'Stopping the start': support for proposed tobacco control policies - a population-based survey in Great Britain 2021-2023.

OBJECTIVES: This study assessed public support for four proposed tobacco control policies in Great Britain: (1) Raising the sales age of tobacco by 1 year every year (Smokefree Generation); (2) Raising the sales age of tobacco from 18 years to 21 years; (3) Providing prescription e-cigarettes as smoking cessation aids to adults who smoke; (4) Restricting e-cigarette advertising to prevent youth uptake. DESIGN: Repeat cross-sectional population-based survey weighted to match the population of Great Britain. SETTING: The survey was conducted in England, Scotland and Wales in September 2021, October 2022 and October 2023. PARTICIPANTS: 6541 adults living in Great Britain. MAIN OUTCOME MEASURES: Support for each policy and year and prevalence ratios (PRs) comparing support between years and subgroups. RESULTS: The most popular policy each year was restricting e-cigarette advertising (74%/79%/85%), followed by raising the sales age to 21 years (50%/58%/64%), providing prescription e-cigarettes (45%/44%/47%) and Smokefree Generation (34%/44%/49%). The largest increases were for policies about the age of sale (Smokefree Generation: 2021/2022 PR=1.28, 95% CI 1.18 to 1.40, 2022/2023 PR=1.12, 95% CI 1.04 to 1.20; raising the age to 21 years: 2021/2022 PR=1.16, 95% CI 1.09 to 1.23, 2022/2023 PR=1.11, 95% CI 1.05 to 1.17). Only 30% opposed Smokefree Generation in 2023 down from 41% in 2021. CONCLUSIONS: Support for each policy increased each year, except for providing prescription e-cigarettes. Restricting e-cigarette advertising was the most popular policy, while support for age of sale policies, in particular for a Smokefree Generation, grew most. TRIAL REGISTRATION: The study protocol was published on the Open Science Framework (https://osf.io/46z2c/) prior to starting the analysis.

A survey of experimental stimulus presentation code sharing in major areas of psychology.

Computer code plays a vital role in modern science, from the conception and design of experiments through to final data analyses. Open sharing of code has been widely discussed as being advantageous to the scientific process, allowing experiments to be more easily replicated, helping with error detection, and reducing wasted effort and resources. In the case of psychology, the code used to present stimuli is a fundamental component of many experiments. It is not known, however, the degree to which researchers are sharing this type of code. To estimate this, we conducted a survey of 400 psychology papers published between 2016 and 2021, identifying those working with the open-source tools Psychtoolbox and PsychoPy that openly share stimulus presentation code. For those that did, we established if it would run following download and also appraised the code's usability in terms of style and documentation. It was found that only 8.4% of papers shared stimulus code, compared to 17.9% sharing analysis code and 31.7% sharing data. Of shared code, 70% ran directly or after minor corrections. For code that did not run, the main error was missing dependencies (66.7%). The usability of the code was moderate, with low levels of code annotation and minimal documentation provided. These results suggest that stimulus presentation code sharing lags behind other forms of code and data sharing, potentially due to less emphasis on such code in open-science discussions and in journal policies. The results also highlight a need for improved documentation to maximize code utility.

A pathway to strengthening open science: comments on the draft South African Ethics in Health Research Guidelines.

The recently released draft South African Ethics in Health Research Guidelines: Principles, Processes and Structures (Draft Guidelines) by the National Health Research Ethics Council recognize open data and provide guiding principles for this in the context of health research in South Africa. While its inclusion is a positive development, there is room for improvement. Although the Draft Guidelines leverage the Draft National Policy on Data and Cloud, it lacks incorporation of other relevant government policies, notably the Draft National Open Science Policy, and fails to sufficiently detail the principles of open science and open access. This limited scope and lack of comprehensive definition and detailed guidance present challenges for researchers in conducting ethical and responsible health research in South Africa. It constrains the Draft Guidelines from fully aligning with national imperatives and from fostering African-centric approaches. To address these issues, it is recommended that the Draft Guidelines integrate broader policies and principles, enhance clarity through comprehensive definitions, provide detailed guidance on open access, and promote African-centric approaches. Implementing these solutions will strengthen the Draft Guidelines, aligning them with national visions of open science, and thereby harnessing the full potential of South Africa's diverse scientific community in advancing health research.

