Psychologists embrace the ethical imperative of protecting research participants from harm. We argue that sharing data should also be considered an ethical imperative. Despite potential risks to participants’ privacy and data confidentiality, sharing data confers benefits to participants and to the community at large by promoting scientific transparency, bolstering reproducibility, and fostering more efficient use of resources. Most of the risks to participants can be mitigated and the benefits of sharing realized through well-established but not yet widespread practices and tools. This chapter serves as a how-to manual for addressing ethical challenges in sharing human data in psychological research in ways that simultaneously protect participants and advance discovery.

In 2019, the Governing Council of the Society for Research in Child Development (SRCD) adopted a Policy on Scientific Integrity, Transparency, and Openness (SRCD, 2019a) and accompanying Author Guidelines on Scientific Integrity and Openness in Child Development (SRCD, 2019b). In this issue, a companion article (Gennetian, Tamis‐LeMonda, & Frank) discusses the opportunities to realize SRCD’s vision for a science of child development that is open, transparent, robust, and impactful. In this article, we discuss some of the challenges associated with realizing SRCD’s vision. In identifying these challenges—protecting participants and researchers from harm, respecting diversity, and balancing the benefits of change with the costs—we also offer constructive solutions.

Interdisciplinary exchange of ideas and tools can accelerate scientific progress. For example, findings from developmental and vision science have spurred recent advances in artificial intelligence and computer vision. However, relatively little attention has been paid to how artificial intelligence and computer vision can facilitate research in developmental science. The current study presents AutoViDev—an automatic video-analysis tool that uses machine learning and computer vision to support video-based developmental research. AutoViDev identifies full body position estimations in real-time video streams using convolutional pose machine-learning algorithms. AutoViDev provides valuable information about a variety of behaviors, including gaze direction, facial expressions, posture, locomotion, manual actions, and interactions with objects. We present a high-level architecture of the framework and describe two projects that demonstrate its usability. We discuss the benefits of applying AutoViDev to large-scale, shared video datasets and highlight how machine learning and computer vision can enhance and accelerate research in developmental science.