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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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23 Best Data Visualization Tools of 2024 (with Examples)

If you are dissatisfied with what you've been able to achieve with your current data visualization software, and you want to try a different one, you have come to the right place.

Or, if you have never tried a data visualization software and you wish first to understand how it works and what the market offers, you are again in the right place.

Through extensive research, we have compiled a list of the absolute best data visualization tools in the industry, ranging from free solutions to enterprise packages.

Continue reading, and find the JavaScript library, non-programmer-optimized tools, industry and concept-specific, or fully-featured mapping, charting, and dashboard solutions for your needs.

What are Data Visualization Tools?

Data Visualization Tools refer to all forms of software designed to visualize data.

Different tools can contain varying features but, at their most basic, data visualization tools will provide you with the capabilities to input datasets and visually manipulate them.

Helping you showcase raw data in a visually digestible graphical format, data visualization tools can ensure you produce customizable bar, pie, Gantt, column, area, doughnut charts, and more.

When you need to handle datasets that contain up to millions of data points, you will need a program that will help you explore, source, trim, implement and provide insights for the data you work with.

A data visualization tool will enable you to automate these processes, so you can interpret information immediately, whether that is needed for your annual reports, sales and marketing materials, identifying trends and disruptions in your audience's product consumption, investor slide decks, or something else.

After you have collected and studied the trends, outliers, and patterns in data you gathered through the data visualization tools, you can make necessary adjustments in business strategy and propel your team closer to better results.

In addition, the more you can implement the valuable insights gained from the graphs, charts, and maps into your work, the more interested and adept you will become at generating intelligent data visualizations, and this loops back into getting actionable insights from the reports.

Through data visualization tools, you build a constructive feedback loop that keeps your team on the right path.

Comparison of Best Data Visualization Tools

Best data visualization software for creating maps and public-facing visualizations..

Available in a variety of ways, including desktop, server, online, prep, free public option, Tableau provides an enormous collection of data connectors and visualizations.

Establishing connections to your data sources is straightforward, and you can import everything from CSV files, Google Ads and Analytics to Microsoft Excel, JSON, PDF files, and Salesforce data.

A variety of chart formats and a robust mapping capability ensure that the designers can create color-coded maps that show geographically important data in the most visually digestible way.

The tool offers a public version that is free to use for everyone that will help you create interactive visualizations and connect to CSV, text, statistical files, Google sheets, web data connectors, and Excel documents.

Tableau Desktop can help you transform, process, and store huge volumes of data with exceptional analytics and powerful calculations from existing data, drag-and-drop reference lines and forecasts, and statistical summaries.

The Desktop option lets you connect to data on-prem or in the cloud, access and combine disparate data without coding, pivot, split, and manage metadata, and there is no limit to how much data it can store, process, or share.

Whereas Tableau Desktop is more suitable for analysts and BI professionals, Tableau Public is for anyone interested in understanding data and sharing those insights through data visualizations (students, journalists, writers, bloggers).

Data visualization example

You can purchase Tableau Creator for $70 per user per month.

• The tool comes in desktop, cloud, server, prep, online options
• Free public version
• Extensive options for securing data without scripting
• Convert unstructured statistical information into comprehensive logical results
• Fully functional, interactive, and appealing dashboards
• Arrange raw data into catchy diagrams
• Support for connections with many data sources, such as HADOOP, SAP, DB Technologies
• More than 250 app integrations

With a user-friendly design and a substantial collection of data connectors and visualizations, Tableau will help you attain high performance with a thriving community & forum and mobile-friendly capacity.

2. Infogram

Fully-featured data visualization tool for non-designers and designers..

Consisting of more than 550 maps, 35 charts, and 20 predesigned templates, Infogram can enable you to prepare a fully responsive, professional presentation on any device.

You can import data from online or PC sources, and you can download infographics in HD quality, supporting multiple file types (PNG, JPG, PDF, GIF, HTML).

If the majority of your data sources are in Excel XLS, JPG, or HTML files, and you want them imported in Infogram as PDF files, you can do a little search and find the best PDF converters .

The process of creating effective visualizations for marketing reports, infographics, social media posts, and maps is simplified with the drag-and-drop editor, allowing non-designers or people without much technical knowledge to generate slick-looking reports, one-pagers, diagrams.

Interactive visualizations are perfect for embedding into websites and apps, and you can update new data to a chart periodically and automatically for updating dashboards, reports, presentations, live feeds, and articles through Infogram's live options.

The software allows you to upload CSV or XLS files from your Google Drive, Dropbox, or OneDrive accounts easily by connecting with the accounts, making any design changes, publishing, and embedding them.

Outside of the free Basic plan, the Pro subscription is $25 per month.

• 550+ map types and 35+ chart types
• Enhanced interactive charts and maps with tooltips, tabs, clickable legends, linking
• Object animations
• Move your graphics to social media, slide decks like Prezi, and other platforms easily
• Make changes on the fly without wholesale revisions
• View and restore earlier versions of your projects
• Generate 13 different reports on website traffic and automatically update the charts with data
• Facilitate top referring sites, mobile usage, number of pages per session, top keywords, and other reports through Google Analytics
• Publish your content on Facebook Instant Articles, Medium, or through WordPress

With a wide selection of chart types and map types that are easy to implement by anyone and a free plan that allows you to publish your content online, you will find Infogram is an excellent solution for downloading data, sharing it privately, and accessing beautiful templates and images.

3. ChartBlocks

Best data visualization tool for embedding charts on any website..

The cloud-based chart-building tool allows you to customize any charts and sync them with any data source, letting you share charts on social media websites, including Facebook and Twitter.

You can import data from any source using their API, including live feeds, with the chart building wizard helping you select the optimal data for your charts before importing on any device of any screen size.

Control all aspects of your charts with hundreds of customization options, allowing you to configure everything from colors, fonts, and sizes to grids and number ticks on your axes.

CartBlocks ensures responsive HTML5 charts that work on any browser or device and the powerful D3.js tool to render your charts as scalable vector graphics and make them ready to be used on retina screens and for high-quality printed documents.

Grab the embed code and share the charts on your website, Twitter, Facebook, and other social media sites.

The Personal plan is $0 per month, and you can scale up to the Professional subscription for $20.

• HTML5 charts that work on any browser, device, and screen size
• Import data from spreadsheets, databases, and live feeds
• Pick the right data for your chart with the chart building wizard
• Design all elements of your charts with hundreds of customization options
• Embed your charts on websites, articles, and across social media
• Set up scheduled imports in the ChartBlocks app
• Optimize your charts for retina screens and high-quality printed documents through D3.js
• Export charts to PNG images
• Export charts as vector graphics (eps, PDF, SVG)
• Remove branding for $20 per month

Helping you create charts on any device and any screen size along with optimizing the charts for high-quality prints, ChartBlocks can offer up to 50 active charts for free for up to 50K monthly views.

4. Datawrapper

Best data visualization software for adding charts and maps to news stories..

Created specifically for adding charts and maps to news stories, Datawrapper is an open-source tool that supports Windows, Mac, and Linux and enables you to connect your visualization to Google Sheets.

Select one of 19 interactive and responsive chart types ranging from simple bars and lines to the arrow, range, and scatter plots, three map types that allow you to create locator maps, thematic choropleth, symbol maps, and more.

Table capabilities provide you with a range of styling options for the responsive bars, columns & line charts, heatmaps, images, search bars & pagination.

Copy your data from the web, Excel, or Google Sheets, and upload CSV/XLS files or provide links to URLs, Google Sheets for live-updating charts.

Copy the embed code into your CMS or website to access the interactive version, or export the chart as a PNG, SVG, or PDF for printing.

Outside the free plan, you can purchase the Custom for $599 per month, but the free one offers unlimited visualizations.

• 19 interactive and responsive charts and 3 map types
• No limits to charts, maps, and tables you can create in the free plan
• All visualizations are private until you activate the publish capability
• Utilize shared folders, Slack & Team integrations, and admin permissions
• Dustwrapper will design a custom chart theme according to the style guides you send
• Export all visualizations as PNG, SVG, PDF formats
• Update charts and tables automatically without republishing through live updating
• Access print-ready PDFs with defined CMYK colors

Datawrapper will create finished visualizations similar to those in the New York Times, with tons of stylization options and practicality for creating graphics and web maps that you can easily copy and share.

JavaScript Library for Manipulating Documents through Data with Reusable Charts.

D3.js is a JavaScript library for manipulating documents based on data, helping you activate data through HTML, SVG, and CSS.

The tool is extremely fast and it supports large datasets and dynamic behaviors for interaction and animation, enabling you to generate an HTML table from an array of numbers or use the same data to create an interactive SVG bar chart with smooth transitions and interaction.

While the platform requires some JavaScript knowledge, there are apps like NVD3 that allow non-programmers to utilize the library, providing reusable charts and chart components without taking away the power of D3.js.

Other similar apps include Plotly's Chart Studio that enables you to import data, compose interactive charts, publish static, printed versions or share interactively.

Ember Charts lets you extend and modify the time series, bar, pie, and scatter charts within the D3.js frameworks.

D3.js is free.

• Free, open-source, customizable
• Extremely fast and supportive of large datasets
• Generate HTML tables from numbers
• Create interactive SVG bar charts with smooth transitions and interaction
• Non-programmers can create complex visualizations
• A diverse collection of official and community-developed modules allows code reuse
• Utilize NVD3, Plotly's Chart Studio, and Ember Charts to get the most out of D3.js's library without coding

Emphasizing web standards, D3.js will help you create high-quality visualizations quickly and share them on the web without anyone having to install any software to view your work.

Best Data Visualization Software for Companies with BI Experience.

Domo is a cloud platform that has a powerful BI tool with a lot of data connectors and a robust data visualization capability that helps you conduct analysis and generate interactive visualizations.

The app helps you simplify administration data and examine important data using graphs and pie charts, and the engine allows you to manipulate ETL operations and conduct data cleansing in the engine after the load with no limits to how much data you can store.

With more than 450 available connectors, some of which are accessible by default and others after requesting URLs from the support team, Domo is highly flexible, and they allow you to load the locally stored CSV files easily.

Explore data in the interactive format through the data warehouse functionality, and conduct data prep, data joining, and ETL tasks.

Access more than 85 different visualizations, create and customize cards and pages, handling everything from text editing and single-data points to creating the apps for the app store.

You need to contact Domo's sales team for a personalized quote.

• Limitless data storage and an extensive range of connectors
• Create advanced charts and maps with filters and drill-downs
• Guide people through analysis with interactive data stories by combining cards, text, and images
• Ensure the teams can self-service while governing access to data
• Refine data with data points, calculated fields, and filters
• Annotate chart data for further commentary
• Define how cards on a dashboard interact with custom links and filters
• Dashboards with KPIs for retail, marketing, data science through different apps
• Encrypt your data with the Workbench tool or use an on-premise VM with Domo querying engine behind your firewall

Domo's powerful BI tool with a lot of data connectors isn't suited for newcomers and is best-suited for businesses that have BI experience that will benefit from the tool's excellent sharing features, limitless data storage, and terrific collaboration capabilities.

7. Google Charts

Best data visualization tool for creating simple line charts and complex hierarchical trees..

The powerful and free data visualization tool Google Charts is specifically designed for creating interactive charts that communicate data and points of emphasis clearly.

The charts are embeddable online, and you can select the most fitting ones from a rich interactive gallery and configure them according to your taste.

Supporting the HTML5 and SVG outputs, Google Charts work in browsers without the use of additional plugins, extracting the data from Google Spreadsheets and Google Fusion Tables, Salesforce, and other SQL databases.

Visualize data through pictographs, pie charts, histograms, maps, scatter charts, column and bar charts, area charts, treemaps, timelines, gauges, and many more.

Google Charts is free.

• Rich interactive chart gallery
• Cross-browser compatibility
• Dynamic data support
• Combo, calendar, candlestick, diff, gauge, Gantt, histograms, intervals, org, scattered, stepped area charts
• Animate modifications made to a chart
• Draw multiple charts on one web page
• Compatible with Android and iOS platforms

Google Charts is a free data visualization platform that supports dynamic data, provides you with a rich gallery of interactive charts to choose from, and allows you to configure them however you want.

