Letter to the Editor. Re: "[Indigenous dataset of Dayak, Malay, and Chinese communities in Sintang Regency, West Kalimantan, Indonesia by A. Gandasari et al., Data in Brief, 2024, 53, 110147]"

Gandasari and colleagues [1] open an important opportunity for us to consider how social scientists might contribute to preserving Indigenous cultures in Indonesia. The focus of these authors was on releasing for open use Indigenous data collected from Dayak, Malay, and Chinese communities in Sintang Regency, West Kalimantan, Indonesia. In this commentary, we respond to key issues raised by Gandasari and colleagues [1] to engage in a productive discussion regarding the broader ethical considerations and implications of publishing such Indigenous datasets in academic journals. The main argument of this commentary is that we need to consider the potential ethical dilemmas that come with extracting and releasing such Indigenous knowledge beyond the contexts within which they are located. Central to this commentary is an assertion regarding the need for researchers to consider the perspectives of Indigenous groups in determining what is ethical conduct regarding the use of their knowledge. Also considered are how attempts by researchers to preserve Indigenous cultures by giving their 'data' away reflect broader concerns around the commodification of what we often refer to as data, but which is often understood by Indigenous peoples as their histories, knowledge, and treasured and often sacred insights into the human condition. Working with such knowledge requires us to engage with Indigenous ethics and issues of power between groups in society.

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.

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.

Design and validation of a conceptual model regarding impact of open science on healthcare research processes

INTRODUCTION

The development and use of digital tools in various stages of research highlight the importance of novel open science methods for an integrated and accessible research system. The objective of this study was to design and validate a conceptual model of open science on healthcare research processes.

METHODS

This research was conducted in three phases using a mixed-methods approach. The first phase employed a qualitative method, namely purposive sampling and semi-structured interview guides to collect data from healthcare researchers and managers. Influential factors of open science on research processes were extracted for refining the components and developing the proposed model; the second phase utilized a panel of experts and collective agreement through purposive sampling. The final phase involved purposive sampling and Delphi technique to validate the components of the proposed model according to researchers' perspectives.

FINDINGS

From the thematic analysis of 20 interview on the study topic, 385 codes, 38 sub-themes, and 14 main themes were extracted for the initial proposed model. These components were reviewed by expert panel members, resulting in 31 sub-themes, 13 main themes, and 4 approved themes. Ultimately, the agreed-upon model was assessed in four layers for validation by the expert panel, and all the components achieved a score of > 75% in two Delphi rounds. The validated model was presented based on the infrastructure and culture layers, as well as supervision, assessment, publication, and sharing.

CONCLUSION

To effectively implement these methods in the research process, it is essential to create cultural and infrastructural backgrounds and predefined requirements for preventing potential abuses and privacy concerns in the healthcare system. Applying these principles will lead to greater access to outputs, increasing the credibility of research results and the utilization of collective intelligence in solving healthcare system issues.

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.

An ode to multidisciplinarity: the 'Bacteria Orchestrate Life' international meeting

If scientists stick to their research expertise without collaborating with other experts in different fields, it could stall the progress of their work in a world where interdisciplinary thinking and working should be second nature. Biologists are at the forefront of this trend. That is why a consortium formed by the faculty of sciences of Tunis El Manar University, Tunisia, the GetGenome Foundation and Learn and Win, decided to organise an international conference on bacteria, a perfect field for multidisciplinarity. For 3 days, from 14 to 16 December 2023, more than 200 interdisciplinary researchers and students of life sciences and more than 20 international speakers and trainers met at the faculty of sciences in Tunis, to discuss microbiology and bacteria from different horizons, from the most fundamental to the most imaginative, with a strong focus on technologies and discoveries. This Meeting Review describes the scientific event and highlights the main results of both the conferences and the practical sessions.

Minimap: An interactive dynamic decision making game for search and rescue missions

Many aspects of humans' dynamic decision-making (DDM) behaviors have been studied with computer-simulated games called microworlds. However, most microworlds only emphasize specific elements of DDM and are inflexible in generating a variety of environments and experimental designs. Moreover, despite the ubiquity of gridworld games for Artificial Intelligence (AI) research, only some tools exist to aid in the development of browser-based gridworld environments for studying the dynamics of human decision-making behavior. To address these issues, we introduce Minimap, a dynamic interactive game to examine DDM in search and rescue missions, which incorporates all the essential characteristics of DDM and offers a wide range of flexibility regarding experimental setups and the creation of experimental scenarios. Minimap specifically allows customization of dynamics, complexity, opaqueness, and dynamic complexity when designing a DDM task. Minimap also enables researchers to visualize and replay recorded human trajectories for the analysis of human behavior. To demonstrate the utility of Minimap, we present a behavioral experiment that examines the impact of different degrees of structural complexity coupled with the opaqueness of the environment on human decision-making performance under time constraints. We discuss the potential applications of Minimap in improving productivity and transparent replications of human behavior and human-AI teaming research. We made Minimap an open-source tool, freely available at  https://github.com/DDM-Lab/MinimapInteractiveDDMGame .

Best practices for cleaning eye movement data in reading research

One challenge that comes with studying eye movement behavior is deciding how to clean the eye movement data (e.g., fixation durations) before conducting analyses. Reading researchers must decide which data cleaning methods they will use and which thresholds they will set to remove eye movements that are not reflective of lexical processing. The purpose of this project was to determine what data cleaning methods are typically used and if there are any consequences of using different data cleaning methods. In the first study, an analysis of 192 recently published articles indicated that there is inconsistency in the reporting and application of data cleaning methods. In the second study, three different data cleaning methods were applied based on the literature analysis in the first study. Analyses were conducted to determine the impact of different data cleaning methods on three commonly studied effects in reading research (frequency, predictability, and length). Overall, standardized estimates decreased for each effect when more data were removed; however, removing more data also resulted in decreased variance. As a result, effects remained significant with each data cleaning method, and simulated power remained high for both a moderate and small sample size. Effect sizes remained consistent for most effects but decreased for the length effect as more data were removed. Seven suggestions are provided that are based on open science practices with the intention of helping researchers, reviewers, and the field as a whole.

