Transparency In Research Research Articles

Status of “African research and its contribution to global health research: a review and an opinion”

Abstract Aim Research is key to the development of any nation, with health research being of high value to society. Research provides data that is useful for the generation and provision of knowledge needed for policy making, planning and strategic management to develop nations. Despite the African population accounting for nearly 14% of the world’s population, little published research originates from African scholars. Purpose The purpose of this article is to highlight the reasons for the lack of robust research by scientists from African low-and-middle-income countries and to emphasize the inequity experienced by African scientists in global health research. Possible solutions to the dearth in African global health research are equally explored. Findings Insufficient research has led to poor growth, development and advancements in health in Africa. A significant gap in African-led and published research, lack of mentorship, inequitable access to research funding and grant eligibility, and increased dependency on foreign organizations have contributed to the lack of sustainability and failure of African research. Conclusion Research is crucial for national development, especially in health. However, African researchers are underrepresented in global publications. To optimize research, local needs and African researchers must be prioritized, and research systems in African institutions must be strengthened. Additionally, the international community must respect, be transparent, and support research development in Africa. These efforts are essential for fostering a robust research environment, addressing global health challenges, and promoting sustainable development across Africa and LMICs.

Openness and transparency in animal research: Why and how

AbstractNonhuman animal models continue to be indispensable in neuroscience research for the foreseeable future. In recent years, animal‐right activists have been increasing the pressure on politicians and policymakers to phase out animal research. To address this pressure, we should adapt our communication habits, to be more open and transparent about (our) animal research and most importantly to expand the methods we use to communicate about our research and increase the extend of this outreach. In this editorial, we discuss the why and how of animal research communication.

Open science practices among authors published in complementary, alternative, and integrative medicine journals: An international, cross-sectional survey.

Open science practices aim to increase transparency in research and increase research availability through open data, open access platforms, and public access. Due to the increasing popularity of complementary, alternative, and integrative medicine (CAIM) research, our study aims to explore current open science practices and perceived barriers among CAIM researchers in their own respective research articles. We conducted an international cross-sectional online survey that was sent to authors that published articles in MEDLINE-indexed journals categorized under the broad subject of "Complementary Therapies" or articles indexed under the MeSH term "Complementary Therapies." Articles were extracted to obtain the names and emails of all corresponding authors. Eight thousand seven hundred eighty-six researchers were emailed our survey, which included questions regarding participants' familiarity with open science practices, their open science practices, and perceived barriers to open science in CAIM with respect to participants' most recently published article. Basic descriptive statistics was generated based on the quantitative data. The survey was completed by 292 participants (3.32% response rate). Results indicate that the majority of participants were "very familiar" (n = 83, 31.68%) or "moderately familiar" (n = 83, 31.68%) with the concept of open science practices while creating their study. Open access publishing was the most familiar to participants, with 51.96% (n = 136) of survey respondents publishing with open access. Despite participants being familiar with other open science practices, the actual implementation of these practices was low. Common barriers participants experienced in implementing open science practices include not knowing where to share the study materials, where to share the data, or not knowing how to make a preprint. Although participants responded that they were familiar with the concept of open science practices, the actual implementation and uses of these practices were low. Barriers included a lack of overall knowledge about open science, and an overall lack of funding or institutional support. Future efforts should aim to explore how to implement methods to improve open science training for CAIM researchers.

