Precisely and Persistently Identifying and Citing Arbitrary Subsets of Dynamic Data
Bibliographic record
Abstract
Precisely identifying arbitrary subsets of data so that these can be re-produced is a daunting challenge in data-driven science, the more so if the underlying data source is dynamically evolving. Yet, most settings exhibit exactly those characteristics: increasingly larger amounts of data being continuously ingested from a range of sources, with error correction and quality improvement processes adding to the dynamics. Yet, for studies to be reproducible, for decision-making to be transparent, and for meta studies to be performed conveniently, having a precise identification mechanism to reference, retrieve and work with such data is essential. The RDA Working Group on Dynamic Data Citation has published 14 recommendations that are centered around time- stamping and versioning evolving data sources and identifying subsets dynamically via persistent identifiers that are assigned to the queries selecting the respective subsets. These principles are generic and work for virtually any kind of data. In the past few years numerous repositories around the globe have implemented these recommendations and deployed solution. This paper provides an overview of the recommendations, reference implementations and pilot systems deployed and analyses key lessons learned from these. This provides a solid basis for institutions and researchers considering adding this functionality to their data infrastructure.
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 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 | 0.109 | 0.300 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.030 | 0.060 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.012 |
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".