The state of diversity among consultant neurologists in the United Kingdom

Equitable representation is key for successful clinical and research work. Inequalities in gender, skin colour, and education have been found among professionals in many medical, surgical and academic fields, but have not been examined yet in the current UK neurologists' workforce. This cross-sectional study examined whether there are disparities in gender, perceived skin colour, education, academic productivity, and career progression among UK neurologists. The names of consultant neurologists working in the UK anytime between December 2021 and March 2022 were found via an online search. Online data were collected regarding education, research activity, academic productivity, impact and progression. A total of 1010 consultant neurologists were found to be working in the UK. There was predominance of men at consultant level, with a university affiliation, and with a full professor position. All 24 female full professors had white skin colour. There were no skin colour or gender disparities in currently being affiliated with a top university, obtaining a Master's degree, or duration between medical degree obtainment and becoming full professors. However, less black or brown neurologists had obtained a PhD, were consultants, andit took them a longer time from medical school graduation until becoming consultants.Less females were currently affiliated with a university and had obtained their medical degree from a top university. There were also skin colour and gender disparities in bibliometrics. In conclusion, this study revealed that there are gender, skin colour, education, academic productivity, and career progression gaps among UK consultant neurologists, which need to be addressed.

Reproducibility of COVID-era infectious disease models

Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus' transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed.

DataPipe: Born-open data collection for online experiments

DataPipe ( https://pipe.jspsych.org ) is a tool that allows researchers to save data from a behavioral experiment directly to the Open Science Framework. Researchers can configure data storage options for an experiment on the DataPipe website and then use the DataPipe API to send data to the Open Science Framework from any Internet-connected experiment. DataPipe is free to use and open-source. This paper describes the design of DataPipe and how it can help researchers adopt the practice of born-open data collection.

Beyond single paradigms, pipelines, and outcomes: Embracing multiverse analyses in psychophysiology

Psychophysiological research is an inherently complex undertaking due to the nature of the data, and its analysis is characterized by many decision points that shape the final dataset and a study's findings. These decisions create a "multiverse" of possible outcomes, and each decision from study conceptualization to statistical analysis can lead to different results and interpretations. This review describes the concept of multiverse analyses, a methodological approach designed to understand the impact of different decisions on the robustness of a study's findings and interpretation. The emphasis is on transparently showcasing different reasonable approaches for constructing a final dataset and on highlighting the influence of various decision points, from experimental design to data processing and outcome selection. For example, the choice of an experimental task can significantly impact event-related brain potential (ERP) scores or skin conductance responses (SCRs), and different tasks might elicit unique variances in each measure. This review underscores the importance of transparently embracing the flexibility inherent in psychophysiological research and the potential consequences of not understanding the fragility or robustness of experimental findings. By navigating the intricate terrain of the psychophysiological multiverse, this review serves as an introduction, helping researchers to make informed decisions, improve the collective understanding of psychophysiological findings, and push the boundaries of the field.

A guide for social science journal editors on easing into open science

Journal editors have a large amount of power to advance open science in their respective fields by incentivising and mandating open policies and practices at their journals. The Data PASS Journal Editors Discussion Interface (JEDI, an online community for social science journal editors: www.dpjedi.org ) has collated several resources on embedding open science in journal editing ( www.dpjedi.org/resources ). However, it can be overwhelming as an editor new to open science practices to know where to start. For this reason, we created a guide for journal editors on how to get started with open science. The guide outlines steps that editors can take to implement open policies and practices within their journal, and goes through the what, why, how, and worries of each policy and practice. This manuscript introduces and summarizes the guide (full guide: https://doi.org/10.31219/osf.io/hstcx ).

geoFOR: A collaborative forensic taphonomy database for estimating the postmortem interval

Accurately assessing the postmortem interval (PMI), or the time since death, remains elusive within forensic science research and application. This paper introduces geoFOR, a web-based collaborative application that utilizes ArcGIS and machine learning to deliver improved PMI predictions. The geoFOR application provides a standardized, collaborative forensic taphonomy database that gives practitioners a readily available tool to enter case information that automates the collection of environmental data and delivers a PMI prediction using statistically robust methods. After case submission, the cross-validating machine learning PMI predictive model results in a R² value of 0.82. Contributors receive a predicted PMI with an 80% confidence interval. The geoFOR database currently contains 2529 entries from across the U.S. and includes cases from medicolegal investigations and longitudinal studies from human decomposition facilities. We present the overall findings of the data collected so far and compare results from medicolegal cases and longitudinal studies to highlight previously poorly understood limitations involved in the difficult task of PMI estimation. This novel approach for building a reference dataset of human decomposition is forensically and geographically representative of the realities in which human remains are discovered which allows for continual improvement of PMI estimations as more data is captured. It is our goal that the geoFOR data repository follow the principles of Open Science and be made available to forensic researchers to test, refine, and improve PMI models. Mass collaboration and data sharing can ultimately address enduring issues associated with accurately estimating the PMI within medicolegal death investigations.

Open-Source System for Real-Time Functional Assessment of In Vitro Filtration Barriers

The integrity of the barrier between blood and the selective filtrate of solutes is important for homeostasis and its disruption contributes to many diseases. Microphysiological systems that incorporate synthetic or natural membranes with human cells can mimic biological filtration barriers, such as the glomerular filtration barrier in the kidney, and they can readily be used to study cellular filtration processes as well as drug effects and interactions. We present an affordable, open-source platform for the real-time monitoring of functional filtration status in engineered microphysiological systems. Using readily available components, our assay can linearly detect real-time concentrations of two target molecules, FITC-labeled inulin and Texas Red-labeled human-serum albumin, within clinically relevant ranges, and it can be easily modified for different target molecules of varying sizes and tags. We demonstrate the platform's ability to determine the concentration of our target molecules automatically and consistently. We show through an acellular context that the platform enables real-time tracking of size-dependent diffusion with minimal fluid volume loss and without manual extraction of media, making it suitable for continuous operational monitoring of filtration status in microphysiological system applications. The platform's affordability and integrability with microphysiological systems make it ideal for many precision medicine applications, including evaluation of drug nephrotoxicity and other forms of drug discovery.

