Introduction

Infographic Statistics: ​Infographics have emerged as a powerful tool in data communication, significantly enhancing content engagement and information retention. Approximately 60% of businesses have integrated infographics into their content strategies, recognizing their effectiveness in conveying complex data succinctly.

Content incorporating images and graphics, such as infographics, can achieve up to 650% higher engagement compared to text-only content. Furthermore, infographics are 30 times more likely to be read than purely textual articles, underscoring their appeal to audiences. The human brain processes visual information more efficiently, with 90% of information transmitted to the brain being visual.

Additionally, 65% of individuals are visual learners, highlighting the importance of visual aids in learning and information dissemination. When information is paired with relevant images, people retain 65% of the information three days later, compared to only 10% when it is presented without visuals.

These statistics demonstrate the critical role of infographics in enhancing communication, improving comprehension, and increasing audience engagement across various platforms.

Tables and charts have long been seen as effective ways to convey data. Much attention has been focused on improving charts, following ideas of human perception and brain function. Tables can also be viewed as two-dimensional representations of data, yet it is only fairly recently that we have begun to apply principles of design that aid the communication of information between the author and reader. In this study, we collated guidelines for the design of data and statistical tables. These guidelines fall under three principles: aiding comparisons, reducing visual clutter, and increasing readability. We surveyed tables published in recent issues of 43 journals in the fields of ecology and evolutionary biology for their adherence to these three principles, as well as author guidelines on journal publisher websites. We found that most of the over 1,000 tables we sampled had no heavy grid lines and little visual clutter. They were also easy to read, with clear headers and horizontal orientation. However, most tables did not aid the vertical comparison of numeric data. We suggest that authors could improve their tables by the right-flush alignment of numeric columns typeset with a tabular font, clearly identify statistical significance, and use clear titles and captions. Journal publishers could easily implement these formatting guidelines when typesetting manuscripts. Methods Once we had established the above principles of table design, we assessed their use in issues of 43 widely read ecology and evolution journals (SI 2). Between January and July 2022, we reviewed the tables in the most recent issue published by these journals. For journals without issues (such as Annual Review of Ecology, Evolution, and Systematics, or Biological Conservation), we examined the tables in issues published in a single month or in the entire most recent volume if few papers were published in that journal on a monthly basis. We reviewed only articles in a traditionally typeset format and published as a PDF or in print. We did not examine the tables in online versions of articles. Having identified all tables for review, we assessed whether these tables followed the above-described best practice principles for table design and, if not, we noted the way in which these tables failed to meet the outlined guidelines. We initially both reviewed the same 10 tables to ensure that we agreed in our assessment of whether these tables followed each of the principles. Having ensured agreement on how to classify tables, we proceeded to review all subsequent journals individually, while resolving any uncertainties collaboratively. These preliminary table evaluations also showed that assessing whether tables used long format or a tabular font was hard to evaluate objectively without knowing the data or the font used. Therefore, we did not systematically review the extent to which these two guidelines were adhered to.

Visual analytics: Exposing the past, understanding the present, and looking to the future Dan Ariely, founder of The Center for Advanced Hindsight once posted on Facebook, “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...” This is especially true in Higher Education as much of the work being done to organize, connect, and analyze big data is happening in the for profit sector. This multimedia presentation (video, photos, and text) has three goals. (1) Discuss how the field visual analytics is tackling the problem of analyzing big data. (2) Explore when visual analytics is superior and inferior to typical statistics. (3) Tactics and tools for Institutional Researchers to use in their everyday work to change data into actionable intelligence.

Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

The dataset primarily contains the following data content:Statistical learning results where stimuli streams of visual features of real objects were statistically learned during the familiarization phase, followed by visual feature-based testing in the test phase;Statistical learning results where participants performed statistical learning on visual feature-based stimulus streams of real objects during the familiarization phase, followed by testing using both visual features and natural sounds in the test phase;Statistical learning results where participants performed statistical learning on simultaneously presented visual feature-based stimulus streams of real objects and auditory stimulus streams of nonsense syllables during the familiarization phase, followed by testing using visual features, natural sounds, and nonsense syllables in the test phase.

