A data-oriented approach to making new molecules as a student experiment: AI-enabling FAIR publication of NMR data for organic esters

Date

2021-08-09

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Wiley

Type

Article

Peer reviewed

Yes

Abstract

The lack of machine-readable data is a major obstacle in the application of NMR in artificial intelligence. As a way to overcome this, a procedure for capturing primary NMR Spectroscopic instrumental data annotated with rich metadata and publication in a 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.

Description

open access article

Keywords

FAIR, NMR Spectroscopy, Data Repository, Metadata Registration, Re-use, Artificial Intelligence, Chemical Education

Citation

Rzepa, H. S. and Kuhn, S. (2021) A data-oriented approach to making new molecules as a student experiment: AI-enabling FAIR publication of NMR data for organic esters. Magnetic Resonance in Chemistry,

Rights

Research Institute

Cyber Technology Institute (CTI)