by Sarah Rutley, Kevin Read and Catherine Boden, University Library, University of Saskatchewan
In the Spring of 2020, University of Saskatchewan (USask) librarians met for the inaugural session of “Data Conversations” – a series of discussions designed to get us thinking about how academic libraries can, do, and should support research data-related activities. We hope to demystify data across disciplinary contexts, build familiarity with key concepts, develop a shared understanding of research data supports, and develop guidance for implementation. The jumping off point for the first meeting was a chapter titled “What is different about data?”.1 Here we share some highlights from our conversation.
Why is research data management (RDM) important? Topics like RDM, and data sharing, reproducibility, and reuse are becoming increasingly salient for research communities. Funders and publishers are making data sharing a requirement, and the scholarly community is looking to develop methods to solve the problem of transparency and reproducibility in science. Researchers are now required to perform new and potentially unfamiliar tasks/duties related to managing their research data. With this push towards openness in research, librarians have already begun to play a significant role. At USask, we are considering the types and levels of data services to offer. We talked about identifying approaches that focus on reducing the researcher burden, identifying collaborations with campus partners, and discussing which services are suitable for specific disciplines. These efforts will be especially important as the Canadian Tri-agency prepares to release new requirements for RDM for grant applications and the newly released Canadian Roadmap for Open Science is implemented incrementally over the next few years.
Speaking the language of the research community. A takeaway from our conversation was the importance of language when engaging with the research community. Using library-centric terminology to engage a research community may hinder a librarian’s ability to establish a connection with a researcher, resulting in a lost opportunity to demonstrate the value of the library’s data-related services. For instance, terms like “e-science” and “cyberinfrastructure”, while used frequently in library research to report on data-related efforts, are not necessarily used by research communities. Research disciplines think about data in different ways, and librarians need to be aware of these distinctions. A researcher in the humanities might not even think of their research materials (e.g., documents, artistic works, historical texts) as ‘data’. Being able to speak the language of the research community will help increase a librarian’s credibility, helping to establish researcher trust.
Researcher perceptions of applicability/relevance. We discussed how (or if) researchers perceive RDM to be relevant to their own work. Reasons to resist formally integrating RDM into research workflows could include: lack of conceptual familiarity with RDM; being unconvinced of associated personal or communal benefits; or the belief that “data” (as source material or scholarly output) belongs to other disciplines. Academic libraries that intend to successfully support RDM will need to work within each of these realities, and more. It was also noted that even where researchers may see the need to integrate RDM within their research lifecycle, practical responsibility may still be experienced as an unacceptable administrative burden. The challenge here is a significant one – if librarians intend to advocate for improved and intensified RDM practices, they must also be prepared to develop services that help harmonize the process for researchers.
This was a productive first conversation, and we look forward to exploring more topics going forward. Stay tuned for our next post on our conversation about indigenous data.
1 In Rice, R., & Southall, J. (2016). The data librarian’s handbook. Facet publishing.