Enabling Data Discovery with the Astrobiology Resource Metadata Standard

As scientific investigations increasingly adopt Open Science practices, reuse of data becomes paramount. However, despite decades of progress in internet search tools, finding relevant astrobiology datasets for an envisioned investigation remains challenging due to the precise and atypical needs of the astrobiology researcher. In response, we have developed the Astrobiology Resource Metadata Standard (ARMS), a metadata standard designed to uniformly describe astrobiology "resources," that is, virtually any product of astrobiology research. Those resources include datasets, physical samples, software (modeling codes and scripts), publications, websites, images, videos, presentations, and so on. ARMS has been formulated to describe astrobiology resources generated by individual scientists or smaller scientific teams, rather than larger mission teams who may be required to use more complex archival metadata schemes. In the following, we discuss the participatory development process, give an overview of the metadata standard, describe its current use in practice, and close with a discussion of additional possible uses and extensions.

Prioritizing open science in space medicine: perspectives following the NASA "Transform to Open Science (TOPS)" Curriculum

The National Aeronautics and Space Administration (NASA) has recently made a long-term commitment towards fostering open science. The NASA Transform to Open Science (TOPS) initiative provides recommendations, best practices, and tools related to open science. The principles of open science include the transparent sharing of data, findings, and methods and is designed to accelerate the pace of discovery and foster collaboration. The goal of open science is to allow data, publications, software, and physical samples to be accessible to all, regardless of being a professional or an amateur. In this paper, we summarize several key points open science that were presented as part of NASA's Open Science 101 Module 1 at an in-person training event in Washington, D.C., and include how open science can be beneficial for researchers and society as a whole.

A survey of researchers' methods sharing practices and priorities

Missing or inaccessible information about the methods used in scientific research slows the pace of discovery and hampers reproducibility. Yet little is known about how, why, and under what conditions researchers share detailed methods information, or about how such practices vary across social categories like career stage, field, and region. In this exploratory study, we surveyed 997 active researchers about their attitudes and behaviors with respect to methods sharing. The most common approach reported by respondents was private sharing upon request, but a substantial minority (33%) had publicly shared detailed methods information independently of their research findings. The most widely used channels for public sharing were connected to peer-reviewed publications, while the most significant barriers to public sharing were found to be lack of time and lack of awareness about how or where to share. Insofar as respondents were moderately satisfied with their ability to accomplish various goals associated with methods sharing, we conclude that efforts to increase public sharing may wish to focus on enhancing and building awareness of existing solutions-even as future research should seek to understand the needs of methods users and the extent to which they align with prevailing practices of sharing.

Making science public: a review of journalists' use of Open Access research

Science journalists are uniquely positioned to increase the societal impact of open research outputs by contextualizing and communicating findings in ways that highlight their relevance and implications for non-specialist audiences. Yet, it is unclear to what degree journalists use open research outputs, such as open access publications or preprints, in their reporting; what factors motivate or constrain this use; and how the recent surge in openly available research seen during the COVID-19 pandemic has affected this. This article examines these questions through a review of relevant literature published from 2018 onwards-particularly literature relating to the COVID-19 pandemic-as well as seminal articles outside the search dates. We find that research that explicitly examines journalists' engagement with open access publications or preprints is scarce, with existing literature mostly addressing the topic tangentially or as a secondary concern, rather than a primary focus. Still, the limited body of evidence points to several factors that may hamper journalists' use of these outputs and thus warrant further exploration. These include an overreliance on traditional criteria for evaluating scientific quality; concerns about the trustworthiness of open research outputs; and challenges using and verifying the findings. We also find that, while the COVID-19 pandemic encouraged journalists to explore open research outputs such as preprints, the extent to which these explorations will become established journalistic practices remains unclear. Furthermore, we note that current research is overwhelmingly authored and focused on the Global North, and the United States specifically. We conclude with recommendations for future research that attend to issues of equity and diversity, and more explicitly examine the intersections of open access and science journalism.

Adapting Open Science and Pre-registration to Longitudinal Research

Open science practices, such as pre-registration and data sharing, increase transparency and may improve the replicability of developmental science. However, developmental science has lagged behind other fields in implementing open science practices. This lag may arise from unique challenges and considerations of longitudinal research. In this paper, preliminary guidelines are provided for adapting open science practices to longitudinal research to facilitate researchers' use of these practices. The guidelines propose a serial and modular approach to registration that includes an initial pre-registration of the methods and focal hypotheses of the longitudinal study, along with subsequent pre- or co-registered questions, hypotheses, and analysis plans associated with specific papers. Researchers are encouraged to share their research materials and relevant data with associated papers, and to report sufficient information for replicability. In addition, there should be careful consideration about requirements regarding the timing of data sharing, to avoid disincentivizing longitudinal research.

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning

OBJECTIVE

To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices.

RESULTS

We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs.

CONCLUSION

The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology.

RPM: An open-source Rotation Platform for open- and closed-loop vestibular stimulation in head-fixed Mice

Head fixation allows the recording and presentation of controlled stimuli and is used to study neural processes underlying spatial navigation. However, it disrupts the head direction system because of the lack of vestibular stimulation. To overcome this limitation, we developed a novel rotation platform which can be driven by the experimenter (open-loop) or by animal movement (closed-loop). The platform is modular, affordable, easy to build and open source. Additional modules presented here include cameras for monitoring eye movements, visual virtual reality, and a micro-manipulator for positioning various probes for recording or optical interference. We demonstrate the utility of the platform by recording eye movements and showing the robust activation of head-direction cells. This novel experimental apparatus combines the advantages of head fixation and intact vestibular activity in the horizontal plane. The open-loop mode can be used to study e.g., vestibular sensory representation and processing, while the closed-loop mode allows animals to navigate in rotational space, providing a better substrate for 2-D navigation in virtual environments. The full build documentation is maintained at https://ranczlab.github.io/RPM/.

SGABU computational platform for multiscale modeling: Bridging the gap between education and research

BACKGROUND AND OBJECTIVE

In accordance with the latest aspirations in the field of bioengineering, there is a need to create a web accessible, but powerful cloud computational platform that combines datasets and multiscale models related to bone modeling, cancer, cardiovascular diseases and tissue engineering. The SGABU platform may become a powerful information system for research and education that can integrate data, extract information, and facilitate knowledge exchange with the goal of creating and developing appropriate computing pipelines to provide accurate and comprehensive biological information from the molecular to organ level.

METHODS

The datasets integrated into the platform are obtained from experimental and/or clinical studies and are mainly in tabular or image file format, including metadata. The implementation of multiscale models, is an ambitious effort of the platform to capture phenomena at different length scales, described using partial and ordinary differential equations, which are solved numerically on complex geometries with the use of the finite element method. The majority of the SGABU platform's simulation pipelines are provided as Common Workflow Language (CWL) workflows. Each of them requires creating a CWL implementation on the backend and a user-friendly interface using standard web technologies. Platform is available at https://sgabu-test.unic.kg.ac.rs/login.

