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        <title>Frontiers in Big Data | Data Analytics for Social Impact section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/big-data/sections/data-analytics-for-social-impact</link>
        <description>RSS Feed for Data Analytics for Social Impact section in the Frontiers in Big Data journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-13T06:12:38.71+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1594374</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1594374</link>
        <title><![CDATA[Toward robust social media sentiment for SMEs: a comparative study of dictionary-based and machine learning approaches with insights for hybrid methodologies]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Heru Susanto</author><author>Aida Sari Omar</author><author>Alifya Kayla Shafa Susanto</author><author>Desi Setiana</author><author>Leu Fang-Yie</author><author>Junaid M. Shaikh</author><author>Asep Insani</author><author>Uus Khusni</author><author>Rachmat Hidayat</author><author>Indra Akbari</author><author>Iwan Basuki</author>
        <description><![CDATA[Small and Medium-sized Enterprises (SMEs) increasingly rely on social media to engage customers, promote products, and enhance workplace collaboration. Customer opinions expressed through comments and posts on platforms such as Facebook and Instagram represent valuable insights, yet their informal and context-specific nature—often characterized by slang, misspellings, and bilingual usage—poses challenges for automated sentiment analysis. This study addresses this gap by comparatively evaluating dictionary-based and machine learning approaches to sentiment classification for SMEs' social media content. Data were collected from a diverse set of SMEs across multiple industries, with a substantial volume of customer comments extracted and pre-processed through tokenization, normalization, stop-word removal, and stemming. A customized dictionary was developed to account for local language variations, while Naïve Bayes and Support Vector Machine (SVM) models were employed as supervised classifiers. The findings indicate that dictionary-based methods, while simple and interpretable, struggle with accuracy when processing informal and localized language, whereas machine learning approaches deliver higher overall performance but require extensive preprocessing and tuning. Moreover, the study highlights the potential of hybrid frameworks that combine the interpretability of dictionary-based models with the adaptability of machine learning classifiers. This research contributes both practically and theoretically by (i) demonstrating the limitations of applying generic sentiment analysis tools in localized SME contexts, (ii) proposing a hybrid sentiment analysis framework tailored to SMEs, and (iii) offering empirical evidence to support digital transformation strategies for SMEs in resource-constrained environments. Ultimately, accurate sentiment analysis can enable SMEs to refine business strategies, strengthen customer engagement, and achieve sustainable growth in the digital economy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1752142</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1752142</link>
        <title><![CDATA[Jingdezhen ceramic culture in the digital era: a qualitative inquiry into digital dissemination and platform innovation]]></title>
        <pubdate>2026-03-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qiuyang Huang</author><author>Zhengjun Chen</author>
        <description><![CDATA[IntroductionDigital platforms have increasingly reshaped the ways in which traditional craft cultures are produced, circulated, and interpreted. While prior research has examined digital heritage broadly, limited attention has been paid to how platform-based dissemination transforms ceramic culture in historically significant craft centers such as Jingdezhen.MethodsThis study adopts a qualitative research design, combining semi-structured interviews with 32 ceramic practitioners and digital ethnography of 58 ceramic-related livestreaming sessions on Douyin.ResultsThe findings reveal three key dynamics: (1) the reconfiguration of craft authority through platform visibility; (2) the emergence of hybrid artisan–educator–entrepreneur identities; and (3) persistent tensions between cultural authenticity and commercial logic in platform-mediated environments.DiscussionBy integrating cultural ecology and platform ecosystem theory, this study contributes to scholarship on digital heritage and provides practical insights for cultural practitioners and heritage institutions navigating digital platform ecosystems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1697392</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1697392</link>
        <title><![CDATA[Dynamic transfer learning with co-occurrence-guided multi-source fusion for urban spatio-temporal crime prediction]]></title>
        <pubdate>2026-02-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chen Cui</author><author>Ziwan Zheng</author><author>Hao Du</author><author>Wen Wang</author>
