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Reproducibility is a fundamental requirement of the scientific process sinceit enables outcomes to be replicated and verified. Computational scientificexperiments can benefit from improved reproducibility for many reasons,including validation of results and reuse by other scientists. However,designing reproducible experiments remains a challenge and hence the need fordeveloping methodologies and tools that can support this process. Here, wepropose a conceptual model for reproducibility to specify its main attributesand properties, along with a framework that allows for computationalexperiments to be findable, accessible, interoperable, and reusable. We presenta case study in ecological niche modeling to demonstrate and evaluate theimplementation of this framework.

Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study