Research Data Management (RDM) at UNB: Results of UNB Libraries’ 2019 Survey
Bibliographic record
Abstract
Research Data Management (RDM) refers to the processes that guide the collection, documentation, storage, sharing, and preservation of original research data. RDM is becoming increasingly important to academic researchers who are or may soon be required to submit research data and data management plans with their publication submissions or funding applications (see the Tri-Agency Statement of Principles on Digital Data Management- https://www.ic.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html). In our efforts to guide and support RDM best practices, UNB Libraries conducted a survey to determine current practices, perceptions, and future needs for managing original research data. The survey questions were focused on collecting information on the extent and types of data generated by UNB researchers, current practices for managing research data, and perceptions of the evolving RDM landscape. The results of this survey help to support our research community by designing RDM services and infrastructure suitable for current and future research needs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.018 | 0.056 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.436 |
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; both teacher heads agree on what is shown here.
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".