Python client for parsing SCOTUS data from multiple sources.
pip install -e firstname.lastname@example.org:newsdev/nyt-clerk.git#egg=clerk python -m clerk.scdb python -m clerk.scotus python -m clerk.scores
- clerk/data/scdb_cases.json: SCDB fields about decided merits cases, excluding the justice and vote details.
- clerk/data/scdb_justices.json: SCDB fields about individual justices.
- clerk/data/scdb_votes.json: SCDB fields about a single justice’s vote in a single case.
- clerk/data/scotus_cases.json: Case data from the SupremeCourt.gov site, including transcripts and audio where available.
- clerk/data/scores_courtterms.json: Data about the ideology of a given Court term.
- clerk/data/scores_justices.json: Data about the ideology and qualifications of a given Justice before they were confirmed.
- clerk/data/scores_justicetterms.json: Data about the ideology of an individual Justice in a given Court term.
SCDB data includes cases from 1946 term to the 2014 term. Many fields need to be mapped to their full values. The SCDB maintains an online codebook with these maps.
The SupremeCourt.gov site has case data from the 2000 term until the present for some cases. * Argument transcripts: 2000-present * Slip opinions (decision PDFs): 2006-present * Oral argument audio: 2010-present
Ideology / Qualification Scores
Martin-Quinn scores measure the relative ideology of a Justice or a Supreme Court term to the median Justice. Andrew Martin and Kevin Quinn wrote an excellent paper about the method.
Segal-Cover scores measure the ideology and qualification of an individual Justice before their appointment to the Court. Jeffrey Segal wrote a summary and shows the raw data as a table (warning: PDF).
- Build inflator for turning JSON into Python objects.
- Think a bit more about the developer API.