Data sharing is increasingly acknowledged to be a feature of a healthy scientific ecosystem, maximizing the benefits from the often costly business of collecting scientific data and enhancing discovery. Thus, timely data sharing from large research projects is an explicitly stated policy of the National Institutes of Health (NIH). Making data openly and freely available and encouraging researchers to use them for additional analyses ensures the maximum return on the scientific investments that the NIH, and ultimately the US taxpayer, have made.The Adolescent Brain Cognitive Development (ABCD) study is a prime and successful example of the open data-sharing philosophy of the NIH. This ambitious 10-year study of brain development and child health in the United States is in its third year of collecting neuroimaging, genetic, and behavioral information and has completed baseline data collection on 11 878 US children who were recruited at age 9 to 10 years. The study is designed to measure brain development using structural and functional magnetic resonance imaging and to investigate the role of various biological, environmental, and behavioral factors on brain, cognitive, and social/emotional development. 1 Researchers have been encouraged to use this rich, open data set.So far, the scientific community has responded. Two batches of ABCD data have been released, the first in February 2018, which included the children from the first year of recruitment, and the second in April 2019, which included the full baseline sample. Multiple research groups have already published analyses on neurobiological correlates of screen time, 2 neurocognitive associations with problem behaviors, 3 construct validity and psychometric properties of a measure of prodromal psychotic-like symptoms, 4 minority sexual orientation and gender identity, 5,6 eating disorders, 7 and neurobiological associations with anhedonia, 8 and more articles are appearing regularly.The uptake of the initial ABCD data set for such diverse analyses is encouraging. However, to use the data most effectively, it is important to understand the strengths as well as the limitations of the data set. For example, while the study sample and design are well suited for conducting cross-sectional and longitudinal analyses, it would not be appropriate to take the ABCD study cohort as fully representative of the US population for the purposes of calculating population prevalence estimates. 9 Yet, some researchers conducting secondary analyses of ABCD data have done so, which could potentially produce misleading conclusions.A Research Letter by Calzo and Blashill 5 describes the sample as a "US representative cohort." These authors similarly describe the sample as "US representative" in a more recent article. 6 Another Research Letter