8. FusionCharts

Best data visualization tool for building beautiful web and mobile dashboards..

FusionCharts is a JavaScript-based solution for creating web and mobile dashboards that can integrate with popular JS frameworks like React, jQuery, Ember, and Angular and server-side programming languages like PHP, Java, Django, and Ruby on Rails.

The tool equips you with 100+ interactive chart types and 2,000+ data-driven maps, including popular options like the bar, column, line, area, and pie, or domain-specific charts like treemaps, heatmaps, Gantt charts, Marimekko charts, gauges, spider charts, and waterfall charts.

In addition to these, FusionCharts provides 2K+ choropleth maps that cover countries and even cities, and the powerful engine supports millions of data points in your browser with no glitches.

Generate charts on the server-side, export the dashboards as PDFs, send reports via email, and FusionCharts will have you covered.

The Basic plan is $499 per year.

• Integrates with popular JS frameworks and server-side programming languages
• 100+ interactive chart types and 2K+ data-driven choropleth maps
• Construct complex charts through dashboards easily with consistent API
• Plot your crucial business data by regions with over 2,000 choropleth maps
• Common charts are supported on older browsers
• Comprehensive documentation for each library or programming language
• Ready-to-use chart examples, industry-specific dashboards and data stories with source codes

With extensive documentation, cross-browser support, and a huge number of chart and map format options, FusionCharts will allow you to build beautiful dashboards for your web and mobile projects while keeping even the most complex charts performing on a high level with consistent API.

9. Chart.js

Simple and flexible data visualization software for including animated, interactive graphs on your website..

Chart.js is a simple and flexible JavaScript charting library that provides eight chart types in total and allows animation and interaction.

Using the HTML5 Canvas for output, Chart.js renders charts across all modern browsers effectively.

You can mix and match bar and line charts to provide a clear visual distinction between datasets, plot complex, sparse datasets on date time, logarithmic, and fully custom scales.

Animate anything with out-of-the-box stunning transitions for data changes.

Chart.js is free.

• The learning curve is almost non-existent
• Compatible with all screen sizes
• Modernized, eye-catching, and pleasing graphs
• Open-source and free
• Visualize your data through 8 different animated, customizable chart types
• Continuous rendering performance across all modern browsers through HTML5 Canvas
• Mix and match bar and line charts for a clear visual distinction between datasets
• Plot complex, sparse datasets on date time, logarithmic, and entirely custom scales
• Redraw charts on window resize

Not only are the Chart.js graphs easy to digest and eye-catching, but the tool allows you to combine different graph forms to translate data into a more tangible output and add numerical JSON data into the Canvas for free.

10. Sisense

#1 data visualization tool for simplifying complex data from multiple sources..

Crunch large datasets and visualize them with beautiful pictures, graphs, charts, maps, and more from a single dashboard.

One of the best data visualization tools that can help you transform data into actionable, applicable components or visualizations, Sisense lets you simplify data analysis by unlocking data from the cloud and on-prem and embed analytics anywhere with a customizable feature.

Create custom experiences and automated multi-step actions to accelerate workflows and integrate AI-powered analytics into workflows, processes, applications, and products.

Before selecting your data sources, you can preview and mash up a couple of data sources before adding them to your schema.

Instead of valuing visualizations for the number of designs and formats it offers, Sisense places the emphasis on the depth of insights the charts expose, providing multidimensional widgets that render interactive visualizations and generate a ton of insights by scrolling the mouse over them or clicking on different sections.

With no subscription plans displayed publicly, you will need to request a quote.

• Assemble and share dashboards
• Crunch large datasets and visualize them through graphs, charts, maps, and pictures
• Transform raw data into actionable, applicable components for visualizations
• Preview and mash up multiple data sources before adding them to your schema
• Enable self-service analytics for your customers code-free
• Advanced predictive intelligence and natural language querying
• Leverage robust embedding capabilities from iFrames to APIs and SDKs
• Pull in data from eBay, Facebook, Quickbooks, PayPal
• Leverage cached data for minimized query costs
• Resolve bottlenecks with in-chip processing

Appealing to seasoned BI users with its comprehensive features, Sisense will help you mash up data and create an analytics app, deploy your work on the cloud, recover your data and safeguard against errors, and help you export data to Excel, CSV, PDF.

11. Workday Adaptive Planning

#1 data visualization tool with the best planning, budgeting, and forecasting capabilities..

Workday's Adaptive Planning data visualization tool is designed to boost your business by helping you make more lucrative decisions, allowing you to plan, budget, and forecast while evaluating multiple scenarios across large datasets.

Collaborate through the web, mobile, or Excel and deliver stunning reports in minutes while quickly optimizing sales resources, increasing rep performances, and improving predictability.

Create dashboards that integrate your actuals and plans, easily manage models and forecasts across integrated data sources, and always extract real-time data.

Automated data consolidation from all sources and use flexible modeling that lets you build on the fly, adjusting the dimensions if needed.

Making the right decisions based on the insights gathered through a comprehensive budgeting and forecasting tool like Workday Adaptive Planning will be more effective when implemented hand in hand with Net Present Value calculation for fostering a more budget-optimized workspace and better investment decisions.

Before starting your free trial and receiving a quote, you will need to write a request to the support team.

• Create dashboards that integrate your actuals and plans
• Drag-and-drop report building features
• Create rep capacity plans to meet topline bookings targets
• Deploy the right quotas and set up balanced territories
• Collaborate on what-if scenarios
• Access audit trails to see what changed, where, and who did it
• Export operational data from GL, payroll, purchasing

When the active planning process is collaborative, comprehensive, and continuous as with Adaptive Planning, the tools and information for building complex dashboards quickly that the software provides are easy to deploy.

12. Grafana

Open-source data visualization tool for integrating with all data sources and using the smoothest graphs..

In Grafana , you can package and present information through a variety of chart types, and if you want to make dynamic dashboards, there are not a lot of visualization tools that make the process simpler than Grafana.

Grafana's open-source data visualization software allows you to create dynamic dashboards and other visualizations.

You can query, visualize, alert on, and understand your metrics no matter where they are stored, and deploy data source permissions, reporting, and usage insights.

Extract data from mixed data sources, apply annotations and customizable alert functions, and extend the software's capabilities via hundreds of available plugins.

Share snapshots of dashboards and invite other users to collaborate through the export functions.

Outside of the Free plan, you can purchase the Pro subscription for $49 per month + usage.

• Creating dynamic dashboards is easy
• Variety of chart types and data sources
• Support for mixed data feeds
• Access for up to 3 members in the Free plan
• Query, visualize, alert on, and understand your metrics
• Data source permissions
• Usage insights
• Apply annotations
• Hundreds of plugins
• Share snapshots of the dashboard

One of the best software for monitoring and alerting, Grafana allows you to write the query to create graphs and alerts, integrate with almost all cloud platforms, and invite other users to collaborate for free.

Best Data Visualization Software to Motivate and Engage Your Employees to Perform Better through Dashboards.

By visualizing performance indicators openly and engagingly, Plecto helps keep your team's morale at a high level and motivates your employees to keep improving.

Plecto allows you to integrate with an unlimited number of data sources, and you can even import data from different sources and filter these across sources.

Visualize your most important KPIs on real-time dashboards and engage your team with the addicting gamification features, sales contests, leaderboards, and instant notifications.

Add data through Excel, SQL, Zapier, or Plecto's REST-based API, display your Plecto account on a TV and access your dashboard on the go through mobile apps for Android, iPhone, and Apple Watch.

The Medium subscription starts at $250 per month when billed monthly.

• Provide data-driven, actionable knowledge about the business, departments, and individuals' performances
• Motivate your team to keep improving through gamification
• Integrate with an unlimited number of data sources
• Import data from different sources and apply filters
• Engage your team through sales contests, leaderboards, and instant notifications
• Add data through Excel, SQL, Zapier, or the software's REST-based API
• Access your Plecto account via TV or through the Android, iPhone, and Apple Watch apps

Plecto will allow the teams and individuals to keep progressing and provide teams with actionable, data-driven knowledge delivered through encouraging gamification practices while connecting them with one of 50+ pre-built integrations or public API.

14. Whatagraph

Best data visualization tool for monitoring and comparing performances of multiple campaigns..

The Whatagraph application allows you to transfer custom data from Google Sheets and API.

Commonly used by marketing professionals for visualizing data and building tailored cross-channel reports, Whatagraph is the best tool for performance monitoring and reporting.

Blend data from different sources and create cross-channel reports so you can compare how the same campaign is performing across different channels.

Create custom reports or utilize the pre-made widgets, with ready-made report templates for different marketing channels like SEO, PPC, social media, and share links with your colleagues so they can access them at all times.

Choose from 30+ integrations that include Facebook Ads, Google Analytics, HubSpot, and more.

The Professional plan will cost you $119 per month.

• Monitor and compare performances of multiple channels and campaigns
• Customize the reports with brand colors, logos, custom domains
• Add custom data with Google Sheets and Public API integrations
• Blend data from different sources and create cross-channel reports
• Ready-made templates for different marketing channels
• Google Analytics, Google Ads, Facebook Ads, Instagram, Twitter, Linkedin, Simplifi, and more integrations
• Automatically deliver reports to clients

Whatagraph allows you to style your reports according to your preferences, monitor and compare performances across multiple campaigns and channels, and blend data from different sources for cross-channel reports.

Best-in-Class Data Visualization Software for Running SQL Queries.

Cluvio will enable you to utilize SQL and R to analyze your data and create appealing, interactive dashboards in a few minutes better than any other tool on the market.

Translate your raw data into numerous professional charts and graphs, and share your dashboard with clients and colleagues without a mandatory log-in.

Scheduled dashboard and report sharing via email (image, PDF, Excel, CSV) are functionalities Cluvio provides to urge the users to view the information, get a regular snapshot, or trigger conversations.

Cluvio's customer service team is definitely worth mentioning as it has gained praise for being fast, informative, accurate, and helpful for a large portion of Cluvio's audience.

Outside of the Free plan, Cluvio's Pro plan is $249 per month.

• Change aggregation, select a specific time range, and filter dashboards by any individual attributes
• Code completion, syntax highlighting, and parameterized queries in the SQL editor
• Turn your codes into reusable snippets
• Monitor data in real-time with SQL alerts
• Automatic suggestions for best data visualization practices
• Run custom R scripts
• Invite an unlimited number of employees in the Pro and Business plans

Not only does Cluvio offer a free plan with three dashboards and 1,000 query executions, but the software comes with complete monitoring and sharing capabilities while allowing you to dig deeper into your statistical analysis and extract more value through SQL and R queries.

16. RAWGraphs

Best data visualization tool for simplifying complicated data through striking visual representation..

RAWGraphs will enable you to generate beautiful data visualizations uploaded as XLSX or CSV files quickly, as well as URL uploads and spreadsheet copies.

The software offers mapping and export visualizations as SVG or PNG images that can be customized by inputting additional parameters.

You can work with delimiter-separated values (CSV. and TSV. files), as well as copy-paste texts from other applications (TextEdit, Excel) and CORS-enabled endpoints (APIs).

Data here is processed only by the web browser, and the charts are available in conventional and unconventional layouts.

The pricing is not provided by the vendor, which is why you will have to contact them to get your quote.

• Work with CSV, TSV files, copy-paste texts from other applications, and APIs
• Receive visual feedback after mapping dataset dimensions
• Export visualizations as SVG and PNG images and embed them on your web page
• No server-side operations or storages are performed so no one can see or copy your data
• Unconventional charts that are hard to produce with other tools
• Simple pie and column charts
• Map the dimensions of your datasets with the visual variables of the selected layouts
• Open the visualizations in your favorite vector graphics editor and improve them

Designed as a tool to provide the missing link between spreadsheet applications (Microsoft Excel, Apple Numbers, OpenRefine) and vector graphics editors (Adobe Illustrator, Inkscape, Sketch), RAWGraphs will help you simplify complex data through powerful visualizations.

17. Visually

Fast and affordable data visualization solution for infographics and interactive websites..

Visually is a data visualization and infographics platform that will help you turn your data into a compelling story, allowing you to convert your numbers into image-based visualizations and streamline the product design processes.