Characteristics, enablers, and barriers to the community health and wellbeing assessment component of the health promotion practice cycle: a scoping review protocol.

OBJECTIVE: The proposed review will describe the characteristics, enablers, and barriers to the community health and well-being assessment (CHWA) component of the health promotion practice cycle. INTRODUCTION: CHWA guides health promotion action in communities and populations. A "critical" approach to CHWA can be adopted, which addresses the social, political, cultural, economic, commercial, and environmental determinants of health and well-being to enhance health equity for priority communities and populations. Although tools exist to guide such a critical approach, little is known about the extent to which these tools are being used or the barriers and enablers to applying best practice CHWA. Such evidence is needed to inform future health promotion CHWA and research. INCLUSION CRITERIA: This review will consider literature that describes CHWA conducted in health promotion practice, focusing on an organizational, social, or geographical community or population. Literature that focuses on clinical practice or a specific health condition will be excluded. METHODS: Scopus, PubMed, Web of Science, and CINAHL (EBSCOhost) will be searched to identify peer-reviewed articles. Google Scholar and Google, as well as Public Health, Health & Medical, and Nursing and Allied Health (ProQuest) databases will be searched for gray literature. Articles will be screened and data extracted by 2 or more independent reviewers. The data extraction tool will be developed by the reviewers based on the JBI template and a critical health promotion approach to CHWA. Data will be analyzed and presented as frequency tables and narrative summaries of the characteristics, enablers, and barriers to CHWA. DETAILS OF THE PROTOCOL ARE AVAILABLE ON OPEN SCIENCE FRAMEWORK: osf.io/jq8th/.

Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care.

Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability. This paper introduces a framework for understanding health care data needs, discussing the challenges and opportunities of open data standards in the field. It emphasizes the necessity of acknowledging diverse data standards, each catering to specific viewpoints and needs, while proposing a categorization of health care data into three domains, each with its distinct characteristics and challenges, along with outlining overarching design requirements applicable to all domains and specific requirements unique to each domain.

Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain.

When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC-NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform. Database URL: https://www.opensciencechain.org.

State of open science in cancer research.

PURPOSE: This study has been focused on assessing the Open Science scenario of cancer research during the period 2011-2021, in terms of the derived scientific publications and raw data dissemination. METHODS: A cancer search equation was executed in the Science Citation Index-Expanded, collecting the papers signed by at least one Spanish institution. The same search strategy was performed in the Data Citation Index to describe dataset diffusion. RESULTS: 50,822 papers were recovered, 71% of which belong to first and second quartile journals. 59% of the articles were published in Open Access (OA) journals. The Open Access model and international collaboration positively conditioned the number of citations received. Among the most productive journals stood out Plos One, Cancers, and Clinical and Translational Oncology. 2693 genomics, proteomics and metabolomics datasets were retrieved, being Gene Expression Omnibus the favoured repository. CONCLUSIONS: There has been an increase in oncology publications in Open Access. Most were published in first quartile journals and received higher citations than non-Open Access articles, as well as when oncological investigation was performed between international research teams, being relevant in the context of Open Science. Genetic repositories have been the preferred for sharing oncology datasets. Further investigation of research and data sharing in oncology is needed, supported by stronger Open Science policies, to achieve better data sharing practices among three scientific main pillars: researchers, publishers, and scientific organizations.

Bridging Biodiversity and Health: The Global Biodiversity Information Facility's initiative on open data on vectors of human diseases.

