Data Integration for the Study of Outstanding Productivity in Biomedical Research
Author(s)
Issue Date
May 13, 2022
Publisher
euroCRIS
Source
Procedia Computer Science 211: 196-200 (2022)
Type
Conference Proceeding
Abstract
Our 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.
Description
Extended abstract to be presented at the CRIS2022 conference in Dubrovnik.-- Event programme available at https://cris2022.srce.hr/#section-program
Presentation delivered remotely. Recording available at https://youtu.be/7kZeL1HGK08
URI
https://dspacecris.eurocris.org/handle/11366/1987
File(s)![Thumbnail Image]()
Name
Aubert_et_al_CRIS2022_Data-Integration-for-the-Study.pdf
Description
Extended abstract (PDF)
Size
422.18 KB
Format
Adobe PDF
Checksum (MD5)
a12356bd6ea495038314871975401e98
Conference(s)