The AI presentation tools market is experiencing rapid growth, driven by increasing demand for efficient and engaging presentations across various sectors. The market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $10 billion by 2033. This surge is fueled by several key factors. Businesses are increasingly adopting AI-powered tools to streamline their workflow, automating tasks like design creation, content generation, and data visualization. The ability to create visually appealing and data-rich presentations with minimal effort is a significant driver. Furthermore, the rising popularity of remote work and virtual presentations has accelerated the adoption of these tools, offering solutions for creating professional presentations regardless of location. The ease of use, integration with other software, and the potential for improved communication and engagement are also major contributors to market growth. Competitive landscape analysis reveals a mix of established players like Microsoft with its Copilot integration and newer entrants offering specialized features. This competitive environment fosters innovation, leading to continuous improvement in the capabilities and accessibility of AI presentation tools. Despite the significant growth potential, certain challenges exist. The initial cost of implementation and the learning curve associated with new software can be barriers for smaller businesses. Concerns regarding data security and privacy, particularly when dealing with sensitive business information, also need to be addressed. However, ongoing advancements in AI technology, coupled with a growing awareness of the benefits offered by these tools, are expected to mitigate these restraints. The market segmentation reveals a strong demand across various industries, including marketing, education, and corporate communications. The diverse range of tools available caters to different user needs and skill levels, from simple slide creation to advanced data visualization and presentation analysis. Future growth will likely be influenced by advancements in natural language processing, generative AI, and enhanced integration with other collaborative platforms.

This repository contains supplementary materials related to the study "𝐒𝐮𝐫𝐯𝐞𝐲 𝐨𝐧 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐁𝐫𝐚𝐳𝐢𝐥𝐢𝐚𝐧 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐨𝐫𝐢𝐞𝐬: 𝐏𝐫𝐨𝐟𝐢𝐥𝐢𝐧𝐠 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐦𝐮𝐥𝐭𝐢𝐯𝐚𝐫𝐢𝐚𝐭𝐞 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬, 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐚 '𝐍𝐞𝐰 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬' 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡". The dataset, figures, exported results, and analysis scripts are included to ensure full transparency and reproducibility of the research findings.

𝐅𝐨𝐥𝐝𝐞𝐫 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞

1_𝐃𝐚𝐭𝐚𝐬𝐞𝐭/ This folder contains the dataset used in the study, formatted for direct use in the Feature Priorizer R Markdown script.

2_𝐅𝐢𝐠𝐮𝐫𝐞𝐬/ All figures generated by the Feature Priorizer are stored here in 600 DPI resolution, ensuring high-quality graphics for publication and analysis.

3_𝐄𝐱𝐩𝐨𝐫𝐭𝐞𝐝/ This folder contains the exported results, including statistical outputs, tables, and processed datasets derived from the analyses.

4_𝐒𝐮𝐩𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐫𝐲_𝐅𝐢𝐥𝐞𝐬/ This folder contains auxiliary files used in generating the Feature Priorizer HTML report, ensuring an enhanced visual presentation and incorporating dynamic statistical quotes. – 𝐬𝐭𝐲𝐥𝐞𝐬.𝐜𝐬𝐬: Defines the formatting of the HTML report, ensuring a consistent visual presentation. logo.html, logo.png, logo.txt – Files related to the project's visual identity. – 𝐒𝐜𝐢𝐞𝐧𝐜𝐞_𝐒𝐭𝐚𝐭𝐬_𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬.𝐣𝐩𝐞𝐠: An image displayed in the HTML report, complementing the section on statistical and scientific reflections. – 𝐬𝐭𝐚𝐭𝐪𝐮𝐨𝐭𝐞_𝐜𝐲𝐜𝐥𝐞_𝐬𝐭𝐚𝐭𝐞.𝐫𝐝𝐬: An RDS file that stores the state of the statistical quotes cycle. This file is dynamically updated to prevent repetitions, ensuring that the quotes presented in the report change with each execution.

5_𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐏𝐫𝐢𝐨𝐫𝐢𝐳𝐞𝐫 – 𝐑 𝐌𝐚𝐫𝐤𝐝𝐨𝐰𝐧 𝐒𝐜𝐫𝐢𝐩𝐭 The "Feature Priorizer" is an R Markdown-based analytical pipeline (Script_Feature_Prioritizer.Rmd) developed to perform the full multivariate analysis workflow presented in the study. The script integrates:

A) Dimensionality reduction (Logistic PCA) B) Unsupervised clustering (K-Means) C) Feature prioritization using the Nihans Index and Pareto Analysis D) Statistical and practical significance assessment (Chi-square test, Cohen's h) E) Automated report generation in HTML format, including figures and tables

