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Presentation . 2023
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Presentation . 2023
License: CC BY
Data sources: Datacite
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Environmental Data Science Book: a computational notebook community showcasing open and reproducible environmental science

Authors: Coca-Castro, Alejandro; Fouilloux, Anne; Hosking, J. Scott;

Environmental Data Science Book: a computational notebook community showcasing open and reproducible environmental science

Abstract

With the great diversity in open data, software and computational resources, environmental data science research and applications have accelerated rapidly. Building upon global communities such as the Turing Way (https://the-turing-way.netlify.app/) and Pangeo, the Environmental Data Science (EDS) book (www.edsbook.org) is a community-driven online resource aimed at educating and leveraging good scientific software and data management practices among environmental scientists through peer-reviewed findable, accessible, interoperable and reusable (FAIR) computational notebooks. The community has published ten DOI-tagged Jupyter notebooks analyzing environmental datasets and models through the Pangeo software stack and HoloViz ecosystem. We expect to increase contributions highlighting scalable Python open-source developments in Pangeo and ecosystems alike in other programming languages e.g Julia and R through partnerships with climate, environmental and open education communities (see for instance the Climate Informatics Reproducibility Challenge, https://cambridge-iccs.github.io/climate-informatics-2023/reproducibility-challenge). In this presentation, we will present our mission and vision, highlight key experiences adapting existing open infrastructure and research object platforms to host and curate our notebooks, and share activities over the next months and how to get involved.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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