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In Search of Outstanding Research Advances: Prototyping the creation of an open dataset of "editorial highlights"
arXiv - CS - Digital Libraries Pub Date : 2020-11-16 , DOI: arxiv-2011.07910
Alexis-Michel Mugabushaka, Jasmin Sadat, Jorge Costa Dantas Faria

A long-standing research question in bibliometrics is how one identifies publications, which represent major advances in their fields, making high impact in there and other areas. In this context, the term "Breakthrough" is often used and commonly used approaches rely on citation links between publications implicitly positing that peers who use or build upon previously published results collectively inform about their standing in terms of advancing the research frontiers. Here we argue that the "Breakthrough" concept is rooted in the Kuhnian model of scientific revolution which has been both conceptually and empirically challenged. A more fruitful approach is to consider various ways in which authoritative actors in scholarly communication system signal the importance of research results. We bring to discussions different "recognition channels" and pilot the creation of an open dataset of editorial highlights from regular lists of notable research advances. The dataset covers the last ten years and includes: the "discoveries of the year" from Science magazine and La Recherche and weekly editorial highlights from Nature ("research highlights") and Science ("editor's choice"). The final dataset includes 230 entries in the "discoveries of the years" (with over 720 references) and about 9,000 weekly highlights (with over 8,000 references).

中文翻译:

寻找杰出的研究进展:创建“编辑亮点”开放数据集的原型

文献计量学中一个长期存在的研究问题是如何识别代表各自领域重大进展、在该领域和其他领域产生重大影响的出版物。在这种情况下,“突破”一词经常被使用,常用的方法依赖于出版物之间的引用链接,暗示使用或建立在先前发表的结果上的同行共同告知他们在推进研究前沿方面的地位。在这里,我们认为“突破”概念植根于科学革命的库恩模型,该模型在概念上和经验上都受到了挑战。一种更有成效的方法是考虑学术交流系统中的权威参与者以各种方式表明研究成果的重要性。我们引入不同的“认可渠道”进行讨论,并从显着研究进展的常规列表中试行创建编辑重点的开放数据集。该数据集涵盖了过去十年,包括:Science 杂志和 La Recherche 的“年度发现”以及 Nature(“研究亮点”)和 Science(“编辑选择”)的每周编辑亮点。最终数据集包括“年度发现”中的 230 个条目(超过 720 个参考文献)和大约 9,000 个每周亮点(超过 8,000 个参考文献)。来自 Science 杂志和 La Recherche 以及 Nature(“研究亮点”)和 Science(“编辑选择”)的每周编辑重点。最终数据集包括“年度发现”中的 230 个条目(超过 720 个参考文献)和大约 9,000 个每周亮点(超过 8,000 个参考文献)。来自 Science 杂志和 La Recherche 以及 Nature(“研究亮点”)和 Science(“编辑选择”)的每周编辑重点。最终数据集包括“年度发现”中的 230 个条目(超过 720 个参考文献)和大约 9,000 个每周亮点(超过 8,000 个参考文献)。
更新日期:2020-11-17
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