README.md

---
license: cc-by-nc-4.0
language:
- en
- fr
size_categories:
- 10K<n<100K
---
# Refugee Law Lab: Canadian Legal Data

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

The Refugee Law Lab supports bulk open-access to Canada legal data to facilitate research and advocacy, focusing on
federal law and law relating to refugees and other people on the move. Bulk open-access helps avoid asymmetrical 
access-to-justice and amplification of marginalization that results when commercial actors leverage proprietary 
legal datasets for profit -- a particular concern in the border control setting.

This dataset includes the unofficial full text of thousdands of court and tribunal decisions at the federal level. 
It can be used for legal analytics (e.g. identifying patterns in legal decision-making), to test ML and NLP tools 
on a bilingual (French and English) dataset of Canadian legal materials, and to pretrain language models.

### Languages

Many documents are available in both English and French. Some are only available in one of the two languages.

## Dataset Structure

### Data Instances

- SCC: Full text of all Supreme Court of Canada decisions, based on the Refugee Law Lab's Supreme Court of Canada Bulk Decisions Dataset (1877 – 2023)
- FCA: Full text of all Federal Court of Appeal (Canada) decisions that have been given a neutral citation, based on 
the Refugee Law Lab's Federal Court of Appeal Bulk Decisions Dataset (2001-2023)
- FC: Full text of all Federal Court (Canada) decisions that have been given a neutral citation, based on 
the Refugee Law Lab's Federal Court Bulk Decisions Dataset (2001-2023)

### Data Fields

- citation1 (string): Legal citation for the document (neutral citation where available)

- citation2 (string): For some documents multiple citations are available (e.g. for some periods
the Supreme Court of Canada provided both official reported citation and neutral citation)

- data_instance (string): Name of the data instance (e.g. "SCC", "FCA", "FC", etc)

- year (int32): Year of the document date, which can be useful for filtering

- name (string): Name of the document, typically the style of cause of a case

- language (string): Language of the document, "en" for English, "fr" for French

- document_date (string): Date of the document, typically the date of a decision (yyyy-mm-dd)

- source_url (string): URL where the document was scraped and where the official version can be found

- scraped_timestamp (string): Date the document was scraped (yyyy-mm-dd)

- unofficial_text (string): Full text of the document (unofficial version, for official version see source_url)

- other (string): Field for additional metadata in JSON format, currently a blank string for most datasets


### Data Splits

The data has not been split, so all files are in the train split. If splitting for training/validation, 
some thought should be given to whether it is necessary to limit to one language or to ensure that both
English and French versions of the same documents are put in the same split.


## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]