This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore L ...

The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

Abstract This paper presents the results of the statistical graphs’ analysis according to the curricular guidelines and its implementation in eighteen primary education mathematical textbooks in Perú, which correspond to three complete series and are from different editorials. In them, through a content analysis, we analyzed sections where graphs appeared, identifying the type of activity that arises from the graphs involved, the demanded reading level and the semiotic complexity task involved. The textbooks are partially suited to the curricular guidelines regarding the graphs presentation by educational level and the number of activities proposed by the three editorials are similar. The main activity that is required in textbooks is calculating and building. The predominance of bar graphs, a basic reading level and the representation of an univariate data distribution in the graph are observed in this study.

Abstract Technical graphic representation presents problems concerning the reduction of dimensionality from 3D to 2D. AEC (architecture, engineering and construction) projects usually adopt the top view paradigm with two-dimensional orthogonal projection. Recently, three major changes in technical representation were the alteration of orthogonal projection into a three-dimensional perspective view, inclusion of oriented object programing as in BIM (Building Information Model) and the interactions with AR (augmented reality). In this context, the present research evaluates the proposal of symbology based on color Hue as done in Cartography and the impact of three-dimensionality of the symbol in the identification of incompatibilities in a project of a residential building. An application of the visual variable color hue was proposed improve readability to representations and evaluations were performed with expert users, using representations in CAD 2D, BIM and AR in top and perspective views. Results indicate the color hue improve the cognitive process of read, interpret and find incompatibilities in civil projects, while the change of point of view contribute to interaction and manipulation in virtual environments. Both shows significance higher than 6% in ANOVA tests.

This part of the data release includes graphical representation (figures) of data of sediment cores collected in 2014 in Monterey Canyon. It is one of five files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in 2014 (cruise ID 2014-615-FA). One spreadsheet (NorthernFlankMontereyCanyonCores_Info.xlsx) contains core name, location, and length. One spreadsheet (NorthernFlankMontereyCanyonCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity and gamma-ray density whole-core logs of vibracores. One zipped folder of .bmp files (NorthernFlankMontereyCanyonCores_Photos.zip) contains continuous core photographs of the archive half of each vibracore. One spreadsheet (NorthernFlankMontereyCanyonCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One .pdf file (NorthernFlankMontereyCanyonCores_Figures.pdf) contains combined displays of data for each vibracore, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file NorthernFlankMontereyCanyon_Figures.pdf. All vibracores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center. Other remaining core material, if available, is archived at MBARI.

This statistic shows the global data visualization market revenue in 2017 and 2023. In 2017, the total value of this market was estimated to be 4.51 billion US dollars. The market is expected to increase to 7.76 billion U.S. dollars by 2023, with a CAGR of 9.47 percent over the forecast period.

The Knowledge Graph Visualization Tool market is experiencing robust growth, driven by the increasing need for organizations to effectively manage and understand complex data relationships. The market's expansion is fueled by the rising adoption of big data analytics, the need for improved data visualization capabilities, and the growing demand for intuitive tools that simplify complex information. Businesses across various sectors, including healthcare, finance, and technology, are leveraging these tools to gain actionable insights from their data, improve decision-making processes, and enhance operational efficiency. The market is segmented by application (e.g., business intelligence, data discovery, risk management) and type (e.g., cloud-based, on-premise). While the cloud-based segment currently dominates, the on-premise segment is expected to witness steady growth due to security and data control concerns in certain industries. Competition is relatively high, with established players and emerging startups vying for market share. The market is geographically diverse, with North America and Europe currently holding significant shares, while the Asia-Pacific region is predicted to show the fastest growth due to increasing digitalization and technological advancements. The forecast period (2025-2033) indicates continued expansion, with a projected Compound Annual Growth Rate (CAGR) that, assuming a conservative estimate based on current market trends and technological advancements, sits around 15%. This growth will be influenced by factors such as the continuous development of advanced visualization techniques, increased integration with artificial intelligence (AI) and machine learning (ML) algorithms, and the growing demand for real-time data analysis. However, challenges remain, including the need for user-friendly interfaces, concerns about data privacy and security, and the high cost of implementation for some organizations, particularly smaller businesses. Nevertheless, the overall market outlook for Knowledge Graph Visualization Tools is positive, presenting significant opportunities for vendors who can successfully address these challenges and cater to the evolving needs of their customers.

