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

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.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2024-09-16
DOI
https://doi.org/10.1038/s41467-024-52446-8
Citations
6
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
6.31

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Danial Lashgari, Aldo Jongejan, Rodrigo García-Valiente, Perry D. Moerland et al.
6 citations
Nature Communications
Scientific Computing and Data Management
6.31
article

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

Danial Lashgari, Aldo Jongejan, Rodrigo García-Valiente, Perry D. Moerland, Adriaan D. Dane, Mia L. Pras‐Raves, Eric Wever, Antoine H. C. van Kampen, Barbera D. C. van Schaik, Daria Balashova, Utkarsh Mahamune
article en
6 citations

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.

Nature CommunicationsVol. 15(1)
Netherlands Center for Occupational Diseases (NL), Netherlands Metabolomics Centre (NL), Public Health Service of Amsterdam (NL), Amsterdam University Medical Centers (NL), Amsterdam University of Applied Sciences (NL), University of Amsterdam (NL)
European Federation of Pharmaceutical Industries and Associations, European Commission
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Scientific Computing and Data Management
6.31
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.