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Abstract Across the globe, the legal land rights and tenure of many Indigenous peoples are yet to be recognized. A growing body of research demonstrates that tenure of Indigenous lands improves livelihoods and protects forests in addition to inherently recognizing human rights. However, the effect of tenure on environmental outcomes has scarcely been tested in regions with high development pressure, such as those with persisting forest–agriculture conflicts. In this paper, we conduct an event study and a difference-in-differences analysis to estimate the average treatment effect of land tenure on forest cover change for 129 Indigenous lands in the Atlantic Forest of Brazil from 1985 to 2019. We found that forest outcomes in Indigenous lands improved following tenure compared to pretenure and that forest outcomes improved in tenured compared to nontenured lands. We also found that formalized tenure, rather than incomplete tenure, was necessary to improve forest outcomes. Our study is the first rigorous analysis of the effect of tenure on Indigenous lands in the globally important Atlantic Forest biome and contributes to a growing body of literature on the role of rights-based approaches to conservation. The evidence presented in this study may support efforts to secure the legal rights and autonomy of Indigenous peoples. 

By learning collaboration skills, statisticians and data scientists can more effectively collaborate with others who possess the skills they may lack. We describe the ASCCR framework, which was developed in the United States for teaching and learning collaboration skills. We hypothesize which aspects of this framework are universal and which are culturally dependent. We provide an example of our initial attempt to translate the Structure component of ASCCR into the Indonesian language and culture. The new acronym of SABAR can help Indonesian students learn, remember, and use specific skills for structuring, organizing, and conducting collaboration meetings. How to effectively teach collaboration skills to Indonesian students in statistics and data science will require more work and is the subject of future research. 

Training complex skills is typically accomplished by means of a trainer or mediator who tailors instruction to the individual trainee. However, facilitated training is costly and labor intensive, and the use of a mediator is infeasible in remote or extreme environments. Imparting complex skills in applications like long-duration human spaceflight, military field operations, or remote medicine may require automated training algorithms. Virtual reality (VR) is an effective, easily programmable, immersive training medium that has been used widely across fields. However, there remain open questions in the search for the most effective algorithms for guiding automated training progression. This study investigates the effects of responsiveness, personalization, and subtask independence on the efficacy of automated training algorithms in VR for training complex, operationally relevant tasks. Thirty-two subjects (16M/16F, 18–54 years) were trained to pilot and land a spacecraft on Mars within a VR simulation using four different automated training algorithms. Performance was assessed in a physical cockpit mock-up. We found that personalization results in faster skill acquisition on average when compared with a standardized progression built for a median subject (p= 0.0050). The standardized progression may be preferable when consistent results are desired across all subjects. Independence of the difficulty adjustments between subtasks may lead to increased skill acquisition, while lockstep in the progression of each subtask increases self-reported flow experience (p= 0.01), fluency (p= 0.02), and absorption (p= 0.01) on the Flow Short Scale. Data visualization suggests that highly responsive algorithms may lead to faster learning progressions and higher skill acquisition for some subjects. Improving transfer of skills from training to testing may require either high responsiveness or a standardized training progression. Optimizing the design of automated, individually adaptive algorithms around the training needs of a group may be useful to increase skill acquisition for complex operational tasks. 

Graduate level statistics education curricula often emphasize technical instruction in theory and methodology but can fail to provide adequate practical training in applications and collaboration skills. We argue that a statistical collaboration center (“stat lab”) structured in the style of the University of Colorado Boulder’s Laboratory for Interdisciplinary Statistical Analysis (LISA) is an effective mechanism for providing graduate students with necessary training in technical, non-technical, and job-related skills. We summarize the operating structure of LISA, and then provide evidence of its positive impact on students via analyses of a survey completed by 123 collaborators who worked in LISA between 2008–15 while it was housed at Virginia Tech. Students described their work in LISA as having had a positive impact on acquiring technical (94%) and non-technical (95%) statistics skills. Five-sixths (83%) of the students reported that these skills will or have helped them advance in their careers. We call for the integration of stat labs into statistics and data science programs as part of a comprehensive and modern statistics education, and for further research on students’ experience in these labs and the impact on student outcomes. 

Abstract Hearing loss has been associated with individual cardiovascular disease (CVD) risk factors and, to a lesser extent, CVD risk metrics. However, these relationships are understudied in clinical populations. We conducted a retrospective study of electronic health records to evaluate the relationship between hearing loss and CVD risk burden. Hearing loss was defined as puretone average (PTA 0.5,1,2,4 ) > 20 dB hearing level (HL). Optimal CVD risk was defined as nondiabetic, nonsmoking, systolic blood pressure (SBP) < 120 and diastolic (D)BP < 80 mm Hg, and total cholesterol < 180 mg/dL. Major CVD risk factors were diabetes, smoking, hypertension, and total cholesterol ≥ 240 mg/dL or statin use. We identified 6332 patients (mean age = 62.96 years; 45.5% male); 64.0% had hearing loss. Sex-stratified logistic regression adjusted for age, noise exposure, hearing aid use, and body mass index examined associations between hearing loss and CVD risk. For males, diabetes, hypertension, smoking, and ≥ 2 major CVD risk factors were associated with hearing loss. For females, diabetes, smoking, and ≥ 2 major CVD risk factors were significant risk factors. Compared to those with no CVD risk factors, there is a higher likelihood of hearing loss in patients with ≥ 2 major CVD risk factors. Future research to better understand sex dependence in the hearing loss-hypertension relationship is indicated. 

The questions we ask and how we ask them will make a difference in how successful we are in meetings, in collaborations and in our careers as statisticians and data scientists. What makes a question good and what makes a good question great? Great questions elicit information useful for accomplishing the tasks of a project and strengthen the statistician–domain expert relationship. Great questions have three parts: the question, the answer and the paraphrasing of the answer to create shared understanding. We discuss three strategies for asking great questions: preface questions with statements about the intent behind asking the question; follow the question with behaviours and actions consistent with the prefaced words including actions such as listening, paraphrasing and summarizing; and model a collaborative relationship via the asking of a great question. We describe the methods and results of a study that shows how questions can be assessed, that statisticians can learn to ask great questions and that those who have learned this skill consider it to be valuable for their careers. We provide practical guidelines for learning how to ask great questions so that statisticians can improve their collaboration skills and thus increase their impact to help address societal challenges. 

The ability to navigate scientific obstacles is widely recognized as a hallmark of a scientific disposition and is one predictor of science, technology, engineering, and mathematics persistence for early-career scientists. However, the development of this competency in undergraduate research has been largely underexplored. This study addresses this gap by examining introductory students’ emotional and behavioral responses to research-related challenges and failures that occur in two sequential research-based courses. We describe commonly reported emotions, coping responses, and perceived outcomes and examine relationships between these themes, student demographics, and course enrollment. Students commonly experience frustration, confusion, and disappointment when coping with challenges and failures. Yet the predominance of students report coping responses likely to be adaptive in academic contexts despite experiencing negative emotions. Being enrolled in the second course of a research-based course sequence was related to several shifts in response to challenges during data collection, including less reporting of confusion and fewer reports of learning to be cautious from students. Overall, students in both the first and second courses reported many positive outcomes indicating improvements in their ability to cope with challenge and failure. We assert that educators can improve research-based educational courses by scaffolding students’ research trials, failures, and iterations to support students’ perseverance.