presentation of data conclusion

Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of storytelling with data, so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

Like this article? Please share

Data Analysis, Data Science, Data Visualization Filed under Design

Related Articles

Filed under Design • March 27th, 2024

How to Make a Presentation Graph

Detailed step-by-step instructions to master the art of how to make a presentation graph in PowerPoint and Google Slides. Check it out!

Filed under Presentation Ideas • January 6th, 2024

All About Using Harvey Balls

Among the many tools in the arsenal of the modern presenter, Harvey Balls have a special place. In this article we will tell you all about using Harvey Balls.

Filed under Business • December 8th, 2023

How to Design a Dashboard Presentation: A Step-by-Step Guide

Take a step further in your professional presentation skills by learning what a dashboard presentation is and how to properly design one in PowerPoint. A detailed step-by-step guide is here!

Leave a Reply

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

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

Partner Center

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons
• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

1.3: Presentation of Data

• Last updated
• Save as PDF
• Page ID 577

Learning Objectives

• To learn two ways that data will be presented in the text.

In this book we will use two formats for presenting data sets. The first is a data list, which is an explicit listing of all the individual measurements, either as a display with space between the individual measurements, or in set notation with individual measurements separated by commas.

Example \(\PageIndex{1}\)

The data obtained by measuring the age of \(21\) randomly selected students enrolled in freshman courses at a university could be presented as the data list:

\[\begin{array}{cccccccccc}18 & 18 & 19 & 19 & 19 & 18 & 22 & 20 & 18 & 18 & 17 \\ 19 & 18 & 24 & 18 & 20 & 18 & 21 & 20 & 17 & 19 &\end{array} \nonumber \]

or in set notation as:

\[ \{18,18,19,19,19,18,22,20,18,18,17,19,18,24,18,20,18,21,20,17,19\} \nonumber \]

A data set can also be presented by means of a data frequency table, a table in which each distinct value \(x\) is listed in the first row and its frequency \(f\), which is the number of times the value \(x\) appears in the data set, is listed below it in the second row.

Example \(\PageIndex{2}\)

The data set of the previous example is represented by the data frequency table

\[\begin{array}{c|cccccc}x & 17 & 18 & 19 & 20 & 21 & 22 & 24 \\ \hline f & 2 & 8 & 5 & 3 & 1 & 1 & 1\end{array} \nonumber \]

The data frequency table is especially convenient when data sets are large and the number of distinct values is not too large.

Key Takeaway

• Data sets can be presented either by listing all the elements or by giving a table of values and frequencies.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

74 Drawing Conclusions From Your Data

As we mentioned earlier, it is important to not just state the results of your statistical analyses. You should interpret the meanings, because this will enable you to answer your research questions. At the end of your analysis, you should be able to conclude whether your hypotheses are confirmed or rejected. To ensure you are able to draw conclusions from your analyses, we offer the following suggestions:

• Highlight key findings from the data. ​
• Making generalized comparisons​
• Assess the right strength of the claim. Are hypotheses supported? To what extent? ​To what extent do generalizations hold?​
• Examine the goodness of fit.

Your conclusions could be framed in statements such as:

“Most respondents …..​”

“Group A (e.g., Young adults) were more likely to ___than group B (older adults)

“Given the low degree of fit, other variables/factors might explain the relationship discovered”

Box 10.10 – Statistical Analysis Checklist

Access and Organize the Dataset

• I have checked whether an Institutional Ethics Review is needed. If it is needed, I have obtained it.
• I have recorded all the ways that I manipulated the data
• I have inspected the data set and have noted the limitations (e.g., sampling, non-response, measurement, coverage) and have inspected it for reliability and validity.
• I have inspected the data to ensure that it meets the requirements and assumptions of the statistical techniques that I wish to perform

Cleaning, Coding, and Recoding

• I have re-coded variables as appropriate.
• I have cleaned and processed the data set to make sure it is ready for analysis.

Research Design

• If it is secondary data I am using, my methodology has documented their method for deriving the data.
• My methodology documented the procedures for the quantitative data analysis.
• I have highlighted my research questions and how my findings relate to them

Statistical Analysis

• I have reported on the goodness of fit measures such as r2 and chi-square for the likelihood ratio test in order to show that your model fits the data well.
• I have not interpreted coefficients for models that do not fit the data.
• I have not merely provided statistical results, I have also interpreted the results.
• You must test relationships. Univariate statistics are not enough for quantitative research.​ Make some inferences supported by tests of significance.​ Correlations, Chi-square, ANOVAs, Regressions (Linear and Logistics) etc. ​
• I have stored all my statistical results in a central file which I can use to write up my results.

Statistical Presentation

• My tables and figures conform to the referencing styles that I am using.
• Report both statistically significant and non-statistically significant results.​ Do not be tempted to ignore the non-statistically significant results. They also tell a story.
• I have avoided generalizations that my statistics cannot make.
• I have discussed all of the relevant demographics

Practicing and Presenting Social Research Copyright © 2022 by Oral Robinson and Alexander Wilson is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Share This Book

30 Examples: How to Conclude a Presentation (Effective Closing Techniques)

By Status.net Editorial Team on March 4, 2024 — 9 minutes to read

Ending a presentation on a high note is a skill that can set you apart from the rest. It’s the final chance to leave an impact on your audience, ensuring they walk away with the key messages embedded in their minds. This moment is about driving your points home and making sure they resonate. Crafting a memorable closing isn’t just about summarizing key points, though that’s part of it, but also about providing value that sticks with your listeners long after they’ve left the room.

Crafting Your Core Message

To leave a lasting impression, your presentation’s conclusion should clearly reflect your core message. This is your chance to reinforce the takeaways and leave the audience thinking about your presentation long after it ends.

Identifying Key Points

Start by recognizing what you want your audience to remember. Think about the main ideas that shaped your talk. Make a list like this:

• The problem your presentation addresses.
• The evidence that supports your argument.
• The solution you propose or the action you want the audience to take.

These key points become the pillars of your core message.

Contextualizing the Presentation

Provide context by briefly relating back to the content of the whole presentation. For example:

• Reference a statistic you shared in the opening, and how it ties into the conclusion.
• Mention a case study that underlines the importance of your message.

Connecting these elements gives your message cohesion and makes your conclusion resonate with the framework of your presentation.

30 Example Phrases: How to Conclude a Presentation

• 1. “In summary, let’s revisit the key takeaways from today’s presentation.”
• 2. “Thank you for your attention. Let’s move forward together.”
• 3. “That brings us to the end. I’m open to any questions you may have.”
• 4. “I’ll leave you with this final thought to ponder as we conclude.”
• 5. “Let’s recap the main points before we wrap up.”
• 6. “I appreciate your engagement. Now, let’s turn these ideas into action.”
• 7. “We’ve covered a lot today. To conclude, remember these crucial points.”
• 8. “As we reach the end, I’d like to emphasize our call to action.”
• 9. “Before we close, let’s quickly review what we’ve learned.”
• 10. “Thank you for joining me on this journey. I look forward to our next steps.”
• 11. “In closing, I’d like to thank everyone for their participation.”
• 12. “Let’s conclude with a reminder of the impact we can make together.”
• 13. “To wrap up our session, here’s a brief summary of our discussion.”
• 14. “I’m grateful for the opportunity to present to you. Any final thoughts?”
• 15. “And that’s a wrap. I welcome any final questions or comments.”
• 16. “As we conclude, let’s remember the objectives we’ve set today.”
• 17. “Thank you for your time. Let’s apply these insights to achieve success.”
• 18. “In conclusion, your feedback is valuable, and I’m here to listen.”
• 19. “Before we part, let’s take a moment to reflect on our key messages.”
• 20. “I’ll end with an invitation for all of us to take the next step.”
• 21. “As we close, let’s commit to the goals we’ve outlined today.”
• 22. “Thank you for your attention. Let’s keep the conversation going.”
• 23. “In conclusion, let’s make a difference, starting now.”
• 24. “I’ll leave you with these final words to consider as we end our time together.”
• 25. “Before we conclude, remember that change starts with our actions today.”
• 26. “Thank you for the lively discussion. Let’s continue to build on these ideas.”
• 27. “As we wrap up, I encourage you to reach out with any further questions.”
• 28. “In closing, I’d like to express my gratitude for your valuable input.”
• 29. “Let’s conclude on a high note and take these learnings forward.”
• 30. “Thank you for your time today. Let’s end with a commitment to progress.”

