Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }
- 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.
- 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.
- 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.
- 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.
- 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.
- 09542d1a-e27b-4bb2-b1d5-cebba0f60ecd description "Discussion and concluding remarks The navigable window for OW ships and PC6 ships along the NSR were investigated in our previous work (Chen et al., 2020), but it is insufficient to evaluate Arctic navigability by a single climate model, even with a high resolution. This study serves as a reference for future changes in sea ice and navigability in the Arctic, including NSR, NWP, and central passage. However, the uncertainty of the models might have affected the results and their reliability in this research. Approximated physical processes and unreal parameters in models are inevitable problems in the geosciences. Differences still existed even when the models were filtered by comparing the historical simulations with the observations of sea ice extent. The abnormal decrease in navigable area at high latitudes (80–90∘ N) in September might be an example. This is against conventional wisdom, but it could be true. The uncertainty of the models is expected to decrease in future prospective research. Different ice types do make a big difference to ship navigability. For example, for the same sea ice thickness (SIT) ⋅ sea ice concentration (SIC) (e.g., SIT ⋅ SIC = 0.3), pack ice (say SIT = 0.6 m thick and SIC = 50 %) has a high degree of freedom that level ice (say SIT = 0.3 m and SIC = 100 %) does not have. Thus, ships are easier to navigate in broken ice floes (Huang et al., 2020b). ATAM is unable to clearly distinguish ice types at first, and this might be a future direction." assertion.
- 52876829-bc09-44c5-954c-c0b1e23c9da0 description "Photo by NOAA on Unsplash" assertion.
- 91431acd-954a-4c9a-ac2f-a9a87e17720d description "Abstract: Antarctica is a largely inhospitable and inaccessible continent that plays a key role in regulating the climate through ocean currents, winds, icebergs drift, and sea-ice concentration and thickness. The study area of this work corresponds to the Weddell Sea, Bellingshausen Sea and the South Atlantic Ocean. These areas are relevant because of supply operations to Antarctic stations and scientific and tourist activities. The Antarctic Peninsula is the most visited region of the continent for tourist and research vessels and requires special efforts in the development and dissemination of updated ice information for Safety of Navigation. For this purpose, it is critical to have information that discriminates the origin of the ice from land and open water, sea-ice concentration, and stage of development. The high recurrence of cloud cover over the Antarctic Peninsula during the summer hinders the operational use of visible/infra red satellite imagery, therefore access to Synthetic Aperture Radar (SAR) sensors is considered to be a high priority. Between 2018 and 2020, with the launch of the SAOCOM (Satélite Argentino de Observación Con Microondas) constellation, Argentina has evidenced an increase in the availability of SAR images for sea-ice operations. This paper presents the current state of routine production of operational ice charts at the Argentine Naval Hydrographic Service for mariners in the vicinity of the Antarctic Peninsula and South Atlantic Ocean and d" assertion.
- j.marpol.2015.12.027 description "Abstract The rapid Arctic summer sea ice reduction in the last decade has lead to debates in the maritime industries on the possibility of an increase in cargo transportation in the region. Average sailing times on the North Sea Route along the Siberian Coast have fallen from 20 days in the 1990s to 11 days in 2012–2013, attributed to easing sea ice conditions along the Siberian coast. However, the economic risk of exploiting the Arctic shipping routes is substantial. Here a detailed high-resolution projection of ocean and sea ice to the end of the 21st century forced with the RCP8.5 IPCC emission scenario is used to examine navigability of the Arctic sea routes. In summer, opening of large areas of the Arctic Ocean previously covered by pack ice to the wind and surface waves leads to Arctic pack ice cover evolving into the Marginal Ice Zone. The emerging state of the Arctic Ocean features more fragmented thinner sea ice, stronger winds, ocean currents and waves. By the mid 21st century, summer season sailing times along the route via the North Pole are estimated to be 13–17 days, which could make this route as fast as the North Sea Route." 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 "Bibliographic Research Object created by Jean Iaquinta on sea-ice forecasting and navigability in the Arctic." assertion.
- df6591e6-c326-4d28-92fb-cb9d59786ac7 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.
- 32e083d2-895a-4261-9f46-58b02a560519 description "Image showing interactive plot of IceNet seasonal forecasts of Artic sea ice according to four lead times and months in 2020" assertion.
