A collection of files used for a data visualization project for the Digital Humanities Praxis course at the Graduate Center, CUNY. The files represent raw data (csv), data used for the visualization(s) (gephi), and the visualizations themselves (pdf). A write-up on the project can be located at the GC Academic Commons site: http://dhpraxis14.commons.gc.cuny.edu/2014/11/12/its-big-data-to-me-data-viz-part-2

Dataset

This dataset was created by Serhii Kotiv

Contents

Data Visualization 2 (trail)

## Overview

Data Visualization 2 (trail) is a dataset for object detection tasks - it contains Food 5Sze annotations for 7,580 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Mohamed Aldamrdash

Released under Apache 2.0

Contents

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.

Dataset

This dataset was created by Bharat Kumar

Contents

In 2012, GreyNet published a page on its website and made accessible the first edition of IDGL, International Directory of Organizations in Grey Literature . The latest update of this PDF publication was in August 2016, providing a list of some 280 organizations in 40 countries worldwide that have contact with the Grey Literature Network Service. The listing appears by country followed by the names of the organizations in alphabetical order, which are then linked to a URL.This year GreyNet International marks its Twenty Fifth Anniversary and seeks to more fully showcase organizations, whose involvement in grey literature is in one or more ways linked to GreyNet.org. Examples of which include: members, partners, conference hosts, sponsors, authors, service providers, committee members, associate editors, etc.This revised and updated edition of IDGL will benefit from the use of visualization software mapping the cities in which GreyNet’s contacts are located. Behind each point of contact are a number of fields that can be grouped and cross-tabulated for further data analysis. Such fields include the source, name of organization, acronym, affiliate’s job title, sector of information, subject/discipline, city, state, country, ISO code, continent, and URL. Eight of the twelve fields require input, while the other four fields do not.The population of the study was derived by extracting records from GreyNet’s in-house, administrative file. Only recipients on GreyNet’s Distribution List as of February 2017 were included. The records were then further filtered and only those that allowed for completion of the required fields remained. This set of records was then converted to Excel format, duplications were removed, and further normalization of field entries took place. In fine, 510 records form the corpus of this study. In the coming months, an in-depth analysis of the data will be carried out - the results of which will be recorded and made visually accessible.The expected outcome of the project will not only produce a revised, expanded, and updated publication of IDGL, but will also provide a visual overview of GreyNet as an international organization serving diverse communities with shared interests in grey literature. It will be a demonstration of GreyNet’s commitment to research, publication, open access, education, and public awareness in this field of library and information science. Finally, this study will serve to pinpoint geographic and subject based areas currently within as well as outside of GreyNet’s catchment.

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

Construction Data Visualization Software Market Outlook

According to our latest research, the global construction data visualization software market size reached USD 2.1 billion in 2024, driven by the increasing adoption of digital solutions across the construction industry. The market is poised to exhibit a robust CAGR of 13.2% from 2025 to 2033, with the total market value expected to reach USD 6.1 billion by 2033. This significant growth is fueled by the rising demand for real-time data analytics, improved project transparency, and the growing complexity of construction projects worldwide. The construction data visualization software market is witnessing accelerated adoption as organizations strive to enhance project efficiency, reduce costs, and mitigate risks through actionable insights derived from advanced data visualization tools.

The construction data visualization software market is experiencing remarkable momentum as the industry increasingly recognizes the value of data-driven decision-making. One of the primary growth factors is the escalating complexity of construction projects, which necessitates robust tools for managing, analyzing, and visualizing vast datasets. As projects become larger and more intricate, stakeholders require comprehensive dashboards and visual analytics to track progress, monitor budgets, and anticipate potential risks. This has resulted in a surge in demand for construction data visualization software, which empowers users to convert raw data into actionable insights, streamline workflows, and facilitate better communication across teams. Furthermore, the integration of Building Information Modeling (BIM) and other digital construction technologies is amplifying the need for sophisticated visualization solutions, enabling seamless collaboration and data sharing among architects, engineers, contractors, and project owners.

