Time to Professionalise Data Stewardship
Bibliographic record
Abstract
Materials presented at the workshop "Time to Professionalise Data Stewardship", held during the Open Science Fair in Porto on 17 September 2019. This workshop consisted of a mix of case study presentations and group discussions focussing on the need for professionalising data stewardship roles and how to go from theory and policy to practice and implementation. The “Turning FAIR into Reality” Report of the European Commission FAIR Data Expert Group gives high priority to “the increased provision and professionalisation of data stewardship”. In particular, it states: “New job profiles need to be defined and education programmes put in place to train the large cohort of data scientists and data stewards required to support the transition to FAIR.” A number of current initiatives seek to define these job profiles and address the supply of relevant skills. One example is the FAIR4S Skills Framework defined under the EOSCpilot WP7 on Skills and Capability. Another example is the EOSC FAIRsFAIR project that supports data stewardship training. The workshop aimed to raise awareness of such initiatives among those facing the practical commitments and real experience of building data stewardship roles. As a starting point for discussion, case studies from relevant initiatives explain how they support those intending to hire and train data stewards. In breakout groups, the participants debated what are the current problems and obstacles facing those who are implementing data stewardship (e.g. training and career development), and how current skill and training frameworks and job profiles can help.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.895 | 0.800 |
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".