Data-Driven Insights for a Holistic Understanding of Research Culture

Research environments (and their measurement and tracking) are becoming increasingly complex, with rapid Artificial Intelligence (AI) advancements, interdisciplinary collaboration, and global connectivity shaping the way research knowledge is created and disseminated. Within this dynamic landscape, universities hold an ever increasing collection of valuable data, which are stored within core operational systems including research information systems, research management systems or grants databases, human resource systems and course management systems. This treasure trove of information, often overlooked and underutilised, not only serves as a valuable tool in guiding strategic decisions, but also could be further used to provide a comprehensive approach to monitoring research integrity and culture. This critical reflection follows extensive conversations and debates around defining and assessing ‘research culture’. How can we possibly measure something that has, up until now, been viewed as merely a concept? How can we generate useful metrics that reflect the culture of a research institution? Our reflections will draw attention to the potential of leveraging readily accessible information to gauge and benchmark research integrity and culture practices. We discuss how regular habitual integration of these data sources enables continuous monitoring and measurement of research culture as well as the ability to assist in the assessment of interventions or initiatives designed to improve it. We reflect on how this approach ensures that University leaders have a consistent and up-to-date understanding of the research environment through which they can identify strengths, pinpoint areas for improvement, and cultivate a more robust, inclusive, and transparent research ecosystem. The current paper, through illustrative examples from one UK-based institution, explores the potential in harnessing existing data, such as collaborative trends and prevailing research practices, to gain valuable insights into the dynamics of academic research. In addition, we explore the advantages and drawbacks of using this data to develop potential metrics that can be used to recognise and reward a healthy research culture.

Identifying AI-Generated Research Papers: Methods and Considerations

Recent advancements in natural language generation (NLG) have revolutionized content creation, enabling artificial intelligence (AI) tools to produce coherent and seemingly authentic texts, including scholarly papers. While AI-generated content offers efficiencies in speed and volume, concerns over authenticity, ethical implications, and academic integrity persist. This review explores methods and considerations for identifying AI-generated research papers, emphasizing the need to distinguish between human-authored and AI-generated content to uphold scholarly standards and ensure transparency in research. Key detection techniques include textual analysis, metadata examination, and content evaluation. Ethical concerns regarding AI's role in research are also discussed, underscoring the importance of ongoing research to refine identification methodologies and maintain research integrity.

Recommendations for sharing network data and materials

Abstract One of the goals of open science is to promote the transparency and accessibility of research. Sharing data and materials used in network research is critical to these goals. In this paper, we present recommendations for whether, what, when, and where network data and materials should be shared. We recommend that network data and materials should be shared, but access to or use of shared data and materials may be restricted if necessary to avoid harm or comply with regulations. Researchers should share the network data and materials necessary to reproduce reported results via a publicly accessible repository when an associated manuscript is published. To ensure the adoption of these recommendations, network journals should require sharing, and network associations and academic institutions should reward sharing.

Enhancing ethical standards in clinical trials: A deep dive into regulatory compliance, informed consent, and participant rights protection frameworks

This study critically examines the ethical standards governing clinical trials, focusing on regulatory compliance, informed consent, and the protection of participant rights. The purpose is to assess the current ethical frameworks and explore emerging trends that shape the evolving landscape of clinical research. Through a detailed review of existing literature, this paper analyzes how global regulatory bodies and ethical guidelines, such as Good Clinical Practice (GCP) and the Declaration of Helsinki, guide the ethical conduct of trials while identifying gaps that have emerged due to advances in technology and the globalization of clinical research. Key findings reveal that while informed consent remains central to participant autonomy, challenges persist in ensuring true comprehension across diverse populations, particularly in the context of complex interventions and digital tools. The growing use of artificial intelligence (AI), decentralized trial designs, and precision medicine introduces both opportunities and ethical dilemmas, especially regarding data privacy, participant selection, and regulatory harmonization in multinational trials. Conclusions emphasize the need for adaptive ethical frameworks that address the unique risks and benefits associated with these emerging trends. Recommendations include strengthening global regulatory harmonization, enhancing transparency in AI-driven research, and ensuring that participant rights, particularly data security and informed consent, are prioritized in evolving clinical trial designs. These steps are crucial for maintaining public trust and advancing medical research ethically and responsibly.

3.B. Scientific session: Why do we need clear understanding of digital public health interventions?

