Research Outcomes Research Articles

Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks

Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outcomes, often leading to the wasteful expenditure of time, manpower, and material resources. Consequently, the accurate identification of cell lines is paramount. However, traditional identification methods, which often involve staining and culturing procedures, are not only time-consuming but also laborious. This underscores the need for a novel approach that enables rapid and automated cell line identification, thereby enhancing research efficiency and accuracy. Raman spectroscopy, renowned for its label-free, rapid, and noninvasive nature, offers invaluable molecular insights into samples, making it a widely utilized technique in the biological field. In this study, the identification of one normal and five cancer cell lines was achieved using a sparrow search algorithm-convolutional neural networks (SSA-CNN), considering both the full spectra and fingerprint region perspectives. The SSA-CNN model demonstrated exceptional performance, not just in binary classification, but also in accurately distinguishing among six cell lines. It achieved the highest accuracy (around 95 %), and the lowest standard error (≤3%), for both the full spectra and fingerprint region. Based on the highly accurate SSA-CNN model, proposed the application of gradient-weighted class activation mapping (Grad-CAM) to visualize the Raman feature peaks. Upon comparing the visualized Raman features with reported biomarkers, found that not only were common biomolecules such as glucose, proteins, and liquids visualized, but specific feature peaks also aligned with reported biomarkers. The aforementioned results clearly demonstrated that the proposed strategy not only classifies cancer cell lines with remarkable accuracy but also served as a valuable tool for the discovery of novel biomarkers.

Propaganda of legal knowledge about Kazakhstan citizenship

In the article problematic issues surrounding the promotion of legal knowledge regarding Kazakhstan citizenship were considered, recognizing citizenship as a fundamental legal institution shaping modern Kazakhstan society's statehood. The research conducts a comprehensive analysis and synthesis of the theory and practice of disseminating legal knowledge about citizenship, aiming to identify solutions to normative consolidation challenges. Through a combination of general scientific and specialized legal methods, including analysis and theoretical generalization, as well as an examination of legal norms on citizenship, the study identifies the main trends in improving legal knowledge about citizenship. The main research outcome underscores the necessity to enhance legal measures aimed at expanding the use of forms and methods for disseminating legal knowledge about Kazakhstan citizenship. As a result, the article proposes supplementing citizenship legislation with norms mandating government bodies to provide information and advice on citizenship issues. It suggests widening public awareness about the importance of citizenship as a legal institution and supplementing political and legal acts with provisions to increase Kazakhstani society's awareness of citizenship.

Comprehensive guidelines for appropriate statistical analysis methods in research.

The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it can significantly impact the reliability and quality of the research outcomes. This study explores a comprehensive guideline for systematically choosing appropriate statistical analysis methods, with a particular focus on the statistical hypothesis testing stage and categorization of variables. By providing a detailed examination of these aspects, this study aims to provide researchers with a solid foundation for informed methodological decision making. Moving beyond theoretical considerations, this study delves into the practical realm by examining the null and alternative hypotheses tailored to specific statistical methods of analysis. The dynamic relationship between these hypotheses and statistical methods is thoroughly explored, and a carefully crafted flowchart for selecting the statistical analysis method is proposed. Based on the flowchart, we examined whether exemplary research papers appropriately used statistical methods that align with the variables chosen and hypotheses built for the research. This iterative process ensures the adaptability and relevance of this flowchart across diverse research contexts, contributing to both theoretical insights and tangible tools for methodological decision-making. This study emphasizes the importance of a scientific and rational approach for the selection of statistical analysis methods. By providing comprehensive guidelines, insights into the null and alternative hypotheses, and a practical flowchart, this study aims to empower researchers and enhance the overall quality and reliability of scientific studies.

Pathways for Strengthening Lived Experience Leadership for Transformative Systems Change: Reflections on Research and Collective Change Strategies.

