Post-publication critique at top-ranked journals across scientific disciplines: a cross-sectional assessment of policies and practice

Journals exert considerable control over letters, commentaries and online comments that criticize prior research (post-publication critique). We assessed policies (Study One) and practice (Study Two) related to post-publication critique at 15 top-ranked journals in each of 22 scientific disciplines (N = 330 journals). Two-hundred and seven (63%) journals accepted post-publication critique and often imposed limits on length (median 1000, interquartile range (IQR) 500–1200 words) and time-to-submit (median 12, IQR 4–26 weeks). The most restrictive limits were 175 words and two weeks; some policies imposed no limits. Of 2066 randomly sampled research articles published in 2018 by journals accepting post-publication critique, 39 (1.9%, 95% confidence interval [1.4, 2.6]) were linked to at least one post-publication critique (there were 58 post-publication critiques in total). Of the 58 post-publication critiques, 44 received an author reply, of which 41 asserted that original conclusions were unchanged. Clinical Medicine had the most active culture of post-publication critique: all journals accepted post-publication critique and published the most post-publication critique overall, but also imposed the strictest limits on length (median 400, IQR 400–550 words) and time-to-submit (median 4, IQR 4–6 weeks). Our findings suggest that top-ranked academic journals often pose serious barriers to the cultivation, documentation and dissemination of post-publication critique

Effects of go/no-go training on food-related action tendencies, liking and choice

This is the final version. Available on open access from the Royal Society via the DOI in this recordData accessibility: All study data and analysis scripts are freely available on the Open Science Framework (https://osf.io/hz2nb/). The study protocol was preregistered prior to data collection at https://osf.io/wav8p. The data are provided in electronic supplementary material [86].Inhibitory control training effects on behaviour (e.g. 'healthier' food choices) can be driven by changes in affective evaluations of trained stimuli, and theoretical models indicate that changes in action tendencies may be a complementary mechanism. In this preregistered study, we investigated the effects of food-specific go/no-go training on action tendencies, liking and impulsive choices in healthy participants. In the training task, energy-dense foods were assigned to one of three conditions: 100% inhibition (no-go), 0% inhibition (go) or 50% inhibition (control). Automatic action tendencies and liking were measured pre- and post-training for each condition. We found that training did not lead to changes in approach bias towards trained foods (go and no-go relative to control), but we warrant caution in interpreting this finding as there are important limitations to consider for the employed approach-avoidance task. There was only anecdotal evidence for an effect on food liking, but there was evidence for contingency learning during training, and participants were on average less likely to choose a no-go food compared to a control food after training. We discuss these findings from both a methodological and theoretical standpoint and propose that the mechanisms of action behind training effects be investigated further.Economic and Social Research Council (ESRC)Biotechnology and Biological Sciences Research Council (BBSRC)European Research Council (ERC

Effects of go/no-go training on food-related action tendencies, liking and choice

Inhibitory control training effects on behaviour (e.g. ‘healthier’ food choices) can be driven by changes in affective evaluations of trained stimuli, and theoretical models indicate that changes in action tendencies may be a complementary mechanism. In this preregistered study, we investigated the effects of food-specific go/no-go training on action tendencies, liking and impulsive choices in healthy participants. In the training task, energy-dense foods were assigned to one of three conditions: 100% inhibition (no-go), 0% inhibition (go) or 50% inhibition (control). Automatic action tendencies and liking were measured pre- and post-training for each condition. We found that training did not lead to changes in approach bias towards trained foods (go and no-go relative to control), but we warrant caution in interpreting this finding as there are important limitations to consider for the employed approach–avoidance task. There was only anecdotal evidence for an effect on food liking, but there was evidence for contingency learning during training, and participants were on average less likely to choose a no-go food compared to a control food after training. We discuss these findings from both a methodological and theoretical standpoint and propose that the mechanisms of action behind training effects be investigated further

Embedding open and reproducible science into teaching: A bank of lesson plans and resources

