Data Science Studies Research Articles

From hearing to seeing: Linking auditory and visual place perceptions with soundscape-to-image generative artificial intelligence

People experience the world through multiple senses simultaneously, contributing to our sense of place. Prior quantitative geography studies have mostly emphasized human visual perceptions, neglecting human auditory perceptions at place due to the challenges in characterizing the acoustic environment vividly. Also, few studies have synthesized the two-dimensional (auditory and visual) perceptions in understanding human sense of place. To bridge these gaps, we propose a Soundscape-to-Image Diffusion model, a generative Artificial Intelligence (AI) model supported by Large Language Models (LLMs), aiming to visualize soundscapes through the generation of street view images. By creating audio-image pairs, acoustic environments are first represented as high-dimensional semantic audio vectors. Our proposed Soundscape-to-Image Diffusion model, which contains a Low-Resolution Diffusion Model and a Super-Resolution Diffusion Model, can then translate those semantic audio vectors into visual representations of place effectively. We evaluated our proposed model by using both machine-based and human-centered approaches. We proved that the generated street view images align with our common perceptions, and accurately create several key street elements of the original soundscapes. It also demonstrates that soundscapes provide sufficient visual information places. This study stands at the forefront of the intersection between generative AI and human geography, demonstrating how human multi-sensory experiences can be linked. We aim to enrich geospatial data science and AI studies with human experiences. It has the potential to inform multiple domains such as human geography, environmental psychology, and urban design and planning, as well as advancing our knowledge of human-environment relationships.

Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies

Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies

AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization

This comprehensive review explores the transformative role of artificial intelligence (AI) and data science in the renewable energy sector, with a particular focus on solar and wind energy optimization. The study systematically examines the intersection of AI and renewable energy, highlighting the emergence of AI-driven solutions and their impact on enhancing the efficiency, reliability, and sustainability of renewable energy systems. The review begins with an overview of renewable energy and its growing importance in the global energy mix, emphasizing the critical role of AI in this sector. It then delves into the methodological approach, outlining the research strategy and criteria for selecting relevant AI and data science studies in renewable energy. This includes a detailed analysis of data collection and synthesis techniques used to identify key AI innovations and trends in solar and wind energy optimization. The core of the review comprises an extensive literature survey on AI applications in solar and wind energy systems. It covers fundamental principles of AI in renewable energy, state-of-the-art data science techniques, and emerging trends such as novel AI algorithms and their integration into renewable energy grids. The study evaluates the technological, economic, and environmental impacts of AI in renewable energy, addressing challenges and proposing solutions. Furthermore, the review discusses the role of standards and regulatory frameworks in AI-driven renewable energy and the implications for stakeholders. It concludes with a summary of AI's role in enhancing renewable energy, future prospects, and recommendations for industry leaders and policymakers. This review provides a thorough understanding of the current state and future potential of AI in renewable energy, offering valuable insights for researchers, industry professionals, and policymakers engaged in the field of sustainable energy.

Identifying Soccer Players' Playing Styles: A Systematic Review.

Identifying playing styles in football is highly valuable for achieving effective performance analysis. While there is extensive research on team styles, studies on individual player styles are still in their early stages. Thus, the aim of this systematic review was to provide a comprehensive overview of the existing literature on player styles and identify research areas required for further development, offering new directions for future research. Following the PRISMA guidelines for systematic reviews, we conducted a search using a specific strategy across four databases (PubMed, Scopus, Web of Science, and SPORTDiscus). Inclusion and exclusion criteria were applied to the initial search results, ultimately identifying twelve studies suitable for inclusion in this review. Through thematic analysis and qualitative evaluation of these studies, several key findings emerged: (a) a lack of a structured theoretical framework for player styles based on their positions within the team formation, (b) absence of studies investigating the influence of contextual variables on player styles, (c) methodological deficiencies observed in the reviewed studies, and (d) disparity in the objectives of sports science and data science studies. By identifying these gaps in the literature and presenting a structured framework for player styles (based on the compilation of all reported styles from the reviewed studies), this review aims to assist team stakeholders and provide guidance for future research endeavors.