To create your memorable data visualizations, Visually's team will handpick from a selection of 1,000 of the best data journalists, designers, and developers to deliver your designs in record time.

Collaborate with world-class designers to create infographics that stand out, with the software keeping you in direct contact with your creative team and assigning you a dedicated rep to be at your disposal during the streamlining of the production.

Create presentations and slideshows that leave long-lasting impressions with a normal delivery time of 19 days.

Data personalization example

You will need to submit a request to get your quote.

• Create world-class presentations and slideshows and attention-grabbing infographics
• Usual delivery time of 16 days for infographics and 19 days for slideshows and presentations
• Corporate reports, sales decks, and slideshares for startups, Fortune 50 companies
• Share content on social media channels
• Collaboration with premium data journalists, designers, and developers
• Convert your numbers into image-based visualizations
• Keep direct contact with the creative team during the infographics, presentation production process

Visually creates beautiful content that grabs the attention of large crowds on social media, conveys the intended message in a captivating way, and connects you with the world's biggest experts while allowing you to keep direct contact during their work in the production stages, which guarantees satisfactory infographics and presentations.

#1 Data Visualization Tool for Converting Data into Useful Diagrams.

Looker will help you develop and streamline accurate data models and visualize your codes in interactive diagrams.

Equipping you with a dashboard through which you can explore and analyze your data deeply, Looker lets you select funnel, maps, timelines, donut multiples, snakey, treemap, combined charts, or gauge multiple visualizations.

You can configure your specific visualization, and the software will add it to your Looker workflow, helping you maximize your impact and tell a compelling story.

Without proper organization, any data you pull for visualizations will give subpar results, which is why you need to properly manage, update, and track data by choosing one of the best database software that will help you generate realistic and productive projections in your visualizations.

Set up filters for individuals or groups dynamically, separating one dashboard for sales reps, one for customer success managers, and another for external viewers.

Develop robust and accurate data models and reduce errors while understanding the relationships, behaviors, and extensions of different LookML objects.

As with many other vendors, you will need to send a request to receive your quote.

• Visualize your codes in interactive diagrams
• Explore and analyze your data deeply
• Choose from a variety of chart types, multiple chart and map frameworks, or configure your own
• Build effective, action-oriented dashboards and presentations
• Easy to detect changes and irregularities in your data
• The software adds your tailored visualizations to your Looker workflows
• Set up dynamic filters for groups and individuals and separate the dashboards for sales reps and external viewers
• Visualize data with subtotal in tables

Providing a modern API to integrate your workflows, Looker allows you to explore your data to intrinsic detail and bring your stories to life through compelling visualizations while compartmentalizing the dashboards for different uses.

19. Chartist.js

Best data visualization tool for smaller teams in need of simple, responsive charts..

Chartist.js is an open-source charting JavaScript library that has a lightweight interface that is flexible for integrations.

Create responsive, scalable, and great-looking charts while availing of the simple handling, great flexibility while using clear separation of concerns (styling with CSS and controlling with JS), SVG for illustration, and more.

The app is fully responsive and DPI independent, which results in GUI being displayed at a consistent size regardless of the resolution of the screen.

Chartist.js ensures responsive configuration with media queries, allowing compatibility with a wide variety of devices and screen sizes.

Specifying the style of your chart in the CSS will enable you to use the amazing CSS animations and transitions and apply them to your SVG elements.

Chartist's SVG animation API ensures almost limitless animation possibilities, and you can style your charts with CSS in @media queries.

The configuration override mechanism based on media queries helps you conditionally control the behavior of your charts when necessary.

Lastly, you should know that the app is fully built and customizable with SaaS.

Chartist.js is free.

• Create responsive, scalable, great-looking charts
• A lightweight interface that's flexible for integrations
• Implement your style through the DOM hooks
• Rich, responsive support for multiple screen sizes
• Comprehensive grid, color, label layout options
• Advanced SSL animations
• Multi-line labels
• SVG animations with SMIL
• Control the behavior of your charts with the configuration override mechanism

Offering great response configuration to media queries and high flexibility for use in the separation of concerns, Chartist.js is here to help you create highly customized, responsive charts and allow you to utilize SVG for illustrations.

20. Sigma.js

Single-purpose data visualization tool for creating network graphs..

Sigma.js allows you to create embeddable, interactive, and responsive graphs, helping you customize your drawing and allowing you to publish the final result on any website.

To make the networks' manipulation on web pages as smooth and as fast as possible, Sigma.js will equip you with features such as Canvas and WebGL renderers, as well as mouse & touch support.

You can add your functions directly to your scripts and render the nodes and edges how you want them to be.

Through the Public API, you can modify data, move the camera, refresh the rendering, listen to events, and more.

Sigma.js can allow you to create JSON and GEXF-encoded graphs with the related plugin covering loading and parsing of the files.

Sigma.js is a free, open-source tool.

• WebGL and Canvas rendering
• Rescale when the container's size changes
• Render nodes and edges according to your liking
• Move the camera, refresh the rendering, listen to events, and modify your data through the Public API
• Display simple interactive publications of network and rich web applications
• Update data and refresh the drawing when you want
• Use plugins for animating the graphs or applying force-directed layouts to your graphs

Sigma.js is a dedicated graph drawing service that will help you embed graphs in websites and apps easily while allowing you to make changes and refresh the graphs anytime you want.

Best Data Visualization Software for Building Complex Data Models Quickly through its Associative Engine.

Innovatively providing data visualization services, Qlik will help you attain data from various sources quickly while automatically maintaining data association and supporting numerous forms of data presentation.

Capture large volumes of data and generate reports quickly and automatically while extracting valuable insights from transparent reporting functionalities and identify trends and information to help you make best-practice decisions.

Get an understanding of the information quickly through powerful global search and selections with interactive dashboards.

Combine, load, visualize, and explore your data, and activate the assistance from the insight advisor for chart creation, association recommendation, and data preparation.

Qlik Sense Business plan will cost you $30 per user per month.

• Build complex data models and dashboards quickly
• Simplifies data load and data modeling
• Aggregate structured data from different sources and build simple data models through snowflake or star schemas
• Simplified operation querying
• Generate reports quickly and automatically
• Identify trends to make best-practice decisions
• An attentive, knowledgeable support team that is receptive to feedback
• Get assistance on chart creation, association recommendation, and data preparation with the insight advisor

Allowing you to discover important insights through conversational analytics and insight advisor, rapidly develop custom apps, new visualizations, or extensions, and embed fully interactive analytics within the apps and processes, Qlik will let you visualize with clear intent and context through the most engaging graphs.

22. Polymaps

Dedicated javascript library for mapping and complete data visualization..

Designed specifically for mapping, Polymaps is a free, open-source JavaScript library for creating interactive, dynamic maps, using CSS to design and SVG to display your data through numerous types of visual presentation styles.

You can use the CSS mechanism to customize the visuals of your maps, after which you can easily embed them onto any website or apps.

The software supports large-scale and rich data overlays on interactive maps and SVR-rendered vector files, along with powerful graphical operations like compositing, geometric transformations, and image processing.

Apply styling via CSS operations, and utilize the CSS3 animations and transitions.

The software provides a factory method for constructing the required objects internally which results in shorter code and faster execution when compared to the traditional JS constructors.

Polymaps is 100% free.

• Large-scale data overlays
• CSS3 animations and transitions
• Private members can hide the internal state
• Refine the geometry to display greater details when zooming in
• Compositing, geometric transformations, and image processing
• Shorter code and faster execution
• Compatible and robust API

Polymaps is known for its speed when loading large amounts of data in full range, allowing it to run compositing, image processing, and geometric transforms, as well as supporting and processing of rich data on dynamic maps.

23. Microsoft Power BI

Best data visualization tool for fostering a data-driven culture with business intelligence for all..

Microsoft's Power BI is a data visualization and business intelligence tool combined into one that allows you to convert data from various data sources into interactive, engaging, and story-presenting dashboards and reports.

Providing reliable connections to your data sources on-prem and in the cloud, Power BI is ready to equip you with data exploration through natural language querying and real-time updates on the dashboard.

Save time and make data prep easier with modeling tools, and reclaim hours in a day using the self-service power query, ingestion, transforming, and integration tools.

Dig deeper into data and find patterns that lead to actionable insights, and use features like quick measures, grouping, forecasting, and clustering.

Activate the powerful DAX formula language and give advanced users full control over their models.

Power BI Pro is $9.99 per user per month.

• Access data from Dynamics 365, Salesforce, Azure SQL DB, Excel, SharePoint, and hundreds of other supported sources
• Pre-built and custom data connectors
• Natural language querying
• Real-time dashboard updates
• Design your reports with theming, formatting, and layout tools
• Quick measures, grouping, forecasting, and clustering
• Assign full control over models to advanced users through the DAX formula language
• Sensitivity labeling, end-to-end encryption, and real-time access monitoring

In Power BI, you can handle everything from managing reports using SaaS solutions to engaging in data exploration using the natural language query while accessing reliable data sources, which you can easily convert into interactive dashboards and reports that you can share across the whole organization.

Types of Data Visualization Methods

Starting with the most familiar one, column charts are a time-efficient method of showing comparisons among different sets of data.

A column chart will contain data labels along the horizontal axis with measured metrics or values presented on the vertical axis.

With column charts, you can track monthly sales figures, revenue per landing page, and similar information, while you can use the pie charts to demonstrate components or proportions between the elements of one whole.

You can find many more chart types like the Mekko, bar, line, scatter plot, area, waterfall, and many more.

Plots are data visualization methods used to distribute two or more datasets over a 2D or 3D space to represent the relationship between these sets and the parameters on the plot.

Scatter and bubble plots are some of the most commonly used data visualization methods, while the more complex box plots are more frequently utilized for visualizing relationships between large volumes of data.

A bubble plot is an extension of the scatter plot used to look at the relationships between three numeric variables.

Box plot is a data visualization method used for expanatory data analysis, visually displaying the distribution of numerical data and distortion through displaying the data quartiles and averages.

Maps allow you to locate elements on relevant objects and areas, which is where we can start to divide them into geographical maps, building plans, website layouts, and the most popular map visualization types include heat maps, distribution maps, and cartograms.

Heat maps are graphical representations of data where values are depicted by different colors.

Distribution maps are data visualization arrangements used to indicate the distribution of a particular feature in an area, and they can be qualitative that explore qualities or characteristics, quantitative where the value of data is shown in the form of counts and numbers.

The distribution of continuous variables like temperature, pressure, rainfall are represented by lines of equal value.

4. Diagrams and Matrices

Diagrams are used to illustrate complex data relationships and links, and they include various types of data in one visualization.

They can be hierarchical, network, flowchart, Venn, multidimensional, tree-like, etc.

Matrix is one of the advanced data visualization techniques that help determine and process the correlation between multiple continuously updating datasets.

What to Look for In Data Visualization Software Tools

Before getting into the specific functionalities, let's establish the fundamentals required when purchasing a data visualization tool.

1. Ease of Use

Any data visualization software you choose must have easy-to-use features and a user-friendly interface for the less technically skilled employees.

While there are code-heavy data visualization tools packed with advanced features, these tools need to be well balanced if your team consists of both seasoned IT users and less-experienced workers.

Tableau has a steeper learning curve than other platforms on this list, for example, but this is balanced out with an extremely user-friendly design and a l arge community of users.

That's not to say Tableau doesn't require substantial training, but the completeness of the tool in all aspects makes the training process worth the effort.

Similarly, Sisense will appeal to seasoned BI users while potentially causing frustration with the newcomers.

Apart from the natural language query in the third-party apps, Sisense's UI doesn't match the level of user-friendliness needed to satisfy less-knowledgeable users.

Online training systems with well-organized support teams have helped battle this significantly.

Most importantly, Sisense provides phenomenal data visualization service and equips their target intermediate and highly-skilled business analysts with transparency, and lifts most of their burden without having to buy additional tools.

2. Data Connectivity

Quality data visualization software will equip you with the capability to connect with or extract important information from external sources when you encounter data absence problems.

If you want to import data from both online and PC sources while being able to download infographics in HD quality and connect with numerous file types like PNG, JPG, PDF, GIF, HTML, and more, Infogram could be the comprehensive data connectivity solution you need.

