Data curation preservation issues: budgets, costs, staffing and skills

Data curation helps keep research data usable, understandable and trustworthy after a project has ended. It includes choosing what should be kept, organising it, describing it, preserving it and making it available for later use. The difficult part is that preservation depends on more than technical systems. It also depends on money, staff time and institutional planning. When these are weak, data may no longer remain findable, accessible, interoperable and reusable, even though these are the aims of the FAIR principles (Wilkinson et al., 2016).

Budgeting is one of the main problems. Many institutions still treat preservation as if it ends once data has been deposited in a repository. In practice, data needs continuing support. Storage, backup systems, metadata work, file migration, security, software maintenance and repository management all require regular funding. The Digital Preservation Coalition (2015) makes the point that digital preservation depends on sustainable planning and resources, not technology alone. Without stable budgets, an institution may collect data but still fail to protect it properly.

Costs also rise because digital data is fragile. File formats can become outdated, storage media can fail, software can change and documentation can become separated from the data. Because of this, institutions need to carry out active preservation work such as format migration, fixity checks, metadata improvement and disaster recovery planning. Whyte and Tedds (2011) argue that research data management should be part of the whole research lifecycle. This matters because poor planning at the start of a project often creates bigger preservation costs later. Good data management plans, clear standards and early researcher support can reduce those costs.

Staffing is another major issue. Data curation needs people who understand information management and the research context in which the data was created. The DCC Curation Lifecycle Model shows that curation includes several connected stages, including data creation, appraisal, preservation and reuse (Higgins, 2008). These stages cannot be handled by software alone. Curators must judge data quality, create useful metadata, consider ethical issues, choose suitable preservation formats and support future users. Johnston et al. (2018) also explain that curation expertise is often spread across different parts of an institution, so shared staffing models can help where resources are limited.


Skills shortages make the problem worse. Data curators need technical knowledge, metadata skills, awareness of digital preservation, communication skills and an understanding of legal and ethical responsibilities. Academic librarians are increasingly asked to support research data services, but many still need training in areas such as data documentation, repository use and data management planning (Fuhr, 2022). Tenopir et al. (2015) also found that libraries face rising demand for data services while staffing and training remain common barriers.

Institutions can respond by treating data preservation as a core responsibility rather than an optional service. This means writing clear preservation policies, setting aside dedicated budgets, training staff and encouraging librarians, IT staff and researchers to work together. Shared infrastructure can also reduce pressure on institutions that cannot manage everything alone. If budgets, costs, staffing and skills are ignored, valuable data can be lost, misunderstood or made impossible to reuse. Effective preservation protects the data, but it also protects research quality, accountability and future knowledge.


REFERENCE 

Digital Preservation Coalition. (2015). Digital preservation handbook.

Fuhr, J. (2022). Research data management training and support for academic librarians.

Higgins, S. (2008). The DCC Curation Lifecycle Model.

Johnston, L., Carlson, J., Hudson-Vitale, C., Imker, H., Kozlowski, W., Olendorf, R., & Stewart, C. (2018). How important is data curation? Gaps and opportunities for academic libraries.

Tenopir, C., Sandusky, R. J., Allard, S., & Birch, B. (2015). Research data management services in academic research libraries and perceptions of librarians.

Whyte, A., & Tedds, J. (2011). Making the case for research data management.

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship.

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