Learn Chart Digitization: 5 Free Practice Datasets

Chart digitization is a learned skill — clicking gets faster, precision improves, you develop a feel for which workflow fits which chart. Deliberate practice on charts with known answers is the fastest way to build it.

Five practice charts, easiest first. Each has an answer key and a walkthrough. Work in order or jump to the type you’re extracting this week.

The five workshops

#	Workshop	Type	Difficulty	Time	What you learn
1	Extract a simple bar chart	5 bars	★☆☆☆☆	5 min	The four-step loop end-to-end
2	Multi-series line chart	3 series × 12 months	★★☆☆☆	12 min	Per-series extraction discipline
3	Dense scatter with auto-extract	250 points, 3 clusters	★★★☆☆	8 min	Color-based auto-extraction
4	Log-scale chart	Semi-log, 12 points	★★★★☆	10 min	Log calibration trick
5	Kaplan-Meier reconstruction	2-arm step function	★★★★★	15 min	Step-function precision; survival data

Each is self-contained. The recommended sequence builds progressively, but jump if you came from a specific problem.

What you’ll have after all five

• A baseline accuracy number per chart type — the thing you want when deciding how much to trust your extractions.
• Familiarity with manual-click and color-based-auto workflows.
• Intuition for which chart types reward extra precision.
• Self-knowledge about your failure modes — most operators have a recurring error the workshops surface.

How to grade yourself

Every workshop publishes ground-truth values. After your extraction:

1. Export your data as CSV.
2. Open your CSV and the answer key in a spreadsheet or Python.
3. Compute MAE per series.
4. Compare to the workshop’s target.

Most workshops target MAE under 1.5% of y-range — careful-operator level. If you’re above, the workshop’s “common mistakes” section usually identifies what went wrong.

The Python recipe for MAE:

import csv

# Load your extracted CSV
extracted = {}
with open('my_extraction.csv') as f:
    for row in csv.DictReader(f):
        extracted[row['x']] = float(row['y'])

# Compare to ground truth (paste from workshop answer key)
truth = {'Acme': 36.6, 'Bolt': 23.5, 'Crux': 61.5, 'Delta': 17.5, 'Echo': 52.7}

mae = sum(abs(extracted[k] - v) for k, v in truth.items()) / len(truth)
y_range = max(truth.values()) - min(truth.values())
print(f"MAE: {mae:.2f} ({100*mae/y_range:.1f}% of y-range)")

For log charts, see the log workshop’s grading section — you want log-space MAE, not linear.

The charts in detail

Workshop 1: bar chart

Five bars, vendor satisfaction on 0-100. No tricks. Cleanest introduction to the four-step workflow.

Start workshop 1 →

Workshop 2: multi-series line

Three product lines across twelve months, with crossings. Per-series discipline prevents losing track of which point belongs where. AI fails by swapping series at crossings; you’ll do it cleanly with named groups.

Start workshop 2 →

Workshop 3: dense scatter

250 points in three color-coded clusters. Where manual clicking stops scaling. Color-based auto-extraction with per-cluster tolerance — 90 seconds instead of 15 minutes.

Start workshop 3 →

Workshop 4: log-scale chart

Twelve points of exponential decay on a semi-log y-axis. The chart type AI gets most catastrophically wrong (40%+ MAE). Teaches the log-calibration trick — calibrate at visible powers of ten, toggle to log.

Start workshop 4 →

Workshop 5: Kaplan-Meier survival curve

Two-arm step function at 6-month intervals. Standard shape for clinical trial reporting and a frequent meta-analysis reconstruction target. Teaches step-corner placement and per-arm extraction. Pairs with our meta-analysis guide.

Start workshop 5 →

Where these charts come from

All five come from our open-source benchmark harness. The same charts score AI tools in our comparison posts — so “ChatGPT had 41% MAE on the log chart” is the exact same chart you can practice on.

Chart code, ground-truth JSON, and AI runners are in benchmarks/.

What to do after the workshops

• Researchers / meta-analysts: Data extraction for meta-analysis — full systematic-review workflow.
• Financial analysts: Chart screenshot to Excel — XLSX export with chart embedded.
• Asking if AI works: The limits of AI chart extraction — pillar post.

Want more workshops?

Workshop 6 (dual-axis), 7 (stacked area), and 8 (forest plots) are planned. To request a chart type, see the GitHub issues.