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- legal
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- court
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- opinions
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---
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<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>
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# Collaborative Open Legal Data (COLD) - Cases
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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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---
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- [Formats](#formats)
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- [File structure](#file-structure)
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- [Data dictionary](#data-dictionary)
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- [
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- [Pipeline source code](https://github.com/harvard-lil/cold-cases-export)
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---
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[☝️ Go back to Summary](#summary)
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##
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- legal
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- court
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- opinions
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size_categories:
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- 1M<n<10M
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---
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<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>
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# Collaborative Open Legal Data (COLD) - Cases
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COLD Cases is a dataset of 8.3 million United States legal decisions with text and metadata, formatted as one JSON object per decision.
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The total dataset size is approximately 104GB of uncompressed JSON.
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This dataset exists to support the open legal movement exemplified by projects like
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[Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law) and
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[LegalBench](https://hazyresearch.stanford.edu/legalbench/).
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A key input to legal understanding projects is caselaw -- the published, precedential decisions of judges deciding legal disputes and explaining their reasoning.
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United States caselaw is collected and published as open data by [CourtListener](https://www.courtlistener.com/), which maintains scrapers to aggregate data from
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a wide range of public sources.
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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
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(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
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data removed. By consolidating the data engineering for preprocessing caselaw in an
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[open source](https://github.com/harvard-lil/cold-cases-export)
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pipeline maintained by the Harvard Law School Library, we ensure
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that downstream machine learning and natural language processing projects can use consistent, high quality representations of cases for legal understanding tasks.
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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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---
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## Links
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- [Data nutrition label](https://datanutrition.org/labels/v3/?id=c29976b2-858c-4f4e-b7d0-c8ef12ce7dbe) (DRAFT). ([Archive](https://perma.cc/YV5P-B8JL)).
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- [Pipeline source code](https://github.com/harvard-lil/cold-cases-export)
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## Table of Contents
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- [Formats](#formats)
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- [File structure](#file-structure)
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- [Data dictionary](#data-dictionary)
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- [Notes on appropriate use](#appropriate-use)
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---
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[☝️ Go back to Summary](#summary)
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## Notes on appropriate use
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When using this data, please keep in mind:
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* 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.**
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* Nevertheless, **public court decisions frequently contain statements about individuals that are not true**. Court decisions often contain claims that are disputed,
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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
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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
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to provide factual personal information. Applications should carefully consider whether use of this data will inform about the law, or mislead about individuals.
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* **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
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at the time of the decision. Use of this data to generate statements about the law requires integration of a large amount of context --
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the skill typically provided by lawyers -- rather than simple data retrieval.
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To mitigate privacy risks, we have filtered out cases [blocked or deindexed by CourtListener](https://www.courtlistener.com/terms/#removal). Researchers who
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require access to the full dataset without that filter may rerun our pipeline on CourtListener's raw data.
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