6_𝐅𝐢𝐥𝐞𝐬 𝐑𝐞𝐥𝐚𝐭𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐏𝐫𝐢𝐨𝐫𝐢𝐳𝐞𝐫 – 𝐒𝐜𝐫𝐢𝐩𝐭_𝐅𝐞𝐚𝐭𝐮𝐫𝐞_𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞𝐫.𝐑𝐦𝐝: The R Markdown script that executes the entire analytical pipeline – 𝐒𝐜𝐫𝐢𝐩𝐭_𝐅𝐞𝐚𝐭𝐮𝐫𝐞_𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞𝐫.𝐡𝐭𝐦𝐥: The automatically generated HTML report containing all results, figures, and statistical summaries – 𝐈𝐧𝐬𝐭𝐚𝐥𝐥_𝐩𝐚𝐜𝐤𝐚𝐠𝐞𝐬.𝐑𝐦𝐝: A helper script that installs all necessary R packages for running the Feature Priorizer

Data Visualization Software Market Outlook

The global data visualization software market size was valued at approximately USD 8.4 billion in 2023 and is projected to reach around USD 19.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.8% from 2024 to 2032. The significant growth factor driving this market is the increasing need for data-driven decision-making across various industries.

The surge in big data and the growing complexity of data generated by enterprises have fueled the demand for data visualization software. Businesses are increasingly recognizing the importance of translating complex datasets into comprehensible visual formats to derive meaningful insights and strategic decisions. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) with data visualization tools is further providing an impetus to market growth by enabling predictive and prescriptive analytics.

Another critical growth factor is the rising adoption of cloud-based solutions. Cloud deployment not only offers scalability and flexibility but also reduces the total cost of ownership, making it an attractive option for organizations of all sizes. Additionally, the increased penetration of internet and mobile devices has led to the proliferation of data, necessitating the use of advanced visual analytics tools to harness and interpret this data efficiently. Organizations are also investing in data visualization software to enhance operational efficiency, improve customer experience, and gain a competitive edge in the market.

The market is also witnessing significant growth due to the increasing importance of data governance and compliance. With stringent data privacy regulations like GDPR, CCPA, and HIPAA, organizations are compelled to adopt robust data visualization software to ensure data is managed and reported accurately. Moreover, the growing trend of remote work and the need for real-time data access and collaboration platforms have further accelerated the demand for data visualization tools. These tools facilitate seamless collaboration among teams, enabling them to make informed decisions swiftly.

Visual Analytics is playing a pivotal role in transforming the way organizations interpret and utilize data. By combining interactive visual interfaces with advanced analytics, visual analytics tools enable users to explore complex datasets more intuitively. This approach not only enhances the comprehension of data but also facilitates the identification of patterns and trends that might otherwise remain hidden. As businesses strive to make data-driven decisions, the demand for visual analytics solutions is expected to rise significantly. These tools empower users to interact with data in real-time, offering dynamic insights that can be crucial for strategic planning and operational efficiency. Moreover, visual analytics is becoming increasingly essential in industries where quick decision-making is critical, such as finance, healthcare, and retail.

Regionally, North America holds the largest market share due to the early adoption of advanced technologies and the presence of major market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digital transformation, increasing investments in IT infrastructure, and the growing number of SMEs adopting data visualization tools in countries like China and India are key drivers for this regional growth.

Component Analysis

The data visualization software market is segmented into software and services. The software segment dominates the market, driven by the increasing need for sophisticated tools that can handle large volumes of data and present it in an easily digestible format. Solutions within this segment include standalone software, embedded analytics, and dashboards. These tools help businesses make data-driven decisions, identify trends, and uncover insights that were previously hidden in spreadsheets and raw data.

Within the software segment, standalone software holds a significant share. These are comprehensive solutions that provide a wide range of functionalities, from basic charts and graphs to complex data visualization techniques like heat maps, scatter plots, and bubble charts. The growing integration of AI and ML technologies into these software solutions is enabling more advanced analytics capabilities, such as predictive and prescriptive ana

Snapshot img

Visual Content Market Size 2025-2029

The visual content market size is forecast to increase by USD 1.24 billion at a CAGR of 5.1% between 2024 and 2029.

The market, encompassing digital stock images and software-generated graphics, continues to experience significant growth In the US. Key drivers include the increasing demand for digital content in various sectors such as real estate, education, and digital marketing. A catalyst for this growth is the rising preference for visuals like 360-degree images and videos. However, the market faces challenges, including limited online video consumption due to slow internet speeds. As digital marketing becomes more prevalent, the need for high-quality, visually engaging content is increasingly important. This trend is expected to continue, with advancements in technology further enhancing the potential of visual content to captivate audiences and drive engagement.

