Data Visualization Methods Research Articles

Application of the Bootstrap Method in Behavioral Data Analysis and the Data Visualization Method of Choice from Three Objects

Application of the Bootstrap Method in Behavioral Data Analysis and the Data Visualization Method of Choice from Three Objects

Reconciling chemical flame retardant exposure and fire risk in domestic furniture.

Evidence suggests that standards for resistance of furniture to ignition may lead to an increase in use of chemical flame retardants (CFRs). This is motivating the development of new approaches that maintain high levels of fire safety while facilitating a reduction in use of CFRs. However, reconciling potential fire risk with use of CFRs in relation to specific policy objectives is challenging. To inform the development of a new policy in the UK for the fire safety of furniture, we developed for domestic furniture quantitative models of fire risk and potential for CFR exposure. We then combined the models to determine if any lower fire risk, higher CFR exposure categories of furniture were identifiable. We applied a novel mixed-methods approach to modelling furniture fire risk and CFR exposure in a data-poor environment, using literature-based concept mapping, qualitative research, and data visualisation methods to generate fire risk and CFR exposure models and derive furniture product rankings. Our analysis suggests there exists a cluster of furniture types including baby and infant products and pillows that have comparable overall properties in terms of lower fire risk and higher potential for CFR exposure. There are multiple obstacles to reconciling fire risk and CFR use in furniture. In particular, these include a lack of empirical data that would allow absolute fire risk and exposure levels to be quantified. Nonetheless, it seems that our modelling method can potentially yield meaningful product clusters, providing a basis for further research.

Traveling to Audiences

As African countries gained independence, filmmakers saw cinema as a weapon for decolonization. With the increasing disappearance of cinema venues following the global economic crisis of the 1970s, however, films were rarely able to reach local audiences. In the twenty-first century, especially since 2010, new audience-centered curatorial and managerial approaches have turned film festivals into key platforms for introducing African cinema to local audiences. This article investigates one of these approaches, namely, the decentralization of festival spaces, using one Senegalese film festival as a case study. Instead of asking audiences to travel to a festival venue, the Festival Films Femmes Afrique—the first film festival in Senegal dedicated to films about women—travels to local audiences, offering a variety of free activities across a wide range of venues and spaces. This study relies on data visualization methods as well as practice-based ethnographic research. The focus on the decentralization of festival spaces seeks to encourage further research on creative curatorial and managerial strategies to build and engage with diverse audiences, by increasing the accessibility of programs and boosting the circulation of films that are often marginalized in global distribution platforms.

Enterprise management system analytical tools

High-quality information and analytical content can be used as a basis for making optimal and informed management decisions by analysing the impact of factors on business efficiency, which will allow us to explore the weaknesses of an enterprise and plan the directions of its strategic development. The study aims to provide a scientific synthesis of theoretical aspects and outline practice-oriented approaches to organising analytical support for the management system at an enterprise. The following methods were used: theoretical aspects were analysed using the generalisation, systematisation and grouping methods; the use of statistical analysis and business process modelling methods for the needs of enterprise management was analysed; the components of Data Science analysis were considered; the main material was presented using the descriptive method. Analytical tools of the enterprise management system were analysed. The emphasis is placed on the procedures of statistical analysis and modelling of business processes, application of Data Science tools and visualisation methods. The external and internal sources of information for the needs of enterprise management are allocated. Varieties of analytical tools that contribute to understanding the essence of problematic issues and determine the search for optimal and reasonable management decisions are presented. The SWOT-analysis matrix as an instrument for optimal combination of information modelling methods, analytical procedures and visualisation of the analysis results is presented. The need to use IT tools to improve the quality of the results of analytical procedures is emphasised. The expediency of developing analytical tools for the needs of optimising the enterprise management system using Data Science technology is substantiated. The advantages of using Big Data for making management decisions are determined. The components of Data Science analysis in the enterprise management system are allocated. The possibility of solving business problems or determining the enterprise development strategy by structuring and visualising a large array of data to identify patterns of enterprise development is emphasised. The tools of analytical support for the needs of organising the system of economic security of enterprise are generalised. The practical value of the work lies in the fact that an algorithm of actions for organising analytical support of the management system at an enterprise is proposed

Methodological Bases for Assessing the Formation of an Effective Digital Ecosystem of the Regional Economy (on the Example of the Regions of the CFD)