What if you want good data connectivity for free?

Thanks to D3.js , this is possible as well, with the tool being the perfect JavaScript library for manipulating documents based on data, letting you access data through HTML, SVG, and CSS.

All this flexibility comes with additional benefits of the tool being extremely fast, supporting large datasets and dynamic behaviors for interaction and animation.

Adding on the themes in the previous paragraphs, D3.js accommodates this robust data connectivity with apps like NVD3 that non-programmers can use to still get good results in the library.

When you search for a tool that supports the SVG and HTML5 outputs, yet enables you to work in browsers without additional plugins, it's hard to rival Google Charts as it allows you to extract data from Google Spreadsheets, Google Fusion Tables, Salesforce (and Salesforce alternatives ), and other SQL databases.

3. Employee Skill Level

Now that you have determined the fundamentals you look for in the tool, you should turn inward and see what your team can offer to the tool.

Not only will you avoid sudden training costs during the learning process, but knowing the limitations of your employees will help you select a data visualization tool to get you the results you strive for and challenge the employees to develop faster.

4. Let's Talk Refinements

• Data visualization – Analyze data in visual form, such as patterns, charts, graphs, maps, trends, correlations, and so on.
• Role-based access management – Regulate access levels for individuals, including data and administration.
• Historical snapshots – Create snapshots of your data samples and workspace and access them as records later in the process.
• Template creation – Save previously used color schemes and combinations as templates and reuse them again in future projects.
• Visual analytics – Analyze enormous amounts of data through powerful and interactive reporting capabilities.
• Visual discovery – Find patterns, sequences, and outliers in datasets through visual analysis without necessarily creating data models.
• Data cleansing – Filter through the redundant and inaccurate residual information from various formats, and keep your database pure.
• In-place filtering – Filter off specific data by value, type, category, or other criteria with dropdowns, checkboxes, radio buttons, sliders, and more.
• Email reporting – Receive constant information and visual statistical reports about your data through scheduled emails.
• Mobile user support – Access your data and monitor ongoing operations outside of the working environment.

Which Data Visualization Tool Should I Choose?

Do you want a tool that will give you access to an enormous collection of data connectors and visualizations, allow you to create maps and public-facing visualizations that stand out, generating the most accurate forecasts and statistical summaries?

Of course, you do.

Everyone looking for a data visualization solution wants those things, and there is no better tool today to help you master self-service business intelligence like Tableau .

Maybee empowering your employees to perform better is at the top of your priorities, and you would like to engage your employees in an alternative way.

Plecto is a tool that will let you add data from all SQL databases, including Zapier and REST-based API, and allow you to integrate with an unlimited number of them while proactively motivating your team to improve through gamification and other unique features.

If you want to construct complex data models but you want to do it quickly with a tool that will help you attain data from various sources and even advise you on the best practices for chart creation, association recommendation, and data preparation, look no further than Qlik .

Just like Jumpeau, with Sisense , you know you are getting the cream of the crop from picture, graph, chart, and map visualizations and high-volume dataset management, reaching the depth of insights through the actionable visualizations that is unrivaled.

Was This Article Helpful?

Martin luenendonk.

Martin loves entrepreneurship and has helped dozens of entrepreneurs by validating the business idea, finding scalable customer acquisition channels, and building a data-driven organization. During his time working in investment banking, tech startups, and industry-leading companies he gained extensive knowledge in using different software tools to optimize business processes.

This insights and his love for researching SaaS products enables him to provide in-depth, fact-based software reviews to enable software buyers make better decisions.

Data Collection, Presentation and Analysis

• First Online: 25 May 2023

Cite this chapter

• Uche M. Mbanaso 4 ,
• Lucienne Abrahams 5 &
• Kennedy Chinedu Okafor 6  

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This chapter covers the topics of data collection, data presentation and data analysis. It gives attention to data collection for studies based on experiments, on data derived from existing published or unpublished data sets, on observation, on simulation and digital twins, on surveys, on interviews and on focus group discussions. One of the interesting features of this chapter is the section dealing with using measurement scales in quantitative research, including nominal scales, ordinal scales, interval scales and ratio scales. It explains key facets of qualitative research including ethical clearance requirements. The chapter discusses the importance of data visualization as key to effective presentation of data, including tabular forms, graphical forms and visual charts such as those generated by Atlas.ti analytical software.

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Uche M. Mbanaso

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Kennedy Chinedu Okafor

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Mbanaso, U.M., Abrahams, L., Okafor, K.C. (2023). Data Collection, Presentation and Analysis. In: Research Techniques for Computer Science, Information Systems and Cybersecurity. Springer, Cham. https://doi.org/10.1007/978-3-031-30031-8_7

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How To Present Research Data?

Tong seng fah.

MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia

Aznida Firzah Abdul Aziz

Introduction.

The result section of an original research paper provides answer to this question “What was found?” The amount of findings generated in a typical research project is often much more than what medical journal can accommodate in one article. So, the first thing the author needs to do is to make a selection of what is worth presenting. Having decided that, he/she will need to convey the message effectively using a mixture of text, tables and graphics. The level of details required depends a great deal on the target audience of the paper. Hence it is important to check the requirement of journal we intend to send the paper to (e.g. the Uniform Requirements for Manuscripts Submitted to Medical Journals 1 ). This article condenses some common general rules on the presentation of research data that we find useful.

SOME GENERAL RULES

• Keep it simple. This golden rule seems obvious but authors who have immersed in their data sometime fail to realise that readers are lost in the mass of data they are a little too keen to present. Present too much information tends to cloud the most pertinent facts that we wish to convey.
• First general, then specific. Start with response rate and description of research participants (these information give the readers an idea of the representativeness of the research data), then the key findings and relevant statistical analyses.
• Data should answer the research questions identified earlier.
• Leave the process of data collection to the methods section. Do not include any discussion. These errors are surprising quite common.
• Always use past tense in describing results.
• Text, tables or graphics? These complement each other in providing clear reporting of research findings. Do not repeat the same information in more than one format. Select the best method to convey the message.

Consider these two lines:

• Mean baseline HbA 1c of 73 diabetic patients before intervention was 8.9% and mean HbA 1c after intervention was 7.8%.
• Mean HbA 1c of 73 of diabetic patients decreased from 8.9% to 7.8% after an intervention.

In line 1, the author presents only the data (i.e. what exactly was found in a study) but the reader is forced to analyse and draw their own conclusion (“mean HbA 1c decreased”) thus making the result more difficult to read. In line 2, the preferred way of writing, the data was presented together with its interpretation.

• Data, which often are numbers and figures, are better presented in tables and graphics, while the interpretation are better stated in text. By doing so, we do not need to repeat the values of HbA 1c in the text (which will be illustrated in tables or graphics), and we can interpret the data for the readers. However, if there are too few variables, the data can be easily described in a simple sentence including its interpretation. For example, the majority of diabetic patients enrolled in the study were male (80%) compare to female (20%).
• Using qualitative words to attract the readers’ attention is not helpful. Such words like “remarkably” decreased, “extremely” different and “obviously” higher are redundant. The exact values in the data will show just how remarkable, how extreme and how obvious the findings are.

“It is clearly evident from Figure 1B that there was significant different (p=0.001) in HbA 1c level at 6, 12 and 18 months after diabetic self-management program between 96 patients in intervention group and 101 patients in control group, but no difference seen from 24 months onwards.” [Too wordy]

Changes of HbA 1c level after diabetic self-management program.

The above can be rewritten as:

“Statistical significant difference was only observed at 6, 12 and 18 months after diabetic self-management program between intervention and control group (Fig 1B)”. [The p values and numbers of patients are already presented in Figure 1B and need not be repeated.]

• Avoid redundant words and information. Do not repeat the result within the text, tables and figures. Well-constructed tables and graphics should be self-explanatory, thus detailed explanation in the text is not required. Only important points and results need to be highlighted in the text.

Tables are useful to highlight precise numerical values; proportions or trends are better illustrated with charts or graphics. Tables summarise large amounts of related data clearly and allow comparison to be made among groups of variables. Generally, well-constructed tables should be self explanatory with four main parts: title, columns, rows and footnotes.

• Title. Keep it brief and relate clearly the content of the table. Words in the title should represent and summarise variables used in the columns and rows rather than repeating the columns and rows’ titles. For example, “Comparing full blood count results among different races” is clearer and simpler than “Comparing haemoglobin, platelet count, and total white cell count among Malays, Chinese and Indians”.

*WC, waist circumference (in cm)

†SBP, systolic blood pressure (in mmHg)

‡DBP, diastolic blood pressure (in mmHg)

£LDL-cholesterol (in mmol/L)

*Odds ratio (95% confidence interval)

†p=0.04

‡p=0.01

• Footnotes. These add clarity to the data presented. They are listed at the bottom of tables. Their use is to define unconventional abbreviation, symbols, statistical analysis and acknowledgement (if the table is adapted from a published table). Generally the font size is smaller in the footnotes and follows a sequence of foot note signs (*, †, ‡, §, ‖, ¶, **, ††, # ). 1 These symbols and abbreviation should be standardised in all tables to avoid confusion and unnecessary long list of footnotes. Proper use of footnotes will reduce the need for multiple columns (e.g. replacing a list of p values) and the width of columns (abbreviating waist circumference to WC as in table 1B )
• Consistent use of units and its decimal places. The data on systolic blood pressure in Table 1B is neater than the similar data in Table 1A .
• Arrange date and timing from left to the right.
• Round off the numbers to fewest decimal places possible to convey meaningful precision. Mean systolic blood pressure of 165.1mmHg (as in Table 1B ) does not add much precision compared to 165mmHg. Furthermore, 0.1mmHg does not add any clinical importance. Hence blood pressure is best to round off to nearest 1mmHg.
• Avoid listing numerous zeros, which made comparison incomprehensible. For example total white cell count is best represented with 11.3 ×10 6 /L rather than 11,300,000/L. This way, we only need to write 11.3 in the cell of the table.
• Avoid too many lines in a table. Often it is sufficient to just have three horizontal lines in a table; one below the title; one dividing the column titles and data; one dividing the data and footnotes. Vertical lines are not necessary. It will only make a table more difficult to read (compare Tables 1A and ​ and1B 1B ).
• Standard deviation can be added to show precision of the data in our table. Placement of standard deviation can be difficult to decide. If we place the standard deviation at the side of our data, it allows clear comparison when we read down ( Table 1B ). On the other hand, if we place the standard deviation below our data, it makes comparison across columns easier. Hence, we should decide what we want the readers to compare.
• It is neater and space-saving if we highlight statistically significant finding with an asterisk (*) or other symbols instead of listing down all the p values ( Table 2 ). It is not necessary to add an extra column to report the detail of student-t test or chi-square values.

Graphics are particularly good for demonstrating a trend in the data that would not be apparent in tables. It provides visual emphasis and avoids lengthy text description. However, presenting numerical data in the form of graphs will lose details of its precise values which tables are able to provide. The authors have to decide the best format of getting the intended message across. Is it for data precision or emphasis on a particular trend and pattern? Likewise, if the data is easily described in text, than text will be the preferred method, as it is more costly to print graphics than text. For example, having a nicely drawn age histogram is take up lots of space but carries little extra information. It is better to summarise it as mean ±SD or median depends on whether the age is normally distributed or skewed. Since graphics should be self-explanatory, all information provided has to be clear. Briefly, a well-constructed graphic should have a title, figure legend and footnotes along with the figure. As with the tables, titles should contain words that describe the data succinctly. Define symbols and lines used in legends clearly.

Some general guides to graphic presentation are:

• Bar charts, either horizontal or column bars, are used to display categorical data. Strictly speaking, bar charts with continuous data should be drawn as histograms or line graphs. Usually, data presented in bar charts are better illustrated in tables unless there are important pattern or trends need to be emphasised.

