Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }
- parting-the-sea-to-save-venice description "The natural-color Landsat images above show some of the MOSE engineering efforts that are visible above the water line near the Lido Inlet. The top image was acquired on June 20, 2000, by the Enhanced Thematic Mapper+ on Landsat 7. The second image, from the Operational Land Imager on Landsat 8, was collected on September 4, 2013. Turn on the image comparison tool to make the changes easier to see. (Note that Landsat 8 has a greater dynamic range than Landsat 7, so the Landsat 8 image is crisper the Landsat 7 image.)" assertion.
- 10.1016%2Fj.marpolbul.2021.112124 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.
- content 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.
- ?dataset=69623:EU_CAMS_SURFACE_NO2_G description "CAMS NITROGEN DIOXIDE" assertion.
- ?dataset=69625:EU_CAMS_SURFACE_O3_G description "CAMS OZONE" assertion.
- ?dataset=69627:EU_CAMS_SURFACE_PM25_G description "CAMS SURFACE PARTICULATE METTER D<2.5" assertion.
- 0869e396-3733-4aff-8fb2-94c8937b28aa 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.
- 53aa90bf-c593-4e6d-923f-d4711ac4b0e1 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.
- 2a2b6f01-be2e-414e-af08-d882aa995a71 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.
- 998dccd6-7192-4d88-af39-6018c71e6bdf 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.
- 084b7991-70e0-48c1-af37-5bf6e1e21196 description "## 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.
- 49c08d71-ef52-4ae1-9ddb-cd43bc204d84 description "Dataset shows monthly values and error bars." assertion.
- 885820ec-21c6-4045-96cd-716e2ae42102 description "Compare air quality and water quality in the Venice Lagoon for two different dates." assertion.
- 9777f9c8-388f-49e7-b027-a1dc933c2398 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.
- da17f3c5-cc88-46fe-bd6e-3e7c278f8df0 description "The goal is to compare values of NO2 water quality before and during the covid-19 lockdown." assertion.
- c2c64bf9-7625-4442-9ca9-dcd978b1d38b description "Integration of data on Air and Water quality in the Venice Lagoon to assess the impact of the Covid-19 Lockdown" assertion.
- NO2_EUROPE_ADAMAPI2019-03-01_2021-06-30.nc description "NO2 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube." assertion.
- full description "Although all the technical components supporting fully orchestrated Digital Twins (DT) currently exist, what remains missing is a conceptual clarification and analysis of a more generalized concept of a DT that is made FAIR, that is, universally machine actionable. This methodological overview is a first step toward this clarification. We present a review of previously developed semantic artifacts and how they may be used to compose a higher-order data model referred to here as a FAIR Digital Twin (FDT). We propose an architectural design to compose, store and reuse FDTs supporting data intensive research, with emphasis on privacy by design and their use in GDPR compliant open science." assertion.
- c797fdc7-0330-45de-8970-fedf5ee89cb1 description "Bibliography and other related articles and documents of interest for the Destination Earth Initiative." assertion.
- 1b43f352-a63c-4476-8ba7-8379e1dbde75 description "Web article about the collaboration between CINECA and the Destination Earth initiative for the adoption of new HPC technologies." assertion.
- 3ca945fa-c503-45bf-8d3d-930bdb0651c9 description "5 pillars for realising Destination Earth" assertion.
- 8d7ce936-46bc-4023-b06f-2198580f3802 description "Presentation given at ECMWF by Shainer Graham" assertion.
- 871a1786-bc6a-4e60-a160-3f57e3869d35 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.
- 4a2af672-c0f3-46f3-8eca-bdf08c8a1b8d description "Research Object demonstrating sea ice forecasting using IceNet. The corresponding Jupyter Notebook has been published in the Environmental Data Science book." assertion.
- 632fe6e6-78fc-4b1b-8c5f-0b991e854cf9 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.
- 7a09c382-3acd-4ee5-af4a-896b78da763a 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.
- 89f226c9-2b9b-4921-a3a5-e9f24bf82b64 description "Recording of the presentation given at AGU2022." assertion.
- 4691cb04-70c5-4c7b-aa76-65850ddd509c description "This RO provides the Jupyter notebook used to post process the Sound Pressure Levels (SPLs) data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded." assertion.
