From Hard Drives to Globus: Supporting new workflows for large data transfer in libraries
Bibliographic record
Abstract
As data continues to grow in size and volume, there is an equally growing need to provide new technical solutions to support large data transfer within academic library data services. This search for digital solutions became even more urgent with the outbreak of the COVID-19 pandemic, as restrictions on contact meant that existing workflows were no longer possible in a remote environment. This presentation summarizes recent work by Scholars Portal, a consortial library technology service, to develop infrastructure to support the transfer of big data in delivering library data services to students and researchers. It will focus on the use of Globus, a large data transfer tool, and workflows for integration into two different data repository systems - Dataverse & Scholars GeoPortal. We will discuss current workflows for the transfer of large files, and some of the use cases in academic libraries both now and into the future.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".