RESULTS

The main dashboard of the SGABU platform is divided into sections for each field of research, each one of which includes a subsection of datasets and multiscale models. The datasets can be presented in a simple form as tabular data, or using technologies such as Plotly.js for 2D plot interactivity, Kitware Paraview Glance for 3D view. Regarding the models, the usage of Docker containerization for packing the individual tools and CWL orchestration for describing inputs with validation forms and outputs with tabular views for output visualization, interactive diagrams, 3D views and animations.

CONCLUSIONS

In practice, the structure of SGABU platform means that any of the integrated workflows can work equally well on any other bioengineering platform. The key advantage of the SGABU platform over similar efforts is its versatility offered with the use of modern, modular, and extensible technology for various levels of architecture.

Open Science Practices in Psychiatric Genetics: A Primer

Open science ensures that research is transparently reported and freely accessible for all to assess and collaboratively build on. Psychiatric genetics has led among the health sciences in implementing some open science practices in common study designs, such as replication as part of genome-wide association studies. However, thorough open science implementation guidelines are limited and largely not specific to data, privacy, and research conduct challenges in psychiatric genetics. Here, we present a primer of open science practices, including selection of a research topic with patients/nonacademic collaborators, equitable authorship and citation practices, design of replicable, reproducible studies, preregistrations, open data, and privacy issues. We provide tips for informative figures and inclusive, precise reporting. We discuss considerations in working with nonacademic collaborators and distributing research through preprints, blogs, social media, and accessible lecture materials. Finally, we provide extra resources to support every step of the research process.

[The different models of scientific journals]

Introduction

Scientific journals are the main source of scientific data, ensuring their registration, validation, distribution and archiving. With over 2.6 million scientific articles published each year, the turnover of scientific journals exceeds $25 billion annually. Five publishers share nearly half of this lucrative market. Scientists are the key players in the process, but other stakeholders have gradually been introduced, building various business models whose similarities and differences are described here.

Concepts underlying scientific publication

Open access to scientific papers dates back to the scientific and technical revolution of the 17th century. However, its evolution has been considerably boosted by the development of the Internet and the recognition of science as "commons".Scientific integrity is under the control of research institutions to ensure the prevention of fraud and misconduct in the course of scientific production. Usually, the scientific integrity is questioned during the manuscript reviewing process which may result in identification of flaws.

Models of scientific publications

In the historical model, readers pay for access to the document. Authors are not remunerated and renounce copyright on their articles to the publisher. The limits of the historical model became clear in the 90s, facing the cost of publishing, reduction in the number of subscribers, development of the Internet and willingness to improve manuscript evaluation.With the development of the Internet and the paradigm of open access, publishers proposed a new model in the 2000s, replacing the cost of access to articles for the reader with the payment of Article Processing Charges (APCs) paid by the author or its institution ("author pays" model). In this model, the content of the article can be freely reproduced and used, provided that the original author is credited. In addition to the evaluation of the manuscript which remains a critical factor, the cost of publication appears inequitable. However, all or part of the APCs may be waived, particularly for authors from low- and middle-income countries.For the past 15 years or so, publishers, learned societies and academic or research institutions (including libraries) have been seeking to publish reliable, open access manuscripts that respect scientific integrity while being affordable for the author.Predatory journals emerged in the late 2000s, taking advantage of the success of the authorpays model to capture APCs. Lacking a proper evaluation process resulting in poor-quality publications, these journals are rejected by most scientific institutions. On the other hand, they are particularly attractive in low- and middle-income countries because of their aggressive commercial practices (insistent invitation to submit a manuscript, low rejection rate, rapid publication, reduced APC, etc.).The purpose of each journal is to secure its economic model. This goes through ensuring its visibility, which is determined by the number of citations (online and social media citations) rather than the quality of the articles published.

Peer review

This very old concept has not been widely used until the 20th century. In the historical model, manuscript evaluation is generally carried out by members of the learned society that publishes the journal. Evaluation can be either unblinded, single-blinded (referee is anonymous) or double-blinded (author and referee are anonymous). Several studies have shown that blind procedures do not alter the quality of the evaluation. Since the early 90s, post-publication evaluation has emerged, of which there are several variants. The aim is to shorten times to publication and open up the evaluation process more widely in order to limit the bias. Apart from the fact that this system does not guarantee a better evaluation of the manuscript, its main disadvantage is that the article is accessible without validation of the data collection and analysis throughout the entire process, which can be lengthy.

Cost and funding of scientific journals

The cost of an article depends on charges that vary according to the conditions and places of production. Reviewers are volunteers. On the other hand, manuscript management, editing and distribution are carried out by professionals, which entail financial charges. Some of these costs are lower in low- and medium-resource countries, where economies of scale and higher benefits are possible.

Conclusion

The limits of the historical model have led to the development of several business models of scientific journals, that are in constant evolution, especially the author pays model which promises open access to publications but impacts scientific production. However, the evaluation of scientific production is heterogeneous due to a limited pool of reviewers inadequately selected. Scientific publishing is looking for solutions to find a virtuous model that respects open science, open access to data and scientific integrity. The "Diamond open access" model, free of charge for both readers and authors with the guarantee of an irrevocable license to reproduce the content of the article provided that the original source is cited, perfectly fits.

Two common issues in synchronized multimodal recordings with EEG: Jitter and latency

Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., muscle activities, eye movement, pupil diameters, or body kinematics data) is ubiquitous in human neuroscience research. However, the precise time alignment of multiple data from heterogeneous sources (i.e., devices) is often arduous due to variable recording parameters of commercially available research devices and complex experimental setups. In this review, we introduced the versatility of a Lab Streaming Layer (LSL)-based application that can overcome two common issues in measuring multimodal data: jitter and latency. We discussed the issues of jitter and latency in multimodal recordings and the benefits of time-synchronization when recording with multiple devices. In addition, a computer simulation was performed to highlight how the millisecond-order jitter readily affects the signal-to-noise ratio of the electrophysiological outcome. Together, we argue that the LSL-based system can be used for research requiring precise time-alignment of datasets. Studies that detect stimulus-induced transient neural responses or test hypotheses regarding temporal relationships of different functional aspects with multimodal data would benefit most from LSL-based systems.

