Nanopublications

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f18af8df-aeb1-4c4e-9334-da5557f6b4ea description "The Jupyter notebook used to post process SPLs data and to create graphs/tables." 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.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.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.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.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.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.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.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.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.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.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.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.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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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-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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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