TY - JOUR
T1 - How research data deliver non-academic impacts
T2 - A secondary analysis of UK Research Excellence Framework impact case studies
AU - Jensen, Eric A.
AU - Wong, Paul
AU - Reed, Mark S.
N1 - Publisher Copyright:
Copyright: © 2022 Jensen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/3
Y1 - 2022/3
N2 - This study investigates how research data contributes to non-academic impacts using a secondary analysis of high-scoring impact case studies from the UK’s Research Excellence Framework (REF). A content analysis was conducted to identify patterns, linking research data and impact. The most prevalent type of research data-driven impact related to “practice” (45%), which included changing how professionals operate, changing organizational culture and improving workplace productivity or outcomes. The second most common category was “government impacts”, including reducing government service costs and enhancing government effectiveness or efficiency. Impacts from research data were developed most frequently through “improved institutional processes or methods” (40%) and developing impact via pre-analyzed or curated information in reports (32%), followed by “analytic software or methods” (26%). The analysis found that research data on their own rarely generate impacts. Instead they require analysis, curation, product development or other forms of significant intervention to leverage broader non-academic impacts.
AB - This study investigates how research data contributes to non-academic impacts using a secondary analysis of high-scoring impact case studies from the UK’s Research Excellence Framework (REF). A content analysis was conducted to identify patterns, linking research data and impact. The most prevalent type of research data-driven impact related to “practice” (45%), which included changing how professionals operate, changing organizational culture and improving workplace productivity or outcomes. The second most common category was “government impacts”, including reducing government service costs and enhancing government effectiveness or efficiency. Impacts from research data were developed most frequently through “improved institutional processes or methods” (40%) and developing impact via pre-analyzed or curated information in reports (32%), followed by “analytic software or methods” (26%). The analysis found that research data on their own rarely generate impacts. Instead they require analysis, curation, product development or other forms of significant intervention to leverage broader non-academic impacts.
UR - http://www.scopus.com/inward/record.url?scp=85126124393&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0264914
DO - 10.1371/journal.pone.0264914
M3 - Article
SN - 1932-6203
VL - 17
JO - PLoS ONE
JF - PLoS ONE
IS - 3 March
M1 - e0264914
ER -