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替代计量视角下学术成果影响力评估:NISO AMMP项目剖析 被引量:5

The Impact Assessment of Academic Output in the Altmetrics Perspective:NISO AMMP Analysis
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摘要 [目的/意义]对美国国家信息标准协会(NISO)发布的"替代计量评价指标的推荐实施规范"(AMMP)进行研究,以推动我国图书馆界对AMMP项目的深入了解,突破传统学术评价局限,促进替代计量在国内的应用以及新科学交流评价体系的构建。[方法/过程]介绍AMMP产生的背景、发展、主要内容及特点,同时对该推荐实践规范推广和相关组织应用进行总结。在此基础上,对国内Altmetrics未来发展、一流期刊建设和研究数据集平台建设等提出建议。[结果/结论]AMMP通过分析利益相关者共性需求,明确替代计量适用成果类型,针对研究数据、永久标识符、数据质量等方面提出一系列推荐实施规范,为数据提供者或数据集成者的实践提供依据,有益于促进替代计量评价指标的标准化建设。 [Purpose/significance]Research on the output of the NISO Alternative Assessment Metrics Project(AMMP)issued by the National Information Standards Organization to promote the in-depth understanding of the AMMP in the Chinese library community,break through the limitations of traditional academic evaluation,and promote the application of the Altmetrics in our country and the construction of new scientific exchange evaluation system.[Method/process]This peper introduced the background,progress,main content and characteristics of the AMMP and concluded the promotion and application of the recommended practices at the same time.On the basis of above,suggestions were made for the future domestic development of Altmetircs,the construction of research dataset platforms and so on.[Result/conclusion]By analyzing the common needs of stakeholders,the AMMP clarifies the types of the achievements adapted to the Altmetrics,comes up with a series of recommended practices on the aspects of research data,persistent identifier,data quality and so on.It provides a basis for the practice of data providers or data integrators,which is beneficial for standardization of Altmetrics.
作者 张雪蕾 邵晶 尹飞 南希 邹秀英 王晓彤 Zhang Xuelei;Shao Jing;Yin Fei;Nan Xi;Zou Xiuying;Wang Xiaotong(Xi'an Jiaotong University Library,Xi'an 710049;Claremont Graduate University Library,Claremont 91711;University of Oregon Libraries,Eugene 97403)
出处 《图书情报工作》 CSSCI 北大核心 2020年第2期94-104,共11页 Library and Information Service
基金 国家社会科学基金项目"中美电子资源国家标准比较研究"(项目编号:16BTQ027)研究成果之一。
关键词 AMMP 替代计量 研究数据 数据质量 AMMP Altmetrics research data data quality
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