to 42%). The PM reductions due to limiting transportation and fuel combustion in institutional and commercial buildings were partly offset by increases of PM emissions from the activities at home in some of the cities. The NOx concentrations during the lockdown were on average 49% lower than those at weekends of the previous years in all cities. The lockdown effect on O-3 production was similar to 10% higher than the weekend effect in Southern Europe and 38% higher in Wuhan, while for PM the lockdown had the same effect as weekends in Southern Europe (similar to 6% of difference). This study highlights the challenge of reducing the formation of secondary pollutants such as O-3 even with strict measures to control primary pollutant emissions. These results are relevant for designing abatement policies of urban pollution. text/plain Ampli fied ozone pollution in cities during the COVID-19 lockdown 2021-12-10 09:58:58.521856+00:00 service-account-generation-service Ecology psychology diffusion of covid 19 suggested strategy determine the diffusion transmission dynamics medicine city health epidemic transmission dynamics air pollution strategy infectivity Sci. Total Environ diffusion health sector Coccia viral infectivity PM10 factor Northern Italy environment human-to-human transmission Total Environ accelerate viral infectivity federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 7311 https://api.rohub.org/api/ros/814757a0-5d2f-4573-90c5-4b6676714487/crate/download/ 2021-12-10 09:59:01.828213+00:00 2025-03-05 00:50:44.213463+00:00 2021-12-10 09:59:01.828213+00:00 This study has two goals. The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats similar to COVID-19 having an accelerated viral infectivity in society. Using data on sample of N= 55 Italian province capitals, and data of infected individuals at as of April 7th, 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 mu mor less in diameter) or ozone. In particular, hinterland cities with average high number of days exceeding the limits set for PM10 (and also having a lowwind speed) have a very high number of infected people on 7th April 2020 (arithmetic mean is about 2200 infected individuals, with average polluted days greater than 80 days per year), whereas coastal cities also having days exceeding the limits set for PM10 or ozone but with high wind speed have about 944.70 average infected individuals, with about 60 average polluted days per year; moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3350 infected individuals, 7th April 2020), whereas cities having less than 100 days of air pollution per year, they have a lower average number of infected people (about 10(14) individuals). The findings here also suggest that tominimize the impact of future epidemics similar to COVID-19, the max number of days per year that Italian provincial capitals or similar industrialized cities can exceed the limits set for PM10 or for ozone, considering theirmeteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities seems to be a more important predictor in the initial phase of diffusion of viral infectivity (on 17th March 2020, b(1)= 1.27, p > 0.001) than interpersonal contacts (b(2)= 0.31, p < 0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7th April 2020 with b'(1) = 0.81, p < 0.001) also because of the indirect effect of lockdown, whereas regression coefficient of transmission based on interpersonal contacts has a stable level (b'(2)= 0.31, p < 0.01). This result reveals that accelerated transmission dynamics of COVID-19 is due tomainly to the mechanismof air pollution-to-human transmission (airborne viral infectivity) rather than human-to-human transmission. Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region and/or city (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study here suggest that a proactive strategy to cope with future epidemics is also to apply especially an environmental and sustainable policy based on reduction of levels of air pollution mainly in hinterland and polluting cities- (having low wind speed, high percentage of moisture and number of fog days)-that seem to have an environment that foster a fast transmission dynamics of viral infectivity in society. Hence, in the presence of polluting industrialization in regions that can trigger the mechanism of air pollutionto-human transmission dynamics of viral infectivity, this study must conclude that a comprehensive strategy to prevent future epidemics similar to COVID-19 has to be also designed in environmental and socioeconomic terms, that is also based on sustainability science and environmental science, and not only in terms of biology, medicine, healthcare and health sector. (C) 2020 Elsevier B.V. All rights reserved. application/ld+json https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487 Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID MANUAL Foglini, Federica. "Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487. 184 https://api.rohub.org/api/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44/download/ 2021-12-10 09:59:04.387384+00:00 2021-12-10 09:59:04.388609+00:00 This study has two goals. The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats similar to COVID-19 having an accelerated viral infectivity in society. Using data on sample of N= 55 Italian province capitals, and data of infected individuals at as of April 7th, 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 mu mor less in diameter) or ozone. In particular, hinterland cities with average high number of days exceeding the limits set for PM10 (and also having a lowwind speed) have a very high number of infected people on 7th April 2020 (arithmetic mean is about 2200 infected individuals, with average polluted days greater than 80 days per year), whereas coastal cities also having days exceeding the limits set for PM10 or ozone but with high wind speed have about 944.70 average infected individuals, with about 60 average polluted days per year; moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3350 infected individuals, 7th April 2020), whereas cities having less than 100 days of air pollution per year, they have a lower average number of infected people (about 10(14) individuals). The findings here also suggest that tominimize the impact of future epidemics similar to COVID-19, the max number of days per year that Italian provincial capitals or similar industrialized cities can exceed the limits set for PM10 or for ozone, considering theirmeteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities seems to be a more important predictor in the initial phase of diffusion of viral infectivity (on 17th March 2020, b(1)= 1.27, p > 0.001) than interpersonal contacts (b(2)= 0.31, p < 0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7th April 2020 with b'(1) = 0.81, p < 0.001) also because of the indirect effect of lockdown, whereas regression coefficient of transmission based on interpersonal contacts has a stable level (b'(2)= 0.31, p < 0.01). This result reveals that accelerated transmission dynamics of COVID-19 is due tomainly to the mechanismof air pollution-to-human transmission (airborne viral infectivity) rather than human-to-human transmission. Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region and/or city (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study here suggest that a proactive strategy to cope with future epidemics is also to apply especially an environmental and sustainable policy based on reduction of levels of air pollution mainly in hinterland and polluting cities- (having low wind speed, high percentage of moisture and number of fog days)-that seem to have an environment that foster a fast transmission dynamics of viral infectivity in society. Hence, in the presence of polluting industrialization in regions that can trigger the mechanism of air pollutionto-human transmission dynamics of viral infectivity, this study must conclude that a comprehensive strategy to prevent future epidemics similar to COVID-19 has to be also designed in environmental and socioeconomic terms, that is also based on sustainability science and environmental science, and not only in terms of biology, medicine, healthcare and health sector. (C) 2020 Elsevier B.V. All rights reserved. text/plain Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID 2021-12-10 09:59:04.387384+00:00 service-account-generation-service Photographic industry Ecology Earth observation Svalbard Operational Activities SIOS Remote Sensing Working Group Norway RS observation remote sensing training course geology medicine State of the Environmental Science in Svalbard scientific discipline pandemic research data remote sensing campaign newspaper Svalbard open enrollment Remote Sensing Working Group SIOS's earth observation SIOS remote sensing service training course information federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 7427 https://api.rohub.org/api/ros/60c926fe-f511-4350-ab0d-b9dc8b0e0e97/crate/download/ 2021-12-10 09:59:07.786336+00:00 2025-03-05 01:19:07.322452+00:00 2021-12-10 09:59:07.786336+00:00 Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of research institutions studying the environment and climate in and around Svalbard. SIOS is developing an efficient observing system, where researchers share technology, experience, and data, work together to close knowledge gaps, and decrease the environmental footprint of science. SIOS maintains and facilitates various scientific activities such as the State of the Environmental Science in Svalbard (SESS) report, international access to research infrastructure in Svalbard, Earth observation and remote sensing services, training courses for the Arctic science community, and open access to data. This perspective paper highlights the activities of SIOS Knowledge Centre, the central hub of SIOS, and the SIOS Remote Sensing Working Group (RSWG) in response to the unprecedented situation imposed by the global pandemic coronavirus (SARS-CoV-2) disease 2019 (COVID-19). The pandemic has affected Svalbard research in several ways. When Norway declared a nationwide lockdown to decrease the rate of spread of the COVID-19 in the community, even more strict measures were taken to protect the Svalbard community from the potential spread of the disease. Due to the lockdown, travel restrictions, and quarantine regulations declared by many nations, most physical meetings, training courses, conferences, and workshops worldwide were cancelled by the first week of March 2020. The resumption of physical scientific meetings is still uncertain in the foreseeable future. Additionally, field campaigns to polar regions, including Svalbard, were and remain severely affected. In response to this changing situation, SIOS initiated several operational activities suitable to mitigate the new challenges resulting from the pandemic. This article provides an extensive overview of SIOS's Earth observation (EO), remote sensing (RS) and other operational activities strengthened and developed in response to COVID-19 to support the Svalbard scientific community in times of cancelled/postponed field campaigns in Svalbard. These include (1) an initiative to patch up field data (in situ) with RS observations, (2) a logistics sharing notice board for effective coordinating field activities in the pandemic times, (3) a monthly webinar series and panel discussion on EO talks, (4) an online conference on EO and RS, (5) the SIOS's special issue in the Remote Sensing (MDPI) journal, (6) the conversion of a terrestrial remote sensing training course into an online edition, and (7) the announcement of opportunity (AO) in airborne remote sensing for filling the data gaps using aerial imagery and hyperspectral data. As SIOS is a consortium of 24 research institutions from 9 nations, this paper also presents an extensive overview of the activities from a few research institutes in pandemic times and highlights our upcoming activities for the next year 2021. Finally, we provide a critical perspective on our overall response, possible broader impacts, relevance to other observing systems, and future directions. We hope that our practical services, experiences, and activities implemented in these difficult times will motivate other similar monitoring programs and observing systems when responding to future challenging situations. With a broad scientific audience in mind, we present our perspective paper on activities in Svalbard as a case study. application/ld+json https://w3id.org/ro-id/60c926fe-f511-4350-ab0d-b9dc8b0e0e97 SIOS's Earth Observation (EO), Remote Sensing (RS), and Operational Activities in Response to COVID-19 MANUAL Foglini, Federica. "SIOS's Earth Observation (EO), Remote Sensing (RS), and Operational Activities in Response to COVID-19." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/60c926fe-f511-4350-ab0d-b9dc8b0e0e97. 537 https://api.rohub.org/api/resources/7736b11b-c4f3-4bdf-bec0-3bfa831c3463/download/ 2021-12-10 09:59:10.555798+00:00 2021-12-10 09:59:10.557166+00:00 Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of research institutions studying the environment and climate in and around Svalbard. SIOS is developing an efficient observing system, where researchers share technology, experience, and data, work together to close knowledge gaps, and decrease the environmental footprint of science. SIOS maintains and facilitates various scientific activities such as the State of the Environmental Science in Svalbard (SESS) report, international access to research infrastructure in Svalbard, Earth observation and remote sensing services, training courses for the Arctic science community, and open access to data. This perspective paper highlights the activities of SIOS Knowledge Centre, the central hub of SIOS, and the SIOS Remote Sensing Working Group (RSWG) in response to the unprecedented situation imposed by the global pandemic coronavirus (SARS-CoV-2) disease 2019 (COVID-19). The pandemic has affected Svalbard research in several ways. When Norway declared a nationwide lockdown to decrease the rate of spread of the COVID-19 in the community, even more strict measures were taken to protect the Svalbard community from the potential spread of the disease. Due to the lockdown, travel restrictions, and quarantine regulations declared by many nations, most physical meetings, training courses, conferences, and workshops worldwide were cancelled by the first week of March 2020. The resumption of physical scientific meetings is still uncertain in the foreseeable future. Additionally, field campaigns to polar regions, including Svalbard, were and remain severely affected. In response to this changing situation, SIOS initiated several operational activities suitable to mitigate the new challenges resulting from the pandemic. This article provides an extensive overview of SIOS's Earth observation (EO), remote sensing (RS) and other operational activities strengthened and developed in response to COVID-19 to support the Svalbard scientific community in times of cancelled/postponed field campaigns in Svalbard. These include (1) an initiative to patch up field data (in situ) with RS observations, (2) a logistics sharing notice board for effective coordinating field activities in the pandemic times, (3) a monthly webinar series and panel discussion on EO talks, (4) an online conference on EO and RS, (5) the SIOS's special issue in the Remote Sensing (MDPI) journal, (6) the conversion of a terrestrial remote sensing training course into an online edition, and (7) the announcement of opportunity (AO) in airborne remote sensing for filling the data gaps using aerial imagery and hyperspectral data. As SIOS is a consortium of 24 research institutions from 9 nations, this paper also presents an extensive overview of the activities from a few research institutes in pandemic times and highlights our upcoming activities for the next year 2021. Finally, we provide a critical perspective on our overall response, possible broader impacts, relevance to other observing systems, and future directions. We hope that our practical services, experiences, and activities implemented in these difficult times will motivate other similar monitoring programs and observing systems when responding to future challenging situations. With a broad scientific audience in mind, we present our perspective paper on activities in Svalbard as a case study. text/plain SIOS's Earth Observation (EO), Remote Sensing (RS), and Operational Activities in Response to COVID-19 2021-12-10 09:59:10.555798+00:00 service-account-generation-service Meteorology Ecology impacts of COVID-19 lockdown Mississippi study of the impacts of COVID-19 lockdown time-series prediction model inorganic chemistry United States of America Res. Lett medicine statistics lithium Qin research Robert E. Lee time series forecast C. Association US city urban mobility nationwide NO2 concentration lockdown lockdown mobility Wong study C. Association between NO2 concentration report Latvian environment Liu NO2 Ratti Qin study of the impact channel diversity federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5346 https://api.rohub.org/api/ros/1167bfef-0c7c-4278-add5-49e85669e4b9/crate/download/ 2021-12-10 09:59:13.216859+00:00 2025-03-05 12:47:08.582493+00:00 2021-12-10 09:59:13.216859+00:00 The massive lockdown of global cities during the COVID-19 pandemic is substantially improving the atmospheric environment, which for the first time, urban mobility is virtually reduced to zero, and it is then possible to establish a baseline for air quality. By comparing these values with pre-COVID-19 data, it is possible to infer the likely effect of urban mobility and spatial configuration on the air quality. In the present study, a time-series prediction model is enhanced to estimate the nationwide NO2 concentrations before and during the lockdown measures in the United States, and 54 cities are included in the study. The prediction generates a notable NO2 difference between the observations if the lockdown is not considered, and the changes in urban mobility can explain the difference. It is found that the changes in urban mobility associated with various road textures have a significant impact on NO2 dispersion in different types of climates. application/ld+json https://w3id.org/ro-id/1167bfef-0c7c-4278-add5-49e85669e4b9 Association between NO2 concentrations and spatial configuration: a study of the impacts of COVID-19 lockdowns in 54 US cities MANUAL Foglini, Federica. "Association between NO2 concentrations and spatial configuration: a study of the impacts of COVID-19 lockdowns in 54 US cities." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/1167bfef-0c7c-4278-add5-49e85669e4b9. 274 https://api.rohub.org/api/resources/114ea0d4-8d42-4a3a-8025-b1aeb1f16d9c/download/ 2021-12-10 09:59:16.265687+00:00 2021-12-10 09:59:16.266495+00:00 The massive lockdown of global cities during the COVID-19 pandemic is substantially improving the atmospheric environment, which for the first time, urban mobility is virtually reduced to zero, and it is then possible to establish a baseline for air quality. By comparing these values with pre-COVID-19 data, it is possible to infer the likely effect of urban mobility and spatial configuration on the air quality. In the present study, a time-series prediction model is enhanced to estimate the nationwide NO2 concentrations before and during the lockdown measures in the United States, and 54 cities are included in the study. The prediction generates a notable NO2 difference between the observations if the lockdown is not considered, and the changes in urban mobility can explain the difference. It is found that the changes in urban mobility associated with various road textures have a significant impact on NO2 dispersion in different types of climates. text/plain Association between NO2 concentrations and spatial configuration: a study of the impacts of COVID-19 lockdowns in 54 US cities 2021-12-10 09:59:16.265687+00:00 service-account-generation-service Meteorology Ecology meteorology areas in France physics Piazzola onshore wind Continental Areas virus wind area weather aerosol humidity sea spray pollution contaminant France aerosol parent aerosol property property sea-spray aerosol virus survey France study propagation transport contamination of the population influence conduct continental area meteorological conditions Bruch chemistry virus transport virus propagation federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 4887 https://api.rohub.org/api/ros/52066ce3-642d-446f-9189-ed0c641bfac5/crate/download/ 2021-12-10 09:59:19.179989+00:00 2025-03-05 00:55:13.910473+00:00 2021-12-10 09:59:19.179989+00:00 Human behaviors probably represent the most important causes of the SARS-Cov-2 virus propagation. However, the role of virus transport by aerosols-and therefore the influence of atmospheric conditions (temperature, humidity, type and concentration of aerosols)-on the spread of the epidemic remains an open and still debated question. This work aims to study whether or not the meteorological conditions related to the different aerosol properties in continental and coastal urbanized areas might influence the atmospheric transport of the SARS-Cov-2 virus. Our analysis focuses on the lockdown period to reduce the differences in the social behavior and highlight those of the weather conditions. As an example, we investigated the contamination cases during March 2020 in two specific French areas located in both continental and coastal areas with regard to the meteorological conditions and the corresponding aerosol properties, the optical depth (AOD) and the Angstrom exponent provided by the AERONET network. The results show that the analysis of aerosol ground-based data can be of interest to assess a virus survey. We found that moderate to strong onshore winds occurring in coastal regions and inducing humid environment and large sea-spray production episodes coincides with smaller COVID-19 contamination rates. We assume that the coagulation of SARS-Cov-2 viral particles with hygroscopic salty sea-spray aerosols might tend to inhibit its viral infectivity via possible reaction with NaCl, especially in high relative humidity environments typical of maritime sites. application/ld+json https://w3id.org/ro-id/52066ce3-642d-446f-9189-ed0c641bfac5 Influence of Meteorological Conditions and Aerosol Properties on the COVID-19 Contamination of the Population in Coastal and Continental Areas in France: Study of Offshore and Onshore Winds MANUAL Foglini, Federica. "Influence of Meteorological Conditions and Aerosol Properties on the COVID-19 Contamination of the Population in Coastal and Continental Areas in France: Study of Offshore and Onshore Winds." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/52066ce3-642d-446f-9189-ed0c641bfac5. 295 https://api.rohub.org/api/resources/c9e881e5-00e8-4a6a-927d-74acf2db4218/download/ 2021-12-10 09:59:22.107663+00:00 2021-12-10 09:59:22.108484+00:00 Human behaviors probably represent the most important causes of the SARS-Cov-2 virus propagation. However, the role of virus transport by aerosols-and therefore the influence of atmospheric conditions (temperature, humidity, type and concentration of aerosols)-on the spread of the epidemic remains an open and still debated question. This work aims to study whether or not the meteorological conditions related to the different aerosol properties in continental and coastal urbanized areas might influence the atmospheric transport of the SARS-Cov-2 virus. Our analysis focuses on the lockdown period to reduce the differences in the social behavior and highlight those of the weather conditions. As an example, we investigated the contamination cases during March 2020 in two specific French areas located in both continental and coastal areas with regard to the meteorological conditions and the corresponding aerosol properties, the optical depth (AOD) and the Angstrom exponent provided by the AERONET network. The results show that the analysis of aerosol ground-based data can be of interest to assess a virus survey. We found that moderate to strong onshore winds occurring in coastal regions and inducing humid environment and large sea-spray production episodes coincides with smaller COVID-19 contamination rates. We assume that the coagulation of SARS-Cov-2 viral particles with hygroscopic salty sea-spray aerosols might tend to inhibit its viral infectivity via possible reaction with NaCl, especially in high relative humidity environments typical of maritime sites. text/plain Influence of Meteorological Conditions and Aerosol Properties on the COVID-19 Contamination of the Population in Coastal and Continental Areas in France: Study of Offshore and Onshore Winds 2021-12-10 09:59:22.107663+00:00 service-account-generation-service Meteorology Ecology ultrafine particles number concentration observation site suburban area in Italy physics particles concentration Donateo medicine magnetic field measure website particle suburb concentration flux measurement site flux Italy particle source PM10 ultrafine particles concentration lockdown work decrease limitation concentration fluxes in a suburban area fluxes of SARS-CoV-2 lockdown chemistry Italy turbulent flux federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 4445 https://api.rohub.org/api/ros/40d5834d-5081-4190-93f8-952808686088/crate/download/ 2021-12-10 09:59:25.107630+00:00 2025-03-05 00:55:11.644009+00:00 2021-12-10 09:59:25.107630+00:00 In order to slow the spread of SARS-CoV-2, governments have implemented several restrictive measures (lockdown, stay-in-place, and quarantine policies). These provisions have drastically changed the routines of residents, altering environmental conditions in the affected areas. In this context, our work analyzes the effects of the reduced emissions during the COVID-19 period on the ultrafine particles number concentration and their turbulent fluxes in a suburban area. COVID-19 restrictions did not significantly reduce anthropogenic related PM10 and PM2.5 levels, with an equal decrement of about 14%. The ultrafine particle number concentration during the lockdown period decreased by 64% in our measurement area, essentially due to the lower traffic activity. The effect of the restriction measures and the reduction of vehicles traffic was predominant in reducing concentration rather than meteorological forcing. During the lockdown in 2020, a decrease of 61% in ultrafine particle positive fluxes can be observed. At the same time, negative fluxes decreased by 59% and our observation site behaved, essentially, as a sink of ultrafine particles. Due to this behavior, we can conclude that the principal particle sources during the lockdown were far away from the measurement site. application/ld+json https://w3id.org/ro-id/40d5834d-5081-4190-93f8-952808686088 Impact on Ultrafine Particles Concentration and Turbulent Fluxes of SARS-CoV-2 Lockdown in a Suburban Area in Italy MANUAL Foglini, Federica. "Impact on Ultrafine Particles Concentration and Turbulent Fluxes of SARS-CoV-2 Lockdown in a Suburban Area in Italy." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/40d5834d-5081-4190-93f8-952808686088. 192 https://api.rohub.org/api/resources/e76849ec-97fb-4204-b263-a15e70dae87f/download/ 2021-12-10 09:59:28.201666+00:00 2021-12-10 09:59:28.202641+00:00 In order to slow the spread of SARS-CoV-2, governments have implemented several restrictive measures (lockdown, stay-in-place, and quarantine policies). These provisions have drastically changed the routines of residents, altering environmental conditions in the affected areas. In this context, our work analyzes the effects of the reduced emissions during the COVID-19 period on the ultrafine particles number concentration and their turbulent fluxes in a suburban area. COVID-19 restrictions did not significantly reduce anthropogenic related PM10 and PM2.5 levels, with an equal decrement of about 14%. The ultrafine particle number concentration during the lockdown period decreased by 64% in our measurement area, essentially due to the lower traffic activity. The effect of the restriction measures and the reduction of vehicles traffic was predominant in reducing concentration rather than meteorological forcing. During the lockdown in 2020, a decrease of 61% in ultrafine particle positive fluxes can be observed. At the same time, negative fluxes decreased by 59% and our observation site behaved, essentially, as a sink of ultrafine particles. Due to this behavior, we can conclude that the principal particle sources during the lockdown were far away from the measurement site. text/plain Impact on Ultrafine Particles Concentration and Turbulent Fluxes of SARS-CoV-2 Lockdown in a Suburban Area in Italy 2021-12-10 09:59:28.201666+00:00 service-account-generation-service Meteorology Ecology South Italy meteorology nanoparticle concentration submicron particle concentration physics medicine chromium pandemic website nanoparticle particle coronavirus concentration pandemic lockdown Lecce reduction Lamezia Terme ACTRIS network Lamezia Terme sites of Southern Italy coronavirus pandemic lockdown number concentration lockdown guaranteed annual wage betterment outbreak distribution atmospheric nanoparticle concentration percentage reduction infection Dinoi chemistry Lecce coronavirus infection outbreak federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5734 https://api.rohub.org/api/ros/36235e43-d179-496e-84bf-2a8a6c3e7a17/crate/download/ 2021-12-10 09:59:31.781022+00:00 2025-03-05 00:55:10.786546+00:00 2021-12-10 09:59:31.781022+00:00 During the new coronavirus infection outbreak, the application of strict containment measures entailed a decrease in most human activities, with the consequent reduction of anthropogenic emissions into the atmosphere. In this study, the impact of lockdown on atmospheric particle number concentrations and size distributions is investigated in two different sites of Southern Italy: Lecce and Lamezia Terme, regional stations of the GAW/ACTRIS networks. The effects of restrictions are quantified by comparing submicron particle concentrations, in the size range from 10 nm to 800 nm, measured during the lockdown period and in the same period of previous years, from 2015 to 2019, considering three time intervals: prelockdown, lockdown and postlockdown. Different percentage reductions in total particle number concentrations are observed, -19% and -23% in Lecce and -7% and -4% in Lamezia Terme during lockdown and postlockdown, respectively, with several variations in each subclass of particles. From the comparison, no significant variations of meteorological factors are observed except a reduction of rainfall in 2020, which might explain the higher levels of particle concentrations measured during prelockdown at both stations. In general, the results demonstrate an improvement of air quality, more conspicuous in Lecce than in Lamezia Terme, during the lockdown, with a differed reduction in the concentration of submicronic particles that depends on the different types of sources, their distance from observational sites and local meteorology. application/ld+json https://w3id.org/ro-id/36235e43-d179-496e-84bf-2a8a6c3e7a17 Impact of the Coronavirus Pandemic Lockdown on Atmospheric Nanoparticle Concentrations in Two Sites of Southern Italy MANUAL Foglini, Federica. "Impact of the Coronavirus Pandemic Lockdown on Atmospheric Nanoparticle Concentrations in Two Sites of Southern Italy." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/36235e43-d179-496e-84bf-2a8a6c3e7a17. 217 https://api.rohub.org/api/resources/b6fad1b7-1afd-4d69-a13c-30506334c57b/download/ 2021-12-10 09:59:34.605282+00:00 2021-12-10 09:59:34.606484+00:00 During the new coronavirus infection outbreak, the application of strict containment measures entailed a decrease in most human activities, with the consequent reduction of anthropogenic emissions into the atmosphere. In this study, the impact of lockdown on atmospheric particle number concentrations and size distributions is investigated in two different sites of Southern Italy: Lecce and Lamezia Terme, regional stations of the GAW/ACTRIS networks. The effects of restrictions are quantified by comparing submicron particle concentrations, in the size range from 10 nm to 800 nm, measured during the lockdown period and in the same period of previous years, from 2015 to 2019, considering three time intervals: prelockdown, lockdown and postlockdown. Different percentage reductions in total particle number concentrations are observed, -19% and -23% in Lecce and -7% and -4% in Lamezia Terme during lockdown and postlockdown, respectively, with several variations in each subclass of particles. From the comparison, no significant variations of meteorological factors are observed except a reduction of rainfall in 2020, which might explain the higher levels of particle concentrations measured during prelockdown at both stations. In general, the results demonstrate an improvement of air quality, more conspicuous in Lecce than in Lamezia Terme, during the lockdown, with a differed reduction in the concentration of submicronic particles that depends on the different types of sources, their distance from observational sites and local meteorology. text/plain Impact of the Coronavirus Pandemic Lockdown on Atmospheric Nanoparticle Concentrations in Two Sites of Southern Italy 2021-12-10 09:59:34.605282+00:00 service-account-generation-service Meteorology Ecology Chem. Phys United Kingdom World Health Organization BC source ambient black carbon ecology Europe due to COVID-19 lockdown Evangeliou medicine World Health Organization pandemic Eastern Europe chemist black carbon country Eastern Europe reduction Germany black carbon Europe black carbon emission Brem Europe France lockdown lockdown lockdown period air quality pollutant emission light absorption impact British Telecom emission China chemistry Italy lockdown impact Spain Eckhardt Wuhan federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 6340 https://api.rohub.org/api/ros/629b2be8-2457-44e9-bf8f-8aaba3fde6a8/crate/download/ 2021-12-10 09:59:37.625454+00:00 2025-03-05 00:48:40.947446+00:00 2021-12-10 09:59:37.625454+00:00 Following the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for COVID-19 in December 2019 in Wuhan (China) and its spread to the rest of the world, the World Health Organization declared a global pandemic in March 2020. Without effective treatment in the initial pandemic phase, social distancing and mandatory quarantines were introduced as the only available preventative measure. In contrast to the detrimental societal impacts, air quality improved in all countries in which strict lockdowns were applied, due to lower pollutant emissions. Here we investigate the effects of the COVID-19 lockdowns in Europe on ambient black carbon (BC), which affects climate and damages health, using in situ observations from 17 European stations in a Bayesian inversion framework. BC emissions declined by 23 kt in Europe (20% in Italy, 40% in Germany, 34% in Spain, 22% in France) during lockdowns compared to the same period in the previous 5 years, which is partially attributed to COVID-19 measures. BC temporal variation in the countries enduring the most drastic restrictions showed the most distinct lockdown impacts. Increased particle light absorption in the beginning of the lockdown, confirmed by assimilated satellite and remote sensing data, suggests residential combustion was the dominant BC source. Accordingly, in central and Eastern Europe, which experienced lower than average temperatures, BC was elevated compared to the previous 5 years. Nevertheless, an average decrease of 11% was seen for the whole of Europe compared to the start of the lockdown period, with the highest peaks in France (42 %), Germany (21 %), UK (13 %), Spain (11 %) and Italy (8 %). Such a decrease was not seen in the previous years, which also confirms the impact of COVID-19 on the European emissions of BC. application/ld+json https://w3id.org/ro-id/629b2be8-2457-44e9-bf8f-8aaba3fde6a8 Changes in black carbon emissions over Europe due to COVID-19 lockdowns MANUAL Foglini, Federica. "Changes in black carbon emissions over Europe due to COVID-19 lockdowns." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/629b2be8-2457-44e9-bf8f-8aaba3fde6a8. 405 https://api.rohub.org/api/resources/5446a5a9-d79e-41e6-9673-626bb47b240f/download/ 2021-12-10 09:59:40.457611+00:00 2021-12-10 09:59:40.458628+00:00 Following the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for COVID-19 in December 2019 in Wuhan (China) and its spread to the rest of the world, the World Health Organization declared a global pandemic in March 2020. Without effective treatment in the initial pandemic phase, social distancing and mandatory quarantines were introduced as the only available preventative measure. In contrast to the detrimental societal impacts, air quality improved in all countries in which strict lockdowns were applied, due to lower pollutant emissions. Here we investigate the effects of the COVID-19 lockdowns in Europe on ambient black carbon (BC), which affects climate and damages health, using in situ observations from 17 European stations in a Bayesian inversion framework. BC emissions declined by 23 kt in Europe (20% in Italy, 40% in Germany, 34% in Spain, 22% in France) during lockdowns compared to the same period in the previous 5 years, which is partially attributed to COVID-19 measures. BC temporal variation in the countries enduring the most drastic restrictions showed the most distinct lockdown impacts. Increased particle light absorption in the beginning of the lockdown, confirmed by assimilated satellite and remote sensing data, suggests residential combustion was the dominant BC source. Accordingly, in central and Eastern Europe, which experienced lower than average temperatures, BC was elevated compared to the previous 5 years. Nevertheless, an average decrease of 11% was seen for the whole of Europe compared to the start of the lockdown period, with the highest peaks in France (42 %), Germany (21 %), UK (13 %), Spain (11 %) and Italy (8 %). Such a decrease was not seen in the previous years, which also confirms the impact of COVID-19 on the European emissions of BC. text/plain Changes in black carbon emissions over Europe due to COVID-19 lockdowns 2021-12-10 09:59:40.457611+00:00 service-account-generation-service Ecology federica.foglini@ismar.cnr.it Federica Foglini geology 58.410407812669014 0.6472741365432739 patient management 3.5924232527759634 5.5 Int. J. 40.88830829523187 62.6 health 4.88929889298893 5.3 emergency action 5.878510777269758 9.0 As the Italian health system is regionally based, COVID-19 emergency actions are based on a general lockdown imposed by national authority and then management at local level by 21 regional authorities. 12.28733459357278 19.5 pandemic response plan 11.691704768125406 17.9 Lombardy 8.671586715867159 9.4 pandemic 6.4575645756457565 7.0 service-account-enrichment 5564 https://api.rohub.org/api/ros/2cb6e395-bccb-4a36-a88a-f52f222e6aa3/crate/download/ 2021-12-10 09:59:43.703633+00:00 2025-03-05 00:46:18.902167+00:00 2021-12-10 09:59:43.703633+00:00 As the Italian health system is regionally based, COVID-19 emergency actions are based on a general lockdown imposed by national authority and then management at local level by 21 regional authorities. Therefore, the pandemic response plan developed by each region led to different approaches. The aim of this paper is to analyze whether differences in patient management may have influenced the local course of the epidemic. The analysis on the 21 Italian regions considers the strategies adopted in terms of hospitalization, treatment in the ICU and at home. Moreover, an in-depth analysis was carried out on: Lombardia, which adopted a hospitalization approach; Veneto, which tended to confine patients at home; and Emilia Romagna, which adopted a mixed hospitalization-home based approach. The majority of regions implemented a home-based approach, while the hospital approach was followed in three regions (Lombardia, Piemonte, and Lazio), mainly limited to the first period of the outbreak. All regions in the later phases tended to reduce hospitalization, preferring to confine patients at home. This comparison, highlighting the different phases of the pandemic, outlined that the adoption of home-based practices contributed to limiting infection rates among patients and health professionals as well as decreasing the number of deaths. application/ld+json https://w3id.org/ro-id/2cb6e395-bccb-4a36-a88a-f52f222e6aa3 Analysis of the Different Approaches Adopted in the Italian Regions to Care for Patients Affected by COVID-19 MANUAL https://w3id.org/ro-id/5c04d90a-500e-4319-bc89-51d65e892a66 https://w3id.org/ro-id/9ecc8253-d098-4fd4-8894-1c7eaf681b03 https://w3id.org/ro-id/3dbf25f9-9d2f-472f-8cb4-dce90b009dc2 https://w3id.org/ro-id/69c2993a-7b19-4642-9040-a335b918648f https://w3id.org/ro-id/7b222b62-9f3a-4fd9-b209-68a4207783a7 https://w3id.org/ro-id/c6b2bab9-60eb-4131-8f1f-65ed82e08459 https://w3id.org/ro-id/d162c5bb-a120-4f73-bfa8-778c64f56e6d https://w3id.org/ro-id/1b047bc7-e592-4ba3-a67a-395fa4e9ba65 https://w3id.org/ro-id/260bcbb2-dd5a-4f70-b088-ce2fdaac260d https://w3id.org/ro-id/283da705-81e5-48df-b01f-c37f5840461c https://w3id.org/ro-id/34ca3f01-3815-44dc-a292-11da9fd6ecf2 https://w3id.org/ro-id/414da530-9606-4ff0-9ce1-cb7b2ead6c80 https://w3id.org/ro-id/5ce8058d-a913-4a6a-ad72-b7a49fc41d1d https://w3id.org/ro-id/733d2e94-a0fd-4a76-8c3b-9887ced0bb49 https://w3id.org/ro-id/8a43c305-ebc5-4b8f-bea3-75be459974a7 https://w3id.org/ro-id/9c974d86-d31e-418c-ad04-1bb4e9ae490e https://w3id.org/ro-id/9d86b31a-0a6d-402b-ae29-ed8c320ba39a https://w3id.org/ro-id/9fa35b61-3f90-480e-9bae-ace65cd5efe3 https://w3id.org/ro-id/a7442850-e8cf-41ef-a23a-7ec782b55505 https://w3id.org/ro-id/e87edd68-8af9-48f6-9c8e-7f29913db96e https://w3id.org/ro-id/eed92be3-806c-4f18-8907-8fb515f70374 https://w3id.org/ro-id/0c04cefc-bd79-4a35-bda4-fd236ae00b8f https://w3id.org/ro-id/63a7da15-138a-4e30-8cad-aa57d1c032b1 https://w3id.org/ro-id/a0a24214-f7a1-4bfa-877a-c4ce8e5ea022 https://w3id.org/ro-id/c2797563-6aa3-4d6a-8e74-a7cd122ce144 https://w3id.org/ro-id/5b3154cb-cbd8-42fe-90e2-05df59beed82 https://w3id.org/ro-id/772151fd-a140-4e48-9c7a-d9b61e106f93 https://w3id.org/ro-id/84b31486-d952-481e-8aff-f338cdd25499 https://w3id.org/ro-id/a46e6eb5-7d19-4e53-8d4b-789a11d516f9 https://w3id.org/ro-id/af35ae6b-7e7b-4f5b-8a15-ba5e55cf906a https://w3id.org/ro-id/3f60f9c4-1b50-4f4d-806b-ac6a7c3e4347 https://w3id.org/ro-id/50c7826d-25a4-4f46-8ca1-9b0b5a81571c https://w3id.org/ro-id/8296c3cc-f553-4298-86ea-b9eca0fcfd46 https://w3id.org/ro-id/9fd6ba00-aeb4-4e9e-96db-9520bc5f63cf https://w3id.org/ro-id/a05527bb-e071-476f-894f-df5f7f9cbb9c https://w3id.org/ro-id/a5fd6650-e8ba-401c-add8-dd81cfddccbf https://w3id.org/ro-id/bb7a5ec9-2874-4a19-b588-a106e2b39741 https://w3id.org/ro-id/c1891194-360f-42fa-b46a-8c4af5a33fdc https://w3id.org/ro-id/c2ccd306-b323-4578-b664-36139727e8c9 https://w3id.org/ro-id/d25fab40-4250-4877-aa5a-5a491b0feb17 https://w3id.org/ro-id/f8bb1f0f-a4b3-4b55-9736-f698775b68b3 https://w3id.org/ro-id/f9cd9900-1faa-4506-b6b0-b13c7378f1ea https://w3id.org/ro-id/b7cf6d88-379d-4e12-b0f0-c88a38a04751 https://w3id.org/ro-id/f7c33079-5ec6-48db-abac-7e05a600c0f2 https://w3id.org/ro-id/0f74e125-e765-4ce2-a534-f5811c080910 https://w3id.org/ro-id/1a692bc1-c508-4075-b2a7-c91f81797559 https://w3id.org/ro-id/1de81368-789a-4fdf-afe7-ea08bceff14d https://w3id.org/ro-id/23411119-c5d2-4dc3-8f53-dd3920c17465 https://w3id.org/ro-id/747fb36a-cab1-45b0-bf56-832bb30851b4 https://w3id.org/ro-id/7841a673-faa7-47bb-b58b-3846950e7001 https://w3id.org/ro-id/92d6b73e-c950-46d7-94dc-f4e001e9c974 https://w3id.org/ro-id/a93e292f-e018-4173-9996-be5c588c22f7 https://w3id.org/ro-id/d7dc09a6-17b6-4bc4-882c-9fde30427577 https://w3id.org/ro-id/ea324d03-324b-42e0-8402-41e7f7a116fa https://w3id.org/ro-id/1f8af77d-5d2d-45f7-b168-70a300971b37 https://w3id.org/ro-id/3d0607ef-f907-4d28-9ec9-fc1751b29808 https://w3id.org/ro-id/52771653-358c-4717-8beb-b17eedec0973 https://w3id.org/ro-id/6ba387b2-c79b-4f1c-baf1-192f97b88bfc https://w3id.org/ro-id/9d8c05aa-712d-4d86-83f5-a08b2c66c25f https://w3id.org/ro-id/ec018e1d-a1e1-457f-bdab-6669b2ce39f8 Foglini, Federica. "Analysis of the Different Approaches Adopted in the Italian Regions to Care for Patients Affected by COVID-19." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/2cb6e395-bccb-4a36-a88a-f52f222e6aa3. 208 https://api.rohub.org/api/resources/e78611f1-e1ff-4282-94aa-ae2a4c2e4116/download/ 2021-12-10 09:59:47.189985+00:00 2021-12-10 09:59:47.191036+00:00 As the Italian health system is regionally based, COVID-19 emergency actions are based on a general lockdown imposed by national authority and then management at local level by 21 regional authorities. Therefore, the pandemic response plan developed by each region led to different approaches. The aim of this paper is to analyze whether differences in patient management may have influenced the local course of the epidemic. The analysis on the 21 Italian regions considers the strategies adopted in terms of hospitalization, treatment in the ICU and at home. Moreover, an in-depth analysis was carried out on: Lombardia, which adopted a hospitalization approach; Veneto, which tended to confine patients at home; and Emilia Romagna, which adopted a mixed hospitalization-home based approach. The majority of regions implemented a home-based approach, while the hospital approach was followed in three regions (Lombardia, Piemonte, and Lazio), mainly limited to the first period of the outbreak. All regions in the later phases tended to reduce hospitalization, preferring to confine patients at home. This comparison, highlighting the different phases of the pandemic, outlined that the adoption of home-based practices contributed to limiting infection rates among patients and health professionals as well as decreasing the number of deaths. text/plain Analysis of the Different Approaches Adopted in the Italian Regions to Care for Patients Affected by COVID-19 2021-12-10 09:59:47.189985+00:00 testing 5.1660516605166045 5.6 Pecoraro, F; Luzi, D; Clemente, F. Analysis of the Different Approaches Adopted in the Italian Regions to Care for Patients Affected by COVID-19. 54.631379962192824 86.7 Piedmont https://www.wikidata.org/wiki/Q1216 J. 6.058446186742693 8.5 Emilia Romagna 6.365313653136531 6.9 patient 20.171062009978616 28.3 Moreover, an in-depth analysis was carried out on: Lombardia, which adopted a hospitalization approach; Veneto, which tended to confine patients at home; and Emilia Romagna, which adopted a mixed hospitalization-home based approach. 12.917454316320102 20.5 Epidemic Health/Diseases and conditions/Communicable disease/Epidemic medicine 79.69924812030075 21.2 plan 4.704797047970479 5.1 earth sciences 41.589592187330986 0.4608744978904724 Emilia Romagna https://www.wikidata.org/wiki/Q1263 Analysis of the Different Approaches Adopted in the Italian Regions to Care for Patients Affected by COVID-19. 11.909262759924385 18.9 phase 3.5055350553505535 3.8 volume 18 0.914435009797518 1.4 Health Health Res. public health 2021 3.3311561071195293 5.1 Lombardy https://www.wikidata.org/wiki/Q1210 Venetia 4.1339985744832495 5.8 Health facility Health/Health facility hospitalization 9.04059040590406 9.8 hospitalization approach 9.732201175702155 14.9 research 3.044280442804428 3.3 Venetia 7.380073800738007 8.0 (Int. J. Environ. 6.931316950220542 11.0 hospitals 20.30075187969925 5.4 lockdown 4.52029520295203 4.9 hospitalization 5.844618674269421 8.2 Luzi 6.557377049180326 9.2 geochemistry 41.589592187330986 0.4608744978904724 Capital punishment Crime, law and justice/Law enforcement/Punishment (criminal)/Capital punishment covid 19 17.177476835352813 24.1 patient 27.398523985239855 29.700000000000003 health system 5.355976485956891 8.2 Adoption Society/Family/Adoption life sciences 100.0 1.6412524580955505 Clemente 8.838203848895224 12.4 pandemic 4.1339985744832495 5.8 earth sciences 58.410407812669014 0.6472741365432739 F. Analysis 10.192444761225943 14.3 Lazio https://www.wikidata.org/wiki/Q1282 Venetia https://www.wikidata.org/wiki/Q1243 Pecoraro 7.483962936564504 10.5 F. Analysis of the different approach 11.887655127367735 18.2 authority 5.350553505535055 5.8 care for patient 6.7276290006531685 10.3 Res. Public Health 2021, Volume 18, issue 3) 1.3232514177693764 2.1 approach 3.5055350553505535 3.8 life sciences (general) 100.0 1.6412524580955505 Emilia Romagna 3.9201710620099783 5.5 Lombardy 5.48823948681397 7.7 service-account-generation-service Ecology psychology urban video Zabini Int. J. computer video forest environment medicine television system pandemic video inhalation value mean effective pressure health effect intrinsic value forest effective Study of the restorative effect Gavazzi forest experience prolonged lockdown Gronchi lockdown J. potency study biogenic volatile organic compounds benchmark environment participant benchmark value effect MP. comparative Study federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5860 https://api.rohub.org/api/ros/b3c216f9-6567-49d0-9c9c-900582f5ed52/crate/download/ 2021-12-10 09:59:50.276706+00:00 2025-03-05 00:47:00.204820+00:00 2021-12-10 09:59:50.276706+00:00 The prolonged lockdown imposed to contain the COrona VIrus Disease 19 COVID-19 pandemic prevented many people from direct contact with nature and greenspaces, raising alarms for a possible worsening of mental health. This study investigated the effectiveness of a simple and affordable remedy for improving psychological well-being, based on audio-visual stimuli brought by a short computer video showing forest environments, with an urban video as a control. Randomly selected participants were assigned the forest or urban video, to look at and listen to early in the morning, and questionnaires to fill out. In particular, the State-Trait Anxiety Inventory (STAI) Form Y collected in baseline condition and at the end of the study and the Part II of the Sheehan Patient Rated Anxiety Scale (SPRAS) collected every day immediately before and after watching the video. The virtual exposure to forest environments showed effective to reduce perceived anxiety levels in people forced by lockdown in limited spaces and environmental deprivation. Although significant, the effects were observed only in the short term, highlighting the limitation of the virtual experiences. The reported effects might also represent a benchmark to disentangle the determinants of health effects due to real forest experiences, for example, the inhalation of biogenic volatile organic compounds (BVOC). application/ld+json https://w3id.org/ro-id/b3c216f9-6567-49d0-9c9c-900582f5ed52 Comparative Study of the Restorative Effects of Forest and Urban Videos during COVID-19 Lockdown: Intrinsic and Benchmark Values MANUAL Foglini, Federica. "Comparative Study of the Restorative Effects of Forest and Urban Videos during COVID-19 Lockdown: Intrinsic and Benchmark Values." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/b3c216f9-6567-49d0-9c9c-900582f5ed52. 374 https://api.rohub.org/api/resources/e9dabf30-3b11-48ad-a755-cdd05ca83b36/download/ 2021-12-10 09:59:52.847900+00:00 2021-12-10 09:59:52.849079+00:00 The prolonged lockdown imposed to contain the COrona VIrus Disease 19 COVID-19 pandemic prevented many people from direct contact with nature and greenspaces, raising alarms for a possible worsening of mental health. This study investigated the effectiveness of a simple and affordable remedy for improving psychological well-being, based on audio-visual stimuli brought by a short computer video showing forest environments, with an urban video as a control. Randomly selected participants were assigned the forest or urban video, to look at and listen to early in the morning, and questionnaires to fill out. In particular, the State-Trait Anxiety Inventory (STAI) Form Y collected in baseline condition and at the end of the study and the Part II of the Sheehan Patient Rated Anxiety Scale (SPRAS) collected every day immediately before and after watching the video. The virtual exposure to forest environments showed effective to reduce perceived anxiety levels in people forced by lockdown in limited spaces and environmental deprivation. Although significant, the effects were observed only in the short term, highlighting the limitation of the virtual experiences. The reported effects might also represent a benchmark to disentangle the determinants of health effects due to real forest experiences, for example, the inhalation of biogenic volatile organic compounds (BVOC). text/plain Comparative Study of the Restorative Effects of Forest and Urban Videos during COVID-19 Lockdown: Intrinsic and Benchmark Values 2021-12-10 09:59:52.847900+00:00 service-account-generation-service Ecology red zone visitation of garden ugs distance land use sports medicine city pandemic case study territory exercise motivation country Pol. 2021 Ugolini visitor of ugs feeling of deprivation feelings of deprivation survey visitation lockdown loss study total missing ugs Italian case study related feelings of deprivation respondent visitor feeling federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5410 https://api.rohub.org/api/ros/affd2ec0-acae-4476-b51c-47c23ec3c73b/crate/download/ 2021-12-10 09:59:56.002024+00:00 2025-03-05 02:47:03.164025+00:00 2021-12-10 09:59:56.002024+00:00 This study investigated perceptions and behavioral patterns related to urban green space (UGS) in Italian cities, during the period of national lockdown imposed due to the outbreak of SARS-CoV-2 in the spring of 2020. A survey was used to examine the responses of population groups in different municipal areas, comparing those in government-defined ?red zones?, mostly in the northern regions of the country, with ?non-red zones? in the rest of the country, where the rate of infection was much lower. A total of 2100 respondents participated in the survey. The majority of respondents declared themselves to be habitual users of UGS, especially of parks or green areas outside the town ? mainly visiting for relaxation and physical exercise, but also for observing nature. In the northern regions people more commonly reported the adoption of sustainable practices, in terms of the utilization of tools for green mobility. During the lockdown, habits changed significantly: only one third of respondents reported visiting UGS, with frequent visits made mainly for the purpose of walking the dog. Other motivations included the need for relaxing, mostly in the red zones, and for physical exercise in non-red zones. The reduction in travel to urban parks was accompanied by increased visitation of gardens and other green spaces in close proximity, as social distancing and other regulations imposed restrictions on movement. In all regions, respondents who could not physically access UGS expressed a feeling of deprivation which was exacerbated by living in towns located in red zones, being a usual visitor of UGS and having no green view from the window. The extent to which these visitors missed UGS depended on the frequency of visitation before the pandemic and the UGS distance, as well as the type of previous activity. In fact, those activities that were most common before the pandemic were missed the most, reinforcing the importance of green areas for social gathering, sports, and observing nature ? but simply ?spending time outdoors? was also mentioned, even by those who visited UGS during the lockdown, as the time outdoors was not enough or not fully enjoyed. The feeling of missing UGS was only partially alleviated by the green view from the window ? only a more open view to a natural landscape or adaptation to a view with little greenery reduced such feeling. application/ld+json https://w3id.org/ro-id/affd2ec0-acae-4476-b51c-47c23ec3c73b Usage of urban green space and related feelings of deprivation during the COVID-19 lockdown: Lessons learned from an Italian case study MANUAL Foglini, Federica. "Usage of urban green space and related feelings of deprivation during the COVID-19 lockdown: Lessons learned from an Italian case study." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/affd2ec0-acae-4476-b51c-47c23ec3c73b. 221 https://api.rohub.org/api/resources/85dc7cc6-07ba-43ff-ab97-50080d3c7eec/download/ 2021-12-10 09:59:58.851804+00:00 2021-12-10 09:59:58.853622+00:00 This study investigated perceptions and behavioral patterns related to urban green space (UGS) in Italian cities, during the period of national lockdown imposed due to the outbreak of SARS-CoV-2 in the spring of 2020. A survey was used to examine the responses of population groups in different municipal areas, comparing those in government-defined ?red zones?, mostly in the northern regions of the country, with ?non-red zones? in the rest of the country, where the rate of infection was much lower. A total of 2100 respondents participated in the survey. The majority of respondents declared themselves to be habitual users of UGS, especially of parks or green areas outside the town ? mainly visiting for relaxation and physical exercise, but also for observing nature. In the northern regions people more commonly reported the adoption of sustainable practices, in terms of the utilization of tools for green mobility. During the lockdown, habits changed significantly: only one third of respondents reported visiting UGS, with frequent visits made mainly for the purpose of walking the dog. Other motivations included the need for relaxing, mostly in the red zones, and for physical exercise in non-red zones. The reduction in travel to urban parks was accompanied by increased visitation of gardens and other green spaces in close proximity, as social distancing and other regulations imposed restrictions on movement. In all regions, respondents who could not physically access UGS expressed a feeling of deprivation which was exacerbated by living in towns located in red zones, being a usual visitor of UGS and having no green view from the window. The extent to which these visitors missed UGS depended on the frequency of visitation before the pandemic and the UGS distance, as well as the type of previous activity. In fact, those activities that were most common before the pandemic were missed the most, reinforcing the importance of green areas for social gathering, sports, and observing nature ? but simply ?spending time outdoors? was also mentioned, even by those who visited UGS during the lockdown, as the time outdoors was not enough or not fully enjoyed. The feeling of missing UGS was only partially alleviated by the green view from the window ? only a more open view to a natural landscape or adaptation to a view with little greenery reduced such feeling. text/plain Usage of urban green space and related feelings of deprivation during the COVID-19 lockdown: Lessons learned from an Italian case study 2021-12-10 09:59:58.851804+00:00 service-account-generation-service Information science Social sciences contact-tracing app Andrienko Inf. Technol. Comande avoiding location tracking Journal of geophysical research. Biogeosciences location data medicine technology personal data collection dynamics application software control to individual citizen chain drive store vision help COVID-19 containment ethics Inf. Technol. collection tracking containment privacy Lehmann van den Hoven tracing personal data information rebirth citizen detection locating Hoven Nanni federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 6571 https://api.rohub.org/api/ros/aab8e785-b624-46b2-8686-e3ae2e4c477f/crate/download/ 2021-12-10 10:00:02.683249+00:00 2025-03-05 00:59:13.131445+00:00 2021-12-10 10:00:02.683249+00:00 The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' personal data stores, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. application/ld+json https://w3id.org/ro-id/aab8e785-b624-46b2-8686-e3ae2e4c477f Give more data, awareness and control to individual citizens, and they will help COVID-19 containment MANUAL Foglini, Federica. "Give more data, awareness and control to individual citizens, and they will help COVID-19 containment." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/aab8e785-b624-46b2-8686-e3ae2e4c477f. 630 https://api.rohub.org/api/resources/b85e8888-e545-4ae3-b7c9-d8841539f4b4/download/ 2021-12-10 10:00:06.346850+00:00 2021-12-10 10:00:06.348041+00:00 The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' personal data stores, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. text/plain Give more data, awareness and control to individual citizens, and they will help COVID-19 containment 2021-12-10 10:00:06.346850+00:00 service-account-generation-service Mathematics Biology SIR model epidemic spreading infection lockdown medicine health epidemic infection mathematical model applications to COVID-19 infection forecast Zanella mathematical modeling spread health system spreading evolution lockdown application social contact statistical analysis modelling information contact infection Math. Biosci Italy Pavia scattering Health Protection Agency federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 4571 https://api.rohub.org/api/ros/b42df712-bd99-44f6-822c-ef4e3825ca4a/crate/download/ 2021-12-10 10:00:09.455335+00:00 2025-03-05 01:19:09.443199+00:00 2021-12-10 10:00:09.455335+00:00 Lockdown and social distancing, as well as testing and contact tracing, are the main measures assumed by the governments to control and limit the spread of COVID-19 infection. In reason of that, special attention was recently paid by the scientific community to the mathematical modeling of infection spreading by including in classical models the effects of the distribution of contacts between individuals. Among other approaches, the coupling of the classical SIR model with a statistical study of the distribution of social contacts among the population, led some of the present authors to build a Social SIR model, able to accurately follow the effect of the decrease in contacts resulting from the lockdown measures adopted in various European countries in the first phase of the epidemic. The Social SIR has been recently tested and improved through a fruitful collaboration with the Health Protection Agency (ATS) of the province of Pavia (Italy), that made it possible to have at disposal all the relevant data relative to the spreading of COVID-19 infection in the province (half a million of people), starting from February 2020. The statistical analysis of the data was relevant to fit at best the parameters of the mathematical model, and to make short-term predictions of the spreading evolution in order to optimize the response of the local health system. application/ld+json https://w3id.org/ro-id/b42df712-bd99-44f6-822c-ef4e3825ca4a Social contacts, epidemic spreading and health system. Mathematical modeling and applications to COVID-19 infection MANUAL Foglini, Federica. "Social contacts, epidemic spreading and health system. Mathematical modeling and applications to COVID-19 infection." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/b42df712-bd99-44f6-822c-ef4e3825ca4a. 282 https://api.rohub.org/api/resources/c6bd1af1-12e6-4742-89f6-684f3c31447e/download/ 2021-12-10 10:00:12.746743+00:00 2021-12-10 10:00:12.748168+00:00 Lockdown and social distancing, as well as testing and contact tracing, are the main measures assumed by the governments to control and limit the spread of COVID-19 infection. In reason of that, special attention was recently paid by the scientific community to the mathematical modeling of infection spreading by including in classical models the effects of the distribution of contacts between individuals. Among other approaches, the coupling of the classical SIR model with a statistical study of the distribution of social contacts among the population, led some of the present authors to build a Social SIR model, able to accurately follow the effect of the decrease in contacts resulting from the lockdown measures adopted in various European countries in the first phase of the epidemic. The Social SIR has been recently tested and improved through a fruitful collaboration with the Health Protection Agency (ATS) of the province of Pavia (Italy), that made it possible to have at disposal all the relevant data relative to the spreading of COVID-19 infection in the province (half a million of people), starting from February 2020. The statistical analysis of the data was relevant to fit at best the parameters of the mathematical model, and to make short-term predictions of the spreading evolution in order to optimize the response of the local health system. text/plain Social contacts, epidemic spreading and health system. Mathematical modeling and applications to COVID-19 infection 2021-12-10 10:00:12.746743+00:00 service-account-generation-service Medical science federica.foglini@ismar.cnr.it Federica Foglini Students Education/Teaching and learning/Students symptom 28.014616321559075 23.0 service-account-enrichment 6017 https://api.rohub.org/api/ros/052b3693-fd97-4b09-b853-d7db9701d81e/crate/download/ 2021-12-10 10:00:16.155139+00:00 2025-03-05 00:46:22.782245+00:00 2021-12-10 10:00:16.155139+00:00 The first case of infection by SARS-CoV-2 (i.e., COVID-19) was officially recorded by the Italian National Health Service on 21 February 2020. Respiratory tract manifestations are the most common symptoms, such as gastrointestinal symptoms (GISs) like nausea or sickness, diarrhea, and anorexia, and psychological effects may be reported in affected individuals. However, similar symptoms may be observed in healthy people as a consequence of an anxiety state. Methods: We analyzed GISs and anxiety state during the COVID-19 lockdown period; from 9 March 2020 to 4 May 2020. A web-based survey consisting of 131 items was administered to 354 students affiliated with the School of Medicine of the University Magna Graecia of Catanzaro; Italy. A set of statistical analyses was performed to analyze the relationships among the answers to assess a correlation between the topics of interest. Results: The statistical analysis showed that 54.0% of interviewed reported at least one GISs, 36.16% of which reported a positive history for familial GISs (FGISs). The 354 subjects included in our cohort may be stratified as follows: 25.99% GISs and FGISs, 27.97% GISs and no-FGISs, 10.17% no-GISs and FGISs, 35.87% no-GISs and no-FGISs. Results indicated an anxiety state for 48.9% of respondents, of which 64.74% also presented GISs. In addition, considered dietary habits, we detect the increased consumption of hypercaloric food, sweetened drinks, and alcoholic beverages. Conclusions: The increase of GISs during the lockdown period in a population of medical students, may be correlated to both dietary habits and anxiety state due to a concern for one's health. application/ld+json https://w3id.org/ro-id/052b3693-fd97-4b09-b853-d7db9701d81e Anxiety and Gastrointestinal Symptoms Related to COVID-19 during Italian Lockdown MANUAL https://w3id.org/ro-id/0dd45ca3-1797-4c8f-b3fa-ca0116072406 https://w3id.org/ro-id/7116c03c-f8d0-47df-8134-d1ef265758c8 https://w3id.org/ro-id/b52decfb-6a43-4972-9f67-6d568258c7e4 https://w3id.org/ro-id/2c3cb1ac-bf9d-4004-ac1d-5029c73f7c69 https://w3id.org/ro-id/6e9dcb42-2c43-4150-a8b6-11c2dc99bd3d https://w3id.org/ro-id/f702b2b8-f31e-4bd7-b89b-1fbd8fcc807d https://w3id.org/ro-id/03e33c63-27b8-4080-9786-bfd5b99bb148 https://w3id.org/ro-id/285dc4bb-3177-4533-9f25-7718bff11fd6 https://w3id.org/ro-id/2b04bc8b-80eb-40e6-9191-755cac3c3ae4 https://w3id.org/ro-id/2b2a73d2-a759-4153-be68-f1f5f8b2e9b1 https://w3id.org/ro-id/334cbdd8-1283-4c2e-977d-1080395f5c25 https://w3id.org/ro-id/5298a7b9-1be1-470d-bc44-4257b5946204 https://w3id.org/ro-id/85d6a9f9-9aef-480c-8ad9-f93a52adbfb1 https://w3id.org/ro-id/b14cb911-bdf2-4624-812a-b6dc5ecd9d9c https://w3id.org/ro-id/b5ab6e0d-d517-40f3-85ef-1eee6b2d6b65 https://w3id.org/ro-id/beabb81f-8c70-4bf6-80c0-9bb364d238ad https://w3id.org/ro-id/d9a39101-50a6-4c32-94cc-09c41636d38e https://w3id.org/ro-id/edf3193b-0822-4f53-9745-04a061eb55ed https://w3id.org/ro-id/9d0cb60a-435e-4d27-91c1-e02aaa1baec2 https://w3id.org/ro-id/fd783551-b810-4194-b5ec-b53efea4b082 https://w3id.org/ro-id/02071902-1412-492c-819b-c25ca99ba62e https://w3id.org/ro-id/3b94bf59-bf17-4c9a-b4a5-32daac5b4fc6 https://w3id.org/ro-id/437c1717-7b5e-4223-8e27-8815721e4bdd https://w3id.org/ro-id/60654b10-9e25-4896-9047-4ce687be898e https://w3id.org/ro-id/aef56b88-c528-4b05-aa67-9ad67f226b8e https://w3id.org/ro-id/bc5faa75-1c56-4971-8571-60611bf27d2c https://w3id.org/ro-id/c30be2f6-95bc-4e14-8d44-adb9f0c8bd9b https://w3id.org/ro-id/0c964fed-50f8-4878-930b-dc81f527c78a https://w3id.org/ro-id/16eece8f-854e-4a73-9a7b-2cd5f5e3dae8 https://w3id.org/ro-id/17169dd5-fba7-4014-994c-015d1e366e28 https://w3id.org/ro-id/49495e93-c983-49bc-81cb-bb191a409c37 https://w3id.org/ro-id/4b223565-22a7-462a-8e09-3e0dc9463da1 https://w3id.org/ro-id/63ab0b0f-f5b7-4272-992d-f366c3946d0d https://w3id.org/ro-id/74f5a107-4138-42d7-aa34-df2199ec5b3a https://w3id.org/ro-id/adbeb591-4465-4469-85c1-b9b72748660a https://w3id.org/ro-id/b40663ab-4f5b-42a5-8968-22a1ca6e73d8 https://w3id.org/ro-id/bb08402a-6773-448d-8215-1f87591d85dd https://w3id.org/ro-id/d89ceb3d-bead-42ff-a757-9e6255b5d490 https://w3id.org/ro-id/f43518f4-7a31-40c3-b624-0da6b7d76dee https://w3id.org/ro-id/549e442a-e3b0-4d53-9e05-0770fafb728b https://w3id.org/ro-id/ca6559ce-2b7a-4e51-8157-2d6dd27e5477 https://w3id.org/ro-id/0f9eab0d-1461-4f98-8bd1-3fc84292ae54 https://w3id.org/ro-id/3dd6120f-234e-4ad1-a29f-a9f93ee26da7 https://w3id.org/ro-id/3fcaf1ca-67d5-4d55-acce-8e6298363a37 https://w3id.org/ro-id/49988441-1f7c-4ed2-ae17-ebef5154a0c1 https://w3id.org/ro-id/4c438556-3369-4d42-8e40-7cb8936e64c8 https://w3id.org/ro-id/5d0dcb27-0deb-4902-9835-ccb372de5eb3 https://w3id.org/ro-id/c991cca7-b191-41b4-86d6-b5550e499786 https://w3id.org/ro-id/da1261e3-d35a-451f-985b-e8032b02e8de https://w3id.org/ro-id/e11ed944-6983-46a8-9eba-0d43e7b9e6af https://w3id.org/ro-id/26e66e3c-f36f-42e5-ac9a-5db7254aebd4 https://w3id.org/ro-id/60bad0d0-a8a0-4f9f-b51e-dff9f504b113 https://w3id.org/ro-id/8646893a-dc7c-4810-8115-906a66da5fe7 https://w3id.org/ro-id/9350dfed-6064-4f52-a677-f50638a3fba0 https://w3id.org/ro-id/94a4979f-6bc1-4bab-8722-da662dcbb139 https://w3id.org/ro-id/c2c2f8b8-a840-4f70-9e5a-aa38c2bb0535 https://w3id.org/ro-id/3b586a65-c442-4f6b-af81-79d6d1ac20c7 https://w3id.org/ro-id/e6a20e36-75d6-4fcc-aa34-6d02b7dcb022 Foglini, Federica. "Anxiety and Gastrointestinal Symptoms Related to COVID-19 during Italian Lockdown." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/052b3693-fd97-4b09-b853-d7db9701d81e. 271 https://api.rohub.org/api/resources/e283b8b5-21a7-46c9-9848-e3ca02f22209/download/ 2021-12-10 10:00:18.644677+00:00 2021-12-10 10:00:18.645775+00:00 The first case of infection by SARS-CoV-2 (i.e., COVID-19) was officially recorded by the Italian National Health Service on 21 February 2020. Respiratory tract manifestations are the most common symptoms, such as gastrointestinal symptoms (GISs) like nausea or sickness, diarrhea, and anorexia, and psychological effects may be reported in affected individuals. However, similar symptoms may be observed in healthy people as a consequence of an anxiety state. Methods: We analyzed GISs and anxiety state during the COVID-19 lockdown period; from 9 March 2020 to 4 May 2020. A web-based survey consisting of 131 items was administered to 354 students affiliated with the School of Medicine of the University Magna Graecia of Catanzaro; Italy. A set of statistical analyses was performed to analyze the relationships among the answers to assess a correlation between the topics of interest. Results: The statistical analysis showed that 54.0% of interviewed reported at least one GISs, 36.16% of which reported a positive history for familial GISs (FGISs). The 354 subjects included in our cohort may be stratified as follows: 25.99% GISs and FGISs, 27.97% GISs and no-FGISs, 10.17% no-GISs and FGISs, 35.87% no-GISs and no-FGISs. Results indicated an anxiety state for 48.9% of respondents, of which 64.74% also presented GISs. In addition, considered dietary habits, we detect the increased consumption of hypercaloric food, sweetened drinks, and alcoholic beverages. Conclusions: The increase of GISs during the lockdown period in a population of medical students, may be correlated to both dietary habits and anxiety state due to a concern for one's health. text/plain Anxiety and Gastrointestinal Symptoms Related to COVID-19 during Italian Lockdown 2021-12-10 10:00:18.644677+00:00 anxiety state 10.72695035460993 12.1 medicine 51.492537313432834 13.8 similar symptom 4.389173372348208 6.0 J. Clin 6.6489361702127665 7.5 F. Anxiety 10.815602836879433 12.2 However, similar symptoms may be observed in healthy people as a consequence of an anxiety state. 9.064539521392312 12.5 statistical analysis 4.628501827040195 3.8 ingestion 3.89768574908648 3.2 result 13.885505481120585 11.4 Catanzaro https://www.wikidata.org/wiki/Q3883 manifestation 4.141291108404385 3.4 from Mar-9-2020 to May-4-2020 Eating disorder Health/Diseases and conditions/Mental and behavioural disorder/Eating disorder gastrointestinal symptom 57.79078273591807 79.0 volume 10 6.2179956108266285 8.5 Music Arts, culture and entertainment/Arts and entertainment/Music symptom 19.592198581560286 22.1 respiratory tract manifestation 8.851499634235553 12.1 gastrointestinal symptom 6.028368794326242 6.8 Italian National Health Service 6.071689831748356 8.3 anxiety state 16.56516443361754 13.6 life sciences (general) 100.0 1.6781120300292969 relate to covid 19 2.7798098024871987 3.8 Customs and tradition Arts, culture and entertainment/Culture/Customs and tradition (J. Clin. 6.453952139231327 8.9 Boffoli 6.471631205673759 7.3 Mediterranean Sea https://www.wikidata.org/wiki/Q4918 linguistics 41.7910447761194 11.2 no-FGISs 4.432624113475177 5.0 airway 5.237515225334958 4.3 Respiratory tract manifestations are the most common symptoms, such as gastrointestinal symptoms (GISs) like nausea or sickness, diarrhea, and anorexia, and psychological effects may be reported in affected individuals. 13.12545322697607 18.1 Med. 5.003625815808557 6.9 Abenavoli, L; Cinaglia, P; Lombardo, G; Boffoli, E; Scida, M; Procopio, AC; Larussa, T; Boccuto, L; Zanza, C; Longhitano, Y; Fagoonee, S; Luzza, F. Anxiety and Gastrointestinal Symptoms Related to COVID-19 during Italian Lockdown. 57.36040609137055 79.1 earth sciences 100.0 1.7650614976882935 respiratory tract 3.368794326241135 3.8 Health Health habit 3.89768574908648 3.2 covid 19 14.273049645390074 16.1 psychology 6.716417910447761 1.8 disease 3.89768574908648 3.2 FGISs 4.964539007092199 5.6 Musical instrument Arts, culture and entertainment/Arts and entertainment/Music/Musical instrument Mediterranean Sea 7.673568818514008 6.3 Results indicated an anxiety state for 48.9% of respondents, of which 64.74% also presented GISs. 8.992023205221175 12.4 Diseases and conditions Health/Diseases and conditions consumption of hypercaloric food 3.5113386978785663 4.8 life sciences 100.0 1.6781120300292969 Procopio 6.205673758865248 7.0 medical student 4.019488428745433 3.3 anxiety state due to a concern 4.169714703730798 5.7 issue 6 6.2179956108266285 8.5 on Feb-21-2020 issue 4.141291108404385 3.4 Journal of geophysical research. Biogeosciences 6.471631205673759 7.3 Italy https://www.wikidata.org/wiki/Q38 geology 100.0 1.7650614976882935 service-account-generation-service Mathematics volume 11 propagation of the infection evolution of an epidemic sci rep 2021 lockdown strategy epidemic model medicine mathematics book development epidemic angstrom strategy vaccination policy events issue 1 epidemic multiple lockdown mathematics of multiple lockdown vaccination policy lockdown sci newsgroup impact infection Scala federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 3581 https://api.rohub.org/api/ros/a9bf0ba2-1a84-467f-b2f8-d0ddd2d38ac1/crate/download/ 2021-12-10 10:00:21.666929+00:00 2025-03-05 02:47:40.494268+00:00 2021-12-10 10:00:21.666929+00:00 While vaccination is the optimal response to an epidemic, recent events have obliged us to explore new strategies for containing worldwide epidemics, like lockdown strategies, where the contacts among the population are strongly reduced in order to slow down the propagation of the infection. By analyzing a classical epidemic model, we explore the impact of lockdown strategies on the evolution of an epidemic. We show that repeated lockdowns have a beneficial effect, reducing the final size of the infection, and that they represent a possible support strategy to vaccination policies. application/ld+json https://w3id.org/ro-id/a9bf0ba2-1a84-467f-b2f8-d0ddd2d38ac1 The mathematics of multiple lockdowns MANUAL Foglini, Federica. "The mathematics of multiple lockdowns." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/a9bf0ba2-1a84-467f-b2f8-d0ddd2d38ac1. 83 https://api.rohub.org/api/resources/4d287a73-7ec7-477c-b7e4-3aa8f729e319/download/ 2021-12-10 10:00:25.747593+00:00 2021-12-10 10:00:25.748657+00:00 While vaccination is the optimal response to an epidemic, recent events have obliged us to explore new strategies for containing worldwide epidemics, like lockdown strategies, where the contacts among the population are strongly reduced in order to slow down the propagation of the infection. By analyzing a classical epidemic model, we explore the impact of lockdown strategies on the evolution of an epidemic. We show that repeated lockdowns have a beneficial effect, reducing the final size of the infection, and that they represent a possible support strategy to vaccination policies. text/plain The mathematics of multiple lockdowns 2021-12-10 10:00:25.747593+00:00 service-account-generation-service Biology cytology Rizzari TMEM16F Journal of geophysical research. Biogeosciences anatomy medicine americium cell lung protein disease drug cell membrane ion channel Congressional Medal of Honor thrombosis pathogenesis inhibit TMEM16 protein niclosamide syncytium Braga Barclay spike protein microscopy syncytia covid 19 pharmacology spike-induced syncytium lung thrombosis syncytia result Schneider drug niclosamide Braga block SARS-CoV-2 spike-induced syncytium federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5944 https://api.rohub.org/api/ros/53cb4ba4-7529-4924-9e30-46d4bf7d55f7/crate/download/ 2021-12-10 10:05:51.703731+00:00 2025-03-05 00:47:51.014292+00:00 2021-12-10 10:05:51.703731+00:00 COVID-19 is a disease with unique characteristics that include lung thrombosis(1), frequent diarrhoea(2), abnormal activation of the inflammatory response(3) and rapid deterioration of lung function consistent with alveolar oedema(4). The pathological substrate for these findings remains unknown. Here we show that the lungs of patients with COVID-19 contain infected pneumocytes with abnormal morphology and frequent multinucleation. The generation of these syncytia results from activation of the SARS-CoV-2 spike protein at the cell plasma membrane level. On the basis of these observations, we performed two high-content microscopy-based screenings with more than 3,000 approved drugs to search for inhibitors of spike-driven syncytia. We converged on the identification of 83 drugs that inhibited spike-mediated cell fusion, several of which belonged to defined pharmacological classes. We focused our attention on effective drugs that also protected against virus replication and associated cytopathicity. One of the most effective molecules was the antihelminthic drug niclosamide, which markedly blunted calcium oscillations and membrane conductance in spike-expressing cells by suppressing the activity of TMEM16F (also known as anoctamin 6), a calcium-activated ion channel and scramblase that is responsible for exposure of phosphatidylserine on the cell surface. These findings suggest a potential mechanism for COVID-19 disease pathogenesis and support the repurposing of niclosamide for therapy. application/ld+json https://w3id.org/ro-id/53cb4ba4-7529-4924-9e30-46d4bf7d55f7 Drugs that inhibit TMEM16 proteins block SARS-CoV-2 spike-induced syncytia MANUAL Foglini, Federica. "Drugs that inhibit TMEM16 proteins block SARS-CoV-2 spike-induced syncytia." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/53cb4ba4-7529-4924-9e30-46d4bf7d55f7. 369 https://api.rohub.org/api/resources/05b54af3-ce56-4474-9603-9d49e56e97c6/download/ 2021-12-10 10:05:55.162102+00:00 2021-12-10 10:05:55.163555+00:00 COVID-19 is a disease with unique characteristics that include lung thrombosis(1), frequent diarrhoea(2), abnormal activation of the inflammatory response(3) and rapid deterioration of lung function consistent with alveolar oedema(4). The pathological substrate for these findings remains unknown. Here we show that the lungs of patients with COVID-19 contain infected pneumocytes with abnormal morphology and frequent multinucleation. The generation of these syncytia results from activation of the SARS-CoV-2 spike protein at the cell plasma membrane level. On the basis of these observations, we performed two high-content microscopy-based screenings with more than 3,000 approved drugs to search for inhibitors of spike-driven syncytia. We converged on the identification of 83 drugs that inhibited spike-mediated cell fusion, several of which belonged to defined pharmacological classes. We focused our attention on effective drugs that also protected against virus replication and associated cytopathicity. One of the most effective molecules was the antihelminthic drug niclosamide, which markedly blunted calcium oscillations and membrane conductance in spike-expressing cells by suppressing the activity of TMEM16F (also known as anoctamin 6), a calcium-activated ion channel and scramblase that is responsible for exposure of phosphatidylserine on the cell surface. These findings suggest a potential mechanism for COVID-19 disease pathogenesis and support the repurposing of niclosamide for therapy. text/plain Drugs that inhibit TMEM16 proteins block SARS-CoV-2 spike-induced syncytia 2021-12-10 10:05:55.162102+00:00 service-account-generation-service Biology biology reporter system Nat. Commun neuronal phenotype fluorescent reporter monogenic disease medicine photon gene cell disease epilepsy gram reporter neuron central nervous system phenotype mosaicism microscopy generation mosaic model mosaic genetics GM. modelling Commun genotype knockout mosaic genetic mosaicism inclusion disorder Trovato Landi modelling genetic mosaicism federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5271 https://api.rohub.org/api/ros/9641a891-0c97-4a21-bcc5-3c0e7f974ede/crate/download/ 2021-12-10 10:05:58.607206+00:00 2025-03-05 00:56:54.503963+00:00 2021-12-10 10:05:58.607206+00:00 Genetic mosaicism, a condition in which an organ includes cells with different genotypes, is frequently present in monogenic diseases of the central nervous system caused by the random inactivation of the X-chromosome, in the case of X-linked pathologies, or by somatic mutations affecting a subset of neurons. The comprehension of the mechanisms of these diseases and of the cell-autonomous effects of specific mutations requires the generation of sparse mosaic models, in which the genotype of each neuron is univocally identified by the expression of a fluorescent protein in vivo. Here, we show a dual-color reporter system that, when expressed in a floxed mouse line for a target gene, leads to the creation of mosaics with tunable degree. We demonstrate the generation of a knockout mosaic of the autism/epilepsy related gene PTEN in which the genotype of each neuron is reliably identified, and the neuronal phenotype is accurately characterized by two-photon microscopy. application/ld+json https://w3id.org/ro-id/9641a891-0c97-4a21-bcc5-3c0e7f974ede Modelling genetic mosaicism of neurodevelopmental disorders in vivo by a Cre-amplifying fluorescent reporter MANUAL Foglini, Federica. "Modelling genetic mosaicism of neurodevelopmental disorders in vivo by a Cre-amplifying fluorescent reporter." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/9641a891-0c97-4a21-bcc5-3c0e7f974ede. 346 https://api.rohub.org/api/resources/863f7285-2401-467b-9d3b-485498dc267d/download/ 2021-12-10 10:06:01.685172+00:00 2021-12-10 10:06:01.686020+00:00 Genetic mosaicism, a condition in which an organ includes cells with different genotypes, is frequently present in monogenic diseases of the central nervous system caused by the random inactivation of the X-chromosome, in the case of X-linked pathologies, or by somatic mutations affecting a subset of neurons. The comprehension of the mechanisms of these diseases and of the cell-autonomous effects of specific mutations requires the generation of sparse mosaic models, in which the genotype of each neuron is univocally identified by the expression of a fluorescent protein in vivo. Here, we show a dual-color reporter system that, when expressed in a floxed mouse line for a target gene, leads to the creation of mosaics with tunable degree. We demonstrate the generation of a knockout mosaic of the autism/epilepsy related gene PTEN in which the genotype of each neuron is reliably identified, and the neuronal phenotype is accurately characterized by two-photon microscopy. text/plain Modelling genetic mosaicism of neurodevelopmental disorders in vivo by a Cre-amplifying fluorescent reporter 2021-12-10 10:06:01.685172+00:00 service-account-generation-service Mathematics Lombardy Journal of geophysical research. Biogeosciences outbreak in Lombardy, Italy optimization problem medicine number disease epicenter symptom replica system approach China forecast the COVID-19 outbreak Google Inc. approach Introduction Italy Lombardy simulator compartmental modelling novel coronavirus disease 2019 optimisation estimate outbreak modelling numerical optimization approach Toraldo bell China Italy reproduction number R-0 Russo reproduction number R-e optimization approach federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 7330 https://api.rohub.org/api/ros/bce36619-9ad0-4291-81e1-f1808b1a7afa/crate/download/ 2021-12-10 10:06:05.025294+00:00 2025-03-05 02:46:55.810297+00:00 2021-12-10 10:06:05.025294+00:00 Introduction Italy became the second epicenter of the novel coronavirus disease 2019 (COVID-19) pandemic after China, surpassing by far China's death toll. The disease swept through Lombardy, which remained in lockdown for about two months, starting from the 8th of March. As of that day, the isolation measures taken in Lombardy were extended to the entire country. Here, assuming that effectively there was one case zero that introduced the virus to the region, we provide estimates for: (a) the day-zero of the outbreak in Lombardy, Italy; (b) the actual number of asymptomatic infected cases in the total population until March 8; (c) the basic (R-0)and the effective reproduction number (R-e) based on the estimation of the actual number of infected cases. To demonstrate the efficiency of the model and approach, we also provide a tentative forecast two months ahead of time, i.e. until May 4, the date on which relaxation of the measures commenced, on the basis of the COVID-19 Community Mobility Reports released by Google on March 29. Methods To deal with the uncertainty in the number of the actual asymptomatic infected cases in the total population Volpert et al. (2020), we address a modified compartmental Susceptible/ Exposed/ Infectious Asymptomatic/ Infected Symptomatic/ Recovered/ Dead (SEIIRD) model with two compartments of infectious persons: one modelling the cases in the population that are asymptomatic or experience very mild symptoms and another modelling the infected cases with mild to severe symptoms. The parameters of the model corresponding to the recovery period, the time from the onset of symptoms to death and the time from exposure to the time that an individual starts to be infectious, have been set as reported from clinical studies on COVID-19. For the estimation of the day-zero of the outbreak in Lombardy, as well as of the effective per-day transmission rate for which no clinical data are available, we have used the proposed SEIIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March. This was accomplished by solving a mixed-integer optimization problem. Based on the computed parameters, we also provide an estimation of the basic reproduction number R-0 and the evolution of the effective reproduction number R-e. To examine the efficiency of the model and approach, we ran the simulator to forecast the epidemic two months ahead of time, i.e. from March 8 to May 4. For this purpose, we considered the reduction in mobility in Lombardy as released on March 29 by Google COVID-19 Community Mobility Reports, and the effects of social distancing and of the very strict measures taken by the government on March 20 and March 21, 2020. Results Based on the proposed methodological procedure, we estimated that the expected day-zero was January 14 (min-max rage: January 5 to January 23, interquartile range: January 11 to January 18). The actual cumulative number of asymptomatic infected cases in the total population in Lombardy on March 8 was of the order of 15 times the confirmed cumulative number of infected cases, while the expected value of the basic reproduction number R-0 was found to be 4.53 (min-max range: 4.40- 4.65). On May 4, the date on which relaxation of the measures commenced the effective reproduction number was found to be 0.987 (interquartiles: 0.857, 1.133). The model approximated adequately two months ahead of time the evolution of reported cases of infected until May 4, the day on which the phase I of the relaxation of measures was implemented over all of Italy. Furthermore the model predicted that until May 4, around 20% of the population in Lombardy has recovered (interquartile range: similar to 10% to similar to 30%). application/ld+json https://w3id.org/ro-id/bce36619-9ad0-4291-81e1-f1808b1a7afa Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach MANUAL