What will be the Size of the Visual Content Market During the Forecast Period?

Request Free Sample

The market continues to expand as businesses recognize the power of engaging, shareable content to capture audience attention and drive performance. The human brain processes visual information 60,000 times faster than text, making infographics, videos, photos, and interactive visuals effective tools for conveying complex information and boosting brand awareness. For example, a brand may include a CTA in an infographic, inviting users to sign up for a newsletter or download an e-book. Visual content drives ROI through increased traffic, backlinks, and calls to action. 



Platforms and others provide businesses with a range of image-based and interactive content solutions. As the market evolves, expect to see a continued focus on creating high-quality, shareable visuals that resonate with audiences and deliver measurable results. Visual capitalists are leveraging a variety of formats, including pictures, diagrams, charts, online videos, slide decks, native video, and ultimate guides, to present complex data and insights in an engaging and accessible way.

How is this Visual Content Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Stock images
  Stock video


Application

  Editorial
  Commercial


License Model

  RF
  RM


End-user

  Media and entertainment
  Advertising
  Corporate
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan


  Middle East and Africa



  South America

By Product Insights

The stock images segment is estimated to witness significant growth during the forecast period.

The market experienced significant growth in 2024, with stock images leading the segment. The proliferation of digital photography, driven by the easy accessibility and affordability of digital single-lens reflex (DSLR) cameras, has contributed to market expansion. Notably, there has been an increasing trend of collaborations among companies, enabling them to broaden their offerings, reach larger audiences, and enhance customer value. The market exhibits minimal price differentiation based on picture resolution due to the transition to mobile and online platforms. The demand for responsive web design has fueled the need for high-quality, small images, leading to advancements in image resolution technology. Visual content encompasses various formats, including infographics, videos, YouTube, Hubspot, and social media, among others.

Get a glance at the Visual Content Industry report of share of various segments Request Free Sample

The stock images segment was valued at USD 3.38 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market holds the largest share In the global visual content industry. The US is the primary contributor to this market's growth due to the increasing demand for video content among commercial consumers. Factors such as enhanced broadband penetration and faster internet speeds facilitate smoother video consumption. Furthermore, the proliferation of social media platforms like Facebook and Instagram In the US fuels market expansion. Visual content encompasses various formats, including infographics, videos, YouTube, Hubspot, and interactive visuals. These ele

The global market for Visual Analytics in Education is experiencing robust growth, driven by the increasing adoption of data-driven decision-making in educational institutions and the rising need for effective learning analytics. The market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of big data within educational settings—from student performance data to resource allocation—demands sophisticated analytical tools for effective interpretation and strategic planning. Secondly, the shift towards personalized learning experiences necessitates visual analytics capabilities to identify individual student needs and tailor educational interventions accordingly. Furthermore, research institutions increasingly leverage visual analytics for complex data analysis across diverse disciplines, further driving market growth. Cloud-based visual analytics solutions are gaining significant traction due to their scalability, cost-effectiveness, and accessibility. The market segmentation reveals a strong preference for cloud-based visual analytics over on-premise solutions, reflecting the ongoing digital transformation in education. Educational institutions, universities, and research institutions represent the dominant application segments, while "others" (e.g., tutoring centers, online learning platforms) show promising growth potential. Key players such as Oracle, Tableau, and Qlik Technologies are actively vying for market share through innovative product offerings and strategic partnerships with educational institutions. Geographical analysis indicates strong market presence in North America and Europe, although significant growth opportunities exist in the Asia-Pacific region due to increasing investment in educational technology and infrastructure. While data privacy concerns and the initial investment costs associated with implementing visual analytics solutions present challenges, the long-term benefits in terms of improved student outcomes, optimized resource allocation, and enhanced research capabilities are expected to outweigh these restraints.

Visual Data Discovery Market Outlook

The global visual data discovery market size was valued at approximately USD 9.1 billion in 2023 and is projected to reach USD 22.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.8%. This robust growth is driven by an increasing demand for tools that allow businesses to better interpret and utilize vast amounts of data. The surge in big data analytics and the need for real-time insights are significant factors propelling market expansion. Organizations are increasingly recognizing the competitive advantage offered by visual data discovery solutions, which facilitate enhanced decision-making processes through more intuitive data interpretation.

