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arXiv 1604.04896cs.DL
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来自出版物致谢的资金数据:覆盖范围,使用和限制

Funding Data from Publication Acknowledgements: Coverage, Uses and Limitations

Nicola Grassano, Daniele Rotolo, Josh Hutton, Frédérique Lang, Michael M. Hopkins

摘要:

本文通过探索新兴方法的鲁棒性和可比性来生成对政策制定有用的资助景观,从而有助于研究资助系统分析方法的发展。我们使用了一个新的数据集,手动提取和编码的数据,在2011年代表英国癌症研究的7,510篇出版物的资金来源,并将这些“参考数据”与Web of Science(WoS)和MEDLINE/PubMed提供的资金数据进行比较。研究结果显示,WoS资金数据的召回率较高(约93%)。相比之下,MEDLINE/PubMed数据检索到的英国癌症出版物中只有不到一半承认至少有一个资助者。相反,这两个数据库都有很高的精确度(+90%):即很少有未向资助者确认的出版物被确定为有资助数据。尽管如此,MEDLINE/PubMed和WoS分别在约75%和32%的病例中未正确列出英国癌症出版物中承认的资助者。“参考数据”的英国癌症研究资助系统,然后作为一个案例研究,以展示实用的资助数据的战略情报应用程序(例如,绘图的资助景观,比较资助者的研究组合)。

英文摘要:

This article contributes to the development of methods for analysing research funding systems by exploring the robustness and comparability of emerging approaches to generate funding landscapes useful for policy making. We use a novel dataset of manually extracted and coded data on the funding acknowledgements of 7,510 publications representing UK cancer research in the year 2011 and compare these 'reference data' with funding data provided by Web of Science (WoS) and MEDLINE/PubMed. Findings show high recall (about 93%) of WoS funding data. By contrast, MEDLINE/PubMed data retrieved less than half of the UK cancer publications acknowledging at least one funder. Conversely, both databases have high precision (+90%): i.e. few cases of publications with no acknowledgement to funders are identified as having funding data. Nonetheless, funders acknowledged in UK cancer publications were not correctly listed by MEDLINE/PubMed and WoS in about 75% and 32% of the cases, respectively. 'Reference data' on the UK cancer research funding system are then used as a case-study to demonstrate the utility of funding data for strategic intelligence applications (e.g. mapping of funding landscape, comparison of funders' research portfolios).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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