首页> 外文期刊>Aslib Proceedings >Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS and F1000Prime
【24h】

Usefulness of altmetrics for measuring the broader impact of research: A case study using data from PLOS and F1000Prime

机译:高度测量法对研究更广泛影响的有用性:使用PLOS和F1000Prime数据进行的案例研究

获取原文
获取原文并翻译 | 示例
       

摘要

Purpose - The purpose of this case study is to investigate the usefulness of altmetrics for measuring the broader impact of research. Design/methodology/approach - This case study is based on a sample of 1,082 the Public Library of Science (PLOS) journal articles recommended in F1000. The data set includes altmetrics which were provided by PLOS. The F1000 data set contains tags on papers which were assigned by experts to characterise them. Findings - The most relevant tag for altmetric research is "good for teaching", as it is assigned to papers which could be of interest to a wider circle of readers than the peers in a specialised area. One could expect papers with this tag to be mentioned more often on Facebook and Twitter than those without this tag. The results from regression models were able to confirm these expectations: papers with this tag show significantly higher Facebook and Twitter counts than papers without this tag. This clear association could not be seen with Mendeley or Figshare counts (that is with counts from platforms which are chiefly of interest in a scientific context). Originality/value - The results of the current study indicate that Facebook and Twitter, but not Figshare or Mendeley, might provide an indication of which papers are of interest to a broader circle of readers (and not only for the peers in a specialist area), and could therefore be useful for the measurement of the societal impact of research.
机译:目的-本案例研究的目的是研究高度测量法对研究更广泛影响的有用性。设计/方法/方法-此案例研究基于F1000中推荐的1,082个公共科学图书馆(PLOS)期刊文章样本。数据集包括由PLOS提供的高度测量。 F1000数据集包含纸张上的标签,这些标签是由专家分配以对其进行表征的。发现-与高度测量研究最相关的标签是“对教学有益”,因为它被分配给论文,与专业领域的同行相比,更广泛的读者群体可能会对它感兴趣。人们可能希望带有此标签的论文比没有此标签的论文在Facebook和Twitter上被更多地提及。回归模型的结果能够证实这些期望:带有该标签的论文显示的Facebook和Twitter人数明显高于没有该标签的论文。与Mendeley或Figshare计数(即在科学背景下最受关注的平台的计数)看不到这种明确的关联。原创性/价值-本研究的结果表明,Facebook和Twitter(而非Figshare或Mendeley)可能提供了更广泛的读者感兴趣的论文的指示(不仅是专业领域的同行) ,因此可能对衡量研究的社会影响有用。

著录项

  • 来源
    《Aslib Proceedings》 |2015年第3期|305-319|共15页
  • 作者

    Lutz Bornmann;

  • 作者单位

    Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society, Munich, Germany;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Facebook; Altmetrics; Mendeley; Twitter; F1000; Figshare;

    机译:脸书;高度度量;门德利推特;F1000;无花果;
  • 入库时间 2022-08-17 23:15:38

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号