Perspectives on systematic review protocol registration: a survey amongst stakeholders in the clinical research publication process

BACKGROUND

As systematic reviews (SRs) inform healthcare decisions, it is key that they address relevant questions and use rigorous methodology. Registration of SR protocols helps researchers identify relevant topics for future reviews and aims to prevent bias and duplication of effort. However, most SRs protocols are currently not registered, despite its significance. To guide future recommendations to enhance preregistration of SRs, it is important to gain a comprehensive understanding of the perspectives within the research community. Therefore, this study aims to examine the experiences with and factors of influence (barriers and facilitators) on prospective SR registration amongst researchers, peer reviewers and journal editors.

METHODS

Two different surveys were distributed to two groups: researchers and journal editors both identified from an existing sample of SRs. Researchers who indicated to have peer reviewed a SR were surveyed on their perspectives as peer reviewers as well. Survey design and analysis were informed by the Consolidated Framework for Implementation Research (CFIR). Shared and unique subthemes from the perspectives of researchers, peer reviewers and journal editors were identified and linked to the SR registration process (Innovation), to team, organisation (Inner setting) and (inter)national research community (Outer setting), and to characteristics of researchers, peer reviewers or journal editors (Individuals).

RESULTS

The survey's response rates were 65/727 (9%) for researchers, of which 37 were peer reviewers, and 22/308 (7%) for journal editors. Most respondents (n = 76, 94%) were familiar with SR protocol registration and 81% of researchers had registered minimally one SR protocol. Shared SR registration process subthemes were the importance and advantages of SR protocol registration, as well as barriers such as a high administrative burden. Shared subthemes regarding the inner and outer setting centred on journal processes, external standards and time. Shared individual factors were knowledge, skills and awareness.

CONCLUSIONS

The majority of the respondents were familiar with SR protocol registration and had a positive attitude towards it. This study identified suboptimal registration process, administrative burden and lack of mandatory SR protocol registration as barriers. By overcoming these barriers, SR protocol registration could contribute more effectively to the goals of open science.

SYSTEMATIC REVIEW REGISTRATION

osf.io/gmv6z.

Global change research needs international collaboration

Tackling the grand challenges of global climate change for the sustainability of ecological and societal systems requires data and expertise from Russia, the world's largest country that has the longest Arctic shoreline and the largest forest biome, peatland and permafrost zones. Academic relations and scientific collaborations with Russian scholars and institutions must continue despite the ensuing geopolitical crisis since 2022.

"On the ruins of seriality": The scientific journal and the nature of the scientific life

Twenty-first-century discourse on science has been marked by narratives of crisis. Science is said to be experiencing crises of public trust, of peer review and publishing, of reproducibility and replicability, and of recognition and reward. The dominant response has been to "repair" the scientific literature and the system of scientific publishing through open science. This paper places the current predicament of scholarly communication in historical perspective by exploring the evolution of the scientific journal in the second half of the twentieth century. I focus on a new genre of scientific journal invented by Dutch commercial publishers shortly after World War II, and on its effects on the nature of the scientific life. I show that profit-oriented publishers and discipline-building scientists worked together to make postwar science more open, while also arguing that formats of scientific publication have their own agency.

FAB: A "Dummy's" program for self-paced forward and backward reading

The self-paced reading paradigm has been popular and widely used in psycholinguistic research for several decades. The tool described in this paper, FAB (Forward and Backward reading), is a tool created to hopefully and maximally reduce the coding demands and simplify the operation costs for experimental researchers and clinical researchers who are doing experimental work, in their designing, coding, implementing, and analyzing self-paced reading tasks. Its basis in web languages (HTML, JavaScript) also promotes experimental implementation and material sharing in our era of open science. In addition, FAB has a unique forward-and-backward mode that can track regressive-like behaviors that are usually only recordable using eye-tracking or mouse-tracking equipment. In this paper, the specific application and usage of FAB is demonstrated in one laboratory and two online validation experiments. We hope this free and open-sourced tool can benefit research in a diverse range of contexts where self-paced reading is desirable.

Trail making test B in postoperative delirium: a replication study

Background

The Trail Making Test B (TMT-B) is indicative of cognitive flexibility and several other cognitive domains. Previous studies suggest that it might be associated with the risk of developing postoperative delirium, but evidence is limited and conflicting. We therefore aimed to replicate the association of preoperative TMT-B results with postoperative delirium.

Methods

We included older adults (≥65 yr) scheduled for major surgery and without signs of dementia to participate in this binational two-centre longitudinal observational cohort study. Presurgical TMT-B scores were obtained. Delirium was assessed twice daily using validated instruments. Logistic regression was applied and the area under the receiver operating characteristic curve calculated to determine the predictive performance of TMT-B. We subsequently included covariates used in previous studies for consecutive sensitivity analyses. We further analysed the impact of outliers, missing or impaired data.

Results

Data from 841 patients were included and of those, 151 (18%) developed postoperative delirium. TMT-B scores were statistically significantly associated with the incidence of postoperative delirium {odds ratio per 10-s increment 1.06 (95% confidence interval [CI] 1.02-1.09), P=0.001}. The area under the receiver operating characteristic curve was 0.60 ([95% CI 0.55-0.64], P<0.001). The association persisted after removing 21 outliers (1.07 [95% CI 1.03-1.07], P<0.001). Impaired or missing TMT-B data (n=88) were also associated with postoperative delirium (odds ratio 2.74 [95% CI 1.71-4.35], P<0.001).

Conclusions

The TMT-B was associated with postoperative delirium, but its predictive performance as a stand-alone test was low. The TMT-B alone is not suitable to predict delirium in a clinical setting.

Clinical trial registration

NCT02265263. (https://clinicaltrials.gov/ct2/show/results/NCT02265263).

Deep Generative Models: The winning key for large and easily accessible ECG datasets?

Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored.

