One Name at a Time: How Die Zeit Built a Searchable Database of Nazi Party Members – Global Investigative Journalism Network

EARLY BIRD TICKETS - Online Investigative Journalism Week (IJ WEEK)

July 31, 2026 • 23:59

day

days

hour

hours

min

mins

sec

secs

Donate

Accessibility Settings

reset all

text size

decrease<br>increase

color options

monochrome<br>muted color<br>dark

reading tools

isolation<br>ruler

other

no motion<br>large cursor

Image: Screenshot, DIE ZEIT

Stories

&bull;

Topics

» Data Journalism

One Name at a Time: How Die Zeit Built a Searchable Database of Nazi Party Members

by

Hanna Duggal

• June 26, 2026

Related Resources

Muckraking the Past: Using Archives to Seek Accountability

Investigating War Crimes: Tracing War Criminals

Tracking Far-Right Suspects Across Continents: A 10-Year Hunt for Spanish Fugitives

‘Rewriting History’: Tips for Investigating Events Years, Decades, Or Centuries Later

Explore the Resource Center<br>&rarr;

Share

Tweet<br>Facebook<br>Print<br>LinkedIn<br>WhatsApp<br>Telegram<br>Reddit<br>Republish

In the final days of World War II, as the German Reich collapsed, Nazi officials ordered millions of party membership cards to be destroyed. The vast card index that documented membership across Germany survived largely because a paper mill operator, Hanns Huber, chose to hand the records over to the advancing US forces rather than pulp them.

The surviving material represents one of the largest repositories of information about party membership. Together, the surviving records cover approximately 90% of all Nazi Party memberships between 1925 and 1945, although significant gaps remain.<br>For decades, discovering whether a relative had belonged to the Nazi Party — officially the National Socialist German Workers’ Party (NSDAP) — required either a formal request to Germany’s Federal Archives or trudging through microfilm copies housed at the US National Archives in Washington, DC. Earlier this year, when the US National Archives began releasing digitized versions of the records online, public interest was so high that the website briefly crashed.

For German newspaper Die Zeit, the archive presented an opportunity. While the records had become publicly available, navigating them remained a challenge. Millions of membership cards were scattered across thousands of PDF files, making individual searches near impossible.

A team of reporters, data journalists, and data scientists set out to change that. A few weeks after the archive’s release was reported in German media, Die Zeit had built a searchable database that transformed millions of scanned membership cards into an accessible public resource. The project has since attracted millions of searches, with users uncovering the names of grandparents, great-uncles, and other relatives whose party membership had long remained hidden within the archive.

The database is also challenging a persistent misconception about Nazi Germany. While conscription to the Wehrmacht — the German armed forces under the Nazis — was compulsory, membership in the Nazi Party itself was not. Despite some professions carrying heavy social pressure to sign up, it remained voluntary to join.

Gregor Aisch, a visual data journalist at Die Zeit, and Andreas Loos, head of the newspaper’s data science and AI desk, spoke to GIJN about how the project came together, the role artificial intelligence played in making the archive searchable, and the unexpected response that followed publication.

Using AI to Build a Searchable Archive

The foundation of the project lay in two vast collections of membership records: the Central Card Index and the regional Gau Card Index. Political scientist Jürgen Falter, who spent decades studying the Nazi Party, estimates that only 44% of the Central Index and 77% of the Gau Index survived the war. Even so, the surviving material represents one of the largest repositories of information about party membership. Together, the surviving records cover approximately 90% of all Nazi Party memberships between 1925 and 1945, although significant gaps remain.

Andreas Loos, head of Die Zeit’s data science and AI desk. Image: Courtesy of Loos

Transforming that archive into a searchable database required processing 5,442 PDF files, each containing roughly 3,000 pages of scanned membership cards. Many members appear in both indexes, creating duplicate records that needed to be accounted for.

“We used AI mainly to extract textual information from images,” Loos explains. “The models could cope with even difficult visual data: each register card may contain a mix of many fonts and handwriting, including Kurrent and Sütterlin, two old German handwriting scripts. All this could be read.”

Optical character recognition (OCR) converts images into machine-readable text. For the NSDAP project, the team combined several OCR systems, relying heavily on Google’s Gemini LLM to interpret handwritten and printed information from the cards.

For Die Zeit the...