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To this, is added the pervasive and growing effects of human-induced perturbations of the climate system. International frameworks provide foundations for the protection of deep-sea ecosystems, but the lack of standardized criteria for the identification of areas deserving protection, insufficient legislative instruments and poor implementation hinder an efficient set up in practical terms. Here, we discuss the international legal frameworks and management measures in relation to the status of habitats and key species in the deep Mediterranean Basin. By comparing the results of a multi-criteria decision analysis (MCDA) and of expert evaluation (EE), we identify priority deep-sea areas for conservation and select five criteria for the designation of future protected areas in the deep Mediterranean Sea. Our results indicate that areas (1) with high ecological relevance (e.g., hosting endemic and locally endangered species and rare habitats),(2) ensuring shelf-slope connectivity (e.g., submarine canyons), and (3) subject to current and foreseeable intense anthropogenic impacts, should be prioritized for conservation. 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In order to obtain the concept id of the term that is going to be matched against the gene list, the workflow Get concept suggestions from term, needs to run first. matchConceptProfileList: the gene list we want to match (order) against a particular concept queryConceptProfileList: the concept (or gene list) we want to match the query against" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Prioritize gene list related to a concept /list of concepts" } ], "http://schema.org/sdDatePublished": [ { "@value": "2014-02-25 16:12:14.228000+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://bioschemas.org/ComputationalWorkflow" ] }, { "@id": "https://w3id.org/ro-id/d1b1d427-2332-428e-a205-726ac7a0e951/resources/7aa50a5a-bc9a-4639-9e04-1fecc9bb5002", "http://schema.org/author": [ { "@id": "https://www.google.com/accounts/o8/id?id=AItOawlLcpRhy-5MtgIVFxuwLWcFFys5ZTC7w2c" } ], "http://schema.org/contentSize": [ { "@value": "203555", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/resources/7aa50a5a-bc9a-4639-9e04-1fecc9bb5002/download/" } ], "http://schema.org/creator": [ { "@id": "https://www.google.com/accounts/o8/id?id=AItOawlLcpRhy-5MtgIVFxuwLWcFFys5ZTC7w2c" } ], "http://schema.org/dateCreated": [ { "@value": "2014-02-25 16:06:54.756000+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-25 09:07:55.632943+00:00" } ], "http://schema.org/description": [ { "@value": "This workflow annotates a comma separated gene list with a predefined concept set as for example Biological processes or Disease/syndrome. To obtain the particular id for each concept set (e.g. \"5\" for Biological processes), the workflow listPredefinedConceptSets needs to run first. The workflow is using the anni web services" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Annotate a gene list with Biological processes" } ], "http://schema.org/sdDatePublished": [ { "@value": "2014-02-25 16:06:54.756000+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://bioschemas.org/ComputationalWorkflow" ] }, { "@id": "https://w3id.org/ro-id/d1b1d427-2332-428e-a205-726ac7a0e951/resources/99ba79b8-8172-4026-93b2-affd7320122f", "http://schema.org/author": [ { "@id": "https://www.google.com/accounts/o8/id?id=AItOawlLcpRhy-5MtgIVFxuwLWcFFys5ZTC7w2c" } ], "http://schema.org/contentSize": [ { "@value": "69369", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/resources/99ba79b8-8172-4026-93b2-affd7320122f/download/" } ], "http://schema.org/creator": [ { "@id": "https://www.google.com/accounts/o8/id?id=AItOawlLcpRhy-5MtgIVFxuwLWcFFys5ZTC7w2c" } ], "http://schema.org/dateCreated": [ { "@value": "2014-02-26 13:14:06.663000+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-25 09:07:56.525975+00:00" } ], "http://schema.org/description": [ { "@value": "This workflow takes two concept ids as input and returns the top ranking \"B\" concepts according to Swanson's ABC model of discovery, where the relationships AB and BC are known and reported in the literature, and the implicit relationship AC is a putative new discovery. It might also be the case that AC is already known. In that case AC does not represent a new discovery but will still be returned (see workflow example values). 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The user can run this workflow with any term of interest as for example \"human\", \"htt\", \"Transcription\" etc, and will get suggestions for concept ids together with descriptions. 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The workflow is using the anni web services" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Annotate a gene list with Biological processes" } ], "http://schema.org/sdDatePublished": [ { "@value": "2013-11-05 13:07:17.887000+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://bioschemas.org/ComputationalWorkflow" ] }, { "@id": "https://w3id.org/ro-id/95a9036b-bee9-48fb-81eb-7e9a90014293/resources/a8461986-76a4-49b2-8153-4c899df13854", "http://schema.org/author": [ { "@id": "https://www.google.com/accounts/o8/id?id=AItOawnOAeiyuU0cZ91YBD1EW7d43AlWw8xdALU" } ], "http://schema.org/contentSize": [ { "@value": "188023", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/resources/a8461986-76a4-49b2-8153-4c899df13854/download/" } ], "http://schema.org/creator": [ { "@id": "https://www.google.com/accounts/o8/id?id=AItOawnOAeiyuU0cZ91YBD1EW7d43AlWw8xdALU" } ], "http://schema.org/dateCreated": [ { "@value": "2013-11-05 13:07:43.493000+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-25 09:27:04.663380+00:00" } ], "http://schema.org/description": [ { "@value": "This workflow can prioritize genes that are related to a specific concept, e.g. HTT. 