Bibliographic record
Abstract
Governments and industry are increasingly recognizing the potential benefits of publicly available data for social, scientific, and business innovation. The growth of data on public platforms has made data discoverability challenging. This is exacerbated by inconsistent adherence to data publishing standards. Comma-Separated Values (CSV) is a prevalent Open Data format used in various domains for its straightforwardness. Yet, CSV data often lacks strict specification compliance, complicating automated discovery and extraction. Our work introduces two novel methods to enhance Open Data metadata for improved data discovery. The first, Pytheas, automates the discovery of topological structural metadata in CSV files — identifying table locations and elements by leveraging column value coherency. We evaluated Pytheas over two manually annotated data sets totaling 4511 CSV files sourced from portals in four English-speaking countries. We show that Pytheas compares favorably to state-of-the-art approaches in accuracy and generalizability. Finally, we introduce a confidence measure for table discovery and demonstrate its value in error identification. The second method, Gnomon, focuses on the unification of semantics metadata of table attributes found in supplementary documentation files in Open CSV. We propose a metadata model for capturing common table attribute semantics, and use a two-step unification approach: an ensemble of classifiers to discern schema matching between a source schema and the target metadata model, followed by a Weighted Bipartite b-Matching (WBbM) step to enforce domain constraints. Evaluating Gnomon on 228 files from English-speaking government and scientific Open Data portals showed that it outperforms current schema matching techniques, with WBbM proving effective in the alignment task. We anticipate that these approaches will empower researchers and industry professionals across a wide spectrum of domains, highlighting their potential to advance the landscape of data science and collaboration
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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 | 0.039 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".