This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 licence (CC BY-NC-SA 4.0): research, academic, personal and other non-commercial use is permitted with attribution; adaptations must be shared under the same licence; commercial use is not permitted. The original public source data remains under the Government Open Data License - India (GODL-India). Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.

Health-StatsQA

Licence: CC BY-NC-SA 4.0 — research and non-commercial use only; derivatives must be shared under the same licence. Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai

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1,000 health-statistics questions about Indian districts, each asked in English and Hindi (2,000 questions in all). Every answer is read or computed by code from two sibling releases: IndiaDiseaseWatch (HMIS disease counts and outbreak labels) and the Bharat Health Service Atlas (HMIS service indicators, Rural Health Statistics facility counts). Each question comes with the data rows its answer is computed from, so a model can be tested from memory (closed_book) and reading the data (oracle).

Quickstart

from datasets import load_dataset

ds = load_dataset("judicialmind/Health-StatsQA", split="test")     # or load_dataset("json", data_files="Health-StatsQA/data/test.jsonl")
q = ds[0]
print(q["lang"], q["template"], q["answer_type"], "->", q["answer"])
print(q["question"])
print(q["oracle_context"])

en disease_month number -> 2.0
According to India's Health Management Information System (HMIS), how many emergency attendances for snake bite were reported by health facilities in Imphal West district, Manipur in July 2020? Reply with a single number.
emergency attendances for snake bite | Imphal West, Manipur
2020-04: 0
2020-05: 1
2020-06: 2
2020-07: 2
2020-10: 1
2020-11: 0
2020-12: 0
2021-01: 1
2021-02: 0
2021-03: 0

Evaluate a model

cd Health-StatsQA
python evaluate.py prompts --condition closed_book --out prompts.jsonl     # or --condition oracle
# run your model on each prompt, write preds.jsonl lines {"id": ..., "response": "<raw answer text>"}
python evaluate.py score preds.jsonl
python evaluate.py score --gold                                            # parser check: gold answers score 1.0

all                              {"n": 2000, "accuracy": 1.0}
answer_type=choice               {"n": 400, "accuracy": 1.0}
...
lang=hi                          {"n": 1000, "accuracy": 1.0}
template=change_pct              {"n": 200, "accuracy": 1.0}
...
pairs                            {"n": 1000, "both_correct": 1.0, "en_hi_agreement": 1.0}

evaluate.py needs only the Python standard library. The oracle prompt is the oracle_context rows followed by the question. Scores: accuracy overall, by language, template and answer type; pairs.both_correct = share of questions answered correctly in both English and Hindi, pairs.en_hi_agreement = share where the two languages are both right or both wrong.

Scoring:

number: the first number in the response (Indian digit grouping, Devanagari digits and a trailing % accepted; the question's year is skipped when another number is present) is correct within rel_tolerance of the answer (1% for percentages, exact for counts), or when it equals the answer after rounding to the answer's decimals.
choice: the response names the right district and no other choice (case-insensitive).
yes_no: the response answers with the right word (yes / no, हाँ / नहीं).

Files

data/test.jsonl: 2,000 questions, one per line.
sources.parquet: per-resource attribution (provider, title, URL).
evaluate.py: prompt builder and scorer.

Schema

field	meaning
`id`	question id (`<pair_id>_<lang>`)
`pair_id`	links the English and Hindi versions of a question
`question`	question text; district and state names stay in Latin script, as in the LGD directory
`lang`	`en` or `hi`
`answer`	gold answer: a number, a district name, or yes/no (`yes`/`no` in English, `हाँ`/`नहीं` in Hindi)
`answer_type`	`number`, `choice` or `yes_no`
`choices`	the options for `choice` and `yes_no` questions
`unit`	`count`, `%` or empty
`scale`	unit multiplier of the answer (1.0)
`rel_tolerance`	0.01 for percentages, else 0 (exact)
`year`, `fy`, `month`	the period asked about (`fy` like `FY2019-20`; `month` as `YYYY-MM-01`)
`state_lgd`, `district_lgd`, `level`, `place`	LGD codes, `district`, and the place name
`source`	release the answer comes from (`IndiaDiseaseWatch` or `Bharat-Health-Service-Atlas`)
`template`	question template (below)
`conditions`	`["closed_book", "oracle"]`
`split`	`test`
`oracle_context`	the data rows the answer is computed from

Templates (per language)

template	questions
`change_pct`	100
`compare_districts`	100
`disease_fy_total`	150
`disease_month`	200
`outbreak_yes_no`	100
`rhs_count`	100
`service_ratio`	150
`state_argmax`	100

How each template's answer is computed:

disease_month: the reported count for one district, disease and month.
disease_fy_total: the financial-year sum, using only years with all 12 months reported.
service_ratio: a ratio from the atlas, in percent.
compare_districts: which of two districts reported more, in the same state and year; the two values differ by more than 5%.
state_argmax: the district with the most cases among a state's fully-reporting districts, with a lead of more than 5%.
change_pct: the year-on-year change in percent, from a base of at least 20.
rhs_count: a facility count.
outbreak_yes_no: the IndiaDiseaseWatch label, with the rule stated in the question; balanced between yes and no.

The Hindi questions are written from templates, not machine-translated.

Coverage

Districts across India; HMIS months from April 2009 to March 2022 (disease and service questions) and Rural Health Statistics years 2016 to 2021 (facility questions); 2,000 questions in English and Hindi over 8 templates.

Source data

Derived from 2 resources published on the Open Government Data Platform India (data.gov.in), snapshot May 2026, under the Government Open Data License - India. Per-resource attribution (provider, title, resource URL) is in the attribution column of sources.parquet.

Providers: Ministry of Health and Family Welfare (HMIS), Ministry of Health and Family Welfare (RHS, via NITI Aayog NDAP).

Answers derive from IndiaDiseaseWatch and Bharat-Health-Service-Atlas; their sources.parquet files list every underlying resource.

Original data: published by Ministry of Health and Family Welfare (Health Management Information System, HMIS; Rural Health Statistics, via NITI Aayog NDAP) on the Open Government Data Platform India (data.gov.in) and, where marked, NITI Aayog's National Data and Analytics Platform (NDAP), under the Government Open Data License - India (GODL-India). The original public data remains under GODL-India; the CC BY-NC-SA 4.0 licence of this repository covers JudicialMind's compilation, derived fields, annotations, benchmarks and models.

Citation

@misc{judicialmind2026healthstatsqa,
  title={Health-StatsQA: Bilingual English-Hindi Health Statistics Questions about Indian Districts},
  author={JudicialMind},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/judicialmind/Health-StatsQA}}
}

License

CC BY-NC-SA 4.0 (see LICENSE): research, academic, personal and other non-commercial use, with attribution to JudicialMind and to the original data publishers; adaptations must be shared under the same licence. Commercial use requires a separate licence.

Commercial use: contact surya@judicialmind.ai or hello@judicialmind.ai.

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