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[ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.5281/zenodo.4543739" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-28 14:18:39.324751+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-29 18:08:07.800368+00:00" } ], "http://schema.org/description": [ { "@value": "By deploying JupyterLab with PANGEO, CESM and ESMValTool conda environments as a new Climate Galaxy interactive tool, we are aiming at bridging the gap between climate scientists and non-climate specialists.\n\nGalaxy is an open, web-based platform for accessible, reproducible, and transparent computational research. One of the strength of Galaxy is that it does not require programming experience and allow researchers to easily upload data, run complex tools and workflows in a reproducible manner, and visualize results. Galaxy Climate is quite new and aims at offering tools to everyone interested in Climate Science so that they can analyse and visualize climate data produced by climate scientists. However, climate scientists and in particular climate modellers have very different working practices: they often like to use command lines for running climate models and thanks to the PANGEO community (a community platform for Big Data geoscience) the Jupyter ecosystem has become very popular with several deployments of JupyterHubs dedicated to climate data analysis. By deploying JupyterLab with PANGEO, CESM and ESMValTool conda environments as a new Climate Galaxy interactive tool (https://live.usegalaxy.eu/?tool_id=interactive_tool_climate_notebook), we are aiming at bridging the gap between climate scientists and non-climate specialists. On this poster, we will show typical use cases both for research (https://nordicesmhub.github.io/eosc-nordic-climate-demonstrator/02-use-cases/) and for teaching (https://nordicesmhub.github.io/NEGI-Abisko-2019/intro)." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Climate JupyterLab as an interactive tool in Galaxy" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-28 14:18:39.324751+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/PresentationDigitalDocument" ] }, { "@id": "https://doi.org/10.5281/zenodo.6394185", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.5281/zenodo.6394185" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-29 17:55:05.034625+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-29 18:08:05.535928+00:00" } ], "http://schema.org/description": [ { "@value": "This is a tarball for the Docker climate-JupyterLab image - Version 2021-03-18.\n\nTo use it:\n\ndownload the image file docker-climate-notebook-2021-03-18.tar\nload it with docker with the command: docker load --input docker-climate-notebook-2021-03-18.tar\nlaunch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-climate-notebook:2021-03-18\nstart your favorite web browser and go to: http://localhost:7777/ipython/\nSee https://github.com/NordicESMhub/docker-climate-notebook for more details" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Docker climate-JupyterLab image Version 2021-03-18" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-29 17:55:05.034625+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/SoftwareApplication" ] }, { "@id": "https://github.com/NordicESMhub/docker-climate-notebook", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://github.com/NordicESMhub/docker-climate-notebook" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-29 12:01:31.834492+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-29 18:08:06.488065+00:00" } ], "http://schema.org/description": [ { "@value": "This github repository contains all the sources required for building the docker containers that are made available in Quay Container Registry." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Source code for building the docker container (github repository)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-29 12:01:31.834492+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/SoftwareSourceCode" ] }, { "@id": "https://jupyterlab.readthedocs.io/en/stable/", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://jupyterlab.readthedocs.io/en/stable/" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-28 14:14:45.648769+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-29 18:08:02.576584+00:00" } ], "http://schema.org/description": [ { "@value": "Link to the online JupyterLab documentation." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "JupyterLab Documentation" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-28 14:14:45.648769+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/SoftwareApplication" ] }, { "@id": "https://orcid.org/0000-0002-3079-6586", "http://schema.org/affiliation": [ { "@value": "University of Freiburg, Freiburg (Germany)" } ], "http://schema.org/email": [ { "@value": "bjoern.gruening@gmail.com" } ], "http://schema.org/name": [ { "@value": "Björn Grüning" } ], "@type": [ "http://xmlns.com/foaf/0.1/Agent" ], "https://w3id.org/dingo#orcid": [ { "@value": "0000-0002-3079-6586" } ] }, { "@id": "https://quay.io/repository/nordicesmhub/docker-climate-notebook", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://quay.io/repository/nordicesmhub/docker-climate-notebook" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-29 11:58:28.213223+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2022-03-29 18:08:06.281346+00:00" } ], "http://schema.org/description": [ { "@value": "These docker images (different tags) correspond to the docker images built for Galaxy Climate JupyterLab.\nThe docker images can be used within Galaxy and as standalone docker images.\nYou can use the same images we use in Galaxy on your local computer or any other platform:\n\n1. 