        <description><![CDATA[Spatio-temporal crime prediction is crucial for optimizing police resource allocation but faces challenges including data sparsity, which hinders models from extracting effective patterns and limits robustness—and the underutilization of cross-type crime co-occurrence correlations. To address these issues, we propose a transfer learning approach that explores underlying cross-type relationships, enabling the sharing of spatio-temporal features across crime types and alleviating data sparsity. An adaptive weight updating mechanism is incorporated to enhance the perception of distinct crime categories, while the impacts of points of interest (POIs), meteorological factors, and other features are also analyzed. Experiments on real-world data from a Chinese city show that our model comprehensively captures latent features across crime types, thereby enhancing predictive performance and robustness, particularly for crime types with sparse data. Moreover, it effectively incorporates environmental features, further improving crime prediction performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1679897</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1679897</link>
        <title><![CDATA[Examining the influence of deterrent and enhancement factors on QR-code mobile payment continuance intention: insights from PLS-SEM and IPMA analysis]]></title>
        <pubdate>2026-01-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ashikur Rahman</author><author>Fahmid al Farid</author><author>Mohammad Abul Bashar</author><author>Jia Uddin</author><author>Arif Mahmud</author><author>Hezerul Abdul Karim</author>
        <description><![CDATA[IntroductionThe rise of contactless payment has made quick response (QR) code-mobile payment (QR-MP) platform increasingly popular among mobile financial service (MFS) users, especially in emerging economies. It has been demonstrated that the ongoing use of QR payments can significantly drive the growth of emerging economies. However, despite its importance, the continued use of this technology has not been satisfactory. Thus, this study seeks to explore the modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, including four additional constructs: amotivation (AM), alternative attractiveness (AA), QR transaction anxiety (QTA), and transaction convenience (TC) to examine the MFS users' sustained usage of QR payment.MethodsData were collected from 247 MFS users in Bangladesh using an online survey and analyzed through SEM-PLS and non-linear analysis of IPMA.ResultsThe research findings reveal that effort expectancy is the most influential factor, and that both moderator factors, QTA and TC, are significant. However, social influence and hedonic motivation were found to be insignificant. Furthermore, our extended research model explains 76.5% of the variance in CINT without the moderation effect.DiscussionThe IPMA findings help to find the best-performing variables and provide practical insights for this study. Theoretical and managerial implications are provided to enrich the existing literature on the study of information technology, indicating how MFS providers in developing countries can retain their existing users.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1718366</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1718366</link>
        <title><![CDATA[Unequal access in a digital age: women's digital exclusion and socioeconomic inequalities in Vietnam]]></title>
        <pubdate>2025-12-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chi Thi Lan Pham</author><author>Quyen Thi Tu Bui</author><author>Anh Ha Le</author><author>Long Quynh Khuong</author>
        <description><![CDATA[IntroductionAccess to information and communication technologies (ICTs) and the skills to use them are essential for inclusive development and digital participation. As Vietnam accelerates its digital transformation, ensuring that women are not left behind is critical to achieving the Sustainable Development Goals (SDGs), particularly SDG 5 (Gender Equality) and SDG 9 (Industry, Innovation, and Infrastructure). This study investigates the extent and socioeconomic patterning of digital exclusion among women in Vietnam.MethodsWe utilized nationally representative data from the 2021 Multiple Indicator Cluster Survey (MICS), which covered 10,770 women aged 15–49. Digital exclusion was defined in terms of (1) no ICT access (no use of computer, internet, or mobile phone in the past 3 months) and (2) no ICT skills (unable to perform any of nine standard digital tasks).ResultsResults show that 4.28% of women lacked digital access and 72.85% lacked digital skills. Inequalities were stark: access was lowest among ethnic minorities (19.55%) and the poorest quintile (17.10%), compared to 1.980.31% in the majority and richest groups. The digital skills gap was even wider, with 95.51% of the poorest women lacking ICT skills vs. 41.23% of the richest. Multivariable logistic regressions confirmed that ethnicity, wealth, rural residence, and older age were key predictors of exclusion.ConclusionThese findings underscore the urgent need for inclusive digital policies that extend beyond infrastructure to address gendered and socioeconomic barriers to digital literacy. Without targeted efforts, digital rollouts may widen existing inequalities and undermine SDG progress.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1682151</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1682151</link>