Another significant driver propelling the construction data visualization software market is the growing emphasis on cost control and resource optimization. Construction firms are under increasing pressure to deliver projects on time and within budget, while maintaining high standards of quality and safety. Data visualization tools play a crucial role in this regard by providing real-time visibility into project metrics, enabling early identification of cost overruns, schedule delays, and resource bottlenecks. By leveraging advanced analytics and interactive dashboards, construction companies can make informed decisions, optimize resource allocation, and improve overall project performance. The adoption of cloud-based solutions further enhances accessibility and scalability, allowing organizations of all sizes to harness the power of data visualization without significant upfront investments in IT infrastructure.

The rapid digital transformation sweeping across the construction sector is also a key catalyst for market expansion. As the industry embraces technologies such as the Internet of Things (IoT), artificial intelligence, and machine learning, the volume and complexity of construction data are increasing exponentially. Construction data visualization software is evolving to accommodate these trends, offering advanced features such as predictive analytics, automated reporting, and customizable dashboards. These capabilities enable stakeholders to gain deeper insights into project dynamics, anticipate challenges, and drive continuous improvement. Additionally, the growing focus on sustainability and regulatory compliance is prompting construction firms to adopt data visualization tools that facilitate transparent reporting and support environmentally responsible decision-making.

From a regional perspective, North America currently dominates the construction data visualization software market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of major industry players, high digital adoption rates, and a strong focus on innovation and process optimization. Europe and Asia Pacific are also witnessing rapid growth, fueled by increasing infrastructure investments, government initiatives promoting digitalization, and the rising adoption of cloud-based construction solutions. Emerging markets in Latin America and the Middle East & Africa are expected to experience steady growth, supported by urbanization trends and the modernization of construction practices. Overall, the global construction data visualization software market is set to witness robust expansion across all major reg

This project was done to analyze sales data: to identify trends, top-selling products, and revenue metrics for business decision-making. I did this project offered by MeriSKILL, to learn more and be exposed to real-world projects and challenges that will provide me with valuable industry experience and help me develop my data analytical skills.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20837845%2Fe3561db319392bf9cc8b7d3fcc7ed94d%2F2019%20Sales%20dashboard.png?generation=1717273572595587&alt=media" alt=""> More on this project is on Medium

Research dissemination and knowledge translation are imperative in social work. Methodological developments in data visualization techniques have improved the ability to convey meaning and reduce erroneous conclusions. The purpose of this project is to examine: (1) How are empirical results presented visually in social work research?; (2) To what extent do top social work journals vary in the publication of data visualization techniques?; (3) What is the predominant type of analysis presented in tables and graphs?; (4) How can current data visualization methods be improved to increase understanding of social work research? Method: A database was built from a systematic literature review of the four most recent issues of Social Work Research and 6 other highly ranked journals in social work based on the 2009 5-year impact factor (Thomson Reuters ISI Web of Knowledge). Overall, 294 articles were reviewed. Articles without any form of data visualization were not included in the final database. The number of articles reviewed by journal includes : Child Abuse & Neglect (38), Child Maltreatment (30), American Journal of Community Psychology (31), Family Relations (36), Social Work (29), Children and Youth Services Review (112), and Social Work Research (18). Articles with any type of data visualization (table, graph, other) were included in the database and coded sequentially by two reviewers based on the type of visualization method and type of analyses presented (descriptive, bivariate, measurement, estimate, predicted value, other). Additional revi ew was required from the entire research team for 68 articles. Codes were discussed until 100% agreement was reached. The final database includes 824 data visualization entries.

A variety of cumulative (over reporting periods) data points for Rescue Plan projects: individual recipient count, business recipient count, non profit recipient count, and expenditure.-- Additional Information: Category: ARPA Update Frequency: As Necessary-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=61045

This package includes a Tableau file and good/bad figures for the visual sequencing disorder group. Dataset: Medical Cost.csv is used for the creation of visualizations.

This dataset contains the raw experimental data and supplementary materials for the "Asymmetry Effects in Virtual Reality Rod and Frame Test". The materials included are:

•  Raw Experimental Data: older.csv and young.csv
•  Mathematica Notebooks: a collection of Mathematica notebooks used for data analysis and visualization. These notebooks provide scripts for processing the experimental data, performing statistical analyses, and generating the figures used in the project.
•  Unity Package: a Unity package featuring a sample scene related to the project. The scene was built using Unity’s Universal Rendering Pipeline (URP). To utilize this package, ensure that URP is enabled in your Unity project. Instructions for enabling URP can be found in the Unity URP Documentation.