Art driven by visual representations of chemical space

Science and art have been connected for centuries. With the development of new computational methods, new scientific disciplines have emerged, such as computational chemistry, and related fields, such as cheminformatics. Chemoinformatics is grounded on the chemical space concept: a multi-descriptor space in which chemical structures are described. In several practical applications, visual representations of the chemical space of compound datasets are low-dimensional plots helpful in identifying patterns. However, the authors propose that the plots can also be used as artistic expressions. This manuscript introduces an approach to merging art with chemoinformatics through visual and artistic representations of chemical space. As case studies, we portray the chemical space of food chemicals and other compounds to generate visually appealing graphs with twofold benefits: sharing chemical knowledge and developing pieces of art driven by chemoinformatics. The art driven by chemical space visualization will help increase the application of chemistry and art and contribute to general education and dissemination of chemoinformatics and chemistry through artistic expressions. All the code and data sets to reproduce the visual representation of the chemical space presented in the manuscript are freely available at https://github.com/DIFACQUIM/Art-Driven-by-Visual-Representations-of-Chemical-Space- . Scientific contribution: Chemical space as a concept to create digital art and as a tool to train and introduce students to cheminformatics.

YCharOS open antibody characterisation data: Lessons learned and progress made

YCharOS is a collaborative initiative aimed at characterising antibodies against the entire human proteome. As of August 2023, they have presented comprehensive knockout characterisation data for 812 antibodies and 78 proteins using techniques such as Western blot, immunoprecipitation, and immunofluorescence. YCharOS consolidates its data into reports (one protein per report) available on Zenodo, a public repository controlled by CERN, to ensure open access. To enhance the visibility of their work, the group is progressively converting their Zenodo reports into F1000 articles, collected on the YCharOS Gateway, and indexed via PubMed. Their data is also accessible through searches on the Antibody Registry. The provided data is a valuable resource for researchers when selecting antibodies for specific applications, although certain limitations should be considered. The data accumulated thus far has illuminated the extent of the problem when poorly performing antibodies are employed in research. While the scientific community was already aware that this was likely a widespread issue, the establishment of a collaborative open science project with industry partners introduces an innovative solution that holds the potential to yield significant returns on investment in the public interest. This potential is substantiated by the number of antibodies that have either been withdrawn or had their recommended usage altered by the vendor. However, despite the discovery of high-performing renewable antibodies for most of the studied proteins, this accounts for a tiny fraction of the human proteome and the commercial antibody market. To realise the full potential of this work, end-users must adjust their antibody procurement and usage practises in line with the provided data. This editorial offers a guide on how individual scientists can utilise the YCharOS data, in addition to sharing the insights gained from the data thus far with the wider scientific community.

Open Science Standards at Journals that Inform Evidence-Based Policy

Evidence-based policy uses intervention research to inform consequential decisions about resource allocation. Research findings are often published in peer-reviewed journals. Because detrimental research practices associated with closed science are common, journal articles report more false-positives and exaggerated effect sizes than would be desirable. Journal implementation of standards that promote open science-such as the transparency and openness promotion (TOP) guidelines-could reduce detrimental research practices and improve the trustworthiness of research evidence on intervention effectiveness. We evaluated TOP implementation at 339 peer-reviewed journals that have been used to identify evidence-based interventions for policymaking and programmatic decisions. Each of ten open science standards in TOP was not implemented in most journals' policies (instructions to authors), procedures (manuscript submission systems), or practices (published articles). Journals implementing at least one standard typically encouraged, but did not require, an open science practice. We discuss why and how journals could improve implementation of open science standards to safeguard evidence-based policy.

Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine

RATIONALE AND OBJECTIVES

Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the radiology and nuclear medicine papers on AI in terms of transparency and open science.

MATERIALS AND METHODS

A systematic literature search was performed in PubMed to identify original research studies on AI. The search was restricted to studies published in Q1 and Q2 journals that are also indexed on the Web of Science. A random sampling of the literature was performed. Besides six baseline study characteristics, a total of five availability items were evaluated. Two groups of independent readers including eight readers participated in the study. Inter-rater agreement was analyzed. Disagreements were resolved with consensus.

RESULTS

Following eligibility criteria, we included a final set of 194 papers. The raw data was available in about one-fifth of the papers (34/194; 18%). However, the authors made their private data available only in one paper (1/161; 1%). About one-tenth of the papers made their pre-modeling (25/194; 13%), modeling (28/194; 14%), or post-modeling files (15/194; 8%) available. Most of the papers (189/194; 97%) did not attempt to create a ready-to-use system for real-world usage. Data origin, use of deep learning, and external validation had statistically significantly different distributions. The use of private data alone was negatively associated with the availability of at least one item (p<0.001).

CONCLUSION

Overall rates of availability for items were poor, leaving room for substantial improvement.

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

Exploring the use of preprints in dentistry

OBJECTIVE

This study aims to assess the use, impact, and dissemination of preprints in dentistry.

METHODS

This is a meta-research study with a cross-sectional design. We included preprints published in dentistry, regardless of the year of publication. Searches were performed in the medRxiv.org and Preprints.org platforms and restricted to English. One researcher extracted the data, and another researcher verified data consistency. The following data were extracted: year of publication, country of the corresponding author, number of abstract and full-text views and downloads, Altmetric attention score, whether the preprint was mentioned in other servers such as Twitter and Publons, number of mentions in other servers, number of citations in the Dimensions database, and whether the preprint had already been published in a peer-reviewed journal. If already published, we extracted the journal's impact factor (JCR 2021) and the number of citations in the Dimensions database. We conducted a descriptive analysis of the extracted characteristics and explored relationships between metrics using the Spearman correlation.

RESULTS

We identified 276 preprints. Most of the studies were published between 2020 and 2022 (n = 229), especially those from ten countries. The most-cited preprint and published article are the same study. Only the correlation between the number of preprint citations and peer-reviewed article citations in the Dimensions database showed a large positive association (Spearman's rho = 0.5809).

CONCLUSION

Preprints gained popularity over the last several years due to the COVID-19 pandemic and reached a larger audience, especially on platforms such as Twitter.

CLINICAL SIGNIFICANCE

Preprint publishing allows faster dissemination of science for the benefit of society.

Open hardware in microscopy

The field of microscopy has been empowering humankind for many centuries by enabling the observation of objects that are otherwise too small to detect for the naked human eye. Microscopy techniques can be loosely divided into three main branches, namely photon-based optical microscopy, electron microscopy, and scanning probe microscopy with optical microscopy being the most prominent one. On the high-end level, optical microscopy nowadays enables nanometer resolution covering many scientific disciplines ranging from material sciences over the natural sciences and life sciences to the food sciences. On the lower-end level, simplified hardware and openly available description and blueprints have helped to make powerful microscopes widely available to interested scientists and researchers. For this special issue, we invited contributions from the community to share their latest ideas, designs, and research results on open-source hardware in microscopy. With this collection of articles, we hope to inspire the community to further increase the accessibility, interoperability, and reproducibility of microscopy. We further touch on the standardization of methodologies and devices including the use of computerized control of data acquisition and data analysis to achieve high quality and efficiency in research and development.

