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Replacing traditional journals with a more modern solution is not a new idea. Here, we propose ways to overcome the social dilemma underlying the decades of inaction. Any solution needs to not only resolve the current problems but also be capable of preventing takeover by corporations: it needs to replace traditional journals with a decentralized, resilient, evolvable network that is interconnected by open standards and open-source norms under the governance of the scholarly community. It needs to replace the monopolies connected to journals with a genuine, functioning and well-regulated market. In this new market, substitutable service providers compete and innovate according to the conditions of the scholarly community, avoiding sustained vendor lock-in. Therefore, a standards body needs to form under the governance of the scholarly community to allow the development of open scholarly infrastructures servicing the entire research workflow. We propose a redirection of money from legacy publishers to the new network by funding bodies broadening their minimal infrastructure requirements at recipient institutions to include modern infrastructure components replacing and complementing journal functionalities. Such updated eligibility criteria by funding agencies would help realign the financial incentives for recipient institutions with public and scholarly interest., Competing Interests: We declare we have no competing interests., (© 2023 The Authors.)

This study reports an independent replication of the findings presented by Smaldino and McElreath (Smaldino, McElreath 2016 R. Soc. Open Sci. 3 , 160384 (doi:10.1098/rsos.160384)). The replication was successful with one exception. We find that selection acting on scientist's propensity for replication frequency caused a brief period of exuberant replication not observed in the original paper due to a coding error. This difference does not, however, change the authors' original conclusions. We call for more replication studies for simulations as unique contributions to scientific quality assurance., (© 2023 The Authors.)

Symptoms of insomnia are an important risk factor for the development of mental disorders, especially during stressful life periods such as the coronavirus disease 2019 (COVID-19) pandemic. However, up to now, most studies have used cross-sectional data, and the prolonged impact of insomnia symptoms during the pandemic on later mental health remains unclear. Therefore, we investigated insomnia symptoms as a predictor of other aspects of mental health across 6 months, with altogether seven assessments (every 30 days, t0-t6), in a community sample (N = 166-267). Results showed no mean-level increase of insomnia symptoms and/or deterioration of mental health between baseline assessment (t0) and the 6- month follow-up (t6). As preregistered, higher insomnia symptoms (between persons) across all time points predicted reduced mental health at the 6-month follow-up. Interestingly, contrary to our hypothesis, higher insomnia symptoms at 1 month, within each person (i.e., compared to that person's symptoms at other time points), predicted improved rather than reduced aspects of mental health 1 month later. Hence, we replicated the predictive effect of averagely increased insomnia symptoms on impaired later mental health during the COVID-19 pandemic. However, we were surprised that increased insomnia symptoms at 1 month predicted aspects of improved mental health 1 month later. This unexpected effect might be specific for our study population and a consequence of our study design. Overall, increased insomnia symptoms may have served as a signal to engage in, and successfully implement, targeted countermeasures, which led to better short-term mental health in this healthy sample., (© 2022 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

For a given research question, there are usually a large variety of possible analysis strategies acceptable according to the scientific standards of the field, and there are concerns that this multiplicity of analysis strategies plays an important role in the non-replicability of research findings. Here, we define a general framework on common sources of uncertainty arising in computational analyses that lead to this multiplicity, and apply this framework within an overview of approaches proposed across disciplines to address the issue. Armed with this framework, and a set of recommendations derived therefrom, researchers will be able to recognize strategies applicable to their field and use them to generate findings more likely to be replicated in future studies, ultimately improving the credibility of the scientific process., (© 2021 The Authors.)

Does it matter if your personality fits in with the personalities of the people where you live? The present study explored the links between person-city personality fit and self-esteem. Using data from 543,934 residents of 860 U.S. cities, we examined the extent to which the fit between individuals' Big Five personality traits and the Big Five traits of the city where they live (i.e., the prevalent traits of the city's inhabitants) predicts individuals' self-esteem. To provide a benchmark for these effects, we also estimated the degree to which the fit between person and city religiosity predicts individuals' self-esteem. The results provided a nuanced picture of the effects of person-city personality fit on self-esteem: We found significant but small effects of fit on self-esteem only for openness, agreeableness, and conscientiousness, rather than effects for all Big Five traits. Similar results and effect sizes were observed for religiosity. We conclude with a discussion of the relevance and limitations of this study., (© The Author(s) 2016.)

Since the development of D scores for the Implicit Association Test, few studies have examined whether there is a better scoring method. In this contribution, we tested the effect of four relevant parameters for IAT data that are the treatment of extreme latencies, the error treatment, the method for computing the IAT difference, and the distinction between practice and test critical trials. For some options of these different parameters, we included robust statistic methods that can provide viable alternative metrics to existing scoring algorithms, especially given the specificity of reaction time data. We thus elaborated 420 algorithms that result from the combination of all the different options and test the main effect of the four parameters with robust statistical analyses as well as their interaction with the type of IAT (i.e., with or without built-in penalty included in the IAT procedure). From the results, we can elaborate some recommendations. A treatment of extreme latencies is preferable but only if it consists in replacing rather than eliminating them. Errors contain important information and should not be discarded. The D score seems to be still a good way to compute the difference although the G score could be a good alternative, and finally it seems better to not compute the IAT difference separately for practice and test critical trials. From this recommendation, we propose to improve the traditional D scores with small yet effective modifications.

Family research aims to explore family processes, but is often limited to the examination of unidirectional processes. As the behavior of 1 person has consequences that go beyond that individual, family functioning should be investigated in its full complexity. The social relations model (SRM; Kenny & La Voie, 1984) is a conceptual and analytical model that can disentangle family data from a round-robin design at 3 different levels: the individual level (actor and partner effects), the dyadic level (relationship effects), and the family level (family effect). Its statistical complexity may however be a hurdle for family researchers. The user-friendly R package fSRM performs almost automatically those rather complex SRM analyses and introduces new possibilities for assessing differences between SRM means and between SRM variances, both within and between groups of families. Using family data on negative processes, different type of research questions are formulated and corresponding analyses with fSRM are presented., ((c) 2015 APA, all rights reserved).)