Improving your data publishing workflow with the Frictionless Data Field Guide

The Frictionless Data Field Guide provides step-by-step instructions for improving data publishing workflows. The field guide introduces new ways of working informed by the Frictionless Data suite of software that data publishers can use independently, or adapt into existing personal and organisational workflows.

Data quality and automation of data processing are essential in creating useful and effective data publication workflows. Speed of publication, and lowering costs of publication, are two areas that are directly enhanced by having better tooling and workflows to address quality and automation.

At Open Knowledge International, we think that it is important for everybody involved in the publication of data to have access to tools that help automate and improve the quality of data, so this field guide details open data publication approaches with a focus on user-facing tools for anyone interested in publishing data.

All of the Frictionless Data tools that are included in this field guide are built with open data publication workflows in mind, with a focus on tabular data, and there is a high degree of flexibility for extended use cases, handling different types of open data. The software featured in this field guide are all open source, maintained by Open Knowledge International under the Frictionless Data umbrella and designed to be modular.

The preparation and delivery of the Frictionless Data Field Guide  has been made possible by the Open Data Institute, who received funding from Innovate UK to build “data infrastructure, improve data literacy, stimulate data innovation and build trust in the use of data” under the pubtools programme.

Feel free to engage the Frictionless Data team and community on Gitter.

The Frictionless Data project is a set of simple specifications to address common data description and data transport issues. The overall aim is to reduce friction in working with data and to do this by making it as easy as possible to transport data between different tools and platforms for further analysis. At the heart of Frictionless Data is the Data Package, which is a simple format for packaging data collections together with a schema and descriptive metadata. For over ten years, the Frictionless Data community has iterated extensively on tools and libraries that address various causes of friction in working with data, and this work culminated in the release of v1 specifications in September 2017.