Turning Open Government Data Portals into Interactive Databases
Bibliographic record
Abstract
The launch of open governmental data portals (OGDPs), such as data.gov, data.gov.in, and open.canada.ca, have popularized the open data movement of the last decade, which now includes numerous other portals from other public or private institutions. These portals publish large numbers of datasets related to a very wide range of topics. Although the amount of datasets in OGDPs are increasing, the functionalities provided by most of the OGDPs to the end users are limited to finding the datasets based on the title and description, and downloading the actual files. This limitation hinders the end users, especially those without technical skills, to find the open data files and make use of them. \n \nThis thesis presents Governor, a web application developed to make open data tables more accessible to the end users in several ways. First, Governor facilitates searching the actual records in the original tables in OGDP. Second, Governor allows users to preview the tables in the web browser directly without downloading them. Third, Governor allows users to integrate multiple tables to form enriched datasets. A key feature here is automatically finding and suggesting joinable and unionable tables to users based on the latest state of their integrated tables. These operations are performed in the web browser interactively through a few clicks without using a programming language or a spreadsheet software. Lastly, Governor provides a set of features to summarize the provenance of integrated tables allowing users and their collaborators to easily trace back the values in integrated tables to the original tables in the OGDP.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".