Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }
- edbbcddb-8e80-4cc8-96bd-30ed44319dcf description "Related publication of the modelling published in OCEANS 2021" assertion.
- README.html description "The damast documentation contains basic information for using damast python package as well as information on damast API." assertion.
- damast description "damask source code repository for creating of reproducible data processing pipelines." assertion.
- 0b1fcfe6-ab98-4df7-be19-e952de9776c6 description "Data-centric Research Object aggregating known AIS public datasets that can be used for instance with damast to create a data pipeline." assertion.
- 42613c9d-9bea-4bc9-b475-1ac886a05d57 description "This work has been derived from work that is part of the T-SAR project (https://www.simula.no/research/projects/t-sar). The TSAR project aims at demonstrating that recent advances in Artificial Intelligence (AI) can be leveraged in the automatic detection of False Data Injection Attacks (FDIA) in transport infrastructures. Some derived work is mainly part of the specific data processing for the 'maritime' domain but could be applied in different domains such as air traffic control, and connected cars." assertion.
- 42613c9d-9bea-4bc9-b475-1ac886a05d57 description "This work has been derived from work that is part of the T-SAR project (https://www.simula.no/research/projects/t-sar). The TSAR project aims at demonstrating that recent advances in Artificial Intelligence (AI) can be leveraged in the automatic detection of False Data Injection Attacks (FDIA) in transport infrastructures. Some derived work is mainly part of the specific data processing for the 'maritime' domain but could be applied in different domains such as air traffic control, and connected cars. " assertion.
- beac91cb-7857-4e5c-b8c4-a0ea0e7511b1 description "Sketch showing how damast tackles metadata information to improve the FAIRness of data pipelines." assertion.
- c2472ec9-4add-4ecd-bf97-2de4ce0f8529 description "Sketch showing how to improve data processing pipelines with damast." assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a description "Research Object aggregating resources corresponding to the PhD work of Pierre Barnabe at Simula Research Laboratory." assertion.
- zenodo.7738876 description "A brief presentation (10min) on (a) the challenges encountered when trying to reuse an existing machine learning project and (b) a developed library/software engineering approach to improve the situation." assertion.
- 990129bb-230e-4025-acf8-7a6f2f38a460 description "The research object refers to the Met Office UKV high-resolution atmosphere model data notebook published in the Environmental Data Science book." assertion.
- 08b2afc9-13d5-4be0-9fa4-94648f367473 description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 2394779d-7849-419d-92ea-ca249f903ec1 description "Contains input gridded data used in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data" assertion.
- 2888fcd8-ff55-4459-947d-e107ea10b54f description "Related publication of the sensors presented in the Jupyter notebook" assertion.
- 3f8c9487-01df-4230-be72-968573dd4366 description "Contains outputs, (regridded data and figures), generated in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data" assertion.
- 69eb3a34-c707-4ede-adba-25fe366145e8 description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 72f091b4-26c1-4d41-ab7e-82925e9d1b42 description "Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- 89d8d5d6-6f23-4592-a0d2-d8a9c2d74dff description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- b55f290e-9a57-4ce2-9e91-709163f5de0e description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- bec6a162-4e81-4603-8070-1a50c2c15981 description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- 1d5d87d5-b157-42e0-af96-62ad96207a26 description "The research object refers to the Met Office UKV high-resolution atmosphere model data notebook published in the Environmental Data Science book." assertion.
- 0c8c516f-f5c1-4e60-b9ec-742a7a4f4d10 description "Contains outputs, (regridded data and figures), generated in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data" assertion.
- 1b0e0f73-1c1e-4d8d-9aa6-3be76e1e3d0c description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 3c9a2e27-9238-4f2d-a97d-f3bcd9392189 description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- 44276276-4353-4ad1-ab63-55f54965dd22 description "Contains input gridded data used in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data" assertion.
- 4ea8a763-73d5-4024-9506-358bc12dc6a6 description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- 523656fb-fe5e-4160-9805-9fc76b4751b0 description "Related publication of the sensors presented in the Jupyter notebook" assertion.
