Developing a standardized but extendable framework to increase the findability of infectious disease datasets.
Developing a standardized but extendable framework to increase the findability of infectious disease datasets.
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DOI：
10.1038/s41597-023-01968-9
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发表时间：
2023-02-23
期刊：
影响因子：
9.8
通讯作者：
Hughes, Laura D.
中科院分区：
文献类型：
--
作者：
Tsueng, Ginger;Cano, Marco A. Alvarado;Bento, Jose;Czech, Candice;Kang, Mengjia;Pache, Lars;Rasmussen, Luke, V;Savidge, Tor C.;Starren, Justin;Wu, Qinglong;Xin, Jiwen R.;Yeaman, Michael;Zhou, Xinghua I.;Su, Andrew, I;Wu, Chunlei;Brown, Liliana;Shabman, Reed S.;Hughes, Laura D.
生物医学数据集的规模越来越大，存储在许多存储库中，并面临公平性（可查找性，可访问性，互操作性，可重用性）的挑战。作为一个由来自15个中心的传染病研究人员组成的联盟，我们的目标是采用开放的科学实践，以促进透明度，鼓励可重复性，并通过数据重用加速研究进展。为了提高我们的数据集和计算工具的公平性，我们评估了已建立的生物医学数据存储库的元数据标准。绝大多数都不遵循单一的标准，例如Schema.org，它被通用存储库广泛采用。因此，这些存储库中的数据集无法在Google Dataset Search等聚合项目中找到。我们通过创建一个基于Schema.org的可重用元数据模式来缩小这一差距，并对我们收集的近400个数据集和计算工具进行了编目。这种方法很容易重用，可以创建与社区标准互操作的模式，但可以根据特定的上下文进行定制。我们的方法实现了数据发现，提高了大型研究联盟数据集的可重用性，并加速了研究。最后，我们讨论了公平性超越可验证性的持续挑战。
Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.
影响因子：
9.8
作者：
Barker, Michelle;Hong, Neil P. Chue;Katz, Daniel S.;Lamprecht, Anna-Lena;Martinez-Ortiz, Carlos;Psomopoulos, Fotis;Harrow, Jennifer;Castro, Leyla Jael;Gruenpeter, Morane;Martinez, Paula Andrea;Honeyman, Tom
通讯作者：
Honeyman, Tom