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.

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.

Context

There's a story behind every dataset and here's your opportunity to share yours.

This study is based on a previous study: Amy Zhang, Aditya Ranganathan, Sarah Emlen Metz, Scott Appling, Connie Moon Sehat, Norman Gilmore, Nick B. Adams, Emmanuel Vincent, Jennifer 8. Lee, Martin Robbins, Ed Bice, Sandro Hawke, David Karger, and An Xiao Mina. A Structured Response to Misinformation: Defining and Annotating Credibility Indicators in News Articles. The Web Conference, April 2018 (available here; dataset here )

A number of the questions remain the same from the study ("WebConf 2018"), with several modifications based on the earlier results to elicit more information related to indicators (https://credweb.org/cciv/).

The study has been broken up into 2 major parts:

new annotators reviewing the same articles from the original WebConf 2018 study, and new annotators reviewing new articles

This spreadsheet contains a collection of over 230 data visualisations about public finances from media organisations, journalists, civil society organisations, advocacy groups, civic hackers, companies and public institutions. In order to build the collection I started with a collection of projects derived from another study mapping “open budget data” on digital media (Gray, 2015). Over 65% of the 120 fiscal data projects identified through the study used visualisations to present information about public finances. Examples were also incorporated from other lists, including relevant items from a database of 466 projects from The Guardian and the New York Times from between 2000 and 2015 (Rooze, 2015), as well as from expert data visualisation blogs such as Infosthetics and Visual Complexity. Further examples were solicited from expert mailing lists, forums and targeted outreach via email and social media. Analyses of the data visualisations are forthcoming in several publications. The collection will continue to be updated periodically. If you have suggestions for projects to add, please get in touch: http://jonathangray.org/contact/

References Gray, J. (2015) "Open Budget Data: Mapping the Landscape". Available at: http://dx.doi.org/10.2139/ssrn.2654878Rooze, M. (2015) "News Graphics Collection". Available at: http://collection.marijerooze.nl/

This dataset consists of 75k records. This will be handy to Learn, Practice and Research topics.

id: unique id for a news article

title: the title of a news article

text: the text of the article; could be incomplete.

label: a label that marks the article as potentially unreliable.

1: unreliable

0: reliable

The Data Science Journal, a renowned publication in the field of data analysis and computer science. Headquartered in New York, the organization has been a leading authority on data-driven insights for over two decades. Founded by a team of esteemed researchers and academics, the company's mission is to provide a platform for data scientists and researchers to share their findings and collaborate with peers.

The Data Science Journal's mission is reflected in their extensive repository of data, which includes datasets on various subjects such as machine learning, natural language processing, and data visualization. The data is carefully curated and organized to facilitate seamless access and integration into various projects. With a strong focus on excellence and innovation, the company continues to expand its offerings, staying at the forefront of the rapidly evolving data science landscape.

Data Visualization Software Market Outlook

According to our latest research, the global Data Visualization Software market size reached USD 8.2 billion in 2024, reflecting the sectorÂ’s rapid adoption across industries. With a robust CAGR of 10.8% projected from 2025 to 2033, the market is expected to grow significantly, attaining a value of USD 20.3 billion by 2033. This dynamic expansion is primarily driven by the increasing demand for actionable business insights, the proliferation of big data analytics, and the growing need for real-time decision-making tools across enterprises worldwide.

One of the most powerful growth factors for the Data Visualization Software market is the surge in big data generation and the corresponding need for advanced analytics solutions. Organizations are increasingly dealing with massive and complex datasets that traditional reporting tools cannot handle efficiently. Modern data visualization software enables users to interpret these vast datasets quickly, presenting trends, patterns, and anomalies in intuitive graphical formats. This empowers organizations to make informed decisions faster, boosting overall operational efficiency and competitive advantage. Furthermore, the integration of artificial intelligence and machine learning capabilities into data visualization platforms is enhancing their analytical power, allowing for predictive and prescriptive insights that were previously unattainable.

Another significant driver of the Data Visualization Software market is the widespread digital transformation initiatives across various sectors. Enterprises are investing heavily in digital technologies to streamline operations, improve customer experiences, and unlock new revenue streams. Data visualization tools have become integral to these transformations, serving as a bridge between raw data and strategic business outcomes. By offering interactive dashboards, real-time reporting, and customizable analytics, these solutions enable users at all organizational levels to engage with data meaningfully. The democratization of data access facilitated by user-friendly visualization software is fostering a data-driven culture, encouraging innovation and agility across industries such as BFSI, healthcare, retail, and manufacturing.

The increasing adoption of cloud-based data visualization solutions is also fueling market growth. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making advanced analytics accessible to organizations of all sizes, including small and medium enterprises (SMEs). Cloud-based platforms support seamless integration with other business applications, facilitate remote collaboration, and provide robust security features. As businesses continue to embrace remote and hybrid work models, the demand for cloud-based data visualization tools is expected to rise, further accelerating market expansion. Vendors are responding with enhanced offerings, including AI-driven analytics, embedded BI, and self-service visualization capabilities, catering to the evolving needs of modern enterprises.

In the realm of warehouse management systems (WMS), the integration of WMS Data Visualization Tools is becoming increasingly vital. These tools offer a comprehensive view of warehouse operations, enabling managers to visualize data related to inventory levels, order processing, and shipment tracking in real-time. By leveraging advanced visualization techniques, WMS data visualization tools help in identifying bottlenecks, optimizing resource allocation, and improving overall efficiency. The ability to transform complex data sets into intuitive visual formats empowers warehouse managers to make informed decisions swiftly, thereby enhancing productivity and reducing operational costs. As the demand for streamlined logistics and supply chain management continues to grow, the adoption of WMS data visualization tools is expected to rise, driving further innovation in the sector.