Abstract The rapid proliferation of digital technologies in health and public health has led to various terms and concepts, often used interchangeably or inconsistently. This ambiguity hampers communication, complicates research efforts, and undermines the effectiveness and adaptation of interventions. Without a clear understanding of such concepts, stakeholders face difficulties using, evaluating, and regulating digital public health (DiPH) initiatives. Thus, there is an urgent need to establish a common language - across academic disciplines and in the science-to-practice/policy transfer - to foster collaboration, ensure accountability, and maximize the impact of DiPH interventions globally. This workshop highlights the need for a cross-sectoral understanding of terms and concepts related to DiPH interventions. For this, we identify effective strategies for engaging diverse populations in DiPH interventions, considering accessibility, usability, and cultural sensitivity. Additionally, we present how a more precise understanding of these concepts will impact positive population-health outcomes of DiPH interventions. Finally, we discuss DiPH terminology enhancement regarding data protection, highlighting the boundaries of digital interventions within and outside health policies and public health systems, given the diversity of the legal frameworks applicable in either case. We aim to facilitate interdisciplinary collaboration and knowledge exchange, fostering a shared understanding of terminology and best practices. This will advance DiPH by promoting clarity, rigor, and transparency in research and practice. Shared understandings of key concepts will eventually enhance the design, implementation, and evaluation of DiPH interventions, leading to improved population health outcomes and informing evidence-based policymaking and regulatory frameworks. The workshop consists of four presentations and a 20-minute panel discussion on increasing understanding dissemination. The first talk highlights multi-stakeholder perspectives (including researchers, technicians, health and medical practitioners, and the general public) on the ever-changing terminology of DiPH concepts. The second presentation showcases how these varying perceptions influence the academic representation of DiPH interventions and that interventions - although summarized under the same terminology - drastically vary concerning their use case and characteristics. The third speaker will discuss why a clearer understanding of these terms is needed to increase engagement with DiPH interventions for positive population health outcomes and identify effective DiPH interventions based on evidence. The last talk explains why unclear terminology or standards threaten populations and result in over- or under-regulation of DiPH interventions. Overall, this workshop catalyzes advancing the DiPH agenda in Europe, empowering stakeholders to harness the full potential of technology for promoting population health. Key messages • When we create technical terms, it is our professional approach, characterized by our academic background. It does not necessarily transfer to other domains or the general population. • Digital Public Health can transform global healthcare. However clear & harmonized terminologies are crucial for comparing evolving technologies & paving the way for transformative digital healthcare.

A systematic review protocol of injuries and illness across all the competitive cycling disciplines, including track cycling, mountain biking, road cycling, time trial, cyclocross, gravel cycling, BMX freestyle, BMX racing, e-sport, para-cycling and artistic cycling

IntroductionThe sport of cycling has witnessed phenomenal growth over the past decade. Globally, over 200 million television hours across five continents watched the recent inaugural World Championships in Glasgow, in 2023. The Union Cycliste Internationale (UCI), the world cycling governing body, has highlighted its mission to “promote and support research in cycling epidemiology and medicine, especially for the benefit of lesser-known disciplines” within its 2030 Agenda. This paper outlines a proposed protocol to conduct a systematic review that comprehensively analyses and synthesises the existing literature about cycling-related injuries and illness across all competitive disciplines.MethodsThe Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines will be followed through each stage of this systematic review. Cycling is an umbrella term used for many individual disciplines. Investigation of all types of injuries and/or illnesses sustained during training and/or competition among competitive athletes across all disciplines will be included in this review. A computerised, systematic literature search will be conducted in electronic databases MEDLINE, Scopus, Embase, and Cochrane Library. Independent screening by two reviewers in a two-step process: title/abstract screening followed by full-text review. The reference lists of included articles will be searched to identify any other potentially relevant articles. Narrative synthesis and tabular/charted presentation of the extracted data will be included.DiscussionThis protocol paper outlines the methodology to conduct a systematic review of injuries and illness across all competitive cycling disciplines. The aims of outlining this systematic review protocol are to aid research transparency, help reduce publication bias, prevent selective publication, and prevent the selective reporting of results. Future systematic reviews based on the proposed protocol will summarise the known prevalence, incidences, locations and burden of injury and illness across the sport of cycling.Trial RegistrationThis study has been registered with the PROSPERO International Prospective Register of Systematic Reviews (registration number CRD42024502703).