Summarizing the Main Points

When you reach the end of your presentation, summarizing the main points helps your audience retain the important information you’ve shared. Crafting a memorable summary enables your listeners to walk away with a clear understanding of your message.

Effective Methods of Summarization

To effectively summarize your presentation, you need to distill complex information into concise, digestible pieces. Start by revisiting the overarching theme of your talk and then narrow down to the core messages. Use plain language and imagery to make the enduring ideas stick. Here are some examples of how to do this:

• Use analogies that relate to common experiences to recap complex concepts.
• Incorporate visuals or gestures that reinforce your main arguments.

The Rule of Three

The Rule of Three is a classic writing and communication principle. It means presenting ideas in a trio, which is a pattern that’s easy for people to understand and remember. For instance, you might say, “Our plan will save time, cut costs, and improve quality.” This structure has a pleasing rhythm and makes the content more memorable. Some examples include:

• “This software is fast, user-friendly, and secure.”
• Pointing out a product’s “durability, affordability, and eco-friendliness.”

Reiterating the Main Points

Finally, you want to circle back to the key takeaways of your presentation. Rephrase your main points without introducing new information. This reinforcement supports your audience’s memory and understanding of the material. You might summarize key takeaways like this:

• Mention the problem you addressed, the solution you propose, and the benefits of this solution.
• Highlighting the outcomes of adopting your strategy: higher efficiency, greater satisfaction, and increased revenue.

Creating a Strong Conclusion

The final moments of your presentation are your chance to leave your audience with a powerful lasting impression. A strong conclusion is more than just summarizing—it’s your opportunity to invoke thought, inspire action, and make your message memorable.

Incorporating a Call to Action

A call to action is your parting request to your audience. You want to inspire them to take a specific action or think differently as a result of what they’ve heard. To do this effectively:

• Be clear about what you’re asking.
• Explain why their action is needed.
• Make it as simple as possible for them to take the next steps.

Example Phrases:

• “Start making a difference today by…”
• “Join us in this effort by…”
• “Take the leap and commit to…”

Leaving a Lasting Impression

End your presentation with something memorable. This can be a powerful quote, an inspirational statement, or a compelling story that underscores your main points. The goal here is to resonate with your audience on an emotional level so that your message sticks with them long after they leave.

• “In the words of [Influential Person], ‘…'”
• “Imagine a world where…”
• “This is more than just [Topic]; it’s about…”

Enhancing Audience Engagement

To hold your audience’s attention and ensure they leave with a lasting impression of your presentation, fostering interaction is key.

Q&A Sessions

It’s important to integrate a Q&A session because it allows for direct communication between you and your audience. This interactive segment helps clarify any uncertainties and encourages active participation. Plan for this by designating a time slot towards the end of your presentation and invite questions that promote discussion.

• “I’d love to hear your thoughts; what questions do you have?”
• “Let’s dive into any questions you might have. Who would like to start?”
• “Feel free to ask any questions, whether they’re clarifications or deeper inquiries about the topic.”

Encouraging Audience Participation

Getting your audience involved can transform a good presentation into a great one. Use open-ended questions that provoke thought and allow audience members to reflect on how your content relates to them. Additionally, inviting volunteers to participate in a demonstration or share their experiences keeps everyone engaged and adds a personal touch to your talk.

• “Could someone give me an example of how you’ve encountered this in your work?”
• “I’d appreciate a volunteer to help demonstrate this concept. Who’s interested?”
• “How do you see this information impacting your daily tasks? Let’s discuss!”

Delivering a Persuasive Ending

At the end of your presentation, you have the power to leave a lasting impact on your audience. A persuasive ending can drive home your key message and encourage action.

Sales and Persuasion Tactics

When you’re concluding a presentation with the goal of selling a product or idea, employ carefully chosen sales and persuasion tactics. One method is to summarize the key benefits of your offering, reminding your audience why it’s important to act. For example, if you’ve just presented a new software tool, recap how it will save time and increase productivity. Another tactic is the ‘call to action’, which should be clear and direct, such as “Start your free trial today to experience the benefits first-hand!” Furthermore, using a touch of urgency, like “Offer expires soon!”, can nudge your audience to act promptly.

Final Impressions and Professionalism

Your closing statement is a chance to solidify your professional image and leave a positive impression. It’s important to display confidence and poise. Consider thanking your audience for their time and offering to answer any questions. Make sure to end on a high note by summarizing your message in a concise and memorable way. If your topic was on renewable energy, you might conclude by saying, “Let’s take a leap towards a greener future by adopting these solutions today.” This reinforces your main points and encourages your listeners to think or act differently when they leave.

Frequently Asked Questions

What are some creative strategies for ending a presentation memorably.

To end your presentation in a memorable way, consider incorporating a call to action that engages your audience to take the next step. Another strategy is to finish with a thought-provoking question or a surprising fact that resonates with your listeners.

Can you suggest some powerful quotes suitable for concluding a presentation?

Yes, using a quote can be very effective. For example, Maya Angelou’s “People will forget what you said, people will forget what you did, but people will never forget how you made them feel,” can reinforce the emotional impact of your presentation.

What is an effective way to write a conclusion that summarizes a presentation?

An effective conclusion should recap the main points succinctly, highlighting what you want your audience to remember. A good way to conclude is by restating your thesis and then briefly summarizing the supporting points you made.

As a student, how can I leave a strong impression with my presentation’s closing remarks?

To leave a strong impression, consider sharing a personal anecdote related to your topic that demonstrates passion and conviction. This helps humanize your content and makes the message more relatable to your audience.

How can I appropriately thank my audience at the close of my presentation?

A simple and sincere expression of gratitude is always appropriate. You might say, “Thank you for your attention and engagement today,” to convey appreciation while also acknowledging their participation.

What are some examples of a compelling closing sentence in a presentation?

A compelling closing sentence could be something like, “Together, let’s take the leap towards a greener future,” if you’re presenting on sustainability. This sentence is impactful, calls for united action, and leaves your audience with a clear message.

• How to Build Rapport: Effective Techniques
• Active Listening (Techniques, Examples, Tips)
• Effective Nonverbal Communication in the Workplace (Examples)
• What is Problem Solving? (Steps, Techniques, Examples)
• 2 Examples of an Effective and Warm Letter of Welcome
• Effective Interview Confirmation Email (Examples)

Presentation of Data

Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.

What is Meant by Presentation of Data?

As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.

Presentation of Data Examples

Now, let us discuss how to present the data in a meaningful way with the help of examples.

Consider the marks given below, which are obtained by 10 students in Mathematics:

36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

Find the range for the given data.

Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

The data given is called the raw data.

First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.

Therefore, the lowest mark is 25 and the highest mark is 95.

We know that the range of the data is the difference between the highest and the lowest value in the dataset.

Therefore, Range = 95-25 = 70.

Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.

Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.

Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)

10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.

In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.

For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.

Therefore, the presentation of data is given as below:

The following example shows the presentation of data for the larger number of observations in an experiment.

Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)

95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.

Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.

In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.

Hence, the presentation of data in the grouped frequency table is given below:

Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.

Practice Problems

• The heights of 50 students (in cms) are given below. Present the data using the grouped frequency table by taking the class intervals as 160 -165, 165 -170, and so on.  Data: 161, 150, 154, 165, 168, 161, 154, 162, 150, 151, 162, 164, 171, 165, 158, 154, 156, 172, 160, 170, 153, 159, 161, 170, 162, 165, 166, 168, 165, 164, 154, 152, 153, 156, 158, 162, 160, 161, 173, 166, 161, 159, 162, 167, 168, 159, 158, 153, 154, 159.
• Three coins are tossed simultaneously and each time the number of heads occurring is noted and it is given below. Present the data using the frequency distribution table. Data: 0, 1, 2, 2, 1, 2, 3, 1, 3, 0, 1, 3, 1, 1, 2, 2, 0, 1, 2, 1, 3, 0, 0, 1, 1, 2, 3, 2, 2, 0.

To learn more Maths-related concepts, stay tuned with BYJU’S – The Learning App and download the app today!

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Request OTP on Voice Call

Post My Comment

• Share Share

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

A Reader on Data Visualization

Chapter 7 conclusion.