        <title><![CDATA[Editorial: Navigating the nexus of big data, AI, and public health: transformations, triumphs, and trials in multiple sclerosis care access]]></title>
        <pubdate>2025-10-01T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Immanuel Azaad Moonesar</author><author>M. V. Manoj Kumar</author><author>Khulood Alsayegh</author><author>Ayat Abu-Agla</author><author>Likewin Thomas</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1539724</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1539724</link>
        <title><![CDATA[LISTEN: lived experiences of Long COVID: a social media analysis of mental health and supplement use]]></title>
        <pubdate>2025-06-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sam Martin</author><author>Maya Janse Van Rensburg</author><author>Huong Thien Le</author><author>Charlie Firth</author><author>Abinaya Chandrasekar</author><author>Sigrún Eyrúnardóttir Clark</author><author>Samantha Vanderslott</author><author>Cecilia Vindrola-Padros</author><author>Norha Vera San Juan</author>
        <description><![CDATA[IntroductionLong COVID, or Post-Acute Sequelae of SARS-CoV-2 infection (PASC), is a complex condition characterized by a wide range of persistent symptoms that can significantly impact an individual's quality of life and mental health. This study explores public perspectives on the mental health impact of Long COVID and the use of dietary supplements for recovery, drawing on social media content. It uniquely addresses how individuals with Long COVID discuss supplement use in the absence of public health recommendations.MethodsThe study employs the LISTEN method (“Collaborative and Digital Analysis of Big Qual Data in Time Sensitive Contexts”), an interdisciplinary approach that combines human insight and digital analysis software. Social media data related to Long COVID, mental health, and supplement use were collected using the Pulsar Platform. Data were analyzed using the free-text discourse analysis tool Infranodus and collaborative qualitative analysis methods.ResultsThe findings reveal key themes, including the impact of Long COVID on mental health, occupational health, and the use of food supplements. Analysis of attitudes toward supplement use highlights the prevalence of negative emotions and experiences among Long COVID patients. The study also identifies the need for evidence-based recommendations and patient education regarding supplement use.DiscussionThe findings contribute to a better understanding of the complex nature of Long COVID and inform the development of comprehensive, patient-centered care strategies addressing both physical and mental health needs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1440816</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1440816</link>
        <title><![CDATA[CrowdRadar: a mobile crowdsensing framework for urban traffic green travel safety risk assessment]]></title>
        <pubdate>2025-03-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yigao Wang</author><author>Qingxian Tang</author><author>Wenxuan Wei</author><author>Chenhui Yang</author><author>Dingqi Yang</author><author>Cheng Wang</author><author>Liang Xu</author><author>Longbiao Chen</author>
        <description><![CDATA[As environmental awareness increased due to the surge in greenhouse gases, green travel modes such as bicycles and walking have gradually became popular choices. However, the current traffic environment has many hidden problems that endanger the personal safety of traffic participants and hinder the development of green travel. Traditional methods, such as identifying risky locations after traffic accidents, suffer from the disadvantages of delayed response and lack of foresight. Against this background, we proposed a mobile edge crowdsensing framework to dynamically assess urban traffic green travel safety risks. Specifically, a large number of mobile devices were used to sense the road environment, from which a semantic detection framework detected the traffic high-risk behaviors of traffic participants. Then multi-source and heterogeneous urban crowdsensing data were used to model the travel safety risk to achieve a comprehensive and real-time assessment of urban green travel safety. We evaluated our method by leveraging real-world datasets collected from Xiamen Island. Results showed that our framework could accurately detect traffic high-risk behaviors with average F1-scores of 86.5% and assessed the travel safety risk with R2 of 0.85 outperforming various baseline methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1485493</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1485493</link>
        <title><![CDATA[Use of Bayesian networks in Brazil high school educational database: analysis of the impact of COVID-19 on ENEM in Pará between 2019 and 2022]]></title>
        <pubdate>2025-03-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sandio Maciel Dos Santos</author><author>Marcelino Silva da Silva</author><author>Fábio Manoel França Lobato</author><author>Carlos Renato Lisboa Francês</author>
