The Data Visualization Workshop II: Data Wrangling was a web-based event held on October 18, 2017. This workshop report summarizes the individual perspectives of a group of visualization experts from the public, private, and academic sectors who met online to discuss how to improve the creation and use of high-quality visualizations. The specific focus of this workshop was on the complexities of "data wrangling". Data wrangling includes finding the appropriate data sources that are both accessible and usable and then shaping and combining that data to facilitate the most accurate and meaningful analysis possible. The workshop was organized as a 3-hour web event and moderated by the members of the Human Computer Interaction and Information Management Task Force of the Networking and Information Technology Research and Development Program's Big Data Interagency Working Group. Report prepared by the Human Computer Interaction And Information Management Task Force, Big Data Interagency Working Group, Networking & Information Technology Research & Development Subcommittee, Committee On Technology Of The National Science & Technology Council...

Here is the link for the web application developped fir the data analysis embedded to the BENEFIT-MED project

Context

This dataset was created by scraping different websites and then classifying them into different categories based on the extracted text.

Content

Below are the values each column has. The column names are pretty self-explanatory. website_url: URL link of the website. cleaned_website_text: the cleaned text content extracted from the

Explore numeric attribute data from a feature layer in 3D. Visible numeric fields are displayed and values are ranked from highest to lowest. You can optionally represent each the value as a percentage of the total. Configurable OptionsSupported visualizations include: Point Extrusion: represent attribute data from point features as a vertical line.Point Pulse: represent attribute data from point features as a pulsing circle.Polygon Extrusion: represent attribute data from polygon features as a vertical column.Data RequirementsThis application requires a feature layer with at least one numeric field. For more information, see the Layers help topic for more details.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.Click Create a Web App on the item detail page for a web scene.

Here is the link to access the web application code on the Github public repository.

Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool (http://statistika.mfub.bg.ac.rs/interactive-graph/) demonstrates the overall concept and may promote widespread use of interactive graphics.

Code to generate Additional file 2: Figure S1. (TXT 6 kb)

Code to generate Fig. 1. (TXT 14 kb)

In this lightening strike presentation, Daniel presents various tools for data visualization.

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.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

CENSORED WEB-SITES BY ALL COUNTRIES

Sites that were or are currently banned.

This data was created by each country's own users.

• Some of the sites you have seen may have been active again.

Experimental data can broadly be divided in discrete or continuous data. Continuous data are obtained from measurements that are performed as a function of another quantitative variable, e.g., time, length, concentration, or wavelength. The results from these types of experiments are often used to generate plots that visualize the measured variable on a continuous, quantitative scale. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open-source tool was created with R/shiny that does not require coding skills to operate it. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in 3 different ways: (1) lines with unique colors, (2) small multiples, and (3) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color-blind-friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that are relevant. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and runs locally or online: https://huygens.science.uva.nl/PlotTwist

NASS Data Visualization provides a dynamic web query interface supporting searches by Commodity (e.g. Cotton, Corn, Farms & Land, Grapefruit, Hogs, Oranges, Soybeans, Wheat), Statistic type (automatically refreshed based upon choice of Commodity - e.g. Inventory, Head, Acres Planted, Acres Harvested, Production, Yield) to generate chart, table, and map visualizations by year (2001-2016), as well as a link to download the resulting data in CSV format compatible for updating databases and spreadsheets. Resources in this dataset:Resource Title: NASS Data Visualization web site. File Name: Web Page, url: https://nass.usda.gov/Data_Visualization/index.php Query interface with visualization of results as charts, tables, and maps.

Source data files used to generate visualizations presented in Figs 4 and 6–11, along with URLs of the corresponding generated visualizations.

Presentation Date: Friday, December 2, 2022 Location: Barcelona, Spain Abstract: An introduction to data visualization concepts, using key concepts from the "10 Questions to Ask when Creating a Visualization" (10QViz.org) website. Presented at "Data Viz for Society." Featured software includes glue (see glueviz.org and gluesolutions.io). Files included are Keynote slides (in .key and .pdf formats)

Description

This is the main data set that was built for the work titled: "A Web Mining Approach to Collaborative Consumption of Food Delivery Services" which is the official institutional research project of Professor Juan C. Correa at Fundación Universitaria Konrad Lorenz.

Categories

Urban Transportation, Consumer, e-Commerce Retail

Acknowledgements & Source

Professor Juan C. Correa at Fundación Universitaria Konrad Lorenz

Data Source

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Image Source

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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