Love notes to our future selves: Digital preservation and data curation
Bibliographic record
Abstract
As the volume of research data grows in both size and complexity, concerns about maintaining access to files that are difficult (or impossible) to migrate, reliant on software that is not openly available or no longer accessible, or of such poor quality that they cannot be reused are heightened by an awareness of the environmental cost of digital storage. While archivists have a long history of practice guiding preservation decision-making and the deaccessioning or removal of records from archives, it is not clear how widely archival appraisal theory informs the approach to research data (Dorey, Hurley, and Knazook 2022). Long-term preservation of digital research data will prove challenging for repositories and preservationists, requiring substantially more information than is typically collected to support decisions about what to keep, how to maintain accessibility, and for how long. Preservationists need information about when the files were created, by whom, and using what software or tools, along with an understanding of the relevance of the data to the community of practice and its perceived long-term value. Curators play a critical role in communicating the informational value of datasets, and through their work with depositors, are in a unique position to collect information that will inform preservation decisions and reduce duplication of effort as data are (re)appraised over time, but they are often disconnected from preservation decision-making. In this panel discussion, we explore how training data curators in archival appraisal can help ensure long-term access to research data. We will hear an overview of a combined data curation and preservation workflow, and 3 institutions will discuss their experiences testing and refining a checklist developed at the Digital Research Alliance of Canada to record appraisal information about incoming datasets. We will end with a panel discussion and share a public version of the checklist.
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 | 0.045 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.026 | 0.037 |
| Scholarly communication | 0.036 | 0.051 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".