        <description><![CDATA[This study examines the impact of the COVID-19 pandemic on academic performance and student participation in the National High School Exam (ENEM) in the state of Pará, Brazil, focusing on the interaction between socioeconomic factors, access to technology, and regional disparities. The research employed a mixed-methods approach, analyzing quantitative data from ENEM results (2020–2022) and qualitative interviews with educators and students. The findings indicate that the pandemic exacerbated pre-existing educational inequalities, particularly affecting low-income students and those enrolled in public schools. The highest dropout rates were recorded among students with a family income of up to one minimum wage, highlighting the barriers posed by limited access to technology and infrastructure for remote learning. A statistical analysis revealed a 20% increase in scores among students with access to computers and the Internet, particularly in private schools. The study also found significant regional differences across Pará's mesoregions, with Marajó and Southeast Pará facing more persistent challenges in reducing dropout rates compared to the Metropolitan Region of Belém. These results underscore the urgent need for region-specific public policies that address disparities in educational resources, including targeted investments in digital infrastructure and teacher training for remote education. The study concludes that comprehensive support programs, including psychological assistance for students, are essential for building a more resilient and equitable educational system capable of withstanding future crises.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1448571</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1448571</link>
        <title><![CDATA[How critical is SME financial literacy and digital financial access for financial and economic development in the expanded BRICS block?]]></title>
        <pubdate>2024-12-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Manoj Kumar M.</author><author>Nasser Almuraqab</author><author>Immanuel Azaad Moonesar</author><author>Udo Christian Braendle</author><author>Ananth Rao</author>
        <description><![CDATA[IntroductionThe expanded BRICS block presents significant opportunities for SMEs (Small and Medium Enterprises), but challenges related to financial literacy and digital access hinder their potential. While global efforts emphasize financial literacy and digitization as key drivers of economic growth, especially in developing regions, their specific impact on SMEs in the BRICS block remains underexplored. This paper contributes to the literature by contextualizing how financial literacy and digital financial access influence SME sustainability and economic progress, particularly in light of ongoing efforts to bridge the digital divide.MethodsUsing Principal Component Analysis to reduce dimensionality, the study uses advanced Random Forest Tree modeling, to evaluate current practices in SME finance, credit access, and digitization.ResultsResults indicate that both financial literacy and digitalization play pivotal roles in driving sustainable economic development, with significant implications for policy interventions aimed at supporting SME growth in emerging economies.DiscussionThis study addresses the crucial intersection of SME financial literacy and digital financial access, focusing on their role in fostering economic development within the expanded BRICS block-a group now comprising major emerging economies that collectively face substantial disparities in financial inclusion. The study results are relevant not only for understanding the BRICS context but also for shaping global strategies toward inclusive financial systems and SME resilience in the digital era.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1449572</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1449572</link>
        <title><![CDATA[Predicting student self-efficacy in Muslim societies using machine learning algorithms]]></title>
        <pubdate>2024-12-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammed Ba-Aoum</author><author>Mohammed Alrezq</author><author>Jyotishka Datta</author><author>Konstantinos P. Triantis</author>
        <description><![CDATA[IntroductionSelf-efficacy is a critical determinant of students' academic success and overall life outcomes. Despite its recognized importance, research on predictors of self-efficacy using machine learning models remains limited, particularly within Muslim societies. This study addresses this gap by leveraging advanced machine learning techniques to analyze key factors influencing students' self-efficacy.MethodsAn empirical dataset collected was used to examine self-efficacy among secondary school students in Muslim societies. Four machine learning algorithms-Decision Tree, Random Forest, XGBoost, and Neural Network-were employed to predict self-efficacy using two demographic variables and 10 socio-emotional, cognitive, and regulatory factors. The predictors included culturally relevant variables such as religious/spiritual beliefs and collectivist-individualist