- 15fac8f9-b9ec-4a0e-8d60-6d9563ecdd60 description "Link to open the file in Notebooks, an environment based on Jupyter and the EGI cloud service: It allows to post process SPLs data and to create graphs/tables. A valid EGI account is required." assertion.
- 2b2bbff5-3a54-42f5-9889-123bacb66828 description "Example of SPLs input file. HDF5 format, according to to ICES (International Council for the Exploration of the Sea) continuous noise data portal specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx)." assertion.
- 551e4e13-b14a-42fe-b654-788dd2fa2220 description "Some examples of output files" assertion.
- 9424b92f-ac08-4c1d-b702-49f33508a9ca description "Map of stations with their coordinates" assertion.
- 94c1d183-c6a0-44db-8ec7-262cb699e822 description "The Jupyter notebook used to post process SPLs data and to create graphs/tables." assertion.
- c9bf4fa6-e56f-4ea4-b9ea-cd73c2c5fc26 description "20 and 60 seconds SPLs dataset" assertion.
- ca7bd76d-d492-45cd-a23a-1d7fb5903ca7 description "Continuous Noise Database (https://underwaternoise.ices.dk/continuous), 2022. ICES, Copenhagen" assertion.
- dd76312c-945d-461f-9719-17d5abc73df6 description "EU-Interreg Italy-Croatia 2014/2020 – CBC Program (Contract number 10043643)" assertion.
- SPL_PostProcessing_HDF5.ipynb description "Link to open the file in Notebooks, an environment based on Jupyter and the EGI cloud service: It allows to post process SPLs data and to create graphs/tables. A valid EGI account is required." assertion.
- SPL_PostProcessing_HDF5.ipynb description "Link to EGI Jupyer HUB: It allows to post process spl data and to create graphs/tables" assertion.
- continuous description "Continuous Noise Database (https://underwaternoise.ices.dk/continuous), 2022. ICES, Copenhagen" assertion.
- continuous description "Continuous Noise Database (https://underwaternoise.ices.dk/continuous), 2022. ICES, Copenhagen" assertion.
- soundscape description "EU-Interreg Italy-Croatia 2014/2020 – CBC Program (Contract number 10043643)" assertion.
- soundscape description "EU-Interreg Italy-Croatia 2014/2020 – CBC Program (Contract number 10043643)" assertion.
- 2f352829-b3e1-45f3-99f2-786c52587485 description "This RO provides the Jupyter notebook used to post process the Sound Pressure Levels (SPLs) data obtained within the Soundscape Project - SOUNDSCAPES IN THE NORTH ADRIATIC SEA AND THEIR IMPACT ON MARINE BIOLOGICAL RESOURCES (https://www.italy-croatia.eu/web/soundscape) where more of 1 year of continuos underwater noise data (march 2020 - june 2021) were recorded." assertion.
- 30ecc79b-fa5a-4f52-9e0c-043b972cc852 description "Map of stations with their coordinates" assertion.
- 5864b820-e87b-453e-974f-1c1046ce8156 description "Some examples of output files" assertion.
- 9b4f23a4-edd7-4088-b7f1-d750dc89af89 description "Example of SPLs input file. HDF5 format, according to to ICES (International Council for the Exploration of the Sea) continuous noise data portal specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx)." assertion.
- f18af8df-aeb1-4c4e-9334-da5557f6b4ea description "The Jupyter notebook used to post process SPLs data and to create graphs/tables." assertion.
- zenodo.7472152 description "20 and 60 seconds SPLs dataset" assertion.
- 73e65f3b-c8eb-4aca-b7dc-af81e4ec9b4a description "This presentation has been given at the Data Managers Network meeting on Tuesday 22 November 2022. The topic of the meeting was "EOSC in practise" where different speakers gave their perspectives and experience with involvement in EOSC." assertion.
- 2cc72630-5f90-4284-9f30-ce8b647935b2 description "1st slide of Anne Fouilloux's presentation." assertion.
- 5c0b4dc8-8ddc-4377-8e10-88022bd34ddf description "Slides for Anne Fouilloux's presentation at UiO Data Manager Meeting on EOSC in practise." assertion.
- 99001ed6-eafc-4363-ad93-b3da218a7187 description "Timeline with Anne Fouilloux's EOSC journey." assertion.