As we conclude our brief study on data visualization, it is clear that the field is rich in potential applications in diverse disciplines, at the same time we need to be aware of its practical and ethical complexities. In the previous chapters, we have presented some important theoretical and practical principles to keep in mind when designing a data visualization. We have also discussed and critiqued several examples of data visualizations, learning common pitfalls and helpful tricks along the way. As we have seen, developing an effective and ethical data visualization is a complex process. In this chapter we will touch upon the future of data visualization and additional resources for data visualizers.

7.1 The Future of Data Visualization

Towler ( 2015 )

Data visualization is entering a new era. Emerging sources of intelligence, theoretical developments and advances in multidimensional imaging are reshaping the potential value that analytics and insights can provide, with visualization playing a key role. The principles of effective data visualization won’t change. However, nextgen technologies and evolving cognitive frameworks are opening new horizons, moving data visualization from art to science.

Looking back, much attention has been given to the principles of effective data visualization, such as substance, context and actionability. As timeless tenets that will continue to be important, regardless of medium or format, a brief review seems in order:

Effective data visualization should be substantive And while creative visuals can enhance interest and memory, embellishment can’t make up for lack of substance. According to purist Edward Tufte, “Every single pixel should testify directly to content.”

Visualization should be accurate and contextual. David McCandless’s Billion Dollar O’Gram provides an example of how greater meaning can be added by incorporating the bigger picture. According to McCandless, “Absolute figures in a connected world don’t give you the whole picture. They’re not as true as they could be. We need relative figures that are connected to other data so that we can see a fuller picture.”

Billion Dollar O’Gram

(Source: Towler ( 2015 ) )

• More than anything else, data visualization should facilitate decision-making , a goal that is difficult to achieve for many. According to a recent [KPMG study] (International 2015 ) , while data and analytics are deemed increasingly important to organizations, generating actionable insights remains a top challenge.

7.1.1 Recent Developments

Consider the Internet of Things, Network and Complexity Theories, and recent developments in multidimensional visualization, we can clearly see how data visualization has evolved along with the field.

The Internet has transformed the way we visualize information through a better understanding of networks and an explosion in profile, behavioral and attitudinal data. Sociograms, for example, have gone from relatively simple graphs to multifaceted relational maps, as illustrated in the following two charts, courtesy of the Journal of Social Structure and the Leadership Learning Community.

Figure 2:Pre-Internet sociogram

Figure 3:Internet-age sociogram

The Internet of Things is expected to have a similar impact, with billions of connected devices capturing human and machine activity. Fully capitalizing on the data generated will require further advances in our ability to synthesize and display spatiotemporal activities.

Network Theory has been in use for decades, with its earliest applications largely in social structure analysis. More recently, Network Theory is being applied to understand relationships and interactions in a variety of domains, such as crime prevention and disease management. Dirk Brockmann and Dirk Helbling’s work modeling the spread of infectious diseases provides an example of the power that Network Theory holds.

7.1.2 Future Direction

Towler ( 2015 ) As the world becomes increasingly interconnected and interdependent, opportunities to generate value through data visualization will only increase. The Internet of Things will have a profound effect on the role that data visualization can play in organizations and society, improving our ability to understand how humans and machines interact with each other and the environment. Application of evolving cognitive frameworks, such as Network and Complexity Theories, will help us better reflect dynamic and intricate structural dependencies. Advances in multidimensional visualization will allow us to more effectively synthesize and explore spatiotemporal conditions.

Below are the salient points from the Data Visualization Summit 2016 (Source: Denham ( n.d. ) ) key note address highlighting the future of data visualization.

• Supporting big data 3 Vs at scale: like 100X for Volume, 10X for Velocity, 5X for Variety.
• The end-to-end set up time will be much shorter, the learning curve to be a data viz expert would be within days.
• More flexible user interfaces with easier ways to surface complicated cases w/o any further advanced coding for discovering the underlying knowledge and relationships, such as social graphing, 3+ dimensional graphing, etc.
• More integration could happen here, such as new intelligent features in data viz application for predictive modeling, pattern discovery, automatic alerting, etc.
• Given the recent crazy popularity in AR technology for Pokemon Go, interesting data viz application to support AR or VR will soon become available

With more and more data, the field of Data Science is challenged with the task of making meaningful sense out of the data. Data visualization tools slice and dice the data and present insights to scientists and data visualization will be the center piece in the analytics domain. Few of the fields which are really pushing its limits are Artificial Intelligence, Virtual Reality, Augmented Reality and Real Time Analytics based on Live Streaming data. An interesting article on the future of data visualization in the year 2019 and beyond (Source: Studio ( 2019 ) ) based on a survey from the data visualization community.

7.2 Key Lessons Going Forward

7.2.1 laying the groundwork for data visualization.

(SAS 2014 ) Before implementing new technology, there are some steps you need to take. Not only do you need to have a solid grasp on your data, you also need to understand your goals, needs and audience. Preparing your organization for data visualization technology requires that you first:

• Understand the data you’re trying to visualize, including its size and cardinality (the uniqueness of data values in a column).
• Determine what you’re trying to visualize and what kind of information you want to communicate.
• Know your audience and understand how it processes visual information.
• Use a visual that conveys the information in the best and simplest form for your audience.

Once you’ve answered those initial questions about the type of data you have and the audience who’ll be consuming the information, you need to prepare for the amount of data you’ll be working with. Big data brings new challenges to visualization because large volumes, different varieties and varying velocities must be taken into account. Plus, data is often generated faster that it can be managed and analyzed.

7.2.2 How Is It Being Used?

(SAS 2014 ) Regardless of industry or size, all types of businesses are using data visualization to help make sense of their data. Here’s how.

Comprehend information quickly By using graphical representations of business information, businesses are able to see large amounts of data in clear, cohesive ways – and draw conclusions from that information. And since it’s significantly faster to analyze information in graphical format (as opposed to analyzing information in spreadsheets), businesses can address problems or answer questions in a more timely manner.

Identify relationships and patterns Even extensive amounts of complicated data start to make sense when presented graphically; businesses can recognize parameters that are highly correlated. Some of the correlations will be obvious, but others won’t. Identifying those relationships helps organizations focus on areas most likely to influence their most important goals.

Pinpoint emerging trends Using data visualization to discover trends – both in the business and in the market – can give businesses an edge over the competition, and ultimately affect the bottom line. It’s easy to spot outliers that affect product quality or customer churn, and address issues before they become bigger problems.

Communicate the story to others Once a business has uncovered new insights from visual analytics, the next step is to communicate those insights to others. Using charts, graphs or other visually impactful representations of data is important in this step because it’s engaging and gets the message across quickly.

Improved collaboration among work teams Data visualization makes it easier to gain vital information, identify trends, collaborate at a likeminded pace, and from there put decisions to action — all with much greater speed than is generally allowed by presentations that lack the proper visual tools.

Establish Clear Correlation Between Business Operations And Activities Since the data is visualized in a highly comprehensive and interactive manner, establishing existing or possible correlations between various business operations comes in easy here. This gives business leaders a clear insight into business performance and needs for further strategies. Data visualization allows decision makers to make sense out of the visible patterns and parallel operations in terms of the overall business performance. It also allows them to establish a correlation between the daily tasks and the long-term outcomes for these in terms of business performance.

###Helpful tips for a good visualization * Don’t try to be too cute. Using all the fancy features of a visualization tool can lead to complex and confusing graphics. Just because you have a flashy hammer, it does not mean that everything is a nail. Don’t lose your purpose in your infatuation with a new tool or feature. Stick to good design principles, and keep it simple (as was mentioned in the discussion about Patterns).

Don’t provide more data than you need to tell your story. Humans have a tendency to want to create visualizations with multi-drill downs, filters, and tables. This is fine for ad hoc analysis, but if we are presenting a visual with a single story or argument, we must make sure every piece of data included plays a meaningful part in the story.

Have more data to show what-ifs. A great way to provide more information in a visualization is to use filters to provide what-if views. Having filters for different scenarios, options, and time slices provide the user a way to review and discover various perspectives on your data. Just make sure the user does not have to work too hard to get there and that they do not get lost in the data.

Deliver value in your visualization. Many graphs may be aesthetically pleasing or exciting but deliver no value. A graph with good value should deliver important insights to aid decision-making or prompt an action. Keep in mind, the main objective of a visualization is to deliver value, and each design decision should help achieve this.

