PostgREST Data Provider for React-Admin: Bootstrap the creation of user interfaces on top of PostgreSQL databases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In today’s data-driven world, vast amounts of data are stored in relational databases like i2b2, often using middleware applications for delivery. PostgreSQL, a widely used open-source DBMS, offers advanced features, including Foreign Data Wrappers (FDWs) for integration with other DBMSs. However, accessing data typically requires SQL knowledge. RESTful APIs simplify data interactions, and tools like PostgREST convert PostgreSQL databases into RESTful APIs. Our work introduces a PostgREST Data Provider that bridges React-Admin with PostgREST. A demo application showcases its capabilities, using KeyCloak for authentication and integrating an i2b2 database with FDW, fuzzy full-text search with ZomboDB, and utilizing GRASCCO discharge letters linked to i2b2 patients. • Seamlessly integrate PostgREST APIs with React-admin for efficient data management. • Demonstration app showcases advanced search with ZomboDB and a full-text search using GRASCCO and FHIR representation. • Leverage PostgreSQL foreign data wrappers (FDWs) to interact with diverse data sources seamlessly. • Unified authentication with KeyCloak supports multiple data providers in one application. • Open-source provider under MIT license encourages customization and enhancement.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it