Author
Listed:
- Antoine H. C. Kampen
(Epidemiology and Data Science
Methodology
Amsterdam Institute for Immunology and Infectious Diseases
University of Amsterdam)
- Utkarsh Mahamune
(Epidemiology and Data Science
Methodology
Amsterdam Institute for Immunology and Infectious Diseases)
- Aldo Jongejan
(Epidemiology and Data Science
Methodology)
- Barbera D. C. Schaik
(Epidemiology and Data Science
Methodology
Amsterdam Institute for Immunology and Infectious Diseases)
- Daria Balashova
(Epidemiology and Data Science
Methodology
Amsterdam Institute for Immunology and Infectious Diseases)
- Danial Lashgari
(Epidemiology and Data Science
Methodology
Amsterdam Institute for Immunology and Infectious Diseases)
- Mia Pras-Raves
(Laboratory Genetic Metabolic Diseases
Amsterdam UMC)
- Eric J. M. Wever
(Laboratory Genetic Metabolic Diseases
Amsterdam UMC)
- Adrie D. Dane
(Epidemiology and Data Science
Methodology
Amsterdam UMC)
- Rodrigo García-Valiente
(Epidemiology and Data Science
Methodology
Amsterdam Institute for Immunology and Infectious Diseases)
- Perry D. Moerland
(Epidemiology and Data Science
Methodology
Amsterdam Institute for Immunology and Infectious Diseases)
Abstract
Reproducibility of computational research is often challenging despite established guidelines and best practices. Translating these guidelines into practical applications remains difficult. Here, we present ENCORE, an approach to enhance transparency and reproducibility by guiding researchers in how to structure and document a computational project. ENCORE builds on previous efforts in computational reproducibility and integrates all project components into a standardized file system structure. It utilizes pre-defined files as documentation templates, leverages GitHub for software versioning, and includes an HTML-based navigator. ENCORE is designed to be agnostic to the type of computational project, data, programming language, and ICT infrastructure, and does not rely on specific software tools. We also share our group’s experience using ENCORE, highlighting that the most significant challenge to the routine adoption of approaches like ours is the lack of incentives to motivate researchers to dedicate sufficient time and effort to ensure reproducibility.
Suggested Citation
Antoine H. C. Kampen & Utkarsh Mahamune & Aldo Jongejan & Barbera D. C. Schaik & Daria Balashova & Danial Lashgari & Mia Pras-Raves & Eric J. M. Wever & Adrie D. Dane & Rodrigo García-Valiente & Perry, 2024.
"ENCORE: a practical implementation to improve reproducibility and transparency of computational research,"
Nature Communications, Nature, vol. 15(1), pages 1-14, December.
Handle:
RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52446-8
DOI: 10.1038/s41467-024-52446-8
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