Seven easy steps to open science: An annotated reading list

The open science movement is rapidly changing the scientific landscape. Because exact definitions are often lacking and reforms are constantly evolving, accessible guides to open science are needed. This paper provides an introduction to open science and related reforms in the form of an annotated reading list of seven peer-reviewed articles, following the format of Etz, Gronau, Dablander, Edelsbrunner, and Baribault (2018). Written for researchers and students-particularly in psychological science-it highlights and introduces seven topics: understanding open science; open access; open data, materials, and code; reproducible analyses; preregistration and registered reports; replication research; and teaching open science. For each topic, we provide a detailed summary of one particularly informative and actionable article and suggest several further resources. Supporting a broader understanding of open science issues, this overview should enable researchers to engage with, improve, and implement current open, transparent, reproducible, replicable, and cumulative scientific practices

Perspectives on Scientific Error

Theoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings.</p

Perspectives on scientific error

Theoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings

Perspectives on scientific error

Theoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings

As novas situações propostas de Pais e filhos em São Paulo: a tradução da poética originária de Stanislavski no contexto nacional

Recent discussions within the mathematical psychology community have focused on how Open Science practices may apply to cognitive modelling. Lee et al. (2019) sketched an initial approach for adapting Open Science practices that have been developed for experimental psychology research to the unique needs of cognitive modelling. While we welcome the general proposal of Lee et al. (2019), we believe a more fine-grained view is necessary to accommodate the adoption of Open Science practices in the diverse areas of cognitive modelling. Firstly, we suggest a categorization for the diverse types of cognitive modelling, which we argue will allow researchers to more clearly adapt Open Science practices to different types of cognitive modelling. Secondly, we consider the feasibility and usefulness of preregistration and lab notebooks for each of these categories and address potential objections to preregistration in cognitive modelling. Finally, we separate several cognitive modelling concepts that we believe Lee et al. (2019) conflated, which should allow for greater consistency and transparency in the modelling process. At a general level, we propose a framework that emphasizes local consistency in approaches while allowing for global diversity in modelling practices

Citation Patterns Following a Strongly Contradictory Replication Result: Four Case Studies From Psychology

Replication studies that contradict prior findings may facilitate scientific self-correction by triggering a reappraisal of the original studies; however, the research community’s response to replication results has not been studied systematically. One approach for gauging responses to replication results is to examine how they affect citations to original studies. In this study, we explored postreplication citation patterns in the context of four prominent multilaboratory replication attempts published in the field of psychology that strongly contradicted and outweighed prior findings. Generally, we observed a small postreplication decline in the number of favorable citations and a small increase in unfavorable citations. This indicates only modest corrective effects and implies considerable perpetuation of belief in the original findings. Replication results that strongly contradict an original finding do not necessarily nullify its credibility; however, one might at least expect the replication results to be acknowledged and explicitly debated in subsequent literature. By contrast, we found substantial citation bias: The majority of articles citing the original studies neglected to cite relevant replication results. Of those articles that did cite the replication but continued to cite the original study favorably, approximately half offered an explicit defense of the original study. Our findings suggest that even replication results that strongly contradict original findings do not necessarily prompt a corrective response from the research community. </jats:p