Toward a Data Curation Network in Canada: Outcomes of the Canadian Data Curation Forum
Bibliographic record
Abstract
Data curation is an iterative process that adds value to scholarship by optimizing research datasets for current use, as well as future discovery and reuse. Moreover, it ensures that datasets developed and archived by researchers epitomize the FAIR guiding principles of Findability, Accessibility, Interoperability, and Reusability (Wilkinson et al., 2016). In order to meet these aspirations, data curators require a unique complement of disciplinary knowledge, information management skills, and software expertise. While advances are being made in automating data curation processes, the heterogeneous and multidisciplinary nature of research data commonly requires human curators to define FAIR standards for datasets and collaborate with researchers to realize them. Given that individual curators within research groups, organizations, and institutions are unlikely to possess all of the required skills and expertise, there is a significant need for training opportunities that expand their capabilities, and for higher-level coordination that standardizes practices across organizations. To address this need, McMaster University, in partnership with the Canadian Association of Research Libraries’ Portage Network and the Portage Network Curation Expert Group (CEG) (Portage Network, 2019), organized the first Canadian Research Data Curation Forum to promote and advance the practice of data curation in Canada. The event took place in Hamilton, Ontario from Oct. 16-18, 2019. The main goals of this event were to provide (1) a community-building stakeholder forum to better understand the current state and unmet needs of data curation practice in Canada, (2) professional development for data curators, and (3) a clear articulation of the next steps for the development of this profession in Canada. This poster will provide a summary of the event and describe the primary outcomes, including lessons learned, current data curation gaps and challenges, proposed models for a digital curation network in Canada, and the planned next steps to enact the vision expressed by the Canadian data curation community.
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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 | 0.075 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.054 | 0.009 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".