Using and reusing data

 


What happens to data after research is completed? In many cases, valuable datasets are stored away and rarely revisited. Yet within data curation, data is no longer viewed as something created for a single purpose only. Instead, it is increasingly recognised as a long-term intellectual asset capable of generating new knowledge, supporting innovation, and strengthening future research. However, while the reuse of curated datasets offers many opportunities, I believe its success depends on more than simply making information available online.

The use of data remains central to research, decision making, and knowledge production across different disciplines. Data allows researchers, organisations, and governments to identify patterns, evaluate problems, and make informed decisions. Boté and Térmens (2019) explain that datasets are generated throughout the research lifecycle using different tools and methods, ranging from surveys to digital devices. In my view, the value of data lies not only in its preservation but also in its practical application. Properly curated data can support innovation, improve service delivery, and strengthen evidence-based policy making. Without meaningful use, data simply becomes stored information rather than a resource capable of influencing society and future research.

Boté and Térmens (2019) further explain that data reuse involves finding, processing, and analysing existing datasets to generate new knowledge. This process supports research transparency and interdisciplinary collaboration, especially in today’s digital research environment. Although this sounds straightforward, secondary analysis is not always simple. Data collected in one context may easily lose meaning when interpreted in another. In my view, this is where proper digital stewardship becomes essential. Curating data is not only about preserving files but also about preserving context, reliability, and usability.

One concept that continues shaping modern research data management is the FAIR data principles, which encourage data to be Findable, Accessible, Interoperable, and Reusable. Wilkinson et al. (2022) argue that these principles improve the long-term value and accessibility of research data. However, I believe institutions sometimes focus too heavily on accessibility while overlooking documentation and metadata. A dataset may be publicly available, but without clear explanations of how the information was collected, organised, or interpreted, reuse can easily result in misinterpretation. Good metadata therefore becomes just as important as the dataset itself because it preserves meaning and trustworthiness.

Another issue that stand out to me from the discussion on data reuse is ethics. Boté and Térmens (2019) emphasise concerns surrounding informed consent, anonymity, privacy, and authorship recognition. While Open Science initiatives encourage sharing information, not all datasets should automatically be reused without limitations. Sensitive information from health, education, or social research requires careful handling to avoid exposing participants to harm. I strongly believe that ethical stewardship should remain at the centre of research reuse practices because trust is what ultimately gives research its credibility and long-term value.

At the same time, reusing existing datasets contributes greatly to sustainability in research and information management. Instead of repeatedly collecting similar information, researchers can save time, reduce costs, and build upon existing knowledge. Kim and Yoon (2023) further note that reusable data strengthens research transparency and scientific collaboration. Building on this argument, I believe institutions that invest in proper digital preservation and repository management are better positioned to support innovation and future knowledge creation.

Ultimately, using and reusing data is not simply a technical process within data curation. It is a responsibility that requires balance between openness, ethics, preservation, and trustworthiness. As digital information continues to grow, the future of research will depend not on how much data we collect, but on how responsibly and intelligently we preserve, interpret, and reuse it.

https://youtu.be/hUHuBGzIWw4?si=AyXu-Bcx6NfutUan

References

Boté, J.-J., & Térmens, M. (2019). Reusing data: Technical and ethical challenges. DESIDOC Journal  of Library & Information Technology, 39(6), 329–337.

Kim, Y., & Yoon, A. (2023). Emerging trends in research data reuse and stewardship practices. Journal    of the Association for Information Science and Technology, 74(2), 145–159.

Tenopir, C., Dalton, E. D., Allard, S., Frame, M., Pjesivac, I., Birch, B., et al. (2015). Changes in data    sharing and data reuse practices and perceptions among scientists worldwide. PLoS ONE, 10(8),          e0134826.

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2022). The FAIR guiding principles for          scientific data management and stewardship. Scientific Data, 9(1), 1–9.

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