In recent years, data visualization has been gaining space in journalism, not only in the specialized press, but also in the general press. The objective of this article is to analyze whether there are differences between the impact of receiving a traditional news item and that of a news item with data visualization, in terms of interest, comprehension and attitudes toward data visualization. For this, a study (N = 700) was carried out with two experimental conditions (traditional news vs. news with data visualization), using scientific and health communication news. Moderated mediation analysis were performed to understand how data visualization affects factors such as attitude, or interest, and affects public comprehension. The results showed significant indirect effects that indicate that reading a data visualization news item increases comprehension and, with it, positive attitudes toward data visualization. Variables related to comprehension and interest have been found to have a significant impact on attitudes toward data viewing, opening new lines of research to delve into the factors that affect data-driven news performance.

Methods for 3D‐imaging of biological samples are experiencing unprecedented development, with tools such as X‐ray micro‐computed tomography (μCT) becoming more accessible to biologists. These techniques are inherently suited to small subjects and can simultaneously image both external and internal morphology, thus offering considerable benefits for invertebrate research. However, methods for visualising 3D‐data are trailing behind the development of tools for generating such data. Our aim in this article is to make the processing, visualisation and presentation of 3D‐data easier, thereby encouraging more researchers to utilise 3D‐imaging. Here, we present a comprehensive workflow for manipulating and visualising 3D‐data, including basic and advanced options for producing images, videos and interactive 3D‐PDFs, from both volume and surface‐mesh renderings. We discuss the importance of visualisation for quantitative analysis of invertebrate morphology from 3D‐data, and provide example figures illustrating the different options for generating 3D‐figures for publication. As more biology journals adopt 3D‐PDFs as a standard option, research on microscopic invertebrates and other organisms can be presented in high‐resolution 3D‐figures, enhancing the way we communicate science.

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

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

The purpose of this code is to produce a line graph visualization of COVID-19 data. This Jupyter notebook was built and run on Google Colab. This code will serve mostly as a guide and will need to be adapted where necessary to be run locally. The separate COVID-19 datasets uploaded to this Dataverse can be used with this code. This upload is made up of the IPYNB and PDF files of the code.

AERS data CSV and compressed (ZIP) files are available for download in the table below. For information about VAERS data, please view the pdf icon VAERS Data Use Guide [PDF - 310KB], which contains the following information:

Important information about VAERS from the FDA Brief description of VAERS Cautions on interpreting VAERS data Definitions of terms Description of files List of commonly used abbreviations Select the desired time interval to download VAERS data. Each data set is available for download as a compressed (ZIP) file or as individual CSV files. Each compressed file contains the three CSV files listed for a specific data set.

IntroductionThis study examines the utilization, challenges, and design principles of data visualization approaches, focusing on their applications within AI-assisted decision-making contexts, by reviewing relevant literature. We explore the types of visualization approaches used and the challenges users face. We also examine key visual elements that influence understanding and the evaluation methods used to assess these visualizations.MethodsA systematic literature review (SLR) adhering to PRISMA protocols was carried out across five major academic databases, resulting in 127 relevant studies published from 2011 to July 2024. We synthesize insights from existing visualization approaches used in decision-making, and evaluates key aspects such as usability, interactivity, accessibility, and cognitive load management.ResultsWe identified a range of visualization forms including charts, graphs, dashboards, and interactive platforms aimed at enhancing data exploration and insight extraction. The identified challenges include achieving a balance between complexity and usability, fostering intuitive design, and providing sufficient training to aid accurate interpretation of complex data. Specific visual elements, such as color usage, symbolic representation, and data density control, are highlighted as essential for enhancing user comprehension and supporting effective decision-making. Interactive and customizable visualizations tailored to individual cognitive styles proved especially effective. We further underscore the importance of diverse evaluation methods, including usability testing, surveys, and cognitive assessments, to iteratively refine visualization approaches based on user feedback.DiscussionOur findings suggest that users benefit most from customizable, interactive approaches that cater to varied cognitive preferences and incorporate continuous training to reduce interpretive biases. This research contributes to best practice development for designing accessible, effective visualization approaches suited to the complex decision-making needs in data-centric environments.