Specialized collection of 0 free data visualization SVG illustrations from the technology & electronics category. Data visualization illustrations including bar charts, network graphs, and information graphics Examples include: bar chart, network graph.

The global Knowledge Graph Visualization Tool market is poised for substantial growth, projected to reach approximately $2,500 million by 2025, with an anticipated Compound Annual Growth Rate (CAGR) of around 18-22% through 2033. This expansion is primarily fueled by the escalating demand for sophisticated data analysis and interpretation across diverse industries. Key drivers include the burgeoning volume of complex, interconnected data and the increasing recognition of knowledge graphs as powerful tools for uncovering hidden patterns, relationships, and actionable insights. The ability of these tools to transform raw data into intuitive, visual representations is critical for stakeholders to make informed decisions, enhance operational efficiency, and gain a competitive edge. Sectors like finance, where fraud detection and risk assessment are paramount, and healthcare, for drug discovery and personalized medicine, are leading this adoption. Educational institutions are also leveraging these tools for more engaging and effective learning experiences, further broadening the market's reach. The market's trajectory is further shaped by the continuous innovation in visualization techniques and the integration of advanced AI and machine learning capabilities. The emergence of both structured and unstructured knowledge graph types caters to a wider array of data complexities, allowing businesses to harness insights from both highly organized databases and free-form text or multimedia content. While the potential is immense, market restraints include the initial complexity and cost associated with implementing and maintaining knowledge graph solutions, as well as the need for specialized skill sets to manage and interpret the data effectively. However, as the technology matures and becomes more accessible, these challenges are expected to diminish, paving the way for widespread adoption. Geographically, North America and Europe are currently dominant markets due to their advanced technological infrastructure and early adoption rates, but the Asia Pacific region is rapidly emerging as a significant growth area driven by its large digital economy and increasing investments in data analytics. This comprehensive report delves into the dynamic landscape of Knowledge Graph Visualization Tools, providing an in-depth analysis of market dynamics, key players, and future projections. The study period spans from 2019 to 2033, with a base year of 2025, offering a thorough examination of historical trends (2019-2024) and forecasting future growth during the forecast period of 2025-2033. The estimated year for market assessment is also 2025. The report aims to equip stakeholders with actionable insights, forecasting a market value that is projected to reach into the millions of USD.

Reproducibility package containing data, R script and final plot of Figure 1 of this blog post on data visualisation in neuroscience:https://garstats.wordpress.com/2016/07/28/neuroscience-group-results/

Following the format of the Open Graph Benchmark (OGB), we design four prediction tasks of relations (mag-write, mag-cite) and higher-order patterns (tags-math, DBLP-coauthor) and construct the corresponding datasets over heterogeneous graphs and hypergraphs [1]. The original ogb-mag dataset only contains features for 'paper'-type nodes. We add the node embedding provided by [2] as raw features for other node types in MAG(P-A)/(P-P). For these four tasks, the model is evaluated by one positive query paired with a certain number of randomly sampled negative queries (1:1000 by default, except for tags-math 1:100).

