LEAF: Laboratory Equipment Adapter Framework

Data silos

Data is essential to research and scientific processes. Likewise, it is also essential for Industry4.0 digital innovations such as machine learning, artificial intelligence, digital twins, and digital shadows. Yet laboratory, or field, equipment lack the capacity to automatically transfer data from a device to a public or private cloud based environment. The lack of data transfer means that for many researchers, technicians, and data providers they must manually extract data, limiting the usability of such data to after the fact, rather than in real-time when corrective actions could be performed. In larger set-ups (i.e., large-scale research facilties; more ambitious experiments, etc) data may have to extracted from multiple devices increasing the data processing time, time that could be spent performing more constructive tasks. If experiments utilise multiple devices that come from different manufacturers the ‘simple task’ of integrating data in manufacturer specific formats with manufacturer specific units and standards can place a significant burden on resources. Overcoming these barriers to data integration - or the siloing of data - would enable data to be used in real-time.

To resolve these issues we developed LEAF, or the Laboratory Equipment Adapter Framework, a modular open-source framework that enables automated, standardised, and interoperable data collection from diverse laboratory equipment to support digitisation and integration within modern data-driven research environments. Giving your devices the capabilities of Internet of Things (IoT) without replacing (manufacturer) software.

What is an adapter?

Adapters are containers of modules (and an interpreter) that, when joined, can take new information from equipment, transform it into a standard structure, and output or store it. Adapters have been developed for equipment that exists in-house, however new adapters can be developed to take into account other equipment.