Digital preservation, archives management, format migration, transformation, at scale, normalization: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
In a recent scoping study we have inquired into the data management needs of several research groups at the University of Porto and concluded that data quality and ease of on-line data manipulation are among the most valued features of a data repository.This paper describes the ensuing approach to data curation, designed to streamline the data depositing process and built on two components: a curation workflow and a data repository.The workflow involves a data curator who will assist researchers in providing meaningful descriptions for their data, while a DSpace repository was customised to satisfy common data deposit and exploration requirements.Storing the datasets as XML documents, the repository allows curators to deposit new datasets using Excel spreadsheets as an intermediate format, allowing the data to be queried on-line and the results retrieved in the same format.This dedicated repository provides the grounds for collecting researcher feedback on the curation process.
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 | 0.044 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.032 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".