Foglini, Federica. "Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/bce36619-9ad0-4291-81e1-f1808b1a7afa. 266 https://api.rohub.org/api/resources/32451a9e-6f5a-4fc7-ab0e-875998dfebe5/download/ 2021-12-10 10:06:08.003535+00:00 2021-12-10 10:06:08.004550+00:00 Introduction Italy became the second epicenter of the novel coronavirus disease 2019 (COVID-19) pandemic after China, surpassing by far China's death toll. The disease swept through Lombardy, which remained in lockdown for about two months, starting from the 8th of March. As of that day, the isolation measures taken in Lombardy were extended to the entire country. Here, assuming that effectively there was one case zero that introduced the virus to the region, we provide estimates for: (a) the day-zero of the outbreak in Lombardy, Italy; (b) the actual number of asymptomatic infected cases in the total population until March 8; (c) the basic (R-0)and the effective reproduction number (R-e) based on the estimation of the actual number of infected cases. To demonstrate the efficiency of the model and approach, we also provide a tentative forecast two months ahead of time, i.e. until May 4, the date on which relaxation of the measures commenced, on the basis of the COVID-19 Community Mobility Reports released by Google on March 29. Methods To deal with the uncertainty in the number of the actual asymptomatic infected cases in the total population Volpert et al. (2020), we address a modified compartmental Susceptible/ Exposed/ Infectious Asymptomatic/ Infected Symptomatic/ Recovered/ Dead (SEIIRD) model with two compartments of infectious persons: one modelling the cases in the population that are asymptomatic or experience very mild symptoms and another modelling the infected cases with mild to severe symptoms. The parameters of the model corresponding to the recovery period, the time from the onset of symptoms to death and the time from exposure to the time that an individual starts to be infectious, have been set as reported from clinical studies on COVID-19. For the estimation of the day-zero of the outbreak in Lombardy, as well as of the effective per-day transmission rate for which no clinical data are available, we have used the proposed SEIIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March. This was accomplished by solving a mixed-integer optimization problem. Based on the computed parameters, we also provide an estimation of the basic reproduction number R-0 and the evolution of the effective reproduction number R-e. To examine the efficiency of the model and approach, we ran the simulator to forecast the epidemic two months ahead of time, i.e. from March 8 to May 4. For this purpose, we considered the reduction in mobility in Lombardy as released on March 29 by Google COVID-19 Community Mobility Reports, and the effects of social distancing and of the very strict measures taken by the government on March 20 and March 21, 2020. Results Based on the proposed methodological procedure, we estimated that the expected day-zero was January 14 (min-max rage: January 5 to January 23, interquartile range: January 11 to January 18). The actual cumulative number of asymptomatic infected cases in the total population in Lombardy on March 8 was of the order of 15 times the confirmed cumulative number of infected cases, while the expected value of the basic reproduction number R-0 was found to be 4.53 (min-max range: 4.40- 4.65). On May 4, the date on which relaxation of the measures commenced the effective reproduction number was found to be 0.987 (interquartiles: 0.857, 1.133). The model approximated adequately two months ahead of time the evolution of reported cases of infected until May 4, the day on which the phase I of the relaxation of measures was implemented over all of Italy. Furthermore the model predicted that until May 4, around 20% of the population in Lombardy has recovered (interquartile range: similar to 10% to similar to 30%). text/plain Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach 2021-12-10 10:06:08.003535+00:00 service-account-generation-service Social sciences volume 10 age structure of the population social interaction mechanisms for the Covid-19 epidemic mobility restriction epidemic spreading sci rep 2020 lockdown strength Quattrociocchi medicine behavior epidemic interaction mechanisms relevance restriction infectious disease impact age class interaction exit mechanisms spread exit age structure lockdown mobility importance social contact intervention Italy inter-regional mobility matrix Scala federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5418 https://api.rohub.org/api/ros/75a096fc-0b3e-4f13-96cc-9e34f22d226c/crate/download/ 2021-12-10 10:06:11.123834+00:00 2025-03-05 02:47:47.644030+00:00 2021-12-10 10:06:11.123834+00:00 We develop a minimalist compartmental model to study the impact of mobility restrictions in Italy during the Covid-19 outbreak. We show that, while an early lockdown shifts the contagion in time, beyond a critical value of lockdown strength the epidemic tends to restart after lifting the restrictions. We characterize the relative importance of different lockdown lifting schemes by accounting for two fundamental sources of heterogeneity, i.e. geography and demography. First, we consider Italian Regions as separate administrative entities, in which social interactions between age classes occur. We show that, due to the sparsity of the inter-Regional mobility matrix, once started, the epidemic spreading tends to develop independently across areas, justifying the adoption of mobility restrictions targeted to individual Regions or clusters of Regions. Second, we show that social contacts between members of different age classes play a fundamental role and that interventions which target local behaviours and take into account the age structure of the population can provide a significant contribution to mitigate the epidemic spreading. Our model aims to provide a general framework, and it highlights the relevance of some key parameters on non-pharmaceutical interventions to contain the contagion. application/ld+json https://w3id.org/ro-id/75a096fc-0b3e-4f13-96cc-9e34f22d226c Time, space and social interactions: exit mechanisms for the Covid-19 epidemics MANUAL Foglini, Federica. "Time, space and social interactions: exit mechanisms for the Covid-19 epidemics." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/75a096fc-0b3e-4f13-96cc-9e34f22d226c. 203 https://api.rohub.org/api/resources/303c913d-7af8-4738-8374-d389cd781e70/download/ 2021-12-10 10:06:14.183453+00:00 2021-12-10 10:06:14.184617+00:00 We develop a minimalist compartmental model to study the impact of mobility restrictions in Italy during the Covid-19 outbreak. We show that, while an early lockdown shifts the contagion in time, beyond a critical value of lockdown strength the epidemic tends to restart after lifting the restrictions. We characterize the relative importance of different lockdown lifting schemes by accounting for two fundamental sources of heterogeneity, i.e. geography and demography. First, we consider Italian Regions as separate administrative entities, in which social interactions between age classes occur. We show that, due to the sparsity of the inter-Regional mobility matrix, once started, the epidemic spreading tends to develop independently across areas, justifying the adoption of mobility restrictions targeted to individual Regions or clusters of Regions. Second, we show that social contacts between members of different age classes play a fundamental role and that interventions which target local behaviours and take into account the age structure of the population can provide a significant contribution to mitigate the epidemic spreading. Our model aims to provide a general framework, and it highlights the relevance of some key parameters on non-pharmaceutical interventions to contain the contagion. text/plain Time, space and social interactions: exit mechanisms for the Covid-19 epidemics 2021-12-10 10:06:14.183453+00:00 service-account-generation-service Social sciences the economy Bonaccorsi mobility restriction human mobility restriction segregation effect mobility contraction lockdown restriction lockdown strategy aluminium strategy restriction result social consequence mobility consequences of human mobility restriction Acad lockdown mobility Natl. Acad limitation information Facebook economic situation national diversity federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5327 https://api.rohub.org/api/ros/55f5ab15-13df-4205-b2e5-2d5d80107369/crate/download/ 2021-12-10 10:06:17.575361+00:00 2025-03-05 00:57:44.982573+00:00 2021-12-10 10:06:17.575361+00:00 In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near-real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown. application/ld+json https://w3id.org/ro-id/55f5ab15-13df-4205-b2e5-2d5d80107369 Economic and social consequences of human mobility restrictions under COVID-19 MANUAL Foglini, Federica. "Economic and social consequences of human mobility restrictions under COVID-19." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/55f5ab15-13df-4205-b2e5-2d5d80107369. 304 https://api.rohub.org/api/resources/edbb0b87-359c-4381-9289-52f1604ebf29/download/ 2021-12-10 10:06:20.410214+00:00 2021-12-10 10:06:20.411042+00:00 In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near-real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown. text/plain Economic and social consequences of human mobility restrictions under COVID-19 2021-12-10 10:06:20.410214+00:00 service-account-generation-service Neurobiology diffusion of covid 19 Prezioso possible involvement trigger complication World Health Organization possible involvement of SARS-CoV-2 Journal of geophysical research. Biogeosciences novel coronavirus medicine World Health Organization number pandemic pneumonia epidemic coronavirus complication capital of Hubei, China Wuhan Hubei diffusion Italy pneumonia Coronavirus disease 2019 involvement of SARS-CoV-2 Three Italy of the COVID-19 epidemic Italian National Institute of Health capital involvement infection China Hubei Italy The Three Italy of the COVID-19 epidemic Wuhan kill federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 4282 https://api.rohub.org/api/ros/ac4dc70c-fdcc-4065-b297-7cce379c56a5/crate/download/ 2021-12-10 10:06:23.621238+00:00 2025-03-05 02:47:43.863841+00:00 2021-12-10 10:06:23.621238+00:00 Coronavirus disease 2019 (COVID-19), first reported in Wuhan, the capital of Hubei, China, has been associated to a novel coronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In March 2020, the World Health Organization declared the SARS-CoV-2 infection a global pandemic. Soon after, the number of cases soared dramatically, spreading across China and worldwide. Italy has had 12,462 confirmed cases according to the Italian National Institute of Health (ISS) as of March 11, and after the lockdown of the entire territory, by May 4, 209,254 cases of COVID-19 and 26,892 associated deaths have been reported. We performed a review to describe, in particular, the origin and the diffusion of COVID-19 in Italy, underlying how the geographical circulation has been heterogeneous and the importance of pathophysiology in the involvement of cardiovascular and neurological clinical manifestations. application/ld+json https://w3id.org/ro-id/ac4dc70c-fdcc-4065-b297-7cce379c56a5 The Three Italy of the COVID-19 epidemic and the possible involvement of SARS-CoV-2 in triggering complications other than pneumonia MANUAL Foglini, Federica. "The Three Italy of the COVID-19 epidemic and the possible involvement of SARS-CoV-2 in triggering complications other than pneumonia." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/ac4dc70c-fdcc-4065-b297-7cce379c56a5. 262 https://api.rohub.org/api/resources/c64d9709-1f85-4cdf-bcb0-9b5346cfe881/download/ 2021-12-10 10:06:26.377332+00:00 2021-12-10 10:06:26.378328+00:00 Coronavirus disease 2019 (COVID-19), first reported in Wuhan, the capital of Hubei, China, has been associated to a novel coronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In March 2020, the World Health Organization declared the SARS-CoV-2 infection a global pandemic. Soon after, the number of cases soared dramatically, spreading across China and worldwide. Italy has had 12,462 confirmed cases according to the Italian National Institute of Health (ISS) as of March 11, and after the lockdown of the entire territory, by May 4, 209,254 cases of COVID-19 and 26,892 associated deaths have been reported. We performed a review to describe, in particular, the origin and the diffusion of COVID-19 in Italy, underlying how the geographical circulation has been heterogeneous and the importance of pathophysiology in the involvement of cardiovascular and neurological clinical manifestations. text/plain The Three Italy of the COVID-19 epidemic and the possible involvement of SARS-CoV-2 in triggering complications other than pneumonia 2021-12-10 10:06:26.377332+00:00 service-account-generation-service Medical science volume 13 multiple-choice web-form survey nutrition counselling Maffoni De Giuseppe body mass index during COVID-19 pandemic lockdown Journal of geophysical research. Biogeosciences anatomy medicine book pandemic way of life body mass index public health web form multiple-choice issue 4 pandemic lockdown counselling survey mean Delta BMI Italian Online-Survey Delta BMI lockdown issue food lifestyle change information conduct nutrition Biino dietetics pandemic federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5779 https://api.rohub.org/api/ros/69e2fce8-0d65-49a3-ad8c-de9b4eef3070/crate/download/ 2021-12-10 10:06:29.565487+00:00 2025-03-05 01:01:21.020930+00:00 2021-12-10 10:06:29.565487+00:00 Background. COVID-19 pandemic has imposed a period of contingency measures, including total or partial lockdowns all over the world leading to several changes in lifestyle/eating behaviours. This retrospective cohort study aimed at investigating Italian adult population lifestyle changes during COVID-19 pandemic Phase 1 lockdown (8 March-4 May 2020) and discriminate between positive and negative changes and BMI (body mass index) variations (Delta BMI). Methods. A multiple-choice web-form survey was used to collect retrospective data regarding lifestyle/eating behaviours during Phase 1 in the Italian adult population. According to changes in lifestyle/eating behaviours, the sample was divided into three classes of changes: negative change, no change, positive change. For each class, correlations with Delta BMI were investigated. Results. Data were collected from 1304 subjects (973F/331M). Mean Delta BMI differed significantly (p < 0.001) between classes, and was significantly related to water intake, alcohol consumption, physical activity, frequency of craving or snacking between meals, dessert/sweets consumption at lunch. Conclusions. During Phase 1, many people faced several negative changes in lifestyle/eating behaviours with potential negative impact on health. These findings highlight that pandemic exacerbates nutritional issues and most efforts need to be done to provide nutrition counselling and public health services to support general population needs. application/ld+json https://w3id.org/ro-id/69e2fce8-0d65-49a3-ad8c-de9b4eef3070 Lifestyle Changes and Body Mass Index during COVID-19 Pandemic Lockdown: An Italian Online-Survey MANUAL Foglini, Federica. "Lifestyle Changes and Body Mass Index during COVID-19 Pandemic Lockdown: An Italian Online-Survey." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/69e2fce8-0d65-49a3-ad8c-de9b4eef3070. 220 https://api.rohub.org/api/resources/02388d59-ced9-426d-801d-9139b1f6a721/download/ 2021-12-10 10:06:32.824356+00:00 2021-12-10 10:06:32.825234+00:00 Background. COVID-19 pandemic has imposed a period of contingency measures, including total or partial lockdowns all over the world leading to several changes in lifestyle/eating behaviours. This retrospective cohort study aimed at investigating Italian adult population lifestyle changes during COVID-19 pandemic Phase 1 lockdown (8 March-4 May 2020) and discriminate between positive and negative changes and BMI (body mass index) variations (Delta BMI). Methods. A multiple-choice web-form survey was used to collect retrospective data regarding lifestyle/eating behaviours during Phase 1 in the Italian adult population. According to changes in lifestyle/eating behaviours, the sample was divided into three classes of changes: negative change, no change, positive change. For each class, correlations with Delta BMI were investigated. Results. Data were collected from 1304 subjects (973F/331M). Mean Delta BMI differed significantly (p < 0.001) between classes, and was significantly related to water intake, alcohol consumption, physical activity, frequency of craving or snacking between meals, dessert/sweets consumption at lunch. Conclusions. During Phase 1, many people faced several negative changes in lifestyle/eating behaviours with potential negative impact on health. These findings highlight that pandemic exacerbates nutritional issues and most efforts need to be done to provide nutrition counselling and public health services to support general population needs. text/plain Lifestyle Changes and Body Mass Index during COVID-19 Pandemic Lockdown: An Italian Online-Survey 2021-12-10 10:06:32.824356+00:00 service-account-generation-service https://orcid.org/0000-0002-2736-0052 Federica Foglini https://orcid.org/0000-0002-2736-0052 5530 https://api.rohub.org/api/ros/5a0a6ad0-a912-48a1-9309-c6503d63931f/crate/download/ mailto:service-account-generation-service 2021-12-10 10:06:35.892700+00:00 2025-03-05 00:50:02.929436+00:00 2021-12-10 10:06:35.892700+00:00 The infection caused by COVID-19 (i.e. corona virus disease 2019) has caused more than 5.2 million cases and more than 337,000 deaths worldwide. Italy was the European epicenter for virus spread and one with most cases and deaths. The first Italian patient was diagnosed on February 18(th), a young man hospitalized in Lombardy (Northern Italy). The Italian government not only isolated the village where he lived, but a few days later put the entire country in lockdown. We have here analyzed the COVID-19 Italian data during the first three months after the outbreak and the effect of lockdown. COVID-19 virus has a high transmission rate and is associated with high fatality rate especially in the older population. The initial reproduction rate of the virus (R0) in Italy was between 2.1 and 3.3 in different Italian regions, with a doubling time between 2.7 and 3.2 days. The number of confirmed cases has now reached 229,000 but after the lockdown R0 is finally below 1. Despite the lockdown, the number of infected and deceased patients in Italy was very high, with a lethality rate higher than in other countries. It is likely that number of cases is underestimating the real since the number of asymptomatic and paucisymptomatic is relatively high. It is important to investigate which patients are more vulnerable and also if other co-factors can account for this high fatality rate, since this pandemia is far from being resolved. application/ld+json https://w3id.org/ro-id/5a0a6ad0-a912-48a1-9309-c6503d63931f COVID-19 Infection Pandemic: From the Frontline in Italy http://eurovoc.europa.eu/5881 http://w3id.org/ro-id/rohub/model#subject/-1054707894 http://w3id.org/ro-id/rohub/model#subject/-1651654742 http://w3id.org/ro-id/rohub/model#subject/-1730379577 http://w3id.org/ro-id/rohub/model#subject/-2042622210 http://w3id.org/ro-id/rohub/model#subject/-503298211 http://w3id.org/ro-id/rohub/model#subject/-900704710 http://w3id.org/ro-id/rohub/model#subject/100000516 http://w3id.org/ro-id/rohub/model#subject/100000563 http://w3id.org/ro-id/rohub/model#subject/100000602 http://w3id.org/ro-id/rohub/model#subject/100000839 http://w3id.org/ro-id/rohub/model#subject/100012215 http://w3id.org/ro-id/rohub/model#subject/100166262 http://w3id.org/ro-id/rohub/model#subject/1009549104 http://w3id.org/ro-id/rohub/model#subject/1120076258 http://w3id.org/ro-id/rohub/model#subject/126742 http://w3id.org/ro-id/rohub/model#subject/13160596 http://w3id.org/ro-id/rohub/model#subject/1363162178 http://w3id.org/ro-id/rohub/model#subject/1412263058 http://w3id.org/ro-id/rohub/model#subject/1983462063 http://w3id.org/ro-id/rohub/model#subject/2103630808 http://w3id.org/ro-id/rohub/model#subject/221244 http://w3id.org/ro-id/rohub/model#subject/24521 http://w3id.org/ro-id/rohub/model#subject/369037759 http://w3id.org/ro-id/rohub/model#subject/38959 http://w3id.org/ro-id/rohub/model#subject/46560 http://w3id.org/ro-id/rohub/model#subject/61177 http://w3id.org/ro-id/rohub/model#subject/70969475 http://w3id.org/ro-id/rohub/model#subject/74351906 MANUAL Foglini, Federica. "COVID-19 Infection Pandemic: From the Frontline in Italy." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/5a0a6ad0-a912-48a1-9309-c6503d63931f. https://orcid.org/0000-0002-2736-0052 156 https://api.rohub.org/api/resources/ce35056e-dd42-4386-a9a3-59f80a6ff3af/download/ mailto:service-account-generation-service 2021-12-10 10:06:39.122596+00:00 2021-12-10 10:06:39.123386+00:00 The infection caused by COVID-19 (i.e. corona virus disease 2019) has caused more than 5.2 million cases and more than 337,000 deaths worldwide. Italy was the European epicenter for virus spread and one with most cases and deaths. The first Italian patient was diagnosed on February 18(th), a young man hospitalized in Lombardy (Northern Italy). The Italian government not only isolated the village where he lived, but a few days later put the entire country in lockdown. We have here analyzed the COVID-19 Italian data during the first three months after the outbreak and the effect of lockdown. COVID-19 virus has a high transmission rate and is associated with high fatality rate especially in the older population. The initial reproduction rate of the virus (R0) in Italy was between 2.1 and 3.3 in different Italian regions, with a doubling time between 2.7 and 3.2 days. The number of confirmed cases has now reached 229,000 but after the lockdown R0 is finally below 1. Despite the lockdown, the number of infected and deceased patients in Italy was very high, with a lethality rate higher than in other countries. It is likely that number of cases is underestimating the real since the number of asymptomatic and paucisymptomatic is relatively high. It is important to investigate which patients are more vulnerable and also if other co-factors can account for this high fatality rate, since this pandemia is far from being resolved. text/plain cite.txt 2021-12-10 10:06:39.122596+00:00 mailto:service-account-enrichment mailto:service-account-generation-service Medical science psychology Journal of geophysical research. Biogeosciences Trivellini related quality of life volume 9 medicine health angstrom volume welfare quality of life Italian adolescent CQ period peer relationship emotion relationship health quality of life health promotion process of health quality of life in Italian adolescent study experience aim mood Mastorci information teen Standard Condition A. health-related quality of life CQ federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 4811 https://api.rohub.org/api/ros/d0501e2b-d445-42cd-9c1d-e06d2c00fd2e/crate/download/ 2021-12-10 10:06:42.441770+00:00 2025-03-05 00:52:20.609974+00:00 2021-12-10 10:06:42.441770+00:00 Coronavirus disease 2019 (COVID-19) outbreak represented an experience of social isolation potentially leading to changes in the health quality of life. The aim of this study is to investigate the health-related quality of life during quarantine in early adolescents. Data were collected from 1,289 adolescents (mean age, 12.5; male, 622), at the beginning of the school year (September 2019, Standard Condition, SC) as part of the AVATAR project and during Phase 1 of the Italian lockdown (mid-late April 2020) (COVID-19 Quarantine, CQ) using an online questionnaire. In the CQ period, with respect to SC, adolescents showed lower perception in the dimensions, such as psychological (p = 0.001), physical well-being (p = 0.001), mood/emotion (p = 0.008), autonomy (p = 0.001), and financial resources (p = 0.018). Relationship with the family (p = 0.021) and peers (p = 0.001), as well as the perception of bullying (p = 0.001) were reduced. In lifestyle, adolescents developed higher adherence to the Mediterranean diet (p = 0.001). Adolescents living in the village had greater reduction in both autonomy (p = 0.002) and peer relationships (p = 0.002). Moreover, the perception of physical well-being was lower in those living in the city instead of those living in the countryside (p = 0.03), in an apartment instead of a detached house (p = 0.002), and in those who did not have green space (p = 0.001). Gender effect emerged for the psychological (p = 0.007) and physical well-being (p = 0.001), mood/emotion (p = 0.001), and self-perception (p = 0.001). The study showed that health-related quality of life during quarantine changed in its psychosocial dimensions, from mood and self-esteem to social relationships, helping to define the educational policies at multiple points in the promotion process of health. application/ld+json https://w3id.org/ro-id/d0501e2b-d445-42cd-9c1d-e06d2c00fd2e Health-Related Quality of Life in Italian Adolescents During Covid-19 Outbreak MANUAL Foglini, Federica. "Health-Related Quality of Life in Italian Adolescents During Covid-19 Outbreak." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/d0501e2b-d445-42cd-9c1d-e06d2c00fd2e. 208 https://api.rohub.org/api/resources/fe790e5e-39b6-4491-a37d-23ec745a671c/download/ 2021-12-10 10:06:45.619191+00:00 2021-12-10 10:06:45.620260+00:00 Coronavirus disease 2019 (COVID-19) outbreak represented an experience of social isolation potentially leading to changes in the health quality of life. The aim of this study is to investigate the health-related quality of life during quarantine in early adolescents. Data were collected from 1,289 adolescents (mean age, 12.5; male, 622), at the beginning of the school year (September 2019, Standard Condition, SC) as part of the AVATAR project and during Phase 1 of the Italian lockdown (mid-late April 2020) (COVID-19 Quarantine, CQ) using an online questionnaire. In the CQ period, with respect to SC, adolescents showed lower perception in the dimensions, such as psychological (p = 0.001), physical well-being (p = 0.001), mood/emotion (p = 0.008), autonomy (p = 0.001), and financial resources (p = 0.018). Relationship with the family (p = 0.021) and peers (p = 0.001), as well as the perception of bullying (p = 0.001) were reduced. In lifestyle, adolescents developed higher adherence to the Mediterranean diet (p = 0.001). Adolescents living in the village had greater reduction in both autonomy (p = 0.002) and peer relationships (p = 0.002). Moreover, the perception of physical well-being was lower in those living in the city instead of those living in the countryside (p = 0.03), in an apartment instead of a detached house (p = 0.002), and in those who did not have green space (p = 0.001). Gender effect emerged for the psychological (p = 0.007) and physical well-being (p = 0.001), mood/emotion (p = 0.001), and self-perception (p = 0.001). The study showed that health-related quality of life during quarantine changed in its psychosocial dimensions, from mood and self-esteem to social relationships, helping to define the educational policies at multiple points in the promotion process of health. text/plain Health-Related Quality of Life in Italian Adolescents During Covid-19 Outbreak 2021-12-10 10:06:45.619191+00:00 service-account-generation-service Pharmacology Pharmacy Turkey Ciancimino Journal of geophysical research. Biogeosciences gender stereotype behavior health gender stereotype questionnaire system gender role Turkey welfare Bulgan emergency from covid 19 Italy Europe compare the influence health emergency Bulgan well-being in Italy Europe influence of gender stereotype Tintori influence l. compliance emergency Italy Eur. Rev. Med emancipation process federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 6144 https://api.rohub.org/api/ros/130bd478-289c-438c-b9e9-8ce20ae58f6d/crate/download/ 2021-12-10 10:06:48.990464+00:00 2025-03-05 00:47:00.660389+00:00 2021-12-10 10:06:48.990464+00:00 OBJECTIVE: The hypothesis that gender stereotypes influence human behaviour and relational well-being is widely accepted in the literature. However, a comparison based on scientific assumptions is necessary to deeply understand the mechanisms activated by stereotypes in conditions of stress. The global health emergency from COVID-19 offers the opportunity to compare countries with different socio-cultural conditions, whose population has been subjected to the same stressful event during the lockdown phase. SUBJECTS AND METHODS: The same questionnaire was disseminated in both Italy and Turkey during their respective lockdown phases. 140,000 interviews were collected in Italy and 10,000 in Turkey, a number big enough to obtain useful information for a comparative analysis in relation to behaviours, attitudes and well-being. also using the recursive regression models. RESULTS: The results, based on scientific data, show that gender stereotypes are much more rooted in Turkey than in Italy, where the emancipation process of the population is more advanced, producing profound social changes and decreasing differences between men and women in terms of behaviour and reactions to difficult situations, such as the present one. CONCLUSIONS: Stereotypes, which are hostile to any opposite evidence, affect individual behaviours and attitudes to the point that, within a specific context, they play a protective role against the uncertainty during a period of health emergency, inducing people to seek shelter in pre-established and widespread behavioural models. According to the data analysis, this has happened in Turkey more than in Italy. The results show that within a culture still strongly pervaded by these social conditioning, especially at the presence of low levels of education. the adherence to gender roles constitutes a protective factor of the individual well-being against external stress factors. application/ld+json https://w3id.org/ro-id/130bd478-289c-438c-b9e9-8ce20ae58f6d Comparing the influence of gender stereotypes on well-being in Italy and Turkey during the COVID-19 lockdown MANUAL Foglini, Federica. "Comparing the influence of gender stereotypes on well-being in Italy and Turkey during the COVID-19 lockdown." