TY - JOUR
T1 - Data Integration for the Study of Outstanding Productivity in Biomedical Research
AU - Aubert, Clément
AU - Balas, E. Andrew
AU - Townsend, Tiffany
AU - Sleeper, Noah
AU - Tran, C. J.
N1 - Funding Information:
This work was supported by the grant R01 GM146338 from the NIH National Institute of General Medical Sciences in the SCISIPBIO program. The authors would like to thank the reviewers for their interesting comments, that greatly improved our presentation.
Publisher Copyright:
© 2022 The Author(s).
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Biomedical Research
KW - Matching of Research Databases
KW - Research Evaluation
KW - Scientific Performance
UR - http://www.scopus.com/inward/record.url?scp=85145226295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145226295&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2022.10.191
DO - 10.1016/j.procs.2022.10.191
M3 - Conference article
AN - SCOPUS:85145226295
SN - 1877-0509
VL - 211
SP - 196
EP - 200
JO - Procedia Computer Science
JF - Procedia Computer Science
IS - C
T2 - 15th International Conference on Current Research Information Systems, CRIS 2022
Y2 - 12 May 2022 through 14 May 2022
ER -