UTILIZING SCIENCE DATA TO INCREASING THE NUMBER MSME DEBTORS AT PT.BANK CENTRAL ASIA.TBK (CASE STUDY OF PT. BANK CENTRAL ASIA.TBK KCU TEBING TINGGI)

This study aims to increase the number of MSME debtors at the BCA Tebing Tinggi Branch. Since the enactment of Bank Indonesia Regulation Number 23/13/PBI/2021 concerning the Macroprudential Inclusive Financing Ratio (RPIM) for Conventional Commercial Banks, Sharia Commercial Banks, and Sharia Business Units. So Commercial Banks began to adjust the percentage of the use of funds that would be used to finance MSMEs and PBR. BCA Tebing Tinggi Branch is committed to meeting the increase in the percentage of RPIM. One way that can be used to explore Potential Funding is by Utilizing Data Science. Data science studies data, especially quantitative data, with the aim of finding hidden patterns in the data. Researchers will study profile information and transaction patterns in accounts to find MSME customers who are given the right financing. This study processes data using the Machine Learning method with the Random Forest algorithm.

Misleading Repurposing on Twitter

We present the first in-depth and large-scale study of misleading repurposing, in which a malicious user changes the identity of their social media account via, among other things, changes to the profile attributes in order to use the account for a new purpose while retaining their followers. We propose a definition for the behavior and a methodology that uses supervised learning on data mined from the Internet Archive's Twitter Stream Grab to flag repurposed accounts. We found over 100,000 accounts that may have been repurposed. Of those, 28% were removed from the platform after 2 years, thereby confirming their inauthenticity. We also characterize repurposed accounts and found that they are more likely to be repurposed after a period of inactivity and deleting old tweets. We also provide evidence that adversaries target accounts with high follower counts to repurpose, and some make them have high follower counts by participating in follow-back schemes. The results we present have implications for the security and integrity of social media platforms, for data science studies in how historical data is considered, and for society at large in how users can be deceived about the popularity of an opinion. The data and the code is available at https://github.com/tugrulz/MisleadingRepurposing.

58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk

OBJECTIVES/GOALS: To determine the signs, symptoms, and diagnoses that are significantly upregulated in cases of long COVID while identifying risk factors and demographics that increase one’s likelihood of developing long COVID. METHODS/STUDY POPULATION: This is a retrospective, big data science study. Data from Veterans Affairs (VA) medical centers across the United States between the start of 2020 and the end of 2022 were utilized. Our cohort consists of 316,782 individuals with positive COVID-19 tests recorded in the VA EHR with a history of ICD10-CM diagnosis codes in the record for case-control comparison. We looked at all new diagnoses that were not present in the six months before COVID diagnosis but were present in the time period from one month after COVID through seven months after. We determined which were significantly enriched and calculated odds ratios for each, organized by long COVID subtypes by medical specialty / affected organ system. Demographic analyses were also performed for long COVID patients and patients without any new long COVID ICD10-CM codes. RESULTS/ANTICIPATED RESULTS: This profile shows disorders that are highly upregulated in the post-COVID population and provides strong evidence for a broad definition of long COVID. By breaking this into subtypes by medical specialty, we define cardiac long COVID, neurological long COVID, pulmonary long COVID, and eight others. The long COVID cohort was older with more comorbidities than their non-long COVID counterparts. We also noted any differences regarding sex, race, ethnicity, severity of acute COVID-19 symptoms, vaccination status, as well as some analysis regarding medications taken. DISCUSSION/SIGNIFICANCE: This profile can be utilized to decisively define long COVID as a clinical diagnosis and will lead to consistence in future research. Elucidating an actionable model for long COVID will help clinicians identify those in their care that may be experiencing long COVID, allowing them to be admitted into more intensive monitoring and treatment programs.

