Lintol: a more techie overview…

Lintol will provide a standalone automated system incorporating pre-existing, high quality data validation tools such as CSV Lint and Goodtables-py, and offer new plug-ins for additional data types. For example, Lintol will be distinct in offering the following functions:

“Checking that geospatial points (in, say a GeoJSON dataset are within boundaries…Analysing how many points in a 3D point set are outside a bounding sphere…Detecting whether common name or address strings, or column names, can be found in a dataset, as an anonymization re-confirmation…Validating data against registers – for example, given a column containing council names, that all are on the relevant register of local government names…Scoring validation against relevant W3C and ISO standards”

We plan to keep adding to this functionality and expanding Lintol’s capabilities.

In addition to its validating functions, we are developing Lintol so that in the future it could be integrated into any established Open Data platform, for example Octopub.

For us, it’s key that the principles of Open Data and transparency are part of Lintol’s genetic code, and we’re using a Python based open source workflow engine.

How do I get started?

Lintol will be officially available from March 2018, but in the meantime we’re looking for Data Owners and Publishers to try the prototype for free. One of our team will be on hand to guide you
through the process, and will come out in person to any local customers who’re willing to be testers.