• Line graphs are most appropriate in tracking changing values between variables over a period of time or when the changing values are continuous data. Independent variables (e.g. time) are usually on the X-axis and dependant variables (for example, HbA 1c ) are usually on the Y-axis. The trend of HbA 1c changes is much more apparent with Figure 1B than Figure 1A , and HbA 1c level at any time after intervention can be accurately read in Figure 1B .
• Pie charts should not be used often as any data in a pie chart is better represented in bar charts (if there are specific data trend to be emphasised) or simple text description (if there are only a few variables). A common error is presenting sex distribution of study subjects in a pie chart. It is simpler by just stating % of male or female in text form.
• Patients’ identity in all illustrations, for example pictures of the patients, x-ray films, and investigation results should remain confidential. Use patient’s initials instead of their real names. Cover or blackout the eyes whenever possible. Obtain consent if pictures are used. Highlight and label areas in the illustration, which need emphasis. Do not let the readers search for details in the illustration, which may result in misinterpretation. Remember, we write to avoid misunderstanding whilst maintaining clarity of data.

Papers are often rejected because wrong statistical tests are used or interpreted incorrectly. A simple approach is to consult the statistician early. Bearing in mind that most readers are not statisticians, the reporting of any statistical tests should aim to be understandable by the average audience but sufficiently rigorous to withstand the critique of experts.

• Simple statistic such as mean and standard deviation, median, normality testing is better reported in text. For example, age of group A subjects was normally distributed with mean of 45.4 years old kg (SD=5.6). More complicated statistical tests involving many variables are better illustrated in tables or graphs with their interpretation by text. (See section on Tables).
• We should quote and interpret p value correctly. It is preferable to quote the exact p value, since it is now easily obtained from standard statistical software. This is more so if the p value is statistically not significant, rather just quoting p>0.05 or p=ns. It is not necessary to report the exact p value that is smaller than 0.001 (quoting p<0.001 is sufficient); it is incorrect to report p=0.0000 (as some software apt to report for very small p value).
• We should refrain from reporting such statement: “mean systolic blood pressure for group A (135mmHg, SD=12.5) was higher than group B (130mmHg, SD= 9.8) but did not reach statistical significance (t=4.5, p=0.56).” When p did not show statistical significance (it might be >0.01 or >0.05, depending on which level you would take), it simply means no difference among groups.
• Confidence intervals. It is now preferable to report the 95% confidence intervals (95%CI) together with p value, especially if a hypothesis testing has been performed.

The main core of the result section consists of text, tables and graphics. As a general rule, text provides narration and interpretation of the data presented. Simple data with few categories is better presented in text form. Tables are useful in summarising large amounts of data systemically and graphics should be used to highlight evidence and trends in the data presented. The content of the data presented must match the research questions and objectives of the study in order to give meaning to the data presented. Keep the data and its statistical analyses as simple as possible to give the readers maximal clarity.

Contributor Information

Tong Seng Fah, MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia.

Aznida Firzah Abdul Aziz, MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia.

FURTHER READINGS

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.

2. Column chart

It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.

3. Pie Charts

Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.

4. Bar charts

A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.

5. Histograms

It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.

6. Box plots

Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.

Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.

All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.

Importance of Data Presentation

Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.

Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.

If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.

There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.

To impress the higher management and top brass of a firm, effective presentation of data is needed.

Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.

Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-

• Many consumers or higher authorities are interested in the interpretation of data, not the raw data itself. Therefore, after the analysis of the data, users should represent the data with a visual aspect for better understanding and knowledge.
• The user should not overwhelm the audience with a number of slides of the presentation and inject an ample amount of texts as pictures that will speak for themselves.
• Data presentation often happens in a nutshell with each department showcasing their achievements towards company growth through a graph or a histogram.
• Providing a brief description would help the user to attain attention in a small amount of time while informing the audience about the context of the presentation
• The inclusion of pictures, charts, graphs and tables in the presentation help for better understanding the potential outcomes.
• An effective presentation would allow the organization to determine the difference with the fellow organization and acknowledge its flaws. Comparison of data would assist them in decision making.

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Skills for Learning : Research Skills

Data analysis is an ongoing process that should occur throughout your research project. Suitable data-analysis methods must be selected when you write your research proposal. The nature of your data (i.e. quantitative or qualitative) will be influenced by your research design and purpose. The data will also influence the analysis methods selected.

We run interactive workshops to help you develop skills related to doing research, such as data analysis, writing literature reviews and preparing for dissertations. Find out more on the Skills for Learning Workshops page.

We have online academic skills modules within MyBeckett for all levels of university study. These modules will help your academic development and support your success at LBU. You can work through the modules at your own pace, revisiting them as required. Find out more from our FAQ What academic skills modules are available?

Quantitative data analysis

Broadly speaking, 'statistics' refers to methods, tools and techniques used to collect, organise and interpret data. The goal of statistics is to gain understanding from data. Therefore, you need to know how to:

• Produce data – for example, by handing out a questionnaire or doing an experiment.
• Organise, summarise, present and analyse data.
• Draw valid conclusions from findings.

There are a number of statistical methods you can use to analyse data. Choosing an appropriate statistical method should follow naturally, however, from your research design. Therefore, you should think about data analysis at the early stages of your study design. You may need to consult a statistician for help with this.

Tips for working with statistical data

• Plan so that the data you get has a good chance of successfully tackling the research problem. This will involve reading literature on your subject, as well as on what makes a good study.
• To reach useful conclusions, you need to reduce uncertainties or 'noise'. Thus, you will need a sufficiently large data sample. A large sample will improve precision. However, this must be balanced against the 'costs' (time and money) of collection.
• Consider the logistics. Will there be problems in obtaining sufficient high-quality data? Think about accuracy, trustworthiness and completeness.
• Statistics are based on random samples. Consider whether your sample will be suited to this sort of analysis. Might there be biases to think about?
• How will you deal with missing values (any data that is not recorded for some reason)? These can result from gaps in a record or whole records being missed out.
• When analysing data, start by looking at each variable separately. Conduct initial/exploratory data analysis using graphical displays. Do this before looking at variables in conjunction or anything more complicated. This process can help locate errors in the data and also gives you a 'feel' for the data.
• Look out for patterns of 'missingness'. They are likely to alert you if there’s a problem. If the 'missingness' is not random, then it will have an impact on the results.
• Be vigilant and think through what you are doing at all times. Think critically. Statistics are not just mathematical tricks that a computer sorts out. Rather, analysing statistical data is a process that the human mind must interpret!

Top tips! Try inventing or generating the sort of data you might get and see if you can analyse it. Make sure that your process works before gathering actual data. Think what the output of an analytic procedure will look like before doing it for real.

(Note: it is actually difficult to generate realistic data. There are fraud-detection methods in place to identify data that has been fabricated. So, remember to get rid of your practice data before analysing the real stuff!)

Statistical software packages

Software packages can be used to analyse and present data. The most widely used ones are SPSS and NVivo.

SPSS is a statistical-analysis and data-management package for quantitative data analysis. Click on ‘ How do I install SPSS? ’ to learn how to download SPSS to your personal device. SPSS can perform a wide variety of statistical procedures. Some examples are:

• Data management (i.e. creating subsets of data or transforming data).
• Summarising, describing or presenting data (i.e. mean, median and frequency).
• Looking at the distribution of data (i.e. standard deviation).
• Comparing groups for significant differences using parametric (i.e. t-test) and non-parametric (i.e. Chi-square) tests.
• Identifying significant relationships between variables (i.e. correlation).

NVivo can be used for qualitative data analysis. It is suitable for use with a wide range of methodologies. Click on ‘ How do I access NVivo ’ to learn how to download NVivo to your personal device. NVivo supports grounded theory, survey data, case studies, focus groups, phenomenology, field research and action research.

• Process data such as interview transcripts, literature or media extracts, and historical documents.
• Code data on screen and explore all coding and documents interactively.
• Rearrange, restructure, extend and edit text, coding and coding relationships.
• Search imported text for words, phrases or patterns, and automatically code the results.

Qualitative data analysis

Miles and Huberman (1994) point out that there are diverse approaches to qualitative research and analysis. They suggest, however, that it is possible to identify 'a fairly classic set of analytic moves arranged in sequence'. This involves:

• Affixing codes to a set of field notes drawn from observation or interviews.
• Noting reflections or other remarks in the margins.
• Sorting/sifting through these materials to identify: a) similar phrases, relationships between variables, patterns and themes and b) distinct differences between subgroups and common sequences.
• Isolating these patterns/processes and commonalties/differences. Then, taking them out to the field in the next wave of data collection.
• Highlighting generalisations and relating them to your original research themes.
• Taking the generalisations and analysing them in relation to theoretical perspectives.

        (Miles and Huberman, 1994.)

Patterns and generalisations are usually arrived at through a process of analytic induction (see above points 5 and 6). Qualitative analysis rarely involves statistical analysis of relationships between variables. Qualitative analysis aims to gain in-depth understanding of concepts, opinions or experiences.

Presenting information

There are a number of different ways of presenting and communicating information. The particular format you use is dependent upon the type of data generated from the methods you have employed.

Here are some appropriate ways of presenting information for different types of data:

Bar charts: These   may be useful for comparing relative sizes. However, they tend to use a large amount of ink to display a relatively small amount of information. Consider a simple line chart as an alternative.

Pie charts: These have the benefit of indicating that the data must add up to 100%. However, they make it difficult for viewers to distinguish relative sizes, especially if two slices have a difference of less than 10%.

Other examples of presenting data in graphical form include line charts and  scatter plots .

Qualitative data is more likely to be presented in text form. For example, using quotations from interviews or field diaries.

• Plan ahead, thinking carefully about how you will analyse and present your data.
• Think through possible restrictions to resources you may encounter and plan accordingly.
• Find out about the different IT packages available for analysing your data and select the most appropriate.
• If necessary, allow time to attend an introductory course on a particular computer package. You can book SPSS and NVivo workshops via MyHub .
• Code your data appropriately, assigning conceptual or numerical codes as suitable.
• Organise your data so it can be analysed and presented easily.
• Choose the most suitable way of presenting your information, according to the type of data collected. This will allow your information to be understood and interpreted better.

Primary, secondary and tertiary sources

Information sources are sometimes categorised as primary, secondary or tertiary sources depending on whether or not they are ‘original’ materials or data. For some research projects, you may need to use primary sources as well as secondary or tertiary sources. However the distinction between primary and secondary sources is not always clear and depends on the context. For example, a newspaper article might usually be categorised as a secondary source. But it could also be regarded as a primary source if it were an article giving a first-hand account of a historical event written close to the time it occurred.

• Primary sources
• Secondary sources
• Tertiary sources
• Grey literature

Primary sources are original sources of information that provide first-hand accounts of what is being experienced or researched. They enable you to get as close to the actual event or research as possible. They are useful for getting the most contemporary information about a topic.

Examples include diary entries, newspaper articles, census data, journal articles with original reports of research, letters, email or other correspondence, original manuscripts and archives, interviews, research data and reports, statistics, autobiographies, exhibitions, films, and artists' writings.

Some information will be available on an Open Access basis, freely accessible online. However, many academic sources are paywalled, and you may need to login as a Leeds Beckett student to access them. Where Leeds Beckett does not have access to a source, you can use our  Request It! Service .

Secondary sources interpret, evaluate or analyse primary sources. They're useful for providing background information on a topic, or for looking back at an event from a current perspective. The majority of your literature searching will probably be done to find secondary sources on your topic.

Examples include journal articles which review or interpret original findings, popular magazine articles commenting on more serious research, textbooks and biographies.

The term tertiary sources isn't used a great deal. There's overlap between what might be considered a secondary source and a tertiary source. One definition is that a tertiary source brings together secondary sources.

Examples include almanacs, fact books, bibliographies, dictionaries and encyclopaedias, directories, indexes and abstracts. They can be useful for introductory information or an overview of a topic in the early stages of research.

Depending on your subject of study, grey literature may be another source you need to use. Grey literature includes technical or research reports, theses and dissertations, conference papers, government documents, white papers, and so on.

Artificial intelligence tools

Before using any generative artificial intelligence or paraphrasing tools in your assessments, you should check if this is permitted on your course.

If their use is permitted on your course, you must  acknowledge any use of generative artificial intelligence tools  such as ChatGPT or paraphrasing tools (e.g., Grammarly, Quillbot, etc.), even if you have only used them to generate ideas for your assessments or for proofreading.