The growth of the visual data discovery market is primarily fueled by the escalating data volumes generated across various industry verticals. As businesses increasingly rely on data-driven strategies, the need for sophisticated tools that can simplify complex data sets into easily understandable visuals has surged. The ability to visualize data in a more intuitive manner aids in faster and more accurate decision-making, thereby improving business operations and outcomes. Additionally, the rising adoption of advanced technologies such as artificial intelligence and machine learning has enhanced the capabilities of visual data discovery tools, allowing for more precise data analysis and prediction models. The integration of these technologies into visual analytics solutions is providing businesses with a more comprehensive view of their data, thereby driving market growth.

Moreover, the trend towards digital transformation across industries is a significant catalyst for the expansion of the visual data discovery market. Companies are increasingly investing in digital tools to streamline their operations and gain insights into their business processes. Visual data discovery solutions are integral to these digital initiatives as they provide a powerful means to interpret and utilize data effectively. The increasing adoption of cloud computing is another factor contributing to the market's growth. Cloud-based visual data discovery solutions offer scalability, ease of access, and cost-efficiency, making them an attractive option for businesses of all sizes. This shift towards cloud-based services is expected to continue, further propelling the market forward.

The market's growth is also supported by the rising demand for self-service business intelligence tools. Organizations are looking for solutions that empower their employees to access and analyze data without relying heavily on IT departments. Visual data discovery tools that offer user-friendly interfaces and interactive dashboards are becoming increasingly popular in this regard. They allow employees across different departments to gain insights from data quickly and independently, fostering a data-driven culture within the organization. This democratization of data is playing a crucial role in the market's expansion, as it aligns with the broader trend towards decentralized decision-making in businesses.

Regionally, North America is anticipated to hold the largest share of the visual data discovery market. The presence of numerous key players and the rapid adoption of advanced technologies across various industries contribute to this dominance. The region's established IT infrastructure and high investment in research and development further propel market growth. Meanwhile, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by increasing digitalization and the growing emphasis on data-driven decision-making in emerging economies. The rapid expansion of industries such as retail, healthcare, and BFSI in countries like China and India is creating significant opportunities for market players in this region.

Component Analysis

The component segment of the visual data discovery market can be broadly categorized into software and services. Software tools form the backbone of visual data discovery solutions, providing the necessary platforms and applications for data analysis and visualization. These software solutions are designed to handle large volumes of data and present them in a visually appealing and easy-to-understand format. The demand for advanced software tools is driven by their ability to provide real-time insights and enhance data-driven decision-making. Companies are increasingly investing in software that offers comprehensive analytics capabilities, including predictive analytics and interactive dashboards, which are essential for gaining competitive advantages.

Subjects

MEG was recorded in 7 healthy subjects (5 young right-handed and 1 left-handed and 1 elderly) in the waking state with their eyes open or closed, who were sitting in a comfortable chair. The experimental technique was approved by the ethical commission of the Institute of Higher Nervous Activity and Neurophysiology of RAS (protocol No. 5 dated 02.12.2020).

Equipment

MEG was recorded on a VectorView device (Elekta Neuromag Oy, Finland), which was placed inside a magnetically protected chamber made of multilayer permalloy (AK3b, Vacuumschmelze GmbH, Germany). Before MEG recording, the coordinates of anatomical reference points (left and right preauricular points and nasion) were determined, as well as indicator coils attached to the surface of the scalp of the subject in the upper part of the forehead and behind the auricles. These points were determined using a FASTRAK 3D digitizer (Polhemus, USA). Each subject had a virtual model of the brain and head obtained from an anatomical 3D MRI taken the day before (file: MRI_V1_7.zip).

Registration and pre-processing

The subject's head was covered by a helmet, which is part of a fiberglass Dewar vessel with an array of sensors immersed in liquid helium. The subject sat down in such a way that the surface of the head was as close as possible to the sensors. The magnetic signal was recorded from 102 triplets, each of which consisted of 1 magnetometer and 2 gradiometers at rest with eyes closed and upon presentation of visual and speech stimuli. Recording was performed with a sampling frequency of 1000 Hz in a bandwidth of 0.1–330 Hz and was processed by the MaxFilter program (Elekta Neuromag Oy, Finland), which eliminates artifacts (the tSSS method—spatio-temporal separation of signals). The signal levels were corrected in accordance with the data on the position of the subject's head in relation to the MEG sensors. The position of the head during the experiment was controlled using special inductors.