The Activating Lived Experience Leadership (ALEL) project was a South Australian participatory action research project that aimed to improve the ways lived experience is recognised, valued and integrated across mental health and social sector systems. ALEL was completed during 2019-2021, where it engaged 182 participants in generating community action and research knowledge. Our paper discusses the project's processes of building a collective partnership among lived experience leaders and other leaders from within the sector, so that the actions and strategies identified through research could be implemented by systems-level impact. We describe the collaborative process and key learnings that resulted in eight key action areas for transformative systems change in South Australia. The project invited a diverse range of self-identified lived experience and other leaders to be involved in a PAR process featuring formal qualitative research (focus groups, surveys and interviews) as well as community development activities (leaders' summit meetings, consultations, training and community of practice meetings). These processes were used to help us describe the purpose, achievements and potential of lived experience leadership. Project priorities and systems-level analysis was also undertaken with lived experience sector leaders and project advisors across two leaders' summit meetings, integrating research outcomes with sector planning to define high-level actions and a vision for transformational change. Participatory action research as informed by systems change and collective impact strategies assisted the project to generate detailed findings about the experiences and complexities of lived experience leadership, and collective responses of how systems could better support, be accountable to and leverage lived experience perspectives, experience and peer-work approaches. Systems change to define, value and embed lived experience leadership benefits from collective efforts in both formal research and sector development activities. These can be used to generate foundational understandings and guidance for working together in genuine ways for transforming mental health and social sector systems, experience and outcomes. Members of lived experience communities codesigned the project, and contributed to project governance and the development of all findings and project reports.

PROGRESS: A Patient-centered Engagement Infrastructure and Multi-level Approach to Enrich Diversity, Equity, and Inclusion in a National Randomized Online Behavioral Pain Treatment Study

Twenty percent of individuals experience chronic pain worldwide posing significant challenges to those living with it. Pain research is crucial for developing and characterizing effective strategies to reduce the burden of chronic pain. Traditional research approaches often yield homogeneous study samples that poorly generalize and have unknown applicability across diverse patient populations. The Pain Relief with Online Groups that Empower Skills-based Symptom Reduction (PROGRESS) study aims to address disparities in pain research engagement and patient outcomes through the intentional inclusion of people with varied backgrounds and experiences of pain, and through a multilevel design informed by diverse stakeholder recommendations. The composition of three advisory boards (Patient Engagement and Diversity Board, Local Patient Advisory Board, and the National Patient Advisory Panel) prioritized diversity in patient/expert advisor background, geographic location, race, and ethnicity. Our engagement approach aligns with the Foundational Expectations for Partnerships in Research by Patient-Centered Outcomes Research Institute (PCORI), which emphasizes diverse representation, early and ongoing engagement, dedicated funds for advisor compensation, collaborative decision making, meaningful participation, and continuous assessment. The first 24 months of study advisor engagement has yielded multiple recruitment strategies resulting in a study population enriched with a breadth of identities within PROGRESS (e.g., inclusive patient-facing materials). Lessons learned underscore the importance of investing time in building patient and stakeholder relationships, trust, and embracing diverse viewpoints amongst the study team. PROGRESS demonstrates the potential of diverse patient-centered engagement to support evidence-based outcomes and practices that are more inclusive, equitable, and representative of the broader population. PerspectiveThe PROGRESS study demonstrates how diverse patient engagement and inclusive advisory boards enhance research outcomes. By aligning with PCORI standards and employing innovative recruitment strategies, it highlights the vital role of stakeholder relationships and diverse perspectives. Key lessons learned emphasize adaptive strategies and continuous feedback for advancing equitable pain research.

Contextual drivers of energy demand and supply from renewable sources

This study investigates the socio-economic and environmental drivers shaping renewable energy demand and supply within a meso-level setting (Italian regions, 2004–2020). Employing a panel vector error correction mechanism (PVECM) and an integrative biplot analysis, the research outcomes reveal a market equilibrium between demand and supply in the short and long run. Human capital accumulation is identified as pivotal in driving the renewable energy transition. Significant regional heterogeneities indicate challenges in achieving convergence towards sustainability and socio-economic development and well-being. Key empirical implications include enhancing education and vocational training to strengthen human capital and implementing tailored regional strategies for sustainable infrastructure development. These findings support policymakers in formulating targeted policies for a resilient, balanced energy future and addressing regional disparities in renewable energy adoption.

Understanding the Impacts on Automobile Leasing Firm Loss in China: An Analysis of Vehicle Residual Value Risk and Lease Price Patterns

ABSTRACTThe paper addresses vehicle depreciation and pricing issues in the automobile leasing industry in China. Under the current industry climate, operating lease firms face large residual value risk, and therefore, how to price contracts that minimises such risk is an important question. By analysing market data of used vehicles, an exponential decay function was developed to model the price dynamics across different brands and model series. Specifically, we estimate the depreciation rate directly from the data. In addition, by considering all revenue and cost components and converting them into present value terms, appropriate deposit and rents combintions were sought under different profit expectations. Length of contracts, age of vehicles and purchase cost factors were also considered to detect any further patterns in rents. The research outcomes will provide guidance on lease pricing given the current development of the industry, thereby improving firms’ profitability position as well as helping maintain their competitiveness and growth.

