University of Alberta Dataverse: A journey from standalone to a hosted community platform
Bibliographic record
Abstract
University of Alberta Library Dataverse (UALD) is a deployment of the open source research data repository software developed and supported by the Harvard Dataverse Project, providing essential support in helping researchers at the University of Alberta (UA) to store, manage, share, and disseminate their research data since its first deployment in 2014. It enhances findability, accessibility, interoperability, and reusability (FAIR) of deposited content and its associated intellectual output. However, it has become increasingly difficult to sustain such an imperative and essential service in the face of a litany of challenges, including: continued increase of research data in terms of rate, volume and diversity; reduced and uncertain budgets; lack of localized IT personnel with specialized knowledge and skills due to the centralization of campus IT infrastructure and personnel; and fast changes of technologies such as cloud computing and storage. With these challenges in mind, it was decided to migrate the UALD from a standalone application to Scholars Portal’s dataverse, a collaborative platform and service operating at a national level across Canada. The collaboration allows participatory institutions to leverage shared computing infrastructure and resources, to tap into the pool of dedicated expertises, to stay at the forefront of cutting-edge technologies, to increase exposure of research data, and most importantly to overcome crippling challenges to provide sustainable services with cost-efficiency. The migration activities largely took place throughout 2021, with many valuable lessons learned. The journey of this migration is presented.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.034 |
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".