- 5cf6b7f5-5d81-484b-b73e-c24a2eba46b4 description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 739c80cf-f81b-43ad-be6f-70e737bd4845 description "Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- fa07ebe9-65f6-41db-9072-76ea57a0cc27 description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 750e4516-2748-46de-aeea-72bd5f12bc7d description "The research object refers to the Met Office UKV high-resolution atmosphere model data notebook published in the Environmental Data Science book." assertion.
- 03f0c5aa-d4b7-42e0-9cc8-5878ede875d3 description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 1d141bf1-4786-4f2e-9d52-eac34c66ef51 description "Contains input gridded data used in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data" assertion.
- 1f9eacd8-996b-430f-86dc-a6c830bbcb0f description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 324a1a07-a27a-418e-a961-d2a58ce1d28b description "Contains outputs, (regridded data and figures), generated in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data" assertion.
- 6c9eabdc-4f52-464f-bfa7-947fa8b27e20 description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 88d5147a-77fd-4aa2-8b9c-126caa93d7b6 description "Related publication of the sensors presented in the Jupyter notebook" assertion.
- b00a5f7b-cdc8-4ac0-b505-dc5829f0c571 description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- dc37e435-16ca-4f33-bce5-4d68ed02edcb description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- f91fd224-bbce-4e28-9919-7eda95c55bbf description "Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- f66e2dcd-c7f9-4ef0-9856-34ac088be93a description "The research object refers to the Met Office UKV high-resolution atmosphere model data notebook published in the Environmental Data Science book." assertion.
- 0e4721bf-3530-4c43-a9e9-e8808a8bdfaf description "Contains outputs, (regridded data and figures), generated in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data" assertion.
- 10173ca4-d9a4-47d5-8e49-472cba6f32eb description "Related publication of the sensors presented in the Jupyter notebook" assertion.
- 3236ac5a-fb75-45b5-9f37-ab5a5aefcd77 description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 352f6402-4bc6-470a-ae31-cd74962652f8 description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- 5dae8a2d-5ade-4533-92d0-f2eacaa17955 description "Contains input gridded data used in the Jupyter notebook of Met Office UKV high-resolution atmosphere model data" assertion.
- 6194d670-a5bd-472b-a105-0c2e05892cb3 description "Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- 7dacf5b3-6178-4826-934f-1574819aa640 description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- d3aff03f-c079-4250-9b4f-dde1b97cdc5b description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- e010eb30-05bc-40f1-8b16-0896ae39d5d3 description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- b167006f-4678-4b8a-8e8b-224befdd8038 description "This Research Object aggregates all the different Research Objects and resources used for presenting the Environmental Data Science Book at AGU 2022. The Environmental Data Science book is a living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science. The Environmental Data Science is: a book a community a global collaboration We target to make sense of: environmental systems environmental data and sensors innovative research in Environmental Data Science open-source tools for Environmental Data Science We hope you find the content in the resource helpful. The resource and executable notebooks are free under a CC-BY licence and OSI-approved MIT license, respectively." assertion.
- 0811c0ce-8e21-4368-8af1-f29c4181c75d description "Open science communities are pushing the boundaries of how we approach scientific research. With advancements in computing, software, and data management, the tools are available to transform science into a truly open, collaborative, and inclusive space. By following open science practices, we can increase accessibility of scientific research and findings, improve collaboration, and facilitate high quality, reproducible science. This session will showcase success stories in the Earth and space sciences and highlight a range of open science platforms, datasets, and computational tools. Join this session for real-world examples of how open science practices have empowered and enabled scientists across disciplines to carry out successful research projects." assertion.
- 137c3863-4e6b-4f13-876e-32534856cbfd description "Research Object demonstrating sea ice forecasting using IceNet. The corresponding Jupyter Notebook has been published in the Environmental Data Science book." assertion.
- 54d872a8-14ef-4291-ba35-8a7393004530 description "Recording of the presentation given at AGU2022." assertion.
- d08daf3e-3171-42be-935a-3889d5ae0ee0 description "This research object is a fork from RO examplifying Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. Its main purpose is to show how to make derivative work and keep all the history of contributions and contributors." assertion.
- ea5e95c4-9f22-4ce5-ba9a-22fc7257cd27 description "This summary report describes a preliminary remote sensing analysis of the coseismic displacement map occurred following the two main event occurred in Turkey-Syria on February 2023. Two pairs of ALOS-2 WD images along the descending and ascending orbit were processed to derive coseismic displacement maps retrieved by applying the standard two-steps InSAR approach. The displacement component of the ground motion were computed as well." assertion.
