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Analytical code sharing practices in biomedical research.

Nitesh Kumar Sharma¹, Ram Ayyala², Dhrithi Deshpande¹

¹Titus Family Department of Clinical Pharmacy, University of Southern California, Los Angeles, California, United States.

Peerj. Computer Science

|July 10, 2024

Summary

This summary is machine-generated.

Most biomedical studies fail to share analytical code, hindering reproducibility. Enhancing code sharing practices is crucial for scientific transparency and accelerating discoveries.

Keywords:

Accessibility Code sharing Data sharing Open-access Open-source Reproducibility Transparency

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Area of Science:

Biomedical research
Computational biology
Data science

Background:

Data-driven computational analysis is vital in modern biomedical research.
Lack of shared research outputs (data, code, methods) impedes study transparency and reproducibility.
Many published studies are irreproducible due to insufficient sharing of documentation, code, and data.

Purpose of the Study:

To analyze the extent of code sharing in biomedical research publications.
To identify factors associated with code availability and reproducibility.
To propose strategies for improving code sharing practices.

Main Methods:

Comprehensive analysis of 453 biomedical manuscripts published between 2016-2021.
Assessment of code availability, data sharing, and code organization.
Statistical analysis to identify associations between code availability statements, analysis type, and code sharing.

Main Results:

50.1% of analyzed manuscripts failed to share analytical code.
Most studies sharing code did not share additional research outputs like data.
Only 10% of articles organized code in a structured, reproducible manner.
Code availability statements were significantly associated with increased code availability.
Studies performing secondary analyses were more likely to share code than primary analyses.

Conclusions:

There is a significant deficit in code sharing practices within biomedical research, impacting reproducibility.
Promoting code availability statements and structured code organization is essential.
Prioritizing open science practices, including code and data sharing, is necessary to enhance scientific rigor, collaboration, and accelerate discoveries.