This Project aims to transform field research by overcoming the 'digitisation bottleneck', and by dramatically reducing the cost of creating digital and FAIR-compliant data. This project will make quality, comprehensive datasets the norm. It would re-engineer FAIMS Mobile, a mature and stable, but dated, system for producing custom electronic notebooks for data collection in diverse field situations. The proposed Platform, FAIMS 3.0 Electronic Field Notebooks (FAIMS 3.0) aims to reduce the cost of robust data collection and improve the efficiency, quality, and integrity of field research. Customised applications generated by the platform and tailored to the requirements of diverse research communities (including academic, government, and industry users) would accelerate knowledge generation by producing FAIR datasets and documenting research processes, supporting large-scale studies and improving transparency. The proposed platform would mint cross-platform data applications, with robust offline functionality. Customisations would be created using text files (generatable via a web application with a graphical interface) to facilitate sharing and reuse - and intrinsically document of field process and data schemas, further contributing to Open Science goals. FAIMS 3.0 would extend existing fieldwork-specific features of FAIMS (capture of varied data, bi-directional sync, backup, versioning, automation, data annotations, aids to good practice), improve performance, and add functionality, especially around self-service customisation and deployment. Following good software design principles (do one thing well), the Platform will focus on field data capture, then federate with other platforms and services that focus on later stages of the data lifecycle. The platform will integrate with existing virtual environments for data analysis and repositories (Cloudstor and SWAN, AVRE Geo DeVL, Alveo, Open Context, and tDAR) in order to bridge the data capture and processing stages of fieldwork and facilitate a seamless workflow from data creation, through cleaning and analysis, to publication and archiving.