Question Paper Solutions of chapter Diagrammatic and Graphical representation of Numerical Data of Numerical and statistical Methods, 5th Semester , Bachelor of Computer Application 2020-2021

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)

This dataset contains graphs and a movie. Both show visualizations of rotational curves in the transversal plane within a Standardized Gait Cycle from Vertebra prominens downwards, ending at the pelvis. They display 201 anonymous healthy people aged 18-70 years walking at 2,3,4, and 5 km/h on a treadmill. They are based on a SPSS (v23) syntax file and a relating graph template that can be found at our datasets as well. Files are numbered subsequently across all speeds and can be linked by number to its non-standardized counterpart in a further dataset. Positive values show vertebral body rotation to the left, negative values show rotation to the right. Percent of the Standardized Gait Cycle (0-100%) is displayed on the abscissa, always starting with Initial Contact of the right foot. Within a Standardized Gait Cycle the duration of the stance phase right is expected to be 60% (Perry, 1992). As can be seen in the graphs, interpolating spline functions work for average walking speed measurements leading to a more precise determination of relevant and characteristic points (e.g. maxima, phase shifts, lumbar and thoracic movement behavior), thereby aiding in in the clarification of individual features.

Discover the booming Knowledge Graph Visualization Tool market! Our analysis reveals a $2 billion market in 2025, projected to reach $6.5 billion by 2033, driven by big data analytics and AI. Explore key trends, restraints, and regional insights for informed business decisions.

The global Knowledge Graph Visualization Tool market is projected to grow from XXX million in 2025 to XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The market growth is attributed to the increasing adoption of knowledge graphs by enterprises to organize and visualize complex data, the rising need for efficient data exploration and analysis, and the growing popularity of artificial intelligence (AI) and machine learning (ML). The increasing investments in research and development activities by market players to enhance the capabilities of knowledge graph visualization tools are further fueling the market growth. The market is segmented based on application, type, and region. By application, the market is categorized into various sectors such as healthcare, finance, retail, manufacturing, and government. By type, the market is divided into cloud-based and on-premises solutions. Regionally, the market is analyzed across North America, Europe, Asia Pacific, Middle East & Africa, and South America. Key market players include [Company Names]. The competitive landscape of the market is characterized by the presence of established vendors and emerging startups offering innovative solutions. Strategic partnerships, mergers and acquisitions, and product innovation are some of the key strategies adopted by market participants to gain a competitive edge. This report provides a comprehensive overview of the Knowledge Graph Visualization Tool market. It includes market sizing, segmentation, competitive analysis, and key trends. The report also provides insights into the factors driving the market and the challenges it faces.

Description

This dataset contains a list of 186 Digital Humanities projects leveraging information visualisation techniques. Each project has been classified according to visualisation and interaction methods, narrativity and narrative solutions, domain, methods for the representation of uncertainty and interpretation, and the employment of critical and custom approaches to visually represent humanities data.

Classification schema: categories and columns

The project_id column contains unique internal identifiers assigned to each project. Meanwhile, the last_access column records the most recent date (in DD/MM/YYYY format) on which each project was reviewed based on the web address specified in the url column.
The remaining columns can be grouped into descriptive categories aimed at characterising projects according to different aspects:

Narrativity. It reports the presence of information visualisation techniques employed within narrative structures. Here, the term narrative encompasses both author-driven linear data stories and more user-directed experiences where the narrative sequence is determined by user exploration [1]. We define 2 columns to identify projects using visualisation techniques in narrative, or non-narrative sections. Both conditions can be true for projects employing visualisations in both contexts. Columns:

• non_narrative (boolean)

• narrative (boolean)

Domain. The humanities domain to which the project is related. We rely on [2] and the chapters of the first part of [3] to abstract a set of general domains. Column:

• domain (categorical):

• History and archaeology

• Art and art history

• Language and literature

• Music and musicology

• Multimedia and performing arts

• Philosophy and religion

• Other: both extra-list domains and cases of collections without a unique or specific thematic focus.