There is an increased awareness of the importance of data publication, data sharing, and open science to support research, monitoring and control of vector-borne disease (VBD). Here we describe the efforts of the Global Biodiversity Information Facility (GBIF) as well as the World Health Special Programme on Research and Training in Diseases of Poverty (TDR) to promote publication of data related to vectors of diseases. In 2020, a GBIF task group of experts was formed to provide advice and support efforts aimed at enhancing the coverage and accessibility of data on vectors of human diseases within GBIF. Various strategies, such as organizing training courses and publishing data papers, were used to increase this content. This editorial introduces the outcome of a second call for data papers partnered by the TDR, GBIF and GigaScience Press in the journal GigaByte. Biodiversity and infectious diseases are linked in complex ways. These links can involve changes from the microorganism level to that of the habitat, and there are many ways in which these factors interact to affect human health. One way to tackle disease control and possibly elimination, is to provide stakeholders with access to a wide range of data shared under the FAIR principles, so it is possible to support early detection, analyses and evaluation, and to promote policy improvements and/or development.

Open government data use: The Brazilian states and federal district cases.

PURPOSE: This paper presents the results of an online survey and subsequent interviews investigating whether, how, and why public administrations of Brazilian states and the federal district (Federation Units) use open government data. According to the literature reviewed, the questions were categorized into four big groups: benefits, barriers, enablers, and drivers. DESIGN/METHODOLOGY/APPROACH: The Survey method, based on a questionnaire followed by interviews, was used to collect and analyze data from the open data officers of 26 Brazilian Federation Units. FINDINGS: The use of open government data is controversial as responses from the questionnaires and interviews do not match and raise questions about how well-represented each Federation Unit was. Evidence of open government data use was found. Among others, findings showed that political leadership committed to using open data facilitates and motivates public agents to use these data. Additionally, interviews indicated that the lack of human resources with the knowledge, skills, and capabilities to use open data is a relevant barrier to data use. Findings also revealed that open government data mainly support policy and decision-making processes. PRACTICAL IMPLICATIONS: This research contributed to the open data and public administration fields. It portrays diverse realities of open government data use and institutionalization in Brazilian state and district public administrations. In addition, it provides lists of open government data use benefits, barriers, drivers, and enablers from the perspective of these administrations so that they can benchmark against each other and improve their OGD use. ORIGINALITY AND RESEARCH IMPLICATIONS: For academia, this research provides empirical evidence of the factors influencing public administrations' use of open government data at the subnational level in Brazil. Even though Brazil ranks high on OGD global assessments, few studies on its use and reuse in the public sector were identified. This is one of the first academic studies focusing on open government data use in the country. It also contributes by offering to the academic community two instruments, a questionnaire and an interview protocol, which can be applied to other public settings to expand this study's results or open new research paths by applying them to other contexts.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration.

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

A registered report survey of open research practices in psychology departments in the UK and Ireland.

Open research practices seek to enhance the transparency and reproducibility of research. While there is evidence of increased uptake in these practices, such as study preregistration and open data, facilitated by new infrastructure and policies, little research has assessed general uptake of such practices across psychology university researchers. The current study estimates psychologists' level of engagement in open research practices across universities in the United Kingdom and Ireland, while also assessing possible explanatory factors that may impact their engagement. Data were collected from 602 psychology researchers in the United Kingdom and Ireland on the extent to which they have implemented various practices (e.g., use of preprints, preregistration, open data, open materials). Here we present the summarized descriptive results, as well as considering differences between various categories of researcher (e.g., career stage, subdiscipline, methodology), and examining the relationship between researcher's practices and their self-reported capability, opportunity, and motivation (COM-B) to engage in open research practices. Results show that while there is considerable variability in engagement of open research practices, differences across career stage and subdiscipline of psychology are small by comparison. We observed consistent differences according to respondent's research methodology and based on the presence of institutional support for open research. COM-B dimensions were collectively significant predictors of engagement in open research, with automatic motivation emerging as a consistently strong predictor. We discuss these findings, outline some of the challenges experienced in this study, and offer suggestions and recommendations for future research. Estimating the prevalence of responsible research practices is important to assess sustained behaviour change in research reform, tailor educational training initiatives, and to understand potential factors that might impact engagement.

"I'm telling you my story, not publishing a blog": Considerations and suggestions on data sharing in qualitative health psychology research on sensitive topics.

