Laying the Foundation: A Pilot National Research Data Bootcamp
Bibliographic record
Abstract
Current research data management (RDM) training in Canada is largely based around ‘one-shot’ sessions focused on Data Management Plans (DMPs) and data deposits, and often misses key concepts and activities of computational reproducibility that are essential to maintaining data integrity throughout a project’s lifecycle. While there are other, more computationally-focused workshops offered nationally, feedback from participants suggests that these training sessions can be overly advanced and fast-paced for those new to these technologies.To begin addressing this gap in data training, librarians and graduate students from four Canadian universities, supported by funding from the Digital Research Alliance of Canada, will pilot a week-long, 20-hour national data bootcamp, aimed at graduate students and early career researchers with little or no computational background. The bootcamp, that will be delivered in May 2025, will walk participants through the research data lifecycle using a mock project, and focus on connecting research data management best practices (documentation, storage, sharing, and preservation) to best practices in scholarly integrity (registrations and computationally reproducible workflows), which are intricately connected but oftentimes presented as separate practices. In addition to delivering the bootcamp, a key output will be robust and openly accessible asynchronous training materials for each session of the bootcamp. The broader vision for this program is to develop additional bootcamps in the coming years, which would aim to define “introductory”, “intermediate”, “advanced”, and “expert” level training, and provide a seamless educational trajectory in data and computational training.This presentation will begin by introducing the context in which this pilot was developed, including gaps and opportunities in the Canadian training landscape. We will then discuss the process of developing and delivering the program, and will conclude by addressing lessons learned and future directions.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
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".