Ensuring Adherence to Standards in Experiment-Related Metadata Entered Via Spreadsheets.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-14 DOI:10.1038/s41597-025-04589-6
Martin J O'Connor, Josef Hardi, Marcos Martínez-Romero, Sowmya Somasundaram, Brendan Honick, Stephen A Fisher, Ajay Pillai, Mark A Musen
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Abstract

Scientists increasingly recognize the importance of providing rich, standards-adherent metadata to describe their experimental results. Despite the availability of sophisticated tools to assist in the process of data annotation, investigators generally seem to prefer to use spreadsheets when supplying metadata, despite the limitations of spreadsheets in ensuring metadata consistency and compliance with formal specifications. In this paper, we describe an end-to-end approach that supports spreadsheet-based entry of metadata, while ensuring rigorous adherence to community-based metadata standards and providing quality control. Our methods employ several key components, including customizable templates that represent metadata standards and that can inform the spreadsheets that investigators use to author metadata, controlled terminologies and ontologies for defining metadata values that can be accessed directly from a spreadsheet, and an interactive Web-based tool that allows users to rapidly identify and fix errors in their spreadsheet-based metadata. We demonstrate how this approach is being deployed in a biomedical consortium known as HuBMAP to define and collect metadata about a wide range of biological assays.

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确保通过电子表格输入的实验相关元数据符合标准。
科学家们越来越认识到提供丰富的、符合标准的元数据来描述他们的实验结果的重要性。尽管有一些复杂的工具可以帮助数据注释过程,但在提供元数据时,调查人员似乎通常更喜欢使用电子表格,尽管电子表格在确保元数据一致性和符合正式规范方面存在局限性。在本文中,我们描述了一种支持基于电子表格的元数据输入的端到端方法,同时确保严格遵守基于社区的元数据标准并提供质量控制。我们的方法采用了几个关键组件,包括表示元数据标准的可定制模板,它可以通知调查人员用于创建元数据的电子表格,用于定义可从电子表格直接访问的元数据值的受控术语和本体,以及允许用户快速识别和修复基于电子表格的元数据中的错误的交互式基于web的工具。我们演示了这种方法是如何在一个名为HuBMAP的生物医学联盟中部署的,以定义和收集关于各种生物测定的元数据。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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