These datasets track where institutions failed, where systems drifted, and where patterns hid in plain sight. One hundred years of American life — measured, visualized, and published open.
From 1892 to 1995, the US averaged 17,000 deportations per year. After 1996, the system scaled by an order of magnitude.
1892195019962026
Vol. I · 10 of 10 live
The Reckoning
A century of American institutional failure. The data does not take sides. It keeps score.
Each entry pairs a primary dataset with the moment its line bends. Ten entries form one argument: that the twentieth century is best understood not as a sequence of events but as a sequence of slopes.
A century of things that don't fit. Mass hysteria. Classified documents. Anomalous signals. Ghost geographies. Serious methodology. Uncanny data.
Vol. III · Announced
The Self
Personal scale. Every entry is about the visitor. Your name. Your neighborhood. Your ancestors. Your birth year's cultural fingerprint.
Vol. IV · 2027
The Future
The first hundred years of the internet. A century of climate forcing. Same methodology. Forward-facing questions.
Vol. V · 2028
The Beautiful
A century of American aesthetic culture as data. Color in painting. Sentiment in music. Architecture mapped against economic conditions.
About the project
Why this exists.
One Hundred Years is an independent research and visualization project. Each entry takes a single dataset — one that spans at least a century of American life — and asks what patterns become visible only at that scale.
The project is built by one person. There is no newsroom, no institution, no funding. The work is its own justification. Every dataset is public. Every methodology is published. Every line of code is open.
The name comes from the conviction that a century of data is enough to see things that are invisible at shorter timescales — and that most of these datasets have never been visualized with the care they deserve.
Press & contact
For press inquiries, interviews, or collaboration proposals:
hello@onehundredyears.report
You are free to excerpt, quote, and embed any visualization from this project with attribution. Formal citation format is below.
Methodology
How the analysis works.
M1Station Anomaly Engine
Per-station daily anomalies computed against a 1901–2000 baseline. Each station's record is cleaned, gap-filled where coverage allows, and scored against its own historical distribution. No interpolation between stations.
Name frequency time series modeled as rise-peak-decay curves. Half-life extraction, contagion modeling, and cross-name correlation. Trigger event detection via changepoint analysis on first-derivative signals.
Used in · The Given
Each entry publishes its own methodology tab with full technical detail, known limitations, and confidence ratings. The methods listed here are summaries.
Haynes, J. (2026). One Hundred Years: [Entry Title]. onehundredyears.report. Retrieved [date].
Replace [Entry Title] with the specific entry name and [date] with your access date. BibTeX available in each entry's methodology tab.
Dispatches
Get notified when a new entry goes live.
No spam. No tracking pixels. One email per entry — roughly every few months. Unsubscribe anytime.
"The data does not take sides. It keeps score."
Every dataset is open. Every model is documented. Every finding is labeled — HIGH CONFIDENCE, CANDIDATE, or SPECULATIVE. Nothing is overstated. Code is MIT licensed. Data is CC0. No ads. No tracking. No paywall. Ever.