Anne Claire Fouilloux

• Fouilloux, Anne Claire (2020). EOSC-Nordic & NICEST2 NeIC projects within INES context.
• Fouilloux, Anne Claire (2020). Getting your hands-on climate data (Galaxy Training Materials).. [www ]. Show summary

  The e-learning aims at familiarzing you with Climate Science and the terminology used by climate scientists. The target audience is not a climate scientist but anyone interested in learning about climate.

• Fouilloux, Anne Claire (2020). K3D Jupyter notebook extension for 3D visualization..
• Fouilloux, Anne Claire (2020). Visualize Climate data with Panoply netCDF viewer. [www ]. Show summary

  The practical aims at familiarzing you with the Panoply Galaxy interactive environment. Panoply is among the most popular tool to visualize geo-referenced data stored in Network Common Data Form (netCDF). It provides a graphical interface for inspecting (show metadata) and visualizing netCDF data. It supports many features to customize your plots and we will introduce some of them in this lesson.

• Fouilloux, Anne Claire; Hasan, Adil; Lukkarinen, Ari & Struthers, Hamish (2020). EOSC-Nordic Climate Science Workbench roadmap.
• Fouilloux, Anne Claire; Hasan, Adil; Lukkarinen, Ari & Struthers, Hamish (2020). Nordic climate scientists are turning to Open Science.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). Climate JupyterLab as an interactive tool in Galaxy.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). FATES on GALAXY facilitates ecologist and climate modeler collaboration.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). Functionally Assembled Terrestrial Ecosystem Simulator (FATES) with Community Land Model in Galaxy.
• Hasan, Adil; Fouilloux, Anne Claire & Jacquemot, Christine (2020). Integrating maDMP with the data management process. Show summary

  he data management process for a research project covers the collection, storage, transformation, analysis and archiving or deletion of the data. Each of these stages can involve some complex and detailed steps. Projects in the same discipline may have very similar, or even the same, series of steps in a given stage. For example, more than one project may make use of the same multi-purpose detector. For each project the transformation from the raw data into more physical quantities (such as pulse signals from a detector into three-dimensional coordinates) could be common. If one project were able to define such a workflow and include it as part of their DMP, other projects would be able to reuse it. Capturing these workflows in the DMP would more accurately reflect the data management process and provide some value to follow-up, or related projects that have similar workflows and which would be able to reuse these workflows. The task we set ourselves for the hackathon was to take some specific workflows from a portion of the data management process for the Climate community in the EOSC-Nordic project and see how we could capture the workflow within the maDMP schema. This report is a summary of work done by the Something team at the RDA Hackathon on machine-actionable Data Management Plans that took place between 27th and 29th May 2020

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