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/130bd478-289c-438c-b9e9-8ce20ae58f6d. 270 https://api.rohub.org/api/resources/cc942625-3590-4c7c-90d2-fa9e252bf997/download/ 2021-12-10 10:06:51.674882+00:00 2021-12-10 10:06:51.676645+00:00 OBJECTIVE: The hypothesis that gender stereotypes influence human behaviour and relational well-being is widely accepted in the literature. However, a comparison based on scientific assumptions is necessary to deeply understand the mechanisms activated by stereotypes in conditions of stress. The global health emergency from COVID-19 offers the opportunity to compare countries with different socio-cultural conditions, whose population has been subjected to the same stressful event during the lockdown phase. SUBJECTS AND METHODS: The same questionnaire was disseminated in both Italy and Turkey during their respective lockdown phases. 140,000 interviews were collected in Italy and 10,000 in Turkey, a number big enough to obtain useful information for a comparative analysis in relation to behaviours, attitudes and well-being. also using the recursive regression models. RESULTS: The results, based on scientific data, show that gender stereotypes are much more rooted in Turkey than in Italy, where the emancipation process of the population is more advanced, producing profound social changes and decreasing differences between men and women in terms of behaviour and reactions to difficult situations, such as the present one. CONCLUSIONS: Stereotypes, which are hostile to any opposite evidence, affect individual behaviours and attitudes to the point that, within a specific context, they play a protective role against the uncertainty during a period of health emergency, inducing people to seek shelter in pre-established and widespread behavioural models. According to the data analysis, this has happened in Turkey more than in Italy. The results show that within a culture still strongly pervaded by these social conditioning, especially at the presence of low levels of education. the adherence to gender roles constitutes a protective factor of the individual well-being against external stress factors. text/plain Comparing the influence of gender stereotypes on well-being in Italy and Turkey during the COVID-19 lockdown 2021-12-10 10:06:51.674882+00:00 service-account-generation-service Pharmacology Pharmacy federica.foglini@ismar.cnr.it Federica Foglini needs 9.822263797942002 10.5 spread 3.928905519176801 4.2 Europe https://www.wikidata.org/wiki/Q46 Emergency incident Disaster, accident and emergency incident/Accident and emergency incident/Emergency incident Parsi 7.7227722772277225 7.8 environmental sciences 39.63620155735672 0.6543404459953308 Apr-2-2020 life sciences (general) 100.0 1.84152752161026 social contact 4.4901777362020585 4.8 medicine 26.666666666666668 2.0 Religious leader Religion and belief/Religious leader emotions 8.51262862488307 9.1 scientific terms 9.333333333333334 0.7 geology 60.36379844264328 0.996525228023529 Ciancimino 9.009900990099009 9.1 covid 19 12.77227722772277 12.899999999999999 La Longa 12.265512265512266 17.0 from Mar-22-2020 impact 10.009354536950422 10.7 computer science 28.0 2.1 earth sciences 60.36379844264328 0.996525228023529 lack 5.9405940594059405 6.0 Principal Components Analysis 3.1024531024531026 4.3 Cerbara 7.227722772277228 7.3 scarcity 4.770813844714686 5.1 aim 6.361085126286249 6.8 OBJECTIVE: Social distancing is crucial in order to flatten the curve of COVID-19 virus spreading. 9.021406727828746 11.8 study 15.341440598690363 16.4 La Longa 7.425742574257426 7.5 Palomba 14.356435643564357 14.5 Europe 5.425631431244154 5.8 Cerbara, L; Ciancimino, G; Crescimbene, M; La Longa, F; Parsi, MR; Tintori, A; Palomba, R. A nation-wide survey on emotional and psychological impacts of COVID-19 social distancing. 65.59633027522935 85.8 lack of social contacts 4.112554112554113 5.7 Psychology Science and technology/Social sciences/Psychology Maslow 4.9504950495049505 5.0 Music Arts, culture and entertainment/Arts and entertainment/Music Isolation, scarcity of resources and the lack of social contacts may have produced a negative impact on people's emotions and psychological well-being. 7.262996941896024 9.5 This study demonstrates the existence of links between negative emotions and primary needs that mainly refer to the first three levels of Maslow's pyramid. 7.339449541284403 9.6 Psychology Science and technology/Social sciences/Psychology Weather Weather Maslow's hierarchy of needs 3.3910533910533913 4.7 nation-wide survey 5.411255411255412 7.5 psychology 30.666666666666664 2.3 survey 8.514851485148515 8.6 (Eur. Rev. Med. 10.779816513761467 14.1 welfare 5.893358278765201 6.3 subsistence needs 9.812409812409813 13.6 want 7.857811038353602 8.4 Eur. Rev. Med 50.721500721500725 70.3 life sciences 100.0 1.84152752161026 emotions 5.544554455445544 5.6 virus spreading 7.647907647907648 10.6 linguistics 5.333333333333333 0.4 service-account-enrichment 6268 https://api.rohub.org/api/ros/c34fcf5c-7b68-4820-b3bc-162892cc174a/crate/download/ 2021-12-10 10:06:54.641957+00:00 2025-03-05 00:46:20.076035+00:00 2021-12-10 10:06:54.641957+00:00 OBJECTIVE: Social distancing is crucial in order to flatten the curve of COVID-19 virus spreading. Isolation, scarcity of resources and the lack of social contacts may have produced a negative impact on people's emotions and psychological well-being. This study aims to explore the reasons and the ways through which social distancing generates negative emotions in individuals who experienced the lockdown. To a larger extent, the objective is to check the existence of relations between negative emotions and the satisfaction of basic needs. MATERIALS AND METHODS: In Italy 140,656 online interviews were collected from March 22 to April 2, 2020. Data analysis was carried out using mono and bivariate statistical analysis, K-means clustering and the Principal Components Analysis (PCA). The parameters for the identification of six clusters were: the intensity of the respondent's basic emotions and the layers of Maslow's hierarchy of needs. RESULTS: The majority of people involved in an emergency situation, implying a collapse of social contacts, experience some kind of emotional reactions. In our study, we found a correlation between basic emotions and Maslow's hierarchy of needs. In times of crisis, the most basic needs are the physiological ones. Fear, anger and sadness are predominant in all population groups; anger and disgust mainly appear when people are exposed to the risk of not being able to meet subsistence needs, thus perceiving a lack of economic security. CONCLUSIONS: The well-known Maslow's theory of human needs seems to fit well with the outbreak of negative emotions in the context of COVID-19. This study demonstrates the existence of links between negative emotions and primary needs that mainly refer to the first three levels of Maslow's pyramid. As a result of COVID-19 worldwide pandemic, many people have been sucked into the bottom layers of the pyramid. This change in individual basic needs has triggered a relevant transformation in individual emotional status and a shift towards negative emotions. application/ld+json https://w3id.org/ro-id/c34fcf5c-7b68-4820-b3bc-162892cc174a A nation-wide survey on emotional and psychological impacts of COVID-19 social distancing MANUAL https://w3id.org/ro-id/2e8a8077-1200-45e1-8d22-c85a07fba85c https://w3id.org/ro-id/36cea813-057d-4200-96e8-a02e6dda30f3 https://w3id.org/ro-id/5954ba12-c228-4e45-bc73-83dbb6920a50 https://w3id.org/ro-id/a5da7c22-e344-4474-893c-be34ed87a6bc https://w3id.org/ro-id/bd6c59bd-b975-4168-87b0-fd089cab86df https://w3id.org/ro-id/14845d35-7fe9-45ad-8683-61bb89572114 https://w3id.org/ro-id/dd9c9d64-2c98-4f47-a638-8e8ab17b392b https://w3id.org/ro-id/05ba2522-9161-4420-805e-927f5432c2e1 https://w3id.org/ro-id/142dc626-6793-48ee-bba5-6bdf163badbd https://w3id.org/ro-id/2da0472e-e0af-455d-9d9b-8a35d9410fea https://w3id.org/ro-id/2f6225de-5946-4563-b63f-426c810cf6d3 https://w3id.org/ro-id/525fd32b-643b-4b33-be5a-8b5331b00f5e https://w3id.org/ro-id/66ba459e-0a84-465a-a359-7793d304afb5 https://w3id.org/ro-id/6887fa46-2150-4627-bfc1-01acfb13e00d https://w3id.org/ro-id/73e8df93-171c-4fe0-bc50-a25870d8aead https://w3id.org/ro-id/8021975b-eb03-4371-a8fb-8e8f5a81ed37 https://w3id.org/ro-id/ab4868c2-cb79-48c1-b95f-33607204d182 https://w3id.org/ro-id/b43ca91d-0589-451e-925b-faeeddeb2484 https://w3id.org/ro-id/f37b62b4-d2b9-47ab-b2d1-129270ef66a8 https://w3id.org/ro-id/f7b5522b-02f2-4c8c-b0d2-20b404acc530 https://w3id.org/ro-id/25b86141-9ca6-4f6b-b15a-2a56144a7575 https://w3id.org/ro-id/37980fde-3310-4250-b3e1-678cba79367d https://w3id.org/ro-id/5fa9cfea-3f68-4f08-a5df-203f6bfde867 https://w3id.org/ro-id/eb306019-1796-463e-85e3-e2b362fbf5a8 https://w3id.org/ro-id/189e02d2-cdb8-4b61-a385-c7afb9496979 https://w3id.org/ro-id/2e9e5cc1-8efe-4fc2-85c8-aa6fcefb7c79 https://w3id.org/ro-id/8476ff3e-5432-47af-b3eb-41c3742de3b8 https://w3id.org/ro-id/9525b76f-b6d7-4127-9978-95cd81a26647 https://w3id.org/ro-id/a1628f74-796c-40b1-a096-50c871ad12ee https://w3id.org/ro-id/a17969b2-5cf6-436d-a4f4-a6df6998dbb5 https://w3id.org/ro-id/e40c6a38-55fe-4c7e-bbc1-3ef2dba86c5f https://w3id.org/ro-id/1fc25e98-5566-4218-a8af-4e300e836bc9 https://w3id.org/ro-id/37aa9b20-ff9d-4fd2-98fc-b17ee2823896 https://w3id.org/ro-id/3e2a594c-9d91-4f32-bce1-355f67dda076 https://w3id.org/ro-id/5fedd852-54cd-4b57-8ff6-a7b6a469110d https://w3id.org/ro-id/637061aa-f169-4d41-b7ad-1c528d805c91 https://w3id.org/ro-id/792ee71f-788b-472c-a349-010a8e114ee4 https://w3id.org/ro-id/79bab1dc-b337-430e-8c58-5d00df848885 https://w3id.org/ro-id/92682146-4f29-4c29-b3fc-a7e3320125ac https://w3id.org/ro-id/a68416bd-bf14-4adc-97e6-6f9220ac92dc https://w3id.org/ro-id/bb281765-c361-4aad-ad47-7de3c51e1d20 https://w3id.org/ro-id/d0ce22fa-177e-44e4-bf71-426b449b8988 https://w3id.org/ro-id/eb723d84-b5d9-438f-8273-e02a988ee727 https://w3id.org/ro-id/f54c40c7-debc-4947-94f3-0fcb19fd5582 https://w3id.org/ro-id/26cc9920-a42f-44f8-9554-bf8ff213948d https://w3id.org/ro-id/ba21c90c-9e0d-41c5-8966-e0a701ecf84c https://w3id.org/ro-id/459664d0-c15a-4e75-a918-13ccae306538 https://w3id.org/ro-id/60f487ee-001d-412e-b4f7-76e06f2990df https://w3id.org/ro-id/82033b03-a3ef-402a-bf7c-49244c57b098 https://w3id.org/ro-id/a21e818a-c6ad-4071-a1e9-8820a366a42a https://w3id.org/ro-id/a4d37d46-e6c3-4cf0-a1ff-05cfeb8a1b35 https://w3id.org/ro-id/ad5f3e9a-3936-4476-9c48-abd2bf77271e https://w3id.org/ro-id/b452ed95-d705-4cde-8177-3b3ec0616d3a https://w3id.org/ro-id/bca5c8d6-d006-4057-8adc-25059b9679a3 https://w3id.org/ro-id/e3b0d645-d21a-40e0-ad42-6817f36bd772 https://w3id.org/ro-id/6da6ab3a-0c23-4d3c-aebc-5fe28b23d072 https://w3id.org/ro-id/811781bc-b4e6-4f0a-af71-89e11bdc6f47 https://w3id.org/ro-id/991fbb61-8936-45db-aedf-4a15554e6e73 https://w3id.org/ro-id/9a97d922-7067-4350-8bc7-a929620c3c32 https://w3id.org/ro-id/aa095a41-ab9c-4220-89c1-1ca6073b1fcd https://w3id.org/ro-id/26037b68-872f-4c77-8266-3470628f4e94 https://w3id.org/ro-id/49e20c11-b5b2-493b-930f-e84f6af91403 Foglini, Federica. "A nation-wide survey on emotional and psychological impacts of COVID-19 social distancing." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/c34fcf5c-7b68-4820-b3bc-162892cc174a. 258 https://api.rohub.org/api/resources/231907a0-a7f8-406f-a0b6-662e865aaa09/download/ 2021-12-10 10:06:57.728687+00:00 2021-12-10 10:06:57.729582+00:00 OBJECTIVE: Social distancing is crucial in order to flatten the curve of COVID-19 virus spreading. Isolation, scarcity of resources and the lack of social contacts may have produced a negative impact on people's emotions and psychological well-being. This study aims to explore the reasons and the ways through which social distancing generates negative emotions in individuals who experienced the lockdown. To a larger extent, the objective is to check the existence of relations between negative emotions and the satisfaction of basic needs. MATERIALS AND METHODS: In Italy 140,656 online interviews were collected from March 22 to April 2, 2020. Data analysis was carried out using mono and bivariate statistical analysis, K-means clustering and the Principal Components Analysis (PCA). The parameters for the identification of six clusters were: the intensity of the respondent's basic emotions and the layers of Maslow's hierarchy of needs. RESULTS: The majority of people involved in an emergency situation, implying a collapse of social contacts, experience some kind of emotional reactions. In our study, we found a correlation between basic emotions and Maslow's hierarchy of needs. In times of crisis, the most basic needs are the physiological ones. Fear, anger and sadness are predominant in all population groups; anger and disgust mainly appear when people are exposed to the risk of not being able to meet subsistence needs, thus perceiving a lack of economic security. CONCLUSIONS: The well-known Maslow's theory of human needs seems to fit well with the outbreak of negative emotions in the context of COVID-19. This study demonstrates the existence of links between negative emotions and primary needs that mainly refer to the first three levels of Maslow's pyramid. As a result of COVID-19 worldwide pandemic, many people have been sucked into the bottom layers of the pyramid. This change in individual basic needs has triggered a relevant transformation in individual emotional status and a shift towards negative emotions. text/plain A nation-wide survey on emotional and psychological impacts of COVID-19 social distancing 2021-12-10 10:06:57.728687+00:00 needs 7.326732673267327 7.4 Italy https://www.wikidata.org/wiki/Q38 psychological impact 3.535353535353536 4.9 Science and technology Science and technology environmental science and management 39.63620155735672 0.6543404459953308 objective 4.851485148514852 4.9 social 10.28999064546305 11.0 well-being 4.356435643564357 4.4 Parsi 7.296538821328345 7.8 service-account-generation-service Botany biology scientists' research attention botany research attention plants 2021 botany scientist organism angiosperm research factory interest conservation botanist attention flowers bias bias research bias trait scientists' research interest colour trait effort care plant scientists' research attention plant conservation effort plant life attention distributed flowers federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 3997 https://api.rohub.org/api/ros/2c23ff8e-9ef5-4cc2-a042-c6760bd74b90/crate/download/ 2021-12-10 10:07:00.901185+00:00 2025-03-05 01:14:09.380870+00:00 2021-12-10 10:07:00.901185+00:00 Despite the perception that plant science focuses on strictly scientific criteria, this analysis finds that there is an aesthetic bias in regards to which plants, based on certain traits, receive more research attention. Scientists' research interests are often skewed toward charismatic organisms, but quantifying research biases is challenging. By combining bibliometric data with trait-based approaches and using a well-studied alpine flora as a case study, we demonstrate that morphological and colour traits, as well as range size, have significantly more impact on species choice for wild flowering plants than traits related to ecology and rarity. These biases should be taken into account to inform more objective plant conservation efforts. application/ld+json https://w3id.org/ro-id/2c23ff8e-9ef5-4cc2-a042-c6760bd74b90 Plant scientists' research attention is skewed towards colourful, conspicuous and broadly distributed flowers MANUAL Foglini, Federica. "Plant scientists' research attention is skewed towards colourful, conspicuous and broadly distributed flowers." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/2c23ff8e-9ef5-4cc2-a042-c6760bd74b90. 227 https://api.rohub.org/api/resources/a2e2eaa5-f226-492e-8460-dc778e832dab/download/ 2021-12-10 10:07:04.087045+00:00 2021-12-10 10:07:04.088081+00:00 Despite the perception that plant science focuses on strictly scientific criteria, this analysis finds that there is an aesthetic bias in regards to which plants, based on certain traits, receive more research attention. Scientists' research interests are often skewed toward charismatic organisms, but quantifying research biases is challenging. By combining bibliometric data with trait-based approaches and using a well-studied alpine flora as a case study, we demonstrate that morphological and colour traits, as well as range size, have significantly more impact on species choice for wild flowering plants than traits related to ecology and rarity. These biases should be taken into account to inform more objective plant conservation efforts. text/plain Plant scientists' research attention is skewed towards colourful, conspicuous and broadly distributed flowers 2021-12-10 10:07:04.087045+00:00 service-account-generation-service Geology Botany biology beginning of Heinrich Stadial botany forest withdrawal Ravazzi Last Glacial Maximum.The palynology 2020 Alpine foreland birch-sedge community trees pollen swamp peat floodplain Casaletto Ceredano record forest community withdrawal foreland pollen record flood at the beginning of Heinrich Stadial N-Italy beginning vegetation sedge birch pine woodland floodplain swamp community federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 6189 https://api.rohub.org/api/ros/f0b9d343-156c-46e2-aa72-efb121a12265/crate/download/ 2021-12-10 10:07:07.030356+00:00 2025-03-05 12:47:15.829950+00:00 2021-12-10 10:07:07.030356+00:00 The southern Alpine foreland, facing windward to moist southern airmasses, is daimed to have supported forest vegetation throughout the Last Glaciation. Here we present a multiproxy paleoecological record from a compressed peat, uncovered at Casaletto Ceredano, N-Italy, spanning the interval from 33 to 30.5 kyr cal BP. Stratigraphically, it underlies a fluvioglacial belt attributed to the Last Glacial Maximum.The peat records a floodplain swamp community with tree birch and tall sedges, pine woodlands in upland areas, and only limited patches of open vegetation. Plant macrofossils - bark, charcoal, wood and fruits - establish the predominant role of Betulu pubescens group (downy birch) in the anoxic wetland, thanks to its ability to enhance gas exchange through a distinctive type of bark lenticels. A fire-induced birch-to-pine cyde is repeated twice along the 2500 years-time span covered by the peat layer. The climate reconstructed from modern pollen analogs compares to northern boreal zone, with Tjuly <15 degrees C, excluding warm-temperate trees. A major flood sealing the swamp with minerogenic silt is precisely dated to 30,497 +/- 594 yr cal BP (2 sigma uncertainty). Here, the pollen record shows a substantial forest withdrawal, and development of grasslands and semideserts, pointing to co-factorial action of increased climate continentality and of river dynamics. According to teleconnections with the Atlantic and Arctic framework of the stadial-interstadial climate variability, the age and pattern of this event are consistent with the onset of Heinrich Stadial 3, causing a lockdown of moist westerlies and of their Rossby waves in the W-Mediterranean. (C) 2020 Elsevier B.V. All rights reserved. application/ld+json https://w3id.org/ro-id/f0b9d343-156c-46e2-aa72-efb121a12265 Birch-sedge communities, forest withdrawal and flooding at the beginning of Heinrich Stadial 3 at the southern Alpine foreland MANUAL Foglini, Federica. "Birch-sedge communities, forest withdrawal and flooding at the beginning of Heinrich Stadial 3 at the southern Alpine foreland." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/f0b9d343-156c-46e2-aa72-efb121a12265. 284 https://api.rohub.org/api/resources/7ba72c0c-694e-464a-b488-342c736d7c0e/download/ 2021-12-10 10:07:09.858283+00:00 2021-12-10 10:07:09.859316+00:00 The southern Alpine foreland, facing windward to moist southern airmasses, is daimed to have supported forest vegetation throughout the Last Glaciation. Here we present a multiproxy paleoecological record from a compressed peat, uncovered at Casaletto Ceredano, N-Italy, spanning the interval from 33 to 30.5 kyr cal BP. Stratigraphically, it underlies a fluvioglacial belt attributed to the Last Glacial Maximum.The peat records a floodplain swamp community with tree birch and tall sedges, pine woodlands in upland areas, and only limited patches of open vegetation. Plant macrofossils - bark, charcoal, wood and fruits - establish the predominant role of Betulu pubescens group (downy birch) in the anoxic wetland, thanks to its ability to enhance gas exchange through a distinctive type of bark lenticels. A fire-induced birch-to-pine cyde is repeated twice along the 2500 years-time span covered by the peat layer. The climate reconstructed from modern pollen analogs compares to northern boreal zone, with Tjuly <15 degrees C, excluding warm-temperate trees. A major flood sealing the swamp with minerogenic silt is precisely dated to 30,497 +/- 594 yr cal BP (2 sigma uncertainty). Here, the pollen record shows a substantial forest withdrawal, and development of grasslands and semideserts, pointing to co-factorial action of increased climate continentality and of river dynamics. According to teleconnections with the Atlantic and Arctic framework of the stadial-interstadial climate variability, the age and pattern of this event are consistent with the onset of Heinrich Stadial 3, causing a lockdown of moist westerlies and of their Rossby waves in the W-Mediterranean. (C) 2020 Elsevier B.V. All rights reserved. text/plain Birch-sedge communities, forest withdrawal and flooding at the beginning of Heinrich Stadial 3 at the southern Alpine foreland 2021-12-10 10:07:09.858283+00:00 service-account-generation-service Medical science psychology Stress-induced increase induced increase of spatial Pseudoneglect neglect increment lockdown in Italy effects of lockdown testing session anatomy medicine health pandemic stress asymmetry testing result cancel induced increase cognition COVID-19 Student Stress Scale Radial Arm Maze session coping increase of spatial Pseudoneglect lockdown negligence left hemisphere right brain rise left conclusion lockdown-related stress Italy South Dakota Somma spatial Pseudoneglect federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 6825 https://api.rohub.org/api/ros/e2c753f3-255f-4f6a-9547-43166aa457dd/crate/download/ 2021-12-10 10:07:12.582298+00:00 2025-03-05 00:59:09.610730+00:00 2021-12-10 10:07:12.582298+00:00 Background The measures taken to contain the coronavirus disease 2019 (COVID-19) pandemic, such as the lockdown in Italy, do impact psychological health; yet, less is known about their effect on cognitive functioning. The transactional theory of stress predicts reciprocal influences between perceived stress and cognitive performance. However, the effects of a period of stress due to social isolation on spatial cognition and exploration have been little examined. The aim of the present study was to investigate the possible effects and impact of the COVID-19 pandemic on spatial cognition tasks, particularly those concerning spatial exploration, and the physiological leftward bias known as pseudoneglect. A right-hemisphere asymmetry for spatial attention processes crucially contributes to pseudoneglect. Other evidence indicates a predominantly right-hemisphere activity in stressful situations. We also analyzed the effects of lockdown on coping strategies, which typically show an opposite pattern of hemispheric asymmetry, favoring the left hemisphere. If so, then pseudoneglect should increase during the lockdown and be negatively correlated with the efficacy of coping strategies. Methods One week before the start of the lockdown due to COVID-19 in Italy (T1), we had collected data from a battery of behavioral tests including tasks of peri-personal spatial cognition. During the quarantine period, from late April to early May 2020 (T2), we repeated the testing sessions with a subgroup of the same participants (47 right-handed students, mean age = 20, SD = 1.33). At both testing sessions, participants performed digitized neuropsychological tests, including a Cancellation task, Radial Arm Maze task, and Raven's Advanced Progressive Matrices. Participants also completed a newly developed COVID-19 Student Stress Scale, based on transactional models of stress, and the Coping Orientation to Problems Experienced-New Italian Version (COPE-NIV) to assess coping orientation. Results The tendency to start cancelation from a left-sided item, to explore first a left-sided arm of the maze, and to choose erroneous response items on the left side of the page on Raven's matrices increased from T1 to T2. The degree of pseudoneglect increment positively correlated with perceived stress and negatively correlated with Positive Attitude and Problem-Solving COPE-NIV subscales. Conclusion Lockdown-related stress may have contributed to increase leftward bias during quarantine through a greater activation of the right hemisphere. On the other hand, pseudoneglect was decreased for better coping participants, perhaps as a consequence of a more balanced hemispheric activity in these individuals. application/ld+json https://w3id.org/ro-id/e2c753f3-255f-4f6a-9547-43166aa457dd Further to the Left: Stress-Induced Increase of Spatial Pseudoneglect During the COVID-19 Lockdown MANUAL Foglini, Federica. "Further to the Left: Stress-Induced Increase of Spatial Pseudoneglect During the COVID-19 Lockdown." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/e2c753f3-255f-4f6a-9547-43166aa457dd. 246 https://api.rohub.org/api/resources/77f060ce-4b26-495f-a2b1-09e7bc2a3e8b/download/ 2021-12-10 10:07:14.828890+00:00 2021-12-10 10:07:14.830768+00:00 Background The measures taken to contain the coronavirus disease 2019 (COVID-19) pandemic, such as the lockdown in Italy, do impact psychological health; yet, less is known about their effect on cognitive functioning. The transactional theory of stress predicts reciprocal influences between perceived stress and cognitive performance. However, the effects of a period of stress due to social isolation on spatial cognition and exploration have been little examined. The aim of the present study was to investigate the possible effects and impact of the COVID-19 pandemic on spatial cognition tasks, particularly those concerning spatial exploration, and the physiological leftward bias known as pseudoneglect. A right-hemisphere asymmetry for spatial attention processes crucially contributes to pseudoneglect. Other evidence indicates a predominantly right-hemisphere activity in stressful situations. We also analyzed the effects of lockdown on coping strategies, which typically show an opposite pattern of hemispheric asymmetry, favoring the left hemisphere. If so, then pseudoneglect should increase during the lockdown and be negatively correlated with the efficacy of coping strategies. Methods One week before the start of the lockdown due to COVID-19 in Italy (T1), we had collected data from a battery of behavioral tests including tasks of peri-personal spatial cognition. During the quarantine period, from late April to early May 2020 (T2), we repeated the testing sessions with a subgroup of the same participants (47 right-handed students, mean age = 20, SD = 1.33). At both testing sessions, participants performed digitized neuropsychological tests, including a Cancellation task, Radial Arm Maze task, and Raven's Advanced Progressive Matrices. Participants also completed a newly developed COVID-19 Student Stress Scale, based on transactional models of stress, and the Coping Orientation to Problems Experienced-New Italian Version (COPE-NIV) to assess coping orientation. Results The tendency to start cancelation from a left-sided item, to explore first a left-sided arm of the maze, and to choose erroneous response items on the left side of the page on Raven's matrices increased from T1 to T2. The degree of pseudoneglect increment positively correlated with perceived stress and negatively correlated with Positive Attitude and Problem-Solving COPE-NIV subscales. Conclusion Lockdown-related stress may have contributed to increase leftward bias during quarantine through a greater activation of the right hemisphere. On the other hand, pseudoneglect was decreased for better coping participants, perhaps as a consequence of a more balanced hemispheric activity in these individuals. text/plain Further to the Left: Stress-Induced Increase of Spatial Pseudoneglect During the COVID-19 Lockdown 2021-12-10 10:07:14.828890+00:00 service-account-generation-service Medical science psychology stress single-photon emission computed tomography study period effects of the COVID-19 pandemic perfusion imaging Mediterranean Sea myocardial perfusion imaging number of procedure A. Effects of the COVID-19 pandemic medicine percentage pandemic stress imaging single photon emission computed tomography perfusion system result Mediterranean Sea cardiology imaging imaging for ischemic heart disease finding Mol. imaging 2021 number ischemic heart disease report University of Napoli Federico II stress SPECT-MPI study Nappi myocardial federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 5523 https://api.rohub.org/api/ros/0423aa4d-8fe6-47c3-97a5-38f0fef60808/crate/download/ 2021-12-10 10:07:17.462613+00:00 2025-03-05 00:57:47.085535+00:00 2021-12-10 10:07:17.462613+00:00 Purpose We assessed the effects of the COVID-19 pandemic on myocardial perfusion imaging (MPI) for ischemic heart disease during the lockdown imposed by the Italian Government. Methods We retrospectively reviewed the number and the findings of stress single-photon emission computed tomography (SPECT)-MPI performed between February and May 2020 during the COVID-19 pandemic at the University of Napoli Federico II. The number and the findings of stress SPECT-MPI studies acquired in the corresponding months of the years 2017, 2018, and 2019 were also evaluated for direct comparison. Results The number of stress SPECT-MPI studies performed during the COVID-19 pandemic (n = 123) was significantly lower (P < 0.0001) compared with the mean yearly number of procedures performed in the corresponding months of the years 2017, 2018, and 2019 (n = 413). Yet, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with the mean percentage value of the corresponding period of the years 2017, 2018, and 2019 (34%). Conclusion The number of stress SPECT-MPI studies was significantly reduced during the COVID-19 pandemic compared with the corresponding months of the previous 3 years. The lack of difference in the prevalence of abnormal SPECT-MPI studies between the two study periods strongly suggests that many patients with potentially abnormal imaging test have been missed during the pandemic. application/ld+json https://w3id.org/ro-id/0423aa4d-8fe6-47c3-97a5-38f0fef60808 Effects of the COVID-19 pandemic on myocardial perfusion imaging for ischemic heart disease MANUAL Foglini, Federica. "Effects of the COVID-19 pandemic on myocardial perfusion imaging for ischemic heart disease." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/0423aa4d-8fe6-47c3-97a5-38f0fef60808. 297 https://api.rohub.org/api/resources/45738cd1-06a7-495a-a8f9-d2a057422c55/download/ 2021-12-10 10:07:20.180596+00:00 2021-12-10 10:07:20.181578+00:00 Purpose We assessed the effects of the COVID-19 pandemic on myocardial perfusion imaging (MPI) for ischemic heart disease during the lockdown imposed by the Italian Government. Methods We retrospectively reviewed the number and the findings of stress single-photon emission computed tomography (SPECT)-MPI performed between February and May 2020 during the COVID-19 pandemic at the University of Napoli Federico II. The number and the findings of stress SPECT-MPI studies acquired in the corresponding months of the years 2017, 2018, and 2019 were also evaluated for direct comparison. Results The number of stress SPECT-MPI studies performed during the COVID-19 pandemic (n = 123) was significantly lower (P < 0.0001) compared with the mean yearly number of procedures performed in the corresponding months of the years 2017, 2018, and 2019 (n = 413). Yet, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with the mean percentage value of the corresponding period of the years 2017, 2018, and 2019 (34%). Conclusion The number of stress SPECT-MPI studies was significantly reduced during the COVID-19 pandemic compared with the corresponding months of the previous 3 years. The lack of difference in the prevalence of abnormal SPECT-MPI studies between the two study periods strongly suggests that many patients with potentially abnormal imaging test have been missed during the pandemic. text/plain Effects of the COVID-19 pandemic on myocardial perfusion imaging for ischemic heart disease 2021-12-10 10:07:20.180596+00:00 service-account-generation-service Medical science G. spatiotemporal analysis volume 13 incidence data mathematics parametric data modeling spatiotemporal analysis Journal of geophysical research. Biogeosciences medicine statistics book analyzation research data dynamics dataset effectiveness system issue 3 mathematical analysis Sebastiani understanding Spassiani spatiotemporal statistical analysis Venetia Palu issue statistical analysis information novel methodology incidence decision Italy federica.foglini@ismar.cnr.it Federica Foglini service-account-enrichment 4647 https://api.rohub.org/api/ros/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/crate/download/ 2021-12-10 10:07:23.364596+00:00 2025-03-05 01:19:14.514021+00:00 2021-12-10 10:07:23.364596+00:00 (1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model's parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings. application/ld+json https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b Spatiotemporal Analysis of COVID-19 Incidence Data MANUAL Foglini, Federica. "Spatiotemporal Analysis of COVID-19 Incidence Data." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b. 130 https://api.rohub.org/api/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048/download/ 2021-12-10 10:07:26.094439+00:00 2021-12-10 10:07:26.095871+00:00 (1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model's parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings. text/plain Spatiotemporal Analysis of COVID-19 Incidence Data 2021-12-10 10:07:26.094439+00:00 service-account-generation-service Earth sciences noise data 15.040183696900115 13.1 sound pressure level 10.082644628099173 6.1 sound pressure 10.24793388429752 6.2 Newspaper Arts, culture and entertainment/Mass media/Newspaper This RO provides the Jupyter notebook used to process the Sound Pressure Levels, SPL, data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded. 42.08416833667335 42.0 Ro 8.099173553719009 4.9 acoustics 16.379310344827587 3.8 Adriatic Sea 8.264462809917354 5.0 computer science 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17.369384765625 43.068887774169625 b648e25c-27f3-480f-9bfb-9b661078d205 POLYGON ((17.369384765625 43.068887774169625, 17.193603515625 43.15710884095329, 17.017822265625 43.27720532212024, 16.8310546875 43.35713822211053, 16.63330078125 43.45291889355465, 16.446533203125 43.5326204268101, 16.23779296875 43.48481212891603, 15.99609375 43.50075243569041, 15.919189453124998 43.59630591596548, 15.963134765625 43.644025847699496, 15.6005859375 43.8028187190472, 15.49072265625 43.9058083561574, 15.227050781249998 44.071800467511565, 15.1171875 44.19795903948531, 15.468749999999998 44.26093725039923, 14.94140625 44.66865287227321, 14.94140625 44.91035917458495, 14.820556640625 45.09679146394738, 14.326171874999998 45.336701909968134, 14.150390625 45.0657615477031, 13.963623046874998 44.84029065139799, 13.853759765625 44.85586880735725, 13.645019531249998 45.089035564831036, 13.51318359375 45.44471679159555, 13.721923828124998 45.56021795715051, 13.765869140624998 45.62940492064501, 13.5791015625 45.75985868785574, 13.524169921874998 45.71385093029221, 13.205566406249998 45.744526980468436, 13.095703125 45.69850658738846, 13.128662109375 45.62172169252446, 12.41455078125 45.398449976304086, 12.568359375 45.55252525134013, 12.513427734375 45.56021795715051, 12.3046875 45.460130637921004, 12.1728515625 45.398449976304086, 12.117919921874998 45.26715476332791, 12.337646484375 45.1742925240767, 12.3046875 45.07352060670971, 12.513427734375 44.95702412512118, 12.447509765625 44.88701247981298, 12.3046875 44.78573392716592, 12.315673828125 44.42593442145313, 12.359619140624998 44.24519901522129, 12.6123046875 44.000717834282774, 12.908935546875 43.874138181474734, 13.128662109375 43.74728909225908, 13.304443359375 43.636075155965784, 13.53515625 43.58039085560784, 13.634033203125 43.51668853502906, 13.634033203125 43.42100882994726, 13.68896484375 43.30119623257966, 13.82080078125 43.13306116240612, 13.875732421875 42.99661231842139, 13.9306640625 42.84375132629021, 13.99658203125 42.73894375124377, 17.369384765625 43.068887774169625)) service-account-enrichment https://w3id.org/ro-id/7b86ece5-b588-416b-9c98-30bb63a5b9bc 14303279 https://api.rohub.org/api/ros/3f68f32e-1b6d-4689-acee-b797ba2c8429/crate/download/ 2021-12-13 16:00:05.573722+00:00 2025-03-05 01:19:11.866349+00:00 2021-12-13 16:00:05.573722+00:00 This RO provides the Jupyter notebook used to process the Sound Pressure Levels, SPL, data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded. SPL data were calculated from wav data recorded by Develogic SonoVault Hydrophones (https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501). application/ld+json https://w3id.org/ro-id/3f68f32e-1b6d-4689-acee-b797ba2c8429 Underwater Noise, SPLs, Soundscape Sound Pressure Levels Post Processing within the Soundscape project MANUAL https://w3id.org/ro-id/3f68f32e-1b6d-4689-acee-b797ba2c8429/06c1a003-8c2a-40c2-b115-820fbb33d5c0 https://w3id.org/ro-id/2f20fcaa-42c9-48d8-ab3a-c8acd44d1e28 https://w3id.org/ro-id/3674dda1-dd88-478b-9c2e-53d00704bc12 https://w3id.org/ro-id/bd501fb1-f573-4803-a7b7-a6c495c2286c https://w3id.org/ro-id/c5092ae1-e3bb-488f-89a5-3b2d01489262 https://w3id.org/ro-id/649a5869-ff6c-48bd-8132-a39007bac527 https://w3id.org/ro-id/0fbc4d6a-e4a3-4777-8e4b-1a585bf40944 https://w3id.org/ro-id/11e45088-ae36-428e-a461-d62ccbb7f815 https://w3id.org/ro-id/25fd6b8d-14a1-4fdf-97d8-8b22a05da0cb https://w3id.org/ro-id/30602e3f-185c-4bba-bc22-61b5f8552751 https://w3id.org/ro-id/389f8b2e-6820-4fa1-a0c8-c58e7aad46d4 https://w3id.org/ro-id/779ae693-ccb2-4c28-ae7a-ad6be816f650 https://w3id.org/ro-id/81c0aef3-4e77-427a-a19f-d2085fa658b0 https://w3id.org/ro-id/ace49971-f483-4248-922c-a15769b99d77 https://w3id.org/ro-id/ed2c5977-510e-49e6-aa27-8138e1b247e6 https://w3id.org/ro-id/f3c1a1c2-f3e9-4c78-903e-f01219bbbfc7 https://w3id.org/ro-id/877e343f-4d77-491d-8c06-f325544d3efa https://w3id.org/ro-id/b0c78366-5e8a-4a8c-8191-6e99eed32373 https://w3id.org/ro-id/12b644a7-467a-4873-a0ac-768b6e9af758 https://w3id.org/ro-id/848db731-4319-487b-913b-6333c609dbbe https://w3id.org/ro-id/96ea8b56-8dae-4128-a148-3bda457bea6a https://w3id.org/ro-id/b3004080-0b1a-46a1-aa09-333cbee58682 https://w3id.org/ro-id/55d91882-14ad-4aef-81e6-1701556e5042 https://w3id.org/ro-id/5e9cc950-49df-4609-8b24-e10745d6aedc https://w3id.org/ro-id/b9c67d2a-451d-44f3-9315-279fd7f1b5a7 https://w3id.org/ro-id/c1f9855e-a2b3-4d57-b2ed-8402d7c62917 https://w3id.org/ro-id/d01ffc2e-0f9f-437c-85c0-841ac593b326 https://w3id.org/ro-id/d2492db4-2ec1-485c-8702-626789fad68f https://w3id.org/ro-id/d7e10bba-22f6-43c1-8d42-4d9a750d5448 https://w3id.org/ro-id/7dbbf907-6f28-40d1-802a-1f2638ae38db https://w3id.org/ro-id/a05f5f10-b881-4575-aca2-5cb9c48f7aa7 https://w3id.org/ro-id/08dfc44f-c145-44e3-83c5-fd4faf4bae78 https://w3id.org/ro-id/782d497a-fceb-43b4-8289-487ce25f9ba7 https://w3id.org/ro-id/aea918d6-376e-4598-8f25-898875ca69d3 https://w3id.org/ro-id/d2b17193-8158-462b-ade0-9e272b97bc30 https://w3id.org/ro-id/e160338e-ad32-4f76-94af-5a0212c27f7f https://w3id.org/ro-id/18baa18a-3d16-4924-84a3-8915e6ac3bc3 https://w3id.org/ro-id/c860026c-0561-47c0-a408-fd280b1e1ffc https://w3id.org/ro-id/e1ff0de8-4ed6-4087-8bee-510faa65a2ed https://w3id.org/ro-id/441c8151-f9c0-451f-9f74-69bf37c66576 https://w3id.org/ro-id/a2687f4b-2e9e-4b78-a52b-c22e3aae3201 Petrizzo, Antonio, Fantina Madricardo, Marta Picciulin, and Michol Ghezzo. "Sound Pressure Levels Post Processing within the Soundscape project." ROHub. Dec 13 ,2021. https://w3id.org/ro-id/3f68f32e-1b6d-4689-acee-b797ba2c8429. Here some results results Here some information metadata Jupyter notebooks here notebook data input input 1628249 https://api.rohub.org/api/resources/01899869-a5a8-48c0-980f-d304eaa51b26/download/ 2022-07-05 12:44:30.318843+00:00 2022-07-05 12:44:34.124435+00:00 Map of stations with their coordinates image/png Stations map 2022-07-05 12:44:30.318843+00:00 https://notebooks.egi.eu/user/da47d3640f619a02cb075c15d288fc09e053bf46b90d26ec335392acdfae866b@egi.eu/doc/tree/datahub/Reliance/Soundscape/SPL_PostProcessing_HDF5.ipynb 2022-07-06 15:19:37.054357+00:00 2022-12-19 13:57:22.861507+00:00 Link to open the file in Notebooks, an environment based on Jupyter and the EGI cloud service: It allows to post process spl data and to create graphs/tables. A valid EGI account is required. Jupyter notebook for SPLs processing 2022-07-06 15:19:37.054357+00:00 3034992 https://api.rohub.org/api/resources/391ec38c-80e9-4b6a-bedb-e2d1c6096cc0/download/ 2022-07-05 12:33:28.328984+00:00 2022-07-06 15:15:38.306210+00:00 Some examples of output files application/zip Some examples of output files 2022-07-05 12:33:28.328984+00:00 5833087 https://api.rohub.org/api/resources/3e6b9429-3e1b-43f7-96e7-b113e269a9cb/download/ 2022-07-06 10:25:59.163869+00:00 2022-12-19 13:58:32.793859+00:00 The Jupyter notebook used to post process spl data and to create graphs/tables. application/zip Jupyter notebook for processing SPL data. 2022-07-06 10:25:59.163869+00:00 https://underwaternoise.ices.dk/continuous 2022-07-05 12:42:32.932255+00:00 2022-07-05 12:42:33.578063+00:00 Continuous Noise Database (https://underwaternoise.ices.dk/continuous), 2022. ICES, Copenhagen Format of input file 2022-07-05 12:42:32.932255+00:00 1095417 https://api.rohub.org/api/resources/5cc44fb0-e5b5-43cd-9545-42d4d73a1331/download/ 2022-07-06 15:07:05.061088+00:00 2022-07-06 15:07:07.414275+00:00 image/png workflowPostProcessing.png 2022-07-06 15:07:05.061088+00:00 4863608 https://api.rohub.org/api/resources/7dcb955b-b147-42e3-a618-24135bccf73b/download/ 2022-07-05 12:05:48.435779+00:00 2022-07-06 15:18:46.856004+00:00 Example of SPL input file. HDF5 format, according to to ICES (International Council for the Exploration of the Sea) continuous noise data portal specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx). Example of SPL input file 2022-07-05 12:05:48.435779+00:00 https://www.italy-croatia.eu/web/soundscape 2022-07-06 10:06:13.532986+00:00 2022-07-06 10:06:14.585274+00:00 EU-Interreg Italy-Croatia 2014/2020 – CBC Program (Contract number 10043643) Soundscape Project 2022-07-06 10:06:13.532986+00:00 https://doi.org/10.5281/zenodo.6653258 2022-07-05 12:52:22.146283+00:00 2022-12-21 13:28:01.729449+00:00 Full SPL dataset is located in Zenodo Full SPL dataset 2022-07-05 12:52:22.146283+00:00 Mar-2020 - Jun-2021 soundscape 11.111111111111112 6.7 sound pressure level 10.447761194029852 6.3 Adriatic Sea https://www.wikidata.org/wiki/Q13924 hydrophone 5.785123966942149 3.5 sound pressure Levels Post 11.940298507462687 10.4 False https://w3id.org/ro-id/3f68f32e-1b6d-4689-acee-b797ba2c8429 2022-07-06 15:27:15.164812+00:00 https://w3id.org/ro-id/users/antonio.petrizzo%40ve.ismar.cnr.it life sciences (general) 100.0 0.35060247778892517 AND 6.446280991735537 3.9 Language Arts, culture and entertainment/Culture/Language atmospheric sciences 100.0 0.6904757022857666 Biology Science and technology/Natural science/Biology life sciences 100.0 0.35060247778892517 of 1 year soundscape 10.743801652892563 6.5 SPL data 46.84270952927669 40.8 earth sciences 100.0 0.6904757022857666 Hardware Economy, business and finance/Economic sector/Computing and information technology/Hardware http 8.955223880597016 5.4 database 21.982758620689655 5.1 data 20.729684908789388 12.5 physics 16.810344827586206 3.9 SPL data were calculated from wav data recorded by Develogic SonoVault Hydrophones (https://w3id.org/ro-id/6640422d-57ed-4814-b0d0-8eb4ee85f501) 44.48897795591183 44.4 Jupyter notebook 12.769485903814262 7.7 SPL 23.383084577114428 14.1 soundscapes in the North Adriatic sea 7.921928817451206 6.9 Soundscape Project 12.603648424543948 7.6 Develogic SonoVault hydrophone 18.2548794489093 15.9 Sound Pressure Levels Post Processing within the Soundscape project. 13.42685370741483 13.4 data 20.49586776859504 12.4 http 8.760330578512397 5.3 CNR ISMAR Venice antonio.petrizzo@ve.ismar.cnr.it Antonio Petrizzo direttore@ismar.cnr.it CNR-ISMAR CNR ISMAR fantina.madricardo@ve.ismar.cnr.it Fantina Madricardo CNR ISMAR marta.picciulin@ve.ismar.cnr.it Marta Picciulin CNR ISMAR michol.ghezzo@ve.ismar.cnr.it Michol Ghezzo 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)) Earth sciences aerospace engineering subset satellite data data cube download satellite data research Adam subset data cube from the Adam platform aim Giorgio Castellan Valentina Grande service-account-enrichment 375624 https://api.rohub.org/api/ros/34d648b3-0014-4a19-8469-40b9380ca4c3/crate/download/ 2021-12-14 09:50:09.833282+00:00 2025-03-05 00:47:49.415599+00:00 2021-12-14 09:50:09.833282+00:00 This Research Object demonstrate how to discover, subset and download satellite data stored in a Data Cube from the ADAM Platform application/ld+json https://w3id.org/ro-id/34d648b3-0014-4a19-8469-40b9380ca4c3 Discover and subset satellite data from the ADAM Platform MANUAL Castellan, Giorgio, Valentina Grande, and Valentina Grande. "Discover and subset satellite data from the ADAM Platform." ROHub. Dec 14 ,2021. https://w3id.org/ro-id/34d648b3-0014-4a19-8469-40b9380ca4c3. input biblio output tool https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7 2021-12-14 09:53:22.621865+00:00 2021-12-14 09:53:22.622327+00:00 Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown 2021-12-14 09:53:22.621865+00:00 https://notebooks.egi.eu/user/b285232f8bf5dc90b023ebc0c44ea0e0b883a51043a5c57b1d43b522a25548c9@egi.eu/lab/tree/datahub/Reliance/SatelliteDataVeniceLagoon/RO_Scenario%204%20%E2%80%9CAnalysis%20from%20satellite%20data%20%E2%80%93%20Environmental%20monitoring%20from%20space%E2%80%9D.ipynb 2022-02-25 12:50:56.148874+00:00 2022-02-25 13:58:49.651309+00:00 Jupyter Notebook for discovering, accessing and processing and visualizing RELIANCE data cube of Chl-a and Kd490 Retrieve and visualize satellite data on Chl-a and Kd490 extracted from ADAM Platform 2022-02-25 12:50:56.148874+00:00 156145 https://api.rohub.org/api/resources/750bb6dc-081f-4bd0-9fe0-9ed5b4774289/download/ 2022-02-25 15:06:01.447041+00:00 2022-02-25 15:06:08.312874+00:00 Retrieve and visualize satellite data on Chl-a and Kd490 extracted from ADAM Platform TEST Retrieve and visualize satellite data on Chl-a and Kd490 extracted from ADAM Platform TEST 2022-02-25 15:06:01.447041+00:00 320980 https://api.rohub.org/api/resources/e98a0571-f002-460e-9c59-c45fec720572/download/ 2021-12-14 10:32:45.827889+00:00 2021-12-14 10:32:45.829067+00:00 image/jpeg Schematic illustration of ADAM Platform Data Cube exploration and use 2021-12-14 10:32:45.827889+00:00 Earth sciences Giorgio Castellan Federica Foglini geophysics 100.0 0.4130299687385559 False https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140 2022-03-29 07:03:12.335211+00:00 https://w3id.org/ro-id/users/federica.foglini%40ismar.cnr.it geology 100.0 0.8256934881210327 False https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140 2022-03-24 18:42:55.007788+00:00 https://w3id.org/ro-id/users/federica.foglini%40ismar.cnr.it covid 19 12.191958495460442 9.4 Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea 69.26926926926926 69.2 Environmental monitoring from space. 8.708708708708707 8.7 Satellite technology Economy, business and finance/Economic sector/Computing and information technology/Satellite technology geosciences 100.0 0.4130299687385559 water 11.142061281337048 8.0 earth sciences 100.0 0.8256934881210327 effects of COVID-19 lockdown 15.256008359456635 14.6 2022-03-29 08:47:03.080974+00:00 https://w3id.org/ro-id/users/federica.foglini%40ismar.cnr.it https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140 True environmental monitoring 11.413748378728926 8.8 clarity 12.5810635538262 9.7 analysis of satellite data 25.705329153605014 24.6 Adriatic Sea https://www.wikidata.org/wiki/Q13924 analysis from satellite data 17.03239289446186 16.3 water clarity 30.407523510971785 29.1 Adriatic Sea 11.142061281337048 8.0 environmental monitoring from space 11.598746081504702 11.1 satellite data 23.47600518806745 18.1 analysis 14.902506963788301 10.7 analysis 13.618677042801558 10.5 lockdown 14.785992217898833 11.4 environmental monitoring 11.420612813370472 8.2 space 5.153203342618385 3.7 service-account-enrichment https://w3id.org/ro-id/64aee73b-a05c-433f-bbf7-2ed35ec42601 https://w3id.org/ro-id/15e9432f-53ee-4ea8-b1a3-6fdcaca7cf9e https://w3id.org/ro-id/28ff4f3e-c3f8-4bf0-8591-8fa36c378faa 534285 https://api.rohub.org/api/ros/d0694eaf-a561-4c9f-9a70-17c296da2140/crate/download/ 2021-12-14 10:41:17.716553+00:00 2025-03-05 00:46:18.160924+00:00 2021-12-14 10:41:17.716553+00:00 Collection and analysis of satellite data to monitor the effects of COVID-19 lockdown on water clarity in the north Adriatic Sea application/ld+json https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140 Analysis from satellite data – Environmental monitoring from space MANUAL https://w3id.org/ro-id/89a6f481-4dca-4d83-8958-482cdfc8814f https://w3id.org/ro-id/58b69fff-0412-4708-b68d-db3a1137eaa8 https://w3id.org/ro-id/af6719b5-00a1-4edf-b86d-e4dc5a4cd65c https://w3id.org/ro-id/bb9a179d-6146-403f-a2c9-91577f1d6cab https://w3id.org/ro-id/cbe25979-901c-4377-a9f6-82c94799cf06 https://w3id.org/ro-id/cfd3cdf0-bfa6-4833-a70c-1588cf522b8b https://w3id.org/ro-id/dd8632c4-5e0d-43ec-b56a-cd9e18bdf445 https://w3id.org/ro-id/dda61067-35e4-493d-a083-a1e87960273d https://w3id.org/ro-id/f9de1d09-c194-4570-aef2-91dee3343d41 https://w3id.org/ro-id/ffcb0735-4acc-4c8b-861b-fed257521c9b https://w3id.org/ro-id/23ac69fb-ec11-4405-b8b3-9d4b06238074 https://w3id.org/ro-id/62433058-0f04-4140-8ee1-b7c5b989454f https://w3id.org/ro-id/543c53e8-36e9-461c-a034-0f22f13fe135 https://w3id.org/ro-id/3a33bdbe-b4cf-4dc2-970b-2233b932d331 https://w3id.org/ro-id/6b105d4a-ac78-4135-bea2-d73867d8df4a https://w3id.org/ro-id/6f4c4040-f8cb-49b4-b551-22d32d651689 https://w3id.org/ro-id/bb1c78e4-c8ae-4299-b41c-8b9908cc90e3 https://w3id.org/ro-id/c04f237c-b4c2-4c6e-a666-84b4c9340143 https://w3id.org/ro-id/c5f04e54-5c37-4d3e-bb84-d8121c14e9cb https://w3id.org/ro-id/f40f3dc4-eb20-491b-b3b2-b2dbbe7d5e98 https://w3id.org/ro-id/0a59c39e-c6d1-4376-9df8-ac31a9e1a4ba https://w3id.org/ro-id/5548e324-463c-4711-be55-8db49f48c232 https://w3id.org/ro-id/6247dfac-3621-4541-be10-3fe0f88fe6d0 https://w3id.org/ro-id/7cf638dd-af76-4552-a5d9-5002c40b46c8 https://w3id.org/ro-id/90465225-4586-42c5-b1f0-51062095eb42 https://w3id.org/ro-id/9485bb39-d73b-456a-ba13-e9511360b7e9 https://w3id.org/ro-id/baa90a63-6cd9-4278-8e6b-c564c3408360 https://w3id.org/ro-id/40f339fa-74de-4c1e-9a66-75d453b82d62 https://w3id.org/ro-id/53ae4f33-7137-4e50-8d24-6b21f419d84f https://w3id.org/ro-id/ff1fce2e-3433-470f-bb9f-7a063ff1f243 Castellan, Giorgio, Federica Foglini, and Federica Foglini. "Analysis from satellite data – Environmental monitoring from space." ROHub. Dec 14 ,2021. https://w3id.org/ro-id/d0694eaf-a561-4c9f-9a70-17c296da2140. Results Results Discover, subset, download and visualize satellite data stored in a Data Cube from the ADAM Platform Method Satellite data on Chl-a and Kd490 Satellite_data 449579 https://api.rohub.org/api/resources/71f544d8-a629-4bd9-bb31-85434b0d8054/download/ 2021-12-14 14:38:21.837867+00:00 2021-12-14 14:38:21.839710+00:00 image/jpeg Analysis from satellite data – Environmental monitoring from space during COVID-19 lockdown 2021-12-14 14:38:21.837867+00:00 68452 https://api.rohub.org/api/resources/b32e5199-0f1d-417a-9601-2a477d02144a/download/ 2021-12-14 14:32:01.240770+00:00 2021-12-14 14:33:32.254153+00:00 image/png Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea 2021-12-14 14:32:01.240770+00:00 https://w3id.org/ro-id/894d3a33-8340-497d-beaf-5b9d85c9bfc7 2021-12-14 10:44:17.433059+00:00 2021-12-14 10:46:24.530504+00:00 Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown Satellite data on water clarity in the Venice Lagoon during the COVID 19 lockdown 2021-12-14 10:44:17.433059+00:00 70005 https://api.rohub.org/api/resources/d9b17ba2-5597-4bee-9bc0-944451fa7484/download/ 2021-12-14 14:33:06.849172+00:00 2021-12-14 14:33:42.230343+00:00 image/png Diffuse attenuation coefficient at 490 nm (Kd490) in 2018 in the north Adriatic Sea 2021-12-14 14:33:06.849172+00:00 https://w3id.org/ro-id/34d648b3-0014-4a19-8469-40b9380ca4c3 2021-12-14 10:44:48.894463+00:00 2021-12-14 10:46:31.116080+00:00 Discover and subset satellite data from the ADAM Platform Discover and subset satellite data from the ADAM Platform 2021-12-14 10:44:48.894463+00:00 clarity 12.95264623955432 9.3 collection 13.091922005571032 9.4 collection 11.932555123216602 9.2 lockdown 15.459610027855154 11.1 Analysis from satellite data – 22.02202202202202 22.0 result 4.735376044568246 3.4 Meteorology Applied sciences South Korea Environmental Protection Agency levelO transportation sector emission Sao Paulo Brazil Mar world s major cities lockdown data meteorology W.M. Keck Science Department lockdown event World s air pollution toan air quality index particulate matter level Tehran World Health Organization Russia ecology Moscow Madrid Spain Mar May TotalOutdoorphysicalexercise lockdown lockdown Policy International Agency for Research on Cancer air quality data Claremont McKenna College Historical Climatology Network Los Angeles GHCN Daily Pitzer College United States of America availablefor Los Angeles Carcinogenicto Humans medicine February March May pandemic data air pollution particulate big city air quality index World Meteorological Organization Johannesburg The World air quality project pollution nitrogen dioxyde Mexico City New York Mexico City Mexico Mar Beijing reduction São Paulo New York Los Angeles Sensitive Groups Germany Europe economic activity TotalOutdoorphysicalexercise Berlin Mexico City Europe covid pandemic lockdown lockdown lockdown Lima NO SO WAQI Scripps College air quality impact of the Covid-19 Lockdown Tehran Iran Mar Apr TotalShops emission China Delhi Istanbul PM. Spain Los Angeles U.S.A Mar Wuhan pollutant discharge 01xtthb56 University of Oslo 04jcwf484 Nordic e-Infrastructure Collaboration 2022-06-02 12:43:01.718570+00:00 https://w3id.org/ro-id/users/annefou%40geo.uio.no https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1 0484ac9e-8c6a-4da8-8a3f-010038adc98c POLYGON ((-25.07812321186066 29.331104081765925, -25.07812321186066 70.25945200030641, 45.3515625 70.25945200030641, 47.109375 28.92163128242129, -25.07812321186066 29.331104081765925)) 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 4ccabd8a-b96f-4dff-9621-ed6e072341e7 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 8d187ea0-44c3-4c43-841e-01327371e516 POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) POLYGON ((-25.07812321186066 29.331104081765925, -25.07812321186066 70.25945200030641, 45.3515625 70.25945200030641, 47.109375 28.92163128242129, -25.07812321186066 29.331104081765925)) -25.07812321186066 29.331104081765925, -25.07812321186066 70.25945200030641, 45.3515625 70.25945200030641, 47.109375 28.92163128242129, -25.07812321186066 29.331104081765925 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 e359cf97-fad5-4683-81a8-d81a2537bb71 POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) service-account-enrichment https://w3id.org/ro-id/0b6c81fb-1eff-49e8-96b5-5b238f3ae72f https://w3id.org/ro-id/66f2ccf5-971a-4ca6-bc31-1b2cf173f6ea https://w3id.org/ro-id/a161c8f5-694c-435e-9d4a-79ab1cb29c5b https://w3id.org/ro-id/d217ca99-f2ab-40e1-9020-09525ee35826 https://w3id.org/ro-id/e2606530-f5fa-4bcd-9d96-5e6a43401373 12570533 https://api.rohub.org/api/ros/53aa90bf-c593-4e6d-923f-d4711ac4b0e1/crate/download/ 2021-12-19 21:18:33.231894+00:00 2025-03-05 00:55:11.226154+00:00 2021-12-19 21:18:33.231894+00:00 The COVID-19 pandemic has led to significant reductions in economic activity, especially during lockdowns. Several studies has shown that the concentration of nitrogen dioxyde and particulate matter levels have reduced during lockdown events. Reductions in transportation sector emissions are most likely largely responsible for the NO2 anomalies. In this study, we analyze the impact of lockdown events on the air quality using data from Copernicus Atmosphere Monitoring Service over Europe and at selected locations. application/ld+json https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1 air-quality copernicus europe Jupyter Notebook Impact of the Covid-19 Lockdown on Air quality over Europe MANUAL False https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1/6a4321e6-fab9-40a9-838c-7e8ac14b1422 Anne Foilloux, Jean Iaquinta, and Simone Mantovani. "Impact of the Covid-19 Lockdown on Air quality over Europe." ROHub. Dec 19 ,2021. https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1. POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) POLYGON ((-25.07812321186066 29.331104081765925, -25.07812321186066 70.25945200030641, 45.3515625 70.25945200030641, 47.109375 28.92163128242129, -25.07812321186066 29.331104081765925)) POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) POLYGON ((-25.000012 29.999997, 44.999988 29.999997, 44.999988 71.999997, -25.000012 71.999997, -25.000012 29.999997)) This folder contains the Jupyter Notebooks used for this analysis tools This folder contains data and information generated by the Jupyter notebooks. output Bibliography and other references used for this work. biblio Information about the input data used for executing the Jupyter notebook input https://nordicesmhub.github.io/RELIANCE/science/notebooks/air_quality_lockdown.html 2022-02-28 12:09:37.504067+00:00 2022-02-28 12:09:38.028034+00:00 Rendered notebook showing the impact of the Covid-19 Lockdown on Air quality over Europe text/html Rendered jupyter notebook on impact of lockdown on air quality 2022-02-28 12:09:37.504067+00:00 https://raw.githubusercontent.com/NordicESMhub/RELIANCE/main/content/science/notebooks/air_quality_lockdown.ipynb 2022-05-29 20:03:12.795653+00:00 2022-06-01 14:35:47.916370+00:00 Study the impact of the lockdown during the covid-19 pandemic on different cities in Europe. Impact of the lockdown on air quality (Jupyter Notebook) 2022-05-29 20:03:12.795653+00:00 https://zenodo.org/record/7513765/files/PM2_5_EUROPE_ADAMAPI2019-03-01_2021-06-30.nc 2023-01-08 14:43:48.246969+00:00 2023-01-08 14:44:24.439601+00:00 PM2.5 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube application/x-netcdf PM2.5 PM2.5 CAMS over Europe March-June 2019, 2020 and 2021 2023-01-08 14:43:48.246969+00:00 https://reliance.adamplatform.eu/?dataset=69623:EU_CAMS_SURFACE_NO2_G 2022-05-31 09:59:58.575398+00:00 2022-10-21 17:40:36.922187+00:00 Link to ADAM viewer to explore NO2 CAMS data cube collection. 