Orbura AutoScientist Data Visualization Dataset
Dataset Description
A synthetic instruction-tuning corpus for data visualization tasks, built for the AutoScientist Challenge Part 2 (Data Visualization). Every example is deterministically generated — no human annotation, no LLM-generated labels — so the ground truth is exact and reproducible.
Task types
| Task | Weight | Description |
|---|---|---|
chart_qa |
45% | Arithmetic, comparison, and trend questions about chart data (max, min, sum, avg, median, range, pct_total, ratio, rank, above_avg, trend, difference, counterfactual, percentage_change) |
chart_to_code |
15% | Generate matplotlib code from a chart type + data table |
fix_code |
10% | Repair common matplotlib bugs (typos, missing imports, mismatched lengths, invalid kwargs, string values, swapped axes) |
code_to_desc |
10% | Describe what a matplotlib code block produces, including key statistics |
style_transfer |
10% | Modify an existing plot (change chart type, add grid, rotate labels, change color) |
chart_choice |
5% | Select the most appropriate chart type for given data |
data_to_code |
5% | Convert CSV data into a matplotlib chart |
Chart types covered
- line — trend visualization
- bar — category comparison
- scatter — correlation
- pie — proportion of a whole
Multimodal extension
A 100-row multimodal pilot (generate_multimodal_pilot.py) generates rendered
chart images (PNG) with chart-QA pairs. This extends the text-only dataset with
visual reasoning: the model sees a rendered chart and answers questions about
it. The pilot covers bar, grouped bar, stacked bar, line, multi-line, scatter,
pie, donut, area, and mixed (bar + line overlay) chart types.
Files
| File | Rows | Description |
|---|---|---|
adaption_train_10k.jsonl |
10,000 | Training set (column-mapped for Adaption) |
adaption_val_2k.jsonl |
2,000 | Validation set (held-out) |
adaption_pilot_500.jsonl |
500 | Small pilot for quick iteration |
train_10k.jsonl |
10,000 | Training set (raw, pre-Adaption-mapping) |
val_2k.jsonl |
2,000 | Validation set (raw, pre-Adaption-mapping) |
multimodal_pilot/ |
100 | Rendered chart images + metadata |
Schema
| Field | Description |
|---|---|
instruction |
Task prompt |
input |
Context (data table, CSV, code, or chart metadata) |
output |
Ground-truth completion (deterministic) |
task |
Sub-task name |
category |
Always data_visualization |
messages |
Chat-formatted version for SFT |
Generation
Generated deterministically by generate_full.py with seed 42 (train) and
seed 2024 (val). The Adaption column mapping is applied by
prepare_adaption.py.
python3 generate_full.py # → train_10k.jsonl + val_2k.jsonl
python3 prepare_adaption.py # → adaption_train_10k.jsonl + adaption_val_2k.jsonl
python3 generate_multimodal_pilot.py # → multimodal_pilot/
Augmentation
The dataset is designed to be augmented via Adaption Adaptive Data with:
reasoning_traces: enabledprompt_rephrase: disabled (rewrites system prompts, causing train/inference mismatch — learned from Part 1 post-mortem)deduplication: enabled
Usage
import json
with open("adaption_train_10k.jsonl") as f:
for line in f:
example = json.loads(line)
# example["instruction"], example["input"], example["output"], example["task"]
License
Apache 2.0