Relevance. To develop and implement effective measures for the development of regional digital ecosystems, it is necessary not only to understand their qualitative content, but also to accurately quantify it, which makes it possible to determine the level of development of the digital ecosystem of the regional economy (DEER). The purpose is development and testing of a methodological approach to assessing the formation of an effective digital ecosystem of the regional economy (on the example of the regions of the Central Federal District). Objective: identification of key shortcomings of existing methodological approaches to assessing various aspects of digitalization; formulation of requirements for the methodological approach to assessing the formation of an effective DEER; development of an algorithm for assessing the formation of an effective DEER; formation of a system of indicators characterizing the base of interaction, the environment of interaction, the effects of digitalization; approbation of the developed methodological approach on the example of the regions of the Central Federal District. Methodology. The methodological basis of the study was: the method of statistical processing of information; graphical method of statistical data visualization; methods of statistical analysis and synthesis. Results. The key shortcomings of the existing methodological approaches to assessing various aspects of digitalization are identified. The requirements for the methodological approach to assessing the formation of an effective DEER are formulated. An algorithm for evaluating the formation of an effective DEER has been developed. Based on the data provided by the Federal State Statistics Service, a system of indicators was formed that characterizes the base of interaction, the interaction environment, and the effects of digitalization. The developed methodological approach was tested on the example of the regions of the Central Federal District. Conclusions. The developed methodological approach to assessing the formation of an effective DEER makes it possible to reliably assess not only the level of its development, but also the factors that stimulate and hinder the effective functioning of the digital ecosystem of the region's economy. The results of the author's methodology can be applied in the practical activities of regional government bodies when making managerial decisions and developing plans and programs for the development of regional digital economic ecosystems and improving the efficiency of their functioning.

Analysis of the Problem of Import Substitution in the Chemical Industry and its Impact on Other Sectors of the Economy

In Russia, the issues of import substitution are now more acute than ever. In this article, these problems are examined in relation to various sectors of the economy, and first of all, to the chemical industry, which is the basis for technological progress and whose development makes it possible to create conditions for the emergence and implementation of innovations in other industries. The author of the study set out to analyze the situation with import substitution in the chemical industry, as well as related industries, using Rosstat data, existing research results on similar issues, and also based on her own experience. In the course of the work, the main trends in import substitution and existing opportunities for increasing it were identified. Scientific tools such as analysis, synthesis, statistical method and data visualization method were used as a methodological basis. The research results reflected in this article can be useful both to researchers of the development of high-tech industries, and to representatives of federal and regional executive authorities, and other interested parties.

Research on the relationship between CO2 emission and GDP growth with data visualization methods

Climate change is a major global challenge, and CO2 emissions are a significant contributor to this issue. This paper uses data visualization methods to explore the relationship between CO2 emissions and GDP growth. To ensure the reliability of research results, data is collected from some authoritative institutions, including the World Bank and the Intergovernmental Panel on Climate Change (IPCC). The paper uses regression analysis and data visualization techniques to analyze the relationship between CO2 emissions and GDP growth. The results indicate that there is a positive correlation between GDP growth and CO2 emissions, which suggests that economic growth is linked to an increase in emissions. The findings of this paper can contribute to a better understanding of the relationship between economic growth and environmental sustainability.

Examining students’ course trajectories using data mining and visualization approaches

The heterogeneous data acquired by educational institutes about students’ careers (e.g., performance scores, course preferences, attendance record, demographics, etc.) has been a source of investigation for Educational Data Mining (EDM) researchers for over two decades. EDM researchers have primarily focused on course-specific data analyses of students’ performances, and rare attempts are made at the domain level that may benefit the educational institutes at large to gauge and improve their institutional effectiveness. Our work aims to fill this gap by examining students’ transcripts data for identifying similar groups of students and patterns that might associate with these different cohorts of students based on: (a) difficulty level of a course category, (b) formation of course trajectories, and, (c) transitioning of students between different performance groups. We have exploited descriptive data mining and visualization methods to analyze transcript data of 1398 undergraduate Computer Science students of a private university in Pakistan. The dataset includes students’ transcript data of 124 courses from nine distinct course categories. In the end, we have discussed our findings in detail, challenges, and, future work directions.

