Data Integration for the Study of Outstanding Productivity in Biomedical Research

Clément Aubert, E. Andrew Balas, Tiffany Townsend, Noah Sleeper, C. J. Tran

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)196-200
Number of pages5
JournalProcedia Computer Science
Issue numberC
StatePublished - 2022
Event15th International Conference on Current Research Information Systems, CRIS 2022 - Dubrovnik, Croatia
Duration: May 12 2022May 14 2022


  • Biomedical Research
  • Matching of Research Databases
  • Research Evaluation
  • Scientific Performance

ASJC Scopus subject areas

  • Computer Science(all)


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