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Check out the agenda and register for this online interactive event by 4 December, 23.59 CET.\n\nOver 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):\n\n• Climate action (SDG 13)\n• Industry, Innovation & infrastructure (SDG 9)\n• Good health & well-being (SDG 3)\n\nThere will also be a session with use cases related to Open Science more broadly.\n\nWhy ‘use cases’?\n\nThe demonstrative, first-hand format of the event will enable real research communities to show how\ntheir work can be leveraged by EOSC.\n\nResearchers, disciplinary groups and anyone interested to learn about both EOSC-related tools and\nservices for data sharing and discoverability as well as discipline-related solutions are invited to the\nwebinar. 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For instance, being able to organize meetings/hackathons (online or face to face) with both experts from NorESM and EC-EARTH has been highlighted as an important requirements by those involved in the GPU hackathon.\nCode refactoring and best software practices are the most important component for efficient usage of new architecture, including EuroHPC." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "NICEST2 - D4.5: First report on the identified bottlenecks for an efficient usage of Nordic ESMs on EuroHPC" } ], "http://schema.org/sdDatePublished": [ { "@value": "2023-01-08 21:06:32.125399+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://w3id.org/ro/terms/earth-science#Deliverable" ] }, { "@id": "https://doi.org/10.5281/zenodo.4944686", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.5281/zenodo.4944686" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2023-01-08 21:03:28.636733+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:17:22.369015+00:00" } ], "http://schema.org/description": [ { "@value": "This report summarizes the first NICEST2 hackathon with FAIR experts and Earth System Model specialists to understand what needs to be done to make climate data FAIR. It will help us to define our roadmap for FAIR Climate in the Nordics." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "NICEST2 - D3.3: Report on NICEST2 FAIR climate data hackathon" } ], "http://schema.org/sdDatePublished": [ { "@value": "2023-01-08 21:03:28.636733+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://w3id.org/ro/terms/earth-science#Deliverable" ] }, { "@id": "https://doi.org/10.5281/zenodo.5571344", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.5281/zenodo.5571344" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2023-01-08 21:08:01.968412+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:17:27.394767+00:00" } ], "http://schema.org/description": [ { "@value": "The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system Models (ESMs) that is not widely used in the Nordics yet. A hackathon/workshop was held on March 12, 2021 as a joint event between the INES, NICEST2 and IS-ENES3 projects. During this hackathon, we identified the needs for specific diagnostics for the Nordics that could help researchers to diagnose strengths and deficiencies of current ESMs. We also discussed how to better organize access to data and share resources within the Nordics." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "NICEST2 - D2.1: Short report from the Nordic ESM diagnostics hackathon" } ], "http://schema.org/sdDatePublished": [ { "@value": "2023-01-08 21:08:01.968412+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://w3id.org/ro/terms/earth-science#Deliverable" ] }, { "@id": "https://doi.org/10.5281/zenodo.5571416", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.5281/zenodo.5571416" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2023-01-08 21:10:14.737755+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:17:27.065885+00:00" } ], "http://schema.org/description": [ { "@value": "The Nordic climate modeling community consists of research groups at universities, national meteorological institutes and research institutes, and holds demonstrable world class excellence in the field. Several of these groups contribute to the development of both global and regional climate models (GCMs and RCMs,respectively) in international projects with collaborations within Europe and the US. However, many users, including PhDs and postdocs, are developing and/or running Earth System Models (ESMs) for more fundamental scientific research and sensitivity studies, and/or cross-disciplinary research (economy & climate, biodiversity, etc.) and they do not always benefit from the advances, technologies or resources leveraged by these large projects/consortiums. One concrete example is IS-ENES project (https://is.enes.org/) where only one university, namely Linköpings Universitet (not part of the NICEST2 consortium) from the Nordics is involved; Nordic contributions are mostly from Meteorological services and Research Institutes. From a practical point of view this translates in a lot of time/energy wasted “reinventing the wheel”, repeated simulations, lack of transparency, suboptimal use of the infrastructures, etc. In this context, supporting these researchers and realizing the benefits of Open Science and EOSC are our priorities within NICEST2 and WP4." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "NICEST2 - D4.1: Identification of the Nordic ESM community needs for ESM workflows" } ], "http://schema.org/sdDatePublished": [ { "@value": "2023-01-08 21:10:14.737755+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://w3id.org/ro/terms/earth-science#Deliverable" ] }, { "@id": "https://nordicesmhub.github.io/NorESM_user_workshop_2021/intro.html", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://nordicesmhub.github.io/NorESM_user_workshop_2021/intro.html" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2023-01-08 20:50:23.537300+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:17:27.609612+00:00" } ], "http://schema.org/description": [ { "@value": "Training material on containers for ESM. 