Trends in Israeli clinical trials registration “MyTrial”

BackgroundClinical trial registration is critical for research transparency and integrity. Since 2005, the Declaration of Helsinki has required prospective registration of trials before subject recruitment. In Israel, the MyTrial registry was established in 2015 to register interventional trials and became mandatory in 2016 for ethical approval. The study aimed to analyze the registration practices, challenges, and trends in clinical trial registration in Israel using the local registry “MyTrial”.MethodsA total of 3,895 clinical trial records from 2011 to December 2022 were retrieved from the MyTrial platform and subjected to descriptive analysis.ResultsA significant increase occurred from 2016 to 2021 due to mandated registration, with a peak in 2020 (733 trials) and a decrease in 2022 (462 trials), likely due to COVID-19. Most of the trials included drugs (56%) or medical devices (33%). Geographically, 53% were from central Israel. Only 39% of the patients were registered at both MyTrial and ClinicalTrials.gov. 65% had no blinding. 47% featured unregistered products. 56% had not started recruitment. Since 2016, the number of advanced therapy trials has steadily increased, reaching 19 in 2022. There are gaps between registered trials and official government reports.ConclusionThese findings provide valuable insights into the current landscape of clinical trial registration in Israel and highlight the need for improvements in compliance with prospective registration and adherence to the WHO-ICTRP standards.

Factors shaping innovative behavior: A meta‐analysis of technology adoption studies in agriculture

AbstractThis study conducts a meta‐regression analysis to synthesize the marginal effects of 12 factors that frequently appear in empirical studies examining farmer's technology adoption behavior. The analysis includes 187 observational studies on technology adoption in agriculture, which are published in 32 peer‐reviewed journals in the broader field of agricultural economics, covering farmer's adoption in 47 countries for a diverse range of agricultural technologies. Using this broad meta‐dataset, we investigate whether each of the 12 determinants has a true effect on technology adoption rates and examine whether Type I and Type II publication bias are present in the adoption literature. Our results reveal that while most determinant factors significantly affect adoption rates, their marginal effects are generally of small magnitude and vary considerably by technology type and country group. Additionally, our results provide evidence of the presence of Type I publication bias in half of the factors considered and Type II publication bias in nearly all, underscoring the need for caution when interpreting results in the adoption literature by researchers and policymakers. Overall, the findings highlight the critical need for proactive measures to address publication bias and promote more transparent and credible research practices in agricultural economics.

Many randomized trials in a large systematic review were not registered and had evidence of selective outcome reporting: a metaepidemiological study

ObjectivesThe primary objectives were to describe characteristics of trial registration in the chronic low back pain (CLBP) field and assess the association of trial registration status (registered vs unregistered, prospectively registered vs retrospectively registered) with risk of bias, sufficient sample size, quality of reporting, and treatment effect estimates. Secondary objectives were to describe trial registration consistency with the final report and assess its association with risk of bias, sufficient sample size, and treatment effect estimates. Study Design and SettingA cross-sectional metaepidemiological study of trials included in a large Cochrane review on exercise treatments for CLBP. We extracted relevant trial and registration information and assessed trials’ risk of bias using the Cochrane Risk of Bias 1 tool. We performed descriptive analyses, logistic regressions, and subgroup meta-analyses. ResultsWe included 361 trials, of which 23.3% were prospectively registered. Registered trials had lower risk of bias (odds ratio [OR] 0.6; 95% confidence interval [CI] 0.5, 0.7) and higher reporting quality (OR 1.6; 95% CI 1.4, 1.8) than unregistered trials. Prospectively registered trials were more likely to have low risk of reporting bias (OR 2.7; 95% CI 1.2, 6.5) and higher quality of reporting (OR 1.3; 95% CI 1.1, 1.6) than retrospectively registered trials. Trial registration status was not associated with effect estimates. Among prospectively registered trials, 64.3% clearly defined primary outcome(s) in their registration, 58.3% had consistent sample sizes, and 22.6% had no evidence of selective outcome reporting. Trials that clearly defined primary outcome(s) were more likely to report larger effect estimates for pain intensity (mean difference −15.8; 95% CI −22.7, −8.9 vs −6.0; 95% CI −10.6, −1.5; Q = 6.7, P = .01), although the difference was small, the 95% CIs overlapped, and no difference was found for functional limitations. ConclusionA small proportion of trials in the CLBP field were registered prospectively and many presented registration inconsistencies. Registered trials tend to have lower risk of bias and higher quality of reporting. Policies are needed to improve prospective registration and registration consistency in the field. Plain Language SummaryProspective trial registration is the practice of documenting the planned methods of a randomized controlled trial on a publicly available online platform (ie, website) before enrolling participants. Medical journals require trialists to prospectively register their trials to encourage the conduct of high-quality research and reduce the chance of trialists changing their research plan to report only positive or significant results (known as selective outcome reporting). We investigated whether trialists within the chronic low back pain field were registering their trials, and whether they followed their registered research plan.We used data from a large systematic review of 456 trials that tested the effectiveness of exercise as a treatment for chronic low back pain. We assessed each trials' registration status and whether prospectively registered trials had inconsistencies between their registered research plan and their research conduct (eg, evidence of selective outcome reporting). We also looked at the association among trial registration with trials’ quality of reporting (ie, a marker of research transparency), risk of bias (ie, a marker of research quality), and the amount of low back pain improvement reported by the trials (ie, effect estimates).We found that less than 25% of trials were prospectively registered, and many had inconsistencies between their registered research plan and their research conduct. Overall, registered trials had lower risk of bias and higher quality of reporting. However, trial registration status and selective outcome reporting were not associated with effect estimates (the amount of back pain improvement reported by trials). Our findings highlight the need for trialists and journals to better follow trial registration guidelines and policies in the chronic low back pain field. Knowledge users should be cautious when consuming information from unregistered trials as they appear to be more likely to have quality concerns.