Understanding your audience is the most difficult and important part of developing a visualization. Different audiences will feel differently about the same chart. So when we develop the argument from the data, we should keep our intended audience in mind.

Share what you have found. A visualization can be an effective influencer if used in the correct way and at the correct time.

Think as author / reader When designing graphs, try to stand at the reader side, to check whether the graph is making sense or if there is any misleading information being showcased. As a reader, one needs to detect if there is any visualization deception in the graph. Before making any conclusion, understanding details and information the graph is providing like legend, caption, numbers, etc. is important.

Do Calculations Accurate representation of data is necessary. Sometimes the original data cannot strongly support the main thesis. In that case, some extra calcuations based on the original dataset could make the idea more clear.

It should be ethical Visualization : Research has found that even if viewers do not support an idea, data presented in charts can persuade viewers on the subject matter. It means that visualization can also be used to mislead either unintentionally or intentionally. Misleading, incomprehensible, or incredible data visualization can jeopardize people’s trust, goodwill, or faith in research and advocacy on vital human rights issues. So, certain standards should be followed in order to generate meaningful, accurate and ethical visuals.

7.3 Additional Resources for Aspiring Data Visualizers

The field of data visualization is huge, and the capability of linking storytelling and data-experience design is a commodity. An aspiring visualizer seeking to further broaden his/her knowledge might find these additional resources helpful.

7.3.1 Tableau Community

(Tableau Software 2018 a ) helps you to explore Tableau further :

• It will help us enhance our learning
• Get answers for most of your doubts In tableau
• Post new questions and crowd source answers
• Attend events, seminars and join conferences conducted locally/ globally
• Give back to the community once you become an expert in that field

There are very active Tableau social media groups (Tableau Software 2018 b ) :

• Tableau Enthusiasts: LinkedIn Group (19K members)
• Tableau Software Fans & Friends: LinkedIn Group (45k members)

Describe Artists with Emoji . Using the data from Spotify, the author listed the 10 most distinctive emoji used in the playlists related to popular artists. The table being used in this visual is very straight-forward to link artist to the emojis and is very easy to compare among artists. When you hover over the emoji, further information is presented.

Anaconda Python for visualization (na 2017 b ) :

Anaconda Python provides different packages that can help with more robust data sets and provide different packages that can offer a variety of visualization solutions (na 2017 b ) .

Bokeh One of them is Bokeh an open source interactive visualization for Python (na 2019 ) . Elegant, concise versatile graphics . High performance interactivity over large streaming data sets . Easily create interactive plots, dashboard and data applications

Matplot (na 2017 a )

Is a Python 2D plotting library and object oriented. This package can do the following: . Lines, bars and markers . Images, contours and fields . Statistics graphs . Pie and polar charts . Text, labels and annotations . Pyplot . Others Other packages: Datashader & Holoviews Anaconda community (na 2017 b ) provides many solution and resources for all visualization packages.

Altair Altair is a declarative statistical visualization library for Python (Visualization 2019 ) There are gallerries and it is a powerful and concise visualization tool.

7.3.2 D3.js Community

D3 stands for Data-Driven Documents .The best place to start with D3.js (version 5.9.2) is the D3.js official portal itself ( https://d3js.org/ ). It has features like:

• Introduction, with the help of code snippets.
• Installation guide.
• Features guide.
• It also has a reference guide.
• Newsletters.
• Blogs which introduces D3.js with examples.
• Video lectures which makes the learning experience more interesting.

D3.js has a large and growing presence on GitHub .One can find,clone and use all the needed materials. The content is as follows :

• It has a source repository( https://github.com/d3/d3 ) which allows users to clone ad use the packages.
• It links you to vaious othrer platforms for discussion like: stack overflow ( https://stackoverflow.com/questions/tagged/d3.js ), Google Group ( https://groups.google.com/forum/#!forum/d3-js ), Slack ( https://d3js.slack.com/ ).
• It has an extensive tutorials repository on GitHub ( https://github.com/d3/d3/wiki/Tutorials ).
• It also has a Gallery on GitHub with code snippets for each visualization( https://github.com/d3/d3/wiki/Gallery )
• Links to official ( https://github.com/d3/d3/blob/master/API.md ) and community based modules( https://www.npmjs.com/search?q=keywords:d3-module ) for D3.js .

7.3.3 Blogs

Here are some blogs recommended by Tableau (Tableau Software 2018 c ) :

7.3.4 Useful Links on Data Visualization Resources, Trends, and Tutorials

Towler, Will. 2015. “Data Visualization: The Future of Data Visualization.” http://analytics-magazine.org/data-visualization-the-future-of-data-visualization/ .

International, KPMG. 2015. “Going Beyond the Data: Turning Data from Insights into Value.” https://home.kpmg/content/dam/kpmg/pdf/2015/10/Going_beyond_the_%20data_Turning_Insights_Into_Value_2016-04-21%2022-22-44.pdf .

Denham, Laura. n.d. “Q&A with Speaker of Data Visualization Summit.” https://channels.theinnovationenterprise.com/articles/speaker-snapshot-chi-yi-kuan-director-of-business-analytics-at-linkedin .

Studio, Depict Data. 2019. “The Future of Data Visualization: Predictions for 2019 and Beyond.” https://depictdatastudio.com/the-future-of-data-visualization-predictions-for-2019-and-beyond .

SAS. 2014. “Data Visualization - What It Is and Why It Matters.” https://www.sas.com/en_us/insights/big-data/data-visualization.html .

Tableau Software. 2018a. “Tableau Community.” https://community.tableau.com/welcome .

Tableau Software. 2018b. “Tableau Groups.” https://www.linkedin.com/search/results/groups/?keywords=tableau{\&}origin=SWITCH{\_}SEARCH{\_}VERTICAL .

na. 2017b. “Python and Packages.” https://www.anaconda.com/ .

na. 2019. “Welcome to Bokeh.” https://bokeh.pydata.org/en/latest/ .

na. 2017a. “Matplotlib.” https://matplotlib.org/ .

Visualization, Altair Data. 2019. “Altair, Declarative Visualization in Python.” https://altair-viz.github.io .

Tableau Software. 2018c. “The 10 best data visualization blogs to follow.” https://www.tableau.com/learn/articles/best-data-visualization-blogs .

Catalogue, The Data Visualisation. 2018. “Charts- Data Visualization.” https://datavizcatalogue.com/search.html .

Kosara, Robert. 2018a. “Eagereyes.” https://eagereyes.org/ .

Research, Tableau. 2018. “Robert Kosara- Research.” https://research.tableau.com/user/robert-kosara .

Kosara, Robert. 2018b. “Twitter Handle- Robert Kosara.” https://twitter.com/eagereyes?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor .

FlowingData. 2018. “Flowing Data-Website.” http://flowingdata.com/ .

Infogram. 2018. “Infogram- Data Visualization.” https://infogram.com/page/data-visualization .

beautifuldata.net. 2018. “Beautiful Data.” http://beautifuldata.net/ .

Sandberg, Michael. 2018. “Michael Sandberg’s Data Visualization Blog.” https://datavizblog.com/ .

Bureau, United States Census. n.d. “United States Census Bureau Data Visualization Gallery.” https://www.census.gov/dataviz/ .

Agency, Federal Emergency Management. 2018. “Federal Emergency Management Agency Data Visualization.” https://www.fema.gov/data-visualization .

Marr, Bernard. 2017. “The 7 Best Data Visualization Tools in 2017.” https://www.forbes.com/sites/bernardmarr/2017/07/20/the-7-best-data-visualization-tools-in-2017/#77840a9d6c30 .

Lazarevich, Katherine. 2018a. “Data Visualization in Business: How Data Visualization Impacts Your Business Strategy.” https://www.iotforall.com/data-visualization-strategy-for-business/ .

McCeady, Ryan. 2017. “How Writers Use Misleading Graphs to Manipulate You.” https://venngage.com/blog/misleading-graphs/ .