- 9cf3fc98-5d87-4548-a111-af2c2a5440ce description "Demo for EOSC-Future: Turning FAIR and Open Science into Reality. The example shown is about the "impact of the Covid-19 Lockdown on Air quality over Europe using Copernicus and EOSC project services"." assertion.
- b24ab3e8-29b2-4cb9-aadc-816b9027f855 description "Answering research questions with EOSC, March 10, 2022. Climate Data Scientist Anne Fouilloux and her team were faced with a research question: In France, have there been changes in air quality over the course of the COVID-19 pandemic? With the help of compute services available through EOSC, Anne was able to search for European air quality data analysis. NAVIGATING EOSC Check our infographic and follow Anne as she: - searches for European air quality data via OpenAIRE|Explore; - selects a software (an EOSC Jupyter notebook); - orders the notebook on the EOSC marketplace; - accesses and aggregates research from the RELIANCE project; - performs data analysis with air quality data in France; - shares a new research object (via a B2Drop folder)." assertion.
- 1gtlJ8WmYpC-O7b0YBQKWzMUTRFRuJy5S?usp=sharing description "Link to google drive folder containing input data (csv format) used for detection of anomalies with AIS satellite data." assertion.
- www.nmea.org description "The National Marine Electronics Association, is a worldwide, member based trade organization revolving around marine electronics interface standards, marine electronics installer training, and its annual marine electronics conference & expo. The NMEA and its members are committed to enhancing the technology and safety of marine electronics through installer training and interface standards. NMEA members promote professionalism within the marine electronics industry. NMEA installer trainings and certifications are recognized by many major electronics manufacturers for installation, support and warranty." assertion.
- d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe description "AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway). The dataset contains AIS data (satellite + other) on a global coverage for 2020. There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. The csv files have one header line: mmsi;lon;lat;date_time_utc;sog;cog;true_heading;nav_status;rot;message_nr;source where: mmsi (integer): MMSI number of the vessel (AIS identifier). All records belonging to the same vessel will have the same identifier; lon (float): Geographical longitude (WGS84) between -180 to 180; lat (float): Geographical latitude (WGS84) between -90 to 90; date_time_utc (datetime): Date and Time (in UTC) when position was recorded by AIS. It is represented as: YYYY-MM-DD HH:MM:SS (for instance 2020-01-01 00:00:00); sog (float): Speed over ground (knots); cog (float): Course over ground (degrees); true_heading (integer): Heading (degrees) of the vessel's hull. A value of 511 indicates there is no heading data; nav_status (integer): Navigation status according to AIS Specification; rot (integer): rate of turn; message_nr (integer): message number; source (integer): source is the source of AIS data ('g' for ground or 's' for satellite); One row in the CSV file corresponds to one message." assertion.
- 32057316-113a-48de-bede-927c605d3e58 description "This document explains where to find input data on Simula's computing resources (EX3)" assertion.
- 7f3d4137-258e-42a7-9e9a-e798ac2f22e2 description "Sketch showing a boat transmitting data to the satellite receiver." assertion.
- zenodo.7413790 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7415523 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7415565 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7415613 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7415840 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7415948 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7416056 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7416092 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7416098 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds" assertion.
- zenodo.7416100 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7416110 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7416118 description "There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day. The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020. Follow the link to get the description of the content of the CSV file." assertion.
- zenodo.7418694 description "AIS raw data (ASCII) provided by Statsat AS in the context of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway). The file contains AIS messages. The documentation is also provided in this archive. The file is a NMEA text file. This file has not been used for training the deep learning method (T-SAR project). Decoded, it may not correspond exactly to what is in the data folder." assertion.
- 165616 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.
- 18269477-c1b8-4aa8-9b0e-372c7bb6b65c 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.
- 9ca6c1ca-a4bd-40d5-a199-c310f94edb0b 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.
- fcdb9bc7-5bec-4955-a1cc-cc809e3d5117 description "Recording of the presentation given at AGU2022." assertion.
- polar-modelling-icenet.html description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- polar-modelling-icenet.html description "Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book" assertion.
- polar-modelling-icenet.html description "Rendered version for the original notebook. It has been used as input for this Research Object." assertion.
