Using publicly available data from 299 preregistered replications from the social sciences, we found that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the article characteristics, study design and results, author information, and replication effort. To understand why, we analyzed the textual differences between replicable and nonreplicable studies. Our findings suggest that the language in replicable studies is transparent and confident, written in a detailed and complex manner, and generally exhibits markers of truthful communication, possibly demonstrating the researchers\' confidence in the study. Nonreplicable studies, however, are vaguely written and have markers of persuasion techniques, such as the use of positivity and clout. Thus, our findings allude to the possibility that authors of nonreplicable studies are more likely to make an effort, through their writing, to persuade readers of their (possibly weaker) results.

Open science practices are designed to enhance the utility, integrity, and credibility of scientific research. This article highlights how preregistration in open science practice can be leveraged to enhance the rigor and transparency of single-case experimental designs within an applied behavior analysis framework. We provide an overview of the benefits of preregistration including increased transparency, reduced risk of researcher bias, and improved replicability, and we review the specific contexts under which these practices most benefit the proposed framework. We discuss potential concerns with and unique considerations for preregistering experiments that use single-case designs, with practical guidance for researchers who are seeking to preregister their studies. We present a checklist as a tool for researchers in applied behavior analysis to use for preregistration and provide recommendations for our field to strengthen the contingencies for open science practices that include preregistration.

This paper investigates the ethical implications of applying open science (OS) practices on disruptive technologies, such as generative AIs. Disruptive technologies, characterized by their scalability and paradigm-shifting nature, have the potential to generate significant global impact, and carry a risk of dual use. The tension arises between the moral duty of OS to promote societal benefit by democratizing knowledge and the risks associated with open dissemination of disruptive technologies. Van Rennselaer Potter\'s \'third bioethics\' serves as the founding horizon for an ethical framework to govern these tensions. Through theoretical analysis and concrete examples, this paper explores how OS can contribute to a better future or pose threats. Finally, we provide an ethical framework for the intersection between OS and disruptive technologies that tries to go beyond the simple \'as open as possible\' tenet, considering openness as an instrumental value for the pursuit of other ethical values rather than as a principle with prima facie moral significance.

Chemistry is a reproducible science whose pillars - synthesis and analysis - actually comprise a huge collection of highly reproducible experimental methods to synthesize and analyze substances. The historical development of chemistry, furthermore, shows that reproducibility of methods has been the companion of novelty and creative innovation. The \"publish or perish\" principle dominating global academia since over two decades, however, intrinsically contributes to the publication of non-reproducible research outcomes also in chemistry. A study on reproducibility of chemistry research seems therefore timely, especially now that chemists are slowly but inevitably adopting open science and its tools such as the preprint, open access, and data sharing. We conclude presenting three simple guidelines for enhanced publication of research findings in chemistry.

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual\'s behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to determine the (social) context of a person, and accelerometers to measure movement. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers must make multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. The implementation of open science practices is only beginning to emerge in digital phenotyping studies and varies widely across studies. Well-intentioned researchers may fail to report on some decisions due to the variety of choices that must be made. To address this issue and enhance reproducibility in digital phenotyping studies, we propose the adoption of preregistration as a way forward. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Therefore, our objective was to develop a preregistration template that is easy to use and understandable for researchers. Additionally, we explain this template and provide resources to assist researchers in making informed decisions regarding data collection, cleaning, and analysis. Overall, we aim to make researchers\' choices explicit, enhance transparency, and elevate the standards for studies utilizing passive smartphone measures.

In response to the replication crisis in psychology, the scientific community has advocated open science practices to promote transparency and reproducibility. Although existing reviews indicate inconsistent and generally low adoption of open science in psychology, a current-day, detailed analysis is lacking. Recognising the significant impact of false memory research in legal contexts, we conducted a preregistered systematic review to assess the integration of open science practices within this field, analysing 388 publications from 2015 to 2023 (including 15 replications and 3 meta-analyses). Our findings indicated a significant yet varied adoption of open science practices. Most studies (86.86%) adhered to at least one measure, with publication accessibility being the most consistently adopted practice at 73.97%. While data sharing demonstrated the most substantial growth, reaching about 75% by 2023, preregistration and analysis script sharing lagged, with 20-25% adoption in 2023. This review highlights a promising trend towards enhanced research quality, transparency, and reproducibility in false memory research. However, the inconsistent implementation of open science practices may still challenge the verification, replication, and interpretation of research findings. Our study underscores the need for a comprehensive adoption of open science to improve research reliability and validity substantially, fostering trust and credibility in psychology.