Visual and verbal stimuli

After recording the background MEG for 3 minutes with closed eyes, the subject opened his eyes on command and observed the fixation point on the projection screen. After 15 seconds, stimulation was started and the subject's responses were received in the form of pressing a button. In response to the 0 degrees and 90 degrees stimuli, the subject had to press the button with the index finger, and to the 45 degrees and 135 degrees inclined stimuli, the adjacent button with the middle finger. Stimuli lasting 100 ms were presented randomly every 3100±100 ms (intervals between stimuli varied randomly). In two series, 42 stimuli of each orientation were presented. Between the series, the subject rested for 2-3 minutes. Visual stimuli in the form of Gabor contrast gratings (1.9 cycles per angular degree) with dimensions of 5.25 angular degrees and an average brightness of 4 lux were projected onto a screen located at a distance of 95 cm from the subject's eyes using a Panasonic PT-stimulating projector D7700E-K, which is part of the MEG facility. Visual stimulus patterns were generated at http://www.cogsci.nl/pages/gabor-generator with edge parameters: Circular (sharp edge). The samples are contained in the GaborStim.zip file (the names of the sample files correspond to their name in the script file, but do not match their geometric meaning, see table below). The stimulator was programmed using the Presentation software (USA, Neurobehavioral Systems, Inc). Stimulation scripts are contained in the sce.zip file.

Table

Stimulus or response code Type of stimulus or response

STI101_1 Fixation point STI101_2 90 degrees STI101_4 135 degrees

STI101_8 0 degrees STI101_16 45 degrees STI101_32 First button (index finger)

STI101_64 Second button (middle finger)

After 2 series of visual stimuli, the subject closed his eyes and was presented with 3 series of speech stimuli for 2 minutes with a break of 1 minute. In each series, recordings of audio files of 8 separate adjectives of the Russian language were presented, which were repeated 5 times in a pseudo-random order. The series began with 3 words, which were not taken into account in further analysis. The subject listened to the words and had to press the button after he understood the meaning of the presented word. After pressing or no response, the next word followed in 2±1 s. The audio files are contained in the words101_343.zip file (the names correspond to the script file).

Data received

The records are contained in files with the name of the type V1m24r, where V1 is the number of the subject, m is the sex (m/f), 24 is the age, and r is the right-handed subject. This dataset can be easily loaded into the Brainstorm program. Spontaneous and evoked MEG can be used for source localization and reconstruction of traveling waves.

Acknowledgments

The reported study was funded by RFBR, project number 20-015-00475.

Data Visualization Tools Market Outlook

The global data visualization tools market size was valued at $6.5 billion in 2023 and is forecast to reach $14.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.5% over the forecast period. This robust growth can be attributed to the increasing volume of data generated across various industries and the rising need for data-driven decision-making processes. The widespread adoption of advanced analytics, coupled with the growing trend of digital transformation, is fueling the demand for sophisticated data visualization tools.

One of the primary growth drivers for the data visualization tools market is the exponential increase in data volumes. With the proliferation of IoT devices, social media platforms, and digital transactions, both structured and unstructured data are being generated at an unprecedented rate. Enterprises are keen on harnessing this data to gain actionable insights and maintain a competitive edge. Data visualization tools enable organizations to convert complex data sets into intuitive graphical representations, making it easier for stakeholders to comprehend and analyze information quickly and effectively.

Another significant factor propelling the market is the increasing focus on business intelligence (BI) and analytics. Companies across sectors are investing heavily in BI solutions to enhance their decision-making capabilities. Data visualization tools are integral to BI platforms as they help in presenting data through charts, graphs, and dashboards, allowing users to spot patterns, trends, and anomalies. The ability to visualize data dynamically and interactively empowers businesses to make data-driven decisions swiftly, which is crucial in todayÂ’s fast-paced market environment.

The growing demand for personalized customer experiences is also boosting the data visualization tools market. Organizations are leveraging customer data to tailor their products and services to meet specific consumer preferences and needs. Data visualization tools play a critical role in analyzing customer behavior and market trends, enabling companies to develop targeted marketing strategies. This focus on personalization not only enhances customer satisfaction but also drives revenue growth.

In the context of data visualization, Data Lake Visualization is emerging as a critical component for organizations dealing with vast amounts of data. A data lake is a centralized repository that allows businesses to store all their structured and unstructured data at any scale. With the increasing complexity and volume of data, visualizing this data becomes essential to extract meaningful insights. Data Lake Visualization tools enable users to interact with and analyze data stored in data lakes, providing a comprehensive view of data patterns and trends. These tools help in simplifying the process of data exploration and discovery, making it easier for organizations to harness the full potential of their data lakes. By integrating visualization capabilities with data lakes, businesses can enhance their data-driven strategies, improve decision-making, and drive innovation.

