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

TL;DR

This paper presents a framework to enhance reproducibility and FAIR principles in data science, demonstrated through a case study in ecological niche modeling to improve experiment validation and reuse.

Contribution

It introduces a conceptual model and framework for reproducibility and FAIR compliance, specifically applied to ecological niche modeling.

Findings

Framework effectively supports findability, accessibility, interoperability, and reusability.

Case study demonstrates improved reproducibility in ecological niche modeling.

Framework can be generalized to other computational experiments.

Abstract

Reproducibility is a fundamental requirement of the scientific process since it enables outcomes to be replicated and verified. Computational scientific experiments 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 for developing methodologies and tools that can support this process. Here, we propose a conceptual model for reproducibility to specify its main attributes and properties, along with a framework that allows for computational experiments to be findable, accessible, interoperable, and reusable. We present a case study in ecological niche modeling to demonstrate and evaluate the implementation of this framework.