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    • Open access
  • ISSN: 2634-4602 (Online)
  • Editor: Claire Monteleoni University of Colorado Boulder, USA
  • Editorial board
Environmental Data Science is an open access journal dedicated to the use of data-driven approaches to understand environmental processes - including climate change - 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 the geosphere (the solid earth and its processes), cryosphere (e.g. ice, snow, permafrost and tundra), biosphere (ecology), hydrosphere (oceans and fresh water, including the water cycle) or atmosphere (e.g. meteorology, climatology). It also welcomes work that shows how data science can inform societal responses to environmental problems (such as climate change, air quality, energy, natural resources 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.

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