Regionally, North America holds a significant share of the data visualization tools market, owing to the presence of major technology firms and a high adoption rate of advanced analytics solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digitalization, increasing investments in big data analytics, and the growing number of SMEs embracing BI tools. Europe and Latin America are also experiencing steady growth, with enterprises in these regions increasingly recognizing the importance of data visualization in strategic decision-making.

Component Analysis

The data visualization tools market is segmented by component into software and services. The software segment holds the largest market share, driven by the increasing adoption of BI and analytics software across various industries. These software solutions enable users to create and share visual representations of data, facilitating better understanding and communication of insights. The rise in self-service data visualization tools, which allows users without technical expertise to generate reports and dashboards, is further fueling the demand for software solut

Visualization is among the most powerful of data analysis techniques and is readily available in standalone systems or components of everyday software packages. In recent years much work has been done to design and develop visualization systems with reduced entry and usage barriers in order to make visualization available to the masses. Here we describe a novel application of case-based reasoning techniques to help users visualize complex datasets. We exploit an online visualization service, Many Eyes and explore how case based representation of datasets including simple features such as size and content types can produce recommendations of visualization types to assist novice users in the selection of appropriate visualizations. Paper presented at the 20th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2009), Dubiln, 19th-21st August 2009 Science Foundation Ireland Conference web site http://aics.ucd.ie/

ABSTRACT Randomized controlled trials are known to be the best tool to determine the effects of an intervention; however, most healthcare professionals are not able to adequately understand the results. In this report, concepts, applications, examples, and advantages of using visual data as a complementary tool in the results section of original articles are presented. Visual simplification of data presentation will improve general understanding of clinical research.

Fast and robust visual object recognition in young children

Data for "Fast and robust visual object recognition in young children"

Ayzenberg, V., Sener, S.B., Novick, K., & Lourenco, S. F. (2025, accepted). Fast and robust visual object recognition in young children. Science Advances.

The data, code, stimuli, and figures can also be found at: https://github.com/vayzenb/kornet

The kid_object_recognition-data_stimuli_code.zip contains all the files necessary for replicating the results in the paper

The kornet_train_image.zip file contains all the images that were used for training model classifiers

Naming Conventions

Slightly different naming conventions were used as shorthand for the tasks stimuli and analyses. These do not always line up with the labels used in the manuscript.

The repo name KorNet stands for "Kid Object Recognition Networks" -- a play on the CORnet family of models

Tasks

Complete contour conditions is sometimes referred to as 'Outline'

Perturbed contour condition is sometimes referred to as 'Pert' or 'ripple'

Deleted contour condition is sometimes referred to as 'IC' for illusory contours

Model Names

Models were often renamed for the main-text to more precisly specify their architecture and training or for readability. See the model_comparison.ipynb notebook and the modelling/model_loader.py for all model info

Folder Structure

analyses

Contains all files for conducting all high-level statistical analyses presented in the main-text

Human only data figures can be recreated using the sub_analysis.ipynb notebook

Human model comparison figures can be recreated from the model_comparison.ipynb notebook

data

Contains all individual participant data files for both children and adults.

The sub_info.csv file contains subject demographic information for children

figures

Contains all figures presented in the main-text and supplemental materials

modelling

Contains scripts necessary for extracting model activations and conducting classifcation (i.e., decoding). Below I highlight the files most relevant to the main-text

model_loader.py: A general script for loading model architectures, their transforms, and weights

train.py: The training script used for training VoneNet models

extract_acts.py/extract_acts_layerwise.py: Scripts for extracting feature activations for all training and test stimuli, either from the top layer or from pre-specified layers defined in "all_model_layers.csv"

decode_image.py/decode_image_layerwise.py: Scripts for predicting the category of a test image after training on naturalistic images using activations from the extract_acts script

train_sizes.xlsx: An excel sheet summarizing the dataset size and total experience of each model as measured by data_set size x eopochs. Information is drawn from papers or model cards describing each model

results

Summary files that are used in subsequent analyses.

group_data folder: this contains human only summaries seperated by condition, and summaries containing model performance

model folder: contains performance of each model on the test stimuli using each classifier, seperated by # of training images used

natural_image_decoding: performance of each model on the training images

The top-level results folder also has various summary files for generating figures.

stim

Contains all the test stimuli used in the current study (see naming conventions).