- 4c314f75-9979-4a66-a473-a999880d6347 description "Seasonal sea ice forecast for September 2020 (leadtime=3)" assertion.
- a8c3bc47-707e-4305-8dad-9ca3cbdbb0cc description "This Jupyter notebook is the one used for developing while the GitHub repository may contain a slightly older version (but working version e.g. fully tested). This notebook is shared while working on it and may contain errors." 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.
- polar-modelling-icenet.ipynb description "Jupyter Notebook hosted by the Environmental Data Science Book and exemplifying the use of IceNet, a probabilistic deep learning algorithm to compute seasonal sea-ice forecasts over the Arctic." 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.
- c0188f88-4379-491b-8c04-56ae1b7d62d1 description "Assessing the impact of the Covid-19 Lockdown on Air and Water quality in the Venice Lagoon" assertion.
- f4ee220b-ce4f-4ccc-bb73-f5a1c8b626a2 description "Environment requirements for virtual machine" assertion.
- hal-03789563 description "Working paper or preprint, September 2022." assertion.
- mesh16.h5 description "mes16.h5" assertion.
- mesh32.h5 description "mesh32.h5" assertion.
- e7f90fc2-ddcb-4369-9f31-b81123b40533 description "This Research Object contains links to the paper, source code, etc. for the simulations presented in the paper "Multi-compartmental model of glymphatic clearance of solutes in brain tissue" by Poulain, Riseth and Vinje. For now it contains only a minimal working example for running the simulations, but will be reorganized to be more accessible and more easily extended in the near future. Summary of the paper: the Glymphatic system is the subject of numerous pieces of research in Biology. Mathematical modeling plays a considerable role in this field since it can indicate the possible physical effects in this system and validate the biologists' hypotheses. The available mathematical models that describe the system at the scale of the brain (i.e. the macroscopic scale) are often solely based on the diffusion equation and do not consider the fine structures formed by the perivascular spaces. We therefore propose a mathematical model representing the time and space evolution of a mixture flowing through multiple compartments of the brain. We adopt a macroscopic point of view in which the compartments are all present at any point in space. The equations system is composed of two coupled equations for each compartment: One equation for the pressure of a fluid and one for the mass concentration of a molecule. The fluid and solute can move from one compartment to another according to certain membrane conditions modeled by transfer functions. We propose to apply this new modeling framework to the clearance of 14 C-inulin from the rat brain." assertion.
- bf3f9822-8b40-412f-bbf7-ef8af105d2b6 description "Sketch showing the JupyterBook generated from the main Jupyter notebook." assertion.
- environment.yml description "Conda environment for executing Jupyter Notebooks from this Research Object." assertion.
- main_notebook.ipynb description "Jupyter Notebook showing the findings presented in the paper." assertion.
- 6849c036-dd01-4741-89f9-859c0869deb4 description "This document describes the planning phase of tests to characterise the maneuverability of the SWAMP ASV vehicle, executing maneuvers derived from ITTC standards tipically used for ships." assertion.
- 7ba4c887-20b4-49ee-8462-9dcd40a44663 description "This technical report describes the maneuverability test plan for the autonomous surface vehicle SWAMP (Shallow Water Autonomous Multipurpose Platform), adapted from the standard maneuverability tests (IMO and ITTC)." assertion.
- 2f126bfc-d4fb-4d72-914f-bfd121b0cf35 description "This dataset represents the monthly level of tourism arrivals and overnight-stays in Venice city centre, Italy." assertion.
- 4c5e8340-5b09-45b8-bbc3-38f3209a4e9b description "Tourism Arrivals and overnight-stays in Venice - monthly level 2017-2021" assertion.
- 6387 description "" assertion.
- 6387 description "" assertion.
- answering-research-questions-with-eosc 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.
- e4bff7dc-eecc-4a4e-b32c-1c8afe18deb6 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.
- 34f5e089-6750-4384-a856-0e953fad8c80 description "Presentation given at the French Institute on Thursday 1st December 2022." assertion.
- 5b55a0b1-1ceb-4820-aa4d-e304a88389c6 description "1st slide of Anne Fouilloux's presentation." assertion.
- d93553c4-2435-46ff-a858-1e6afa5153ab description "Slides for Anne Fouilloux's presentation at UiO Data Manager Meeting on EOSC in practise." assertion.
