| # Orbura AutoScientist Data Visualization Dataset |
|
|
| ## Dataset Description |
|
|
| A synthetic instruction-tuning corpus for data visualization tasks, built for |
| the [AutoScientist Challenge](https://adaptionlabs.ai/blog/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`. |
|
|
| ```bash |
| 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`: enabled |
| - `prompt_rephrase`: **disabled** (rewrites system prompts, causing |
| train/inference mismatch — learned from Part 1 post-mortem) |
| - `deduplication`: enabled |
|
|
| ## Usage |
|
|
| ```python |
| 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 |
|
|
