Reuse of code in research
P. Stavropoulos 
Description
The reuse of code or software in research refers to the practice of utilising existing code or software to develop new research tools, methods, or applications. It is becoming increasingly important in various scientific fields, including computer science, engineering, and data analysis, because it directly contributes to scientific reproducibility by enabling other researchers to validate the findings without the need to recreate the software or tools from scratch. Additionally, it is an indicator of research quality, as repeated use of code or software often signals robustness and reliability. Furthermore, a high percentage of research projects reusing code within a particular field could be an indication of strong collaboration and trust within the scientific community. This indicator aims to capture the extent to which researchers engage in the reuse of code or software in their research by quantifying the number and proportion of studies that utilise existing code or software. The indicator can be used to assess the level of collaboration and sharing of resources within a specific scientific community or field and to identify potential barriers or incentives for the reuse of code or software in research. Additionally, it can serve as a measure of the quality and reliability of research, as the reuse of code or software can increase the transparency, replicability, and scalability of research findings.
Connections to Academic Indicators
This indicator emphasizes the adoption and utilization of existing code or software in subsequent studies, focusing on its role in enhancing reproducibility, collaboration, and research quality. In contrast, the Use of Code in Research measures the initial incorporation of code or software into research activities, providing insights into its contribution to the research process itself. Furthermore, the Impact of Open Code in Research extends this perspective by evaluating the broader effects of making code or software openly accessible, fostering transparency, and driving innovation across the scientific community.
Metrics
Number of code/software reused in publications
This metric emphasizes the adoption and utilization of existing code or software in subsequent studies, focusing specifically on its role in enhancing reproducibility, collaboration, and research quality. The reuse of code in research strengthens reproducibility by allowing other researchers to validate findings and build upon existing methods and tools.
This closely aligns with the metrics in the Use of Code in Research under the academic indicators, specifically the number of mentions of code or software in publications. For further details on measurement, including text mining tools, and bibliometric databases, refer to the academic indicator.
In the context of reproducibility, the reuse of code indicates that methods and processes described in research publications are transparent and accessible. When researchers reuse code, they signal that the original research is sufficiently documented and functional to support replication. This is a cornerstone of open science, as reproducible research enables validation of results, ensuring the robustness of scientific knowledge and minimizing errors. The extent of code reuse also highlights the community’s trust in the reliability and quality of the code, as widely adopted software is likely to have undergone rigorous validation by multiple users.
Furthermore, the act of reusing code fosters interdisciplinary collaboration and accelerates scientific progress. By building on shared resources rather than duplicating efforts, researchers save valuable time and energy. This collaborative approach to software reuse ensures that scientific communities can focus on advancing new knowledge rather than resolving redundant technical challenges. As a result, code reuse acts as a multiplier for reproducibility, allowing not only the original study but also derivative works to be verified and built upon, expanding the scope of reliable and impactful research.
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_code_in_research.html},
doi = {10.5281/zenodo.14538442},
langid = {en}
}