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exploration and production and marine infrastructures (i.e., cable and pipeline laying), and bioprospecting. 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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To start your JupyterLab:\n\ndocker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook\n\nand you will top open a new terminal and start your favorite web browser.\n\nyour running Jupyter Notebook instance on http://localhost:7777/ipython/.\n\nRemark: for reproducibility purpose, we suggest you use a specific tag e.g.\n\ndocker pull quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b\n\nThen use the same tag when starting your JupyterLab application:\n\ndocker run -p 7777:8888 quay.io/nordicesmhub/docker-pangeo-notebook:1c0f66b" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Docker images for Galaxy Pangeo JupyterLab (Quay Container Registry)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-29 11:58:28.213223+00:00" } ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/4415864d-cc85-4cb9-8b9f-39eaf78dc78a", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.5281/zenodo.5805953" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-03-30 16:52:19.796786+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:49:55.345103+00:00" } ], "http://schema.org/description": [ { "@value": "Dataset used in the Galaxy Pangeo tutorials on Xarray.\n\nData is in netCDF format and is from Copernicus Air Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. This dataset is very small and there is no need to parallelize our data analysis. Parallel data analysis with Pangeo is not covered in this tutorial and will make use of another dataset." } ], "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" } ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/56c7c29c-badc-45ad-bb41-75ba492064e2", "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://w3id.org/ro/earth-scienceJupyterNotebook" ], "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" } ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/3d33b1aa-589b-4680-906b-3fc60e9a18fc", "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/Course", "http://schema.org/MediaObject" ], "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" } ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/9663df26-3adb-40ed-b68e-391c2023ec0b", "@type": [ "http://purl.org/wf4ever/roterms#Sketch", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "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. In this video, we pass an input file (this file will be imported in the Jupyter Notebook /import folder)." } ], "http://schema.org/encodingFormat": [ { "@value": "image/gif" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "How to start Galaxy Pangeo JupyterLab (gif animated)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-30 16:44:58.679622+00:00" } ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/eb754e63-6b12-48e7-b68c-b76e9aa7b774", "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/SoftwareApplication" ], "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" } ], 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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 details" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Docker Galaxy pangeo-JupyterLab image Version 1c0f66b" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-03-29 17:55:05.034625+00:00" } ] }, { "@id": "https://w3id.org/ro-id/aae72e1c-6d73-4c25-a565-f855cfb434b6/resources/a3f045c3-42e7-4896-a4b7-bef646dade6b", "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://purl.org/wf4ever/wf4ever#Script", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentSize": [ { "@value": "5306", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/resources/a3f045c3-42e7-4896-a4b7-bef646dade6b/download/" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ 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The presentation and demo were given during the pan-europeans digital assets supporting research communities.\n\nAgenda of the event:\nOn 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.\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. Attendees will also hear first-hand accounts from early-adopter communities that have\nintegrated some of these core EOSC services.\n\n1 programme, 2 days\n\nCheck out the agenda to get a glimpse of the cases in the programme, in addition to a at the users,\nEU and UN officials who will be weighing in on discussions.\n\nDay 1 – 5 December\n• 09.30-11.00: Digital assets supporting SDG 13: Climate action\n• 11.15-12.00: Digital assets supporting SDG 3: Good health and well-being\n• 12:15-13:00: Discovering services for open science\n\nDay 2 – 6 December\n• 09.00-09.45: Digital assets supporting SDG 9 Industry, innovation and infrastructure\n• 10.00-10.45: Experiences from Early Adopters approaching EOSC: the RELIANCE Open\nchallenge\n• 11.00-12.00: Lessons Learnt from use cases and Looking forward" } ], "http://schema.org/encodingFormat": [ { "@value": "application/ld+json" } ], "http://schema.org/hasPart": [ { "@id": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/resources/35f2e92c-4366-4f23-bd50-2080645e81dd" }, { "@id": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/resources/584a2c4a-1226-4298-b3ea-12d8b7f05b19" }, { "@id": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/resources/66399eea-b67a-4dca-b280-4408ec6aeb3d" }, { "@id": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/resources/768ea044-4432-4771-8cab-5e18c98031fd" }, { "@id": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/resources/8771e967-1344-4704-89e5-50fddc940f7f" }, { "@id": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/resources/b8798992-775a-42c7-a9b1-c7d10905e0ab" }, { "@id": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/resources/cd28444b-2473-4afa-9c9f-367c3b86c82a" }, { "@id": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/resources/e29d96df-b801-495a-997e-2e0fe9c339e9" } ], "http://schema.org/identifier": [ { "@value": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8" } ], "http://schema.org/keywords": [ { "@value": "deep learning" }, { "@value": "forecasting" }, { "@value": "sea-ice" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/mainEntity": [ { "@value": "Presentation" } ], "http://schema.org/name": [ { "@value": "Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC - snapshot" }, { "@value": "Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC" } ], "http://schema.org/publisher": [ { "@id": "https://ror.org/00vn06n10" } ], "http://w3id.org/ro-id/rohub/model#creation_mode": [ { "@value": "MANUAL" } ], "http://www.opengis.net/ont/geosparql#hasGeometry": [ { "@value": "https://w3id.org/ro-id/107487d2-a9d5-4224-8b00-b321e133b6c8/1c144684-1ea3-4cb5-93e6-8cffecf3b849" } ], "https://www.w3.org/ns/iana/link-relations/relation#cite-as": [ { "@value": "Anne Foilloux, Jean Iaquinta, and Alejandro Coca-Castro. \"Pan-European digital assets supporting research communities – Climate Change with RELIANCE & EOSC.\" ROHub. 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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. 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Stripes\n\n**Execution Status:** scheduled\n\n**Executed:** 2025-05-24 11:15:41.585048\n\n\n## Workflow Inputs\n\n### Formal Input Definitions\n\n- **ts_cities.csv** (File)\n\n### Actual Input Files Used\n\n- **ts_cities.csv**\n - Format: `text/plain`\n - Path: `datasets/ts_cities.csv_31e7840b5aedca433fb349714141a239.tabular`\n\n\n## Workflow Parameters\n\n- **input:**\n - __class__: `NoReplacement`\n\n- **adv:**\n - colormap: `RdBu_r`\n - format_date: ``\n - format_plot: ``\n - nxsplit: `None`\n - xname: ``\n\n- **ifilename:**\n - __class__: `ConnectedValue`\n\n- **title:** `My ScienceLive Stripes`\n\n- **variable:** `tg_anomalies_freiburg`\n\n\n## Workflow Outputs\n\n### Formal Output Definitions\n\n- **stripes.png** (File)\n\n### Actual Output Files Generated\n\n- **stripes.png**\n - Format: `application/octet-stream`\n - Path: `datasets/stripes.png_31e7840b5aedca43c0a4f330c3d24460.png`\n\n\n## Rerun Template\n\nTo rerun this workflow:\n\n1. **Workflow:** Climate Stripes\n\n2. 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"@id": "https://doi.org/10.1525/collabra.35903", "@type": [ "http://purl.org/wf4ever/roterms#Paper", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.1525/collabra.35903" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-10-14 12:43:21.299002+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:45:14.242980+00:00" } ], "http://schema.org/description": [ { "@value": "This paper is from Gisela H. 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. While many building blocks are already at hand, systemic changes are necessary in order to create academic environments that support open science practices and encourage scientists from all fields to become more carbon-conscious, ultimately contributing to a sustainable future." } ], "http://schema.org/keywords": [ { "@value": "climate crisis" }, { "@value": "open science" }, { "@value": "sustainability" } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "The Sustainability Argument for Open Science" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-10-14 12:43:21.299002+00:00" } ] }, { "@id": "https://orcid.org/0000-0002-1784-2920", "@type": [ "http://xmlns.com/foaf/0.1/Agent" ], "http://schema.org/affiliation": [ { "@value": "Simula Research Laboratory" } ], "http://schema.org/email": [ { "@value": "annef@simula.no" } ], "http://schema.org/name": [ { "@value": "Anne Fouilloux" } ], "https://w3id.org/dingo#orcid": [ { "@value": "0000-0002-1784-2920" } ] }, { "@id": "https://doi.org/10.5281/zenodo.6589624", "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/PresentationDigitalDocument" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://doi.org/10.5281/zenodo.6589624" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2022-10-14 13:44:35.094465+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2023-02-19 13:45:14.339103+00:00" } ], "http://schema.org/description": [ { "@value": "Sharan, Malvika\n\nIn this talk, I discuss open science as a framework to ensure that all our research components can be easily accessed, openly examined and built upon by others. I will introduce The Turing Way - an open source, open collaboration and community-driven guide to reproducible, ethical and inclusive data science and research. Drawing insights from the project, I will share best practices that researchers should integrate to ensure the highest reproducible and ethical standards from the start of their projects so that their research work is easy to reuse and reproduce at all stages of the development. All attendees will leave the talk understanding the many dimensions of openness and how they can participate in an inclusive, kind and inspiring open source ecosystem as they collaboratively seek to improve research culture. All questions and contributions are welcome at the GitHub repository: https://github.com/alan-turing-institute/the-turing-way.\n\nHome page: https://malvikasharan.github.io/\n\nThis was a closing keynote at Concordia University in Montreal on 27 May 2022." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Open science for enabling reproducible, ethical and collaborative research: Insights from The Turing Way" } ], "http://schema.org/sdDatePublished": [ { "@value": "2022-10-14 13:44:35.094465+00:00" } ] }, { "@id": "https://en.wikipedia.org/wiki/Climate_justice", "@type": [ "http://purl.org/wf4ever/wf4ever#Document", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl": [ { "@value": "https://en.wikipedia.org/wiki/Climate_justice" } ], "http://schema.org/creator": 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