ENCORE: a practical implementation to improve reproducibility and transparency of computational research.

Area of Science:

• Computational Science
• Research Methodology

Background:

• Computational research reproducibility is a persistent challenge.
• Existing guidelines are difficult to translate into practical applications.

Purpose of the Study:

• To present ENCORE, a novel approach for enhancing transparency and reproducibility in computational projects.
• To provide a standardized framework for structuring and documenting research.

Main Methods:

• ENCORE integrates project components into a standardized file system.
• Utilizes pre-defined documentation templates and GitHub for versioning.
• Includes an HTML-based navigator for accessibility.

Main Results:

• ENCORE is designed to be agnostic to project type, data, programming language, and infrastructure.
• Demonstrates a practical method for improving research transparency.
• Identified lack of incentives as the primary barrier to adoption.

Conclusions:

• ENCORE offers a robust solution for improving computational research reproducibility.
• Addressing researcher incentives is crucial for the widespread adoption of reproducibility frameworks.