Keep it Simple, Stupid: Designing a stripped-down DMP Template
Bibliographic record
Abstract
In Canada the long-awaited Tri-Agency policy on Research Data Management was finally released in early 2021. This policy is being rolled out in stages and is starting to require that applications for research funding that include data collection incorporate data management plans (DMPs). As DMPs have not previously been required in most Canadian funding calls, researchers and institutions supporting them are naturally apprehensive about these requirements. At Western University, a library-led working group including representatives from across the institution formed to consider these issues. The group also included members from research ethics, research development, IT, faculty and university administration. The need to support researchers in writing DMPs was recognized as an initial priority. Canada’s DMP Assistant, adapted from the Digital Curation Centre’s (DCC) DMP Online tool, provides a solution for helping novices write data management plans that meet the new requirements. The DMP Assistant walks researchers through a series of questions and provides guidance for answering them. While the group approved of the tool in principle, we felt that many of the questions were redundant or confusing, the way the questions were worded assumed a level of knowledge that many faculty members would not have, and that the guidance supplied was too general. Fortunately, the DMP Assistant allows the creation of custom templates with institution-specific guidance and questions, and the group decided to modify the default template to counter the issues that we saw. This session will discuss the differing perspectives the members of the group brought to this discussion, some of the issues raised with the existing templates, and the choices we made in coming up with a simplified template. We will also share responses from faculty who beta-tested the customized tool.
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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 | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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".