The FAIR DO Lab

The FAIR DO Lab is a configurable structure of services to fulfill generic FAIR Digital Object (FAIR DO) use cases. It is a proposal for research which can be configured and extended to specific needs. As the FAIR DO Lab is easy to run on local computers and its default configuration offers a sandboxed PID service, it is therefore also suited to serve developers as a testing environment.

As its core, it uses the Typed PID Maker, which then will notify other services about its actions, so they can automatically react to those. For example, an indexer service will put new or changed record information into a search engine without any human interaction required.

The FAIR DO Lab is based on its predecessor, the FAIR DO Testbed, which was introduced at the RDA Virtual Plenary 17 Poster Session. In contrast to it, the FAIR DO Lab aims for production ready software rather than experimentation.

Features

• All features of the Typed PID Maker, including creation, updating and resolving PIDs
• Supporting development and training, due to its sandboxed default configuration
• Configurable services, which can be omitted if not required
• (Optional, default) Extendable architecture due to the established message broker “RabbitMQ”, where services get all information about the situation
• (Optional, default) Automated indexing of PID records using Elasticsearch

Roadmap

Currently, the FAIR DO Lab contains services which are not production-ready. While the development can be followed on GitHub, here are the most important points which are planned in near future:

• The Typed PID Maker must reach version 1.0. – Ideally, all services should be production-ready, but as the Typed PID Maker is a vital part of the FAIR DO Lab, we consider its stability as a requirement for production use. As the FAIR DO Lab is a living proposal, we do not have this requirement for all the services, but we offer explicit documentation about the status of the different components.

• Stable and flexible indexing process. – Searching for FAIR DOs is an important use case. We consider the current indexing process to be a proof-of-concept. For a 1.0 release, we plan to make it more flexible and use it to extract even more information about FAIR DOs.