- b0a8864e-415d-42e3-972f-bb66c6d6a4d9 description "The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. Modelling approach IceNet is a probabilistic, deep learning sea ice forecasting system. The model, an ensemble of U-Net networks, learns how sea ice changes from climate simulations and observational data to forecast up to 6 months of monthly-averaged sea ice concentration maps at 25 km resolution. IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. IceNet was implemented in Python 3.7 using TensorFlow v2.2.0. Further details can be found in the Nature Communications paper Seasonal Arctic sea ice forecasting with probabilistic deep learning." assertion.
- 3b853f3d-6613-4dd1-bcf8-d70e397586a7 description "Figure showing the 2 meter temperature from ECMWF ERA5 (monthly mean September to November 2019)" assertion.
- dbcef63e-b902-49d4-8e78-d2623962fd74 description "Derivative work created from forked Research Object. The Jupyter Notebook has been updated" assertion.
- conda-linux-64.lock description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- conda-linux-64.lock description "Lock conda file for linux-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- conda-osx-64.lock description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- conda-osx-64.lock description "Lock conda file for osx-64 OS of the Jupyter notebook hosted by the Environmental Data Science Book" assertion.
- 107487d2-a9d5-4224-8b00-b321e133b6c8 description "Presentation (slides) and demo (video) by Anne Fouilloux for the RELIANCE use case on Climate Change. The presentation and demo were given during the pan-europeans digital assets supporting research communities. Agenda of the event: On 5-6 December 2022, EOSC Future and the INFRAEOSC-07 projects (C-SCALE, DICE, EGI-ACE, OpenAIRE Nexus, Reliance) are hosting an online use case showcase. Check out the agenda and register for this online interactive event by 4 December, 23.59 CET. Over 2 half-day webinars, actual EOSC users will present how their research communities are using EOSC digital assets to address scientific and societal challenges related to 3 UN Sustainable Development Goals (SDGs): • Climate action (SDG 13) • Industry, Innovation & infrastructure (SDG 9) • Good health & well-being (SDG 3) There will also be a session with use cases related to Open Science more broadly. Why ‘use cases’? The demonstrative, first-hand format of the event will enable real research communities to show how their work can be leveraged by EOSC. Researchers, disciplinary groups and anyone interested to learn about both EOSC-related tools and services for data sharing and discoverability as well as discipline-related solutions are invited to the webinar. Attendees will also hear first-hand accounts from early-adopter communities that have integrated some of these core EOSC services. 1 programme, 2 days Check out the agenda to get a glimpse of the cases in the programme, in addition to a at the users, EU and UN officials who will be weighing in on discussions. Day 1 – 5 December • 09.30-11.00: Digital assets supporting SDG 13: Climate action • 11.15-12.00: Digital assets supporting SDG 3: Good health and well-being • 12:15-13:00: Discovering services for open science Day 2 – 6 December • 09.00-09.45: Digital assets supporting SDG 9 Industry, innovation and infrastructure • 10.00-10.45: Experiences from Early Adopters approaching EOSC: the RELIANCE Open challenge • 11.00-12.00: Lessons Learnt from use cases and Looking forward" assertion.
- 8771e967-1344-4704-89e5-50fddc940f7f description "Demonstration given during the webinar. This demo goes with the presentation (slides) and show how the original work was published s a paper in nature communications. The code and data were available and Alejandro Coca-Castro re-used it to create an executable Research Object with a Jupyter Notebook. This Jupyter Notebook examplifies the use of IceNet (probabilistic deep learning to forecast sea-ice). This executable Research Object was forked and deviated work was created e.g. the Jupyter notebook was updated to make it more accessible to people that are not from the Climate community. We use B2DROP to store the new Jupyter notebook and the results to share while doing. Whenever we update the notebook or add figures, the corresponding Research Object is updated live on RoHub. We are now getting close to Open Science e.g. sharing while doing." assertion.
- b8798992-775a-42c7-a9b1-c7d10905e0ab description "Climate change: Collaborative, reproducible and transparent science for seasonal sea-ice forecasting. Digital assets supporting SDG 13: Climate action" assertion.
- e29d96df-b801-495a-997e-2e0fe9c339e9 description "Presentation (same as the google doc) but in pdf format." assertion.
