Collaborative Data Management: Best Practices throughout the Data Life Cycle
Bibliographic record
Abstract
While there is increased recognition of the value of rigorous data management, budgets and resources for this kind of activity are stagnant or decreasing. Perhaps because of this, there has been a growing interest in pursuing collaborative efforts to implement best practices throughout the research data life cycle. Effective collaborations can be local, involving individual researchers or research teams, or large-scale initiatives involving multiple institutions in either informal relationships or formal partnerships such as consortia. When data is collected, processed, archived, or disseminated as part of a collaborative process, the potential for problems is heightened - but so are the rewards. This session will look at examples of effective collaborative data management at all stages of the data life cycle, and consider some of the challenges and potential successes at play when we work together to improve data collection, preservation, and access. Examples will range from landmark projects to emerging initiatives, and include case studies from the Ontario Council of University Libraries (OCUL), an academic library consortium
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.013 | 0.026 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.045 |
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".