Frequently Asked Questions
Doesn’t this already exist?
Various people working with labor market data, including many of us here at the Center for Data Science and Public Policy, have been working on similar projects for quite some time, and there are already many parts of the solution to a fragmented workforce data ecosystem out there. In fact, many of the ideas we’re putting forward directly borrow or build on existing work by O*NET, the Open Knowledge Foundation, and the National Skills Coalition, and use on concepts populat in software (around packaging) and data structures (the semantic web, Google’s DSPL etc). What we are doing is bringing these data sets and ideas together in an effort to increase interoperability and accelorate innovation, transparency, and opportunity.
- Our approach has these distinguishing features:
- Collaborative - we believe new data and tools will only succeed if they are developed in collaboration with major stakeholders.
- Transparent - all our methods and tools are developed in the open, and major decisions about the project are done in a transparent, participatory fashion.
- Ultra-simplicity - we want to keep things as simple as they possibly can be. This includes formats (JSON and CSV), a focus on end-user tool integration etc
- Web orientation - we want an approach that fits naturally with the web (e.g. use of JSON, formats that stream over HTTP)
- Focus on reuse and integration with existing tools
- Distributed and not tied to a given tool or project - this is not about creating a central data marketplace or similar setup. It’s about creating a basic framework that would enable anyone to publish and use datasets more easily and without going through a central broker.
We believe what is outlined above is a different and an especially powerful approach to improving that data ecosystem.
How do Data at Work standards relate Frictionless Data stanards put out by the Open Knowledge Foundation?
The motive behind Data at Work is to make it easier to access, transport and validate critical types of workforce data. We use the Frictionless Data standard because of it is the best available data standard supporting this goal.