Adapter Framework for Equipment Monitoring and Control

What is LEAF?

LEAF, or the Laboratory Equipment Adapter Framework, is an open source, modular, tool for extracting data and metadata from (research) equipment and transferring it via MQTT to an end-point of your choosing. The aim of LEAF is to ensure that data is available for use by individuals, users, technicians, and/or researchers or by tools and services such as Digital Twins and Shadows and AI/ML algorithms when it is most needed. To achieve this aim LEAF has been designed to be as simple as possible to use as well as develop new features

Overview

Written in Python (>3.12) the Laboratory Equipment Adapter Framework (LEAF) implements an Adapter Architecture  designed to monitor and control various equipment types (e.g., bioreactors). The core principle of LEAF is to reduce the barrier to entry as much as possible to develop and deploy adapters for new equipment. The EquipmentAdapters are the functional equipment monitors composed of the rest of the modules (e.g., ProcessModules, PhaseModules, etc.) that perform specific tasks such as event monitoring, data processing, and output transmission.

Code Repository

The LEAF code environment is hosted on GitLab. You can access the repository here. LEAF can be installed via Pythons Python Package Index (PyPi) here.

How to contribute

Interested in using LEAF for your laboratory equipment? Check out the ‘For developers section’.

How to cite

Have you used LEAF and wish to cite it? It can be cited as the following:

Crowther, M., Metcalfe, B. Suarez, C., Corrales, D.C., Astudillo Lagos, J.A., and Koehorst, J.J., in review. 
LEAF: Laboratory equipment adapter framework

Acknowledgements

LEAF has been developed as part of the European Union’s Horizon 2020 research and innovation programme project ‘RI services to promote deep digitalization of Industrial Biotechnology - towards smart biomanufacturing’ (BIOINDUSTRY 4.0, grant agreement n° 101094287 ). and with financial contribution to the UNLOCK initiative (NWO: 184.035.007 ) by the Dutch Research Council (NWO) and Wageningen University & Research. Development of specific equipment adapters has been been funded through Call 823 - “High-Level Training of Human Capital for the Cauca region” of the Ministry of Science, Technology, and Innovation (MinCiencias).