
Environmental Data Science
@envdatascience
Environmental Data Science is an #OpenAccess @CambridgeUP journal dedicated to the use of data science & AI to enhance our understanding of the environment.
ID: 1307969208291143680
https://www.cambridge.org/core/journals/environmental-data-science 21-09-2020 09:06:20
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New article! Leveraging causality and explainability in digital agriculture 👉 bit.ly/42Jd5Dh By Ilias Tsoumas, Vasileios Sitokonstantinou, Georgios Giannarakis, Evagelia Lampiri, Christos E. Athanasiou, Gustau Camps-Valls, Charalampos Kontoes & Ioannis N. Athanasiadis #agriculture



We’re excited to be part of this year’s #EGU25 in beautiful Vienna! If you're attending, come see us at the Cambridge University Press - booth #09 and check out our call for papers related to an EGU workshop - cup.org/4jHn4ja #Geoscience #ClimateTech CambUP Earth and Environmental Science


New article! Decision support for the identification of testate amoebae in microscopy images to detect peat presence in horticultural substrates 👉 bit.ly/3El8T4F By Serge Zaugg, Camille Vögeli, Lena Märki, Clément Duckert & Edward Mitchell #DeepLearning #peat


New article! Robust machine-learned algorithms for efficient grid operation 👉 bit.ly/3GrwzVH By Nicolas Christianson, Christopher Yeh, Tongxin Li, Mehdi Hosseini, Mahdi Torabi Rad, Azarang Golmohammadi and Adam Wierman Caltech Beyond Limits #machinelearning


Recently published! Reflective error: a metric for assessing predictive performance at extreme events 👉 bit.ly/42JGa2R By Robert Edwin Rouse (Engineering Dept), Henry Moss, Scott Hosking (The Alan Turing Institute) , AllanMcRobie and Emily Shuckburgh #MachineLearning


New article! Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters 👉 bit.ly/4j1N0pi By authors from USC, University of Arizona & NASA Langley Research Center #MachineLearning #aerosol #clouds #AtmosphericComposition


New article! Learning complex spatial dynamics of wildlife diseases with machine learning-guided partial differential equations 👉 bit.ly/4d8c9wR By authors from University of South Carolina, U.S. Fish and Wildlife Service, University of Montana & UW–Madison #spatialdynamics #ecology #ecologicaldiffusion #MachineLearning


📢 CALL FOR PAPERS: Connecting Data-Driven and Physical Approaches: Application to Climate Modeling and Earth System Observation This special collection will build upon a workshop at #EGU25. Find out more: bit.ly/4k09cBu julien brajard #climate #AI #forecasting


New article! The influence of correlated features on neural network attribution methods in geoscience 👉 bit.ly/3F8huId By EvanKrell_CC, Antonios Mamalakis, Scott A. King, Philippe Tissot and Imme Ebert-Uphoff #artificialintelligence #AI #neuralnetworks


New article! Discovering effective policies for land-use planning with neuroevolution 👉 bit.ly/3ZoUBHf By Daniel Young, Olivier Francon, Elliot Meyerson, Clemens Schwingshackl, Jacob Bieker, Hugo Cunha, Babak Hodjat & Risto Miikkulainen Open Climate Fix Cognizant