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10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 05 April, 2024 • 17 min read

There are different ways of presenting data, so which one is suited you the most? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

• What are Methods of Data Presentations?
• #1 – Tabular

#3 – Pie chart

#4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quiz and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should set your session with open-ended questions , to avoid dead-communication!

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

What is chart presentation?

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should use charts for presentation?

You should use charts to ensure your contents and visual look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

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Home Blog Presentation Ideas How to Create and Deliver a Research Presentation

How to Create and Deliver a Research Presentation

Every research endeavor ends up with the communication of its findings. Graduate-level research culminates in a thesis defense , while many academic and scientific disciplines are published in peer-reviewed journals. In a business context, PowerPoint research presentation is the default format for reporting the findings to stakeholders.

Condensing months of work into a few slides can prove to be challenging. It requires particular skills to create and deliver a research presentation that promotes informed decisions and drives long-term projects forward.

Table of Contents

What is a Research Presentation

Key slides for creating a research presentation, tips when delivering a research presentation, how to present sources in a research presentation, recommended templates to create a research presentation.

A research presentation is the communication of research findings, typically delivered to an audience of peers, colleagues, students, or professionals. In the academe, it is meant to showcase the importance of the research paper , state the findings and the analysis of those findings, and seek feedback that could further the research.

The presentation of research becomes even more critical in the business world as the insights derived from it are the basis of strategic decisions of organizations. Information from this type of report can aid companies in maximizing the sales and profit of their business. Major projects such as research and development (R&D) in a new field, the launch of a new product or service, or even corporate social responsibility (CSR) initiatives will require the presentation of research findings to prove their feasibility.

Market research and technical research are examples of business-type research presentations you will commonly encounter.

In this article, we’ve compiled all the essential tips, including some examples and templates, to get you started with creating and delivering a stellar research presentation tailored specifically for the business context.

Various research suggests that the average attention span of adults during presentations is around 20 minutes, with a notable drop in an engagement at the 10-minute mark . Beyond that, you might see your audience doing other things.

How can you avoid such a mistake? The answer lies in the adage “keep it simple, stupid” or KISS. We don’t mean dumbing down your content but rather presenting it in a way that is easily digestible and accessible to your audience. One way you can do this is by organizing your research presentation using a clear structure.

Here are the slides you should prioritize when creating your research presentation PowerPoint.

1.  Title Page

The title page is the first thing your audience will see during your presentation, so put extra effort into it to make an impression. Of course, writing presentation titles and title pages will vary depending on the type of presentation you are to deliver. In the case of a research presentation, you want a formal and academic-sounding one. It should include:

• The full title of the report
• The date of the report
• The name of the researchers or department in charge of the report
• The name of the organization for which the presentation is intended

When writing the title of your research presentation, it should reflect the topic and objective of the report. Focus only on the subject and avoid adding redundant phrases like “A research on” or “A study on.” However, you may use phrases like “Market Analysis” or “Feasibility Study” because they help identify the purpose of the presentation. Doing so also serves a long-term purpose for the filing and later retrieving of the document.

Here’s a sample title page for a hypothetical market research presentation from Gillette .

2. Executive Summary Slide

The executive summary marks the beginning of the body of the presentation, briefly summarizing the key discussion points of the research. Specifically, the summary may state the following:

• The purpose of the investigation and its significance within the organization’s goals
• The methods used for the investigation
• The major findings of the investigation
• The conclusions and recommendations after the investigation

Although the executive summary encompasses the entry of the research presentation, it should not dive into all the details of the work on which the findings, conclusions, and recommendations were based. Creating the executive summary requires a focus on clarity and brevity, especially when translating it to a PowerPoint document where space is limited.

Each point should be presented in a clear and visually engaging manner to capture the audience’s attention and set the stage for the rest of the presentation. Use visuals, bullet points, and minimal text to convey information efficiently.

3. Introduction/ Project Description Slides

In this section, your goal is to provide your audience with the information that will help them understand the details of the presentation. Provide a detailed description of the project, including its goals, objectives, scope, and methods for gathering and analyzing data.

You want to answer these fundamental questions:

• What specific questions are you trying to answer, problems you aim to solve, or opportunities you seek to explore?
• Why is this project important, and what prompted it?
• What are the boundaries of your research or initiative? 
• How were the data gathered?

Important: The introduction should exclude specific findings, conclusions, and recommendations.

4. Data Presentation and Analyses Slides

This is the longest section of a research presentation, as you’ll present the data you’ve gathered and provide a thorough analysis of that data to draw meaningful conclusions. The format and components of this section can vary widely, tailored to the specific nature of your research.

For example, if you are doing market research, you may include the market potential estimate, competitor analysis, and pricing analysis. These elements will help your organization determine the actual viability of a market opportunity.

Visual aids like charts, graphs, tables, and diagrams are potent tools to convey your key findings effectively. These materials may be numbered and sequenced (Figure 1, Figure 2, and so forth), accompanied by text to make sense of the insights.

5. Conclusions

The conclusion of a research presentation is where you pull together the ideas derived from your data presentation and analyses in light of the purpose of the research. For example, if the objective is to assess the market of a new product, the conclusion should determine the requirements of the market in question and tell whether there is a product-market fit.

Designing your conclusion slide should be straightforward and focused on conveying the key takeaways from your research. Keep the text concise and to the point. Present it in bullet points or numbered lists to make the content easily scannable.

6. Recommendations

The findings of your research might reveal elements that may not align with your initial vision or expectations. These deviations are addressed in the recommendations section of your presentation, which outlines the best course of action based on the result of the research.

What emerging markets should we target next? Do we need to rethink our pricing strategies? Which professionals should we hire for this special project? — these are some of the questions that may arise when coming up with this part of the research.

Recommendations may be combined with the conclusion, but presenting them separately to reinforce their urgency. In the end, the decision-makers in the organization or your clients will make the final call on whether to accept or decline the recommendations.

7. Questions Slide

Members of your audience are not involved in carrying out your research activity, which means there’s a lot they don’t know about its details. By offering an opportunity for questions, you can invite them to bridge that gap, seek clarification, and engage in a dialogue that enhances their understanding.

If your research is more business-oriented, facilitating a question and answer after your presentation becomes imperative as it’s your final appeal to encourage buy-in for your recommendations.

A simple “Ask us anything” slide can indicate that you are ready to accept questions.

1. Focus on the Most Important Findings

The truth about presenting research findings is that your audience doesn’t need to know everything. Instead, they should receive a distilled, clear, and meaningful overview that focuses on the most critical aspects.

You will likely have to squeeze in the oral presentation of your research into a 10 to 20-minute presentation, so you have to make the most out of the time given to you. In the presentation, don’t soak in the less important elements like historical backgrounds. Decision-makers might even ask you to skip these portions and focus on sharing the findings.

2. Do Not Read Word-per-word

Reading word-for-word from your presentation slides intensifies the danger of losing your audience’s interest. Its effect can be detrimental, especially if the purpose of your research presentation is to gain approval from the audience. So, how can you avoid this mistake?

• Make a conscious design decision to keep the text on your slides minimal. Your slides should serve as visual cues to guide your presentation.
• Structure your presentation as a narrative or story. Stories are more engaging and memorable than dry, factual information.
• Prepare speaker notes with the key points of your research. Glance at it when needed.
• Engage with the audience by maintaining eye contact and asking rhetorical questions.

3. Don’t Go Without Handouts

Handouts are paper copies of your presentation slides that you distribute to your audience. They typically contain the summary of your key points, but they may also provide supplementary information supporting data presented through tables and graphs.

The purpose of distributing presentation handouts is to easily retain the key points you presented as they become good references in the future. Distributing handouts in advance allows your audience to review the material and come prepared with questions or points for discussion during the presentation.

4. Actively Listen

An equally important skill that a presenter must possess aside from speaking is the ability to listen. We are not just talking about listening to what the audience is saying but also considering their reactions and nonverbal cues. If you sense disinterest or confusion, you can adapt your approach on the fly to re-engage them.

For example, if some members of your audience are exchanging glances, they may be skeptical of the research findings you are presenting. This is the best time to reassure them of the validity of your data and provide a concise overview of how it came to be. You may also encourage them to seek clarification.

5. Be Confident

Anxiety can strike before a presentation – it’s a common reaction whenever someone has to speak in front of others. If you can’t eliminate your stress, try to manage it.

People hate public speaking not because they simply hate it. Most of the time, it arises from one’s belief in themselves. You don’t have to take our word for it. Take Maslow’s theory that says a threat to one’s self-esteem is a source of distress among an individual.

Now, how can you master this feeling? You’ve spent a lot of time on your research, so there is no question about your topic knowledge. Perhaps you just need to rehearse your research presentation. If you know what you will say and how to say it, you will gain confidence in presenting your work.

All sources you use in creating your research presentation should be given proper credit. The APA Style is the most widely used citation style in formal research.

In-text citation

Add references within the text of your presentation slide by giving the author’s last name, year of publication, and page number (if applicable) in parentheses after direct quotations or paraphrased materials. As in:

The alarming rate at which global temperatures rise directly impacts biodiversity (Smith, 2020, p. 27).

If the author’s name and year of publication are mentioned in the text, add only the page number in parentheses after the quotations or paraphrased materials. As in:

According to Smith (2020), the alarming rate at which global temperatures rise directly impacts biodiversity (p. 27).

Image citation

All images from the web, including photos, graphs, and tables, used in your slides should be credited using the format below.

Creator’s Last Name, First Name. “Title of Image.” Website Name, Day Mo. Year, URL. Accessed Day Mo. Year.

Work cited page

A work cited page or reference list should follow after the last slide of your presentation. The list should be alphabetized by the author’s last name and initials followed by the year of publication, the title of the book or article, the place of publication, and the publisher. As in:

Smith, J. A. (2020). Climate Change and Biodiversity: A Comprehensive Study. New York, NY: ABC Publications.

When citing a document from a website, add the source URL after the title of the book or article instead of the place of publication and the publisher. As in:

Smith, J. A. (2020). Climate Change and Biodiversity: A Comprehensive Study. Retrieved from https://www.smith.com/climate-change-and-biodiversity.

1. Research Project Presentation PowerPoint Template

A slide deck containing 18 different slides intended to take off the weight of how to make a research presentation. With tons of visual aids, presenters can reference existing research on similar projects to this one – or link another research presentation example – provide an accurate data analysis, disclose the methodology used, and much more.

Use This Template

2. Research Presentation Scientific Method Diagram PowerPoint Template

Whenever you intend to raise questions, expose the methodology you used for your research, or even suggest a scientific method approach for future analysis, this circular wheel diagram is a perfect fit for any presentation study.

Customize all of its elements to suit the demands of your presentation in just minutes.

3. Thesis Research Presentation PowerPoint Template

If your research presentation project belongs to academia, then this is the slide deck to pair that presentation. With a formal aesthetic and minimalistic style, this research presentation template focuses only on exposing your information as clearly as possible.

Use its included bar charts and graphs to introduce data, change the background of each slide to suit the topic of your presentation, and customize each of its elements to meet the requirements of your project with ease.

4. Animated Research Cards PowerPoint Template

Visualize ideas and their connection points with the help of this research card template for PowerPoint. This slide deck, for example, can help speakers talk about alternative concepts to what they are currently managing and its possible outcomes, among different other usages this versatile PPT template has. Zoom Animation effects make a smooth transition between cards (or ideas).

5. Research Presentation Slide Deck for PowerPoint

With a distinctive professional style, this research presentation PPT template helps business professionals and academics alike to introduce the findings of their work to team members or investors.

By accessing this template, you get the following slides:

• Introduction
• Problem Statement
• Research Questions
• Conceptual Research Framework (Concepts, Theories, Actors, & Constructs)
• Study design and methods
• Population & Sampling
• Data Collection
• Data Analysis

Check it out today and craft a powerful research presentation out of it!

A successful research presentation in business is not just about presenting data; it’s about persuasion to take meaningful action. It’s the bridge that connects your research efforts to the strategic initiatives of your organization. To embark on this journey successfully, planning your presentation thoroughly is paramount, from designing your PowerPoint to the delivery.

