It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada
Bibliographic record
Abstract
Objective – This empirical study aims to contribute qualitative evidence on the perspectives of data-related librarians regarding the necessary skills, education, and training for these roles in the context of Canadian academic libraries. A second aim of this study is to understand the perspectives of data-related librarians regarding the specific role of the MLIS in providing relevant training and education. The definition of a data-related librarian in this study includes any librarian or professional who has a conventional title related to a field of data librarianship (i.e., research data management, data services, GIS, data visualization, data science) or any other librarian or professional whose duties include providing data-related services within an academic institution. Methods – This study incorporates in-depth qualitative empirical evidence in the form of 12 semi-structured interviews of data-related librarians to investigate first-hand perspectives on the necessary skills required for such positions and the mechanisms for acquiring and maintaining such skills. Results – The interviews identified four major themes related to the skills required for library-related data services positions, including the perceived importance of experience conducting original research, proficiency in computational coding and quantitative methods, MLIS-related skills such as understanding metadata, and the ability to learn new skills quickly on the job. Overall, the implication of this study regarding the training from MLIS programs concerning data-related librarianship is that although expertise in metadata, documentation, and information management are vital skills for data-related librarians, the MLIS is increasingly less competitive compared with degree programs that offer a greater emphasis on practical experience working with different types of data in a research context and implementing a variety of methodological approaches. Conclusion – This study demonstrates that an in-depth qualitative portrait of data-related librarians within a national academic ecosystem provides valuable new insights regarding the perceived importance of conducting original empirical research to succeed in these roles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.059 | 0.022 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".