EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science

Alejandro Coca-Castro1,3, Scott Hosking1,2,3, and The Environmental Data Science Community3
Alejandro Coca-Castro et al.
  • 1The Alan Turing Institute, Data Science for Science and Humanities Research Programme, London, United Kingdom
  • 2British Antarctic Survey, Artificial Intelligence Lab, Cambridge, United Kingdom
  • 3The Environmental Science Community, Global, Global

With the plethora of open data and computational resources available, environmental data science research and applications have accelerated rapidly. Therefore, there is an opportunity for community-driven initiatives compiling and classifying open-source research and applications across environmental systems (polar, oceans, forests, agriculture, etc). Building upon the Pangeo Gallery, we propose The Environmental Data Science book (, a community-driven online resource showcasing and supporting the publication of data, research and open-source developments in environmental sciences. The target audience and early adopters are i) anyone interested in open-source tools for environmental science; and ii) anyone interested in reproducibility, inclusive, shareable and collaborative AI and data science for environmental applications. Following FAIR principles, the resource provides multiple features such as guidelines, templates, persistent URLs and Binder to facilitate a fully documented, shareable and reproducible notebooks. The quality of the published content is ensured by a transparent reviewing process supported by GitHub related technologies. To date, the community has successfully published five python-based notebooks: two forest-, two wildfires/savanna- and one polar-related research. The notebooks consume common Pangeo stack e.g. intake, iris, xarray, hvplot for interactive visualisation and modelling from Environmental sensor data. In addition to constant feature enhancements of the GitHub repository, we expect to increase inclusivity (multiple languages), diversity (multiple backgrounds) and activity (collaboration and coworking sessions) towards improving scientific software practises in the environmental science community.

How to cite: Coca-Castro, A., Hosking, S., and Community, T. E. D. S.: Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3739,, 2022.