- edit?usp=sharing&ouid=117642930190987755261&rtpof=true&sd=true description "Climate change: Collaborative, reproducible and transparent science for seasonal sea-ice forecasting. Digital assets supporting SDG 13: Climate action" assertion.
- b47069ec-f001-4783-8def-cbd54858f571 description "Presentation (slides) and demo (video) by Anne Fouilloux for the RELIANCE use case on Climate Change. The presentation and demo were given during the pan-europeans digital assets supporting research communities. Agenda of the event: On 5-6 December 2022, EOSC Future and the INFRAEOSC-07 projects (C-SCALE, DICE, EGI-ACE, OpenAIRE Nexus, Reliance) are hosting an online use case showcase. Check out the agenda and register for this online interactive event by 4 December, 23.59 CET. Over 2 half-day webinars, actual EOSC users will present how their research communities are using EOSC digital assets to address scientific and societal challenges related to 3 UN Sustainable Development Goals (SDGs): • Climate action (SDG 13) • Industry, Innovation & infrastructure (SDG 9) • Good health & well-being (SDG 3) There will also be a session with use cases related to Open Science more broadly. Why ‘use cases’? The demonstrative, first-hand format of the event will enable real research communities to show how their work can be leveraged by EOSC. Researchers, disciplinary groups and anyone interested to learn about both EOSC-related tools and services for data sharing and discoverability as well as discipline-related solutions are invited to the webinar. Attendees will also hear first-hand accounts from early-adopter communities that have integrated some of these core EOSC services. 1 programme, 2 days Check out the agenda to get a glimpse of the cases in the programme, in addition to a at the users, EU and UN officials who will be weighing in on discussions. Day 1 – 5 December • 09.30-11.00: Digital assets supporting SDG 13: Climate action • 11.15-12.00: Digital assets supporting SDG 3: Good health and well-being • 12:15-13:00: Discovering services for open science Day 2 – 6 December • 09.00-09.45: Digital assets supporting SDG 9 Industry, innovation and infrastructure • 10.00-10.45: Experiences from Early Adopters approaching EOSC: the RELIANCE Open challenge • 11.00-12.00: Lessons Learnt from use cases and Looking forward" assertion.
- 50a3686b-e084-445b-b6cc-60b5695e3e70 description "Demonstration given during the webinar. This demo goes with the presentation (slides) and show how the original work was published s a paper in nature communications. The code and data were available and Alejandro Coca-Castro re-used it to create an executable Research Object with a Jupyter Notebook. This Jupyter Notebook examplifies the use of IceNet (probabilistic deep learning to forecast sea-ice). This executable Research Object was forked and deviated work was created e.g. the Jupyter notebook was updated to make it more accessible to people that are not from the Climate community. We use B2DROP to store the new Jupyter notebook and the results to share while doing. Whenever we update the notebook or add figures, the corresponding Research Object is updated live on RoHub. We are now getting close to Open Science e.g. sharing while doing." assertion.
- 5af82a3d-b346-4710-915e-228bbe7de4dd description "Presentation (same as the google doc) but in pdf format." assertion.
- environment.yml description "Conda environment when user want to have the same libraries installed without concerns of package versions" assertion.
- 911b0247-5b28-4993-894e-aff28828e643 description "The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. Modelling approach IceNet is a probabilistic, deep learning sea ice forecasting system. The model, an ensemble of U-Net networks, learns how sea ice changes from climate simulations and observational data to forecast up to 6 months of monthly-averaged sea ice concentration maps at 25 km resolution. IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. IceNet was implemented in Python 3.7 using TensorFlow v2.2.0. Further details can be found in the Nature Communications paper Seasonal Arctic sea ice forecasting with probabilistic deep learning." assertion.