- fb680fff-6082-44c1-ba3e-7f451afba565 description "This summary report describes a preliminary remote sensing analysis of the coseismic displacement map occurred subsequently the two main event occurred in Turkey-Syria on February 2023. Two pairs of ALOS-2 images along the descending and ascending orbit were processed to derive coseismic displacement maps retrieved by applying the standard two-steps InSAR approach." assertion.
- 59e63d12-57af-4bcd-8d8f-821b9e096b9b description "This summary report describes a preliminary remote sensing analysis of the coseismic displacement map occurred following the two main event occurred in Turkey-Syria on February 2023. Two pairs of ALOS-2 WD images along the descending and ascending orbit were processed to derive coseismic displacement maps retrieved by applying the standard two-steps InSAR approach. The displacement component of the ground motion were computed as well." assertion.
- d2f2d1f1-97c0-494c-89cc-61d721e83498 description "This summary report describes a preliminary remote sensing analysis of the coseismic displacement map occurred subsequently the two main event occurred in Turkey-Syria on February 2023. Two pairs of ALOS-2 images along the descending and ascending orbit were processed to derive coseismic displacement maps retrieved by applying the standard two-steps InSAR approach." assertion.
- 9da6e0fa-a824-4ff3-b0af-fa98aef14285 description "The report shows some results about the damage caused by the strong earthquakes (M7.9 and M7.5) that hit Turkey on 6 February 2023. The damage is estimated by means of SAR high-resolution images provided by ASI mission COSMO-SkyMed, and by adopting a change detection technique based on correlation of SAR intensity signals." assertion.
- c3a349e7-ca13-4f4d-b5c7-fc7e4ff7fef4 description "Activity report on SAR image processing" assertion.
- 0c6c5b80-9268-4851-8cec-1f9c49cb7f9f description "Coseismic ground displacement of Turkey/Syria earthquakes of 6th February 2023 from Sentinel-1 SAR data" assertion.
- 4176 description "" assertion.
- 4176 description "" assertion.
- 13e6bbe1-ed1a-4e3d-879c-694f1bbab5a0 description "Coseismic ground displacement of Turkey/Syria earthquakes of 6th February 2023 from Sentinel-1 SAR data" assertion.
- aae72e1c-6d73-4c25-a565-f855cfb434b6 description "This Jupyter Docker container is used by the Galaxy Project. It is based on Pangeo notebook docker image (https://github.com/pangeo-data/pangeo-docker-images) and contained a few additional packages required for Galaxy (to exchange data, etc.)." assertion.
- 039f0e1f-ddb5-4a6b-8047-094aeb37b259 description "Copernicus Atmosphere Monitoring Service PM2.5, 2 day forecasts, 24th December 2021 at 12:00 UTC" assertion.
- 3d33b1aa-589b-4680-906b-3fc60e9a18fc description "Training material (hands-on) where Pangeo Notebook is used to learn Xarray. This training is part of the Galaxy Training Network (GTN). In this tutorial, we will learn about Xarray, one of the most used Python library from the Pangeo ecosystem. We will be using data from Copernicus Atmosphere Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. Parallel data analysis with Pangeo is not covered in this tutorial." assertion.
- 3e989ea3-cba3-49b5-b355-8c20a5e46dfb description "These docker images (different tags) correspond to the docker images built for Galaxy Pangeo JupyterLab. The docker images can be used within Galaxy and as standalone docker images. You can use the same images we use in Galaxy on your local computer or any other platform: 1. Pull an existing image locally docker pull quay.io/nordicesmhub/docker-pangeo-notebook 2. Run a pre-build image from docker registry 3. To start your JupyterLab: docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook and you will top open a new terminal and start your favorite web browser. your running Jupyter Notebook instance on http://localhost:7777/ipython/. Remark: for reproducibility purpose, we suggest you use a specific tag e.g. docker pull quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b Then use the same tag when starting your JupyterLab application: docker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b" assertion.
- 4415864d-cc85-4cb9-8b9f-39eaf78dc78a description "Dataset used in the Galaxy Pangeo tutorials on Xarray. Data is in netCDF format and is from Copernicus Air Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset." assertion.