Big Data Analytics and Business Intelligence in Business Marketing: A Review

The aim of this paper is to conduct an extensive study on big data analytics and business intelligence (BI) in marketing that is within the academic research sphere. Research gaps were identified and development for future research on the study was highlighted. A systematic review based on literature which related academic articles indexed in Web of Science and Scopus databases was used. The articles reviewed were based on certain features like theoretical and conceptual characterization; data source; research topic; type and size of data; data analysis techniques and methods used in data collection. The research outcome indicates that there is an increase in the marketing research with analytical technique applies to large quantity of data. However, this research area is limited in scope and methodologies and presents several gaps. A conceptual framework that will help in detecting important business challenges and relate the domain of big data and business intelligence to marketing is missing. This study contributes to exploring systematically the awareness of marketers working in big data and business intelligence.

Organizational and pedagogical foundations of the interaction of educational organizations at all levels: a factor of continuity and consistency in psychological and pedagogical support

This article delves into the vital aspect of implementing psychological and pedagogical support for students across educational organizations of varying levels. Drawing upon the outcomes of domestic research conducted by the authors, the article highlights the inadequate efficacy of psychological and pedagogical support services for students. It also sheds light on the challenges faced by most secondary school teachers in catering to children with special educational needs. To comprehensively assess the state of psychological and pedagogical support for pupils and students across preschool, secondary, technical and vocational, and post-secondary education, a research group conducted an extensive scientific study. The analysis, employing the triangulation method with qualitative and quantitative data, drew from international and domestic experiences, as well as the existing regulatory framework in Kazakhstan concerning psychological and pedagogical support. The study uncovered several problematic issues hindering the successful implementation of psychological and pedagogical support for students. The primary challenges identified by the research group included the prevalence of the medical model of special educational needs over the socio-pedagogical model in the mindset of educators. Additional challenges encompassed an unsystematic approach to psychological and pedagogical support and insufficient internal and external collaboration among educational organizations at all levels, affecting the provision of comprehensive support across all educational tiers. Based on the research findings, the research group concluded the necessity of adopting a systematic approach in providing continuous and comprehensive psychological and pedagogical support for student’s at all educational levels. The article proposes various strategies to implement continuous comprehensive support and outlines organizational and pedagogical conditions that serve as the scientific and methodological basis for developing models of comprehensive psychological and pedagogical support for inclusive educational processes, grounded in continuity and consistency.

Statistical challenges and solutions in multidisciplinary clinical research: Bridging the gap between

This paper delves into the intricate challenges and innovative solutions in applying statistical methodologies within clinical research, aiming to bridge the gap between biostatistics and medicine. The study meticulously examines fundamental biostatistical concepts, addressing the complexities of modern clinical trials and observational studies. Through a comprehensive review of advanced regression models, causal inference techniques, and machine learning algorithms, the paper illuminates the evolving landscape of biostatistics in handling high-dimensional data and confounding variables. The methods employed in this study involve an extensive analysis of current literature, case studies, and practical applications that demonstrate the utility of these advanced methodologies. Key findings reveal that traditional statistical approaches often fall short in capturing the complexities of clinical data, necessitating the adoption of more sophisticated techniques. The integration of non-linear regression models, robust causal inference methods, and machine learning has significantly enhanced the accuracy and reliability of research outcomes, offering deeper insights into patient outcomes and treatment efficacy. Conclusions drawn from this study underscore the critical need for a paradigm shift in clinical research, moving beyond the rigid reliance on p-values towards a more holistic approach that emphasizes effect sizes, confidence intervals, and practical significance. The paper recommends continued innovation in statistical methodologies, particularly the integration of big data analytics and machine learning, to address the growing complexities of biomedical data. Furthermore, it advocates for interdisciplinary collaboration and ethical considerations in the application of these advanced techniques to ensure that biostatistics continues to contribute meaningfully to the advancement of medical science.

