Procedural and reporting guidelines frame the process of scientific practices and communications among researchers and the community at large. In the pursuit of fostering reproducibility, several methodological frameworks have been proposed by several initiatives. Nevertheless, recent studies indicate that data leakage and reproducibility are still prominent challenges. Recent studies have shown the transforming potential of incorporating the FAIR (Findable, Accessible, Interoperable and Reusable) principles in the work-flow of different context such as software and machine learning model development stages to cultivate open science. In this work, we introduce a framework to calibrate reporting guidelines against the FAIR principles in order to foster reproducibility and open science. We adapted the “Best fit” framework synthesis approach to develop the calibration framework. We propose a series of defined workflows to calibrate reporting guidelines with FAIR principles and a use case to demonstrate the process. By integrating FAIR principles with established reporting guidelines, the proposed framework bridges the gap in accommodating both FAIR metrics and reporting guidelines and benefits from advantages of these major integrated components.