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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1002/mrc.5186
|2 doi
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|a eng
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|a Rzepa, Henry S
|e verfasserin
|4 aut
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|a A data-oriented approach to making new molecules as a student experiment
|b artificial intelligence-enabling FAIR publication of NMR data for organic esters
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 03.01.2022
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|a Date Revised 23.02.2022
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|a published: Print-Electronic
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|a ErratumIn: Magn Reson Chem. 2022 Nov;60(11):1032-1043. - PMID 35195296
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|a Citation Status PubMed-not-MEDLINE
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|a © 2021 The Authors. Magnetic Resonance in Chemistry published by John Wiley & Sons Ltd.
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|a The lack of machine-readable data is a major obstacle in the application of nuclear magnetic resonance (NMR) in artificial intelligence (AI). As a way to overcome this, a procedure for capturing primary NMR spectroscopic instrumental data annotated with rich metadata and publication in a Findable, Accessible, Interoperable and Reusable (FAIR) data repository is described as part of an undergraduate student laboratory experiment in a chemistry department. This couples the techniques of chemical synthesis of a never before made organic ester with illustration of modern data management practices and serves to raise student awareness of how FAIR data might improve research quality and replicability. Searches of the registered metadata are shown, which enable actionable finding and accessing of such data. The potential for re-use of the data in AI applications is discussed
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a FAIR
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|a NMR spectroscopy
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|a artificial intelligence
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|a chemical education
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|a data repository
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|a metadata registration
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|a re-use
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|a Kuhn, Stefan
|e verfasserin
|4 aut
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|i Enthalten in
|t Magnetic resonance in chemistry : MRC
|d 1985
|g 60(2022), 1 vom: 09. Jan., Seite 93-103
|w (DE-627)NLM098179667
|x 1097-458X
|7 nnns
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|g volume:60
|g year:2022
|g number:1
|g day:09
|g month:01
|g pages:93-103
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|u http://dx.doi.org/10.1002/mrc.5186
|3 Volltext
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