A High Performance Partnership: Data Librarians and Supercomputer Centres
Bibliographic record
Abstract
As datasets have continued to grow exponentially, data libraries are struggling with ways to provide access to them and support their use. The University of Toronto’s Map & Data Library recently explored options for providing access to one such large dataset: Web of Science Raw Data (XML). Researchers across disciplines are interested in using this dataset to explore citation networks and conduct bibliometrics research. University administrators are also very interested in this dataset for their reports and benchmarking. Querying this dataset can result in a subset of millions of records; thus, the challenge is not just in accessing the data, but also how to work with such a large number of results. To overcome these obstacles, the Map & Data Library has developed a mutually beneficial partnership with our High Performance Computer Service on campus: SciNet. This partnership enabled us to develop a new service that provides access to the Web of Science XML through an environment where researchers can effectively query and work with this dataset. This presentation will focus on our experiences with this project: how it came about; how the relationship was developed and navigated; the challenges in building a sustainable service; what our final solution was; what roles the Map & Data Library and SciNet play in this service; and our future plans to continue to expand this fruitful partnership.
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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 | 0.049 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.022 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.038 | 0.034 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.062 | 0.047 |
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".