orientation. Model performance was assessed using root mean square error (RMSE) and r-squared (R2) metrics to ensure reliability and validity.ResultsThe results showed that Random Forest outperformed the other models in accuracy, as measured by R2 and RMSE metrics. Among the predictors, self-regulation, problem-solving, and a sense of belonging emerged as the most significant factors, contributing to more than half of the model's predictive power. Other variables such as gratitude, forgiveness, empathy, and meaning-making displayed moderate predictive value, while gender, emotion regulation, and collectivist-individualist orientation had minimal impact. Notably, religious/spiritual beliefs and regional factors showed negligible influence on self-efficacy predictions.DiscussionThis study enhances the understanding of factors influencing self-efficacy among students in Muslim societies and offers a data-driven foundation for developing targeted educational interventions. The findings highlight the utility of machine learning in education research, demonstrating its ability to uncover insights for equitable and effective decision-making. By emphasizing the importance of regulatory and socio-emotional factors, this research provides actionable insights to elevate student performance and well-being in diverse cultural contexts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1417752</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1417752</link>
        <title><![CDATA[How does digitally enabled micro-finance promote income equality for the vulnerable in the expanded BRICS block during the pandemic?]]></title>
        <pubdate>2024-12-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Manoj Kumar M. V.</author><author>Nasser Almuraqab</author><author>Immanuel Azaad Moonesar</author><author>Udo Christian Braendle</author><author>Ananth Rao</author>
        <description><![CDATA[IntroductionTech-enabled alternative micro-finance promotes income equality in growing BRICS and Austria across financial crises and pandemics. Are financial access and digital skills equally economically valuable? Our study uses inputs: Human Capital, Alternative Micro-finance, Digitization, Governance, and Entrepreneurship, GDP, inflation, population growth, pandemics, and economic crises using the global 2000–2022 to explain income equality using SWIID Gini disposable and market income index as outputs.MethodsThe study uses Principal component analysis for reducing data dimensionality and collinearity. The study uses OLS, Dynamic Mixed Model, and random forest tree, a machine learning technique, as models to model digitally enable micro-finance.ResultsRFT model diagnostics consistently were better than OLS and GMM. Reduced income inequalities resulted from public and private infrastructure investments, government policy interventions to fight pandemics, economic crises, and conflicts, as well as from expansion in GDP.DiscussionThe study concludes that digitally enabled micro-finance plays a crucial role in reducing income inequalities, particularly during times of crisis. Key policy implications include the need for government support in digital infrastructure to enhance financial inclusion. By pooling their resources, the BRICS block can empower micro-finance organizations to ameliorate disruptions from COVID-19 and economic crises.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1436019</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1436019</link>
        <title><![CDATA[Big data and AI for gender equality in health: bias is a big challenge]]></title>
        <pubdate>2024-10-16T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Anagha Joshi</author>
        <description><![CDATA[Artificial intelligence and machine learning are rapidly evolving fields that have the potential to transform women's health by improving diagnostic accuracy, personalizing treatment plans, and building predictive models of disease progression leading to preventive care. Three categories of women's health issues are discussed where machine learning can facilitate accessible, affordable, personalized, and evidence-based healthcare. In this perspective, firstly the promise of big data and machine learning applications in the context of women's health is elaborated. Despite these promises, machine learning applications are not widely adapted in clinical care due to many issues including ethical concerns, patient privacy, informed consent, algorithmic biases, data quality and availability, and education and training of health care professionals. In the medical field, discrimination against women has a long history. Machine learning implicitly carries biases in the data. Thus, despite the fact that machine learning has the potential to improve some aspects of women's health, it can also reinforce sex and gender biases. Advanced machine learning tools blindly integrated without properly understanding and correcting for socio-cultural sex and gender biased practices and policies is therefore unlikely to result in sex and gender equality in health.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1184444</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1184444</link>