European green energy transition formation and its modern implementation adjustments

Purpose: this article is to conceptualize the reasons of European green energy transition sustainability on the basis of its historic dynamics of its political and economic drivers and its possible developments taking into account adjustments of this process caused by current energy market situation.Methods: system analysis in the context of economics and politics, case-study, statistical method, information analysis and synthesis, data visualization method are applied.Results: the process of formation of modern economic and political drivers of green energy transition from the moment of the origin of this process to the present time has been investigated. The scale of the restructuring of European energy in the first quarter of the 21st century is shown based on the analysis of the dynamics of energy production by different types of generation. The substantiation of the sustainability of the process of greening energy in European countries is formulated, consisting in a combination of political and economic factors. The possible options for the development of the European green transition process in the medium term are evaluated, taking into account the adjustments of market conditions that are caused by the current unstable state of the energy markets.Conclusions and Relevance: green energy transition is caused from a political point of view, firstly, by the popularity of the concept among voters, and secondly, by the fact that it is the only option to ensure energy independence for Europe. From an economic point of view, the green transition acts as a tool for the development of industry, support for innovation and a factor in reducing negative externalities associated with the negative consequences of the use of hydrocarbon energy. The combination of these factors ensures the sustainability of the greening of energy, therefore, the most likely option for its medium-term development will be the resumption of the process after finding temporary solutions for energy supply.

Medicinal patterns of vines used in Chinese herbal medicine: a quantitative study

Ethnopharmacological relevanceThe botanical characteristics of twinning, climbing vine plants conceptually take shape to interlink the meridians and collaterals system throughout the human body by expelling climatic evils (e.g., wind, dampness). Thus, vines have displayed great medicinal properties in traditional Chinese medicine (TCM). Aim of the studyAlthough some popular vine species have been intensively investigated, the comparable features and medicinal specifications among a vast collection of taxonomic groups based on data visualization methods are relatively lacking in attention. Moreover, the translatability of vines from ancient ethnomedical evidence to modern medical system has not been well established. This review tends to quantitatively summarize the strength of vines in healthcare from the perspectives of medicinal part, traditional function, clinical spectrum, phytochemistry divergence, pharmacological attributes, toxicity as well as the progress of proprietary drug development. Materials and methodsMedicinal vines were retrieved from databases of drug standards and curated catalogues. Synonyms of plant origin across different datasets were normalized by accepted scientific names in the World Flora Online. The distribution patterns and rank of plant origin, medicinal parts, traditional functions and target conditions, as well as the correlation between phytochemical composition and clinical applications were analyzed and visualized. ResultsA total of 121 crude drugs from 36 families, 77 genera, 133 species of vines were obtained and analyzed. The Fabaceae, Menispermaceae and Rubiaceae were the highest ranked families of medicinal vines. Not surprisingly, stem was the most dominant medical part. Moreover, “eliminate wind” displayed a hub node in the traditional function co-occurrence network. In addition to joint impediment disorders, these vines particularly displayed a wide range of therapeutic modalities toward conditions from various organ systems. Chemotaxonomic properties-oriented phytochemical analysis was performed and the chemical diversity among medicinal vines complementarily determined a certain group of therapeutic domains. Particularly, the anti-inflammatory effect and antiarthritic effect were highlighted for treating rheumatic diseases. Using integral animal models and cultured cells, modern pharmacological actions of medicinal vines have been largely observed and validated according to their traditional ethnopharmacology. Furthermore, a small proportion of vine species are well-known toxic plants. Successful drug development pipelines in rheumatic, cardiovascular, liver, malignant and infectious diseases have offered the capacity to generate new treatment options that are being sought out from vine plants. ConclusionsMedicinal vines are rich sources of Chinese Material Medica (CMM) and good fit for a variety of clinical manifestations beyond arthritis and rheumatic diseases. In addition to stem, other parts are also popular for both medicines and dietary supplements. Vine plants provide extensive biologically relevant chemical space for developing value-creating drugs. Thus, our analysis can be useful for further motivating and strengthening the preclinical and clinical research of vine-derived remedies.

Quantifying streamflow properties using a calculus‐based differential approach

AbstractThis research quantifies streamflow temporal configuration and demonstrates the advantages of examining the hydrologic record on a day‐to‐day basis, heretofore unavailable with statistical‐based index parameters. The streamflow chronological structure represents a previously untapped wealth of information that would benefit ecohydrology allowing alternate lines of investigation, re‐evaluating past research in a new light and expanding analysis options for ecohydrologists. Innovative approaches are introduced to quantify this important but overlooked hydrologic property. To accomplish this, a non‐index calculus‐based differential approach has been developed using the lag(1) temporal autocorrelation signature of streamflow. The techniques for quantifying discharge (Q), day‐to‐day discharge change (dQ/dt), rate of day‐to‐day discharge change (d2Q/dt2) and sequential discharge summations are explained and presented using updated data visualization methods. A dam release river impact case study for the Colorado River at Lees Ferry, Arizona, demonstrates this novel way of analyzing and comparing discharge datasets. A set of highly customizable tools for this new approach can be used as a stand‐alone analysis or to complement other existing techniques. The result is a better understanding of the hydrologic regime, permitting more focused research and more effective management planning.

