| --- |
| license: cc0-1.0 |
| language: |
| - en |
| tags: |
| - united states |
| - law |
| - legal |
| - court |
| - opinions |
| size_categories: |
| - 1M<n<10M |
| viewer: true |
| --- |
| |
| <a href="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases.png"><img src="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases-banner.webp"/></a> |
|
|
| # Collaborative Open Legal Data (COLD) - Cases |
|
|
| COLD Cases is a dataset of 8.3 million United States legal decisions with text and metadata, formatted as one JSON object per decision. |
| The total dataset size is approximately 104GB of uncompressed JSON. |
|
|
| This dataset exists to support the open legal movement exemplified by projects like |
| [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law) and |
| [LegalBench](https://hazyresearch.stanford.edu/legalbench/). |
| A key input to legal understanding projects is caselaw -- the published, precedential decisions of judges deciding legal disputes and explaining their reasoning. |
| United States caselaw is collected and published as open data by [CourtListener](https://www.courtlistener.com/), which maintains scrapers to aggregate data from |
| a wide range of public sources. |
|
|
| COLD Cases reformats CourtListener's [bulk data](https://www.courtlistener.com/help/api/bulk-data) so that all of the semantic information about each legal decision |
| (the authors and text of majority and dissenting opinions; head matter; and substantive metadata) is encoded in a single JSON object per decision, with extraneous |
| data removed. By consolidating the data engineering for preprocessing caselaw in an |
| [open source](https://github.com/harvard-lil/cold-cases-export) |
| pipeline maintained by the Harvard Law School Library, we ensure |
| that downstream machine learning and natural language processing projects can use consistent, high quality representations of cases for legal understanding tasks. |
|
|
| Prepared by the [Harvard Library Innovation Lab](https://lil.law.harvard.edu) in collaboration with the [Free Law Project](https://free.law/). |
|
|
| --- |
|
|
| ## Links |
| - [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) |
|
|
| --- |
|
|
| ## Summary |
| - [Formats](#formats) |
| - [File structure](#file-structure) |
| - [Data dictionary](#data-dictionary) |
| - [Notes on appropriate use](#appropriate-use) |
|
|
| --- |
|
|
| ## 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. | |
| | `court_jurisdiction` | Code for type of court that presided over the case. One of the following: |
| F: Federal Appellate |
| FD: Federal District |
| FB: Federal Bankruptcy |
| FBP: Federal Bankruptcy Panel |
| FS: Federal Special |
| S: State Supreme |
| SA: State Appellate |
| ST: State Trial |
| SS: State Special |
| TRS: Tribal Supreme |
| TRA: Tribal Appellate |
| TRT: Tribal Trial |
| TRX: Tribal Special |
| TS: Territory Supreme |
| TA: Territory Appellate |
| TT: Territory Trial |
| TSP: Territory Special |
| SAG: State Attorney General |
| MA: Military Appellate |
| MT: Military Trial |
| C: Committee |
| I: International |
| T: Testing | |
| | `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) |
| |
| ## Notes on appropriate use |
| |
| When using this data, please keep in mind: |
| |
| * All documents in this dataset are public information, published by courts within the United States to inform the public about the law. **You have a right to access them.** |
| * Nevertheless, **public court decisions frequently contain statements about individuals that are not true**. Court decisions often contain claims that are disputed, |
| or false claims taken as true based on a legal technicality, or claims taken as true but later found to be false. Legal decisions are designed to inform you about the law -- they are not |
| designed to inform you about individuals, and should not be used in place of credit databases, criminal records databases, news articles, or other sources intended |
| to provide factual personal information. Applications should carefully consider whether use of this data will inform about the law, or mislead about individuals. |
| * **Court decisions are not up-to-date statements of law**. Each decision provides a given judge's best understanding of the law as applied to the stated facts |
| at the time of the decision. Use of this data to generate statements about the law requires integration of a large amount of context -- |
| the skill typically provided by lawyers -- rather than simple data retrieval. |
| |
| To mitigate privacy risks, we have filtered out cases [blocked or deindexed by CourtListener](https://www.courtlistener.com/terms/#removal). Researchers who |
| require access to the full dataset without that filter may rerun our pipeline on CourtListener's raw data. |
| |
| [☝️ Go back to Summary](#summary) |