        <title><![CDATA[The role of big data in financial technology toward financial inclusion]]></title>
        <pubdate>2024-05-07T00:00:00Z</pubdate>
        <category>Review</category>
        <author>David Mhlanga</author>
        <description><![CDATA[In the rapidly evolving landscape of financial technology (FinTech), big data stands as a cornerstone, driving significant transformations. This study delves into the pivotal role of big data in FinTech and its implications for financial inclusion. Employing a comprehensive literature review methodology, we analyze diverse sources including academic journals, industry reports, and online articles. Our findings illuminate how big data catalyzes the development of novel financial products and services, enhances risk management, and boosts operational efficiency, thereby fostering financial inclusion. Particularly, big data's capability to offer insightful customer behavior analytics is highlighted as a key driver for creating inclusive financial services. However, challenges such as data privacy and security, and the need for ethical algorithmic practices are also identified. This research contributes valuable insights for policymakers, regulators, and industry practitioners, suggesting a need for balanced regulatory frameworks to harness big data's potential ethically and responsibly. The outcomes of this study underscore the transformative power of big data in FinTech, indicating a pathway toward a more inclusive financial ecosystem.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1396638</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1396638</link>
        <title><![CDATA[Corrigendum: A community focused approach toward making healthy and affordable daily diet recommendations]]></title>
        <pubdate>2024-04-04T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Joe Germino</author><author>Annalisa Szymanski</author><author>Heather A. Eicher-Miller</author><author>Ronald Metoyer</author><author>Nitesh V. Chawla</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1349116</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1349116</link>
        <title><![CDATA[Redefining governance: a critical analysis of sustainability transformation in e-governance]]></title>
        <pubdate>2024-04-03T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Qaiser Abbas</author><author>Tahir Alyas</author><author>Turki Alghamdi</author><author>Ahmad B. Alkhodre</author><author>Sami Albouq</author><author>Mushtaq Niazi</author><author>Nadia Tabassum</author>
        <description><![CDATA[With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1301903</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1301903</link>
        <title><![CDATA[Editorial: Are machine learning, AI, and big data tools ready to be used for sustainable development? Challenges, and limitations of current approaches]]></title>
        <pubdate>2023-11-21T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Elisa Omodei</author><author>Dohyung Kim</author><author>Manuel Garcia-Herranz</author><author>Vedran Sekara</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1086212</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1086212</link>
        <title><![CDATA[A community focused approach toward making healthy and affordable daily diet recommendations]]></title>
        <pubdate>2023-11-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Joe Germino</author><author>Annalisa Szymanski</author><author>Heather A. Eicher-Miller</author><author>Ronald Metoyer</author><author>Nitesh V. Chawla</author>
        <description><![CDATA[IntroductionMaintaining an affordable and nutritious diet can be challenging, especially for those living under the conditions of poverty. To fulfill a healthy diet, consumers must make difficult decisions within a complicated food landscape. Decisions must factor information on health and budget constraints, the food supply and pricing options at local grocery stores, and nutrition and portion guidelines provided by government services. Information to support food choice decisions is often inconsistent and challenging to find, making it difficult for consumers to make informed, optimal decisions. This is especially true for low-income and Supplemental Nutrition Assistance Program (SNAP) households which have additional time and cost constraints that impact their food purchases and ultimately leave them more susceptible to malnutrition and obesity. The goal of this paper is to demonstrate how the integration of data from local grocery stores and federal government databases can be used to assist specific communities in meeting their unique health and budget challenges.MethodsWe discuss many of the challenges of integrating multiple data sources, such as inconsistent data availability and misleading nutrition labels. We conduct a case study using linear programming to identify a healthy meal plan that stays within a limited SNAP budget and also adheres to the Dietary Guidelines for Americans. Finally, we explore the main drivers of cost of local food products with emphasis on the nutrients determined by the USDA as areas of focus: added sugars, saturated fat, and sodium.Results and discussionOur case study results suggest that such an optimization model can be used to facilitate food purchasing decisions within a given community. By focusing on the community level, our results will inform future work navigating the complex networks of food information to build global recommendation systems.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1236397</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1236397</link>