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Big data collection involves enormous amounts of raw data. To boost the sustainability of corporate value and support business intelligence and decision-making systems, in-depth data analysis is necessary. The data storage, analysis, and visualization methods, as well as the discovery of patterns and linkages, all depend on extensive data analysis. This study aims to process datasets to learn things like how ratings impact market sales transactions and how much of an impact factor connected to consumers and items have on ratings. Elasticsearch and Kibana were used for the dataset processing. This study evaluated traits related to the test parameters using a variety of test procedures. The product is scored as a representation of the product types involved in the sales transaction, and the name is assessed as a reflection of the customer. Kibana and Elasticsearch, a full-text search engine, were used in this work to do extensive data analysis on data sets. It is a visualization tool that is employed in a controlled environment to evaluate how ratings impact market exchanges for electronic goods, and it offers suggestions. The study found a substantial relationship between electronic product sales on the Amazon marketplace from 2012 to 2018. It suggested the importance of buyer constituents as users and how different product categories relate to ratings in business transactions. Doi: 10.28991/HIJ-2023-04-03-09 Full Text: PDF

Embracing Innovative Approaches in Data Science: Investigating Contemporary Trends in Data Collection, Analysis, and Visualization Methods

Today businesses are becoming more productive and their return on investment (ROI) is increasing with the development of new technologies like data science, artificial intelligence and data analytics. In today's trend organizations are dealing with big data and these data can drive the whole organization in many ways. The process of doing data analysis and extracting meaningful insight is known as data science. Most business organizations are taking data driven models to ease their work and for making intelligent business decisions. The life cycle of a data science involves so many steps like understanding the business, data collection, analysis and data modelling etc., and to achieve these steps various new technologies and methods are available. Firstly, the process of data collection has been significantly augmented by artificial intelligence, allowing businesses to gather vast amounts of structured and unstructured data efficiently. This rich pool of data serves as the foundation upon which strategic decisions are made. By leveraging advanced data collection methods, organizations gain invaluable insights into market trends, customer behaviour, and operational patterns, empowering them to make informed, data-driven decisions. Secondly, data analysis, a core element of data science, plays a pivotal role in extracting meaningful insights from the collected data. Through sophisticated analytical techniques, businesses can uncover hidden patterns, correlations, and trends within the data. This deep understanding of the data not only facilitates efficient problem-solving but also enables the identification of opportunities for innovation and growth. Informed by data analysis, businesses can optimize processes, identify cost-saving measures, and enhance overall operational efficiency. Lastly, data visualization techniques such as real-time visualization and augmented analytics empower organizations to transform complex data sets into easily understandable visual representations. Real-time visualization provides businesses with up-to-the-minute insights, enabling them to respond promptly to market changes and emerging trends. Augmented analytics, on the other hand, leverages machine learning algorithms to automate data analysis and present actionable insights in an intuitive manner, further accelerating the decision-making process. In this study the recent trends in data science like artificial intelligence for data collection, augmented analytics and predictive analysis for data analysis and data democratization & real time visualization techniques for data visualization are discussed in detail. This study also presents the tools, key challenges and applications of these recent methods in brief.

Different sized gold nanoparticles for array-based sensing of pesticides and its application for strawberry pollution monitoring

The use of pesticides plays an essential role in improving crop quality and yield, however, it causes air, water, and soil pollution and the residue of these pesticides in agricultural products threatens the ecosystem and human life. Therefore, it is highly desirable to develop rapid, simple, and cost-effective methods for regular monitoring of pesticide residues in agricultural products especially strawberry that is consumed fresh and unpeeled. In this study, gold nanoparticles (AuNPs) of varying sizes have been exploited as sensing units to design a non-enzymatic colorimetric sensor array for the detection and discrimination of various pesticides including; bifenazate (BF), paraquat (PQ), diazinon (DZ), thiometon (TM), and carbendazim (CD) and chlorpyrifos (CP). Because of their strong size- and environmentally-dependent properties, AuNPs with different sizes produced distinguished plasmonic patterns in the presence of pesticides at a vast range of concentrations (25–800 ng mL−1). Plasmonic patterns of sensor units have been analyzed by various data visualization (bar plots and heat maps) and pattern recognition methods (linear discriminant analysis (LDA)). The multivariate calibrations showed linear responses ranging from 50 to 800 ng mL−1 for carbendazim, chlorpyrifos, paraquat, and bifenazate and 25–800 ng mL−1 for diazinon and thiometon. The limit of detection (LOD) was calculated to be 17.7, 22.8, 22.4, 9.7, 7.4, and 23.8 ng mL−1 for carbendazim, chlorpyrifos, paraquat, diazinon, thiometon, and bifenazate respectively. Finally, the applicability of the designed sensor was evaluated in real samples comprising tap water, well water, soil, and fruit, leave, drainage water, and culture substrate of strawberry.

