Toward Best Practices for Unstructured Descriptions of Research Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Achieving the potential of widespread sharing of open research data requires that sharing data is straightforward, supported, and well‐understood; and that data is discoverable by researchers. Our literature review and environment scan suggest that while substantial effort is dedicated to structured descriptions of research data, unstructured fields are commonly available (title, description) yet poorly understood. There is no clear description of what information should be included, in what level of detail, and in what order. These human‐readable fields, routinely used in indexing and search features and reliably federated, are essential to the research data user experience. We propose a set of high‐level best practices for unstructured description of datasets, to serve as the essential starting point for more granular, discipline‐specific guidance. We based these practices on extensive review of literature on research article abstracts; archival practice; experience in supporting research data management; and grey literature on data documentation. They were iteratively refined based on comments received in a webinar series with researchers, data curators, data repository managers, and librarians in Canada. We demonstrate the need for information research to more closely examine these unstructured fields and provide a foundation for a more detailed conversation.
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.
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 | 0.008 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.050 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it