Visualisation of uncertainty and interpretation. Buiding upon the frameworks proposed by [4] and [5], a set of categories was identified, highlighting a distinction between precise and impressional communication of uncertainty. Precise methods explicitly represent quantifiable uncertainty such as missing, unknown, or uncertain data, precisely locating and categorising it using visual variables and positioning. Two sub-categories are interactive distinction, when uncertain data is not visually distinguishable from the rest of the data but can be dynamically isolated or included/excluded categorically through interaction techniques (usually filters); and visual distinction, when uncertainty visually “emerges” from the representation by means of dedicated glyphs and spatial or visual cues and variables. On the other hand, impressional methods communicate the constructed and situated nature of data [6], exposing the interpretative layer of the visualisation and indicating more abstract and unquantifiable uncertainty using graphical aids or interpretative metrics. Two sub-categories are: ambiguation, when the use of graphical expedients—like permeable glyph boundaries or broken lines—visually convey the ambiguity of a phenomenon; and interpretative metrics, when expressive, non-scientific, or non-punctual metrics are used to build a visualisation. Column:

• uncertainty_interpretation (categorical):

• Interactive distinction

• Visual distinction

• Ambiguation

• Interpretative metrics

Critical adaptation. We identify projects in which, with regards to at least a visualisation, the following criteria are fulfilled: 1) avoid repurposing of prepackaged, generic-use, or ready-made solutions; 2) being tailored and unique to reflect the peculiarities of the phenomena at hand; 3) avoid simplifications to embrace and depict complexity, promoting time-consuming visualisation-based inquiry. Column:

• critical_adaptation (boolean)

Non-temporal visualisation techniques. We adopt and partially adapt the terminology and definitions from [7]. A column is defined for each type of visualisation and accounts for its presence within a project, also including stacked layouts and more complex variations. Columns and inclusion criteria:

• plot (boolean): visual representations that map data points onto a two-dimensional coordinate system.

• cluster_or_set (boolean): sets or cluster-based visualisations used to unveil possible inter-object similarities.

• map (boolean): geographical maps used to show spatial insights. While we do not specify the variants of maps (e.g., pin maps, dot density maps, flow maps, etc.), we make an exception for maps where each data point is represented by another visualisation (e.g., a map where each data point is a pie chart) by accounting for the presence of both in their respective columns.

• network (boolean): visual representations highlighting relational aspects through nodes connected by links or edges.

• hierarchical_diagram (boolean): tree-like structures such as tree diagrams, radial trees, but also dendrograms. They differ from networks for their strictly hierarchical structure and absence of closed connection loops.

• treemap (boolean): still hierarchical, but highlighting quantities expressed by means of area size. It also includes circle packing variants.

• word_cloud (boolean): clouds of words, where each instance’s size is proportional to its frequency in a related context

• bars (boolean): includes bar charts, histograms, and variants. It coincides with “bar charts” in [7] but with a more generic term to refer to all bar-based visualisations.

• line_chart (boolean): the display of information as sequential data points connected by straight-line segments.

• area_chart (boolean): similar to a line chart but with a filled area below the segments. It also includes density plots.

• pie_chart (boolean): circular graphs divided into slices which can also use multi-level solutions.

• plot_3d (boolean): plots that use a third dimension to encode an additional variable.

• proportional_area (boolean): representations used to compare values through area size. Typically, using circle- or square-like shapes.

• other (boolean): it includes all other types of non-temporal visualisations that do not fall into the aforementioned categories.

Temporal visualisations and encodings. In addition to non-temporal visualisations, a group of techniques to encode temporality is considered in order to enable comparisons with [7]. Columns:

• timeline (boolean): the display of a list of data points or spans in chronological order. They include timelines working either with a scale or simply displaying events in sequence. As in [7], we also include structured solutions resembling Gantt chart layouts.

• temporal_dimension (boolean): to report when time is mapped to any dimension of a visualisation, with the exclusion of timelines. We use the term “dimension” and not “axis” as in [7] as more appropriate for radial layouts or more complex representational choices.

• animation (boolean): temporality is perceived through an animation changing the visualisation according to time flow.

• visual_variable (boolean): another visual encoding strategy is used to represent any temporality-related variable (e.g., colour).

