PEPhub:用于编辑、共享和验证生物样本元数据的数据库、网络接口和应用程序接口。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Nathan J LeRoy, Oleksandr Khoroshevskyi, Aaron O'Brien, Rafał Stępień, Alip Arslan, Nathan C Sheffield
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引用次数: 0

摘要

背景:随着生物数据的增加,我们需要更多的基础设施来共享这些数据并促进互操作性。虽然我们在数据共享方面投入了大量精力,但对元数据共享的重视程度却相对较低。然而,元数据共享同样重要,而且在某些方面比数据本身的共享范围更广:在此,我们提出了 PEPhub,一种改善生物元数据共享和互操作性的方法。PEPhub 提供了一个应用程序接口(API)、自然语言搜索以及基于用户友好的网络共享和编辑样本元数据表。我们使用 PEPhub 处理了 100,000 多个已发表的生物研究项目,并通过快速语义自然语言搜索对其进行索引。因此,PEPhub 为查找现有生物研究数据或共享新数据提供了一种快速、用户友好的方式。可用性:https://pephub.databio.org。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PEPhub: a database, web interface, and API for editing, sharing, and validating biological sample metadata.

Background: As biological data increase, we need additional infrastructure to share them and promote interoperability. While major effort has been put into sharing data, relatively less emphasis is placed on sharing metadata. Yet, sharing metadata is also important and in some ways has a wider scope than sharing data themselves.

Results: Here, we present PEPhub, an approach to improve sharing and interoperability of biological metadata. PEPhub provides an API, natural-language search, and user-friendly web-based sharing and editing of sample metadata tables. We used PEPhub to process more than 100,000 published biological research projects and index them with fast semantic natural-language search. PEPhub thus provides a fast and user-friendly way to finding existing biological research data or to share new data.

Availability: https://pephub.databio.org.

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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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