Description: This is a bulk open-access dataset in JSON, Parquet and Hugging Face Dataset formats with the full text of all Immigration and Refugee Board (IRB) Refugee Protection Division (RPD) cases provided by the IRB to the Refugee Law Lab, covering the 2002 to 2020 period. Because the IRB is no longer publishing RPD decisions, we consider the dataset to be a legacy dataset. For more recent decisions obtained via Access to Information requests, see the RLLR Bulk Decisions Dataset. The process through which data is collected and processed, as well as code snippets for loading the data, are available in a repository on the Refugee Law Lab Github.
Data: https://github.com/Refugee-Law-Lab/rpd_bulk_data/blob/master/DATA/yearly
Code Repository: https://github.com/Refugee-Law-Lab/rpd_bulk_data
Current Coverage: 2002-2020
Number of Decisions: ~12,500
Languages: English & French
Format: JSON, Parquet, Hugging Face Dataset
License: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Citation: Sean Rehaag, “RAD Bulk Decisions Dataset” (2023), online: Refugee Law Laboratory https://refugeelab.ca/bulk-data/rpd
Programmatic Access in Python (via Hugging Face Datasets):
from datasets import load_dataset import pandas as pd dataset = load_dataset("refugee-law-lab/canadian-legal-data", "RPD", split="train") # convert to dataframe df = pd.DataFrame(dataset) df
Programmatic Access to in Python (via Parquet):
import pandas as pd import requests from io import BytesIO url = 'https://huggingface.co/datasets/refugee-law-lab/canadian-legal-data/resolve/main/RPD/train.parquet' # load data results = requests.get(url) # convert to dataframe df = pd.read_parquet(BytesIO(results.content)) df
Programmatic Access in Python (JSON via GitHub):
import pandas as pd
import requests
import json
start_year = 2002 # First year of data sought (2002+)
end_year = 2020 # Last year of data sought (2020 -)
base_ulr = 'https://raw.githubusercontent.com/Refugee-Law-Lab/rpd_bulk_data/master/DATA/YEARLY/'
data results = []
for year in range(start_year, end_year+1):
url = base_ulr + f'{year}.json'
results.extend(requests.get(url).json())
df = pd.DataFrame(results)
df