Integrating Research Data Management workflows into Islandora 8x
Bibliographic record
Abstract
In collaboration with colleagues from Simon Fraser University and the Islandora Foundation, the Robertson Library at UPEI received funding to enhance Islandora 8x to facilitate the creation of a Research Data Management platform. Building on their experience working with research data in Islandora 7x, the group will seek to leverage the new version of Islandora and add functionality that will support the research data lifecycle from planning to publication. The team will present and provide an update on their work on the Islandora 8x RDM platform related to metadata and discovery, data deposit and curation, data privacy and security, persistent identifiers and citability, the use of vocabularies/ontologies and linked data, and data access and analytics. The team will highlight their work on data management planning tools, integration of external identity sources like the Global Research Identifier Data and ORCID, external metadata sources like DataCite and Crossref, external funding sources like the CrossRef Funder Registry, and DOI minters. Likewise, the team will demonstrate external storage methods and support for large datasets.
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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.018 |
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".