A method for data review in chemical sciences with a focus on data for the characterization of synthetic molecules is described. As current procedures for data curation in chemistry rely almost exclusively on manual checking or peer reviewing, a (semi-)automatic procedure for the evaluation of data assigned to molecular structures is proposed and demonstrated. The information usually required for the identification of isolated compounds is used to clarify whether the data is complete with respect to the available data types and metadata, if it is consistent with the proposed structure and if it is plausible in comparison to simulated data. Spectra prediction and automatic signal comparison are applied to NMR evaluation, mass spectrometry data are evaluated by signal extraction, and machine learning is used for IR analysis. The proposed protocol shows how an integration of different tools for data analysis can help to overcome the challenges of the currently purely manual review and curation efforts for analytical data in synthetic chemistry.

中文翻译：

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（半）有机合成中常见化合物表征数据的自动审查过程


描述了一种化学科学中的数据审查方法，重点是合成分子表征的数据。由于目前的化学数据管理程序几乎完全依赖于人工检查或同行评审，因此提出并演示了一种（半）自动程序来评估分配给分子结构的数据。鉴定分离化合物通常需要的信息用于阐明数据相对于可用数据类型和元数据是否完整，是否与拟议的结构一致，以及与模拟数据相比是否合理。光谱预测和自动信号比较应用于 NMR 评估，通过信号提取评估质谱数据，并使用机器学习进行 IR 分析。拟议的协议展示了不同数据分析工具的集成如何帮助克服目前合成化学分析数据纯人工审查和管理工作的挑战。