2022-10-19T23:00:00Z NO2 CAMS European air quality forecasts: NO2 2022-05-31 09:59:58.575398+00:00 2018-07-12T00:00:00Z Float32 mailto:govoni@meeo.it [1.354510459350422e-07] [0.0] https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1/1d453d7c-72a5-4b23-88a1-4b34fc73a0b0/3bb46e4c-48b1-450f-b109-eee8f83293f2 303782 https://api.rohub.org/api/resources/1fbe484e-51ae-4e16-ac6f-60aa06e298ba/download/ 2021-12-19 21:43:44.493101+00:00 2021-12-19 21:43:44.494175+00:00 Plot over Europe of surface NO2 on May 1st, 2020 image/png Surface NO2 on May 1st, 2020 2021-12-19 21:43:44.493101+00:00 https://zenodo.org/record/7513765/files/O3_EUROPE_ADAMAPI2019-03-01_2021-06-30.nc 2023-01-08 14:42:05.557815+00:00 2023-01-08 14:42:11.840908+00:00 O3 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube application/x-netcdf O3 O3 CAMS over Europe March-June 2019, 2020 and 2021 2023-01-08 14:42:05.557815+00:00 2276065 https://api.rohub.org/api/resources/2a2b6f01-be2e-414e-af08-d882aa995a71/download/ 2021-12-19 21:39:10.012886+00:00 2021-12-19 21:39:10.015415+00:00 In order to fight against the spread of COVID-19, the most hard-hit countries in the spring of 2020 implemented different lockdown strategies. To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2, SO2, CO, PM2.5 and PM10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown. This work shows the reduction of primary pollutants, especially NO2, is mainly due to lockdown policies. However, preexisting local environmental policy regulations also contributed to declining NO2, SO2 and PM2.5 emissions, especially in Asian countries. In addition, higher rainfall during the lockdown period could cause decline of PM2.5, especially in Johannesburg. By contrast, the changes of AQI in ground-level O3 were not significant in most of cities, as meteorological variability and ratio of VOC/NOx are key factors in ground-level O3 formation. application/pdf Impact of the COVID-19 Pandemic Lockdown on Air Quality Pollution in 20 Major cities around the World 2021-12-19 21:39:10.012886+00:00 24998 https://api.rohub.org/api/resources/3303c10a-4bf7-4fb6-a151-76c5eb87935b/download/ 2021-12-19 22:03:31.558894+00:00 2021-12-19 22:03:31.559562+00:00 image/png Total averaged PM2.5 at 12 differents locations 2021-12-19 22:03:31.558894+00:00 762130 https://api.rohub.org/api/resources/488d827e-60ef-4c8c-8a88-54f52201deed/download/ 2021-12-19 21:53:08.959199+00:00 2021-12-19 21:53:08.961483+00:00 image/png Mean O3 for 2019, 2020 and 2021 over March-June 2021-12-19 21:53:08.959199+00:00 878103 https://api.rohub.org/api/resources/499fb80e-b2a8-4263-811f-bdea94730e57/download/ 2021-12-19 21:36:21.971158+00:00 2021-12-19 21:58:09.867133+00:00 Maps of the 3-yearly averages (done over the period March to June) for NO2, PM2.5 and O3. image/png NO2, PM2.5 and O3 averages (March-June) 2021-12-19 21:36:21.971158+00:00 814163 https://api.rohub.org/api/resources/4b4000fd-8cc4-4ebc-a064-c13340789ca9/download/ 2021-12-19 21:50:16.743371+00:00 2021-12-19 21:50:16.744620+00:00 image/png Maximum PM2.5 for 2019, 2020 and 2021 over March-June 2021-12-19 21:50:16.743371+00:00 23748 https://api.rohub.org/api/resources/543722a7-38ed-4a22-a668-b54ca1bf0252/download/ 2021-12-19 22:03:08.055316+00:00 2021-12-19 22:03:08.056108+00:00 image/png Total averaged O3 at 12 differents locations 2021-12-19 22:03:08.055316+00:00 600154 https://api.rohub.org/api/resources/7781b8c4-96f3-4310-878c-ce669b13a95e/download/ 2021-12-19 21:46:19.852299+00:00 2021-12-19 21:48:37.689694+00:00 Maximum NO2 values over Europe during March-June for 3 different years: 2019 (before pandemic), 2020 (during the lockdown) and 2021 (after the lockdown). image/png Maximum NO2 values over Europe during March-June 2021-12-19 21:46:19.852299+00:00 https://reliance.adamplatform.eu/?dataset=69625:EU_CAMS_SURFACE_O3_G 2022-10-21 14:38:13.978164+00:00 2022-10-21 14:38:17.014060+00:00 CAMS European air quality forecasts: Ozone (O3, µg m-3) startDate: 2018-07-12T00:00:00Z endDate: 2022-10-19T23:00:00Z 2022-10-19T23:00:00Z O3 CAMS European air quality forecasts: O3 2022-10-21 14:38:13.978164+00:00 2018-07-12T00:00:00Z Float32 mailto:govoni@meeo.it [2.2007016298175586e-07] [0.0] https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1/79d1e316-23f5-4d93-ba9c-b4e48c291797/b3629551-5113-4f5e-abd2-cc4b509948be 1587662 https://api.rohub.org/api/resources/8ea33e64-8f3f-43bf-8d31-05c278d34672/download/ 2021-12-19 21:58:46.089097+00:00 2021-12-19 21:58:46.090175+00:00 image/png Timeseries of O3 at 12 different locations 2021-12-19 21:58:46.089097+00:00 1692100 https://api.rohub.org/api/resources/a77d7ebb-2df5-4a9a-a64b-4a81b5a85e17/download/ 2021-12-19 21:59:09.118614+00:00 2021-12-19 21:59:09.119566+00:00 image/png Timeseries of NO2 at 12 different locations 2021-12-19 21:59:09.118614+00:00 644109 https://api.rohub.org/api/resources/c115d189-f8f2-49b5-834e-03a5f05aa0dd/download/ 2021-12-19 21:50:38.719125+00:00 2021-12-19 21:50:38.720236+00:00 image/png Mean PM2.5 for 2019, 2020 and 2021 over March-June 2021-12-19 21:50:38.719125+00:00 752977 https://api.rohub.org/api/resources/c327b26a-bad2-43de-9b2a-e3a6287c07ea/download/ 2021-12-19 21:47:47.994101+00:00 2021-12-19 21:47:47.995982+00:00 image/png NO2 mean over March-June for 2019, 2020 and 2021 2021-12-19 21:47:47.994101+00:00 1484738 https://api.rohub.org/api/resources/c7b8d37a-3cf5-4fb4-8464-99f6e9e4a8aa/download/ 2021-12-19 21:56:14.280390+00:00 2021-12-19 21:57:53.915288+00:00 Comparisons of PM2.5 changes for 2019, 2020 and 2021. image/png Timeseries of PM2.5 at 12 different locations 2021-12-19 21:56:14.280390+00:00 24717 https://api.rohub.org/api/resources/ca2b6864-8c0b-48af-890d-60315f491bb9/download/ 2021-12-19 22:02:44.861406+00:00 2021-12-19 22:02:44.863192+00:00 image/png Total averaged NO2 at 12 differents locations 2021-12-19 22:02:44.861406+00:00 https://zenodo.org/record/7513765/files/PM10_EUROPE_ADAMAPI2019-03-01_2021-06-30.nc 2023-01-08 14:46:00.841743+00:00 2023-01-08 14:46:02.707416+00:00 PM10 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube. application/x-netcdf PM10 PM10 CAMS over Europe March-June 2019, 2020 and 2021 2023-01-08 14:46:00.841743+00:00 https://zenodo.org/record/7513765/files/NO2_EUROPE_ADAMAPI2019-03-01_2021-06-30.nc 2023-01-08 14:47:29.354507+00:00 2023-01-08 14:47:31.746084+00:00 NO2 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube. application/x-netcdf NO2 NO2 CAMS over Europe March-June 2019, 2020 and 2021 2023-01-08 14:47:29.354507+00:00 853273 https://api.rohub.org/api/resources/f1a59359-5d27-4c41-8ca3-ecfe895a564c/download/ 2021-12-19 21:51:32.542926+00:00 2021-12-19 21:52:35.622461+00:00 image/png Maximum O3 for 2019, 2020 and 2021 over March-June 2021-12-19 21:51:32.542926+00:00 https://reliance.adamplatform.eu/?dataset=69627:EU_CAMS_SURFACE_PM25_G 2022-05-31 10:01:40.106991+00:00 2022-10-21 17:33:46.237193+00:00 CAMS PM2.5, Data Cube Collection 2022-10-19T23:00:00Z PM2.5 CAMS European air quality forecasts PM2.5, Data Cube Collection 2022-05-31 10:01:40.106991+00:00 2018-07-12T00:00:00Z Float32 mailto:govoni@meeo.it [709.8012084960938] [0.0] https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1/f39bd071-a179-4718-a36f-c8bc1cbe3072/397bacb4-2c79-498b-83e8-148a6c3f1668 2022-06-02 08:01:01.443851+00:00 https://w3id.org/ro-id/users/annefou%40geo.uio.no https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1 2022-06-01 21:26:26.623371+00:00 https://w3id.org/ro-id/users/annefou%40geo.uio.no https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1 2022-06-02 15:47:19.859211+00:00 https://w3id.org/ro-id/users/annefou%40geo.uio.no https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1 2022-06-01 20:47:13.889472+00:00 https://w3id.org/ro-id/users/annefou%40geo.uio.no https://w3id.org/ro-id/53aa90bf-c593-4e6d-923f-d4711ac4b0e1 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 UiO jean.iaquinta@geo.uio.no Jean Iaquinta mantovani@meeo.it Simone Mantovani Meteorology Applied sciences 01xtthb56 University of Oslo 04jcwf484 Nordic e-Infrastructure Collaboration 27878721-73c6-4644-b9b8-077e2ceccc87 POLYGON ((-32.812503576278694 34.98200288425658, -32.812503576278694 73.05677630689567, 42.42188215255738 73.05677630689567, 42.42188215255738 34.98200288425658, -32.812503576278694 34.98200288425658)) 2.3474116623401646 48.87675878577585 POINT (2.3474116623401646 48.87675878577585) ca3cae64-a562-4ebc-ba64-85cabf9e9718 POINT (2.3474116623401646 48.87675878577585) POLYGON ((-32.812503576278694 34.98200288425658, -32.812503576278694 73.05677630689567, 42.42188215255738 73.05677630689567, 42.42188215255738 34.98200288425658, -32.812503576278694 34.98200288425658)) -32.812503576278694 34.98200288425658, -32.812503576278694 73.05677630689567, 42.42188215255738 73.05677630689567, 42.42188215255738 34.98200288425658, -32.812503576278694 34.98200288425658 service-account-enrichment 18332739 https://api.rohub.org/api/ros/374d0d3a-4807-4925-be83-b9eea52356e3/crate/download/ 2021-12-20 11:04:49.605037+00:00 2025-03-05 12:47:13.501356+00:00 2021-12-20 11:04:49.605037+00:00 Collection of scientific articles and other communications related to the impact of COVID-19 pandemic lockdown on air quality pollution. application/ld+json https://w3id.org/ro-id/374d0d3a-4807-4925-be83-b9eea52356e3 covid-19 lockdown Bibliography on air quality before, during and after lockdown. MANUAL January March aerosol air pollution air quality index air quality article big city compendia demand emission era lockdown value earth sciences Air pollution Chemistry Economy Electricity production and distribution Environmental politics Environmental pollution Newspaper Science and technology Scientific paper Weather AQI ECHAM February January Mar New York aerosol air pollution air quality article collection communication covid 19 lockdown major city pollutant pollution value geosciences life sciences ECHAM HAM simulation January February ML model Madrid Spain Mar May TotalOutdoorphysicalexercise Mexico City Mexico Mar aerosol precursor aerosol radiative forcing reduction air quality pollution availablefor Los Angeles bibliography on air quality collection of scientific article demand variability electricity demand value emission reduction factor energy industry impact of covid 19 pandemic lockdown reg AP emission inventory threshold temperature value Bibliography on air quality before, during and after lockdown. Collection of scientific articles and other communications related to the impact of COVID-19 pandemic lockdown on air quality pollution. Figure summarizes the main statistics (normalized meanbias, NMB; normalized root mean square error, NRMSE;and correlation, r) obtained from the comparison betweenmeasured and ML based electricity demand during the first months of for selected countries. In this study, ML models are used for predictingthe fluctuations of electricity demand based on the temper ature (and additional time features) assuming that temper ature is a strong driver of electricity demand (for heatingand air conditioning) However, temperature is obviously notthe only driver of electricity demand variability that can beinfluenced by various other factors (e.g. change of technol ogy, behaviour, regulation) In addition, the GBM modelsused in this study are non parametric, meaning that they can not extrapolate, i.e. predict electricity demand values out side the range of values used during the training phase. The ECHAM HAM and CESM NoT ensembles allow more freedom for temperature adjustment. The ECHAM HAM simulation set up is most similar to the CESM NoT ensemble described below. The average temperature was not availablefor Los Angeles (LA) New York City (NYC) and Sydney, so the maximum temperature was used,as the maximum temperature is important for ozone formation. The daily median AQI was used in this study for each of major cities. The datasetprovides a statistical summary for each of the air pollutant species, all air pollutants are converted toan Air Quality Index (AQI) with the U.S. Environmental Protection Agency (EPA) standard calculation. The same emissions scenario (from Forster et al.) is run as for CESM using monthly emissions. Thepoorest performance was obtained in Finland (r.) dueto a strong negative anomaly (on average) of elec tricity demand in January February compared to pre vious years used for training. in spring the past winter winter https://w3id.org/ro-id/374d0d3a-4807-4925-be83-b9eea52356e3/896f6773-8216-4af9-b7bc-511956267947 https://w3id.org/ro-id/374d0d3a-4807-4925-be83-b9eea52356e3/fb824ab2-b346-462a-8cc6-4d7870b26bef astronomy chemistry computer science ecology linguistics mathematics medicine meteorology physics politics publishing software the economy Associated Press Creative Commons Environmental Protection Agency European Union Google International Agency for Research on Cancer International Energy Agency National Aeronautics and Space Administration Oxford University World Health Organization World Meteorological Organization Africa Antarctica Asia Australia Austria Barcelona Beijing Belarus Belgium Berlin Bosnia-Herzegovina Brazil British Columbia Bulgaria China Claremont Croatia Cyprus Czech Republic Delaware Delhi Denmark Estonia Europe Finland France Georgia Germany Greece Hungary India Iran Ireland Istanbul Italy Japan Johannesburg Latvia Leeds Lima Lithuania Lombardy London Los Angeles Luxemburg Madrid Malta Mexico City Mexico Milan Moldova Moscow Netherlands New Hampshire New York Norway Occident Oxford Paris Peru Poland Portugal Reading Romania Rome Russia Seoul Slovakia Slovenia South Africa South America South Korea Spain Sweden Switzerland Sydney São Paulo Tehran Tokyo Turkey United Kingdom United States of America Utrecht Wuhan Iaquinta, Jean, and Anne Foilloux. "Bibliography on air quality before, during and after lockdown.." ROHub. Dec 20 ,2021. https://w3id.org/ro-id/374d0d3a-4807-4925-be83-b9eea52356e3. POINT (2.3474116623401646 48.87675878577585) POLYGON ((-32.812503576278694 34.98200288425658, -32.812503576278694 73.05677630689567, 42.42188215255738 73.05677630689567, 42.42188215255738 34.98200288425658, -32.812503576278694 34.98200288425658)) biblio 2737177 https://api.rohub.org/api/resources/116dfa4e-7918-403c-b4ef-1f995d91c775/download/ 2021-12-20 11:37:53.147412+00:00 2021-12-20 14:02:16.465674+00:00 The COVID-19 pandemic led to dramatic changes in economic activity in 2020. In this paper the authors use estimates of emission changes for 2020 in two Earth System Models (ESMs) to simulate the impacts of the COVID-19 economic changes. application/pdf Climate Impacts of COVID-19 Induced Emission Changes 2021-12-20 11:37:53.147412+00:00 https://www.frontiersin.org/articles/10.3389/frsc.2021.705051/full 2021-12-20 13:52:44.528252+00:00 2021-12-20 13:52:44.528673+00:00 The COVID-19 pandemic has affected severely the economic structure and health care system, among others, of India and the rest of the world. The magnitude of its aftermath is exceptionally devastating in India, with the first case reported in January 2020, and the number has risen to ~31.3 million as of July 23, 2021. India imposed a complete lockdown on March 25, which severely impacted migrant population, industrial sector, tourism industry, and overall economic growth. Herein, the impacts of lockdown and unlock phases on ambient atmospheric air quality variables have been assessed across 16 major cities of India covering the north-to-south stretch of the country. In general, all assessed air pollutants showed a substantial decrease in AQI values during the lockdown compared with the reference period (2017–2019) for almost all the reported cities across India. On an average, about 30–50% reduction in AQI has been observed for PM2.5, PM10, and CO, and maximum reduction of 40–60% of NO2 has been observed herein, while the data was average for northern, western, and southern India. SO2 and O3 showed an increase over a few cities as well as a decrease over the other cities. Maximum reduction (49%) in PM2.5 was observed over north India during the lockdown period. Furthermore, the changes in pollution levels showed a significant reduction in the first three phases of lockdown and a steady increase during subsequent phase of lockdown and unlock period. Our results show the substantial effect of lockdown on reduction in atmospheric loading of key anthropogenic pollutants due to less-to-no impact from industrial activities and vehicular emissions, and relatively clean transport of air masses from the upwind region. These results indicate that by adopting cleaner fuel technology and avoiding poor combustion activities across the urban agglomerations in India could bring down ambient levels of air pollution at least by 30%. Effect of Lockdown Amid COVID-19 on Ambient Air Quality in 16 Indian Cities 2021-12-20 13:52:44.528252+00:00 https://apps.who.int/iris/bitstream/handle/10665/345329/9789240034228-eng.pdf?sequence=1&isAllowed=y&utm_source=north%20shore%20news&utm_campaign=north%20shore%20news%3A%20outbound&utm_medium=referral 2021-12-20 12:21:55.614484+00:00 2021-12-20 12:21:55.615043+00:00 There is a strong body of evidence to show how air pollution affects different aspects of health at even lower concentrations than previously understood. But here’s what hasn’t changed: every year, exposure to air pollution is still estimated to cause millions of deaths and the loss of healthy years of life. application/pdf WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide 2021-12-20 12:21:55.614484+00:00 12135323 https://api.rohub.org/api/resources/729c0b49-df8a-42de-8059-1a220c51f353/download/ 2021-12-20 11:30:13.530879+00:00 2021-12-20 13:58:53.392534+00:00 In this paper the authors quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Their estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. application/pdf Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns 2021-12-20 11:30:13.530879+00:00 https://earthobservatory.nasa.gov/images/87910/scientists-show-connection-between-gas-flaring-and-arctic-pollution 2021-12-20 11:35:42.183038+00:00 2021-12-20 11:36:06.522620+00:00 Waste natural gas from industrial oil and gas fields could be a source of nitrogen dioxide and black carbon pollution, according to new research. Scientists Show Connection Between Gas Flaring and Arctic Pollution 2021-12-20 11:35:42.183038+00:00 https://www.scitepress.org/Papers/2021/103972/103972.pdf 2021-12-20 13:56:47.380772+00:00 2021-12-20 13:56:47.381322+00:00 This study has used, in a first stage, Sentinel-5P and CAMS service to analyze the air quality in the territory of Iberian Peninsula, as well as assess in detail major cities within the region (Lisbon, Porto and Madrid), for a period from January 2018 to April 2020. On a later stage, the data from Sentinel-3A and 3B allowed the analysis of water quality during the months March, April and May 2020, in the Portuguese coast. Regarding the air quality, NO2 and PM10 levels in the Iberian Peninsula were consistently lower compared to the same periods in the two past years. application/pdf Air and Water Quality Improvement during COVID-19 lockdown 2021-12-20 13:56:47.380772+00:00 https://arxiv.org/pdf/2106.13750.pdf 2021-12-20 13:51:17.829614+00:00 2021-12-20 13:57:34.731243+00:00 In this work the authors investigate the short-term variations in air quality emissions, attributed to the prevention measures, applied in different cities, to mitigate the COVID-19 spread. Part of the analysis employs a variety of machine learning tools. application/pdf Assessing the Lockdown Effects on Air Quality during COVID-19 Era 2021-12-20 13:51:17.829614+00:00 1297785 https://api.rohub.org/api/resources/b37476c6-244e-4b80-8268-03c5fc1c7de6/download/ 2022-03-17 17:37:17.618246+00:00 2022-03-17 17:37:24.230848+00:00 Paris coronavirus. Man wearing a mask walking in front of the Eiffel Tower on the first day of Paris lock-down. Photo by The Paris Photographer on Unsplash image/jpeg Paris during the lockdown 2022-03-17 17:37:17.618246+00:00 291862 https://api.rohub.org/api/resources/bc3d57ea-d82b-4261-9af7-24e949d1d5f5/download/ 2021-12-20 11:28:32.188824+00:00 2021-12-20 13:59:25.789565+00:00 Concawe has undertaken a city-level analysis to quantify the ways in which the Covid-19 lockdown measures have had an impact on air quality in Europe. This article presents the results of the analysis for particulate matter (PM 2.5 ), nitrogen dioxide (NO2 ) and ozone (O3 ). application/pdf How Covid-19 lockdown affected air pollution in Europe — a multi-city analysis 2021-12-20 11:28:32.188824+00:00 2276065 https://api.rohub.org/api/resources/bc6922f1-debf-46ae-ba79-d574f6f5c064/download/ 2021-12-20 11:33:04.445345+00:00 2021-12-20 13:59:59.434971+00:00 To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2 , SO2 , CO, PM 2.5 and PM 10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown. application/pdf Impact of the COVID-19 Pandemic Lockdown on Air Pollution in 20 Major Cities around the World 2021-12-20 11:33:04.445345+00:00 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 UiO jean.iaquinta@geo.uio.no Jean Iaquinta Meteorology Applied sciences 10.13039/501100000781 European Commission job market galaxy history galaxy workflow air quality metrics from cad galaxy workflow air quality metrics prosody air quality air quality analysis galaxy workflow galaxy 01xtthb56 University of Oslo 04jcwf484 Nordic e-Infrastructure Collaboration 101017502 RELIANCE service-account-enrichment 12885 https://api.rohub.org/api/ros/67edd16f-c3d8-4879-a3a5-2d223e7dce6d/crate/download/ 2021-12-20 11:10:15.669133+00:00 2025-03-05 00:59:10.306788+00:00 2021-12-20 11:10:15.669133+00:00 Galaxy workflow and executed Galaxy histories for getting air quality "metrics" from CADS on a specified location application/ld+json https://w3id.org/ro-id/67edd16f-c3d8-4879-a3a5-2d223e7dce6d air quality copernicus galaxy portal Galaxy workflow and Galaxy histories for air quality analysis MANUAL Iaquinta, Jean, and Anne Foilloux. "Galaxy workflow and Galaxy histories for air quality analysis." ROHub. Dec 20 ,2021. https://w3id.org/ro-id/67edd16f-c3d8-4879-a3a5-2d223e7dce6d. output input tools https://workflowhub.eu/workflows/251/diagram?version=1 2021-12-20 13:35:02.816046+00:00 2021-12-20 13:35:20.188670+00:00 Galaxy workflow diagram 2021-12-20 13:35:02.816046+00:00 https://climate.usegalaxy.eu/u/annefou/h/reliance-ds1-gc0-sc3-venise-pm25 2021-12-20 13:24:44.479519+00:00 2021-12-20 13:33:13.274190+00:00 PM2.5 Venice region during 2019, 2020 and 2021 2021-12-20 13:24:44.479519+00:00 https://climate.usegalaxy.eu/u/annefou/h/reliance-ds1-gc0-sc3-marseille-non-polluted-area-pm25 2021-12-20 13:26:36.750183+00:00 2021-12-20 13:32:19.082373+00:00 PM2.5 Marseille (non-polluted region) during 2019, 2020 and 2021 2021-12-20 13:26:36.750183+00:00 https://climate.usegalaxy.eu/u/annefou/h/reliance-ds1-gc0-sc3-venise-nitrogen-dioxyde 2021-12-20 13:25:40.479608+00:00 2021-12-20 13:32:58.949483+00:00 NO2 Venice region during 2019, 2020 and 2021 2021-12-20 13:25:40.479608+00:00 https://climate.usegalaxy.eu/u/annefou/h/reliance-ds1-gc0-sc3-marseille-non-polluted-area-nitrogen-dioxide 2021-12-20 13:27:12.467869+00:00 2021-12-20 13:32:44.023631+00:00 NO2 Marseille (non-polluted region) during 2019, 2020 and 2021 2021-12-20 13:27:12.467869+00:00 https://workflowhub.eu/workflows/251?version=1 2021-12-20 13:31:41.239402+00:00 2021-12-20 13:31:55.845914+00:00 Galaxy workflow used to investigate the lockdown effect on air quality between January 2019 to May 2021 2021-12-20 13:31:41.239402+00:00 https://climate.usegalaxy.eu/u/annefou/h/reliance-ds1-gc0-sc3-vitrolles--nitrogen-dioxide 2021-12-20 13:28:34.482198+00:00 2021-12-20 13:33:30.862203+00:00 NO2 Vitrolles (more industrial area near Marseille) during 2019, 2020 and 2021 2021-12-20 13:28:34.482198+00:00 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 UiO jean.iaquinta@geo.uio.no Jean Iaquinta Meteorology Applied sciences final report Bibliographic Research education research specialization study of the impact of lockdown report Europe finding Europe lockdown aim Bibliographic Research object air quality report main finding impact 01xtthb56 University of Oslo 04jcwf484 Nordic e-Infrastructure Collaboration service-account-enrichment 7483 https://api.rohub.org/api/ros/66613047-4680-459d-8c60-509c349830d5/crate/download/ 2021-12-20 11:25:17.614300+00:00 2025-03-05 00:50:46.501842+00:00 2021-12-20 11:25:17.614300+00:00 This report puts together some of the results obtained on the subject and summarizes the main findings. In particular, it reuses other Research Objects we or other have created, including automatically generated Bibliographic Research objects. application/ld+json https://w3id.org/ro-id/66613047-4680-459d-8c60-509c349830d5 air quality covid-19 lockdown Final report regarding the study of the impact of lockdown on air quality over Europe. MANUAL Iaquinta, Jean, and Anne Foilloux. "Final report regarding the study of the impact of lockdown on air quality over Europe.." ROHub. Dec 20 ,2021. https://w3id.org/ro-id/66613047-4680-459d-8c60-509c349830d5. biblio 2167 https://api.rohub.org/api/resources/e43d2b1c-0d14-4d3d-8500-393fb10610c6/download/ 2021-12-20 13:17:50.788610+00:00 2021-12-20 13:17:50.789312+00:00 Work in progress Draft report on the study of the impact of lockdown on air quality over Europe 2021-12-20 13:17:50.788610+00:00 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 UiO jean.iaquinta@geo.uio.no Jean Iaquinta Meteorology Applied sciences 01xtthb56 University of Oslo 04jcwf484 Nordic e-Infrastructure Collaboration 3d420628-017a-455d-adc5-6c1897230ba8 POINT (2.4154224246740346 48.883295068713096) 2.4154224246740346 48.883295068713096 POINT (2.4154224246740346 48.883295068713096) service-account-enrichment 1131024 https://api.rohub.org/api/ros/5f2e7ab8-8122-41b3-8d53-060e09855fa7/crate/download/ 2021-12-20 11:28:59.678934+00:00 2025-03-05 01:01:13.145695+00:00 2021-12-20 11:28:59.678934+00:00 Compare CAMS analysis with available observations on specific locations. application/ld+json https://w3id.org/ro-id/5f2e7ab8-8122-41b3-8d53-060e09855fa7 EBAS NO2 O3 Observation air quality pm2.5 Jupyter Notebook Jupyter notebook comparing CAMS and air quality measurements MANUAL A: air quality analysis cam data database dependent variable download eba fact format formatting information measure metadata observation earth sciences Air pollution Food and drink Food IT-computer sciences Space programme Jupyter notebook NASA Ames air quality analysis cam data format data database datum eba measurement metadata geosciences mathematical and computer sciences air quality measurement analysis with available observation cams analysis compare cams analysis data format doesn t change data line data section ebas Nasa Ames format ebas data format format confusion specific location Compare CAMS analysis with available observations on specific locations. Data downloaded from the EBAS database through its web portal is provided in a format based on the NASA Ames format. Jupyter notebook comparing CAMS and air quality measurements. The EBAS Nasa Ames format, just as normal NASA Ames, consists of a tabular data section preceded by a header containing the metadata. Use a well established format as long as all metadata can be conveyed rather than inventing a new format that would increase the format confusion. https://w3id.org/ro-id/5f2e7ab8-8122-41b3-8d53-060e09855fa7/62679c1a-e581-4367-9b97-0ca70afb99a7 computer science database software Ames Research Center Iaquinta, Jean, and Anne Foilloux. "Jupyter notebook comparing CAMS and air quality measurements." ROHub. Dec 20 ,2021. https://w3id.org/ro-id/5f2e7ab8-8122-41b3-8d53-060e09855fa7. POINT (2.4154224246740346 48.883295068713096) Contains the tools used for executing this RO tools contains information and links to input data that are needed for executing this executable RO input Contains bibliographic resources related to this RO biblio Contains all the outputs generated by this executable RO output 5197 https://api.rohub.org/api/resources/03e3106c-2500-47d2-8945-27ccb9035599/download/ 2021-12-20 11:59:46.733088+00:00 2021-12-20 11:59:46.734733+00:00 Norway - Hurdal (NO0056R) - low_vol_sampler - pm25_mass - pm25 [2014.01.13-2021.01.04] PM2.5 EBAS Data for Hurdal (NO0056R) 2021-12-20 11:59:46.733088+00:00 https://raw.githubusercontent.com/NordicESMhub/RELIANCE/main/content/science/notebooks/comparisons_with_air_quality_measurements.ipynb 2021-12-20 13:45:01.924320+00:00 2021-12-20 13:45:01.924953+00:00 Working progress. This Jupyter notebook is in Github and is regularly updated. Jupyter Notebook for comparing CAMS with observations during lockdown 2021-12-20 13:45:01.924320+00:00 5317 https://api.rohub.org/api/resources/0f1890e0-3083-4bf7-bb4d-f2b1f999eac8/download/ 2021-12-20 12:01:30.062595+00:00 2021-12-20 12:01:52.652568+00:00 Norway - Hurdal (NO0056R) - low_vol_sampler - pm25_mass - pm25 [2014.01.13-2021.01.04] PM2.5 EBAS Data for Hurdal (NO0056R) 2021-12-20 12:01:30.062595+00:00 102096 https://api.rohub.org/api/resources/2459dc9b-fcad-4673-be8c-f0fec599f73f/download/ 2021-12-20 17:58:47.636937+00:00 2022-04-02 10:07:17.858851+00:00 NO2 timeseries at a given location and for different years (2019, 2020, 2021). Here the timeseries is for NO2 in Madrid. image/png NO2 Timeseries 2019, 2020 and 2021 over Madrid 2021-12-20 17:58:47.636937+00:00 https://nordicesmhub.github.io/RELIANCE/science/notebooks/comparisons_with_air_quality_measurements.html 2022-03-17 17:13:10.017413+00:00 2022-03-17 17:13:11.073035+00:00 This is the rendered version (with jupyter-book) of the Jupyter Notebook comparing CAMS and air quality measurements. text/html Rendered jupyter notebook comparing CAMS and air quality measurements 2022-03-17 17:13:10.017413+00:00 288378 https://api.rohub.org/api/resources/63c02ce2-3601-495d-ae40-3493af5b3609/download/ 2021-12-20 12:47:21.156793+00:00 2021-12-20 12:47:21.158130+00:00 Copernicus Atmosphere Time Series of O3 over several location before, during and after then lockdown (March-June 2019, 2020 and 2021) text/csv CAMS O3 March-June 2019, 2020, 2021 2021-12-20 12:47:21.156793+00:00 811491 https://api.rohub.org/api/resources/7fe38fd0-dc1e-4781-a2a1-c485b0308e0e/download/ 2021-12-20 17:49:10.944373+00:00 2021-12-20 17:52:34.713267+00:00 CAMS timeseries of NO2 for 2019, 2020 and 2021 at selected locations image/png CAMS timeseries 2021-12-20 17:49:10.944373+00:00 