Supporting data for "The intriguing evolution of effect sizes in biomedical research over time: smaller but more often statistically significant"
Dataset type: Software, Metadata
Data released on November 28, 2017
In medicine, effect sizes (ESs) allow the effects of independent variables (including risk/protective factors, or treatment interventions) on dependent variables (e.g. health outcomes) to be quantified. Given that many public health decisions and health care policies are based on ES estimates, it is important to assess how ESs are used in the biomedical literature and to investigate potential trends in their reporting over time.
This dataset therefore contains the comprehensive database of detected ESs in Pubmed, the database of detected ESs in PubMed Central, and the source code of the program that helped to generate these databases through data-mining algorithms.
Read the peer-reviewed publication(s):
Monsarrat, P., & Vergnes, J.-N. (2017). The intriguing evolution of effect sizes in biomedical research over time: smaller but more often statistically significant. GigaScience, 7(1). doi:10.1093/gigascience/gix121