Related Experiment Video
Updated: May 28, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
Published on: May 26, 2020
Data and code (Un)availability in sports meta-analysis studies
P T Axel Wolff1, Kristin L Sainani2, David N Borg3
1Stanford University, Department of Epidemiology and Population Health, 300 Pasteur Dr, Stanford, CA 94305, USA; United States Army Research Institute of Environmental Medicine, 10 General Green Ave, Natick, MA 01760, USA.
Purpose:
The purpose of this study was to estimate the prevalence of data and code availability and sharing practices in meta-analyses from highly ranked sports journals.
Methods:
A MEDLINE search via PubMed on September 11, 2024 identified 228 meta-analyses. We randomly selected 157 studies and assessed availability statements and sharing outcomes. Authors were contacted between October 2024 and January 2025.
Results:
Of 157 studies, 34% (95% CI: 26-42%) had a data availability statement and 13% (95% CI: 8-19%) had a code statement. Overall, 33% (95% CI: 26-41%) shared data and 11% (95% CI: 6-17%) shared code. Prior to author contact, 15% (95% CI: 10-21%) shared data publicly and 3% (95% CI: 1-6%) shared code. Following contact, 22% (95% CI: 15-29%) provided data privately and 8% (95% CI: 5-14%) provided code. Studies with a code statement were more likely to share code (25% vs 9%; RD=16%, p = 0.03). Open-access articles had greater sharing than non-open-access articles for data (53% vs 28%; RD=25%, p = 0.011) and code (22% vs 8%; RD=14%, p = 0.049).
Conclusion:
Despite sharing statement mandates, actual meta-analysis sharing is limited, underscoring the need for greater journal policy enforcement and standardized transparency practices.
Related Concept Videos
Censoring Survival Data
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Statistical Methods for Analyzing Epidemiological Data
Statistical Software for Data Analysis and Clinical Trials
Statistical Methods to Analyze Parametric Data: ANOVA
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Statistical Analysis: Overview
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...