Abstract
The advancement of data-intensive sciences and artificial intelligence-driven sciences has introduced governance challenges for multi-source heterogeneous scientific data across diverse scenarios. Given the intricate entanglement of stakeholders, processes, and content in scientific data governance, this study intends to propose a theoretical framework to elucidate its complex dynamics and inform governance practices. The theoretical framework for scientific data governance consists of three core dimensions: data stakeholders, data lifecycle, and data governance elements. Non-systematic literature review was employed to identify the classification of data stakeholders and data lifecycle, and bibliometric analysis was used to extract the elements of scientific data governance. Meanwhile, based on the elements of data governance, five governance systems have been summarized, including organizational operation system, technical support system, risk prevention and control system, value realization system, and regulatory system.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from Web of Science. A full list of consulted articles and their detailed information is provided in Supplementary Table S1.
Code availability
The software used for bibliometric analysis is Thomson Data Analyzer (TDA) version 3.0.
References
Pesenson, M. Z., Pesenson, I. Z. & McCollum, B. The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch. Advances in Astronomy. (2010).
Hey, T., Tansley, S. & Tolle, K. The Fourth Paradigm: Data-Intensive Scientific Discovery[M]: Microsoft Press (2009).
Shah, S. I. H., Peristeras, V. & Magnisalis, I. DaLiF: a data lifecycle framework for data-driven governments. Journal of Big Data. 8(1) (2021).
Cox, M. & Ellsworth, D. Managing big data for scientific visualization. (1997).
Laney, D. 3D Data Management: Controlling Data Volume, Velocity, and Variety. (2001).
Bello-Orgaz, G., Jung, J. J. & Camacho, D. Social big data: Recent achievements and new challenges. Information Fusion 28, 45–59 (2016).
Li, S. & Yueliang, Z. Research on the Data Governance Framework of Institutional Research Data Repository Alliance. Library Tribune 38(08), 61–7 (2018).
Weill, P. & Ross, J. IT Governance: How Top Performers Manage IT Decision Rights for Superior Results: Harvard Business School Press; 2004.
Brous, P., Janssen, M & Vilminko-Heikkinen, R. Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles (Cham: Springer International Publishing 2016).
Soares, S. IBM InfoSphere: A Platform for Big Data Governance and Process Data Governance. US: MC Press (2013).
Weber, K. & Otto, B. A Contingency Approach to Data Governance (2007).
Hanisch, R. et al. NIST Research Data Framework (RDaF) Version 2.0.[EB/OL]. [2025-08-31], https://doi.org/10.6028/NIST.SP.1500-18r2 (2023).
Fothergill, B. T., Knight, W., Stahl, B. C. & Ulnicane, I. Responsible Data Governance of Neuroscience Big Data. Front Neuroinform 13, 28 (2019).
Nadal, S., Jovanovic, P., Bilalli, B. & Romero, O. Operationalizing and automating Data Governance. J Big Data 9(1), 117 (2022).
Al-Badi, A., Tarhini, A. & Khan, A. I. Exploring Big Data Governance Frameworks. Procedia Computer Science 141, 271–7 (2018).
Zhang, Q. Q., Sun X. B. & Zhang M. C. Data Matters: A Strategic Action Framework for Data Governance. Information & Management. 59(4) (2022).
DGI. DGI Data Governance Framework[EB/OL]. [2025-03-04]. https://datagovernance.com/the-dgi-data-governance-framework/#:~:text=The%20DGI%20Data%20Governance%20Framework%20is%20a%20logical,decisions%20about%20and%20taking%20action%20on%20enterprise%20data.
Mosley, M., Susan Earley, M. B. & Henderson. D. The DAMA Guide to the Data Management Body of Knowledge - DAMA-DMBOK. Technics Publications, LLC DAMA International (2009).
Jang, K. A. & Kim, W. J. Development of data governance components using DEMATEL and content analysis. Journal of Supercomputing 77(4), 3695–709 (2021).
Eke, D. O. et al. International data governance for neuroscience. Neuron 110(4), 600–12 (2022).
IBM. What is data governance?[EB/OL]. [2025-03-04]. https://www.ibm.com/think/topics/data-governance.
Association CCS. Data Governance Standardization White Paper[EB/OL]. [2025-03-05]. https://13115299.s21i.faiusr.com/61/1/ABUIABA9GAAg7YGHjgYo4PjDtgQ.pdf (2021).
O’Doherty, K. C. et al. Toward better governance of human genomic data. Nature Genetics 53(1), 2–8 (2021).
Abraham, R., Schneider, J. & vom Brocke, J. Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management 49, 424–38 (2019).
Khatri, V. & Brown, C. V. Designing Data Governance. Communications of the Acm 53(1), 148–52 (2010).