Take a look and get inspiration from the sample research presentation slides above, put our tips to heart, and transform your research findings into a compelling call to action.

Like this article? Please share

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6 essential it tools for researchers.

Advances in research computing have propelled Pitt to the forefront of research, with the University ranking third nationally in NIH funding in FY23. Researchers utilize robust technology to collect, store, and analyze data; document and manage lab processes; and work with collaborators. Pitt IT supports the work of researchers, so they can focus on doing what they do best: making the University of Pittsburgh one of the top research institutions in the world! Here are the top six tools researchers at Pitt can start using today.

1. Collect Participant Data (Qualtrics)

For research involving self-reporting or participant feedback, you need a robust survey tool that is easy to use. The  Online Survey System (Qualtrics)  enables you to create and distribute surveys for everything from collecting demographic info or tracking daily activity, to monitoring symptoms or exploring participant beliefs. The system requires minimal experience or training, is extremely user-friendly, and is available at no cost to all Pitt researchers, faculty, staff, and students. Qualtrics securely gathers and stores survey data, allows for complex decision trees based on previous responses, provides easy access to raw data, compiles and analyzes results, and creates high-quality charts and graphs to visually present the data.

2. Document and Manage Lab Activities (LabArchives)

Pitt offers  Electronic Research Notebooks (LabArchives)  at no cost to help you keep track of all your notes, data, and findings in one place, from nearly any type of device. ELNs can be used to organize and manage all aspects of lab activity, from study protocols and testing progress to documenting results. Notebooks can include any kind of input, including text, tables, images, and sketches. You can also share and collaborate on notebooks with research partners and lab staff.

In addition, LabArchives Scheduler and Inventory modules help manage and organize all aspects of lab activity. Scheduler provides a calendar and scheduling service to simplify the management and booking of equipment and spaces, such as microscopes, flow cytometers, recording space, microphones, and other items. Inventory enables you to streamline inventory management via a simple, customizable solution that allows you to include inventory types and storage locations, add and manage lab inventory items, and generate shopping lists.

3. Store Large Data Sets (Enterprise Cloud Storage)

Enterprise Cloud Computing (ECC) gives researchers access to virtual computing resources in a safe and reliable way. The most widely used ECC platform for large-scale storage and robust compute power is  Microsoft Azure . Azure provides resources to tailor specific solutions to your data storage needs, without the extra time or cost of purchasing, setting up and maintaining a server. Azure lets you archive data inexpensively, provide shared file storage, and store and compute data in an environment with HIPAA-compliant protections for data privacy.

4. Transfer Large Data Sets (Globus)

Research often generates very large data sets, which presents challenges when transferring and sharing the data with collaborators. Pitt’s  Enterprise Data Transfer Service (Globus)  subscription lets users transfer and share files with ease, no matter the location of you or your collaborators. You can move and share data between on-premises and cloud storage, with external institutions, and between endpoints/devices. Users can also develop applications and access gateways by leveraging Globus’s advanced identity management, single sign-on, search, authorization, and automation capabilities. A free Globus subscription is available to all Pitt researchers, faculty, and students.

5. Visualize Your Results (BioRender) 

BioRender  is used to create and edit scientific diagrams and illustrations for use in posters, papers, presentations, and publications. The web-based app has over 40,000 icons and a large stock image collection that spans subjects such as neuroscience, immunology, microbiology, and more. The app makes it easy to create sophisticated figures by using an intuitive interface with drag and drop tools—no artistic skills needed. BioRender also lets you work collaboratively on figures. Faculty and staff receive BioRender at a significantly discounted price (just $100/year for each user). Students have free access to help them learn how to visually present scientific concepts and data in their projects and papers.

6. Access Supercomputing Resources (ARCS)

The Center for Research Computing (CRC) and the Pittsburgh Supercomputing Center (PSC) work closely with Pitt IT to provide Pitt researchers with the computing power needed to engage in large-scale processing. They are used by researchers in STEM fields, who often engage in large-scale modeling and simulations, as well as those in the humanities, who need high-throughput computing to analyze large repositories of digitized source materials.

The CRC supports leading-edge research with free access to up to 25,000 computational hours on advanced computing hardware and software for fields across the entire research community, along with training and consultation by CRC research faculty. The PSC is a joint venture with Pitt and CMU, and provides forward-looking technology solutions to advance research nationwide while also coordinating the research endeavors of each university to enhance cooperation.

Get Started and Get Help

Learn more about the  IT resources for researchers  on the Pitt IT website, including what each app can do and how to access each service. If you need help with any of the resources or need advice about addressing a technical issue your research project is facing, contact  Pitt IT’s Technology Help Desk . You can also access a wealth of information about support and training services through:

• Pitt Research and Office of Sponsored Programs (OSP)
• Clinical and Translational Science Institute (CTSI)
• Health Sciences Library System (HSLS)

Happy researching!

-- By Karen Beaudway, Pitt IT Blogger

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• Published: 03 May 2024

A dataset for measuring the impact of research data and their curation

• Libby Hemphill   ORCID: orcid.org/0000-0002-3793-7281 1 , 2 ,
• Andrea Thomer 3 ,
• Sara Lafia 1 ,
• Lizhou Fan 2 ,
• David Bleckley   ORCID: orcid.org/0000-0001-7715-4348 1 &
• Elizabeth Moss 1  

Scientific Data volume  11 , Article number:  442 ( 2024 ) Cite this article

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• Research data
• Social sciences

Science funders, publishers, and data archives make decisions about how to responsibly allocate resources to maximize the reuse potential of research data. This paper introduces a dataset developed to measure the impact of archival and data curation decisions on data reuse. The dataset describes 10,605 social science research datasets, their curation histories, and reuse contexts in 94,755 publications that cover 59 years from 1963 to 2022. The dataset was constructed from study-level metadata, citing publications, and curation records available through the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. The dataset includes information about study-level attributes (e.g., PIs, funders, subject terms); usage statistics (e.g., downloads, citations); archiving decisions (e.g., curation activities, data transformations); and bibliometric attributes (e.g., journals, authors) for citing publications. This dataset provides information on factors that contribute to long-term data reuse, which can inform the design of effective evidence-based recommendations to support high-impact research data curation decisions.

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Interdisciplinarity revisited: evidence for research impact and dynamism

Background & summary.

Recent policy changes in funding agencies and academic journals have increased data sharing among researchers and between researchers and the public. Data sharing advances science and provides the transparency necessary for evaluating, replicating, and verifying results. However, many data-sharing policies do not explain what constitutes an appropriate dataset for archiving or how to determine the value of datasets to secondary users 1 , 2 , 3 . Questions about how to allocate data-sharing resources efficiently and responsibly have gone unanswered 4 , 5 , 6 . For instance, data-sharing policies recognize that not all data should be curated and preserved, but they do not articulate metrics or guidelines for determining what data are most worthy of investment.

Despite the potential for innovation and advancement that data sharing holds, the best strategies to prioritize datasets for preparation and archiving are often unclear. Some datasets are likely to have more downstream potential than others, and data curation policies and workflows should prioritize high-value data instead of being one-size-fits-all. Though prior research in library and information science has shown that the “analytic potential” of a dataset is key to its reuse value 7 , work is needed to implement conceptual data reuse frameworks 8 , 9 , 10 , 11 , 12 , 13 , 14 . In addition, publishers and data archives need guidance to develop metrics and evaluation strategies to assess the impact of datasets.

Several existing resources have been compiled to study the relationship between the reuse of scholarly products, such as datasets (Table  1 ); however, none of these resources include explicit information on how curation processes are applied to data to increase their value, maximize their accessibility, and ensure their long-term preservation. The CCex (Curation Costs Exchange) provides models of curation services along with cost-related datasets shared by contributors but does not make explicit connections between them or include reuse information 15 . Analyses on platforms such as DataCite 16 have focused on metadata completeness and record usage, but have not included related curation-level information. Analyses of GenBank 17 and FigShare 18 , 19 citation networks do not include curation information. Related studies of Github repository reuse 20 and Softcite software citation 21 reveal significant factors that impact the reuse of secondary research products but do not focus on research data. RD-Switchboard 22 and DSKG 23 are scholarly knowledge graphs linking research data to articles, patents, and grants, but largely omit social science research data and do not include curation-level factors. To our knowledge, other studies of curation work in organizations similar to ICPSR – such as GESIS 24 , Dataverse 25 , and DANS 26 – have not made their underlying data available for analysis.

This paper describes a dataset 27 compiled for the MICA project (Measuring the Impact of Curation Actions) led by investigators at ICPSR, a large social science data archive at the University of Michigan. The dataset was originally developed to study the impacts of data curation and archiving on data reuse. The MICA dataset has supported several previous publications investigating the intensity of data curation actions 28 , the relationship between data curation actions and data reuse 29 , and the structures of research communities in a data citation network 30 . Collectively, these studies help explain the return on various types of curatorial investments. The dataset that we introduce in this paper, which we refer to as the MICA dataset, has the potential to address research questions in the areas of science (e.g., knowledge production), library and information science (e.g., scholarly communication), and data archiving (e.g., reproducible workflows).

We constructed the MICA dataset 27 using records available at ICPSR, a large social science data archive at the University of Michigan. Data set creation involved: collecting and enriching metadata for articles indexed in the ICPSR Bibliography of Data-related Literature against the Dimensions AI bibliometric database; gathering usage statistics for studies from ICPSR’s administrative database; processing data curation work logs from ICPSR’s project tracking platform, Jira; and linking data in social science studies and series to citing analysis papers (Fig.  1 ).

Steps to prepare MICA dataset for analysis - external sources are red, primary internal sources are blue, and internal linked sources are green.

Enrich paper metadata

The ICPSR Bibliography of Data-related Literature is a growing database of literature in which data from ICPSR studies have been used. Its creation was funded by the National Science Foundation (Award 9977984), and for the past 20 years it has been supported by ICPSR membership and multiple US federally-funded and foundation-funded topical archives at ICPSR. The Bibliography was originally launched in the year 2000 to aid in data discovery by providing a searchable database linking publications to the study data used in them. The Bibliography collects the universe of output based on the data shared in each study through, which is made available through each ICPSR study’s webpage. The Bibliography contains both peer-reviewed and grey literature, which provides evidence for measuring the impact of research data. For an item to be included in the ICPSR Bibliography, it must contain an analysis of data archived by ICPSR or contain a discussion or critique of the data collection process, study design, or methodology 31 . The Bibliography is manually curated by a team of librarians and information specialists at ICPSR who enter and validate entries. Some publications are supplied to the Bibliography by data depositors, and some citations are submitted to the Bibliography by authors who abide by ICPSR’s terms of use requiring them to submit citations to works in which they analyzed data retrieved from ICPSR. Most of the Bibliography is populated by Bibliography team members, who create custom queries for ICPSR studies performed across numerous sources, including Google Scholar, ProQuest, SSRN, and others. Each record in the Bibliography is one publication that has used one or more ICPSR studies. The version we used was captured on 2021-11-16 and included 94,755 publications.

To expand the coverage of the ICPSR Bibliography, we searched exhaustively for all ICPSR study names, unique numbers assigned to ICPSR studies, and DOIs 32 using a full-text index available through the Dimensions AI database 33 . We accessed Dimensions through a license agreement with the University of Michigan. ICPSR Bibliography librarians and information specialists manually reviewed and validated new entries that matched one or more search criteria. We then used Dimensions to gather enriched metadata and full-text links for items in the Bibliography with DOIs. We matched 43% of the items in the Bibliography to enriched Dimensions metadata including abstracts, field of research codes, concepts, and authors’ institutional information; we also obtained links to full text for 16% of Bibliography items. Based on licensing agreements, we included Dimensions identifiers and links to full text so that users with valid publisher and database access can construct an enriched publication dataset.