Launch of Inaugural Issue - Journal of Data Science, Informetrics, and Citation Studies

The first issue of the Journal of Data Science, Informetrics, and Citation Studies is now available online at www.jcitation.org. This journal is devoted to the advancement of data science, informetrics, and citation studies, and aims to provide a forum for researchers in these fields to share their latest findings. The inaugural issue features articles on a variety of topics, including the use of data science in scholarly communication, the impact of data sharing on scientific research, and the role of data citation in academic publishing. We invite you to check out the Journal of Data Science, Informetrics, and Citation Studies and join us in promoting these important fields of research. Data science, informetrics, and citation studies are cutting-edge fields that are constantly evolving. In order to keep up with the latest advancements, the Journal of Data Science, Informetrics, and Citation Studies was created. This peer-reviewed journal covers every aspect of these fields, from theory to practice. The Journal of Data Science, Informetrics, and Citation Studies is a new online journal that aims to provide a forum for scholars working at the intersection of data science, informetrics, and citation studies. The journal will publish original research articles, review articles, case studies, and book reviews. Read more . . .

Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study

This study provides insight into New York City residents' perceptions about violence after the outbreak of Coronavirus disease (COVID-19) based on information from communities in New York City Housing Authority (NYCHA) buildings. In this novel analysis, we used focus group and social media data to confirm or reject findings from qualitative interviews. We first used data from 69 in-depth, semi-structured interviews with low-income residents and community stakeholders to further explore how violence impacts New York City's low-income residents of color, as well as the role of city government in providing tangible support for violence prevention during co-occurring health (COVID-19) and social (anti-Black racism) pandemics. Residents described how COVID-19 and the Black Lives Matter movement impacted safety in their communities while offering direct recommendations to improve safety. Residents also shared recommendations that indirectly improve community safety by addressing long term systemic issues. As the recruitment of interviewees was concluding, researchers facilitated two focus groups with 38 interviewees to discuss similar topics. In order to assess the degree to which the themes discovered in our qualitative interviews were shared by the broader community, we developed an integrative community data science study which leveraged natural language processing and computer vision techniques to study text and images on public social media data of 12 million tweets generated by residents. We joined computational methods with qualitative analysis through a social work lens and design justice principles to most accurately and holistically analyze the community perceptions of gun violence issues and potential prevention strategies. Findings indicate valuable community-based insights that elucidate how the co-occurring pandemics impact residents' experiences of gun violence and provide important implications for gun violence prevention in a digital era.

Feminist Data Studies and the emergence of a new Data Feminist knowledge domain

Mass participation in social networking sites and online life combined with the development of tracking technology facilitates gathering data on unprecedented scales. The uptake of data collecting during the 2010s coincided with the emergence of data science and data studies, along with critical perspectives such as critical data and critical algorithm studies. This paper explores one such critical perspective. Data Feminism merges the theories of intersectional feminism and critical data studies. Bibliometric text analysis of articles, conference papers, essays, and commentary was conducted in VOSviewer software, which found commonalities between terms within texts. The most prominent terms and keywords in the research area of Data Feminism identified in such a manner informed the close reading that followed. Six clusters of terms were identified, with the two largest clusters formed around the terms “big data” and “artificial intelligence” respectively. We also explored the boundaries, movements and centralities within the six clusters.

Perspective: Opportunities for Interdisciplinary and Data Science Study of Soccer

Perspective: Opportunities for Interdisciplinary and Data Science Study of Soccer

Exploring Reasons for Delayed Start-of-Care Nursing Visits in Home Health Care: Algorithm Development and Data Science Study.