- arctic-shipping-routes-are-expanding-faster-than-predicted description "As the climate warms and sea ice melts, trans-Arctic shipping routes are becoming easier to navigate, a prospect that is enticing to freight companies. These routes can cut up to 9,000 kilometers off a one-way trip between East Asia and Europe compared with shipping through the Suez or Panama Canals—shortcuts that clip roughly 40 percent off the voyage. According to a new study, the reality of routine trans-Arctic trade could come sooner than expected. Using satellite data on daily sea ice between 1979 and 2019, the researchers found that the safe navigation season for open-water vessels in the Arctic—trips that could be embarked upon without the help of icebreakers—is already significantly longer than climate models anticipated. With a few exceptions, most shippers avoid the hostile Arctic Ocean. But according to Kuishuang Feng, an ecological economist at the University of Maryland who worked on the new study, observational data shows that rather than being commercially navigable by the middle of the century, as many climate models predict, several trans-Arctic routes are already navigable for large chunks of the year—and they have been for a while. The team found that open-water ships could have been traveling through the Canadian Arctic Archipelago along the fabled Northwest Passage for more than two months of the year during the 2010s. Captains wanting to travel between the Atlantic and Pacific Oceans along the Norwegian and Russian coasts could have done so for even l" assertion.
- htm description "The ablation of Arctic sea ice makes seasonal navigation possible in the Arctic region, which accounted for the apparent influence of sea ice concentration in the navigation of the Arctic route. This paper uses Arctic sea ice concentration daily data from January 1, 2000, to December 31, 2019. We used a sea ice concentration threshold value of 40% to define the time window for navigating through the Arctic Northeast Passage (NEP). In addition, for the year when the navigation time of the NEP is relatively abnormal, we combined with wind field, temperature, temperature anomaly, sea ice age and sea ice movement data to analyze the sea ice conditions of the NEP and obtain the main factors affecting the navigation of the NEP. The results reveal the following: (1) The sea ice concentration of the NEP varies greatly seasonally. The best month for navigation is September. The opening time of the NEP varies from late July to early September, the end of navigation is concentrated in mid-October, and the navigation time is basically maintained at more than 30 days. (2) The NEP was not navigable in 2000, 2001, 2003 and 2004. The main factors are the high amount of multi-year ice, low temperature and the wind field blowing towards the Vilkitsky Strait and sea ice movement. The navigation time in 2012, 2015 and 2019 was longer, and the driving factors were the high temperature, weak wind and low amount of one-year ice. The navigation time in 2003, 2007 and 2013 was shorter, and the influe" assertion.
- 2022GL099157 description "assessments at high temporal resolution are still very limited. To bridge this gap, daily sea ice concentration and thickness from CMIP6 projections are applied to evaluate the future potential of Arctic shipping under multiple climate scenarios. The September navigable area will continue to increase through the 2050s for open-water (OW) ships and the 2040s for Polar Class 6 (PC6) vessels across all scenarios. Quasi-equilibrium states will then ensue for both OW and PC6 ships under SSP245 and SSP585. The sailing time will be shortened, especially for OW ships, while the navigable days for both types of vessels will increase dramatically. PC6 ships will be able to sail the Arctic shipping routes year-round starting in the 2070s when the decadal-averaged global mean surface temperature anomaly hits approximately +3.6°C (under SSP585) compared to pre-industrial times (1850–1900)." assertion.
- 714c9088-075f-43fb-94e0-b397eb195343 description "Abstract paper-1: The retreat of sea ice has been found to be very significant in the Arctic under global warming. It is projected to continue and will have great impacts on navigation. Perspectives on the changes in sea ice and navigability are crucial to the circulation pattern and future of the Arctic. In this investigation, the decadal changes in sea ice parameters were evaluated by the multi-model from the Coupled Model Inter-comparison Project Phase 6, and Arctic navigability was assessed under two shared socioeconomic pathways (SSPs) and two vessel classes with the Arctic transportation accessibility model. The sea ice extent shows a high possibility of decreasing along SSP5-8.5 under current emissions and climate change. The decadal rate of decreasing sea ice extent will increase in March but decrease in September until 2060, when the oldest ice will have completely disappeared and the sea ice will reach an irreversible tipping point. Sea ice thickness is expected to decrease and transit in certain parts, declining by −0.22 m per decade after September 2060. Both the sea ice concentration and volume will thoroughly decline at decreasing decadal rates, with a greater decrease in volume in March than in September. Open water ships will be able to cross the Northern Sea Route and Northwest Passage between August and October during the period from 2045 to 2055, with a maximum navigable percentage in September. The time for Polar Class 6 (PC6) ships will shift to October–December during the period from 2021 to 2030, with a maximum navigable percentage in October. In addition, the central passage will be open for PC6 ships between September and October during 2021–2030." assertion.