Tofu is a Python library for generating synthetic UK Biobank data.
The UK Biobank is a large open-access prospective research cohort study of 500,000 middle aged participants recruited in England, Scotland and Wales. The study has collected and continues to collect extensive phenotypic and genotypic detail about its participants, including data from questionnaires, physical measures, sample assays, accelerometry, multimodal imaging, genome-wide genotyping and longitudinal follow-up for a wide range of health-related outcomes.
Tofu will generate synthetic data which conform to the structure of the baseline data UK Biobank sends researchers by generating random values:
- For categorical variables (single or multiple choices), a random value will be picked from the UK Biobank data dictionary for that field.
- For continous variables, a random value will be generated based on the distribution of values reported for that field on the UK Biobank showcase.
- For date and date/time fields, a random date will be generated.
- For all other fields, such as polymorphic fields, no data will be generated.
Some general observations:
- The
lookupsdirectory contains lookups downloaded from the UK Biobank showcase - they might need to be updated when new fields become available. - Data conform to the structure and schema of the baseline file but are otherwise nonsensical: no checks have been implemented across fields.
- All eid's (patient identifiers) generated from this tool are prefaced with 'fake' in order to avoid confusion with legitimate datasets.
- Dates randomly generated are between 1910 and 1990 again to avoid confusion with real data.
You can find more information on the UK Biobank here:
- The protocol paper in PLOS Medicine
- Presentations from the UK Biobank Scientific Conference contain a lot of information on various aspects of the dataset.
- The UK Biobank Showcase contains information for each field.
- The data dictionaries contain machine-readable metadata for each field.
Generate synthetic data for 100 patients across all baseline fields (not advised unless you really have the entire dataset):
python tofu.py -n 100
Wrote synthetic-20200128181342.csv shape (100,21946).Generate synthetic data for 100 patients for fields 3 and 20002
Note: you do not have to specify the eid in the list of fields since all generated datasets include it by default.
python tofu.py -n 100 --field 3 20002
Wrote synthetic-20200128171143.csv shape (10,103).Generate synthetic data for 100 patients for fields 3 and 20002 and make 10% of values missing.
python tofu.py -n 100 --field 3 20002 -j 10
Wrote synthetic-20200128191124.csv shape (100,103).Generate synthetic data for 100 patients for fields 3 and 20002 and write decoded fields and values (human readable format).
python tofu.py -n 100 --field 3 20002 -H- Pull requests are welcome! :)
- For major changes, please open an issue first to discuss what you would like to change.
- Please make sure to update tests as appropriate.
Many thanks to all who have contributed to this tool:
- Isuru Liyanage for implementing the -H flag for humans
- Alessia Peviani for implementing the -F flag
Spiros Denaxas. (2020). spiros/tofu: Updated release for DOI (Version v1.1). Zenodo. http://doi.org/10.5281/zenodo.3634604