BackgroundDelayed start-of-care nursing visits in home health care (HHC) can result in negative outcomes, such as hospitalization. No previous studies have investigated why start-of-care HHC nursing visits are delayed, in part because most reasons for delayed visits are documented in free-text HHC nursing notes.ObjectiveThe aims of this study were to (1) develop and test a natural language processing (NLP) algorithm that automatically identifies reasons for delayed visits in HHC free-text clinical notes and (2) describe reasons for delayed visits in a large patient sample.MethodsThis study was conducted at the Visiting Nurse Service of New York (VNSNY). We examined data available at the VNSNY on all new episodes of care started in 2019 (N=48,497). An NLP algorithm was developed and tested to automatically identify and classify reasons for delayed visits.ResultsThe performance of the NLP algorithm was 0.8, 0.75, and 0.77 for precision, recall, and F-score, respectively. A total of one-third of HHC episodes (n=16,244) had delayed start-of-care HHC nursing visits. The most prevalent identified category of reasons for delayed start-of-care nursing visits was no answer at the door or phone (3728/8051, 46.3%), followed by patient/family request to postpone or refuse some HHC services (n=2858, 35.5%), and administrative or scheduling issues (n=1465, 18.2%). In 40% (n=16,244) of HHC episodes, 2 or more reasons were documented.ConclusionsTo avoid critical delays in start-of-care nursing visits, HHC organizations might examine and improve ways to effectively address the reasons for delayed visits, using effective interventions, such as educating patients or caregivers on the importance of a timely nursing visit and improving patients’ intake procedures.

Kształcenie specjalistów z zakresu data science na poziomie studiów wyższych w Europie

Purpose: The aim of the paper is to analyse educational programs of data science at institutions of higher learning in Europe for the 2020-2021 academic year. The authors try to answer the following questions: What are the education possibilities for individuals, who would like to follow the professional path of data science analytics? Where and how one may obtain the required competences? What competences may be earned during higher studies? What professions are predestined by data science studies? Design/methodology/approach: The authors conduct the analysis of 100 randomly selected bachelor, major and post-graduate curricula that are found in the didactic offer of various institutions of higher learning. The basic information about the offered courses have been obtained through individual searches in the resources of a number of Internet portals (e.g. Studies in Europe, Academiccourses.com, Studia.gov.pl, Postgrad.com and MastersPortal.com). On the basis of information provided in websites of every indicated institution of higher learning, the characterisation of studies was conducted, with consideration for the following criteria: branch name, academic unit, description of curriculum (preliminary requirements, offered knowledge and skills, potential employment), duration of learning, course type, subject taught. Findings: The conducted research prove the diversity with respect to the offered forms of data scientists’ education. In Europe, this curriculum is offered at bachelor level by ca. 30 institutions of higher learning, at major level - by ca. 170 institutions and at post-graduate level - by ca. 350 institutions. The most varied educational contents are offered in post-graduate courses. All study programs identified by the authors are profiled for the needs of certain disciplines - especially IT and mathematics, statistics and economy. The candidates for higher learning courses are often expected to possess advanced skills in the scope of ICT, and often also professional experience in the field of data analysis. The majority of courses is paid. Depending on the level of education, the course lasts from one to four years. Study programs are dominated by IT subjects, enriched with general curriculum subject modules. The majority of branches makes it possible to earn knowledge and skills in the field of IT and exact sciences, especially the methods of aggregating, storing and processing of Big Data, the rules of correct and effective programming, performing statistical analyses and interpreting their results. Among the identified branches of studies, the surprising is the small number of opportunities directly addressed to graduates of the humanities’ studies, including the widely understood information studies (information management, information broking, library science, information architecture, information science).

High-Temperature Pyrolysis and Combustion of C5–C19 Fatty Acid Methyl Esters (FAMEs): A Lumped Kinetic Modeling Study

