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Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.

An academic scientist's professional success depends on publishing. Publishing norms emphasize novel, positive results. As such, disciplinary incentives encourage design, analysis, and reporting decisions that elicit positive results and ignore negative results. Prior reports demonstrate how these incentives inflate the rate of false effects in published science. When incentives favor novelty over replication, false results persist in the literature unchallenged, reducing efficiency in knowledge accumulation. Previous suggestions to address this problem are unlikely to be effective. For example, a journal of negative results publishes otherwise unpublishable reports. This enshrines the low status of the journal and its content. The persistence of false findings can be meliorated with strategies that make the fundamental but abstract accuracy motive—getting it right—competitive with the more tangible and concrete incentive—getting it published. This article develops strategies for improving scientific practices and knowledge accumulation that account for ordinary human motivations and biases.

When people speak with one another, they tend to adapt their head movements and facial expressions in response to each others' head movements and facial expressions. We present an experiment in which confederates' head movements and facial expressions were motion tracked during videoconference conversations, an avatar face was reconstructed in real time, and naive participants spoke with the avatar face. No naive participant guessed that the computer generated face was not video. Confederates' facial expressions, vocal inflections and head movements were attenuated at 1 min intervals in a fully crossed experimental design. Attenuated head movements led to increased head nods and lateral head turns, and attenuated facial expressions led to increased head nodding in both naive participants and confederates. Together, these results are consistent with a hypothesis that the dynamics of head movements in dyadicconversation include a shared equilibrium. Although both conversational partners were blind to the manipulation, when apparent head movement of one conversant was attenuated, both partners responded by increasing the velocity of their head movements.

Nonverbal visual cues accompany speech to supplement the meaning of spoken words, signify emotional state, indicate position in discourse, and provide back-channel feedback. This visual information includes head movements, facial expressions and body gestures. In this article we describe techniques for manipulating both verbal and nonverbal facial gestures in video sequences of people engaged in conversation. We are developing a system for use in psychological experiments, where the effects of manipulating individual components of nonverbal visual behavior during live face-to-face conversation can be studied. In particular, the techniques we describe operate in real-time at video frame-rate and the manipulation can be applied so both participants in a conversation are kept blind to the experimental conditions.

In the 21st Century, research is increasingly data- and computation-driven. Researchers, funders, and the larger community today emphasize the traits of openness and reproducibility. In March 2017, 13 mostly early-career research leaders who are building their careers around these traits came together with ten university leaders (presidents, vice presidents, and vice provosts), representatives from four funding agencies, and eleven organizers and other stakeholders in an NIH- and NSF-funded one-day, invitation-only workshop titled \"Imagining Tomorrow's University.\" Workshop attendees were charged with launching a new dialog around open research - the current status, opportunities for advancement, and challenges that limit sharing. The workshop examined how the internet-enabled research world has changed, and how universities need to change to adapt commensurately, aiming to understand how universities can and should make themselves competitive and attract the best students, staff, and faculty in this new world. During the workshop, the participants re-imagined scholarship, education, and institutions for an open, networked era, to uncover new opportunities for universities to create value and serve society. They expressed the results of these deliberations as a set of 22 principles of tomorrow's university across six areas: credit and attribution, communities, outreach and engagement, education, preservation and reproducibility, and technologies. Activities that follow on from workshop results take one of three forms. First, since the workshop, a number of workshop authors have further developed and published their white papers to make their reflections and recommendations more concrete. These authors are also conducting efforts to implement these ideas, and to make changes in the university system.  Second, we plan to organise a follow-up workshop that focuses on how these principles could be implemented. Third, we believe that the outcomes of this workshop support and are connected with recent theoretical work on the position and future of open knowledge institutions.

Reproducibility is a defining feature of science. However, because of strong incentives for innovation and weak incentives for confirmation, direct replication is rarely practiced or published. The Reproducibility Project is an open, large-scale, collaborative effort to systematically examine the rate and predictors of reproducibility in psychological science. So far, 72 volunteer researchers from 41 institutions have organized to openly and transparently replicate studies published in three prominent psychological journals in 2008. Multiple methods will be used to evaluate the findings, calculate an empirical rate of replication, and investigate factors that predict reproducibility. Whatever the result, a better understanding of reproducibility will ultimately improve confidence in scientific methodology and findings.

Gilbert et al . conclude that evidence from the Open Science Collaboration’s Reproducibility Project: Psychology indicates high reproducibility, given the study methodology. Their very optimistic assessment is limited by statistical misconceptions and by causal inferences from selectively interpreted, correlational data. Using the Reproducibility Project: Psychology data, both optimistic and pessimistic conclusions about reproducibility are possible, and neither are yet warranted.

A time-series is a sequence of observations ordered by time. Often in the behavioral sciences, these observations are instances of categorical variables and can be represented by a finite set of symbols, or an alphabet. In these sequences, there may exist temporal patterns that are important in understanding the dynamics of behavior. However, these patterns may be nontrivial, that is events in the patterns may be noncontiguous and therefore difficult to detect by standard time-series analyses as these methods generally deal with understanding the structure of behavior at a global level across the entirety of the series. In 1981, Temple Smith and Michael Waterman encountered a similar issue in the field of molecular biology. They developed local sequence alignment as a means to discover nontrivial patterns of similarity in long sequences of DNA and protein, each comprised of elements from an alphabet of size four and twenty respectively. This project will describe methods of local sequence alignment as they exist in the biological sciences and propose and implement analogous methods for use with temporal data.