Is shape in the eye of the beholder? Assessing landmarking error in geometric morphometric analyses on live fish

Geometric morphometrics is widely used to quantify morphological variation between biological specimens, but the fundamental influence of operator bias on data reproducibility is rarely considered, particularly in studies using photographs of live animals taken under field conditions. We examined this using four independent operators that applied an identical landmarking scheme to replicate photographs of 291 live Atlantic salmon (Salmo salar L.) from two rivers. Using repeated measures tests, we found significant inter-operator differences in mean body shape, suggesting that the operators introduced a systematic error despite following the same landmarking scheme. No significant differences were detected when the landmarking process was repeated by the same operator on a random subset of photographs. Importantly, in spite of significant operator bias, small but statistically significant morphological differences between fish from the two rivers were found consistently by all operators. Pairwise tests of angles of vectors of shape change showed that these between-river differences in body shape were analogous across operator datasets, suggesting a general reproducibility of findings obtained by geometric morphometric studies. In contrast, merging landmark data when fish from each river are digitised by different operators had a significant impact on downstream analyses, highlighting an intrinsic risk of bias. Overall, we show that, even when significant inter-operator error is introduced during digitisation, following an identical landmarking scheme can identify morphological differences between populations. This study indicates that operators digitising at least a sub-set of all data groups of interest may be an effective way of mitigating inter-operator error and potentially enabling data sharing.

Reproducibility in Neuroimaging Analysis: Challenges and Solutions

Recent years have marked a renaissance in efforts to increase research reproducibility in psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid foundation of fundamental research-one that will support new theories built on valid findings and technological innovation that works. The increased focus on reproducibility has made the barriers to it increasingly apparent, along with the development of new tools and practices to overcome these barriers. Here, we review challenges, solutions, and emerging best practices with a particular emphasis on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing each in turn. Analytical reproducibility is the ability to reproduce findings using the same data and methods. Replicability is the ability to find an effect in new datasets, using the same or similar methods. Finally, robustness to analytical variability refers to the ability to identify a finding consistently across variation in methods. The incorporation of these tools and practices will result in more reproducible, replicable, and robust psychological and brain research and a stronger scientific foundation across fields of inquiry.

Next steps after airing disagreement on a scientific issue with policy implications: a meta-analysis, multi-lab replication and adversarial collaboration

Canadian policymakers are interested in determining whether farmed Atlantic salmon, frequently infected with Piscine orthoreovirus (PRV), may threaten wild salmon populations in the Pacific Northwest. A relevant work has been published in BMC Biology by Polinksi and colleagues, but their conclusion that PRV has a negligible impact on the energy expenditure and respiratory performance of sockeye salmon is disputed by Mordecai and colleagues, whose re-analysis is presented in a correspondence article. So, what is the true effect and what should follow this unresolved dispute? We suggest a 'registered multi-lab replication with adversaries'.

Biomedical supervisors' role modeling of open science practices

Supervision is one important way to socialize Ph.D. candidates into open and responsible research. We hypothesized that one should be more likely to identify open science practices (here publishing open access and sharing data) in empirical publications that were part of a Ph.D. thesis when the Ph.D. candidates' supervisors engaged in these practices compared to those whose supervisors did not or less often did. Departing from thesis repositories at four Dutch University Medical centers, we included 211 pairs of supervisors and Ph.D. candidates, resulting in a sample of 2062 publications. We determined open access status using UnpaywallR and Open Data using Oddpub, where we also manually screened publications with potential open data statements. Eighty-three percent of our sample was published openly, and 9% had open data statements. Having a supervisor who published open access more often than the national average was associated with an odds of 1.99 to publish open access. However, this effect became nonsignificant when correcting for institutions. Having a supervisor who shared data was associated with 2.22 (CI:1.19-4.12) times the odds to share data compared to having a supervisor that did not. This odds ratio increased to 4.6 (CI:1.86-11.35) after removing false positives. The prevalence of open data in our sample was comparable to international studies; open access rates were higher. Whilst Ph.D. candidates spearhead initiatives to promote open science, this study adds value by investigating the role of supervisors in promoting open science.

A construction and empirical research of the journal disruption index based on open citation data

For many years, the journal evaluation system has been centered on impact indicators, resulting in evaluation results that do not reflect the academic innovation of journals. To solve this issue, this study attempts to construct the Journal Disruption Index (JDI) from the perspective of measuring the disruption of each journal article. In the actual study, we measured the disruption of articles of 22 selected virology journals based on the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI) first. Then we calculated the JDI of 22 virology journals based on the absolute disruption index (D Z ) of the articles. Finally, we conducted an empirical study on the differences and correlations between the impact indicators and disruption indicators as well as the evaluation effect of the disruption index. The results of the study show: (1) There are large differences in the ranking of journals based on disruption indicators and impact indicators. Among the 22 journals, 12 are ranked higher by JDI than Cumulative Impact Factor for 5 years (CIF5), the Journal Index for PR6 (JIPR6) and average Percentile in Subject Area (aPSA). The ranking difference of 17 journals between the two kinds of indicators is greater than or equal to 5. (2) There is a medium correlation between disruption indicators and impact indicators at the level of journals and papers. JDI is moderately correlated with CIF5, JIPR6 and aPSA, with correlation coefficients of 0.486, 0.471 and - 0.448, respectively. D Z was also moderately correlated with Cumulative Citation (CC), Percentile Ranking with 6 Classifications (PR6) and Percentile in Subject Area (PSA) with correlation coefficients of 0.593, 0.575 and - 0.593, respectively. (3) Compared with traditional impact indicators, the results of journal disruption evaluation are more consistent with the evaluation results of experts' peer review. JDI reflects the innovation level of journals to a certain extent, which is helpful to promote the evaluation of innovation in sci-tech journals.

Iranian researchers' perspective about concept and effect of open science on research publication

BACKGROUND

Sharing research outputs with open science methods for different stakeholders causes better access to different studies to solve problems in diverse fields, which leads to equal access conditions to research resources, as well as greater scientific productivity. Therefore, the aim of this study was to perceive the concept of openness in research among Iranian health researchers.