Open Science (OS) aims, in part, to drive greater societal impact of academic research. Government, funder and institutional policies state that it should further democratize research and increase learning and awareness, evidence-based policy-making, the relevance of research to society\'s problems, and public trust in research. Yet, measuring the societal impact of OS has proven challenging and synthesized evidence of it is lacking. This study fills this gap by systematically scoping the existing evidence of societal impact driven by OS and its various aspects, including Citizen Science (CS), Open Access (OA), Open/FAIR Data (OFD), Open Code/Software and others. Using the PRISMA Extension for Scoping Reviews and searches conducted in Web of Science, Scopus and relevant grey literature, we identified 196 studies that contain evidence of societal impact. The majority concern CS, with some focused on OA, and only a few addressing other aspects. Key areas of impact found are education and awareness, climate and environment, and social engagement. We found no literature documenting evidence of the societal impact of OFD and limited evidence of societal impact in terms of policy, health, and trust in academic research. Our findings demonstrate a critical need for additional evidence and suggest practical and policy implications.

Multielement designs are the quintessential design tactic to evaluate outcomes of a functional analysis in applied behavior analysis. Protecting the credibility of the data collection, graphing, and visual analysis processes from a functional analysis increases the likelihood that optimal intervention decisions are made for individuals. Time-series graphs and visual analysis are the most prevalent method used to interpret functional analysis data. The current project included two principal aims. First, we tested whether the graphical construction manipulation of the x-to-y axes ratio (i.e., data points per x- axis to y-axis ratio [DPPXYR]) influenced visual analyst\'s detection of a function on 32 multielement design graphs displaying functional analyses. Second, we investigated the alignment between board certified behavior analysts (BCBAs; N = 59) visual analysis with the modified visual inspection criteria (Roane et al., Journal of Applied Behavior Analysis, 46, 130-146, 2013). We found that the crossed GLMM that included random slopes, random intercepts, and did not include an interaction effect (AIC = 1406.1, BIC = 1478.2) performed optimally. Second, alignment between BCBAs decisions and the MVI appeared to be low across data sets. We also leveraged current best practices in Open Science for raw data and analysis transparency.

Access to scientific data can enable independent reuse and verification; however, most data are not available and become increasingly irrecoverable over time. This study aimed to retrieve and preserve important datasets from 160 of the most highly-cited social science articles published between 2008-2013 and 2015-2018. We asked authors if they would share data in a public repository-the Data Ark-or provide reasons if data could not be shared. Of the 160 articles, data for 117 (73%, 95% CI [67%-80%]) were not available and data for 7 (4%, 95% CI [0%-12%]) were available with restrictions. Data for 36 (22%, 95% CI [16%-30%]) articles were available in unrestricted form: 29 of these datasets were already available and 7 datasets were made available in the Data Ark. Most authors did not respond to our data requests and a minority shared reasons for not sharing, such as legal or ethical constraints. These findings highlight an unresolved need to preserve important scientific datasets and increase their accessibility to the scientific community.

A broad understanding of the aims and objectives of the international open science movement was recently adopted with the 2021 UNESCO Recommendation on Open Science, expanding the focus of open science to include scientific knowledge, infrastructures, knowledge systems and the open engagement of societal actors. In response, recent discussions on science policy practice are shifting to the implementation of open science via national policies. While policy instruments to support some aspects of open science are well-studied, guidance on the emerging \'social\' aspects of open science has lagged, prompting UNESCO to generate guidance. In this paper, several authors of the UNESCO Open Science Toolkit guidance document on \'Engaging societal actors in Open Science\' synthesize the scholarly underpinnings behind its recommendations. This work draws upon a targeted search from academic, policy, and grey literature in the fields of open science and community engagement, with a special focus on citizen science, to derive guidance on how to overcome barriers to the uptake of societal engagement approaches. The results present building blocks of what an enabling environment for the open engagement of societal actors could look like, identifying key considerations and reflecting on opportunities and challenges for progressing and evaluating sound open engagement of societal actors into regional & national (open) science policies.