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

Qualitative research is not finished just because you have determined the main findings or conclusions of your study. Indeed, disseminating the results is an essential part of the research process. By sharing your results with others, whether in written form as scholarly paper or an applied report or in some alternative format like an oral presentation, an infographic, or a video, you ensure that your findings become part of the ongoing conversation of scholarship in your field, forming part of the foundation for future researchers. This chapter provides an introduction to writing about qualitative research findings. It will outline how writing continues to contribute to the analysis process, what concerns researchers should keep in mind as they draft their presentations of findings, and how best to organize qualitative research writing

As you move through the research process, it is essential to keep yourself organized. Organizing your data, memos, and notes aids both the analytical and the writing processes. Whether you use electronic or physical, real-world filing and organizational systems, these systems help make sense of the mountains of data you have and assure you focus your attention on the themes and ideas you have determined are important (Warren and Karner 2015). Be sure that you have kept detailed notes on all of the decisions you have made and procedures you have followed in carrying out research design, data collection, and analysis, as these will guide your ultimate write-up.

First and foremost, researchers should keep in mind that writing is in fact a form of thinking. Writing is an excellent way to discover ideas and arguments and to further develop an analysis. As you write, more ideas will occur to you, things that were previously confusing will start to make sense, and arguments will take a clear shape rather than being amorphous and poorly-organized. However, writing-as-thinking cannot be the final version that you share with others. Good-quality writing does not display the workings of your thought process. It is reorganized and revised (more on that later) to present the data and arguments important in a particular piece. And revision is totally normal! No one expects the first draft of a piece of writing to be ready for prime time. So write rough drafts and memos and notes to yourself and use them to think, and then revise them until the piece is the way you want it to be for sharing.

Bergin (2018) lays out a set of key concerns for appropriate writing about research. First, present your results accurately, without exaggerating or misrepresenting. It is very easy to overstate your findings by accident if you are enthusiastic about what you have found, so it is important to take care and use appropriate cautions about the limitations of the research. You also need to work to ensure that you communicate your findings in a way people can understand, using clear and appropriate language that is adjusted to the level of those you are communicating with. And you must be clear and transparent about the methodological strategies employed in the research. Remember, the goal is, as much as possible, to describe your research in a way that would permit others to replicate the study. There are a variety of other concerns and decision points that qualitative researchers must keep in mind, including the extent to which to include quantification in their presentation of results, ethics, considerations of audience and voice, and how to bring the richness of qualitative data to life.

Quantification, as you have learned, refers to the process of turning data into numbers. It can indeed be very useful to count and tabulate quantitative data drawn from qualitative research. For instance, if you were doing a study of dual-earner households and wanted to know how many had an equal division of household labor and how many did not, you might want to count those numbers up and include them as part of the final write-up. However, researchers need to take care when they are writing about quantified qualitative data. Qualitative data is not as generalizable as quantitative data, so quantification can be very misleading. Thus, qualitative researchers should strive to use raw numbers instead of the percentages that are more appropriate for quantitative research. Writing, for instance, “15 of the 20 people I interviewed prefer pancakes to waffles” is a simple description of the data; writing “75% of people prefer pancakes” suggests a generalizable claim that is not likely supported by the data. Note that mixing numbers with qualitative data is really a type of mixed-methods approach. Mixed-methods approaches are good, but sometimes they seduce researchers into focusing on the persuasive power of numbers and tables rather than capitalizing on the inherent richness of their qualitative data.

A variety of issues of scholarly ethics and research integrity are raised by the writing process. Some of these are unique to qualitative research, while others are more universal concerns for all academic and professional writing. For example, it is essential to avoid plagiarism and misuse of sources. All quotations that appear in a text must be properly cited, whether with in-text and bibliographic citations to the source or with an attribution to the research participant (or the participant’s pseudonym or description in order to protect confidentiality) who said those words. Where writers will paraphrase a text or a participant’s words, they need to make sure that the paraphrase they develop accurately reflects the meaning of the original words. Thus, some scholars suggest that participants should have the opportunity to read (or to have read to them, if they cannot read the text themselves) all sections of the text in which they, their words, or their ideas are presented to ensure accuracy and enable participants to maintain control over their lives.

Audience and Voice

When writing, researchers must consider their audience(s) and the effects they want their writing to have on these audiences. The designated audience will dictate the voice used in the writing, or the individual style and personality of a piece of text. Keep in mind that the potential audience for qualitative research is often much more diverse than that for quantitative research because of the accessibility of the data and the extent to which the writing can be accessible and interesting. Yet individual pieces of writing are typically pitched to a more specific subset of the audience.

Let us consider one potential research study, an ethnography involving participant-observation of the same children both when they are at daycare facility and when they are at home with their families to try to understand how daycare might impact behavior and social development. The findings of this study might be of interest to a wide variety of potential audiences: academic peers, whether at your own academic institution, in your broader discipline, or multidisciplinary; people responsible for creating laws and policies; practitioners who run or teach at day care centers; and the general public, including both people who are interested in child development more generally and those who are themselves parents making decisions about child care for their own children. And the way you write for each of these audiences will be somewhat different. Take a moment and think through what some of these differences might look like.

If you are writing to academic audiences, using specialized academic language and working within the typical constraints of scholarly genres, as will be discussed below, can be an important part of convincing others that your work is legitimate and should be taken seriously. Your writing will be formal. Even if you are writing for students and faculty you already know—your classmates, for instance—you are often asked to imitate the style of academic writing that is used in publications, as this is part of learning to become part of the scholarly conversation. When speaking to academic audiences outside your discipline, you may need to be more careful about jargon and specialized language, as disciplines do not always share the same key terms. For instance, in sociology, scholars use the term diffusion to refer to the way new ideas or practices spread from organization to organization. In the field of international relations, scholars often used the term cascade to refer to the way ideas or practices spread from nation to nation. These terms are describing what is fundamentally the same concept, but they are different terms—and a scholar from one field might have no idea what a scholar from a different field is talking about! Therefore, while the formality and academic structure of the text would stay the same, a writer with a multidisciplinary audience might need to pay more attention to defining their terms in the body of the text.

It is not only other academic scholars who expect to see formal writing. Policymakers tend to expect formality when ideas are presented to them, as well. However, the content and style of the writing will be different. Much less academic jargon should be used, and the most important findings and policy implications should be emphasized right from the start rather than initially focusing on prior literature and theoretical models as you might for an academic audience. Long discussions of research methods should also be minimized. Similarly, when you write for practitioners, the findings and implications for practice should be highlighted. The reading level of the text will vary depending on the typical background of the practitioners to whom you are writing—you can make very different assumptions about the general knowledge and reading abilities of a group of hospital medical directors with MDs than you can about a group of case workers who have a post-high-school certificate. Consider the primary language of your audience as well. The fact that someone can get by in spoken English does not mean they have the vocabulary or English reading skills to digest a complex report. But the fact that someone’s vocabulary is limited says little about their intellectual abilities, so try your best to convey the important complexity of the ideas and findings from your research without dumbing them down—even if you must limit your vocabulary usage.

When writing for the general public, you will want to move even further towards emphasizing key findings and policy implications, but you also want to draw on the most interesting aspects of your data. General readers will read sociological texts that are rich with ethnographic or other kinds of detail—it is almost like reality television on a page! And this is a contrast to busy policymakers and practitioners, who probably want to learn the main findings as quickly as possible so they can go about their busy lives. But also keep in mind that there is a wide variation in reading levels. Journalists at publications pegged to the general public are often advised to write at about a tenth-grade reading level, which would leave most of the specialized terminology we develop in our research fields out of reach. If you want to be accessible to even more people, your vocabulary must be even more limited. The excellent exercise of trying to write using the 1,000 most common English words, available at the Up-Goer Five website ( https://www.splasho.com/upgoer5/ ) does a good job of illustrating this challenge (Sanderson n.d.).

Another element of voice is whether to write in the first person. While many students are instructed to avoid the use of the first person in academic writing, this advice needs to be taken with a grain of salt. There are indeed many contexts in which the first person is best avoided, at least as long as writers can find ways to build strong, comprehensible sentences without its use, including most quantitative research writing. However, if the alternative to using the first person is crafting a sentence like “it is proposed that the researcher will conduct interviews,” it is preferable to write “I propose to conduct interviews.” In qualitative research, in fact, the use of the first person is far more common. This is because the researcher is central to the research project. Qualitative researchers can themselves be understood as research instruments, and thus eliminating the use of the first person in writing is in a sense eliminating information about the conduct of the researchers themselves.