In the effort to mitigate the depletion of fossil fuels and climate change, biodiesels are considered to be one of the most suitable substitutes for petro-diesel in compression ignition engine applications. As a follow up to prior modeling studies for gasoline and jet surrogate fuel components (Zhang, X.; Mani Sarathy, S. Fuel, 2021, 286, 119361), this work proposes a lumped kinetic model for both saturated and unsaturated C5–C19 fatty acid methyl esters (FAMEs) based on the same methodology. The present lumped model includes 52 FAME fuel components, covering a wide range of biodiesel surrogate fuel components, as well as components typically found in biodiesels. This methodology decouples the combustion of FAME fuels into two stages: the pyrolysis of fuel molecules and the oxidation of pyrolysis intermediates. Lumped reaction steps are used to describe the (oxidative) pyrolysis of each fuel molecule, while a detailed model (Aramcomech 2.0) is adopted as the base mechanism to describe the subsequent conversion of these key intermediates. Rate rules adopted for all the FAME fuels are consistent. The present lumped model is validated against experimental data from 20 pure FAMEs and six diesel/biodiesel surrogates, including around 130 sets of validation data. In general, the present lumped model satisfactorily captures most of these validation targets. This lumped model performs comparably with the detailed models developed in the literature under combustion conditions. Combined with the lumped model for 50 hydrocarbon fuels developed in previous work by this group, the lumped kinetic model for FAME fuels developed here can be used to predict the pyrolysis and combustion chemistry of diesel/biodiesel surrogates in CFD simulations after necessary model reduction for the base model. Also, the stoichiometric parameters of the lumped reactions for various pure FAMEs can be used as the database for data science study in FGMech development for real biodiesels.

Is data science a science? The essence of phenomenon and the role of theory in the emerging field

PurposeData science lacks a distinctive identity and a theory-informed approach, both for its own sake and to properly be applied conjointly to the social sciences. This paper’s purposes are twofold: to provide (1) data science an illustration of theory adoption, able to address explanation and support prediction/prescription capacities and (2) a rationale for identification of the key phenomena and properties of data science so that the data speak through a contextual understanding of reality, broader than has been usual.Design/methodology/approachA literature review and a derived conceptual research model for a push–pull approach (adapted for a data science study in the management field) are presented. A real location–allocation problem is solved through a specific algorithm and explained in the light of the adapted push–pull theory, serving as an instance for a data science theory-informed application in the management field.FindingsThis study advances knowledge on the definition of data science key phenomena as not just pure “data”, but interrelated data and datasets properties, as well as on the specific adaptation of the push-pull theory through its definition, dimensionality and interaction model, also illustrating how to apply the theory in a data science theory-informed research. The proposed model contributes to the theoretical strengthening of data science, still an incipient area, and the solution of the location-allocation problem suggests the applicability of the proposed approach to broad data science problems, alleviating the criticism on the lack of explanation and the focus on pattern recognition in data science practice and research.Research limitations/implicationsThe proposed algorithm requires the previous definition of a perimeter of interest. This aspect should be characterised as an antecedent to the model, which is a strong assumption. As for prescription, in this specific case, one has to take complementary actions, since theory, model and algorithm are not detached from in loco visits, market research or interviews with potential stakeholders.Practical implicationsThis study offers a conceptual model for practical location–allocation problem analyses, based on the push–pull theoretical components. So, it suggests a proper definition for each component (the object, the perspective, the forces, its degrees and the nature of the movement). The proposed model has also an algorithm for computational implementation, which visually describes and explains components interaction, allowing further simulation (estimated forces degrees) for prediction.Originality/valueFirst, this study identifies an overlap of push–pull theoretical approaches, which suggests theory adoption eventually as mere common sense, weakening further theoretical development. Second, this study elaborates a definition for the push–pull theory, a dimensionality and a relationship between its components. Third, a typical location–allocation problem is analysed in the light of the refactored theory, showing its adequacy for that class of problems. And fourth, this study suggests that the essence of a data science should be the study of contextual relationships among data, and that the context should be provided by the spatial, temporal, political, economic and social analytical interests.

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

The aim of this paper is to provide an overview of the interrelationship between data science and climate studies, as well as describes how sustainability climate issues can be managed using the Big Data tools. Climate-related Big Data articles are analyzed and categorized, which revealed the increasing number of applications of data-driven solutions in specific areas, however, broad integrative analyses are gaining less of a focus. Our major objective is to highlight the potential in the System of Systems (SoS) theorem, as the synergies between diverse disciplines and research ideas must be explored to gain a comprehensive overview of the issue. Data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. The improved knowledge integration offered by the System of Systems thinking or climate computing has been demonstrated by analysing the possible inter-linkages of the latest Big Data application papers. The analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.

