Reference

Dataset Schema Reference

All Stravoris medical MCQ datasets use this exact schema. Every subject, every row, every file — same columns, same types, same semantics.

Columns

Column	Type	Nullable	Description
`id`	`string`	No	Unique identifier
`subject`	`string`	No	Subject name
`source`	`string`	No	"medmcqa" or "synthetic_YYYY-MM-DD"
`raw_topic`	`string`	Yes	Original topic from source dataset
`syllabus_topic`	`string`	No	Normalized curriculum topic
`question`	`string`	No	Question stem and lead-in
`options`	`list[string]`	No	Exactly 4 answer options
`correct_index`	`integer`	No	Index of correct option (0-3)
`explanation`	`string`	Yes	Explanation of correct answer
`blooms_level`	`string`	No	recall / comprehension / application / analysis
`model_version`	`string`	Yes	Model that generated synthetic questions
`generation_date`	`string`	Yes	ISO date for synthetic questions
`curation_decision`	`string`	No	"accept" or "fix"
`curation_flaws`	`list[string]`	No	Item-writing flaw tags from curation
`curator_notes`	`string`	Yes	Free-text curation notes

Loading Examples

pandas

import pandas as pd
df = pd.read_parquet("anatomy/anatomy.parquet")

HF datasets

from datasets import load_dataset
ds = load_dataset("parquet", data_files="anatomy/anatomy.parquet")

JSONL

import json
with open("anatomy/anatomy.jsonl") as f:
    for line in f:
        q = json.loads(line)
        break

All subjects at once

subjects = ["anatomy", "biochemistry", "cell-biology", "physiology", "pathology", "microbiology"]
full = pd.concat([pd.read_parquet(f"{s}/{s}.parquet") for s in subjects], ignore_index=True)

Key Properties

4 options per question, always
Curated against 24 published item-writing flaws
Bloom's taxonomy tagged
Syllabus-aligned
Provenance tracked

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