Qualitative research plays a pivotal role in health psychology, offering insights into the intricacies of health-related issues. However, the specificity of qualitative methodology presents challenges in adhering to standard open science principles, including data sharing. The guidelines to address these issues are limited. Drawing from the author's experience in conducting in-depth interviews with middle-aged and older adults regarding their sexuality, this article discusses various challenges in implementing data sharing requirements. It emphasizes factors like participants' reasonable reluctance to share in specific populations, the depth of personal information gleaned from comprehensive interviews, concerns surrounding potential data misuse both within and outside academic circles, and the complex issue of obtaining informed consent. A universal approach to data sharing in qualitative research proves impractical, emphasizing the necessity for adaptable, context-specific guidelines that acknowledge the methodology's nuances. Striking a balance between transparency and ethical responsibility requires tailored strategies and thoughtful consideration.

CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications.

BACKGROUND: The analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged. RESULTS: CREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.

The challenges of open data sharing for qualitative researchers.

'Open Science' advocates for open access to scientific research, as well as sharing data, analysis plans and code in order to enable replication of results. However, these requirements typically fail to account for methodological differences between quantitative and qualitative research, and serious ethical problems are raised by the suggestion that full qualitative datasets can or should be published alongside qualitative research papers. Aside from important ethical concerns, the idea of sharing qualitative data in order to enable replication is conceptually at odds with the underpinnings on most qualitative methodologies, which highlight the importance of the unique interpretative function of the researcher. The question of whether secondary analysis of qualitative data is acceptable is key, and in this commentary we argue that there are good conceptual, ethical and economic reasons to consider how funders, researchers and publishers can make better use of existing data.

Improving the integrity and reproducibility of research that uses antibodies: a technical, data sharing, behavioral and policy challenge.

Antibodies are one of the most important reagents used in biomedical and fundamental research, used to identify, and quantify proteins, contribute to knowledge of disease mechanisms, and validate drug targets. Yet many antibodies used in research do not recognize their intended target, or recognize additional molecules, compromising the integrity of research findings and leading to waste of resources, lack of reproducibility, failure of research projects, and delays in drug development. Researchers frequently use antibodies without confirming that they perform as intended in their application of interest. Here we argue that the determinants of end-user antibody choice and use are critical, and under-addressed, behavioral drivers of this problem. This interacts with the batch-to-batch variability of these biological reagents, and the paucity of available characterization data for most antibodies, making it more difficult for researchers to choose high quality reagents and perform necessary validation experiments. The open-science company YCharOS works with major antibody manufacturers and knockout cell line producers to characterize antibodies, identifying high-performing renewable antibodies for many targets in neuroscience. This shows the progress that can be made by stakeholders working together. However, their work so far applies to only a tiny fraction of available antibodies. Where characterization data exists, end-users need help to find and use it appropriately. While progress has been made in the context of technical solutions and antibody characterization, we argue that initiatives to make best practice behaviors by researchers more feasible, easy, and rewarding are needed. Global cooperation and coordination between multiple partners and stakeholders will be crucial to address the technical, policy, behavioral, and open data sharing challenges. We offer potential solutions by describing our Only Good Antibodies initiative, a community of researchers and partner organizations working toward the necessary change. We conclude with an open invitation for stakeholders, including researchers, to join our cause.

Correction to "Tell it like it is: When politically incorrect language promotes authenticity" by Rosenblum et al. (2020).

