Data Science - Methods, infrastructure, and applications

Michel Dumontier, Tobias Kuhn

Research output: Contribution to JournalEditorialAcademic

Abstract

1. About Data Science
Science has always been about data. Observational data points have served as the evidence that has allowed us assess, accept, and discard scientific theories. But in the last decades, scientific data has grown dramatically in both size and importance. Data Science is therefore not a new science discipline, but rather a new pair of glasses – a new paradigm – to look at problems and questions in the existing disciplines with the new possibilities of data analytics in mind. It also stands for the development that data, when properly linked, transcend disciplines and can enable new sorts of interdisciplinary research fields and even breed entirely new areas. The focus on data also immediately highlights other important and urgent issues in contemporary science, namely the reproducibility of results, the responsible treatment of potentially sensitive data, the transparency and openness of scientific data and processes, the attribution and recognition of data gathering and curation efforts, and the now widely accepted requirement that scientific data should be Findable, Accessible, Interoperable, and Reusable (FAIR). With this journal, called Data Science, we intend to give this type of research the focus and attention we think it deserves.
Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalData Science
Volume1
Issue number1-2
Early online date8 Dec 2017
DOIs
Publication statusPublished - 2017

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