Giving Away the Science of School Psychology

Within the field of school psychology, we often put a premium on evidence-based practices, but what is often missing from these conversations is a consideration of replication and open science principles in the evaluation of evidence. Amid growing concerns regarding the replicability of psychological science, calls for greater research transparency, and more careful consideration of the conditions under which a finding might replicate, research suggest that school psychologists’ uptake of open science practices may lag behind that of other social science disciplines. The present paper synthesizes the literature on the replication crisis and corresponding credibility revolution in psychology, describes various open science practices, and considers barriers and adaptations needed to implement these practices in school-based research. The paper ends with recommendations on how to better give away the science of school psychology via transparent research practices so that research consumers can better reap the benefits of evidence-based practice. Impact Statement This paper summarizes the literature on open science, including key concepts relevant to the replication crisis and corresponding credibility revolution. These concepts are then made applicable to the field of school psychology, including our roles as researchers, practitioners, research consumers, and publication editors. By engaging in more transparent research practices, school psychologists can promote a more replicable, reputable, and reliable science, which yields greater benefits and buy-in for the communities we serve.

Harm effects in non-registered versus registered randomized controlled trials of medications: a retrospective cohort study of clinical trials

BackgroundTrial registration aims to address potential bias from selective or non-reporting of findings, and therefore has a vital role in promoting transparency and accountability of clinical research. In this study, we aim to investigate the influence of trial registration on estimated harm effects in randomized controlled trials of medication interventions.MethodsWe searched PubMed for systematic reviews and meta-analyses of randomized trials on medication harms indexed between January 1, 2015, and January 1, 2020. To be included in the analyses, eligible meta-analyses should have at least five randomized trials with distinct registration statuses (i.e., prospectively registered, retrospectively registered, and non-registered) and 2 by 2 table data for adverse events for each trial. To control for potential confounding, trials in each meta-analysis were analyzed within confounder-harmonized groups (e.g., dosage) identified using the Directed Acyclic Graph method. The harm estimates arising from the trials with different registration statuses were compared within the confounder-harmonized groups using hierarchical linear regression. Results are shown as ratio of odds ratio (OR) and 95% confidence interval (CI).ResultsThe dataset consists of 629 meta-analyses of harms with 10,069 trials. Of these trials, 74.3% were registered, and 23.9% were not registered, and for those registered, 70.6% were prospectively registered, while 26.3% were retrospectively registered. In comparison to prospectively registered trials, both non-registered trials (ratio of OR = 0.82, 95%CI 0.68 to 0.98, P = 0.03) and retrospectively registered trials (ratio of OR = 0.75, 95%CI 0.66 to 0.86, P < 0.01) had lower OR for harms based on 69 and 126 confounders-harmonized groups. The OR of harms did not differ between retrospectively registered and non-registered trials (ratio of OR = 1.02, 95%CI 0.85 to 1.23, P = 0.83) based on 76 confounders-harmonized groups.ConclusionsMedication-related harms may be understated in non-registered trials, and there was no obvious evidence that retrospective registration had a demonstrable benefit in reducing such selective or absent reporting. Prospective registration is highly recommended for future trials.

