FAIR Jupyter：一种面向计算笔记本可复现性数据集语义共享与细粒度探索的知识图谱方法
FAIR Jupyter: a knowledge graph approach to semantic sharing and granular exploration of a computational notebook reproducibility dataset
AI总结：
本文提出 FAIR Jupyter 知识图谱方法，将 Jupyter 笔记本可复现性数据集转化为语义三元组并通过 Web 服务提供细粒度查询，以提升数据 FAIR 程度并支持最佳实践识别。
摘要：
数据共享的方式会影响其效用与可复用性。本文展示了如何通过知识图谱进一步调动我们此前以批量方式共享的数据，从而支持更细粒度的探索与查询。原始数据集涉及与生物医学出版物相关联的、托管在 GitHub 上的 Jupyter 笔记本的计算可复现性，包含关于出版物、相关 GitHub 仓库和 Jupyter 笔记本以及这些笔记本可复现性的丰富元数据。我们将该数据集转换为语义三元组，并加载到三元组存储中以构建知识图谱 FAIR Jupyter，并通过 Web 服务对外提供访问。这使得通过可针对特定用例定制的查询进行细粒度数据探索和分析成为可能。此类查询可以提供原始数据集中任意变量的详细信息，突出它们之间的关系，或将图谱中的部分内容与相应外部资源的材料结合起来。我们提供了一组示例查询，涵盖研究和教育中的一系列用例。我们还概述了如何利用这些查询集合对特定内容类型进行画像，既可以单独进行，也可以按类别进行。最后，我们讨论了这种对复杂数据集进行语义增强的共享方式如何既能提升其 FAIR 程度，即其可发现性、可访问性、互操作性和可复用性，又有助于识别和传播最佳实践，尤其是在数据质量、标准化、自动化和可复现性方面。
英文摘要：
The way in which data are shared can affect their utility and reusability. Here, we demonstrate how data that we had previously shared in bulk can be mobilized further through a knowledge graph that allows for much more granular exploration and interrogation. The original dataset is about the computational reproducibility of GitHub-hosted Jupyter notebooks associated with biomedical publications. It contains rich metadata about the publications, associated GitHub repositories and Jupyter notebooks, and the notebooks' reproducibility. We took this dataset, converted it into semantic triples and loaded these into a triple store to create a knowledge graph, FAIR Jupyter, that we made accessible via a web service. This enables granular data exploration and analysis through queries that can be tailored to specific use cases. Such queries may provide details about any of the variables from the original dataset, highlight relationships between them or combine some of the graph's content with materials from corresponding external resources. We provide a collection of example queries addressing a range of use cases in research and education. We also outline how sets of such queries can be used to profile specific content types, either individually or by class. We conclude by discussing how such a semantically enhanced sharing of complex datasets can both enhance their FAIRness, i.e., their findability, accessibility, interoperability, and reusability, and help identify and communicate best practices, particularly with regards to data quality, standardization, automation and reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
