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Other ORP type . 2020
License: CC BY SA
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Other ORP type . 2020
License: CC BY SA
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Other literature type . 2020
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Chemical Data in Life Sciences R&D and the FAIR Principles

appsOther research productkeyboard_double_arrow_right Other ORP type , Other literature type 03 Aug 2020 English Publisher:Zenodo
Authors: Blanke, Gerd; Doerner, Thomas; Lynch, Nick;

doi: 10.5281/zenodo.3970744 , 10.5281/zenodo.3970745

Chemical Data in Life Sciences R&D and the FAIR Principles

Abstract

In recent years, there has been an increasing drive towards scientific data not only being utilized for the specific context for which it has been created, but that data is findable, accessible, interoperable, and reusable beyond, by both humans and machines. This paradigm has been captured and formalized in the FAIR principles. In this paper, we discuss relevance and practical aspects of applying FAIR principles to chemical data in life sciences R&D.

Keywords

Machine Learning, FAIR data, Collaborative research, Artificial intelligence, Chemical data management, FAIR chemistry, Chemical data FAIRness, Chemical data, Chemical data standards, Discussion paper, Chemical master data, FAIR

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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