Environmental Data Science authors should consult the preparing your materials page - which include LaTeX and Word templates - and submit their papers through the EDS ScholarOne site or by selecting Environmental Data Science after using the Overleaf collaborative authoring tool.
Aims and scope
Environmental Data Science (EDS) is an open access journal dedicated to the use of data-driven approaches to understand environmental processes and aid sustainable decision-making. The data and methodological scope is defined broadly to encompass artificial intelligence, machine learning, data mining, computer vision, econometrics and other statistical techniques.
EDS is a venue for application and methods papers, whether they relate to climate change, or to particular environmental systems, e.g. the climate system, the geosphere (the solid earth and its processes), cryosphere (ice, snow, permafrost and tundra), biosphere (ecology), hydrosphere (water cycle) or atmosphere (meteorology, extreme weather events). It also welcomes work that shows how data science can inform societal responses to environmental problems (such as climate change, air quality, energy, transportation, and land use).
EDS promotes open data and data re-use - through data papers that describe valuable environmental data sets - and publishes shorter position papers relevant to the journal’s scope.
Types of article
Environmental Data Science publishes:
- Application papers: Research progress, or tackling a real-world problem, in an environmental field, enabled by data science. For example, AI or data science could be used for understanding of environmental processes, or improving forecasting tools.
- Methods papers: Novel data science methodology inspired by an environmental problem or application. Typically the methodology should be demonstrated in one or more environmental applications.
- Data papers that describe in a structured way, with a short narrative and accompanying metadata, important and re-usable environmental data sets that reside in publicly accessible repositories. These papers promote data transparency and data re-use.
- Survey papers: providing a systematic overview of a method, tool or approach, or a field or subfield that is relevant to environmental data science.
- Position papers: examples include but are not limited to: a) providing an authoritative, personal view on the uptake or obstacles to AI and data science approaches for environmental problems, or b) exploring issues related to the use of environmental data, including ethical, legal and policy issues, as well as data standards, protocols and services. (Note that these are shorter articles: approx. 5,000 words in length)
* All or part of the publication costs for these article types may be covered by one of the agreements Cambridge University Press has made to support open access. For authors not covered by an agreement, and without APC funding, please see this journal's open access options for instructions on how to request an APC waiver.
Special collections
Proposals for special collections of articles - for example originating from a workshop, conference or event - are also considered. See the instructions for submitting a special collection proposal.
Peer review process
Articles submitted to Environmental Data Science are subject to a single-blind peer review process.