Preferred but not Required: Examining Research Data Management Roles in Health Science Librarian Positions
Bibliographic record
Abstract
Introduction: Research data management (RDM) is being recognized as an increasingly important role for librarians. In this paper, the role of health science librarians in supporting research data management endeavors is examined. Methods: All job postings currently (as of April 5th, 2018) available on the University of Toronto’s Faculty of Information (iSchool) job site were analyzed to identify positions related to health science librarianship. The job responsibilities and descriptions were then examined to identify instances where research data management was mentioned. Results: Thirty-two postings from the search results were identified as meeting the inclusion criteria. Of these thirty-two health science librarian postings which were included in the analysis, eight included supporting research data management services, in some capacity, as part of the position description. Discussion/ Conclusion: Through the job posting analysis, a picture emerges where RDM is not consistently seen as a role for health science librarians. However, the literature indicates that in many instances, research data management is already being done by health science librarians, and is a trend which is likely to continue in the future. As such, it is important that research data management services start being acknowledged and reflected in education and job description opportunities.
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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 | 0.035 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".