Requirements:

•  For Data Files: software capable of opening CSV files (e.g., Microsoft Excel, Google Sheets, or any programming language that can read CSV formats).
•  For Mathematica Notebooks: Wolfram Mathematica software to run and modify the notebooks.
•  For Unity Package: Unity Editor version compatible with URP (2019.3 or later recommended). URP must be installed and enabled in your Unity project.

Usage Notes:

•  The dataset facilitates comparative studies between different age groups based on the collected variables.
•  Users can modify the Mathematica notebooks to perform additional analyses.
•  The Unity scene serves as a reference to the project setup and can be expanded or integrated into larger projects.

Citation: Please cite this dataset when using it in your research or publications.

Results of an interview study with twelve experts on their project processes and their recommendations for visualizations in libraries. Recommendations were retrieved with the method SHIRA (Structured Hierarchical Interviewing for Requirement Analysis)[1]. This allows generating concrete qualities and implementation suggestions out of abstract qualities.

The file contains two pages: On the first, a meta-model was constructed out of all identified steps during library visualization projects. On the second, all SHIRA suggestions were gathered and analyzed.

Feel free to contact me if you have any questions!

[1] M. Hassenzahl, R. Wessler, and K.-C. Hamborg. Exploring and understanding product qualities that users desire. Conference on Human-Computer Interaction IHM-HCI’2001, 2, 2001.

Dataset Name: Online Store Dataset

Description: The Online Store dataset is a comprehensive collection of 500 rows of synthetic e-commerce product data. Designed to simulate an online retail environment similar to major e-commerce platforms like Amazon, this dataset includes a diverse range of attributes for each product. The dataset provides valuable insights into product characteristics, pricing, stock levels, and customer feedback, making it ideal for analysis, machine learning, and data visualization projects.

Features:

ID: Unique identifier for each product. Product_Name: Name of the product, generated using random words to simulate real-world product names. Category: Product category (e.g., Electronics, Clothing, Books, Home, Toys, Sports). Price: Product price, ranging from $10 to $500. Stock: Number of items available in stock. Rating: Customer rating of the product (1 to 5 stars). Reviews: Number of customer reviews. Brand: Brand of the product. Date_Added: Date when the product was added to the catalog. Discount: Percentage discount applied to the product. Use Cases:

Data Analysis: Explore trends and patterns in e-commerce product data. Machine Learning: Build and train models for product recommendation, pricing strategies, or customer segmentation. Data Visualization: Create visualizations to analyze product categories, pricing distribution, and customer reviews. Notes:

The data is synthetic and randomly generated, reflecting typical attributes found in e-commerce platforms. This dataset can be used for educational purposes, practice, and experimentation with various data analysis and machine learning techniques.

Given the wide diversity in applications of biological mass spectrometry, custom data analyses are often needed to fully interpret the results of an experiment. Such bioinformatics scripts necessarily include similar basic functionality to read mass spectral data from standard file formats, process it, and visualize it. Rather than having to reimplement this functionality, to facilitate this task, spectrum_utils is a Python package for mass spectrometry data processing and visualization. Its high-level functionality enables developers to quickly prototype ideas for computational mass spectrometry projects in only a few lines of code. Notably, the data processing functionality is highly optimized for computational efficiency to be able to deal with the large volumes of data that are generated during mass spectrometry experiments. The visualization functionality makes it possible to easily produce publication-quality figures as well as interactive spectrum plots for inclusion on web pages. spectrum_utils is available for Python 3.6+, includes extensive online documentation and examples, and can be easily installed using conda. It is freely available as open source under the Apache 2.0 license at https://github.com/bittremieux/spectrum_utils.

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) for all complete quarters so far this year (2016). Offenses occurring at intersections are represented at the X Coordinate and Y Coordinate of the intersection. Crimes occurring anywhere other than an intersection are geo-located to the middle of the block. For additional details, please see the attached data dictionary in the ‘About’ section.

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

PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.