Interactions. A set of categories to assess affordable interactions based on the concept of user intent [8] and user-allowed perceptualisation data actions [9]. The following categories roughly match the manipulative subset of methods of the “how” an interaction is performed in the conception of [10]. Only interactions that affect the aspect of the visualisation or the visual representation of its data points, symbols, and glyphs are taken into consideration. Columns:

• basic_selection (boolean): the demarcation of an element either for the duration of the interaction or more permanently until the occurrence of another selection.

• advanced_selection (boolean): the demarcation involves both the selected element and connected elements within the visualisation or leads to brush and link effects across views. Basic selection is tacitly implied.

• navigation (boolean): interactions that allow moving, zooming, panning, rotating, and scrolling the view but only when applied to the visualisation and not to the web page. It also includes “drill” interactions (to navigate through different levels or portions of data detail, often generating a new view that replaces or accompanies the original) and “expand” interactions generating new perspectives on data by expanding and collapsing nodes.

• arrangement (boolean): the organisation of visualisation elements (symbols, glyphs, etc.) or multi-visualisation layouts spatially through drag and drop or

The global bar graph displays market is anticipated to experience remarkable growth in the coming years, driven by increasing demand from various end-user industries. The market size was valued at USD XXX million in 2025 and is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% from 2025 to 2033. This growth can be attributed to factors such as technological advancements, rising demand for visual data representation, and increasing adoption in sectors like electronics, medical, and aerospace. Among the key segments, the LED and LCD display types are expected to witness significant growth, owing to their superior brightness, clarity, and energy efficiency. The major regions driving the market include North America, Europe, and Asia Pacific. North America holds a dominant market share, with the United States being a notable contributor. The Asia Pacific region is projected to grow at a higher rate during the forecast period, driven by the rapidly expanding electronics and semiconductor industries in countries like China, India, and Japan. Key players in the bar graph displays market include akYtec, Everlight Electronics, Kingbright, Sifam Tinsley, and Texmate, among others. These companies are focusing on innovation, strategic partnerships, and geographical expansion to enhance their market presence.

The graph technology market is experiencing robust growth, driven by the increasing need for advanced data analytics and the rising adoption of artificial intelligence (AI) and machine learning (ML) applications. The market's expansion is fueled by the ability of graph databases to handle complex, interconnected data more efficiently than traditional relational databases. This is particularly crucial in industries like finance (fraud detection, risk management), healthcare (patient relationship mapping, drug discovery), and e-commerce (recommendation systems, personalized marketing). Key trends include the move towards cloud-based graph solutions, the integration of graph technology with other data management systems, and the development of more sophisticated graph algorithms for advanced analytics. While challenges remain, such as the need for skilled professionals and the complexity of implementing graph databases, the overall market outlook remains positive, with a projected Compound Annual Growth Rate (CAGR) – let's conservatively estimate this at 25% – for the forecast period 2025-2033. This growth will be driven by ongoing digital transformation initiatives across various sectors, leading to an increased demand for efficient data management and analytics capabilities. We can expect to see continued innovation in both open-source and commercial graph database solutions, further fueling the market's expansion. The competitive landscape is characterized by a mix of established players like Oracle, IBM, and Microsoft, alongside emerging innovative companies such as Neo4j, TigerGraph, and Amazon Web Services. These companies are constantly vying for market share through product innovation, strategic partnerships, and acquisitions. The presence of both open-source and proprietary solutions caters to a diverse range of needs and budgets. The market segmentation, while not explicitly detailed, likely includes categories based on deployment (cloud, on-premise), database type (property graph, RDF), and industry vertical. The regional distribution will likely show strong growth in North America and Europe, reflecting the higher adoption of advanced technologies in these regions, followed by a steady rise in Asia-Pacific and other developing markets. Looking ahead, the convergence of graph technology with other emerging technologies like blockchain and the Internet of Things (IoT) promises to unlock even greater opportunities for growth and innovation in the years to come.