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Govaart, Simon M. Hofmann, Evelyn Medawar.\n\nEver-increasing anthropogenic greenhouse gas emissions narrow the timeframe for humanity to mitigate the climate crisis. Scientific research activities are resource demanding and, consequently, contribute to climate change; at the same time, scientists have a central role in advancing knowledge, also on climate-related topics. In this opinion piece, we discuss (1) how open science – adopted on an individual as well as on a systemic level – can contribute to making research more environmentally friendly, and (2) how open science practices can make research activities more efficient and thereby foster scientific progress and solutions to the climate crisis. 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{ "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/3e989ea3-cba3-49b5-b355-8c20a5e46dfb", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://quay.io/repository/nordicesmhub/docker-pangeo-notebook" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-29 11:58:28.213223+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:49:55.588074+00:00" } ], "http://schema.org/description": [ { "@value": "These docker images (different tags) correspond to the docker images built for Galaxy Pangeo JupyterLab.\nThe docker images can be used within Galaxy and as standalone docker images.\nYou can use the same images we use in Galaxy on your local computer or any other platform:\n\n1. Pull an existing image locally\n\ndocker pull quay.io/nordicesmhub/docker-pangeo-notebook\n\n2. Run a pre-build image from docker registry\n\n3. 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This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "netCDF input file PM2.5 4 days forecast from December, 22 2020" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-30 16:52:19.796786+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/56c7c29c-badc-45ad-bb41-75ba492064e2", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentSize": [ { "@value": "1729", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/resources/56c7c29c-badc-45ad-bb41-75ba492064e2/download/" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-29 12:08:38.781608+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:49:59.656812+00:00" } ], "http://schema.org/description": [ { "@value": "Default Jupyter Notebook used when starting Galaxy Climate JupyterLab if no other Jupyter Notebook is passed by the user." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Default Jupyter Notebook for Galaxy Climate JupyterLab" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-29 12:08:38.781608+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://w3id.org/ro/earth-scienceJupyterNotebook" ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/3d33b1aa-589b-4680-906b-3fc60e9a18fc", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://training.galaxyproject.org/training-material/topics/climate/tutorials/pangeo-notebook/tutorial.html" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-30 15:59:56.246391+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:49:56.427998+00:00" } ], "http://schema.org/description": [ { "@value": "Training material (hands-on) where Pangeo Notebook is used to learn Xarray. This training is part of the Galaxy Training Network (GTN).\n\nIn this tutorial, we will learn about Xarray, one of the most used Python library from the Pangeo ecosystem.\n\nWe will be using data from Copernicus Atmosphere Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. Parallel data analysis with Pangeo is not covered in this tutorial." } ], "http://schema.org/encodingFormat": [ { "@value": "text/html" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Pangeo Notebook in Galaxy - Introduction to Xarray (GTN)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-30 15:59:56.246391+00:00" } ], "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/Course", "http://schema.org/MediaObject" ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/9663df26-3adb-40ed-b68e-391c2023ec0b", "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentSize": [ { "@value": "29899819", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/resources/9663df26-3adb-40ed-b68e-391c2023ec0b/download/" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-30 16:44:58.679622+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:50:02.589260+00:00" } ], "http://schema.org/description": [ { "@value": "This is a gif animated image showing how to start the Galaxy Pangeo JupyterLab in Galaxy Europe. 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"https://doi.org/10.5281/zenodo.6399102" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-29 17:55:05.034625+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:49:56.124648+00:00" } ], "http://schema.org/description": [ { "@value": "This is a tarball for the Docker Galaxy pangeo-JupyterLab image - Version 1c0f66b.\n\nTo use it:\n\ndownload the image file docker-pangeo-notebook-1c0f66b.tar\nload it with docker with the command: docker load --input docker-pangeo-notebook-1c0f66b.tar\nlaunch the Docker container binding of your data folder (on the local machine) with the /import folder i(inside the container) with the command: docker run -v my_data_folder:/import -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b\nstart your favorite web browser and go to: http://localhost:7777/ipython/\nSee https://github.com/NordicESMhub/docker-pangeo-notebook for more 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