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dc.contributor.authorAubert, Clémenten_US
dc.contributor.authorBalas, Andrewen_US
dc.contributor.authorTownsend, Tiffanyen_US
dc.contributor.authorSleeper, Noahen_US
dc.contributor.authorTran, CJen_US
dc.identifier.citationProcedia Computer Science 211: 196-200 (2022)-
dc.descriptionExtended abstract to be presented at the CRIS2022 conference in Dubrovnik.-- Event programme available at
dc.descriptionPresentation delivered remotely. Recording available at
dc.description.abstractOur goal is to analyze improvement of scientific performance in a multidimensional outcome space, with a focus on American 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. inferring the impact of inclusive practises, 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.en_US
dc.relation.ispartofseriesCRIS2022: 15th International Conference on Current Research Information Systems (Dubrovnik, Croatia, May 12-14, 2022)-
dc.subjectresearch performanceen_US
dc.subjectbiomedical researchen_US
dc.subjectresearch information managementen_US
dc.titleData Integration for the Study of Outstanding Productivity in Biomedical Researchen_US
dc.typeConference Proceedingen_US
dc.relation.conferenceCRIS2022 – Dubrovniken_US
item.openairetypeConference Proceeding-
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