What is this ? In this case study, I use a bike-share company data to evaluate the biking performance between members and casuals, determine if there are any trends or patterns, and theorize what are causing them. I am then able to develop a recommendation based on those findings.

Content: Hi. This is my first data analysis project and also my first time to use R in my work. They are the capstone project for Google Data Analysis Certificate Course offered in Coursera. (https://www.coursera.org/professional-certificates/google-data-analytics) It is about operation data analysis of a frictional bike-share company in Chicago. For detailed background story, please check the pdf file (Case 01.pdf) for reference.

In this case study, I use a bike-share company data to evaluate the biking performance between members and casuals, determine if there are any trends or patterns, and theorize what are causing them by descriptive analysis. I am then able to develop a recommendation based on those findings.

First I will make a background introduction, my business tasks and objectives, and how I obtain the data sources for analysis. Also, they are the R code I worked in RStudio for data processing, cleaning and generating graphs for next part analysis. Next, there are my analysis of bike data, with graphs and charts generated by R ggplot2. At the end, I also provide some recommendations to business tasks, based on the data finding.

I understand that I am just new to data analysis and the skills or code is very beginner level. But I am working hard to learn more in both R and data science field. If you have any idea or feedback. Please feel free to comment.

Stanley Cheng 2021-09-30

This is the Google Data Analytics Data Analysis Certificate Capstone Project Case Study 1.

Raw data will be cleaned via R script to produce a usable data set for visualization. Cleaned data is listed as "divvy_total_cleaned.csv"

Included is a PDF presentation of case study findings, including marketing recommendations.

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.

Presentation Date: Wednesday, July 19, 2023 Location: Bates College, Lewiston, Maine. Abstract: A discussion of how the glue software can be used across a spectrum of applications, from simple, scripted web-based interactions, to dashboards, to fully flexible environments. Presented at the 2023 Gordon Research Conference on Data Visualization in Research and Education. Files included are Keynote slides (in .key and .pdf formats)

Summary of basic properties of empirical distributions that are interesting for data mining.

Objectives: Develop a tool for applying various COVID-19 re-opening guidelines to the more than 120 U.S. Environmental Protection Agency (EPA) facilities.Methods: A geographic information system boundary was created for each EPA facility encompassing the county where the EPA facility is located and the counties where employees commuted from. This commuting area is used for display in the Dashboard and to summarize population and COVID-19 health data for analysis.Results: Scientists in EPA’s Office of Research and Development developed the EPA Facility Status Dashboard, an easy-to-use web application that displays data and statistical analyses on COVID-19 cases, testing, hospitalizations, and vaccination rates.Conclusion: The Dashboard was designed to provide readily accessible information for EPA management and staff to view and understand the COVID-19 risk surrounding each facility. It has been modified several times based on user feedback, availability of new data sources, and updated guidance. The views expressed in this article are those of the authors and do not necessarily represent the views or the policies of the U.S. Environmental Protection Agency.

📊 Sales & Customer Analytics – Tableau Dashboard (PDF & Interactive) 🔍 Overview This dataset includes a Tableau project analysing sales trends & customer insights with an interactive dashboard switch.

The dashboards provide actionable insights into: ✅ Sales performance & revenue trends 📈 ✅ Top-performing products & regions 🌍 ✅ Customer segmentation & behavior analysis 🛍️ ✅ Retention strategies & marketing impact 🎯

📂 Files Included 📄 Sales & Customer Analytics Dashboard (PDF Report) – A full summary of insights. 🎨 Tableau Workbook (.twbx) – The interactive dashboards (requires Tableau). 🖼️ Screenshots – Previews of the dashboards.

🔗 Explore the Interactive Dashboards on Tableau Public :

Sales Dashboard:[https://public.tableau.com/app/profile/egbe.grace/viz/SalesCustomerDashboardsDynamic_17385906491570/CustomerDashboard] Customer Dashboard: [https://public.tableau.com/app/profile/egbe.grace/viz/SalesCustomerDashboardsDynamic_17385906491570/CustomerDashboard]

📌 Key Insights from the Dashboards ✅ Revenue trends show peak sales periods & seasonal demand shifts. ✅ Top-selling products & regions help businesses optimize their strategies. ✅ Customer segmentation identifies high-value buyers for targeted marketing. ✅ Retention analysis provides insights into repeat customer behaviour.

