Data Integration for the Study of Outstanding Productivity in Biomedical Research.
Data Integration for the Study of Outstanding Productivity in Biomedical Research.
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DOI：
10.1016/j.procs.2022.10.191
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Tran, C J
中科院分区：
文献类型：
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作者：
Aubert, Clement;Balas, E Andrew;Townsend, Tiffany;Sleeper, Noah;Tran, C J
我们的目标是在多维结果空间中分析科学绩效的改进，重点是美国的生物医学研究。随着研究数据库的日益多样化，将科学生产力的评估局限于文献计量指标，如出版物数量、期刊影响因子和被引次数，正日益受到挑战。使用更广泛的结果，从出版物到实践改进再到创业成果，克服了目前研究增长的许多限制。然而，结合这些异构数据集会带来三个挑战：1。将各种数据以csv、XML或XLS文件的形式共享到一个公共位置；合并和链接这些数据，有时重叠，3。评估研究成果和包容性实践在多维空间中的影响，这些通常在数据集中缺失。我们想提出我们对第一个挑战的解决方案，并讨论我们在第二个和第三个挑战方面的领先优势。
Our goal is to analyze improvement of scientific performance in a multidimensional outcome space, with a focus on US-based biomedical research. With the growing diversity of research databases, limiting assessment of scientific productivity to bibliometric measures such as number of publications, impact factor of journals and number of citations, is increasingly challenged. Using a wider range of outcomes, from publications through practice improvements to entrepreneurial outcomes, overcomes many current limitations in the study of research growth. However, combining such heterogeneous datasets raise three challenges: 1. gathering in one common place a variety of data shared as csv, xml or xls files, 2. merging and linking this data, that sometimes overlap, 3. assessing the impact of research production and inclusive practices in a multidimensional space, that are often missing from the datasets. We would like to present our solution for the first of those challenges, and discuss our leads for the second and third challenges.