Aims and Scope#

We consider submissions from all areas of environmental science. This includes (but it is not restricted to):

  • introducing relevant environmental datasets irrespective of their modality (image, labels, points, shapes, surface text).

  • describing the outputs of a machine learning/computer vision model suited to our understanding of Planet Earth.

  • showcasing an open-source software suited to Environmental Data Science.

  • pre- or post-processing routines relevant for Environmental Science.

Submissions could be at any stage from notebook idea to a working prototype from an existing GitHub repository. The notebook should address scientific and/or technical aspects that EDS book audience could adopt, reuse, and/or extend for their purposes.

The optimal notebook has between 100 and 500 for physical lines of code in Code Cells, and 500 to 5000 comments in Markdown Cells. We have determined these ranges from the pool of published notebooks (by March 2023) using cloc, a handy open-source tool to count blank lines, comment lines, and physical lines of source code in many programming languages.

Notebook submissions to EDS book must:

  • Be fully open, under the Open Definition. This means that any text content or graphical objects should be under a Creative Commons license (ideally CC-BY) and code components should be under an OSI-approved MIT license.

  • Documentation of computational cells should be complete for self-learning or adoption by EDS book users.

  • Notebook submissions should make a clear contribution to Environmental Data Science using available open-source software.

  • Authors wishing to make a pre-submission enquiry should open an issue on the EDS Book repository.

Themes#

EDS notebooks are categorized under four proposed topics or themes:

  • Exploration: highlights a particular environmental sensor with visualization and interpretation of the corresponding layers of information.

  • Preprocessing: refers to all procedures to clean and prepare environmental data for analysis. The notebook should highlight differences between the raw and preprocessed data.

  • Modelling: comprises models to analyse a given environmental system.

  • Post-processing: refers to post-process routines to fine tune and/or adjust modelling outputs.

We cover further details of submission and reviewing processes in the guidelines section.