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.

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.

The scientific method is predicated on transparency-yet the pace at which transparent research practices are being adopted by the scientific community is slow. The replication crisis in psychology showed that published findings employing statistical inference are threatened by undetected errors, data manipulation and data falsification. To mitigate these problems and bolster research credibility, open data and preregistration practices have gained traction in the natural and social sciences. However, the extent of their adoption in different disciplines is unknown. We introduce computational procedures to identify the transparency of a research field using large-scale text analysis and machine learning classifiers. Using political science and international relations as an illustrative case, we examine 93 931 articles across the top 160 political science and international relations journals between 2010 and 2021. We find that approximately 21% of all statistical inference papers have open data and 5% of all experiments are preregistered. Despite this shortfall, the example of leading journals in the field shows that change is feasible and can be effected quickly.

The increased demand for efficient computation in data analysis encourages researchers in biomedical science to use workflow systems. Workflow systems, or so-called workflow languages, are used for the description and execution of a set of data analysis steps. Workflow systems increase the productivity of researchers, specifically in fields that use high-throughput DNA sequencing applications, where scalable computation is required. As systems have improved the portability of data analysis workflows, research communities are able to share workflows to reduce the cost of building ordinary analysis procedures. However, having multiple workflow systems in a research field has resulted in the distribution of efforts across different workflow system communities. As each workflow system has its unique characteristics, it is not feasible to learn every single system in order to use publicly shared workflows. Thus, we developed Sapporo, an application to provide a unified layer of workflow execution upon the differences of various workflow systems. Sapporo has two components: an application programming interface (API) that receives the request of a workflow run and a browser-based client for the API. The API follows the Workflow Execution Service API standard proposed by the Global Alliance for Genomics and Health. The current implementation supports the execution of workflows in four languages: Common Workflow Language, Workflow Description Language, Snakemake, and Nextflow. With its extensible and scalable design, Sapporo can support the research community in utilizing valuable resources for data analysis.

Many-analysts studies explore how well an empirical claim withstands plausible alternative analyses of the same dataset by multiple, independent analysis teams. Conclusions from these studies typically rely on a single outcome metric (e.g. effect size) provided by each analysis team. Although informative about the range of plausible effects in a dataset, a single effect size from each team does not provide a complete, nuanced understanding of how analysis choices are related to the outcome. We used the Delphi consensus technique with input from 37 experts to develop an 18-item subjective evidence evaluation survey (SEES) to evaluate how each analysis team views the methodological appropriateness of the research design and the strength of evidence for the hypothesis. We illustrate the usefulness of the SEES in providing richer evidence assessment with pilot data from a previous many-analysts study.

Over the past decade, open science (OS) has emerged as a global science policy and research initiative with implications for most aspects of research, including planning, funding, publishing, evaluation, data sharing and access. As OS has gained increasing prominence, it has also faced substantial criticism. Whether it is the worries about the equality of access associated with open-access publishing or the more recent allegations of OS benefitting those who act in the private interest without giving back to OS, there are, indeed, many potential as well as actual harms that can be linked to the practice of OS. These criticisms often revolve around ethical challenges and fairness concerns, prompting the question of whether a comprehensive ethical governance framework is needed for OS. This commentary contends that owing to the heterogeneous nature of the normative foundations of OS and the inherent diversity within scientific practices, a pluralistic and deliberative approach to governance is needed.