- 50f990d7-ec19-4dc4-b98f-c4a3a55fd51f description "This is a tarball for the Docker Galaxy pangeo-JupyterLab image - Version 1c0f66b. To use it: download the image file docker-pangeo-notebook-1c0f66b.tar load it with docker with the command: docker load --input docker-pangeo-notebook-1c0f66b.tar launch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b start your favorite web browser and go to: http://localhost:7777/ipython/ See https://github.com/NordicESMhub/docker-pangeo-notebook for more details" assertion.
- 56c7c29c-badc-45ad-bb41-75ba492064e2 description "Default Jupyter Notebook used when starting Galaxy Climate JupyterLab if no other Jupyter Notebook is passed by the user." assertion.
- 9663df26-3adb-40ed-b68e-391c2023ec0b description "This is a gif animated image showing how to start the Galaxy Pangeo JupyterLab in Galaxy Europe. In this video, we pass an input file (this file will be imported in the Jupyter Notebook /import folder)." assertion.
- a3f045c3-42e7-4896-a4b7-bef646dade6b description "This is the Galaxy Pangeo JupyterLab tool wrapper used by Galaxy to start the Galaxy Pangeo JupyterLab on a Galaxy instance." assertion.
- d83b5c87-96e6-4efd-8c7c-3bc13a643e29 description "This github repository contains all the sources required for building the docker containers that are made available in Quay Container Registry." assertion.
- eb754e63-6b12-48e7-b68c-b76e9aa7b774 description "Link to the online JupyterLab documentation." assertion.
- 0e6ee388-ce22-4237-bf54-74336d1215ce description "OpenAIRE OAWeek - Research communities & climate action; being open to drive change Description This session will invite expert researchers to discuss their scientific impact in climate related topics. Why are they embracing open science practices in their current research workflows? How are they using the resources provided in the European Open Science Cloud (EOSC)? Is open access enabling researchers to contribute better to Climate Change solutions? If you’re curious about these innovative initiatives by the research communities, make sure you attend! Speakers: Anne Fouilloux (RELIANCE) Anabela de Oliveira (EGI-ACE) Bjorn Backeberg (C-SCALE) Prof Spyridon Rapsomanikis, Athena RC, NEANIAS" assertion.
- 18d7d7c3-4a1f-4ef7-a808-ba6c083f9047 description "This session on "Justice and Climate Change" has been held online during the CESM Workshop 2022. Agenda: - Jola Ajibade: "Understanding the complexity of Climate justice and Climate Change"; - Laura Landrum: "SEARCH - Study of Environmental Arctic Change Program"; - Yifan Cheng: "Informing Climate and Land Surface Model Decisions with Indigenous Guidance"; - Panel discussion with speakers." assertion.
- 1d03bf4c-2768-4c3a-9434-c1f750a8aad4 description "Definition of Climate Justice from Wikipedia." assertion.
- 33337cb8-0054-452f-9392-5247fbd22301 description "Sharan, Malvika In this talk, I discuss open science as a framework to ensure that all our research components can be easily accessed, openly examined and built upon by others. I will introduce The Turing Way - an open source, open collaboration and community-driven guide to reproducible, ethical and inclusive data science and research. Drawing insights from the project, I will share best practices that researchers should integrate to ensure the highest reproducible and ethical standards from the start of their projects so that their research work is easy to reuse and reproduce at all stages of the development. All attendees will leave the talk understanding the many dimensions of openness and how they can participate in an inclusive, kind and inspiring open source ecosystem as they collaboratively seek to improve research culture. All questions and contributions are welcome at the GitHub repository: https://github.com/alan-turing-institute/the-turing-way. Home page: https://malvikasharan.github.io/ This was a closing keynote at Concordia University in Montreal on 27 May 2022." assertion.
- 43256dd6-1cfd-4ca1-b737-33a6224665ad description "Definition of Environmental Justice from Wikipedia" assertion.
- 55af9559-4bd0-42a3-9ec5-77d834e21c8a description "Photo by Markus Spiske on Unsplash." assertion.
- 588ede06-712e-48c6-81bf-8f1403ae8d34 description "Slides used by Anne Fouilloux to present her work on Open Science and the link to Climate Justice." assertion.
