The Data Archivist: the archivist’s role in data management and preservation
Bibliographic record
Abstract
Sarah Allain, Systems Archivist Sarah Romkey, Archivematica Program Manager Research data management is undoubtedly a hot topic in digital librarianship today. Increasingly, academic institutions are relying on services within the library to help researchers build data management plans (DMPs) and manage their data for the long term. Data repositories, like institutional repositories, are often managed by the library. While the role of the librarian in research data management is becoming increasingly clear, the role of the archivist is still emerging. Research data, like all digital assets, has digital preservation needs and challenges, but digital preservation has been described by some as a “gap” in current data management practices. Exacerbating the gap is that research data is sometimes created by domain-specific tools and in proprietary formats. In order to fill this gap, some librarians and archivists have been looking to digital preservation systems such as Archivematica to integrate with their data management platforms. This presentation will report on three approaches in the Archivematica user community to preserve research data: An integration between Archivematica and the data management platform Dataverse, which is being tested by the Ontario Council of University Libraries. Secondly, archivists at the Universities of Hull and York in the United Kingdom have been developing Archivematica features to better integrate with new and existing research data management systems. Finally, Compute Canada has piloted Archivematica as an integrated service with its Globus Portal, a data transfer service.
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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 | 0.052 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.038 | 0.045 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".