With the global increase in experimental data artifacts, harnessing them in a unified fashion leads to a major stumbling block - bad metadata. To bridge this gap, this work presents a Natural Language Processing (NLP) informed application, called FAIRMetaText, that compares metadata. Specifically, FAIRMetaText analyzes the natural language descriptions of metadata and provides a mathematical similarity measure between two terms. This measure can then be utilized for analyzing varied metadata, by suggesting terms for compliance or grouping similar terms for identification of replaceable terms. The efficacy of the algorithm is presented qualitatively and quantitatively on publicly available research artifacts and demonstrates large gains across metadata related tasks through an in-depth study of a wide variety of Large Language Models (LLMs). This software can drastically reduce the human effort in sifting through various natural language metadata while employing several experimental datasets on the same topic.

翻译：随着全球实验数据量的增长，如何以统一的方式利用这些数据成为主要障碍——元数据质量低下。为弥补这一差距，本研究提出了一种基于自然语言处理（NLP）的应用程序FAIRMetaText，用于比较元数据。具体而言，FAIRMetaText分析元数据的自然语言描述，并提供两个术语之间的数学相似度度量。该度量可用于分析各种元数据，例如建议合规术语或对相似术语进行分组以识别可替换术语。通过在公开的研究数据上进行的定性和定量评估，该算法的有效性得到验证，并通过深入研究多种大语言模型（LLMs）展示了在元数据相关任务上的显著改进。该软件可大幅减少人工筛选同一主题下多个实验数据集自然语言元数据的工作量。