--- license: cc0-1.0 language: - en tags: - united states - law - legal - court - opinions viewer: false --- # Collaborative Open Legal Data (COLD) - Cases Re-packaged bulk data from [courtlistener.com](https://www.courtlistener.com/help/api/bulk-data), allowing for easy batch processing of open legal data, for example in the context of data science / AI experiments. Prepared by the [Harvard Library Innovation Lab](https://lil.law.harvard.edu) in collaboration with the [Free Law Project](https://free.law/). --- ## Summary - [Formats](#formats) - [File structure](#file-structure) - [Data dictionary](#data-dictionary) - [Data nutrition label](https://datanutrition.org/labels/v3/?id=c29976b2-858c-4f4e-b7d0-c8ef12ce7dbe) (DRAFT). ([Archive](https://perma.cc/YV5P-B8JL)). - [Pipeline source code](https://github.com/harvard-lil/cold-cases-export) --- ## Formats We've released this data in two different formats: ### JSON-L or JSON Lines This format consists of a JSON document for every row in the dataset, one per line. This makes it easy to sample a selection of the data or split it out into multiple files for parallel processing using ordinary command line tools such as `head`, `split` and `jq`. Just about any language you can think of has a ready way to parse JSON data, which makes this version of the dataset more compatible. See: https://jsonlines.org/ ### Apache Parquet Parquet is binary format that makes filtering and retrieving the data quicker because it lays out the data in columns, which means columns that are unnecessary to satisfy a given query or workflow don't need to be read. Parquet has more limited support outside the Python and JVM ecosystems, however. See: https://parquet.apache.org/ [☝️ Go back to Summary](#summary) --- ## File structure Both of these datasets were exported by the same system based on [Apache Spark](https://spark.apache.org/), so within each subdirectory, you'll find a similar list of files: - **_SUCCESS**: This indicates that the job that built the dataset ran successfully and therefore this is a complete dataset. - **.json.gz or .gz.parquet**: Each of these is a slice of the full dataset, encoded in JSON-L or Parquet, and compressed with [GZip](https://www.gnu.org/software/gzip/). - **Hidden `.crc` files**: These can be used to verify that the data transferred correctly and otherwise ignored. [☝️ Go back to Summary](#summary) --- ## Data dictionary Partial glossary of the fields in the data. | Field name | Description | | --- | --- | | `judges` | Names of judges presiding over the case, extracted from the text. | | `date_filed` | Date the case was filed. Formatted in ISO Date format. | | `date_filed_is_approximate` | Boolean representing whether the `date_filed` value is precise to the day. | | `slug` | Short, human-readable unique string nickname for the case. | | `case_name_short` | Short name for the case. | | `case_name` | Fuller name for the case. | | `case_name_full` | Full, formal name for the case. | | `attorneys` | Names of attorneys arguing the case, extracted from the text. | | `nature_of_suit` | Free text representinng type of suit, such as Civil, Tort, etc. | | `syllabus` | Summary of the questions addressed in the decision, if provided by the reporter of decisions. | | `headnotes` | Textual headnotes of the case | | `summary` | Textual summary of the case | | `disposition` | How the court disposed of the case in their final ruling. | | `history` | Textual information about what happened to this case in later decisions. | | `other_dates` | Other dates related to the case in free text. | | `cross_reference` | Citations to related cases. | | `citation_count` | Number of cases that cite this one. | | `precedential_status` | Constrainted to the values "Published", "Unknown", "Errata", "Unpublished", "Relating-to", "Separate", "In-chambers" | | `citations` | Cases that cite this case. | | `court_short_name` | Short name of court presiding over case. | | `court_full_name` | Full name of court presiding over case. | | `opinions` | An array of subrecords. | | `opinions.author_str` | Name of the author of an individual opinion. | | `opinions.per_curiam` | Boolean representing whether the opinion was delivered by an entire court or a single judge. | | `opinions.type` | One of `"010combined"`, `"015unamimous"`, `"020lead"`, `"025plurality"`, `"030concurrence"`, `"035concurrenceinpart"`, `"040dissent"`, `"050addendum"`, `"060remittitur"`, `"070rehearing"`, `"080onthemerits"`, `"090onmotiontostrike"`. | | `opinions.opinion_text` | Actual full text of the opinion. | | `opinions.ocr` | Whether the opinion was captured via optical character recognition or born-digital text. | [☝️ Go back to Summary](#summary)