Version 1
: Received: 22 July 2024 / Approved: 23 July 2024 / Online: 23 July 2024 (17:57:59 CEST)
How to cite:
Shiferaw, K. B.; Balaur, I.; Welter, D.; Waltemath, D.; Zeleke, A. A. CALIFRAME: A Proposed Method of Calibrating Reporting Guidelines with FAIR Principles to Foster Reproducibility of AI Research in Medicine. Preprints2024, 2024071830. https://doi.org/10.20944/preprints202407.1830.v1
Shiferaw, K. B.; Balaur, I.; Welter, D.; Waltemath, D.; Zeleke, A. A. CALIFRAME: A Proposed Method of Calibrating Reporting Guidelines with FAIR Principles to Foster Reproducibility of AI Research in Medicine. Preprints 2024, 2024071830. https://doi.org/10.20944/preprints202407.1830.v1
Shiferaw, K. B.; Balaur, I.; Welter, D.; Waltemath, D.; Zeleke, A. A. CALIFRAME: A Proposed Method of Calibrating Reporting Guidelines with FAIR Principles to Foster Reproducibility of AI Research in Medicine. Preprints2024, 2024071830. https://doi.org/10.20944/preprints202407.1830.v1
APA Style
Shiferaw, K. B., Balaur, I., Welter, D., Waltemath, D., & Zeleke, A. A. (2024). CALIFRAME: A Proposed Method of Calibrating Reporting Guidelines with FAIR Principles to Foster Reproducibility of AI Research in Medicine. Preprints. https://doi.org/10.20944/preprints202407.1830.v1
Chicago/Turabian Style
Shiferaw, K. B., Dagmar Waltemath and Atinkut Alamirrew Zeleke. 2024 "CALIFRAME: A Proposed Method of Calibrating Reporting Guidelines with FAIR Principles to Foster Reproducibility of AI Research in Medicine" Preprints. https://doi.org/10.20944/preprints202407.1830.v1
Abstract
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.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
