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Background: Despite the rise of big-team science and multiinstitutional, multidisciplinary research networks, little research has explored the unique challenges that large, distributed research networks face in ensuring the ethical and responsible conduct of research (RCR) at the network level. Methods: This qualitative case study explored the views of the scientists, engineers, clinicians, and trainees within a large Engineering Research Center (ERC) on ethical and RCR issues arising at the network level. Results: Semi-structured interviews of 26 ERC members were analyzed and revealed five major themes: (1) data sharing, (2) authorship or inventorship credit, (3) ethics and regulation, (4) collaboration, and (5) network leadership, norms, and policy. Interviews revealed cross-laboratory differences and disciplinary differences as sources of challenge. Conclusions: This study illuminates ethical challenges that a large, multi-institutional research network is likely to face. Research collaboration across disciplines, laboratories, and institutions invites conflict over norms and practices. Network leadership requires anticipating, monitoring, and addressing the ethical challenges in order to ensure the network’s ethical and responsible conduct of research and optimize research collaboration. Studying perceived ethical issues that arise at the meso-level of a research network is essential for understanding how to advance network ethics. 

Video-based analysis of practice models have gained prominence in mathematics and science teacher education inservice professional learning. There is a growing body of evidence that these intensive professional learning (PL) models lead to positive impacts on teacher knowledge, classroom instructional practice, and student learning (Roth et al., 2018; Taylor et al., 2017), but they are expensive and difficult to sustain. An online version would have several benefits, allowing for greater reach to teachers and students across the country, but if online models were substantially less effective, then lower impacts would undercut the benefits of greater accessibility. We designed and studied a fully online version of the face-to-face Science Teachers Learning from Lesson Analysis (STeLLA) PL model (Roth, et al., 2011; Roth et al., 2018; Taylor et al., 2017). We conducted a quasi-experimental study comparing online STeLLA to face-to-face STeLLA. Although we found no significant difference in elementary student learning between the online and face-to-face versions ( p = .09), the effect size raises questions. Exploratory analyses suggest that the impact of online STeLLA on students is greater than the impact of a similar number of hours of traditional, face-to-face content deepening PL, but less than the impact of the full face-to-face STeLLA program. Differences in student populations, with higher percentages of students from racial and ethnic groups underserved by schools in the online STeLLA program, along with testing of the online STeLLA model during the pandemic, complicates interpretation of the findings.