论文层面指标中的数据完整性问题。

The Question of Data Integrity in Article-Level Metrics.

作者信息

Gordon Gregg, Lin Jennifer, Cave Richard, Dandrea Ralph

机构信息

Social Science Research Network, Rochester, New York, United States of America.

Public Library of Science, San Francisco, California, United States of America.

出版信息

PLoS Biol. 2015 Aug 21;13(8):e1002161. doi: 10.1371/journal.pbio.1002161. eCollection 2015 Aug.

DOI:10.1371/journal.pbio.1002161

PMID:26296237

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4546647/

Abstract

Interest in and use of article-level metrics (ALMs) has grown rapidly amongst the research community, by researchers, publishers, funders, and research institutions. As this happens, it is critical to ensure secure and reliable data that is trustworthy and can be used by all. Two case studies are presented, which illustrate different approaches to establishing ALM data integrity.

摘要

研究团体、研究人员、出版商、资助者和研究机构对论文层面指标（ALMs）的兴趣和使用迅速增加。在此过程中，确保数据安全可靠、值得信赖且可供所有人使用至关重要。本文介绍了两个案例研究，它们展示了建立ALM数据完整性的不同方法。