Incorporating costs and benefits to the evaluation of uncertain research results: applications to cancer research funding

Abstract Correctness is a key aspiration of the scientific process, yet recent studies suggest that many high-profile findings may be difficult to replicate or require considerable evidence for verification. Proposals to fix these issues typically ask for tighter statistical controls—e.g., stricter p-value thresholds or higher statistical power. However, these approaches often overlook the importance of contemplating research outcomes’ potential costs and benefits. Here, we develop a framework grounded in Bayesian decision theory that seamlessly integrates cost-benefit analysis into evaluating research programs with potentially uncertain results. We derive minimally acceptable pre-study odds and positive predictive values for cost and benefit levels. We show that tolerance to inaccurate results changes dramatically due to uncertainties posed by research. We also show that reducing uncertainties (e.g., by recruiting more subjects) may have limited effects on the expected benefit of continuing specific research programs. We apply our framework to several types of cancer research and their funding. Our analysis shows that highly exploratory research designs are easily justifiable due to their potential benefits, even when probabilistic models suggest otherwise. We discuss how the cost and benefit of research could and should always be part of the toolkit used by scientists, institutions, or funding agencies. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00332

The Scientific Reference Model—A Methodological Approach in the Hypothetical 3D Reconstruction of Art and Architecture

Scientific practice relies on the rigorous documentation of procedures, methods, and outcomes, governed by principles like method verification, objectivity, and source disclosure. In the computer-based hypothetical 3D reconstruction of destroyed or never realised art and architecture, adhering to these principles faces challenges due to evolving software, methods, and data types, leading to a lack of standardised documentation and publication practices for 3D models. Consequently, the traceability, accessibility, and sustainability of research outcomes are compromised. Decades after the advent of computer-aided 3D visualisation in cultural heritage, there is a critical need to define applicable methodology and comprehensive documentation standards. Web-based platforms necessitate technical infrastructures and clear scientific methodologies to ensure understandable model creation and sustainable accessibility to 3D research data. The Scientific Reference Model proposes an accessible academic framework for this kind of 3D reconstruction, aiming to facilitate broad adoption. Developed and tested in research projects and educational contexts, this model aims to establish clear, accessible 3D models on the web, serving as foundational references for future research and knowledge dissemination.

Exploring the Landscape of Polymerase Chain Reaction in Indian Medical Research: A Scientometric Analysis

Background and Aims: The polymerase chain reaction (PCR) has revolutionised medical research in India by offering a rapid and precise method for amplifying deoxyribonucleic acid (DNA) sequences crucial for various applications in healthcare. It aims to investigate publication growth, leading authors, productive journals, collaborating countries, keywords, institutional affiliations and funding agencies involved in PCR-related research within India. Methodology: A scientometric analysis assessed India’s PCR research performance using 10,509 articles from Scopus (2013-2022). Biblioshiny and VOSviewer aided data analysis, while Microsoft Excel facilitated computations. Results: The finding reveals the continuous growth in PCR publications, with significant contributions from researchers like Aman Sharma from the Post Graduate Institute of Medical Education and Research (Chandigarh) and journals like the Indian Journal of Medical Microbiology. Notable contributors hail from the All India Institute of Medical Sciences (AIIMS), New Delhi. International collaborations, especially with the United States, underscore the global outreach of PCR research in India. Keyword analysis reveals emerging trends and research themes in PCR, reflecting its evolving role in addressing contemporary health challenges. Funding agencies such as the Indian Council of Medical Research and the Ministry of Science and Technology, India (DBT) are crucial in supporting PCR research endeavours. Conclusions: This study provides valuable insights into the dynamics of PCR research in India, highlighting areas for future investigation and collaboration to further advance medical research and healthcare outcomes.

The Swedish sleep apnea registry (SESAR) cohort – “Real world data” on a national level