A Geospatial Donation Platform for COVID-19 and Beyond, Leveraging Location – Based Services and Geofencing

The current global scenario is characterized by epidemics and various types of disasters, severely impacting communities' health, living conditions, and economic stability. Especially during such crises, the requirement for essential necessities becomes critical. Existing solution guidelines involve receiving donated items from public agencies through an offline system to provide assistance to victims. However, this implementation faces several limitations, such as a lack of understanding of people's needs in specific areas, leading to mismatches between assistance and actual requirements. Additionally, donators lack adequate information, resulting in further discrepancies between donated items and the victims' genuine needs. The available geospatial platforms primarily support surveillance and monitoring of epidemic or disaster situations but fail to address the management of needs related to donation and receipt. Through an extensive review of the literature and related research, it was identified that a geospatial platform specifically dedicated to donation management has not yet been developed. To address this gap, this research proposes the creation of a novel geospatial platform using data analysis and visualization methods, complemented by the integration of geofencing technology. By employing such an approach, the geospatial platform will play a pivotal role in enhancing disaster and epidemic assistance. It will facilitate efficient data processing and visualization, overcoming the limitations faced by the current offline system. Emphasis will be placed on improving the management of needs concerning donation and receipt, thereby mitigating mismatches between assistance and actual requirements. Consequently, this platform is expected to significantly enhance the effectiveness and targeting of aid distribution during times of crises.

Inflammatory Determinants and Associated Morbidity in Hemodialysis Patients.

Hemodialysis deteriorates patients' physical, metabolic, and mental status. Clinical outcomes derived from inflammation determine a worse status but are less frequently identified. The objective of the study was to identify inflammatory determinants and the effect of SNP-related serum IL-6 and IL-10 levels on associated morbidity in hemodialysis. A sample of hemodialysis patients at IMSS Regional Hospital No.46 in Guadalajara (n = 85) were tested using the Malnutrition Inflammation Score (MIS) and Patient Health Questionnaire-9 (PHQ-9) to assess the associated morbidity. Serum cytokine levels were quantified by enzyme-linked immunosorbent assay (ELISA). The restriction fragment length polymorphism (RFLP) technique was used for analysis of IL-6-572C/G and IL-10-1082A/G. Using data visualization methods, we identified relevant determinants of inflammation. A simple regression model was constructed between predictors and targets with genotypes as covariates. Results showed malnutrition in 85.9% of patients and depressive symptoms in 50.6%. IL-10 was the most relevant inflammatory determinant, with regression coefficients (R2) between 0.05 and 0.11. The GG genotype of IL-10-1082 A/G evinced small effect on both clinical outcomes (δ of 0.35 and 0.37, respectively). Hemodialysis increases the associated morbidity, cytokines act as inflammatory determinants, and genetic variability contributes to the severity of clinical outcomes. Further studies need to refine the causal relationship between inflammation and CKD.

Is greenery green? An analytical comparison between the planned, visual, and perceived green

This research established a comprehensive evaluation system for urban ecological assessment. Through research in the fields of urban planning, urban design, and cognitive psychology, this paper defines three ecological evaluation indexes correspondingly. They measure the vegetation coverage of land (planning green), the visibility of vegetation from the pedestrian’s viewpoint (visual green), and the psychological perception of greenery by human (perceived green). This study uses computerized parametric analysis, computerized deep learning, data visualization, and statistical methods to achieve an accurate description of the three evaluation indicators. This study assumes that the three green values may behave consistently or inconsistently at each point. Therefore, this study, on the one hand, tries to analyze the potential factors affecting each green indicator. On the other hand, by analysing the consistency or discrepancy of the three green values, this research revealed the potential link between urban spatial type and integrated ecological properties. Four areas of Brisbane dominated by different functions were selected for this study (Red Hill and Bardon for residential areas, Brisbane City for downtown CBD, and Woolloongabba for industrial areas). The results of the study demonstrate the credibility and applicability of the three green indicators in different areas, examine the various factors affecting ecology, and provide new design strategies and ideas for urban designers.