        <title><![CDATA[No longer hype, not yet mainstream? Recalibrating city digital twins' expectations and reality: a case study perspective]]></title>
        <pubdate>2023-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Stefano Calzati</author>
        <description><![CDATA[While the concept of digital twin has already consolidated in industry, its spinoff in the urban environment—in the form of a City Digital Twin (CDT)—is more recent. A CDT is a dynamic digital model of the physical city whereby the physical and the digital are integrated in both directions, thus mutually affecting each other in real time. Replicating the path of smart cities, literature remarks that agendas and discourses around CDTs remain (1) tech-centered, that is, focused on overcoming technical limitations and lacking a proper sociotechnical contextualization of digital twin technologies; (2) practice-first, entailing hands-on applications without a long-term strategic governance for the management of these same technologies. Building on that, the goal of this article is to move beyond high-level conceptualizations of CDT to (a) get a cognizant understanding of what a CDT can do, how, and for whom; (b) map the current state of development and implementation of CDTs in Europe. This will be done by looking at three case studies—Dublin, Helsinki, and Rotterdam—often considered as successful examples of CDTs in Europe. Through exiting literature and official documents, as well as by relying on primary interviews with tech experts and local officials, the article explores the maturity of these CDTs, along the Gartner's hype-mainstream curve of technological innovations. Findings show that, while all three municipalities have long-term plans to deliver an integrated, cyber-physical real-time modeling of the city, currently their CDTs are still at an early stage of development. The focus remains on technical barriers—e.g., integration of different data sources—overlooking the societal dimension, such as the systematic involvement of citizens. As for the governance, all cases embrace a multistakeholder approach; yet CDTs are still not used for policymaking and it remains to see how the power across stakeholders will be distributed in terms of access to, control of, and decisions about CDTs.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1054655</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1054655</link>
        <title><![CDATA[North-south scientific collaborations on research datasets: a longitudinal analysis of the division of labor on genomic datasets (1992–2021)]]></title>
        <pubdate>2023-06-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sarah Bratt</author><author>Mrudang Langalia</author><author>Abhishek Nanoti</author>
        <description><![CDATA[Collaborations between scientists from the global north and global south (N-S collaborations) are a key driver of the “fourth paradigm of science” and have proven crucial to addressing global crises like COVID-19 and climate change. However, despite their critical role, N-S collaborations on datasets are not well understood. Science of science studies tend to rely on publications and patents to examine N-S collaboration patterns. To this end, the rise of global crises requiring N-S collaborations to produce and share data presents an urgent need to understand the prevalence, dynamics, and political economy of N-S collaborations on research datasets. In this paper, we employ a mixed methods case study research approach to analyze the frequency of and division of labor in N-S collaborations on datasets submitted to GenBank over 29 years (1992–2021). We find: (1) there is a low representation of N-S collaborations over the 29-year period. When they do occur, N-S collaborations display “burstiness” patterns, suggesting that N-S collaborations on datasets are formed and maintained reactively in the wake of global health crises such as infectious disease outbreaks; (2) The division of labor between datasets and publications is disproportionate to the global south in the early years, but becomes more overlapping after 2003. An exception in the case of countries with lower S&T capacity but high income, where these countries have a higher prevalence on datasets (e.g., United Arab Emirates). We qualitatively inspect a sample of N-S dataset collaborations to identify leadership patterns in dataset and publication authorship. The findings lead us to argue there is a need to include N-S dataset collaborations in measures of research outputs to nuance the current models and assessment tools of equity in N-S collaborations. The paper contributes to the SGDs objectives to develop data-driven metrics that can inform scientific collaborations on research datasets.]]></description>
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