| --- |
| license: mit |
| pretty_name: "EdTech SQL Evaluation (declared learning curve, verified answer key)" |
| tags: |
| - synthetic |
| - text-to-sql |
| - evaluation |
| - data-agents |
| - tabular |
| - sql |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - table-question-answering |
| configs: |
| - config_name: students |
| data_files: "tables/students.csv" |
| - config_name: practice_attempts |
| data_files: "tables/practice_attempts.csv" |
| --- |
| |
| # EdTech SQL Evaluation (declared learning curve, verified answer key) |
|
|
| An **evalpack**: an evaluation database generated from the answer key, not |
| annotated after the fact. A VLDB 2026 audit found 52.8% of BIRD Mini-Dev |
| answer keys wrong because benchmarks annotate answers onto existing |
| databases; this dataset inverts the order. The declared properties (curves, |
| shares, identities) are the specification, the database is generated to |
| satisfy them exactly, and every shipped question was re-verified by |
| executing its gold SQL against these exact files with DuckDB, an engine |
| that shares no code with the generator. |
|
|
| **15,600 rows** across the tables below. |
|
|
| ## What is in it |
|
|
| Tables: students (600 rows), practice_attempts (15,000 rows). |
| |
| A practice log where the monthly correct rate climbs 55% to 68% by |
| declaration and total practice time per month is declared and exact. Every |
| attempt belongs to a student who exists and happens after that student |
| signed up. 17 questions ship; 4 candidates whose rates were unachievable at |
| the generated row counts were dropped by the verification gate rather than |
| shipped wrong (that refusal is the point). |
| |
| ## Files |
| |
| - `tables/*.csv`: the database |
| - `questions.jsonl`: one verified question per line (natural language, |
| gold SQL, expected answer, tags) |
| - `certificate.json`: per-question DuckDB verification plus FK proof |
| - `manifest.json`: spec hash, seed, library versions, dropped candidates |
| - `verify.py`: standalone re-verification (needs only `pip install duckdb`) |
| - `schema.misata.yaml`: the full declaration; regenerate or rotate the pack |
| from it |
| |
| ## Re-verify in thirty seconds |
| |
| ```bash |
| pip install duckdb |
| python verify.py |
| ``` |
| |
| ## Rotate the environment without touching the answer key |
| |
| ```bash |
| pip install misata |
| misata evalpack --config schema.misata.yaml -o rotated_pack --seed 7 |
| ``` |
| |
| A new seed replaces the rows; the declared answers stay the answers, so |
| agents cannot memorize the environment. Same version + same schema + same |
| seed reproduces these bytes exactly. |
| |
| ## Provenance |
| |
| No real data: fully declaration-generated, offline, deterministic |
| ([provenance statement](https://github.com/rasinmuhammed/misata/blob/main/DATA-PROVENANCE.md)). |
| Generated with [misata](https://github.com/rasinmuhammed/misata) (MIT). |
| Where the approach fails is documented in |
| [LIMITATIONS.md](https://github.com/rasinmuhammed/misata/blob/main/LIMITATIONS.md). |
| |
| |
| ## The rest of the shelf |
| |
| Same idea, other domains, every answer re-verified with DuckDB against the |
| shipped files: |
| |
| - [Ecommerce analytics](https://huggingface.co/datasets/rasinmuhammed/ecommerce-analytics-sql-evaluation) |
| - [SaaS finance](https://huggingface.co/datasets/rasinmuhammed/saas-finance-sql-evaluation) |
| - [EdTech practice](https://huggingface.co/datasets/rasinmuhammed/edtech-sql-evaluation) |
| |
| If you want ground truth for a regression rather than for SQL, the |
| [machine degradation set](https://huggingface.co/datasets/rasinmuhammed/predictive-maintenance-remaining-useful-life) |
| carries exact remaining useful life on 100 run-to-failure machines. |
| |
| Generated by [Misata](https://github.com/rasinmuhammed/misata) (MIT). |
| Build your own: [misata.studio](https://www.misata.studio). |
| |
