A data-oriented approach to making new molecules as a student experiment: artificial intelligence-enabling FAIR publication of NMR data for organic esters.
A data-oriented approach to making new molecules as a student experiment: artificial intelligence-enabling FAIR publication of NMR data for organic esters.
复制标题
一种以数据为导向的方法来制造新分子作为学生实验：人工智能支持有机酯 NMR 数据的公平发布。
DOI：
10.1002/mrc.5186
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Rzepa HS
中科院分区：
文献类型：
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作者：
Rzepa HS
缺乏机器可读数据是核磁共振（NMR）在人工智能（AI）中应用的主要障碍。作为一种方法来克服这一点，一个程序捕获的主要NMR光谱仪器数据注释丰富的元数据和出版物在一个可查找的，可解释的，可互操作和可重用的（FAIR）数据存储库中描述的一部分，在化学系的本科生实验室实验。这将从未制作过的有机酯的化学合成技术与现代数据管理实践的说明结合起来，并有助于提高学生对FAIR数据如何提高研究质量和可复制性的认识。显示了已注册元数据的索引，这使得能够对此类数据进行可操作的查找和访问。讨论了在AI应用程序中重复使用数据的潜力。
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.