Data Discovery and Reuse in Data Service Practices: A Global Perspective
Bibliographic record
Abstract
Abstract The proposed panel will address the issues of the discovery and reuse of publicly available data on the web in the context of data service practices from a global perspective. Thousands of data discovery services have appeared around the world since the promotion of “open science”, reproducible research, and the FAIR (Findable, Accessible, Interoperable and Reusable) data principles in the research sector. However, there is also increasing demand for transparency of search algorithms, and in the design, development, evaluation, and deployment of current data search services; this requires a better understanding of how users approach data discovery and interact with data in search settings. From a global perspective, we will identify and discuss the specific system design issues in data discovery and reuse, drawing on our organization of the NTCIR (NII Testbeds and Community for Information access Research) project of Data Search track, the design and evaluation of the data discovery service of the Australian Research Data Commons (ARDC), and studies examining researchers' practices of data discovery and reuse.
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 | 0.109 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.043 | 0.054 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".