当前位置: X-MOL 学术Magn. Reson. Chem. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A data-oriented approach to making new molecules as a student experiment: artificial intelligence-enabling FAIR publication of NMR data for organic esters
Magnetic Resonance in Chemistry ( IF 2 ) Pub Date : 2021-06-09 , DOI: 10.1002/mrc.5186
Henry S Rzepa 1 , Stefan Kuhn 2
Affiliation  

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.

中文翻译:

一种以数据为导向的方法来制造新分子作为学生实验:人工智能使有机酯的 NMR 数据公平出版

机器可读数据的缺乏是核磁共振(NMR)在人工智能(AI)中应用的主要障碍。作为克服这一问题的一种方法,在可查找、可访问、可互操作和可重复使用 (FAIR) 数据存储库中捕获带有丰富元数据和出版物的原始 NMR 光谱仪器数据的过程被描述为化学系本科生实验室实验的一部分. 这将前所未有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。显示了对已注册元数据的搜索,从而可以对此类数据进行可操作的查找和访问。
更新日期:2021-06-09
down
wechat
bug