Improving reproducibility in scientific research

Independent verification of data is a cornerstone of scientific research. The scientific method relies on reproducibility to validate findings and build upon existing work. Ideally, researchers should be able to replicate experiments and confirm results, but this is sometimes not achieved. In biomedical research, irreproducible findings waste resources and undermine credibility. Despite growing awareness, education on improving reproducibility remains insufficient.

What is reproducibility? Reproducibility encompasses direct replication (same design and conditions), analytic replication (reanalysis of original data), systemic replication (different conditions), and conceptual replication (different methods). Failures in direct and analytic replication often stem from poor practices, while systemic and conceptual replication face natural variability.

The reproducibility problem

Key factors contributing to irreproducibility include:

1. Limited access to data, protocols, and materials
   Without shared data and protocols, replication is difficult, slowing progress and increasing variability.
2. Misidentified or contaminated cell lines and microorganisms
   Contamination or genetic drift in mislabeled materials compromises results and reproducibility.
3. Challenges in managing complex datasets
   Large datasets require proper tools and standards; without them, errors and biases hinder replication.
4. Poor experimental design and reporting
   Insufficient design and unclear documentation make studies hard to reproduce and reduce confidence.
5. Cognitive biases such as confirmation and selection bias
   Biases distort interpretation and sampling, undermining reproducibility even in well-planned studies.
6. A competitive culture favoring novel results over negative findings
   Pressure to publish novel data discourages reporting negative results, creating gaps and wasted effort.

Recommended Best Practices

Broad efforts have focused on improving reproducibility in scientific research, leading to the development of recommended practices and guidelines aimed at enhancing transparency, data integrity, and consistency in experimental processes.  Recommended best practices for reproducibility include:

1. Share data and tools – Deposit raw data in public repositories for transparency and collaboration.
2. Use authenticated biomaterials – Verified, low-passage materials improve integrity and traceability.
3. Train on design and analysis – Educate researchers on proper structure and statistical methods.
4. Pre-register studies – Register plans before starting to reduce bias and improve scrutiny.
5. Publish negative data – Share all results to guide research and prevent wasted resources.
6. Describe methods clearly – Report key parameters and analysis details to enable replication.

Taking next steps

Researchers and stakeholders should adopt rigorous practices, ensure transparency in data sharing, and follow guidelines that promote reproducibility and credibility in life science research. ATCC helps achieve these goals by providing authenticated biomaterials and -omics data, enabling traceability and standardization across labs and organizations.

Read the whitepaper, https://www.atcc.org/resources/white-papers/improving-accuracy-and-reproducibility-in-life-science-research