Implications of the Data-Centric Nature of Climate Science for AI & ML
posterposted on 13.07.2021, 21:01 authored by Seth McGinnis
Climate science prioritizes the production and dissemination of data to enhance its value as evidence. The re-use of data in this way depends on how it is packaged. A comparison of the influence of Big Data in biology versus climate science reveals potential hazards associated with the categorization of phenomena. To avoid undesirably constraining downstream research, the development of ontologies and training datasets for machine learning needs to be an open community effort. This poster was presented at the 2021 Earth Science Information Partners (ESIP) Summer Meeting held virtually in July 2021.