Data Minting

Research-grade data extraction. Every cell linked to evidence.

Developed by Karl Rohe, Professor of Statistics at UW‑Madison

Turn PDFs, images, Word docs, and web pages into clean, structured data in minutes.

Step
1

Name your project

Create a workspace or browse community projects

Step
2

Create your extraction template

Describe what you want. datamint.ing's template assistant drafts your template to your specifications in minutes.

Step
3

Mint & inspect your data

Upload documents. Mint the data. Click any cell to see evidence & reasoning

No coding. No cleanup. Quickly iterate. Scale from 10 to 10,000 documents.

Request Access See it in action ↓

Click to inspect the evidence & reasoning

Systematic Review & Evidence Synthesis
Extract PICO elements, risk of bias assessments, and outcomes with complete citations. Every cell traced to source.
Evidence & Reasoning
📋
Click any cell above to see detailed evidence and reasoning

Core Capabilities

Any file, one pipeline

PDFs, images, Word docs, and web pages flow into one structured table.

Template precision

Define fields once. Get consistent output across 10 or 10,000 files.

Evidence on click

Every cell links to the exact source text and model reasoning.

Batch automation

Upload once and run full collections without prompt juggling.

Analysis ready

Export clean tables to CSV, Sheets, or your warehouse.

Predictable pricing

A penny per page of text and a penny per cell of data.

Two components work together to make datamint.ing rigorous, reliable, transparent, and fast:

Part 1: The template makes it systematic

Specifies exactly what to extract.

  • Human-readable document, easy to edit
  • Defines variables, decision criteria and types (e.g. numeric), and how to handle edge cases
  • Acts as a reusable worksheet applied to every document
  • datamint.ing's assistant helps you draft a template in under 5 minutes
Part 2: The minting process is automatic

Given an input document and a template, multiple AI models extract the data independently and then discuss.

  • The discussion verifies evidence and reasoning
  • Replicate extractions provide inter-rater reliability measures
  • The discussion surfaces template ambiguities for refinement
  • Output values have complete audit trail for every cell - quotes, reasoning, confidence

These components create a continuous improvement cycle: inspect extractions → identify edge cases → refine template → re-mint in minutes.

Instant re-extraction

Forgot a field? Add it and re-extract.

Discovered new patterns? Refine and extract again.

Minutes, not weeks. Iteration built-in.

Who it's for

Literature Reviewers & Synthesizers

Systematic reviews, meta-analyses, scoping reviews across any field. Extract study characteristics, outcomes, and quality assessments at scale.

Qualitative & Mixed-Methods Researchers

Code interviews, focus groups, and open-ended data. Extract themes, contradictions, and patterns with speed and precision.

Data Scientists & Computational Researchers

Extract features that were previously unmeasurable at scale. Turn qualitative patterns into quantitative variables. Documents become observations, meanings become features.

Empirical Researchers

Build datasets from published literature in your field. Extract methods, sample sizes, statistical tests, and effect sizes—turn the literature into a structured database.

Domain Specialists

Clinical: adverse events, patient outcomes • Legal: case precedents, contract terms • Policy: stakeholder positions, implementation barriers • Any field with specialized extraction needs.

Institutional & Applied Teams

When non-academic decisions require academic rigor—systematic analysis with complete audit trails and defensible methods.

Illustrations

Evidence Synthesis

Before
8 weeks
After
2 days
180 RCTs extracted. PICO, outcomes, and risk of bias extracted.

Literature Review

Scope
10 years
Papers
2,400
Methodological trends mapped over time. Statistical practices tracked. Sample size evolution identified.

Interview Coding

Interviews
150
Themes
8
Stakeholder positions extracted with supporting quotes. Contradictions identified across all participants.

The systematic reading revolution

Data Extraction Before:

  • What was already structured
  • Simple word counts
  • What manual coders could process

With datamint.ing

  • Any feature you can systematically describe in words
  • Context, meaning, and nuance captured
  • Quickly iterate. Scale from 10 to 10,000 documents

Trusted data cannot be hand-gathered. It must be minted.

Data Minting: like pressing coins from metal. Documents are ore. Templates are stamps. Data is currency.

Don't craft the data—craft the template with our AI assistant. Test it. Refine it. Share it. Scale it.

What You Can Extract

Reporting & Transparency
Audit completeness against standards • Sample size justifications • Preregistration vs. published outcomes • Data & code availability
Quantitative Data & Measurements
Extract statistics, effect sizes, sample demographics • Experimental parameters • Performance metrics • Measurement values with units
Qualitative Coding
Interview themes with exemplar quotes • Stakeholder stances & rationales • Sentiment & emotional valence • Temporal pattern evolution
Claims & Evidence
Claim-evidence pairings • Hedging & certainty language • Author-acknowledged limitations • Causal vs. correlational framing
Arguments & Reasoning
Logical structure & assumptions • Who disagrees with whom • Implementation barriers & facilitators • Interpretation strategies
Specialized Domains
Clinical: adverse events, patient outcomes • Legal: precedent citations, contract terms • Technical: experimental conditions, material specifications

From Any Document Corpus

Research Outputs

Journal articles
Dissertations
Conference papers
Grant proposals
Systematic reviews

Qualitative Data

Interview transcripts
Focus groups
Field notes
Open surveys
Ethnographies

Gray Literature

Policy briefs
White papers
Technical reports
Evaluation reports
NGO publications

Clinical Documents

Case reports
Clinical notes
Adverse event reports
Patient narratives
Trial protocols

Historical & Archival

Letters & correspondence
Meeting minutes
Newspapers
Government archives
Court records

Institutional Records

Committee minutes
Course evaluations
Strategic plans
Accreditation reports
Annual reports

Why this works

Templates, not prompts.

Reusable, testable specs for exactly the data you need.

Full transparency.

Every cell links to quotes, summaries, and reasoning.

Improves with use.

Templates evolve as edge cases surface and teams share.

Provenance: every spreadsheet cell retains source evidence and the AI's reasoning process for complete auditability.

Frequently Asked Questions

What file types are supported?

PDF, HTML, DOCX, images (JPEG, PNG), and more. If it contains text or visual information, DataMint can extract from it.

How fast is the extraction process?

Most users mint their first dataset in under 10 minutes. Once your template is ready, batch processing is automatic and scales to thousands of documents.

How do I verify the results?

Click any cell in the output table to view the exact source text, key quotes, and the model's reasoning. Every extraction includes complete provenance.

Can I reuse templates across projects?

Yes. Templates are reusable, shareable, and versionable. Create once, apply to any number of documents or share with your team.

What happens if I need to extract additional fields?

Add new fields to your template and re-extract in minutes. Iteration is built into the workflow—no need to start over.

Ready to mint your first dataset?

Request Access