METHODS

From the beginning of August to the middle of November 2021, twenty semi-structured interviews were held with Iranian health researchers from different fields using purposeful, snowball, and convenience sampling. The interviews continued until data saturation. Data analysis was performed with thematic analysis using MAXQDA 20. Finally, seven main issues related to open science were identified.

RESULTS

Through analysis of the interviews, 235 primary codes and 173 main codes were extracted in 22 subclasses. After careful evaluation and integration of subclasses and classes, they were finally classified into nine categories and three main themes. Analysis showed that openness in research was related to three main themes: researchers' understanding of open science, the impact of open science on publication and sharing of research, concerns and reluctance to open research.

CONCLUSION

The conditions of access to research output should be specified given the diversity of studies conducted in the field of health; issues like privacy as an important topic of access to data and information in the health system should also be specified. Our analysis indicated that the conditions of publication and sharing of research processes should be stated according to different scopes of health fields. The concept of open science was related to access to findings and other research items regardless of cost, political, social, or racial barriers, which could create collective wisdom in the development of knowledge. The process of publication and sharing of research related to open access applies to all types of outputs, conditions of access, increasing trust in research, creation of diverse publication paths, and broader participation of citizens in research. Open science practices should be promoted to increase the circulation and exploitation rates of knowledge while adjusting and respecting the limits of privacy, intellectual property and national security rights of countries.

Assessing the publishing priorities and preferences among STEM researchers at a large R1 institution

The cost of academic publishing has increased substantially despite the ease with which information can be shared on the web. Open Access publishing is a key mechanism for amplifying research access, inclusivity, and impact. Despite this, shifting to a free-to-read publishing environment requires navigating complex barriers that vary by career status and publishing expectations. In this article, we investigate the motivations and preferences of researchers situated within our large research institution as a case study for publishing attitudes at similar institutions. We surveyed the publishing priorities and preferences of researchers at various career stages in STEM fields as they relate to openness, data practices, and assessment of research impact. Our results indicate that publishing preferences, data management experience and research impact assessment vary by career status and departmental approaches to promotion. We find that open access publishing is widely appreciated regardless of career status, but financial limitations and publishing expectations were common barriers to publishing in Open Access journals. Our findings shed light on publishing attitudes and preferences among researchers at a major R1 research institution, and offer insight into advocacy strategies that incentivize open access publishing.

Big data from small animals: integrating multi-level environmental data into the Dog Aging Project

Environmental exposures can have large impacts on health outcomes. While many resources have been dedicated to understanding how humans are influenced by the environment, few efforts have been made to study the role of built and natural environmental features on animal health. The Dog Aging Project (DAP) is a longitudinal community science study of aging in companion dogs. Using a combination of owner-reported surveys and secondary sources linked through geocoded coordinates, DAP has captured home, yard and neighbourhood variables for over 40,000 dogs. The DAP environmental data set spans four domains: the physical and built environment; chemical environment and exposures; diet and exercise; and social environment and interactions. By combining biometric data, measures of cognitive function and behaviour, and medical records, DAP is attempting to use a big-data approach to transform the understanding of how the surrounding world affects the health of companion dogs. In this paper, the authors describe the data infrastructure developed to integrate and analyse multi-level environmental data that can be used to improve the understanding of canine co-morbidity and aging.

Well-being impact assessment of artificial intelligence - A search for causality and proposal for an open platform for well-being impact assessment of AI systems

In recent years, the well-being impact assessment approach has been applied in the area of Artificial Intelligence (AI). Existing well-being frameworks and tools provide a relevant starting point. Taking into account its multidimensional nature, well-being assessment is well suited to assess both the expected positive effects of the technology as well as unintended negative consequences. To-date the establishment of causal links mostly stems from intuitive causal models. Such approaches neglect the fact that to prove causal links between the operation of an AI system and observed effects is difficult due to the immense complexity of the socio-technical context. This article aims at providing a framework for ascertaining the attribution of effects of observed impact of AI on well-being. An elaborated approach to impact assessment potentially enabling causal inferences is demonstrated. Furthermore, a new Open Platform for Well-Being Impact Assessment of AI systems (OPIA) is introduced, which is based on a distributed community to build reproducible evidence through effective identification, refinement, iterative testing, and cross-validation of expected causal structures.

Publication bias impacts on effect size, statistical power, and magnitude (Type M) and sign (Type S) errors in ecology and evolutionary biology

Collaborative efforts to directly replicate empirical studies in the medical and social sciences have revealed alarmingly low rates of replicability, a phenomenon dubbed the 'replication crisis'. Poor replicability has spurred cultural changes targeted at improving reliability in these disciplines. Given the absence of equivalent replication projects in ecology and evolutionary biology, two inter-related indicators offer the opportunity to retrospectively assess replicability: publication bias and statistical power. This registered report assesses the prevalence and severity of small-study (i.e., smaller studies reporting larger effect sizes) and decline effects (i.e., effect sizes decreasing over time) across ecology and evolutionary biology using 87 meta-analyses comprising 4,250 primary studies and 17,638 effect sizes. Further, we estimate how publication bias might distort the estimation of effect sizes, statistical power, and errors in magnitude (Type M or exaggeration ratio) and sign (Type S). We show strong evidence for the pervasiveness of both small-study and decline effects in ecology and evolution. There was widespread prevalence of publication bias that resulted in meta-analytic means being over-estimated by (at least) 0.12 standard deviations. The prevalence of publication bias distorted confidence in meta-analytic results, with 66% of initially statistically significant meta-analytic means becoming non-significant after correcting for publication bias. Ecological and evolutionary studies consistently had low statistical power (15%) with a 4-fold exaggeration of effects on average (Type M error rates = 4.4). Notably, publication bias reduced power from 23% to 15% and increased type M error rates from 2.7 to 4.4 because it creates a non-random sample of effect size evidence. The sign errors of effect sizes (Type S error) increased from 5% to 8% because of publication bias. Our research provides clear evidence that many published ecological and evolutionary findings are inflated. Our results highlight the importance of designing high-power empirical studies (e.g., via collaborative team science), promoting and encouraging replication studies, testing and correcting for publication bias in meta-analyses, and adopting open and transparent research practices, such as (pre)registration, data- and code-sharing, and transparent reporting.