Comparative analysis between theoretical and simulatory learning methods by data science methodology approach

Data Science is the most trending interdisciplinary science that integrates the steps of data collection, preprocessing data, transforming data, storing data, data visualization, and hence extracting the insights from the data to serve stakeholder’s purposes. Python is commonly used and, along with being a versatile and open-source language, is a favorite tool in Data science studies. The vast libraries are being used to manipulate data and are very simple for even a beginner data scientist to understand. In the present work, we intend to apply the data science methodology to decision making and predictive analysis using the python programming language. We consider the problem of selecting the better mode of study concerning some of the impractical phenomena from physics for the exact understanding of the process. Data collection has been from an educational institute and the comparison has been made between theoretical learning and simulatory learning for selected topics from the vast fields like mechanics, thermodynamics, fluid dynamics, and radioactivity. The steps of data science methodology are germinated to achieve the insights into the data procured and the results are wangled concerning the teaching methodology that could be employed. In the present work, we undertake a comparative study between the theoretical and simulatory modes of teaching by exploring the modes individually through evaluating the responses imparted by a class of high school students. The analysis reported the more inclination of the student’s responses towards the simulatory methods when compared to the theoretical method of learning.

“Bodies not templates”: Contesting dominant algorithmic imaginaries

Through an array of technological solutions and awareness-raising initiatives, civil society mobilizes against an onslaught of surveillance threats. What alternative values, practices, and tactics emerge from the grassroots which point toward other ways of being in the datafied society? Conversing with critical data studies, science and technology studies, and surveillance studies, this article looks at how dominant imaginaries of datafication are reconfigured and responded to by groups of people dealing directly with their harms and risks. Building on practitioner interviews and participant observation in digital rights events and surveying projects intervening in three critical technological issues of our time—the challenges of digitally secure computing, the Internet of Things, and the threat of widespread facial recognition—this article investigates social justice activists, human rights defenders, and progressive technologists as they try to flip dominant algorithmic imaginaries. In so doing, the article contributes to our understanding of how individuals and social groups make sense of the challenges of datafication from the bottom-up.

Big issues for big data: challenges for critical spatial data analytics

In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenges underlying the collection and analysis of big data. In particular, we consider 1) inference when working with usually biased big data, challenging the assumed inferential superiority of data with observations, n, approaching N, the population n -> N. We also emphasise 2) the need for analyses that answer questions of practical significance or with greater emphasis on the size of the effect, rather than the truth or falsehood of a statistical statement; 3) the need to accept messiness in your data and to document all operations undertaken on the data because of this, in support of openness and reproducibility paradigms; and 4) the need to explicitly seek to understand the causes of bias, messiness etc in the data and the inferential consequences of using such data in analyses, by adopting critical approaches to spatial data science. In particular we consider the need to place individual data science studies in a wider social and economic contexts, along with the role of inferential theory in the presence of big data, and issues relating to messiness and complexity in big data.

Addressing Misinformation in Online Social Networks: Diverse Platforms and the Potential of Multiagent Trust Modeling

In this paper, we explore how various social networking platforms currently support the spread of misinformation. We then examine the potential of a few specific multiagent trust modeling algorithms from artificial intelligence, towards detecting that misinformation. Our investigation reveals that specific requirements of each environment may require distinct solutions for the processing. This then leads to a higher-level proposal for the actions to be taken in order to judge trustworthiness. Our final reflection concerns what information should be provided to users, once there are suspected misleading posts. Our aim is to enlighten both the organizations that host social networking and the users of those platforms, and to promote steps forward for more pro-social behaviour in these environments. As a look to the future and the growing need to address this vital topic, we reflect as well on two related topics of possible interest: the case of older adult users and the potential to track misinformation through dedicated data science studies, of particular use for healthcare.