Reuse of data in research
P. Stavropoulos 
Description
The reuse of data in research refers to the practice of utilizing existing data sets for new research questions. It is a common practice in various scientific fields, and it can lead to increased scientific efficiency, reduced costs, and enhanced scientific collaborations. Additionally, the reuse of well-documented data can serve as an independent verification of original findings, thereby enhancing the reproducibility of research. This indicator aims to capture the extent to which researchers engage in the reuse of data in their research, by quantifying the number and proportion of studies that utilize previously collected data. The indicator can be used to assess the level of scientific collaboration and sharing of data within a specific scientific community or field, and to identify potential barriers or incentives for the reuse of data in research. Additionally, it can serve as a measure of the quality and reliability of research, as the reuse of data can increase the transparency, validity, and replicability of research findings.
Connections to Academic Indicators
This indicator emphasizes the adoption and utilization of existing datasets for new research purposes, highlighting its role in enhancing reusability, reproducibility, collaboration, and research efficiency. In contrast, the Use of Data in Research focuses on the initial incorporation of data into research activities and its contributions to academic outputs. Furthermore, the Impact of Open Data in Research extends this perspective by evaluating how openly shared datasets foster transparency, accessibility, and innovation across the scientific community.
Metrics
Number of datasets reused in publications
This metric emphasizes the reuse of datasets in research publications, highlighting its importance in fostering reproducibility, collaboration, and research efficiency. The reuse of datasets serves as an essential mechanism for validating findings and building upon prior research, enhancing the transparency and replicability of scientific outputs.
This aligns closely with the metrics discussed in the Use of Data in Research under the academic indicators. Specifically, the measurement of dataset mentions or citations in publications provides the foundation for assessing both the use and reuse of data. For further details on measurement methodologies, such as text mining, and the role of data repositories, refer to the academic indicator.
In the context of reproducibility, the reuse of datasets reflects the scientific community’s ability to leverage existing data to answer new research questions. It underscores the importance of effective data sharing practices, robust metadata, and clear licensing, as these enable other researchers to trust, access, and incorporate datasets into their work. Furthermore, higher levels of data reuse often indicate stronger collaboration and trust within a scientific field, which are critical for advancing reproducible research.
By interpreting dataset reuse through the lens of reproducibility, this indicator also highlights the extent to which researchers adopt transparent and open research practices. The repeated utilization of datasets ensures that original findings are validated and that the data itself is robust, reliable, and suitable for diverse applications.
Reuse
Citation
@online{apartis2024,
author = {Apartis, S. and Catalano, G. and Consiglio, G. and Costas,
R. and Delugas, E. and Dulong de Rosnay, M. and Grypari, I. and
Karasz, I. and Klebel, Thomas and Kormann, E. and Manola, N. and
Papageorgiou, H. and Seminaroti, E. and Stavropoulos, P. and Stoy,
L. and Traag, V.A. and van Leeuwen, T. and Venturini, T. and
Vignetti, S. and Waltman, L. and Willemse, T.},
title = {Open {Science} {Impact} {Indicator} {Handbook}},
date = {2024},
url = {https://handbook.pathos-project.eu/sections/5_reproducibility/reuse_of_data_in_research.html},
doi = {10.5281/zenodo.14538442},
langid = {en}
}