README.md

---
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/).

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## 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)

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## 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)