Primary source data, made explorable

I wanted to search what the founders actually wrote. Not someone's interpretation. Not a curated excerpt in a textbook. The actual text, alongside everything that came after, in one index. That was the first project. Then it kept going.

Public data should be publicly explorable. Government agencies publish datasets that are technically available but practically inaccessible: bulk downloads, PDFs, paywalled FOIA responses, formats that require a data pipeline to read. Verbatim takes those datasets and makes them searchable, filterable, and visible.

What’s here

Five datasets and one curated feature. Each dataset is a public record that was already available in theory, now available in practice.

• COMPAS — 7,214 criminal defendants scored by an algorithm that predicts recidivism. The data ProPublica used to show the algorithm was biased against Black defendants.
• Senate Stock Trades — 7,740 stock transactions disclosed by U.S. senators under the STOCK Act. Every purchase, every sale, every ticker.
• Fatal Police Shootings — The Washington Post's Fatal Force database. 10,430 people killed by on-duty police officers since 2015.
• Campaign Finance — 3,457 FEC-registered candidates for the 2024 cycle. Who raised what, from where.
• Qualified Immunity — 5,500+ federal appellate qualified immunity cases, 2010–2020. The Institute for Justice's comprehensive dataset of how courts apply the doctrine.
• Vs. — Curated debates using real quotes. Churchill vs. Twain, Lincoln vs. Jefferson. Two voices, one topic, side by side.

The approach

Each dataset gets the same treatment: search, filter, paginate, visualize. No editorializing, no analysis, no conclusions. The data speaks or it doesn't. Verbatim provides the microphone, not the script.

The name is the tell. Not "interpreted." Not "analyzed." Not "summarized." Verbatim.