Environmental Data Science book

With the plethora of open data and computational resources available, environmental data science research and applications have rapidly accelerated. Therefore, there is an opportunity to propose a new cyberinfrastructure to collect and classify open source research and applications in environmental systems (polar, oceans, forests, agriculture, etc.). Inspired by the Pangeo Gallery, we propose The Environmental Data Science book (https://the-environmental-ds-book.netlify.app), a community-driven online resource that showcases and supports the publication of data, research, and open source developments in environmental science. The target audience and early adopters are

Following the FAIR (findable, accessible, interoperable and reusable) principles, the resource offers multiple features such as guidelines, templates, persistent URLs and Binder to facilitate fully documented, shareable and reproducible content. The quality of published content is assured by a transparent review process supported by GitHub-related technologies. To date, the community has successfully published seven Python-based executable content: one agriculture, two forestry, two forest/savanna fire, one ocean research, and one polar research. The content uses open source Python libraries, such as intake, iris, xarray and hvplot, for interactive visualization and modelling of environmental sensor data. In addition to constant feature enhancements to the GitHub repository we hope to increase contributions from other programming languages, e. g. Julia and R, and to organize more community activities (collaboration and coworking sessions) to improve scientific software practices in the environmental science community.