Original stimuli was numerically labelled. See kornet_classes for the corresponding object name for each number

The completed_silhouette folder contains a silhouette versions of the complete contour condition stimuli. These were used to compute curvature and shape envelope statistics

In this lecture we present and discuss cybercartographic ways of representing statistical data with Nunaliit. Nunaliit is an open-source software framework that enables the development of cybercartographic products including atlases. Cybercartographic atlases are on-line dynamic, interactive atlases supporting new forms of geovisualization that go beyond conventional and GIS mapping techniques to stimulate data analysis exploration and display. Researchers at the Geomatics and Cartographic Research Centre have developed several atlas prototypes, including The Cybercartographic Atlas of Canada's Trade with the World which is based in part on data that has now been liberated by Statistics Canada. Several innovative forms of geovisualization, including geo-audification and graphomap, will be presented and discussed to illustrate the potential of cybercartography tools and concepts to design new forms of audio-visual mapping.

This paper proposes a dynamic analytical processing (DAP) visualization tool based on the Bubble-Wall Plot. It can be handily used to develop visual warning systems for visualizing the dynamic analytical processes of hazard data. Comparative analysis and case study methods are used in this research. Based on a literature review of Q1 publications since 2017, 23 types of data visualization approaches/tools are identified, including seven anomaly data visualization tools. This study presents three significant findings by comparing existing data visualization approaches. The primary finding is that no single visualization tool can fully satisfy industry requirements. This finding motivates academics to develop new DAP visualization tools. The second finding is that there are different views of Line Charts and various perspectives on Scatter Plots. The other one is that different researchers may perceive an existing data visualization tool differently, such as arguments between Scatter Plots and Line Charts and diverse opinions about Parallel Coordinate Plots and Scatter Plots. Users’ awareness rises when they choose data visualization tools that satisfy their requirements. By conducting a comparative analysis based on five categories (Style, Value, Change, Correlation, and Others) with 26 subcategories of metric features, results show that this new tool can effectively solve the limitations of existing visualization tools as it appears to have three remarkable characteristics: the simplest cartographic tool, the most straightforward visual result, and the most intuitive tool. Furthermore, this paper illustrates how the Bubble-Wall Plot can be effectively applied to develop a warning system for presenting dynamic analytical processes of hazard data in the coal mine. Lastly, this paper provides two recommendations, one implication, six research limitations, and eleven further study topics.

Raw fMRI data accompanying the NeuroImage 2022 paper. Information about the structure of data acquisition (run types, etc.) can be found in the paper. Processed data (to produce the figures in the paper and any further analysis) can be found on OSF: https://doi.org/10.17605/OSF.IO/HJ5VC

Feature-based attention modulates visual processing beyond the focus of spatial attention. Previous work has reported such spatially-global effects for low-level features such as color and orientation, as well as for faces. Here, using fMRI, we provide evidence for spatially-global attentional modulation for human bodies. Participants were cued to search for one of six object categories in two vertically-aligned images. Two additional, horizontally-aligned, images were simultaneously presented but were never task-relevant across three experimental sessions. Analyses time-locked to the objects presented in these task-irrelevant images revealed that responses evoked by body silhouettes were modulated by the participants’ top-down attentional set, becoming more body-selective when participants searched for bodies in the task-relevant images. These effects were observed both in univariate analyses of the body-selective cortex and in multivariate analyses of the object-selective visual cortex. Additional analyses showed that this modulation reflected response gain rather than a bias induced by the cues, and that it reflected enhancement of body responses rather than suppression of non-body responses. These findings provide evidence for a spatially-global attention mechanism for body shapes, supporting the rapid and parallel detection of conspecifics in our environment.

Quantitative attitutional data was collected form first year social science students regarding their anxiety about learning statistics and their ability to complete and to learn to complete core statistical tasks, at the beginning and end of a statistics teaching module to identify the impact (if any) of the teaching they have received on their anxiety and self-competence ratings. The aim of the project is to examine the impact (if any) of the introduction of visualisation teaching and learning techniques, including Geographical Information Systems in the form of 'crime mapping' data management and analysis software, on student's self-reported experience of, and attitudes toward, quantitative method teaching. To achieve its aim the project will acquire baseline data from undergraduate criminology and sociology students before introducing a targeted intervention - a new teaching module which emphasizes visual learning and teaching strategies - and ascertaining its impact on student learning