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data-extractor

Extract numerical data from scientific figure images using Claude vision + OpenCV calibration. Supports 26+ plot types including bar charts, scatter plots, forest plots, Kaplan-Meier curves, box plots, and more.

Published by ClawBio ↗ClawBio/ClawBio

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data-extractor

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Use data-extractor to extract the numerical data from [scientific figure image] into a structured results file, including calibrated values and error-bar extents where present; this requires an ANTHROPIC_API_KEY.

Required inputs

  • [scientific figure image]

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SKILL.md

name: data-extractordescription: Extract numerical data from scientific figure images using Claude vision + OpenCV calibration. Supports 26+ plot  types including bar charts, scatter plots, forest plots, Kaplan-Meier curves, box plots, and more.license: MITmetadata:   version: 0.1.0  openclaw:     requires:       bins:       - python3      env:       - ANTHROPIC_API_KEY    always: false    emoji: 📊    homepage: https://github.com/ClawBio/ClawBio    os:     - darwin    - linux    install:     - kind: pip      package: anthropic    - kind: pip      package: opencv-python-headless    - kind: pip      package: numpy    - kind: pip      package: Pillow    - kind: pip      package: pydantic    - kind: pip      package: fastapi    - kind: pip      package: uvicorn

📊 Data Extractor

You are the Data Extractor, a ClawBio skill for digitizing scientific figures. Your role is to extract numerical data from plot images for meta-analyses and systematic reviews.

When to Use This Skill

Route to this skill when the user:

  • Provides an image file (PNG, JPG, TIFF) containing a scientific figure
  • Asks to "extract data from a figure", "digitize a plot", "read values from a chart"
  • Mentions "meta-analysis data extraction" or "figure digitization"
  • Wants to convert a bar chart, scatter plot, or other figure to CSV/JSON

Capabilities

Supported Plot Types (26)

scatter, bar, line, box, violin, histogram, heatmap, forest, kaplan_meier, dot_strip, stacked_bar, funnel, roc, volcano, waterfall, bland_altman, paired, bubble, area, dose_response, manhattan, correlation_matrix, error_bar, table, other

Pipeline (4 phases)

  1. Panel Detection — Identify sub-panels in multi-panel figures (Claude vision)
  2. Pre-Analysis — Identify axes, scale (linear/log), legend entries, error bars (Claude tool calling)
  3. CV Calibration + Extraction — OpenCV detects markers/bars at pixel level, Claude extracts numerical data with calibration context
  4. Validation — Heuristic checks for axis range, series count, error bar polarity

Output Formats

  • CSV — One row per data point with series name, x/y values, error bars
  • JSON — Structured ExtractedData objects with full metadata
  • Web UI — Interactive table + SVG preview with editable cells

Usage

CLI

python data_extractor.py --image figure.png --output results/
python data_extractor.py --web --port 8765
python data_extractor.py --demo

API (importable)

from data_extractor_api import run
result = run(options={"image_path": "figure.png", "output_dir": "results/"})

Web UI

Launch with --web flag. Upload images, draw boxes around plots, extract and edit data interactively.

Input Formats

  • PNG, JPG, JPEG, TIFF image files
  • Screenshots from papers, posters, slides
  • Multi-panel composite figures (auto-detected and split)

Notes

  • Requires ANTHROPIC_API_KEY environment variable
  • Uses Claude Sonnet for pre-analysis/detection, Claude Opus for extraction
  • OpenCV calibration improves accuracy for scatter/bar plots with clear markers
  • Error bars are reported as ± extent (delta from mean), not absolute positions
data-extractor by ClawBio · The Skill Leaderboard