To run the notebooks, you can use one of the following options:

1. Self-contained environment (recommended)

2. Open in Google Colab (see table)
3. You can also run the notebooks locally by cloning the repository and installing the required dependencies.

For local development, the next dependencies are required:

• Java 11
• Python 3.12 or later
• uv

Notebook	Language	Framework	Description	Google Colab
autoimmune_colocalisations.ipynb	Python	pyspark	Extract colocalisations for GWAS credible sets associated with autoimmune diseases, including additional metadata about the studies.
autoimmune_credible_set.ipynb	Python	pyspark	Extract GWAS credible sets associated with autoimmune diseases, including additional metadata about the studies and Locus-2-gene assignments.
chembl_evidence_download.ipynb	Python	pyspark	Download ChEMBL evidence data from the Open Targets Platform using rsync. Includes drug-target associations and related evidence.
exploring_ot_datasets.ipynb	Python	pyspark, polars, dask	Explore the Open Targets datasets available at Open Targets Data Downloads. Includes information on diseases, targets, evidence, and associations.
reading_data_from_aws.ipynb	Python	pandas, polars, pyspark	Access Open Targets Platform datasets hosted on AWS S3 using pandas, polars, and pyspark without downloading local copies.