LEAF: Lightweight equipment adapter framework

<ns5:p>Background As our understanding of complex (biological) systems within the (life) sciences increases, so does the continuous demand for data to maintain that understanding. New approaches for knowledge generation, real-time prediction, and decision-making, such as machine learning, do not reduce this data deficit, instead requiring diverse and continuously updated data streams. Furthermore, in order to be reusable, machine-readable and computationally tractable, such diverse datasets must be seamlessly integrated and efficiently processed across different sources, necessitating a form of standardization. However, the amount and diversity of data required far exceed the capabilities of capturing and curating data manually, making manual approaches increasingly infeasible. One solution would be the automation of, or at least enhancement of, digitization of research data. Although automation of research environments and automated data extraction are not new ideas, the issue of vendor-specific software, processes, and data structures makes generic inter-equipment data extraction challenging. A “one-size-fits-all” approach can be difficult with such diverse and disparate systems. Results Here, the Lightweight Equipment Adapter Framework (LEAF) is introduced, a modular open-source system that can be adapted to monitor different equipment and extract data automatically. LEAF is flexible and scalable, enabling integration with various types of communication protocols, instruments, and data formats. By leveraging reusable modules, the framework allows for rapid development of new adapters, minimizing the effort required to support additional equipment. Once deployed, LEAF operates with minimal human intervention, facilitating continuous and automated data collection. Standardisation of data is achieved through automated data collection, ensuring interoperability across different systems and enabling more efficient data analysis and downstream processing. Conclusions LEAF shifts compliance with data standards to the instrument level, generating better metadata, and allowing the integration of data from diverse sources. Ultimately, LEAF addresses the challenge of fragmented laboratory and research automation by providing a unified, adaptable solution for extracting and structuring experimental data.</ns5:p>