Jiang, S. T. et al. Evolutionary patterns and research frontiers in neoadjuvant immunotherapy: a bibliometric analysis. International Journal of Surgery 109(9), 2774–83 (2023).
Zhang, L. L., Ling, J. & Lin, M. W. Carbon neutrality: a comprehensive bibliometric analysis. Environmental Science and Pollution Research 30(16), 45498–514 (2023).
Yan, P. Analysis of the Participation of Archival Departments in Scientific Data Management from the Perspective of Stakeholders. Archives World. 2019(03), 47-9.
Sheng, X. P. & Wu, H. Analysis of the Motivations of Different Stakeholders in the Activity of Open Sharing of Scientific Data. library and Information Services. 63(17), 40–50 (2019).
Smith, K., Seligman, L. & Swarup, V. Everybody share: The challenge of data-sharing systems. Computer. 41(9), 54 (2008).
wang, D. Research on Scientific Data Governance Model from the Perspective of Open Data Maturity: Heilongjiang University; (2022).
Gao, Y., Zhu Z. L., Yang J. An Evolutionary Game Analysis of Stakeholders’ Decision-Making Behavior in Medical Data Sharing. Mathematics. 11(13) (2023).
OECD. OECD principles and guidelines for access to research datafrom public funding[EB/OL]. [2025-02-27]. https://www.oecd.org/en/publications/oecd-principles-and-guidelines-for-access-to-research-data-from-public-funding_9789264034020-en-fr.html (2007).
COMMISSION E. Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020 Version 1.0 [EB/OL]. [2025-02-27]. https://ai.tecnico.ulisboa.pt/files/sites/52/guidelines-scientific-publicationsresearch-data-in-h2020.pdf (2013).
LERU. LERU roadmap for research data[EB/OL]. [2025-02-27]. https://www.leru.org/files/LERU-Roadmap-for-Research-Data-Full-paper.pdf (2013).
International S. Open data in a big data world[EB/OL]. [2025-02-27]. https://council.science/wp-content/uploads/2017/04/open-data-in-big-data-world_long.pdf (2015).
Blazquez, D. & Domenech, J. Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change 130, 99–113 (2018).
Wen, F. F. Research on the Construction of the Policy System for Government Data Opening in China (2019).
Pei, L. & Wang, J. C. Review of Research Progress in Information Lifecycle Management. Journal of Information. 29(09), 7-10+20 (2010).
Simonet, A., Fedak, G. & Ripeanu, M. Active Data: A programming model to manage data life cycle across heterogeneous systems and infrastructures. Future Generation Computer Systems-the International Journal of Escience 53, 25–42 (2015).
Chen, S. X. Research on the Construction of FAIR Evaluation Index System for Scientific Data Platform (2024).
IBM. What is data lifecycle management (DLM)?[EB/OL]. https://www.ibm.com/think/topics/data-lifecycle-management.
Rahul, K. & Banyal, R. K. Data Life Cycle Management in Big Data Analytics. Procedia Computer Science 173, 364–71 (2020).
Shameli-Sendi, A. An efficient security data-driven approach for implementing risk assessment. Journal of Information Security and Applications. 54 (2020).
Jetten, M., Simons, E. & Rijnders, J. The role of CRIS’s in the research life cycle. A case study on implementing a FAIR RDM policy at Radboud University, the Netherlands. Procedia Computer Science 146, 156–65 (2019).
Ball, A. Review of Data Management Lifecycle Models. (2012).
Cao, T. Z. Synergetics Approach to the Study of Collaborative Mechanisms in Public Administration Implementation: Theoretical Rationality and Interdisciplinary Foundations. Journal of Zhejiang Provincial Party School 25(01), 37–42 (2009).
Goel, K., Martin, N. & ter Hofstede, A. Demystifying data governance for process mining: Insights from a Delphi study. Information & Management. 61(5) (2024).
Acknowledgements
The authors would like to acknowledge the Scientific Data Governance and Open Sharing Team for their contributions to the adjustment and refinement of the theoretical framework of scientific data governance. The contributing members include Xiaofeng Jia, Youbing Ran, Meng Yu, and Borui Zhang. This work was supported by the Noncommunicable Chronic Diseases–National Science and Technology Major Project (Grant No. 2023ZD0509701) and the Medical and Health Technology Innovation Project of the Chinese Academy of Medical Sciences (Grant No. 2021-I2M-1-057).
Author information
Authors and Affiliations
Contributions
Q.Y.R. collected and analyzed data and materials, constructed the scientific data governance theoretical framework, and prepared the original draft. H.Z.M. modified the scientific data governance theoretical framework, revised and reviewed the manuscript, and obtained the funding support for the article. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Qiu, Y., Hu, Z. Construction of a Theoretical Framework for Scientific Data Governance. Sci Data (2026). https://doi.org/10.1038/s41597-025-06525-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41597-025-06525-0