But the question really extends beyond the issue of first-person or third-person. Qualitative researchers have choices about how and whether to foreground themselves in their writing, not just in terms of using the first person, but also in terms of whether to emphasize their own subjectivity and reflexivity, their impressions and ideas, and their role in the setting. In contrast, conventional quantitative research in the positivist tradition really tries to eliminate the author from the study—which indeed is exactly why typical quantitative research avoids the use of the first person. Keep in mind that emphasizing researchers’ roles and reflexivity and using the first person does not mean crafting articles that provide overwhelming detail about the author’s thoughts and practices. Readers do not need to hear, and should not be told, which database you used to search for journal articles, how many hours you spent transcribing, or whether the research process was stressful—save these things for the memos you write to yourself. Rather, readers need to hear how you interacted with research participants, how your standpoint may have shaped the findings, and what analytical procedures you carried out.

Making Data Come Alive

One of the most important parts of writing about qualitative research is presenting the data in a way that makes its richness and value accessible to readers. As the discussion of analysis in the prior chapter suggests, there are a variety of ways to do this. Researchers may select key quotes or images to illustrate points, write up specific case studies that exemplify their argument, or develop vignettes (little stories) that illustrate ideas and themes, all drawing directly on the research data. Researchers can also write more lengthy summaries, narratives, and thick descriptions.

Nearly all qualitative work includes quotes from research participants or documents to some extent, though ethnographic work may focus more on thick description than on relaying participants’ own words. When quotes are presented, they must be explained and interpreted—they cannot stand on their own. This is one of the ways in which qualitative research can be distinguished from journalism. Journalism presents what happened, but social science needs to present the “why,” and the why is best explained by the researcher.

So how do authors go about integrating quotes into their written work? Julie Posselt (2017), a sociologist who studies graduate education, provides a set of instructions. First of all, authors need to remain focused on the core questions of their research, and avoid getting distracted by quotes that are interesting or attention-grabbing but not so relevant to the research question. Selecting the right quotes, those that illustrate the ideas and arguments of the paper, is an important part of the writing process. Second, not all quotes should be the same length (just like not all sentences or paragraphs in a paper should be the same length). Include some quotes that are just phrases, others that are a sentence or so, and others that are longer. We call longer quotes, generally those more than about three lines long, block quotes , and they are typically indented on both sides to set them off from the surrounding text. For all quotes, be sure to summarize what the quote should be telling or showing the reader, connect this quote to other quotes that are similar or different, and provide transitions in the discussion to move from quote to quote and from topic to topic. Especially for longer quotes, it is helpful to do some of this writing before the quote to preview what is coming and other writing after the quote to make clear what readers should have come to understand. Remember, it is always the author’s job to interpret the data. Presenting excerpts of the data, like quotes, in a form the reader can access does not minimize the importance of this job. Be sure that you are explaining the meaning of the data you present.

A few more notes about writing with quotes: avoid patchwriting, whether in your literature review or the section of your paper in which quotes from respondents are presented. Patchwriting is a writing practice wherein the author lightly paraphrases original texts but stays so close to those texts that there is little the author has added. Sometimes, this even takes the form of presenting a series of quotes, properly documented, with nothing much in the way of text generated by the author. A patchwriting approach does not build the scholarly conversation forward, as it does not represent any kind of new contribution on the part of the author. It is of course fine to paraphrase quotes, as long as the meaning is not changed. But if you use direct quotes, do not edit the text of the quotes unless how you edit them does not change the meaning and you have made clear through the use of ellipses (…) and brackets ([])what kinds of edits have been made. For example, consider this exchange from Matthew Desmond’s (2012:1317) research on evictions:

The thing was, I wasn’t never gonna let Crystal come and stay with me from the get go. I just told her that to throw her off. And she wasn’t fittin’ to come stay with me with no money…No. Nope. You might as well stay in that shelter.

A paraphrase of this exchange might read “She said that she was going to let Crystal stay with her if Crystal did not have any money.” Paraphrases like that are fine. What is not fine is rewording the statement but treating it like a quote, for instance writing:

The thing was, I was not going to let Crystal come and stay with me from beginning. I just told her that to throw her off. And it was not proper for her to come stay with me without any money…No. Nope. You might as well stay in that shelter.

But as you can see, the change in language and style removes some of the distinct meaning of the original quote. Instead, writers should leave as much of the original language as possible. If some text in the middle of the quote needs to be removed, as in this example, ellipses are used to show that this has occurred. And if a word needs to be added to clarify, it is placed in square brackets to show that it was not part of the original quote.

Data can also be presented through the use of data displays like tables, charts, graphs, diagrams, and infographics created for publication or presentation, as well as through the use of visual material collected during the research process. Note that if visuals are used, the author must have the legal right to use them. Photographs or diagrams created by the author themselves—or by research participants who have signed consent forms for their work to be used, are fine. But photographs, and sometimes even excerpts from archival documents, may be owned by others from whom researchers must get permission in order to use them.

A large percentage of qualitative research does not include any data displays or visualizations. Therefore, researchers should carefully consider whether the use of data displays will help the reader understand the data. One of the most common types of data displays used by qualitative researchers are simple tables. These might include tables summarizing key data about cases included in the study; tables laying out the characteristics of different taxonomic elements or types developed as part of the analysis; tables counting the incidence of various elements; and 2×2 tables (two columns and two rows) illuminating a theory. Basic network or process diagrams are also commonly included. If data displays are used, it is essential that researchers include context and analysis alongside data displays rather than letting them stand by themselves, and it is preferable to continue to present excerpts and examples from the data rather than just relying on summaries in the tables.

If you will be using graphs, infographics, or other data visualizations, it is important that you attend to making them useful and accurate (Bergin 2018). Think about the viewer or user as your audience and ensure the data visualizations will be comprehensible. You may need to include more detail or labels than you might think. Ensure that data visualizations are laid out and labeled clearly and that you make visual choices that enhance viewers’ ability to understand the points you intend to communicate using the visual in question. Finally, given the ease with which it is possible to design visuals that are deceptive or misleading, it is essential to make ethical and responsible choices in the construction of visualization so that viewers will interpret them in accurate ways.

The Genre of Research Writing

As discussed above, the style and format in which results are presented depends on the audience they are intended for. These differences in styles and format are part of the genre of writing. Genre is a term referring to the rules of a specific form of creative or productive work. Thus, the academic journal article—and student papers based on this form—is one genre. A report or policy paper is another. The discussion below will focus on the academic journal article, but note that reports and policy papers follow somewhat different formats. They might begin with an executive summary of one or a few pages, include minimal background, focus on key findings, and conclude with policy implications, shifting methods and details about the data to an appendix. But both academic journal articles and policy papers share some things in common, for instance the necessity for clear writing, a well-organized structure, and the use of headings.

So what factors make up the genre of the academic journal article in sociology? While there is some flexibility, particularly for ethnographic work, academic journal articles tend to follow a fairly standard format. They begin with a “title page” that includes the article title (often witty and involving scholarly inside jokes, but more importantly clearly describing the content of the article); the authors’ names and institutional affiliations, an abstract , and sometimes keywords designed to help others find the article in databases. An abstract is a short summary of the article that appears both at the very beginning of the article and in search databases. Abstracts are designed to aid readers by giving them the opportunity to learn enough about an article that they can determine whether it is worth their time to read the complete text. They are written about the article, and thus not in the first person, and clearly summarize the research question, methodological approach, main findings, and often the implications of the research.

After the abstract comes an “introduction” of a page or two that details the research question, why it matters, and what approach the paper will take. This is followed by a literature review of about a quarter to a third the length of the entire paper. The literature review is often divided, with headings, into topical subsections, and is designed to provide a clear, thorough overview of the prior research literature on which a paper has built—including prior literature the new paper contradicts. At the end of the literature review it should be made clear what researchers know about the research topic and question, what they do not know, and what this new paper aims to do to address what is not known.

The next major section of the paper is the section that describes research design, data collection, and data analysis, often referred to as “research methods” or “methodology.” This section is an essential part of any written or oral presentation of your research. Here, you tell your readers or listeners “how you collected and interpreted your data” (Taylor, Bogdan, and DeVault 2016:215). Taylor, Bogdan, and DeVault suggest that the discussion of your research methods include the following:

• The particular approach to data collection used in the study;
• Any theoretical perspective(s) that shaped your data collection and analytical approach;
• When the study occurred, over how long, and where (concealing identifiable details as needed);
• A description of the setting and participants, including sampling and selection criteria (if an interview-based study, the number of participants should be clearly stated);
• The researcher’s perspective in carrying out the study, including relevant elements of their identity and standpoint, as well as their role (if any) in research settings; and
• The approach to analyzing the data.

