UBC Research Data Management Survey: Health Sciences
Bibliographic record
Abstract
In 2016, the Canadian federal funding agencies introduced the Tri-Agency Statement of Principles on Digital Data Management, which advocates for developing data management plans (DMPs) and making data available for future research. A data management plan addresses questions about: research data types and formats, metadata standards, ethics and legal compliance, data storage and reuse, assignment of data management responsibilities, and resource requirements. With anticipation that DMPs will be increasingly required in grants applications, librarians at University of British Columbia surveyed researchers about their RDM practices and needs in three phases, each of which targets different disciplines: 1) the Sciences and Engineering (fall 2015), 2) the Social Sciences and Humanities (fall 2016), and 3) the Health Sciences (spring 2017). The surveys illuminate disciplinary differences in RDM, and will inform the University in developing infrastructure and services to support researchers in RDM. This report describes findings from the third survey at UBC targeting researchers in the Health Sciences.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.040 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.033 |
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".