The field of infancy research faces a difficult challenge: some questions require samples that are simply too large for any one lab to recruit and test. ManyBabies aims to address this problem by forming large-scale collaborations on key theoretical questions in developmental science, while promoting the uptake of Open Science practices. Here, we look back on the first project completed under the ManyBabies umbrella – ManyBabies 1 – which tested the development of infant-directed speech preference. Our goal is to share the lessons learned over the course of the project and to articulate our vision for the role of large-scale collaborations in the field. First, we consider the decisions made in scaling up experimental research for a collaboration involving 100+ researchers and 70+ labs. Next, we discuss successes and challenges over the course of the project, including: protocol design and implementation, data analysis, organizational structures and collaborative workflows, securing funding, and encouraging broad participation in the project. Finally, we discuss the benefits we see both in ongoing ManyBabies projects and in future large-scale collaborations in general, with a particular eye towards developing best practices and increasing growth and diversity in infancy research and psychological science in general. Throughout the paper, we include first-hand narrative experiences, in order to illustrate the perspectives of researchers playing different roles within the project. While this project focused on the unique challenges of infant research, many of the insights we gained can be applied to large-scale collaborations across the broader field of psychology.

Introduction to Comparative Government and Politics is the first open educational resource (OER) on the topic of comparative politics, and the second OER textbook in political science funded by ASCCC OERI, in what we hope will become a complete library for the discipline. This textbook aligns with the C-ID Course Descriptor for Introduction to Comparative Government and Politics in content and objectives. It is organized thematically, with each chapter accompanied by a case study or a comparative study, one of the main methodological tools used in comparative politics. By contextualizing the concepts, we hope to help students learn the comparative method, which to this day remains one of the most important methodological tools for all researchers.

This document is intended to provide practical guidelines for researchers to follow when examining their data for missingness and making decisions about how to handle that missingness. We primarily offer recommendations for multiple imputation, but also indicate where the same decisional guidelines are appropriate for other types of missing data procedures such as full information maximum likelihood (FIML). Streamlining procedures to address missing data and increasing the transparency of those procedures through consensus on reporting standards is inexorably linked to the goals of open scholarship (i.e., the endeavour to improve openness, integrity, social justice, diversity, equity, inclusivity and accessibility in all areas of scholarly activities, and by extension, academic fields beyond the sciences and academic activities; Pownall et al., 2021). Successfully implementing transparent and accessible guidelines for addressing missing data is also important for Diversity, Equity, Inclusion, and Accessibility (DEIA) improvement efforts (Randall et al., 2021). Structural barriers to participation in research can lead to participants from minoritized groups disproportionately dropping out of longitudinal, developmental studies or not completing measures (Randall et al., 2021). This selection effect can bias model estimates and confidence intervals, leading to unsubstantiated claims about equitable outcomes. In addition to often creating artificially small estimates of inequalities between groups, listwise deletion also limits statistical power for minoritized groups who are already underrepresented in many datasets.

In this activity students will use linear regression to analyze real data on vector-borne diseases such as Malaria, Zika, and LaCrosse Virus. Students will explore how environmental factors such as climate change or population density influence the transmission of these diseases. The activities are designed for face-to-face instruction and may be adapted for an online classroom. This material is based upon work supported by the National Science Foundation under Grant No. 1919613. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Review of Communication in the Real World: An Introduction to Communication Studies: https://drive.google.com/open?id=1tgWb7Hvuwc5JMiUesMRk3Xnh5ev_kh6v7S4MjxdwMXM

Review of The Public Speaking Project: Public Speaking, The Virtual Text
https://drive.google.com/open?id=1uuLLtoCW_Zg8w4bgztKpqdI-udvq0E_9WJZCQkea78I