289519 https://api.rohub.org/api/resources/9637df5a-e396-4d0d-b128-da562185c397/download/ 2021-12-20 12:45:46.877242+00:00 2021-12-20 12:45:46.879557+00:00 Copernicus Atmosphere Time Series of NO2 over several location before, during and after then lockdown (March-June 2019, 2020 and 2021) text/csv CAMS NO2 March-June 1019, 2020, 2021 2021-12-20 12:45:46.877242+00:00 105032 https://api.rohub.org/api/resources/9d6d5b1c-c6f1-4ec1-9041-b78cdc600722/download/ 2021-12-20 11:58:05.551073+00:00 2021-12-20 12:02:18.603819+00:00 Information about EBAS data format application/pdf EBAS Data Format Read-Me 2021-12-20 11:58:05.551073+00:00 288502 https://api.rohub.org/api/resources/bd43e070-f469-4d65-acc4-799b9a527f7e/download/ 2021-12-20 12:47:59.410543+00:00 2021-12-20 12:47:59.412115+00:00 Copernicus Atmosphere Time Series of PM2.5 over several location before, during and after then lockdown (March-June 2019, 2020 and 2021) text/csv CAMS PM2.5 March-June 2019, 2020, 2021 2021-12-20 12:47:59.410543+00:00 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 UiO jean.iaquinta@geo.uio.no Jean Iaquinta Meteorology Applied sciences identification of source ecology pollution event trajectory events pollution origin of events concentrations of pollutant concentration start source sources of pollution event identification case 01xtthb56 University of Oslo 04jcwf484 Nordic e-Infrastructure Collaboration service-account-enrichment 9284 https://api.rohub.org/api/ros/d767e6c3-6cfd-4e68-a989-0b4cbe9236b5/crate/download/ 2021-12-20 11:33:30.052359+00:00 2025-03-05 00:53:52.389586+00:00 2021-12-20 11:33:30.052359+00:00 Trajectory model (backwards) such as FLEXPART or HYSPLIT or LAGRANTO. Backward trajectories to find origin of events where high concentrations of pollutants have been found application/ld+json https://w3id.org/ro-id/d767e6c3-6cfd-4e68-a989-0b4cbe9236b5 atmospheric concentration flexpart modelling particle dispersion model trajectory Identification of sources of pollution events during lockdown MANUAL Anne Foilloux, and Jean Iaquinta. "Identification of sources of pollution events during lockdown." ROHub. Dec 20 ,2021. https://w3id.org/ro-id/d767e6c3-6cfd-4e68-a989-0b4cbe9236b5. Contains information and links to input data that are needed for executing this executable RO input Contains the tools used for executing this RO tools Contains all the outputs generated by this executable RO output Contains bibliographic resources related to this RO biblio https://www.nilu.com/pub/1762338/ 2021-12-20 14:05:41.262377+00:00 2021-12-20 14:07:19.146697+00:00 The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. This version of FLEXPART (10.4) works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). The Lagrangian particle dispersion model FLEXPART version 10.4 2021-12-20 14:05:41.262377+00:00 Nordic e-Infrastructure Collaboration (NeIC) annefou@geo.uio.no Anne Fouilloux 0000-0002-1784-2920 UiO jean.iaquinta@geo.uio.no Jean Iaquinta Applied sciences Adriatic Sea fair perspective supplementary materials of the paper material press perspective Adriatic Sea fragmented geodata supplementary material multi-disciplinary experience Adriatic Sea experience inhomogeneous federica.foglini@ismar.cnr.it Federica Foglini 0000-0002-2736-0052 CNR-ISMAR valentina.grande@bo.ismar.cnr.it Valentina Grande 0000-0002-3489-268X False https://w3id.org/ro-id/97985638-81ed-42b1-ae48-ab432f14db52 2022-02-21 15:48:49.326486+00:00 https://w3id.org/ro-id/users/valentina.grande%40bo.ismar.cnr.it POLYGON ((11.803711205720903 45.86247197535218, 14.264648705720903 45.91141526426032, 20.10058701038361 41.75492216766298, 20.08300915360451 40.29628651711716, 18.290039598941807 39.771391711936175, 14.756836742162706 41.83355344295755, 12.137695848941803 44.071800467511565, 11.803711205720903 45.86247197535218)) 11.803711205720903 45.86247197535218, 14.264648705720903 45.91141526426032, 20.10058701038361 41.75492216766298, 20.08300915360451 40.29628651711716, 18.290039598941807 39.771391711936175, 14.756836742162706 41.83355344295755, 12.137695848941803 44.071800467511565, 11.803711205720903 45.86247197535218 POLYGON ((12.01904296875 40.0208830923139, 12.01904296875 45.80735960788208, 19.9643549323082 45.80735960788208, 19.9643549323082 40.0208830923139, 12.01904296875 40.0208830923139)) 12.01904296875 40.0208830923139, 12.01904296875 45.80735960788208, 19.9643549323082 45.80735960788208, 19.9643549323082 40.0208830923139, 12.01904296875 40.0208830923139 5dba0cd2-0d87-4d68-a195-f65a19b13d86 POLYGON ((12.01904296875 40.0208830923139, 12.01904296875 45.80735960788208, 19.9643549323082 45.80735960788208, 19.9643549323082 40.0208830923139, 12.01904296875 40.0208830923139)) b5175061-11ee-46b0-af27-6519e270b341 POLYGON ((11.803711205720903 45.86247197535218, 14.264648705720903 45.91141526426032, 20.10058701038361 41.75492216766298, 20.08300915360451 40.29628651711716, 18.290039598941807 39.771391711936175, 14.756836742162706 41.83355344295755, 12.137695848941803 44.071800467511565, 11.803711205720903 45.86247197535218)) service-account-enrichment https://w3id.org/ro-id/3e79723f-9082-41f0-bb7e-331c3b2c84bd 763049 https://api.rohub.org/api/ros/97985638-81ed-42b1-ae48-ab432f14db52/crate/download/ 2022-01-11 10:17:48.200180+00:00 2025-03-05 00:46:16.068607+00:00 2022-01-11 10:17:48.200180+00:00 Supplementary materials of the paper "A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective - The Adriatic Sea experience" Oceanologia 2022 (in press) application/ld+json https://w3id.org/ro-id/97985638-81ed-42b1-ae48-ab432f14db52 Adriatic Sea Data model FAIR principles Marine Spatial Data Infrastructure (MSDI) Metadata catalogue WebGIS Bibliographic Resource A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective - The Adriatic Sea experience MANUAL https://w3id.org/ro-id/97985638-81ed-42b1-ae48-ab432f14db52/01c72cbe-835c-4bab-bebb-c6190e152001 https://w3id.org/ro-id/97985638-81ed-42b1-ae48-ab432f14db52/04110680-351f-4898-88cf-8efc485a68e8 CNR-ISMAR Grande, Valentina, and Federica Foglini. "A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective - The Adriatic Sea experience." ROHub. Jan 11 ,2022. https://w3id.org/ro-id/97985638-81ed-42b1-ae48-ab432f14db52. POLYGON ((11.803711205720903 45.86247197535218, 14.264648705720903 45.91141526426032, 20.10058701038361 41.75492216766298, 20.08300915360451 40.29628651711716, 18.290039598941807 39.771391711936175, 14.756836742162706 41.83355344295755, 12.137695848941803 44.071800467511565, 11.803711205720903 45.86247197535218)) POLYGON ((12.01904296875 40.0208830923139, 12.01904296875 45.80735960788208, 19.9643549323082 45.80735960788208, 19.9643549323082 40.0208830923139, 12.01904296875 40.0208830923139)) 160611 https://api.rohub.org/api/resources/13ab86ae-dfbf-40f2-beff-5ed8683bcf04/download/ 2022-01-11 10:36:29.647328+00:00 2022-09-26 14:05:24.608312+00:00 UML structure of the theme Geophysics shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system application/xml Geophysics UML structure 2022-01-11 10:36:29.647328+00:00 199850 https://api.rohub.org/api/resources/160b1eb5-a7bc-4a0b-a7e7-2c1468a61f6e/download/ 2022-01-11 10:37:00.091930+00:00 2022-01-11 10:39:31.225832+00:00 UML structure of the theme Seafloor Mapping shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system application/xml Seafloor Mapping UML structure 2022-01-11 10:37:00.091930+00:00 73078 https://api.rohub.org/api/resources/3996852d-7831-4dcd-8545-08fdc1ecdc58/download/ 2022-01-11 10:37:31.976146+00:00 2022-01-11 10:39:51.280906+00:00 UML structure of the theme Water Column shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system application/xml Water Column UML structure 2022-01-11 10:37:31.976146+00:00 141559 https://api.rohub.org/api/resources/4889392e-3921-44d3-bc27-790b285fd695/download/ 2022-01-11 10:21:14.170980+00:00 2022-01-11 10:58:25.028960+00:00 UML structure built in Enterprise Architect (© Sparx System) representing the theme “Water Column”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes. image/png Water Column Data Model 2022-01-11 10:21:14.170980+00:00 192731 https://api.rohub.org/api/resources/5bf32ec6-68c6-4e6f-b6fc-85a580acf32d/download/ 2022-01-11 10:19:47.491579+00:00 2022-01-11 10:58:45.682934+00:00 UML structure built in Enterprise Architect (© Sparx System) representing the theme “Geology”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes. image/png Geology Data Model 2022-01-11 10:19:47.491579+00:00 75570 https://api.rohub.org/api/resources/5de6e1a4-a358-461c-a32a-242d2d61595f/download/ 2022-09-26 17:04:19.151541+00:00 2022-09-26 17:04:24.581266+00:00 The table shows the complete evaluation of the FAIR principles related to the Marine Spatial Data Infrastructure (MSDI) focus of the scientific paper application/pdf Table S1 of supplementary materials 2022-09-26 17:04:19.151541+00:00 183710 https://api.rohub.org/api/resources/7590862a-8a88-475b-8f9e-7609e6b0edbd/download/ 2022-01-11 10:18:48.276758+00:00 2022-01-11 10:59:07.316811+00:00 UML structure built in Enterprise Architect (© Sparx System) representing the theme “Geophysics”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes. image/png Geophysics Data Model 2022-01-11 10:18:48.276758+00:00 227476 https://api.rohub.org/api/resources/99cdc922-dd1c-44d8-935a-91ce27b7f841/download/ 2022-01-11 10:35:52.060882+00:00 2022-01-11 10:40:09.707715+00:00 UML structure of the theme Geology shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system application/xml Geology UML structure 2022-01-11 10:35:52.060882+00:00 305358 https://api.rohub.org/api/resources/bf860bfe-76ac-48b7-aca6-91ad8490b20f/download/ 2022-01-11 10:20:28.191702+00:00 2022-01-11 10:59:26.436779+00:00 UML structure built in Enterprise Architect (© Sparx System) representing the theme “Seafloor Mapping”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes. image/png Seafloor Mapping Data Model 2022-01-11 10:20:28.191702+00:00 direttore@ismar.cnr.it CNR-ISMAR 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 Chemistry Małgorzata Wolniewicz 10.24424/5yre-b373 2025-06-12 19:29:35.789131+00:00 True 15129 https://api.rohub.org/api/ros/90e03a9e-5748-4640-a657-f71298b2ff57/crate/download/ 2022-01-11 14:40:29.360290+00:00 2025-10-16 10:39:29.454368+00:00 2022-01-11 14:40:29.360290+00:00 In organic chemistry, a homologous series is a sequence of compounds with the same functional group and similar chemical properties in which the members of the series can be branched or unbranched, or differ by -CH2.[1] This can be the length of a carbon chain,[1] for example in the straight-chained alkanes (paraffins), or it could be the number of monomers in a homopolymer such as amylose.[2] Compounds within a homologous series typically have a fixed set of functional groups that gives them similar chemical and physical properties. (For example, the series of primary straight-chained alcohols has a hydroxyl at the end of the carbon chain.) These properties typically change gradually along the series, and the changes can often be explained by mere differences in molecular size and mass. The name "homologous series" is also often used for any collection of compounds that have similar structures or include the same functional group, such as the general alkanes (straight and branched), the alkenes (olefins), the carbohydrates, etc. However, if the members cannot be arranged in a linear order by a single parameter, the collection may be better called a "chemical family" or "class of homologous compounds" than a "series". application/ld+json https://w3id.org/ro-id/90e03a9e-5748-4640-a657-f71298b2ff57 Homologous series - archive Homologous series MANUAL Wolniewicz, Małgorzata. "Homologous series - archive." ROHub. Jan 11 ,2022. https://doi.org/10.24424/5yre-b373. chemical property 9.983079526226733 5.9 chemistry and materials (general) 100.0 0.9161855578422546 quality 4.0201005025125625 4.0 series of primary 15.96638655462185 7.6 chemical property 5.9296482412060305 5.9 alkane 7.839195979899498 7.8 functional group 24.027072758037225 14.2 alkene 5.728643216080402 5.7 homopolymer 3.2160804020100504 3.2 homologous compound 14.705882352941178 7.0 Synthetic and plastic chemicals Economy, business and finance/Economic sector/Chemicals/Synthetic and plastic chemicals earth sciences 100.0 0.851514458656311 physical property 4.92462311557789 4.9 Kerosene-paraffin Economy, business and finance/Economic sector/Energy and resource/Kerosene-paraffin organic chemistry 9.983079526226733 5.9 chemistry 37.26708074534161 12.0 chemistry and materials 100.0 0.9161855578422546 carbon 7.63819095477387 7.6 compound 17.089678510998308 10.1 In organic chemistry, a homologous series is a sequence of compounds with the same functional group and similar chemical properties in which the members of the series can be branched or unbranched, or differ by -CH2. 59.37500000000001 41.8 hydroxyl 6.934673366834171 6.9 organic chemistry 6.130653266331659 6.1 same functional group 13.865546218487397 6.6 Chemistry Science and technology/Natural science/Chemistry series 18.29145728643216 18.2 homologous series 25.630252100840337 12.2 organic chemistry 62.73291925465838 20.2 carbon 12.351945854483926 7.3 geochemistry 100.0 0.851514458656311 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical functional group 14.271356783919598 14.2 carbon chain 29.831932773109248 14.2 chemical compound 10.251256281407034 10.2 Compounds within a homologous series typically have a fixed set of functional groups that gives them similar chemical and physical properties. 26.136363636363637 18.4 series 13.536379018612521 8.0 alkane 13.028764805414552 7.7 monomer 4.824120603015075 4.8 1] This can be the length of a carbon chain,[1] for example in the straight-chained alkanes (paraffins), or it could be the number of monomers in a homopolymer such as amylose. 14.488636363636363 10.2 service-account-enrichment Chemistry Małgorzata Wolniewicz benzene 9.274193548387096 9.2 aromatic hydrocarbon 5.94758064516129 5.9 scent 4.536290322580645 4.5 oxygen atom 4.032258064516129 4.0 Jewellery Arts, culture and entertainment/Arts and entertainment/Fashion/Jewellery aromatic hydrocarbon 8.695652173913043 5.0 carbon atom 15.999999999999998 9.2 monocyclic ring 14.285714285714286 5.7 heterocyclic compound 6.451612903225806 6.4 chemistry and materials 100.0 0.8506659269332886 aromatic compound 29.739130434782613 17.1 aliphatic compound 4.737903225806451 4.7 larger compound 13.533834586466165 5.4 chemical compound 15.826086956521738 9.1 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. 39.583333333333336 20.9 The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. 39.015151515151516 20.6 ring 3.125 3.1 aromatic compound benzene 24.81203007518797 9.9 benzene ring 3.528225806451613 3.5 chemical compound 10.786290322580644 10.7 The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. 21.401515151515152 11.3 geochemistry 100.0 0.4569866955280304 electron 4.435483870967742 4.4 organic compound 3.8306451612903225 3.8 chemistry and materials (general) 100.0 0.8506659269332886 Organic chemical Economy, business and finance/Economic sector/Chemicals/Organic chemical arene 7.304347826086956 4.2 arene 4.939516129032259 4.9 earth sciences 100.0 0.4569866955280304 carbon atom 10.786290322580644 10.7 organic chemistry 65.91639871382637 41.0 chemistry 34.08360128617363 21.2 oxygen atom 17.794486215538846 7.1 nitrogen 3.9314516129032255 3.9 benzene 12.695652173913043 7.3 heterocyclic compound 9.73913043478261 5.6 nitrogen atom 29.573934837092732 11.8 aromatic 19.657258064516128 19.5 service-account-enrichment https://w3id.org/ro-id/41f2cfe8-4c5b-4d35-849a-5e4aa7be5b42 https://w3id.org/ro-id/54c22dc5-ace3-4aaa-be62-b5b4dab97be6 https://w3id.org/ro-id/ba53e480-17bb-466f-b789-3533246d7b43 https://w3id.org/ro-id/de0b3951-0fa7-4b03-a1fa-d5c4da93a476 3749 https://api.rohub.org/api/ros/0c470650-84d9-40e1-bc80-4591a27f6c4d/crate/download/ 2022-01-12 16:34:39.917729+00:00 2025-09-29 14:11:03.453262+00:00 2022-01-12 16:34:39.917729+00:00 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom. application/ld+json https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d chemistry Aromatic compounds MANUAL Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d. False https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d 2022-01-14 22:19:57.392548+00:00 https://w3id.org/ro-id/users/gosiaw%40man.poznan.pl False https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d 2025-07-05 19:04:55.078129+00:00 https://w3id.org/ro-id/users/gosiaw%40man.poznan.pl False https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d 2025-07-05 18:47:59.392957+00:00 https://w3id.org/ro-id/users/gosiaw%40man.poznan.pl False https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d 2025-07-04 09:08:44.261623+00:00 https://w3id.org/ro-id/users/gosiaw%40man.poznan.pl 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 Applied sciences Earth sciences Climatology Earth observation Stockholm Department of Environmental Science Sweden Amazonia South South America AERONET Siberia physics statistics biomass product Far East correlation land India trend of aerosol optical depth burning aod trend increasing biomass trends in AOD Europe use of the Pangeo platform aerosol particle emission agreement Stockholm University Europe study rise trend South North America North Africa emission India North North America Far East service-account-enrichment 3593453 https://api.rohub.org/api/ros/74cd76d8-24f6-48a8-9fd6-db3ddab4e22f/crate/download/ 2022-01-13 14:53:25.137366+00:00 2025-03-05 00:59:13.833840+00:00 2022-01-13 14:53:25.137366+00:00 This study is made possible by the use of the Pangeo platform (Pangeo notebook), which is an open-source community for Big Data geosciences. We investigate regional trends in aerosol optical depth (AOD) from both satellite (MODIS) and ground-based (AERONET stations) measurements over land over the past 20 years. A good agreement is found between the two datasets when the spatial coverage of the stations is homogeneous. AOD increases significantly over northern North-America, India, Siberia and southern South America, while it decreases over Europe, Amazonia and southern North-America. In order to estimate the contribution of trends in biomass burning emissions to regional AOD trends, we use the burnt area of the monthly satellite product GFED4 as a proxy to calculate regional trends in biomass burning emissions for the period 2000-2015. Biomass burning emissions increase significantly over northern North America and East Asia, while they decrease over Europe, southern South America and northern Africa. We calculate the correlation between regional area burned and regional AOD to estimate regions where a significant trend in area burned can lead to a significant trend in AOD. We find that the increasing biomass burning emissions in northern North-America is likely to be the cause of the increase in AOD in this region. We also find that the significant increase in biomass burning emissions over East Asia cannot explain the negative observed trend in AOD. The reduction in anthropogenic aerosol particle emissions is probably the main driver of the AOD trend in this region. Finally, we construct a multivariate linear model to retrieve the global absolute change in AOD, using only the absolute change in regional burned area for the period 2000-2015. We show that the regional burned area is a good parameter for estimating the trend of AOD in most regions, since the covariation between biomass burning and AOD is strong due to their direct link and dependence to similar teleconnections. application/ld+json https://w3id.org/ro-id/74cd76d8-24f6-48a8-9fd6-db3ddab4e22f Global and regional trends in AOD and their link to biomass burning emissions MANUAL Khadir, Théodore . "Global and regional trends in AOD and their link to biomass burning emissions." ROHub. Jan 13 ,2022. https://w3id.org/ro-id/74cd76d8-24f6-48a8-9fd6-db3ddab4e22f. Notebook, functions file, Abstract AOD_and_biomassburning_trends 185161 https://api.rohub.org/api/resources/92ef4b6c-592d-4b86-afbc-a66bad45301d/download/ 2022-01-13 16:46:18.333267+00:00 2022-01-13 16:46:18.334036+00:00 figure to loaded in the notebook image/png Workflow multivariate regression model 2022-01-13 16:46:18.333267+00:00 14832 https://api.rohub.org/api/resources/9617b5e8-0654-4d3f-9af3-97af8dae8f0a/download/ 2022-01-13 16:10:15.528535+00:00 2022-01-13 16:10:15.529439+00:00 application/vnd.openxmlformats-officedocument.wordprocessingml.document Abstract for EGU 2022 2022-01-13 16:10:15.528535+00:00 85022 https://api.rohub.org/api/resources/a395a422-c558-48a7-a9a6-c2da875514cb/download/ 2022-01-13 16:45:28.461286+00:00 2022-01-13 16:46:40.635066+00:00 text/x-python Functions 2022-01-13 16:45:28.461286+00:00 4543223 https://api.rohub.org/api/resources/ee9822f3-9097-49ef-b15a-aa98f50e9de6/download/ 2022-01-13 16:08:16.186435+00:00 2022-01-13 16:48:39.581368+00:00 Introduction / Methodology / Data processing / Analysis Global and regional trends in AOD and their link to biomass burning emissions 2022-01-13 16:08:16.186435+00:00 Théodore Khadir Chemistry service-account-enrichment False https://w3id.org/ro-id/0c470650-84d9-40e1-bc80-4591a27f6c4d 2022-01-14 22:19:57.396191+00:00 https://orcid.org/0000-0003-2388-0744 3481 https://api.rohub.org/api/ros/41f2cfe8-4c5b-4d35-849a-5e4aa7be5b42/crate/download/ 2022-01-12 16:34:39.917729+00:00 2024-03-05 12:17:02.627855+00:00 2022-01-12 16:34:39.917729+00:00 Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom. application/ld+json https://w3id.org/ro-id/41f2cfe8-4c5b-4d35-849a-5e4aa7be5b42 Aromatic compounds - snapshot Aromatic compounds MANUAL Wolniewicz, Małgorzata. "Aromatic compounds." ROHub. Jan 12 ,2022. https://w3id.org/ro-id/41f2cfe8-4c5b-4d35-849a-5e4aa7be5b42. Earth sciences usage of cam air quality analysis analysis from Copernicus Atmosphere Monitoring area analysis usage map air quality reliance service service reliance map of PM10 UiO jeani@uio.no Jean Iaquinta 0000-0002-8763-1643 01xtthb56 University of Oslo 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 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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))) False https://w3id.org/ro-id/b13d7b6a-66bf-40df-84c8-f9c88775b6c1 2022-01-18 18:30:58.768020+00:00 mailto:annefou@geo.uio.no 180573 https://api.rohub.org/api/ros/0d5a0619-14d5-4b45-b925-a9432684f76a/crate/download/ 2022-01-18 18:28:05.432674+00:00 2024-03-05 12:19:08.696869+00:00 2022-01-18 18:28:05.432674+00:00 This Research Object demonstrates how to use CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services and compute monthly map of PM10 over a given geographical area. application/ld+json https://w3id.org/ro-id/0d5a0619-14d5-4b45-b925-a9432684f76a Jupyter notebook demonstrating the usage of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - snapshot MANUAL https://w3id.org/ro-id/0d5a0619-14d5-4b45-b925-a9432684f76a/4b10ec21-8232-4aee-b23d-ee9f37dce383 Anne Foilloux, and Jean Iaquinta. "Jupyter notebook demonstrating the usage of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services - snapshot." ROHub. Jan 18 ,2022. https://w3id.org/ro-id/0d5a0619-14d5-4b45-b925-a9432684f76a. input output tool biblio Daily average of CAMS Particule matter < 10 μm [μg/m3] over Paris in September 2021 Timeseries of particule matter < 10 μm [μg/m3] over Paris in september 2021 This dataset is a data-Cube retrieved from the ADAM platform over France in September 2019 Data-Cube from ADAM platform over France in September 2019 https://datahub.egi.eu/share/117f0e2a8b5d6615974c6a941b093804ch8b23 2022-01-18 18:30:06.117346+00:00 2022-01-18 18:30:54.343006+00:00 https://datahub.egi.eu/share/117f0e2a8b5d6615974c6a941b093804ch8b23 2022-01-18 18:30:06.117346+00:00 This dataset is a data-Cube retrieved from the ADAM platform over France in September 2020 Data-Cube from ADAM platform over France in September 2020 Geojson file used for retrieving data from the ADAM platform over France Geojson for France https://datahub.egi.eu/share/0e87b0cdd21a4c147952d99ed302a957ch3802 2022-01-18 18:30:02.737890+00:00 2022-01-18 18:30:54.402658+00:00 https://datahub.egi.eu/share/0e87b0cdd21a4c147952d99ed302a957ch3802 2022-01-18 18:30:02.737890+00:00 Jupyter Notebook for discovering, accessing and processing RELIANCE data cube, and creating a Research Object with results, and finally publish it in Zenodo Jupyter Notebook of CAMS European air quality analysis from Copernicus Atmosphere Monitoring with RELIANCE services https://datahub.egi.eu/share/f35b2956fc60a70f493e91278e08e3abchf56d 2022-01-18 18:30:26.410290+00:00 2022-01-18 18:30:54.498308+00:00 https://datahub.egi.eu/share/f35b2956fc60a70f493e91278e08e3abchf56d 2022-01-18 18:30:26.410290+00:00 https://datahub.egi.eu/share/e0e426bfbea07306695b82068898bdcachaa15 2022-01-18 18:30:31.670405+00:00 2022-01-18 18:30:54.569917+00:00 https://datahub.egi.eu/share/e0e426bfbea07306695b82068898bdcachaa15 2022-01-18 18:30:31.670405+00:00 Monthly average maps of CAMS Particule matter < 10 μm [μg/m3] over France in 2019, 2020 and 2021 Particule matter < 10 μm [μg/m3] over France for September 2019, 2020 and 2021 Monthly average maps of CAMS Particule matter < 10 μm [μg/m3] over France in 2019, 2020 and 2021 Particule matter < 10 μm [μg/m3] over France for September 2019, 2020 and 2021 https://datahub.egi.eu/share/a078eb09f1e3822c806bdc9cc530288bchf4c2 2022-01-18 18:30:29.465828+00:00 2022-01-18 18:30:54.623804+00:00 https://datahub.egi.eu/share/a078eb09f1e3822c806bdc9cc530288bchf4c2 2022-01-18 18:30:29.465828+00:00 netCDF data corresponding to daily average of CAMS Particule matter < 10 μm [μg/m3] over France for September 2019, September 2020 and September 2021 netCDF data for daily PM10 concentration over France in September 2019, 2020 and 2021 https://datahub.egi.eu/share/e5580c9233f571eacf7eb8ef71c0d7dcch294a 2022-01-18 18:30:23.256523+00:00 2022-01-18 18:30:54.532963+00:00 https://datahub.egi.eu/share/e5580c9233f571eacf7eb8ef71c0d7dcch294a 2022-01-18 18:30:23.256523+00:00 https://datahub.egi.eu/share/0ed7237e5dc09ba8ff353697fef6fc96ch04c1 2022-01-18 18:30:04.435676+00:00 2022-01-18 18:30:54.374174+00:00 https://datahub.egi.eu/share/0ed7237e5dc09ba8ff353697fef6fc96ch04c1 2022-01-18 18:30:04.435676+00:00 154837 https://api.rohub.org/api/resources/c13c349b-c738-4e88-82fa-c67b56f8f08d/download/ 2022-01-18 18:28:39.211870+00:00 2022-01-18 18:30:55.438746+00:00 image/png PM10_september_FR_2019-2021.png 2022-01-18 18:28:39.211870+00:00 Daily average maps of CAMS Particule matter < 10 μm [μg/m3] over France on September 15, 2021 Particule matter < 10 μm [μg/m3] over France on September 15, 2021 https://datahub.egi.eu/share/617299120e542500102b06860f1e6e15ch2f25 2022-01-18 18:30:19.396255+00:00 2022-01-18 18:30:55.512286+00:00 https://datahub.egi.eu/share/617299120e542500102b06860f1e6e15ch2f25 2022-01-18 18:30:19.396255+00:00 This dataset is a data-Cube retrieved from the ADAM platform over France in September 2021 Data-Cube from ADAM platform over France in September 2021 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disorder 32.85457809694793 18.3 mental disorder 3.426791277258567 3.3 distraction 5.919003115264798 5.7 attention 5.815160955347872 5.6 individuals with ADHD 7.416879795396419 5.8 School Education/School Mental and behavioural disorder Health/Diseases and conditions/Mental and behavioural disorder impulsiveness 5.815160955347872 5.6 life sciences 100.0 0.989045262336731 Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function. 22.572178477690287 17.2 life sciences (general) 100.0 0.989045262336731 problem 13.824057450628365 7.7 disorder 12.772585669781932 12.3 difficulty 10.412926391382404 5.8 neurodevelopmental disorder 62.65984654731457 49.0 problem 9.345794392523365 9.0 diagnosis 6.645898234683282 6.4 environmental sciences 100.0 0.6445436477661133 inattention 9.515260323159785 5.3 substance use disorder 19.565217391304348 15.3 emotions 4.984423676012462 4.8 difficulty 6.853582554517134 6.6 behavioral disorder 12.208258527827647 6.8 disorder 10.951526032315979 6.1 Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate. 59.31758530183727 45.2 individual 4.7767393561786085 4.6 symptom 4.569055036344757 4.4 school performance 5.626598465473147 4.4 For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance. 18.11023622047244 13.8 medicine 100.0 12.8 mental disorders 4.731457800511508 3.7 behavioural disorder 7.4766355140186915 7.2 attention deficit hyperactivity disorder 21.599169262720665 20.8 diagnosis 10.23339317773788 5.7 service-account-enrichment https://w3id.org/ro-id/4db62ae8-d09c-4106-80fe-ea026bbebfd7 https://w3id.org/ro-id/6610d218-813a-4f5d-86ca-afd4aa6d6f68 https://w3id.org/ro-id/7389e42f-7757-45fe-a138-12267888c513 https://w3id.org/ro-id/07b99b7b-a209-44cc-86fd-327339b2599c 13873 https://api.rohub.org/api/ros/95388c3d-c10d-40c5-8ac1-7f92ae6ad243/crate/download/ 2022-01-19 13:47:59.181939+00:00 2025-08-30 05:10:28.853066+00:00 2022-01-19 13:47:59.181939+00:00 Attention deficit hyperactivity disorder (ADHD) is a behavioral and neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, which are pervasive, impairing, and otherwise age inappropriate.Some individuals with ADHD also display difficulty regulating emotions, or problems with executive function. For a diagnosis, the symptoms have to be present for more than six months, and cause problems in at least two settings (such as school, home, work, or recreational activities). In children, problems paying attention may result in poor school performance. Additionally, it is associated with other mental disorders and substance use disorders. Although it causes impairment, particularly in modern society, many people with ADHD have sustained attention for tasks they find interesting or rewarding, known as hyperfocus. application/ld+json https://w3id.org/ro-id/95388c3d-c10d-40c5-8ac1-7f92ae6ad243 Attention deficit hyperactivity disorder MANUAL Wolniewicz, Małgorzata, Liza Poltavchenko, and Liza Poltavchenko. "Attention deficit hyperactivity disorder." ROHub. Jan 19 ,2022. https://w3id.org/ro-id/95388c3d-c10d-40c5-8ac1-7f92ae6ad243. Liza Poltavchenko