Identifying genomic data use with the Data Citation Explorer.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Neil Byers, Charles Parker, Chris Beecroft, T B K Reddy, Hugh Salamon, George Garrity, Kjiersten Fagnan
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引用次数: 0

Abstract

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data that was not formally cited. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

使用数据引用资源管理器识别基因组数据的使用。
测序能力的提高,加上出版物和相关数据资源的快速积累,增加了维护文献和基因组数据之间关联的复杂性。由于文献和数据的数量已经超过了人工整理的能力,因此有必要采用自动化方法来维护和确认这些资源之间的关联。在这里,我们介绍数据引用资源管理器(DCE),它能发现未被正式引用的包含基因组数据的文献。这项服务比人工整理方法更具优势,包括资源覆盖范围一致、元数据丰富、记录新用例以及识别冲突元数据。这项服务降低了人工审核的人力成本,提高了美国能源部联合基因组研究所(JGI)维护的基因组元数据的质量,并增加了采用其数据产品的已知出版物的数量。DCE 有助于了解 JGI 的影响,改善数据生成者的信用归属,并通过让科学家了解重复使用如何扩大其原始研究的影响来鼓励数据共享。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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