Recently, there has been a growing emphasis on embedding open and reproducible approaches into research. One essential step in accomplishing this larger goal is to embed such practices into undergraduate and postgraduate research training. However, this often requires substantial time and resources to implement. Also, while many pedagogical resources are regularly developed for this purpose, they are not often openly and actively shared with the wider community. The creation and public sharing of open educational resources is useful for educators who wish to embed open scholarship and reproducibility into their teaching and learning. In this article, we describe and openly share a bank of teaching resources and lesson plans on the broad topics of open scholarship, open science, replication, and reproducibility that can be integrated into taught courses, to support educators and instructors. These resources were created as part of the Society for the Improvement of Psychological Science (SIPS) hackathon at the 2021 Annual Conference, and we detail this collaborative process in the article. By sharing these open pedagogical resources, we aim to reduce the labour required to develop and implement open scholarship content to further the open scholarship and open educational materials movement

Evaluating the Pedagogical Effectiveness of Study Preregistration in the Undergraduate Dissertation

Research shows that questionable research practices (QRPs) are present in undergraduate final-year dissertation projects. One entry-level Open Science practice proposed to mitigate QRPs is “study preregistration,” through which researchers outline their research questions, design, method, and analysis plans before data collection and/or analysis. In this study, we aimed to empirically test the effectiveness of preregistration as a pedagogic tool in undergraduate dissertations using a quasi-experimental design. A total of 89 UK psychology students were recruited, including students who preregistered their empirical quantitative dissertation ( n = 52; experimental group) and students who did not ( n = 37; control group). Attitudes toward statistics, acceptance of QRPs, and perceived understanding of Open Science were measured both before and after dissertation completion. Exploratory measures included capability, opportunity, and motivation to engage with preregistration, measured at Time 1 only. This study was conducted as a Registered Report; Stage 1 protocol: https://osf.io/9hjbw (date of in-principle acceptance: September 21, 2021). Study preregistration did not significantly affect attitudes toward statistics or acceptance of QRPs. However, students who preregistered reported greater perceived understanding of Open Science concepts from Time 1 to Time 2 compared with students who did not preregister. Exploratory analyses indicated that students who preregistered reported significantly greater capability, opportunity, and motivation to preregister. Qualitative responses revealed that preregistration was perceived to improve clarity and organization of the dissertation, prevent QRPs, and promote rigor. Disadvantages and barriers included time, perceived rigidity, and need for training. These results contribute to discussions surrounding embedding Open Science principles into research training

Updating road maps using Particle Filter tracking and Geographic Information Systems

The updating of road network databases is crucial to many Geographic Information System (GIS) applications like navigation, urban planning, as well as emergency and disaster management. The development of a robust methodology for automatic or semi-automatic road extraction and change detection as well as “discovery of paths”, is essential. Such a methodology has to provide accurate and up-to-date results albeit using noisy and infrequent sensor data. In this paper a new approach for a semi-automatic road extraction is presented that utilizes and combines output from particle filtering tracking (sequential Monte Carlo) and GIS techniques. The target tracks may be generated from different sensor sources. In this paper, we consider airborne platforms with GMTI (Ground Moving Target Indication) radar, which is able to continuously monitor the traffic in large areas day and night and at difficult weather conditions. The radar measurements however suffer from limited resolution, measurement noise, false alarms, and missed detections due to small target velocity or terrain shadowing. It is, therefore, difficult to extract the exact path of vehicles, and hence the road coordinates, directly from the sensor measurements. As an intermediate step, we utilize particle filters to generate target tracks from the time series of sensor measurements. The technique is applied to simulated road traffic in different scenarios such as straight roads, cross sections, and traffic roundabouts. The time series of the particle cloud is the input for the road extraction algorithm which constitutes of different kinds of GIS methods. We apply Kernel Density Estimation (KDE) to generate a raster dataset where each cell has a weight according to the density of particles. Based on a threshold, a binary raster is created and converted into a polygon shapefile representing the road segments. Smoothing techniques are applied to provide a more realistic shape of the extracted roads