Environmental Data Science. (August 2018)

• Record Type: Journal Article
• Authors: Gibert, Karina; Horsburgh, Jeffery S.; Athanasiadis, Ioannis N.; Holmes, Geoff
• Is Part Of: Environmental modelling & software. Volume 106(2018)
• Page Start: 4
• Page End: 12
• Publication Date: 2018-08
• Subjects: Data Science -- Environmental science -- Data driven modelling; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.04.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Environmental data are growing in complexity, size, and resolution. Addressing the types of large, multidisciplinary problems faced by today's environmental scientists requires the ability to leverage available data and information to inform decision making. Successfully synthesizing heterogeneous data from multiple sources to support holistic analyses and extraction of new knowledge requires application of Data Science. In this paper, we present the origins and a brief history of Data Science. We revisit prior efforts to define Data Science and provide a more modern, working definition. We describe the new professional profile of a data scientist and new and emerging applications of Data Science within Environmental Sciences. We conclude with a discussion of current challenges for Environmental Data Science and suggest a path forward. Highlights: Data Science connects data with decisions by producing actionable knowledge from data and bridging the Hammond's Fact Gap. Environmental Sciences can benefit from the added value given by Data Science, which is strategic in complex systems. Historical and contemporary views of Environmental Data Science and emerging environmental applications are given. Data Science requires new professional skills. Multidisciplinary working teams are very convenient. Environmental Data Science involve signifficant challenges, making a promising research field in short, mid and long term.