- 8bc8a12e-0824-4a88-90df-83f3a0cdc101 description "NASA data are being used to support environmental and climate justice efforts as highlighted in several use cases showing how scientists and decision-makers are applying a wide combination of datasets to assess the vulnerability and exposure of communities to environmental challenges." assertion.
- d9fe010a-4902-47a4-be6d-aaecdafad361 description "This paper is from Gisela H. Govaart, Simon M. Hofmann, Evelyn Medawar. Ever-increasing anthropogenic greenhouse gas emissions narrow the timeframe for humanity to mitigate the climate crisis. Scientific research activities are resource demanding and, consequently, contribute to climate change; at the same time, scientists have a central role in advancing knowledge, also on climate-related topics. In this opinion piece, we discuss (1) how open science – adopted on an individual as well as on a systemic level – can contribute to making research more environmentally friendly, and (2) how open science practices can make research activities more efficient and thereby foster scientific progress and solutions to the climate crisis. While many building blocks are already at hand, systemic changes are necessary in order to create academic environments that support open science practices and encourage scientists from all fields to become more carbon-conscious, ultimately contributing to a sustainable future." assertion.
- f94f0f76-26e0-4ad0-8ec1-6a0e430af657 description "OpenAIRE participates in the International Open Access Week - Open for Climate Justice 24 - 30 October 2022" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 description "In this study, we focusing on understanding changes in air and water quality during the Covid-19 lockdown in the Venice Lagoon. We are re-using existing Research Objects, and in particular Jupyter Notebooks that were created in previous studies." assertion.
- 09d7eee4-13ea-4df2-adef-4f07a796aa44 description "Integration of data on Air and Water quality in the Venice Lagoon to assess the impact of the Covid-19 Lockdown" assertion.
- 24c5035b-6451-4e84-944f-059b1d224566 description "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" assertion.
- 37d7f44c-b1ac-40ec-8130-b01dc771fb13 description "Reduction in the impact of human-induced factors is capable of enhancing the environmental health. In view of COVID-19 pandemic, lockdowns were imposed in India. Travel, fishing, tourism and religious activities were halted, while domestic and industrial activities were restricted. Comparison of the pre- and post-lockdown data shows that water parameters such as turbidity, nutrient concentration and microbial levels have come down from pre- to post-lockdown period, and parameters such as dissolved oxygen levels, phytoplankton and fish densities have improved. The concentration of macroplastics has also dropped from the range of 138 ± 4.12 and 616 ± 12.48 items/100 m2 to 63 ± 3.92 and 347 ± 8.06 items/100 m2. Fish density in the reef areas has increased from 406 no. 250 m−2 to 510 no. 250 m−2. The study allows an insight into the benefits of effective enforcement of various eco-protection regulations and proper management of the marine ecosystems to revive their health for biodiversity conservation and sustainable utilization." assertion.
- 489f296f-7053-452b-a254-521e4e9ce898 description "NO2 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube." assertion.
- 5048537a-2da7-4e24-89b4-14b4def8d9e2 description "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." assertion.
- 515ff865-2d30-4450-b209-3f79bf89c94f description "Jupyter Notebook to analyse the changes in NO2 during the Covid-19 Lockdown in the Venice Lagoon. Datasets are from Copernicus Atmosphere Monitoring Forecasts and in-situ measurement for water quality" assertion.
- 7719f92a-5ad6-4314-b8ac-6645255a835f description "The goal is to compare values of NO2 water quality before and during the covid-19 lockdown." assertion.
- 80885ab4-6622-4b21-9e52-5e696aa2d377 description "CAMS OZONE" assertion.
- 875fafc1-3eb1-4273-8380-da3e51d5399e description "Bar plot showing NO2 averaged between March and June for 2019 and 2020. The goal is to compare values before and during the covid-19 lockdown." assertion.
- 8fb0d0f3-cfc3-4b09-8920-7837cbe3964d description "Dataset shows monthly values and error bars." assertion.
- 92623c2c-5dd7-4f49-9175-0a67bdb2e549 description "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." assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd description "CAMS NITROGEN DIOXIDE" assertion.
- 957715d6-ad3e-4ef8-bcd7-e993c0f5d6ef description "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." assertion.