- dc0ead47-1b4b-4a63-ac04-da0580f77d8e description "Timeline with Anne Fouilloux's EOSC journey." assertion.
- tz0OqxHvnbw 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.
- 44961612 description "Biography .pdf files" assertion.
- 28499bdf-a0c6-46aa-a96f-50bd9490b8be description "Estimating the penetration of light along the water column from satellite data to map the photic zone in the Mediterranean Sea" assertion.
- 4c253f5a-d427-40c2-9e9f-6063ae087239 description "Amount of light reaching the seabed according to the model in Castellan et al. 2022" assertion.
- b5091d22-9207-4a6e-a04d-b25e3cec6aee description "Requirements to run the Jupyter Notebook" assertion.
- c3f02a4d-eb38-4d3a-bef2-4563dc2cc16c description "Jupyter Notebook reporting the model to estimate the amount of light at seabed" assertion.
- s41598-022-09413-4 description "Castellan, G., Angeletti, L., Montagna, P. et al. Drawing the borders of the mesophotic zone of the Mediterranean Sea using satellite data. Sci Rep 12, 5585 (2022)." assertion.
- 3292 description "" assertion.
- c85584de-179e-4018-aacd-e7a01072cf0d description "Area under mesophotic light conditions in the Mediterranean Sea" assertion.
- global_metadata.html description "github create global metadata and attributes for robotic variables" assertion.
- c39ea824-7501-4223-96a0-da72ae57e5e3 description "The process of creating a FAIR dataset when dealing with novel robotic platform is not standardised. This RO includes the informal description of the process and the links to the resources to create a FAIR dataset when starting from a raw dataset collected with unconventional platforms. The project is scalable to all those experimental data collected with new marine robots, nevertheless can be applied to all the datasets that sit in forgotten drawers. This process wishes to encourage FAIRness in data and when possible data sharing. Robotics can now push the boundaries of observational marine sciences, environmental interventions, coastal and offshore monitoring. Therefore, it is paramount to lay out a data policy capable of enhance data visibility and accessibility. Hopefully this will sound as an invitation to oceanographers and robotics to work together to improve Earth observations and experiment repeatability" assertion.
- S0048969721020210 description "The UNESCO World Heritage site “Venice and its Lagoon”, is one of the top tourist destinations in the world. Since there is a dearth in the literature regarding microplastic leachable compounds and overtourism-related pollutants, the project studied the Head Space-Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC–MS) molecular fingerprint of volatile lagoon water pollutants, to gain insight into the extent of this phenomenon in August 2019. The chromatographic analyses enabled the identification of 40 analytes related to the presence of polymers in seawater, water traffic, and tourists habits. In Italy, on the 10th March 2020, the lockdown restrictions were enforced to control the spread of the SARS-CoV-2 infection; the ordinary urban water traffic around Venice came to a halt, and the ever-growing presence of tourists suddenly ceased. This situation provided a unique opportunity to analyze the environmental effects of restrictions on VOCs load in the Lagoon. 17 contaminants became not detectable after the lockdown period. The statistical analysis indicated that the amounts of many other contaminants significantly dropped. The presence of 9 analytes was not statistically influenced by the lockdown restrictions, probably because of their stronger persistence or continuous input in the environment from diverse sources. Results signify a sharp and encouraging pollution decrease at the molecular level, concomitant with the anthropogenic stress release." assertion.
- b3dd84e2-9a82-4364-a030-7b8a4269744d description "The RO focuses on the monitoring of the stranded and floating marco-litter pollution in the World Heritage Site of the Venice lagoon. The data also consider the covid-19 lockdown period." assertion.
- 0a6fc07f-e7d3-4617-8023-e341ec6a02fb description "Map of the itinerary" assertion.
- 488ab8f9-3af6-4838-83e5-e7576b76af12 description "Marine litter data collected by Legambiente in the lagoon of Venice - year 2016" assertion.
- 5d1adedf-6a03-40e9-866c-aeec4c705944 description "Marine litter data collected by Venice Lagoon Plastic Free in the lagoon of Venice - year 2020" assertion.
- 7e81b980-c241-485b-aa83-1ce1e721b2c9 description "Stranded litter monitoring campaign in Venice" assertion.