Metadata for research data: current practices and trends
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
This paper reports a study that examined the metadata standards and formats used by a select number of research data services, namely Datacite, Dataverse Network, Dryad, and FigShare. These services make use of a broad range of metadata practices and elements. The specific objective of the study was to investigate the number and nature of metadata elements, metadata elements specific to research data, compliance with interoperability and preservation standards, the use of controlled vocabularies for subject description and access and the extent of support for unique identifiers as well as the common and different metadata elements across these services. The study found that there was a variety of metadata elements used by the research data services and that the use of controlled vocabularies was common across the services. It was found that preservation and unique identifiers are central components of the studied services. An interesting observation was the extent of research data specific metadata elements, with Dryad making use of a wider range of metadata elements specific to research data than other services.
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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.009 | 0.104 |
| Open science | 0.007 | 0.008 |
| 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