Much of the scientific literature in existence today is based on model systems and case studies, which help to split research into manageable blocks. The impact of this research can be greatly increased in meta-analyses that combine individual studies published over time to identify patterns across studies; patterns that may go undetected by smaller studies and that may not be the main subject of investigation. However, many potentially useful studies fail to provide sufficient data (typically means, true sample sizes, and measures of variability) to permit meta-analysis. Authors of primary research studies should provide these summary statistics as a minimum, and editors should require them to do so. By putting policies in place that require these summary statistics to be included, or even those that require raw data, editors and authors can maximize the legacy and impact of the research they publish beyond that of their initial target audience.