After the methods section comes a section, variously titled but often called “data,” that takes readers through the analysis. This section is where the thick description narrative; the quotes, broken up by theme or topic, with their interpretation; the discussions of case studies; most data displays (other than perhaps those outlining a theoretical model or summarizing descriptive data about cases); and other similar material appears. The idea of the data section is to give readers the ability to see the data for themselves and to understand how this data supports the ultimate conclusions. Note that all tables and figures included in formal publications should be titled and numbered.

At the end of the paper come one or two summary sections, often called “discussion” and/or “conclusion.” If there is a separate discussion section, it will focus on exploring the overall themes and findings of the paper. The conclusion clearly and succinctly summarizes the findings and conclusions of the paper, the limitations of the research and analysis, any suggestions for future research building on the paper or addressing these limitations, and implications, be they for scholarship and theory or policy and practice.

After the end of the textual material in the paper comes the bibliography, typically called “works cited” or “references.” The references should appear in a consistent citation style—in sociology, we often use the American Sociological Association format (American Sociological Association 2019), but other formats may be used depending on where the piece will eventually be published. Care should be taken to ensure that in-text citations also reflect the chosen citation style. In some papers, there may be an appendix containing supplemental information such as a list of interview questions or an additional data visualization.

Note that when researchers give presentations to scholarly audiences, the presentations typically follow a format similar to that of scholarly papers, though given time limitations they are compressed. Abstracts and works cited are often not part of the presentation, though in-text citations are still used. The literature review presented will be shortened to only focus on the most important aspects of the prior literature, and only key examples from the discussion of data will be included. For long or complex papers, sometimes only one of several findings is the focus of the presentation. Of course, presentations for other audiences may be constructed differently, with greater attention to interesting elements of the data and findings as well as implications and less to the literature review and methods.

Concluding Your Work

After you have written a complete draft of the paper, be sure you take the time to revise and edit your work. There are several important strategies for revision. First, put your work away for a little while. Even waiting a day to revise is better than nothing, but it is best, if possible, to take much more time away from the text. This helps you forget what your writing looks like and makes it easier to find errors, mistakes, and omissions. Second, show your work to others. Ask them to read your work and critique it, pointing out places where the argument is weak, where you may have overlooked alternative explanations, where the writing could be improved, and what else you need to work on. Finally, read your work out loud to yourself (or, if you really need an audience, try reading to some stuffed animals). Reading out loud helps you catch wrong words, tricky sentences, and many other issues. But as important as revision is, try to avoid perfectionism in writing (Warren and Karner 2015). Writing can always be improved, no matter how much time you spend on it. Those improvements, however, have diminishing returns, and at some point the writing process needs to conclude so the writing can be shared with the world.

Of course, the main goal of writing up the results of a research project is to share with others. Thus, researchers should be considering how they intend to disseminate their results. What conferences might be appropriate? Where can the paper be submitted? Note that if you are an undergraduate student, there are a wide variety of journals that accept and publish research conducted by undergraduates. Some publish across disciplines, while others are specific to disciplines. Other work, such as reports, may be best disseminated by publication online on relevant organizational websites.

After a project is completed, be sure to take some time to organize your research materials and archive them for longer-term storage. Some Institutional Review Board (IRB) protocols require that original data, such as interview recordings, transcripts, and field notes, be preserved for a specific number of years in a protected (locked for paper or password-protected for digital) form and then destroyed, so be sure that your plans adhere to the IRB requirements. Be sure you keep any materials that might be relevant for future related research or for answering questions people may ask later about your project.

And then what? Well, then it is time to move on to your next research project. Research is a long-term endeavor, not a one-time-only activity. We build our skills and our expertise as we continue to pursue research. So keep at it.

• Find a short article that uses qualitative methods. The sociological magazine Contexts is a good place to find such pieces. Write an abstract of the article.
• Choose a sociological journal article on a topic you are interested in that uses some form of qualitative methods and is at least 20 pages long. Rewrite the article as a five-page research summary accessible to non-scholarly audiences.
• Choose a concept or idea you have learned in this course and write an explanation of it using the Up-Goer Five Text Editor ( https://www.splasho.com/upgoer5/ ), a website that restricts your writing to the 1,000 most common English words. What was this experience like? What did it teach you about communicating with people who have a more limited English-language vocabulary—and what did it teach you about the utility of having access to complex academic language?
• Select five or more sociological journal articles that all use the same basic type of qualitative methods (interviewing, ethnography, documents, or visual sociology). Using what you have learned about coding, code the methods sections of each article, and use your coding to figure out what is common in how such articles discuss their research design, data collection, and analysis methods.
• Return to an exercise you completed earlier in this course and revise your work. What did you change? How did revising impact the final product?
• Find a quote from the transcript of an interview, a social media post, or elsewhere that has not yet been interpreted or explained. Write a paragraph that includes the quote along with an explanation of its sociological meaning or significance.

The style or personality of a piece of writing, including such elements as tone, word choice, syntax, and rhythm.

A quotation, usually one of some length, which is set off from the main text by being indented on both sides rather than being placed in quotation marks.

A classification of written or artistic work based on form, content, and style.

A short summary of a text written from the perspective of a reader rather than from the perspective of an author.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Talk to our experts

1800-120-456-456

• Presentation of Data

Data Presenting for Clearer Reference

Imagine the statistical data without a definite presentation, will be burdensome! Data presentation is one of the important aspects of Statistics. Presenting the data helps the users to study and explain the statistics thoroughly. We are going to discuss this presentation of data and know-how information is laid down methodically. 

In this context, we are going to present the topic - Presentation of Data which is to be referred to by the students and the same is to be studied in regard to the types of presentations of data. 

Presentation of Data and Information

Statistics is all about data. Presenting data effectively and efficiently is an art. You may have uncovered many truths that are complex and need long explanations while writing. This is where the importance of the presentation of data comes in. You have to present your findings in such a way that the readers can go through them quickly and understand each and every point that you wanted to showcase. As time progressed and new and complex research started happening, people realized the importance of the presentation of data to make sense of the findings.

Define Data Presentation

Data presentation is defined as the process of using various graphical formats to visually represent the relationship between two or more data sets so that an informed decision can be made based on them.

Types of Data Presentation

Broadly speaking, there are three methods of data presentation:

Diagrammatic

Textual Ways of Presenting Data

Out of the different methods of data presentation, this is the simplest one. You just write your findings in a coherent manner and your job is done. The demerit of this method is that one has to read the whole text to get a clear picture. Yes, the introduction, summary, and conclusion can help condense the information.

Tabular Ways of Data Presentation and Analysis

To avoid the complexities involved in the textual way of data presentation, people use tables and charts to present data. In this method, data is presented in rows and columns - just like you see in a cricket match showing who made how many runs. Each row and column have an attribute (name, year, sex, age, and other things like these). It is against these attributes that data is written within a cell.

Diagrammatic Presentation: Graphical Presentation of Data in Statistics

This kind of data presentation and analysis method says a lot with dramatically short amounts of time.

Diagrammatic Presentation has been divided into further categories:

Geometric Diagram

When a Diagrammatic presentation involves shapes like a bar or circle, we call that a Geometric Diagram. Examples of Geometric Diagram

Bar Diagram

Simple Bar Diagram

Simple Bar Diagram is composed of rectangular bars. All of these bars have the same width and are placed at an equal distance from each other. The bars are placed on the X-axis. The height or length of the bars is used as the means of measurement. So, on the Y-axis, you have the measurement relevant to the data. 

Suppose, you want to present the run scored by each batsman in a game in the form of a bar chart. Mark the runs on the Y-axis - in ascending order from the bottom. So, the lowest scorer will be represented in the form of the smallest bar and the highest scorer in the form of the longest bar.

Multiple Bar Diagram

(Image will be uploaded soon)

In many states of India, electric bills have bar diagrams showing the consumption in the last 5 months. Along with these bars, they also have bars that show the consumption that happened in the same months of the previous year. This kind of Bar Diagram is called Multiple Bar Diagrams.

Component Bar Diagram

(image will be uploaded soon)

Sometimes, a bar is divided into two or more parts. For example, if there is a Bar Diagram, the bars of which show the percentage of male voters who voted and who didn’t and the female voters who voted and who didn’t. Instead of creating separate bars for who did and who did not, you can divide one bar into who did and who did not.

