Nason/fjc-judges-database

Federal Judges Biographical Database (FJC)

Every Article III federal judge since 1789 from the Federal Judicial Center's Biographical Directory: demographics, every appointment with its nomination -> confirmation -> commission timeline, chief-judge and senior-status dates, education, career, and other federal judicial service. Six normalised tables joined on nid, plus v_current_judges. 37,768 rows across 6 tables.

Table	Description	Row Count	Column Count
professional_career	One row per professional-career entry as written by the FJC (clerkships, practice, prior judgeships), in sequence	19,313	4
education	One row per degree: school, degree, year	8,153	6
federal_judicial_service	One row per Article III appointment: court, appointing president and party, ABA rating, seat, nomination, committee, Senate vote and confirmation dates, commission date, chief-judge service, senior status, termination and reason	4,774	30
judges	One row per Article III judge, 1789-present: name, birth and death date/place, gender, race or ethnicity (FJC Biographical Directory)	4,074	18
other_nominations_recess	One row per unsuccessful nomination or recess appointment, as written by the FJC	836	4
other_federal_judicial_service	One row per non-Article III federal judicial post (magistrate judge, bankruptcy judge, territorial court ...)	618	5

Query it remotely

INSTALL httpfs; LOAD httpfs;
ATTACH 'https://huggingface.co/datasets/Nason/fjc-judges-database/resolve/main/fjc-judges.duckdb' AS fjc_judges (READ_ONLY);

SELECT appointing_president, COUNT(*) AS judges, ROUND(AVG(confirmation_date - nomination_date)) AS avg_days_to_confirm
FROM "fjc-judges".federal_judicial_service WHERE nomination_date >= '1981-01-01'
GROUP BY 1 ORDER BY MIN(nomination_date);

Or with the datapond packages: pip install datapond / pak::pak("datapond-db/datapond-r").

Build pipeline and caveats: https://github.com/ian-nason/fjc-judges-database Source: https://www.fjc.gov/history/judges (public domain).