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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.
Data collected about individuals is regularly used to make decisions that impact those same individuals. We consider settings where sensitive personal data is used to decide who will receive resources or benefits. While it is well known that there is
Understanding the behaviour of hosts of SARS-CoV-2 is crucial to our understanding of the virus. A comparison of environmental features related to the incidence of SARS-CoV-2 with those of its potential hosts is critical. We examine the distribution
As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science lifecycle, including long-term data sto
We describe a new logical data model, called the concept-oriented model (COM). It uses mathematical functions as first-class constructs for data representation and data processing as opposed to using exclusively sets in conventional set-oriented mode
Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights: * Automation in data science aims to facilitate and transform t