Regionally, North America continues to dominate the Data Visualization Software market due to early technology adoption, a strong presence of leading vendors, and a mature analytics landscape. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid digitalization, increasing IT investments, and the emergence of data-centric business models in countries like China, India

The dataset comprises headlines scraped from Moneycontrol, a financial news website. Each entry represents a single news headline. The dataset typically includes features such as the headline text. It is commonly stored in CSV format and may contain additional information such as timestamps or URLs. Potential uses include text analysis, machine learning, and data visualization. Limitations include potential data quality issues and legal considerations related to web scraping.

Recent environmental permit filings (2021–2024) show projects are backed by overseas capital.

Kind of data story told by the data visualisation, per platform.

Context This dataset contains around 10,000 lines of raw data of articles from CNN news from the year 2013 to 2022. The data were collected using a web crawler. The crawler can be found on my GitHub account (https://github.com/hadasu/CNN_web_crawler ). The crawler scans through the CNN site and extracts various parameters from the HTML of the article. You can edit the crawler to extract as much or as little data as you want.

Content This dataset contains primary data, no type, duplicate, unbalanced data, etc ... For clean data, you can refer to CNN News Articles from 2013 to 2022

Inspiration - Data cleaning - ETL (Extraction, Transformation, and Loading) - Exploratory data analysis

By querying "data centres' energy footprint" on Google News in incognito mode, the candidate has selected and mapped the top 100 results according to the ranking on May 15, 2022.

The use of text in data visualisations across the four platforms.

American Public Media Research Lab is a nonpartisan research organization that brings fact-based information to the public. As a premier data-driven analysis provider, they offer valuable insights into various topics, including politics, public health, and environmental sustainability. With a strong focus on transparency and objectivity, their work helps inform important policy decisions and empower citizens to make informed choices.

Through their website, visitors can access a wealth of data-driven resources, including polls, surveys, and data visualizations. Their research covers a broad range of topics, from partisan political analysis to COVID-19 data and public health stories. Whether exploring carbon capture, lithium mining for electric vehicles, or government investments, American Public Media Research Lab's data-driven approach provides a critical understanding of complex issues. By subscribing to their newsletter or following their social media channels, individuals can stay up-to-date on the latest research and findings.

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.

Local carmakers outpace foreign brands in the world's largest EV market.

This dataset contains visual assets and test data used in the Crypto News Sentiment Agent project, developed as part of the 2025Q1 Google & Kaggle GenAI Intensive Capstone.

It includes:

1. Line and dual-axis charts illustrating BTC price trends, 7-day moving averages, and rolling volatility
2. Sentiment classification bar chart showing positive, neutral, and negative breakdowns of crypto headlines
3. CSV file with 11 negative crypto news headlines (used to test VADER, TextBlob, and RoBERTa sentiment models)

This element involves the development of software that enables easier commanding of a wide range of NASA relevant robots through the Robot Application Programming Interface Delegate (RAPID) robot messaging system and infusing the developed software into flight projects.  In June and July of 2013, RAPID was tested on ISS as the robot messaging software for the Technology Demonstration Mission (TDM) Human Exploration Telerobotics (HET) Surface Telerobotics experiment.  RAPID has also been made available to — and integrated with — the Robot Operating System (ROS), a popular software framework for developing state-of-the-art robots for ground and space. While ROS powers a number of new robots and components such as Robonaut 2’s climbing legs and R5, the addition of RAPID allows these robots to interoperate in collaborative human-robot teams, safely and effectively over time-delayed communications links. The objective this year is to take this space-tested software and extend it to providing video streaming from remote robots and delivering this new capability to the Exploration Ground Data Systems (xGDS) area within HRS.  xGDS will then deliver its software to Science Mission Directorate (SMD) funded field tests to improve the technology readiness moving leading (potentially) to being used for the Lunar Prospector Mission ground data systems.  Success will involve delivering RAPID to xGDS and then xGDS supporting SMD field test.

The team is also developing algorithms for sensors capable of reconstructing remote worlds and efficiently shipping that remote environment back to earth using the RAPID robot messaging system.  This type of system could eventually lead to scientists on earth gain new insights as they are able to step into the remote world.  This sensor also has the ability to engage the public, bringing remote worlds back to earth.  During FY13, this task used science operations personnel from current SMD projects to objectively measure improvement in remote science target selection and decision-making based. The team continues to work with SMD projects to ensure that the technologies being developed are directly responsive to SMD project personnel needs. The objective of this work in FY14 is to expand the range of science operations tasks addressed by the technology, and to perform laboratory demonstrations for JPL/SMD stakeholders of the immersive visualization of data from a sensor using an SMD representative environment.

During 2014, the “Controlling Robots Over Time Delay” project element will develop two technologies:

• Develop RAPID robot messaging for unified cross-center operations platform for TDM, xGDS, and CCSDS
• Sensor Systems for the Construction of Immersive Virtual Environments

Presentation Date: October 23, 2022 Location: Memphis, Tennessee, USA Abstract: Presented the tenth Patrusky Lecture on October 23, 2022, as part of New Horizons in Science, CASW’s annual briefing on emerging research and issues in science. In the talk, Goodman looks into the potentially surprising discoveries that will emerge as astrophysics is reorganized around remarkable new methods that harness the power of visualization, computer simulation, large-scale collaboration, and powerful instruments such as the new James Webb Space Telescope and the European Space Agency’s Gaia mapping project. Files included are Keynote slides (in .key and .pdf formats)

Purpose or intent of the data visualisation used per platform.