Souvenirs from an RDM Professional Development Journey
Bibliographic record
Abstract
Purpose: Describes a professional development journey in Research Data Management (RDM) services funded by the National Library of Medicine. \nSetting: The National Library of Medicine encourages training in data concepts through courses and funding from the National Network of Libraries of Medicine (NNLM) Training Office and Regional Medical Libraries (RMLs). The Medical Sciences Library at Texas A&M University provides data consultations, referrals and workshops, but no formalized data services. \nDescription: A librarian attended the first cohort of the NNLM Biomedical and Health RDM for Librarians course, the subsequent RDM 102, and a site visit with course mentors, all funded through the NNLM Training Office. Data Science Professional Development awards from the South Central RML funded attendance at the Mobilizing Computable Biomedical Knowledge (MCBK) and Transforming Research meetings. Data-related workshops were selected as an initial service to raise awareness of issues and position the Library as a partner for solutions. “Souvenirs” collected at each stop – a toolkit here, an active learning dataset there – were customized, promoted, piloted, and revised. \nOutcome: The workshops nurture conversation with faculty and leadership. An RDM workshop is in an ongoing series for the Health Sciences Center, graduate students did an active learning exercise at orientation, and a best practices workshop from the library is scheduled. Conference information and contacts inform planning. \nConclusion: The RDM professional development journey doesn’t have to be expensive – funding is available. It doesn’t require applying “all the things”. Collect, customize and apply those that produce a quick win and resonate with constituents.
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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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.031 | 0.024 |
| Open science | 0.005 | 0.046 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.037 | 0.021 |
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".