💡 How This Can Help: This dataset and Tableau project can help businesses & analysts uncover key patterns in sales and customer behavior, allowing them to make data-driven decisions to improve growth and customer retention.

💬 Would love to hear your feedback! Let’s discuss the impact of sales analytics in business strategy.

📢 #DataAnalytics #Tableau #SalesAnalysis #CustomerInsights #BusinessIntelligence #DataVisualization

This brief research report presents an experiment investigating how people interpret quantities displayed in pictorial charts. Pictorial charts are a popular form of data visualization in media. They represent different quantities with differently scaled pictures. In the present study, 63 university students answered a 12-item questionnaire containing three different pictorial charts. The study aimed to evaluate how individuals perceive the quantities in the pictorial charts intuitively. Therefore, the students’ answers were not rated as correct or incorrect. Instead, it was analyzed which functional relationship between scale factor and estimated quantity best described people’s interpretation of pictorial charts. The experiment showed that, on average, a model assuming a quadratic relationship fitted best. This result deviates from research that found an overgeneralization of linearity when students compare the areas of two mathematically similar shapes. It may be that the routines for the interpretation of pictures differ considerably depending on whether a person must calculate a quantity arithmetically or is prompted to estimate the quantity based on visual perception.

This details the software code used in RStudio for analysing study (Evaluation of changes in some physico-chemical properties of bottled water exposed to sunlight in Bauchi State, Nigeria) data. The code is provided in .R, .docx and .pdf. For .R, it is accessible directly using RStudio. For .docx and .pdf, copy and paste the commands into RStudio. It includes codes for generating plots used in paper publication Note that you require the dataset used in the study which is accessible from the following DOI:

This project explores the history and trends of the video game console market using datasets on console release dates ,sales and lifespans. I cleaned and organized the data, then visualized key industry patterns. Insights included how price correlates with units sold, if lifespan had an effect on total sales and if certain time periods or generations sold more than others. This project demonstrates skills in data cleaning, exploratory data analysis, and visualization. I have a full detailed case study PDF and Tableau visualizations are included in the attachments.

SLC25A46 is one of the genes found in Charcot-Marie-Tooth disorder. This pathway has been analyzed with expression data. View the pathway with expression data visualization: https://classic.wikipathways.org/index.php/Image:SLC25A46-Pathway.pdf#file. Download the pathway with expression data visualization: [[Image:SLC25A46-Pathway.pdf]]

Presentation Date: Friday, March 1, 2019 Location: Visual Communication Symposium, Rice University, Houston, TX Abstract: Astronomy has long been a field reliant on visualization. First, it was literal visualization—looking at the Sky. Today, though, astronomers are faced with the daunting task of understanding gigantic digital images from across the electromagnetic spectrum and contextualizing them with hugely complex physics simulations, in order to make more sense of our Universe. In this talk, I will explain how new approaches to simultaneously exploring and explaining vast data sets allow astronomers—and other scientists—to make sense of what the data have to say, and to communicate what they learn, to each other and to the public. I will focus on the multi-dimensional linked-view data visualization environment known as “glue” (glueviz.org), explaining how it is being used in astronomy, medical imaging, and geographic information sciences. I will discuss its future potential to expand into all fields where diverse but related multi-dimensional data sets can be profitably analyzed together. Toward the aim of bringing the fruits of visualization to a broader audience, I will also introduce the new “10 Questions to Ask When Creating a Visualization” website, 10QViz.org. Full program downloadable from: https://vcs.rice.edu/sites/g/files/bxs2036/f/VCS%202019%20program%20booklet.pdf

CSV file used for the visualization, "Data Visualization: Claus Oldenberg and Josepf Bueys" (PDF). Original data set retrieved from: https://github.com/tategallery/collection