Dataverse for Research Data : Using Best Practices for Research Data Management in UBC
Bibliographic record
Abstract
The volume of research data around the world is increasing at a phenomenal rate. According to a report of the Canadian Research Data Summit in 2011, “the way that we choose to manage our research data will directly impact our ability to undertake leading edge research and development in the future.” In Canada, as in many developed countries, requirements for data management are being established across a wide range of scholarly disciplines. In this presentation, we will offer UBC Library expertise in managing thousands of research data files with Dataverse software. UBC Abacus Dataverse (http://dvn.library.ubc.ca/dvn/) is open-source software, developed by Harvard, which allows researchers to share, cite, preserve, discover, and analyze research data. Dataverse is designed as a self-serve platform, where individual researchers, research teams, and institutes can create their own account and deposit their own data. Dataverse has proven to be a flexible platform that can support many models for research data management services. It offers a range of features that improve data discoverability and access. It also does a good job of managing data files from a preservation perspective: it manages versions, conducts checksums to maintain data integrity, and supports persistent identifiers. In this session we will cover how to: 1) Create and change records; 2) Metadata, types of metadata, standards; 3) Uploading files, large files, zipping; 4) Version control; 5) Tabular analysis in your browser; 6) Granular access to datasets: public, institutional, groups; 7) UNFs for data analysis; and 7) OAI for discoverability.
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 | 0.088 | 0.207 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.030 | 0.042 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.038 | 0.030 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.062 | 0.103 |
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".