Corpus linguistics will benefit from greater adoption of pre-registration: A novice-friendly split-corpus approach to pre-registration

In this brief article, I contend that the field of corpus linguistics stands to gain significantly from an increased adoption of pre-registration. Pre-registration serves to constrain the almost infinite degree of analytic freedom inherent in corpus analysis, thereby enhancing the transparency, reliability, and potential impact of corpus research. While pre-registration is increasingly popular in fields such as psychology and medicine, its uptake in corpus linguistics remains notably limited. To facilitate the transition toward pre-registration, I describe a straightforward split-corpus approach, ideally suited for corpus linguists new to pre-registration and for both hypothesis-testing and exploratory research. This method involves dividing a corpus into an exploratory set (20–40 % of the corpus) and a confirmatory set (the remaining 60–80 %). The exploratory set allows researchers to freely generate hypotheses and develop analysis plans, while the confirmatory set is then used for a more structured and objective analysis according to the pre-specified protocols. By employing this approach, corpus linguists can effectively balance exploratory flexibility with the rigour of confirmatory analysis, boosting the reliability of corpus findings. An increased uptake of pre-registration may not only bolster recognition of corpus linguistics as a robust empirical field, but it may also encourage a stronger emphasis on the building of cumulative knowledge.

AI in Scientific Research: Empowering Researchers with Intelligent Tools

This article explores the transformative impact of artificial intelligence (AI) on scientific research across various disciplines. It examines how AI-driven tools are revolutionizing data analysis, simulation, and hypothesis generation, particularly in fields such as genomics, climate science, and materials science. The article discusses the acceleration of discovery processes through AI, highlighting its role in enabling sophisticated analysis of complex datasets, developing predictive models, and facilitating automated experimentation. Ethical considerations, including the need for transparency and reproducibility in AI-assisted research, are addressed. The synergy between human creativity and AI capabilities is explored, emphasizing how AI augments human ingenuity and fosters interdisciplinary collaboration. Case studies illustrate successful implementations of AI in scientific inquiry, demonstrating its potential to enhance research methodologies and outcomes. The article also looks ahead to the prospects of AI in scientific research, considering emerging technologies and the evolving role of AI in the scientific process. By providing a comprehensive overview of AI's current applications and future potential in scientific research, this article underscores the pivotal role of AI in advancing scientific knowledge and addressing complex global challenges.

Computational Study Protocol: Leveraging Synthetic Data to Validate a Benchmark Study for Differential Abundance Tests for 16S Microbiome Sequencing Data

Background The utility of synthetic data in benchmark studies depends on its ability to closely mimic real-world conditions and to reproduce results obtained from experimental data. Here, we evaluate the performance of differential abundance tests for 16S metagenomic data. Building on the benchmark study by Nearing et al. (1), who assessed 14 differential abundance tests using 38 experimental datasets in a case-control design, we validate their findings by generating synthetic datasets that mimics the experimental data. We will employ statistical tests to rigorously assess the similarity between synthetic and experimental data and to validate the conclusions on the performance of these tests drawn by Nearing et al. (1). This protocol adheres to the SPIRIT guidelines and is, to our knowledge, the first of its kind in computational benchmark studies. Methods We replicate Nearing et al.’s (1) methodology, incorporating synthetic data simulated using two distinct tools, mirroring each of the 38 experimental datasets. Equivalence tests will be conducted on 43 data characteristics comparing synthetic and experimental data, complemented by principal component analysis for overall similarity assessment. The 14 differential abundance tests will be applied to both synthetic and experimental datasets, evaluating the consistency of significant feature identification and the number of significant features per tool. Correlation analysis and multiple regression will explore how differences between synthetic and experimental data characteristics may affect the results. Conclusions Synthetic data enables the validation of findings through controlled experiments. We assess how well synthetic data replicates experimental data, validate previous findings and delineate the strengths and limitations of synthetic data in benchmark studies. Moreover, to our knowledge this is the first computational benchmark study to systematically incorporate synthetic data for validating differential abundance methods while strictly adhering to a pre-specified study protocol following SPIRIT guidelines, contributing significantly to transparency, reproducibility, and unbiased research.