Reports an error in "Tell it like it is: When politically incorrect language promotes authenticity" by Michael Rosenblum, Juliana Schroeder and Francesca Gino (Journal of Personality and Social Psychology, 2020[Jul], Vol 119[1], 75-103). In the original article, the sample size in the abstract and in the third sentence of the General Discussion section has been corrected to N = 4,922. The Open Science Framework URL for the preregistered hypothesis and analysis plan for Experiment 2 is now available at https://osf.io/8m7sv. The Open Science Framework URL for the preregistered hypothesis and analysis plan for Experiment 3 is now available at https://osf.io/g2ms4. The Open Science Framework URL for the preregistered hypothesis and analysis plan for Experiment 4 is now available at https://osf.io/j3d95/. Data in the Impression measures subsection in Experiment 4 have been updated. The Open Science Framework URL for the preregistered hypothesis and analysis plan for Experiment 6 is now available at https://osf.io/ f5sc4/. Data in the Participants subsection in Experiment 6 have been updated. Data throughout the Results subsection of Experiment 6 have been updated. Figures 7 and 8 have been updated. Data in Footnote 18 have been updated. The online version of this article has been corrected. (The following abstract of the original article appeared in record 2019-46162-001.) When a person's language appears to be political-such as being politically correct or incorrect-it can influence fundamental impressions of him or her. Political correctness is "using language or behavior to seem sensitive to others' feelings, especially those others who seem socially disadvantaged." One pilot study, 6 experiments, and 3 supplemental experiments (N = 4,922) demonstrate that being politically incorrect makes communicators appear more authentic-specifically, less susceptible to external influence-albeit also less warm. These effects, however, are moderated by perceivers' political ideology and how sympathetic perceivers feel toward the target group being labeled politically correctly. In Experiments 1, 2, and 3 using politically incorrect language (e.g., calling undocumented immigrants illegals) made a communicator appear particularly authentic among conservative perceivers but particularly cold among liberal perceivers. However, in Experiment 4 these effects reversed when conservatives felt sympathetic toward the group that was being labeled politically correctly or incorrectly (e.g., calling poor Whites white trash). Experiment 5 tests why political incorrectness can boost authenticity, demonstrating that it makes communicators seem less strategic. Finally, Experiment 6 examines the use of political language in a meaningful field context: perceived persuasion in real political debates. Debaters instructed to be politically correct (vs. politically incorrect) were judged by their uninstructed conversation partners to be easier to persuade during the conversation, although they actually reported being similarly persuaded. Together, these findings demonstrate when and how using politically incorrect language can enhance a person's authenticity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The State of Open Science Practices in Psychometric Studies of Suicide: A Systematic Review.

The adoption of open science practices (OSPs) is crucial for promoting transparency and robustness in research. We conducted a systematic review to assess the frequency and trends of OSPs in psychometric studies focusing on measures of suicidal thoughts and behavior. We analyzed publications from two international databases, examining the use of OSPs such as open access publication, preregistration, provision of open materials, and data sharing. Our findings indicate a lack of adherence to OSPs in psychometric studies of suicide. The majority of manuscripts were published under restricted access, and preregistrations were not utilized. The provision of open materials and data was rare, with limited access to instruments and analysis scripts. Open access versions (preprints/postprints) were scarce. The low adoption of OSPs in psychometric studies of suicide calls for urgent action. Embracing a culture of open science will enhance transparency, reproducibility, and the impact of research in suicide prevention efforts.

A dataset without a code book: ethnography and open science.

This paper reflects upon calls for "open data" in ethnography, drawing on our experiences doing research on sexual violence. The core claim of this paper is not that open data is undesirable; it is that there is a lot we must know before we presume its benefits apply to ethnographic research. The epistemic and ontological foundation of open data is grounded in a logic that is not always consistent with that of ethnographic practice. We begin by identifying three logics of open data-epistemic, political-economic, and regulatory-which each address a perceived problem with knowledge production and point to open science as the solution. We then evaluate these logics in the context of the practice of ethnographic research. Claims that open data would improve data quality are, in our assessment, potentially reversed: in our own ethnographic work, open data practices would likely have compromised our data quality. And protecting subject identities would have meant creating accessible data that would not allow for replication. For ethnographic work, open data would be like having the data set without the codebook. Before we adopt open data to improve the quality of science, we need to answer a series of questions about what open data does to data quality. Rather than blindly make a normative commitment to a principle, we need empirical work on the impact of such practices - work which must be done with respect to the different epistemic cultures' modes of inquiry. Ethnographers, as well as the institutions that fund and regulate ethnographic research, should only embrace open data after the subject has been researched and evaluated within our own epistemic community.