Publication bias, in which positive results are preferentially reported by authors and published by journals, can restrict the visibility of evidence against false claims and allow such claims to be canonized inappropriately as facts.

Funnel plots of the Standardized Mean Difference versus the standard error are prone to distortion, leading to false-positive tests for funnel plot asymmetry, and therefore using the Normalised Mean Difference, or a sample size-based precision estimate, are more reliable alternatives.

The use of Research Resource Identifiers (RRIDs) improves the proper use of cell lines in the biomedical literature.

Great care is needed when interpreting claims about the genetic basis of human variation based on data from genome-wide association studies.

Each stage of the scientific life cycle stands to benefit from the introduction of data science techniques.

Analysis of data on drug-gene interactions suggests that decentralized collaboration will increase the robustness of scientific findings in biomedical research.

A simulation study is used to demonstrate how mistakes in identifying the experimental unit and the unit of analysis can lead to incorrect analyses and inappropriate inferences when reporting research studies.

A new technique called CUT&RUN can map the distribution of proteins on the genome with higher resolution and accuracy than existing approaches.