Show your work: Tools for open developmental science

A metascience investigation of inclusive, open, and reproducible science practices in research posters at the 2021 SRCD biennial meeting

Over the past decade, there has been a growing appreciation of metascience issues in psychological science. Using data collected from 2615 posters presented at the 2021 biennial meeting of the Society for Research in Child Development, this article examines the use of transparent research practices to increase rigor and reproducibility as well as generalizability through greater inclusivity of diverse samples. Research presented through poster presentations was heavily skewed toward quantitative studies featuring American researchers using Western hemisphere samples. Sharing of data/materials, preregistrations, and replications were uncommon. During a time when governments are increasingly requiring more open practices and access, this research provides an important baseline by which developmental science can benchmark progress toward the goals of greater inclusivity and openness.

Daylong egocentric recordings in small- and large-scale language communities: A practical introduction

Daylong egocentric (i.e., participant-centered) recordings promise an unprecedented view into the experiences that drive early language learning, impacting both assumptions and theories about how learning happens. Thanks to recent advances in technology, collecting long-form audio, photo, and video recordings with child-worn devices is cheaper and more convenient than ever. These recording methods can be similarly deployed across small- and large-scale language communities around the world, opening up enormous possibilities for comparative research on early language development. However, building new high-quality naturalistic corpora is a massive investment of time and money. In this chapter, we provide a practical look into considerations relevant for developing and managing daylong egocentric recording projects: Is it possible to re-use existing data? How much time will manual annotation take? Can automated tools sufficiently tackle the questions at hand? We conclude by outlining two exciting directions for future naturalistic child language research.