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首页> 外文期刊>Journal of the American Society for Information Science and Technology >F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations
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F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations

机译:F1000建议作为研究评估的潜在新数据源:与引文比较

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摘要

F1000 is a postpublication peer review service for biological and medical research. F1000 recommends important publications in the biomedical literature, and from this perspective F1000 could be an interesting tool for research evaluation. By linking the complete database of F1000 recommendations to the Web of Science bibliographic database, we are able to make a comprehensive comparison between F1000 recommendations and citations. We find that about 2% of the publications in the biomedical literature receive at least one F1000 recommendation. Recommended publications on average receive 1.30 recommendations, and more than 90% of the recommendations are given within half a year after a publication has appeared. There turns out to be a clear correlation between F1000 recommendations and citations. However, the correlation is relatively weak, at least weaker than the correlation between journal impact and citations. More research is needed to identify the main reasons for differences between recommendations and citations in assessing the impact of publications.
机译:F1000是针对生物学和医学研究的出版后同行评审服务。 F1000推荐了生物医学文献中的重要出版物,从这个角度来看,F1000可能是用于研究评估的有趣工具。通过将F1000建议的完整数据库链接到Web of Science书目数据库,我们可以对F1000建议和引文进行全面比较。我们发现,生物医学文献中约有2%的出版物至少收到一项F1000建议。推荐的出版物平均收到1.30篇推荐,超过90%的推荐在出版物发表后的半年内给出。事实证明,F1000建议与引文之间存在明显的相关性。但是,相关性相对较弱,至少比期刊影响和引文之间的相关性弱。在评估出版物的影响时,需要更多的研究来确定建议与引用之间差异的主要原因。

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