Gather study usage data

ICPSR maintains a relational administrative database, DBInfo, that organizes study-level metadata and information on data reuse across separate tables. Studies at ICPSR consist of one or more files collected at a single time or for a single purpose; studies in which the same variables are observed over time are grouped into series. Each study at ICPSR is assigned a DOI, and its metadata are stored in DBInfo. Study metadata follows the Data Documentation Initiative (DDI) Codebook 2.5 standard. DDI elements included in our dataset are title, ICPSR study identification number, DOI, authoring entities, description (abstract), funding agencies, subject terms assigned to the study during curation, and geographic coverage. We also created variables based on DDI elements: total variable count, the presence of survey question text in the metadata, the number of author entities, and whether an author entity was an institution. We gathered metadata for ICPSR’s 10,605 unrestricted public-use studies available as of 2021-11-16 ( https://www.icpsr.umich.edu/web/pages/membership/or/metadata/oai.html ).

To link study usage data with study-level metadata records, we joined study metadata from DBinfo on study usage information, which included total study downloads (data and documentation), individual data file downloads, and cumulative citations from the ICPSR Bibliography. We also gathered descriptive metadata for each study and its variables, which allowed us to summarize and append recoded fields onto the study-level metadata such as curation level, number and type of principle investigators, total variable count, and binary variables indicating whether the study data were made available for online analysis, whether survey question text was made searchable online, and whether the study variables were indexed for search. These characteristics describe aspects of the discoverability of the data to compare with other characteristics of the study. We used the study and series numbers included in the ICPSR Bibliography as unique identifiers to link papers to metadata and analyze the community structure of dataset co-citations in the ICPSR Bibliography 32 .

Process curation work logs

Researchers deposit data at ICPSR for curation and long-term preservation. Between 2016 and 2020, more than 3,000 research studies were deposited with ICPSR. Since 2017, ICPSR has organized curation work into a central unit that provides varied levels of curation that vary in the intensity and complexity of data enhancement that they provide. While the levels of curation are standardized as to effort (level one = less effort, level three = most effort), the specific curatorial actions undertaken for each dataset vary. The specific curation actions are captured in Jira, a work tracking program, which data curators at ICPSR use to collaborate and communicate their progress through tickets. We obtained access to a corpus of 669 completed Jira tickets corresponding to the curation of 566 unique studies between February 2017 and December 2019 28 .

To process the tickets, we focused only on their work log portions, which contained free text descriptions of work that data curators had performed on a deposited study, along with the curators’ identifiers, and timestamps. To protect the confidentiality of the data curators and the processing steps they performed, we collaborated with ICPSR’s curation unit to propose a classification scheme, which we used to train a Naive Bayes classifier and label curation actions in each work log sentence. The eight curation action labels we proposed 28 were: (1) initial review and planning, (2) data transformation, (3) metadata, (4) documentation, (5) quality checks, (6) communication, (7) other, and (8) non-curation work. We note that these categories of curation work are very specific to the curatorial processes and types of data stored at ICPSR, and may not match the curation activities at other repositories. After applying the classifier to the work log sentences, we obtained summary-level curation actions for a subset of all ICPSR studies (5%), along with the total number of hours spent on data curation for each study, and the proportion of time associated with each action during curation.

Data Records

The MICA dataset 27 connects records for each of ICPSR’s archived research studies to the research publications that use them and related curation activities available for a subset of studies (Fig.  2 ). Each of the three tables published in the dataset is available as a study archived at ICPSR. The data tables are distributed as statistical files available for use in SAS, SPSS, Stata, and R as well as delimited and ASCII text files. The dataset is organized around studies and papers as primary entities. The studies table lists ICPSR studies, their metadata attributes, and usage information; the papers table was constructed using the ICPSR Bibliography and Dimensions database; and the curation logs table summarizes the data curation steps performed on a subset of ICPSR studies.

Studies (“ICPSR_STUDIES”): 10,605 social science research datasets available through ICPSR up to 2021-11-16 with variables for ICPSR study number, digital object identifier, study name, series number, series title, authoring entities, full-text description, release date, funding agency, geographic coverage, subject terms, topical archive, curation level, single principal investigator (PI), institutional PI, the total number of PIs, total variables in data files, question text availability, study variable indexing, level of restriction, total unique users downloading study data files and codebooks, total unique users downloading data only, and total unique papers citing data through November 2021. Studies map to the papers and curation logs table through ICPSR study numbers as “STUDY”. However, not every study in this table will have records in the papers and curation logs tables.

Papers (“ICPSR_PAPERS”): 94,755 publications collected from 2000-08-11 to 2021-11-16 in the ICPSR Bibliography and enriched with metadata from the Dimensions database with variables for paper number, identifier, title, authors, publication venue, item type, publication date, input date, ICPSR series numbers used in the paper, ICPSR study numbers used in the paper, the Dimension identifier, and the Dimensions link to the publication’s full text. Papers map to the studies table through ICPSR study numbers in the “STUDY_NUMS” field. Each record represents a single publication, and because a researcher can use multiple datasets when creating a publication, each record may list multiple studies or series.

Curation logs (“ICPSR_CURATION_LOGS”): 649 curation logs for 563 ICPSR studies (although most studies in the subset had one curation log, some studies were associated with multiple logs, with a maximum of 10) curated between February 2017 and December 2019 with variables for study number, action labels assigned to work description sentences using a classifier trained on ICPSR curation logs, hours of work associated with a single log entry, and total hours of work logged for the curation ticket. Curation logs map to the study and paper tables through ICPSR study numbers as “STUDY”. Each record represents a single logged action, and future users may wish to aggregate actions to the study level before joining tables.

Entity-relation diagram.

Technical Validation

We report on the reliability of the dataset’s metadata in the following subsections. To support future reuse of the dataset, curation services provided through ICPSR improved data quality by checking for missing values, adding variable labels, and creating a codebook.

All 10,605 studies available through ICPSR have a DOI and a full-text description summarizing what the study is about, the purpose of the study, the main topics covered, and the questions the PIs attempted to answer when they conducted the study. Personal names (i.e., principal investigators) and organizational names (i.e., funding agencies) are standardized against an authority list maintained by ICPSR; geographic names and subject terms are also standardized and hierarchically indexed in the ICPSR Thesaurus 34 . Many of ICPSR’s studies (63%) are in a series and are distributed through the ICPSR General Archive (56%), a non-topical archive that accepts any social or behavioral science data. While study data have been available through ICPSR since 1962, the earliest digital release date recorded for a study was 1984-03-18, when ICPSR’s database was first employed, and the most recent date is 2021-10-28 when the dataset was collected.

Curation level information was recorded starting in 2017 and is available for 1,125 studies (11%); approximately 80% of studies with assigned curation levels received curation services, equally distributed between Levels 1 (least intensive), 2 (moderately intensive), and 3 (most intensive) (Fig.  3 ). Detailed descriptions of ICPSR’s curation levels are available online 35 . Additional metadata are available for a subset of 421 studies (4%), including information about whether the study has a single PI, an institutional PI, the total number of PIs involved, total variables recorded is available for online analysis, has searchable question text, has variables that are indexed for search, contains one or more restricted files, and whether the study is completely restricted. We provided additional metadata for this subset of ICPSR studies because they were released within the past five years and detailed curation and usage information were available for them. Usage statistics including total downloads and data file downloads are available for this subset of studies as well; citation statistics are available for 8,030 studies (76%). Most ICPSR studies have fewer than 500 users, as indicated by total downloads, or citations (Fig.  4 ).

ICPSR study curation levels.

ICPSR study usage.

A subset of 43,102 publications (45%) available in the ICPSR Bibliography had a DOI. Author metadata were entered as free text, meaning that variations may exist and require additional normalization and pre-processing prior to analysis. While author information is standardized for each publication, individual names may appear in different sort orders (e.g., “Earls, Felton J.” and “Stephen W. Raudenbush”). Most of the items in the ICPSR Bibliography as of 2021-11-16 were journal articles (59%), reports (14%), conference presentations (9%), or theses (8%) (Fig.  5 ). The number of publications collected in the Bibliography has increased each decade since the inception of ICPSR in 1962 (Fig.  6 ). Most ICPSR studies (76%) have one or more citations in a publication.

ICPSR Bibliography citation types.

ICPSR citations by decade.

Usage Notes

The dataset consists of three tables that can be joined using the “STUDY” key as shown in Fig.  2 . The “ICPSR_PAPERS” table contains one row per paper with one or more cited studies in the “STUDY_NUMS” column. We manipulated and analyzed the tables as CSV files with the Pandas library 36 in Python and the Tidyverse packages 37 in R.

The present MICA dataset can be used independently to study the relationship between curation decisions and data reuse. Evidence of reuse for specific studies is available in several forms: usage information, including downloads and citation counts; and citation contexts within papers that cite data. Analysis may also be performed on the citation network formed between datasets and papers that use them. Finally, curation actions can be associated with properties of studies and usage histories.

This dataset has several limitations of which users should be aware. First, Jira tickets can only be used to represent the intensiveness of curation for activities undertaken since 2017, when ICPSR started using both Curation Levels and Jira. Studies published before 2017 were all curated, but documentation of the extent of that curation was not standardized and therefore could not be included in these analyses. Second, the measure of publications relies upon the authors’ clarity of data citation and the ICPSR Bibliography staff’s ability to discover citations with varying formality and clarity. Thus, there is always a chance that some secondary-data-citing publications have been left out of the bibliography. Finally, there may be some cases in which a paper in the ICSPSR bibliography did not actually obtain data from ICPSR. For example, PIs have often written about or even distributed their data prior to their archival in ICSPR. Therefore, those publications would not have cited ICPSR but they are still collected in the Bibliography as being directly related to the data that were eventually deposited at ICPSR.

In summary, the MICA dataset contains relationships between two main types of entities – papers and studies – which can be mined. The tables in the MICA dataset have supported network analysis (community structure and clique detection) 30 ; natural language processing (NER for dataset reference detection) 32 ; visualizing citation networks (to search for datasets) 38 ; and regression analysis (on curation decisions and data downloads) 29 . The data are currently being used to develop research metrics and recommendation systems for research data. Given that DOIs are provided for ICPSR studies and articles in the ICPSR Bibliography, the MICA dataset can also be used with other bibliometric databases, including DataCite, Crossref, OpenAlex, and related indexes. Subscription-based services, such as Dimensions AI, are also compatible with the MICA dataset. In some cases, these services provide abstracts or full text for papers from which data citation contexts can be extracted for semantic content analysis.

Code availability

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Acknowledgements

We thank the ICPSR Bibliography staff, the ICPSR Data Curation Unit, and the ICPSR Data Stewardship Committee for their support of this research. This material is based upon work supported by the National Science Foundation under grant 1930645. This project was made possible in part by the Institute of Museum and Library Services LG-37-19-0134-19.

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L.H. and A.T. conceptualized the study design, D.B., E.M., and S.L. prepared the data, S.L., L.F., and L.H. analyzed the data, and D.B. validated the data. All authors reviewed and edited the manuscript.

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Hemphill, L., Thomer, A., Lafia, S. et al. A dataset for measuring the impact of research data and their curation. Sci Data 11 , 442 (2024). https://doi.org/10.1038/s41597-024-03303-2

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Title: Building a Smart Guide for Open Research: A Decision Tree Toolkit for Engineering Postgraduate Research Students

Abstract: Open research practices are important for enhancing the transparency, reliability, and visibility of research. Yet, many engineering postgraduate research students find themselves either unaware of or unable to incorporate these practices into their studies. Recognising this gap, an internal open research grant has been secured to build an innovative decision tree toolkit. In collaboration with Michael Stevenson in the office for open research at the University of Manchester and Matt Parkman who is an IT specialist, the decision tree has integrated with an online platform, designed to guide students through the complexities of integrating open research into their work, thereby promoting scientific advancement.

The online decision tree toolkit offers a clear, step-by-step guide for students to publicly share their research findings, engage with open data, and utilise open-source software tools essential for pushing the boundaries of scientific inquiry. It is crafted to break down the barriers that have previously deterred students from adopting open research practices, providing them with the actionable guidance needed to share their data, methodologies, and findings openly. Such transparency not only enhances the reproducibility of research but also cultivates a collaborative scientific community where information and ideas are freely exchanged.

Following the trial phase, the open research team plans to extensively promote this online tool to a broader student audience. Therefore, the tool would further improve awareness among engineering postgraduate research students regarding the crucial role of openness in research. In essence, this toolkit is more than just a resource; it is a catalyst for transforming engineering postgraduate research students into proponents of a transparent, reproducible, and open research ecosystem.

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