Investigation of research quality and transparency in neurosurgery through the utilization of open science practices.

Neurosurgical research is a rapidly evolving field, with numerous studies continuously published. As the body of research grows, upholding high-quality standards becomes increasingly essential. Open science practices offer tools to ensure quality and transparency. However, the prevalence of these practices remains unclear. This study investigated the extent to which neurosurgical publications have implemented open science practices. Five open science practices (preprint, equator guidelines, published peer review comments, preregistration, and open accessibility to data and methods) were measured from five top-ranked neurosurgical journals (Neurosurgery, Journal of Neurosurgery, World Neurosurgery, Neurosurgical Review, and Acta Neurochirurgica), according to Google Scholar. One hundred fifty articles were randomly sampled from January 1, 2022, to December 31, 2023. Two reviewers analyzed these articles for their utilization of open science practices. A third reviewer settled disagreements. One journal required (20%) and three journals (60%) recommended utilizing EQUATOR guidelines. Three journals (60%) allowed preprints, and all five journals (100%) recommended or required preregistration of clinical trials, but only two (40%) recommended preregistration for systematic reviews (Fig.1). All five journals (100%) recommended or required methods to be publicly available, but none (0%) published peer-review comments. Neurosurgical Review utilized the most open science practices, with a mean utilization of 1.4 open science practices per publication versus 0.9 across the other four journals (p < 0.001). Moreover, Neurosurgical Review significantly utilized more open science practices versus Journal of Neurosurgery (p < 0.05) and World Neurosurgery (p < 0.05). Both randomized controlled trials (p < 0.001) and systematic reviews (p < 0.001) significantly utilized more open science practices compared to observational studies. Despite advocacy from neurosurgical journals, the adoption of open science practices still needs improvement. Implementing incentives and clearer requirements may prove beneficial. Promoting these practices is crucial to enhancing research quality in neurosurgery.

Analysis of Accountability and Transparency of Village Fund Allocation Management in Sebani Village, Pandaan District, Pasuruan Regency

The village as the smallest government system demands renewal to support improved village development and the standard of living of village communities that are far from poverty. The various problems in the village are very complex, making it a reason for the village to develop. The development progress in each village is no less important. This research aims to analyze accountability and transparency of village fund allocation management in Sebani Village, Pandaan District, Pasuruan Regency. The method in this study is a descriptive qualitative method by means of direct interviews with informants. The location of this accountability and transparency research is in Sebani Village, Pandaan District, Pasuruan Regency. This research was carried out for a period of 6 months, starting from January 2024 to June 2024. The data is processed using data analysis techniques with stages, namely data reduction, data presentation, and drawing conclusions. The results of the study based on the Regulation of the Minister of Home Affairs Number 20 of 2018 show that the management of village fund allocations in Sebani Village is accountable and transparent in the stages of administration, reporting, and accountability.

Assessing Preregistration Deviations: A Comparative Analysis of Psychologists and Open Science Experts

ABSTRACTUndisclosed deviations between preregistration plans and published articles challenge research transparency. This study examines whether open science experts can aid in assessing such deviations by comparing their evaluations with those of psychology experts. Using content analysis, we compared assessments of 25 preregistration plans and articles, following a psychologist studying preregistration discrepancies framework. The 72.6% agreement rate suggests differences stemmed from distinct perspectives. Psychologists leveraged domain knowledge to scrutinize subtle inconsistencies, while open science experts, deeply committed to transparency principles, focused more on procedural adherence. Open science experts, unfamiliar with psychology's implicit norms, faced challenges in discerning field‐specific knowledge and interpreting ambiguities as non‐deviations due to unclear preregistration formats and ambiguous content. Targeted support and collaborative approaches engaging both expert groups could enhance preregistration adherence evaluations, balancing the open science experts' emphasis on transparent processes with the psychologists' domain expertise, ultimately strengthening research transparency and reproducibility.