Developing Research Data Management Capability: the View from a National Support Service: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
An increasing number of UK Higher Education Institutions (HEIs) are developing Research Data Management (RDM) support services.Their action reflects a changing technical, social and political environment, guided by principles set out in the Research Councils UK (RCUK) Common Principles on Data Policy.These reiterate expectations that publicly-funded research should be openly accessible, requiring that research data are effectively managed.The Engineering and Physical Sciences Research Council (EPSRC) policy framework is particularly significant, as it sets a timeframe for institutions to develop and implement a roadmap for research data management.The UK Digital Curation Centre (DCC) is responding to such changes by supporting universities to develop their capacity and capability for research data management.This paper describes an 'institutional engagement' programme, identifying our approach, and providing examples of work undertaken with UK universities to develop and implement RDM services.We are working with twenty-one HEIs over an eighteen month period, across a range of institution types, with a balance in research strengths and geographic spread.The support provided varies based on needs, but may include advocacy and awareness raising, defining user requirements, policy development, piloting tools and training.Through this programme we will develop a service model for institutional support and a transferable RDM toolkit.
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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 | 0.122 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.049 | 0.032 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".