IntroductionThe Swedish Sleep Apnea Registry (SESAR) collects clinical data from individual obstructive sleep apnea (OSA) patients since 2010. SESAR has recently been integrated with additional national healthcare data. The current analysis presents the SESAR structure and representative clinical data of a national sleep apnea cohort. MethodsClinical data from unselected patients with a diagnosis of OSA are submitted to the SESAR registry. 48 sleep centers report data from diagnosis, treatment starts with Continuous Positive Airway Pressure (CPAP), oral devices (OD), and Upper Airway Surgery (UAS). Data from follow-up are included. SESAR is linked to mandatory national healthcare data (mortality, comorbidities, procedures, prescriptions) and diagnosis-specific quality registries (e.g. stroke, heart failure, diabetes) within the DISCOVERY project. Results83,404 OSA patients have been reported during the diagnostic workup (age 55.4 ± 14.1 years, BMI 30.8 ± 6.5 kg/m2, AHI 25.8 ± 21.6n/h, respectively). At least one cardiometabolic and respiratory comorbidity is recognized in 57 % of female and 53 % of male OSA patients with a linear increase across OSA severity. In 54,468, 7,797, and 390 patients, start of CPAP, OD or UAS treatment is reported, respectively. OD patients have 4 units lower BMI and 10 units lower AHI compared to patients started on CPAP. UAS patients are characterized by 10 years lower age. The degree of daytime sleepiness is comparable between treatment groups with mean Epworth Sleepiness Scale Scores between 9 and 10. ConclusionSESAR is introduced as a large national registry of OSA patients. SESAR provides a useful tool to highlight OSA management and to perform relevant outcome research.

The Coupling Coordination Degree and Its Driving Factors for Water–Energy–Food Resources in the Yellow River Irrigation Area of Shandong Province

Water resources, energy, and food are essential for the development of society, and they are strongly interdependent. The coupling and coordination relationships of the water–energy–food (WEF) system are important for regional resource security and high-quality development. The Yellow River Irrigation Area in Shandong Province, China, is a grain production base and has a substantial impact on national food security. To examine the water, energy, and food subsystem dynamics in this area, an evaluation system for the WEF system was established. A comprehensive weighting method based on game theory was employed to determine index weights. TOPSIS was used to assess the development level of the WEF system. A coupling coordination degree model was used to analyze the evolution of the coupling coordination degree of the WEF system from 2000 to 2020, and a GWR model was constructed to explore the spatial heterogeneity of its driving factors. The findings indicated that the development level of the WEF system in the study area was moderate, with a gradual upward trend. The coupling coordination degree fluctuated between 0.62 and 0.739. The GWR model revealed that temperature had an overall negative effect on the coupling coordination degree, with the greatest impact on the central irrigation area; the slope and NDVI had a negative effect, with increasing intensity from the southwest to the northeast; and rainfall had an overall positive effect, with the greatest impact on the irrigation area near the estuary in the northeast. Overall, the building area ratio had a negative effect on the coupling coordination degree, with exceptions in some areas. These research outcomes provide theoretical support for sustainable agricultural development in the Yellow River irrigation areas of Shandong Province and methodological reference data for studying collaborative resource utilization in irrigation regions.

FEATURES OF THE USE OF THE TERMS “OVERSEAS STUDENT”, “FOREIGN STUDENT” AND “INTERNATIONAL STUDENT” IN DIFFERENT EDUCATIONAL AND CULTURAL CONTEXTS

The article analyzes the peculiarities of using the terms “overseas student”, “foreign student” and “international student” in various educational and cultural contexts. It was found that British scientists prefer to use the term “international student”, because it better reflects the realities of globalization, internationalization and student mobility, which meets the modern needs of educational institutions and the international community. It is emphasized that the term “foreign student” remains the dominant term in Ukrainian education due to its legal certainty and established practice, however “international student” is also gradually coming into use, especially in the context of European integration and educational exchanges. Analysis of the use of the terms “foreign” and “international” student on the example of the British Council and HESA shows how important is the correct definition of students in international education statistics. A change in the HESA counting methodology in 2009, when students were classified by nationality rather than place of residence, revealed a significant difference in the number of international students in the UK (a difference of 32%), highlighting the extent to which counting methodology can influence research outcomes and state policy.

Effects of research funding on the academic impact and societal visibility of scientific research

Assessing the effectiveness of research funding is of significant value to policymakers. Previous studies have mainly concentrated on the academic impact of funded research, yet the exploration of how research funding affects the societal visibility of research has been significantly lacking. Thus, this study addresses this gap by examining papers published by Chinese scholars and compares the effects of funding on papers’ societal visibility (measured by Altmetric Attention Scores) with those for papers' academic impact (measured by citation counts). This study reveals several interesting findings: First, research supported by funding demonstrates a lower societal visibility, albeit a higher academic impact, compared to those without funding. Second, the societal visibility of research supported by small to moderate number of funding sources is still lower than those without research funding. In contrast, a paper's academic impact is higher if it has a higher number of research funding sources. Third, the effects of funding on papers’ academic impact and societal visibility differ by funding mechanisms—having industry funding significantly increases the societal visibility of research. These findings can aid research policymakers’ funding allocation decisions and inform better assessment of research outcomes.

