Framework for Experimental Results (fer): A Foundation for FAIR-Compliant Reporting of Traceable Data

This paper introduces fer (Framework for Experimental Results), a flexible and comprehensive framework designed to enhance the reproducibility, transparency, and traceability of experimental results, fully aligning with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles and Open Science standards. The framework is implemented through three core components: (1) a structured data file to represent experimental results, (2) a set of scripts to ensure reproducibility, and (3) a database to facilitate accessibility and findability. The main contribution of this work is the definition of a flexible, implementation-agnostic data structure grounded in metrological principles –such as the International Vocabulary of Metrology (VIM) and the Guide to the Expression of Uncertainty in Measurement (GUM)– that supports a wide range of scientific domains. The framework’s utility is illustrated through a case study involving a calibrated thermocouple sensor. To support adoption, we introduce ferpy, a Python-based implementation that works with fer files in JSON format. Compared to existing solutions, this framework is format-agnostic, so other file formats can be easily integrated into ferpy, ensuring adaptability to future technologies and diverse research needs and practices. By promoting consistent, transparent, and open data reporting, fer aims to foster improved data sharing, reproducibility, and collaboration across the scientific community.