Leveraging digital methods in the quest for peaceful futures: the interplay of sincere and subjunctive technology affordances in peace mediation

ABSTRACT Efforts to support the resolution of armed conflicts through the facilitation of peace negotiations and dialogues increasingly involve digital technologies. While traditionally perceived as a human-centered activity, peace mediation now commonly entails information- and data-driven methods to enhance talks, support the analysis of conflict stakeholder needs and interests, and ground mediation efforts in better evidence. Digital technologies also promise to make peace efforts more future-oriented by helping to predict or anticipate upcoming developments, build scenarios, and increase readiness for emerging challenges. However, little is known about how such methods can be employed in dialogue and negotiation settings, where participants may have subjective and incompatible views on the conflict context, and more data and evidence don’t necessarily help to determine what a more peaceful future could look like. Through a qualitative study of the use of digitally enhanced dialogue efforts in Yemen and Libya, we demonstrate that future-oriented peacemaking requires the balancing of ‘sincere’ technology affordances that encourage an engagement with the past and present reality of conflict, with ‘subjunctive’ technology affordances that encourage an engagement with possible futures that are more peaceful. In practice, this requires combining data- and evidence-generating methods concerned with the world ‘as is’ with data analysis and visualization methods concerned with how the world ‘should’ or ‘could’ be. Our findings have implications for the study of digital methods in the facilitation of contentious political processes where the provision of data and evidence may create hurting deadlocks.

Data visualisation in scoping reviews and evidence maps on health topics: a cross-sectional analysis

BackgroundScoping reviews and evidence maps are forms of evidence synthesis that aim to map the available literature on a topic and are well-suited to visual presentation of results. A range of data visualisation methods and interactive data visualisation tools exist that may make scoping reviews more useful to knowledge users. The aim of this study was to explore the use of data visualisation in a sample of recent scoping reviews and evidence maps on health topics, with a particular focus on interactive data visualisation.MethodsOvid MEDLINE ALL was searched for recent scoping reviews and evidence maps (June 2020-May 2021), and a sample of 300 papers that met basic selection criteria was taken. Data were extracted on the aim of each review and the use of data visualisation, including types of data visualisation used, variables presented and the use of interactivity. Descriptive data analysis was undertaken of the 238 reviews that aimed to map evidence.ResultsOf the 238 scoping reviews or evidence maps in our analysis, around one-third (37.8%) included some form of data visualisation. Thirty-five different types of data visualisation were used across this sample, although most data visualisations identified were simple bar charts (standard, stacked or multi-set), pie charts or cross-tabulations (60.8%). Most data visualisations presented a single variable (64.4%) or two variables (26.1%). Almost a third of the reviews that used data visualisation did not use any colour (28.9%). Only two reviews presented interactive data visualisation, and few reported the software used to create visualisations.ConclusionsData visualisation is currently underused by scoping review authors. In particular, there is potential for much greater use of more innovative forms of data visualisation and interactive data visualisation. Where more innovative data visualisation is used, scoping reviews have made use of a wide range of different methods. Increased use of these more engaging visualisations may make scoping reviews more useful for a range of stakeholders.

Использование визуализации информации для развития образного мышления

Imaginative thinking surpasses verbal-logical thinking in terms of speed, volume of stored information and the duration of its storage. Therefore, when solving various problems, imaginative thinking has significant advantages. However, when teaching students of non-artistic specialties, often insufficient attention is paid to the effective use of imaginative thinking in professional activities. At the same time, thanks to the use of computer and information technologies, the possibilities of data visualization have grown significantly. In the present work, possible ways of developing imaginative thinking in teaching students of non-artistic specialties using data visualization methods are investigated. The methodological basis of the study was the analysis of scientific publications and generalized practical experience in identifying the main ways of using information visualization for the development of imaginative thinking in the learning process. The article discusses the main modern methods of research visualization and possible ways of using information visualization for the development of imaginative thinking. It is shown that in order to increase the efficiency of using this thinking in professional activities, in the learning process it is necessary to pay more attention to the analysis of the visual presentation of data, as well as teaching the active use of research visualization methods and, in particular, infographics and dashboards.