The Role of e-Learning During Martial Law: The Ukrainian Experience

The research aim is to reveal the role of e-learning during Martial law by the example of Ukrainian experience. It focuses on the following objectives: to outline the advantages and challenges of e-learning; to describe the peculiarities of implementation of e-learning at the different levels of education; to analyse teaching methods used to integrate e-learning; and to develop the recommendations to implement e-learning. The systemic literature review was carried out to provide an objective summary of the current topic. All sources were published between March, 2022 and May, 2024. The literature focused on e-learning in Ukraine or similar conflict-affected areas. All articles were available in English or Ukrainian. To analyse the data, the synthesis method was applied. The findings proved that e-learning became an important tool during emergencies, providing continuity in education. It was explained that e-learning is introduced at primary, secondary, and higher levels of education. It includes synchronous and asynchronous models. Certain teaching methods are used to implement e-learning effectively. They relate to interactive simulations, multimedia content, gamification, discussion forums, and personalized learning. These methods, combined, create a dynamic e-learning environment that enhances students’ educational outcomes, particularly during emergencies. The implementation of e-learning in Ukraine during Martial law requires careful consideration of the recommendations: enhancement of digital infrastructure, adoption of flexible learning, formation of digital literacy, support of mental health, using interactive methods, increase of content quality, regular assessment, encouragement of positive teacher-student interaction, implementation of adaptive learning tools, and protection of students and educators' data. The research outcomes on the role of e-learning during Martial law can be used for the development of strategic plan of implementation of e-learning tools within the learning process in the regions affected by war or other emergencies. Also, the study can contribute to future educational practices, encouraging the development of more innovative learning solutions.

Sustainability Reporting and Financial Performance: Examine the Correlation between Sustainability Disclosures Financial Performance in Publicly Traded Companies

Sustainability reporting has increasingly gained prominence in academia and practice over the past few decades. With growing stakeholder expectations for companies to report on environmental, social, and governance issues, sustainability reporting allows companies to communicate non-financial impacts. While sustainability reporting aims to enhance accountability and transparency, questions remain regarding its influence on corporate financial performance. Some studies have found a positive correlation between sustainability reporting and financial metrics like profitability and share price, arguing reporting leads to more efficient resource management and risk mitigation. However, other research has found no significant relationship or negative impact, noting that reporting requirements incur costs that may outweigh benefits. This comprehensive review aims to provide an in-depth analysis of the existing literature on the correlation between sustainability disclosures and the financial performance of publicly traded companies. Relevant studies from various regions and sectors published between 1990 and 2023 will be reviewed. The methodologies applied, measurement variables, sample sizes, and time frames considered will be evaluated to understand the scope and limitations of each study. Key findings related to the direction and strength of the reported relationship between the two parameters will be summarized and compared. Moderating factors such as company size, industry, cultural context, and disclosure type will also be examined to determine their influence on research outcomes. The review will conclude by outlining existing knowledge gaps, inconsistencies in current empirical evidence, and areas that require further research.

Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors.

As genomic research continues to advance, sharing of genomic data and research outcomes has become increasingly important for fostering collaboration and accelerating scientific discovery. However, such data sharing must be balanced with the need to protect the privacy of individuals whose genetic information is being utilized. This paper presents a bidirectional framework for evaluating privacy risks associated with data shared (both in terms of summary statistics and research datasets) in genomic research papers, particularly focusing on re-identification risks such as membership inference attacks (MIA). The framework consists of a structured workflow that begins with a questionnaire designed to capture researchers' (authors') self-reported data sharing practices and privacy protection measures. Responses are used to calculate the risk of re-identification for their study (paper) when compared with the National Institutes of Health (NIH) genomic data sharing policy. Any gaps in compliance help us to identify potential vulnerabilities and encourage the researchers to enhance their privacy measures before submitting their research for publication. The paper also demonstrates the application of this framework, using published genomic research as case study scenarios to emphasize the importance of implementing bidirectional frameworks to support trustworthy open science and genomic data sharing practices.