More research,
less wrangling
Transform messy data into structured schemas using readable, auditable methods. Perform schema-to-schema transforms for interoperability and data reuse.

Transform messy data into structured schemas using readable, auditable methods. Perform schema-to-schema transforms for interoperability and data reuse.
Your data are the foundation for research and decision-support. Ensure interoperability, transparency and probity by using readable, auditable transformation methods.
Import CSV, XLS or XLSX source files. Derive or coerce unruly data into a defined schema.
Drag 'n drop sequential actions to define structured and readable schema-to-schema transform methods.
Manage teams with authentication, and assign rights and tasks. Schedule and track a calendar of data updates and transforms.
Make API calls to execute transforms, or save methods for local automation. Download restructured data as CSV, Excel, Parquet or Feather.
Document BibTeX-compliant metadata for projects, collections and source data. Validate output data against an associated transformation method.
Deploy the open source Whyqd stack as a standalone data science hub on your own infrastructure, or integrated as a Python package in your software.