A pie chart is a chart where you divide a pie (a circle) into different parts based on the data. Each of the data is first transformed into a percentage and then that percentage figure is multiplied by 3.6 degrees. The result that you get is the angular degree of that corresponding data to be drawn in the pie chart. So, for example, you get 30 degrees as the result, on the pie chart you draw that angle from the center.

Frequency Diagram

Suppose you want to present data that shows how many students have 1 to 2 pens, how many have 3 to 5 pens, how many have 6 to 10 pens (grouped frequency) you do that with the help of a Frequency Diagram. A Frequency Diagram can be of many kinds:

Where the grouped frequency of pens (from the above example) is written on the X-axis and the numbers of students are marked on the Y-axis. The data is presented in the form of bars.

Frequency Polygon

When you join the midpoints of the upper side of the rectangles in a histogram, you get a Frequency Polygon

Frequency Curve

When you draw a freehand line that passes through the points of the Frequency Polygon, you get a Frequency Curve.

Ogive 

Suppose 2 students got 0-20 marks in maths, 5 students got 20-30 marks and 4 students got 30-50 marks in Maths. So how many students got less than 50 marks? Yes, 5+2=7. And how many students got more than 20 marks? 5+4=9. This type of more than and less than data are represented in the form of the ogive. The meeting point of the less than and more than line will give you the Median.

Arithmetic Line Graph

If you want to see the trend of Corona infection vs the number of recoveries from January 2020 to December 2020, you can do that in the form of an Arithmetic Line Graph. The months should be marked on the X-axis and the number of infections and recoveries are marked on the Y-axis. You can compare if the recovery is greater than the infection and if the recovery and infection are going at the same rate or not with the help of this Diagram.

Did You Know?

Sir Ronald Aylmer Fisher is known as the father of modern statistics.

FAQs on Presentation of Data

1. What are the 4 types of Tabular Presentation?

The tabular presentation method can be further divided into 4 categories:

Qualitative

Quantitative

Qualitative classification is done when the attributes in the table are some kind of ‘quality’ or feature. Suppose you want to make a table where you would show how many batsmen made half-centuries and how many batsmen made centuries in IPL 2020. Notice that the data would have only numbers - no age, sex, height is needed. This type of tabulation is called quantitative tabulation.

If you want to make a table that would inform which year’s world cup, which team won. The classifying variable, here, is year or time. This kind of classification is called Temporal classification.

If you want to list the top 5 coldest places in the world. The classifying variable here would be a place in each case. This kind of classification is called Spatial Classification.

2. Are bar charts and histograms the Same?

No, they are not the same. With a histogram, you measure the frequency of quantitative data. With bar charts, you compare categorical data.

3. What is the definition of Data Presentation?

When research work is completed, the data gathered from it can be quite large and complex. Organizing the data in a coherent, easy-to-understand, quick to read and graphical way is called data presentation.

Data Science for Undergraduates: Opportunities and Options (2018)

Chapter: 6 conclusions, 6 conclusions.

Data science education is well into its formative stages of development; it is evolving into a self-supporting discipline and producing professionals with distinct and complementary skills relative to professionals in the computer, information, and statistical sciences. However, regardless of its potential eventual disciplinary status, the evidence points to robust growth of data science education that will indelibly shape the undergraduate students of the future. In fact, fueled by growing student interest and industry demand, data science education will likely become a staple of the undergraduate experience. There will be an increase in the number of students majoring, minoring, earning certificates, or just taking courses in data science as the value of data skills becomes even more widely recognized. The adoption of a general education requirement in data science for all undergraduates will endow future generations of students with the basic understanding of data science that they need to become responsible citizens. Continuing education programs such as data science boot camps, career accelerators, summer schools, and incubators will provide another stream of talent. This constitutes the emerging watershed of data science education that feeds multiple streams of generalists and specialists in society; citizens are empowered by their basic skills to examine, interpret, and draw value from data.

Today, the nation is in the formative phase of data science education, where educational organizations are pioneering their own programs, each with different approaches to depth, breadth, and curricular emphasis (e.g., business, computer science, engineering, information science, math-

ematics, social science, or statistics). It is too early to expect consensus to emerge on certain best practices of data science education. However, it is not too early to envision the possible forms that such practices might take. Nor is it too early to make recommendations that can help the data science education community develop strategic vision and practices. The following is a summary of the findings and recommendations discussed in the preceding four chapters of this report.

Finding 2.1: Data scientists today draw largely from extensions of the “analyst” of years past trained in traditional disciplines. As data science becomes an integral part of many industries and enriches research and development, there will be an increased demand for more holistic and more nuanced data science roles.

Finding 2.2: Data science programs that strive to meet the needs of their students will likely evolve to emphasize certain skills and capabilities. This will result in programs that prepare different types of data scientists.

Recommendation 2.1: Academic institutions should embrace data science as a vital new field that requires specifically tailored instruction delivered through majors and minors in data science as well as the development of a cadre of faculty equipped to teach in this new field.

Recommendation 2.2: Academic institutions should provide and evolve a range of educational pathways to prepare students for an array of data science roles in the workplace.

Finding 2.3: A critical task in the education of future data scientists is to instill data acumen. This requires exposure to key concepts in data science, real-world data and problems that can reinforce the limitations of tools, and ethical considerations that permeate many applications. Key concepts involved in developing data acumen include the following:

• Mathematical foundations,
• Computational foundations,
• Statistical foundations,
• Data management and curation,
• Data description and visualization,
• Data modeling and assessment,
• Workflow and reproducibility,
• Communication and teamwork,
• Domain-specific considerations, and
• Ethical problem solving.

Recommendation 2.3: To prepare their graduates for this new data-driven era, academic institutions should encourage the development of a basic understanding of data science in all undergraduates.

Recommendation 2.4: Ethics is a topic that, given the nature of data science, students should learn and practice throughout their education. Academic institutions should ensure that ethics is woven into the data science curriculum from the beginning and throughout.

Recommendation 2.5: The data science community should adopt a code of ethics; such a code should be affirmed by members of professional societies, included in professional development programs and curricula, and conveyed through educational programs. The code should be reevaluated often in light of new developments.

Finding 3.1: Undergraduate education in data science can be experienced in many forms. These include the following:

• Integrated introductory courses that can satisfy a general education requirement;
• A major in data science, including advanced skills, as the primary field of study;
• A minor or track in data science, where intermediate skills are connected to the major field of study;
• Two-year degrees and certificates;
• Other certificates, often requiring fewer courses than a major but more than a minor;
• Massive open online courses, which can engage large numbers of students at a variety of levels; and
• Summer programs and boot camps, which can serve to supplement academic or on-the-job training.

Recommendation 3.1: Four-year and two-year institutions should establish a forum for dialogue across institutions on all aspects of data science education, training, and workforce development.

Finding 4.1: The nature of data science is such that it offers multiple pathways for students of different backgrounds to engage at levels ranging from basic to expert.

Finding 4.2: Data science would particularly benefit from broad participation by underrepresented minorities because of the many applications to problems of interest to diverse populations.

Recommendation 4.1: As data science programs develop, they should focus on attracting students with varied backgrounds and degrees of preparation and preparing them for success in a variety of careers.

Finding 4.3: Institutional flexibility will involve the development of curricula that take advantage of current course availability and will potentially be constrained by the availability of teaching expertise. Whatever organizational or infrastructure model is adopted, incentives are needed to encourage faculty participation and to overcome barriers.

Finding 4.4: The economics of developing programs has recently changed with the shift to cloud-based approaches and platforms.

Finding 5.1: The evolution of data science programs at a particular institution will depend on the particular institution’s pedagogical style and the students’ backgrounds and goals, as well as the requirements of the job market and graduate schools.

Recommendation 5.1: Because these are early days for undergraduate data science education, academic institutions should be prepared to evolve programs over time. They should create and maintain the flexibility and incentives to facilitate the sharing of courses, materials, and faculty among departments and programs.

Finding 5.2: There is a need for broadening the perspective of faculty who are trained in particular areas of data science to be knowledgeable of the breadth of approaches to data science so that they can more effectively educate students at all levels.

Recommendation 5.2: During the development of data science programs, institutions should provide support so that the faculty can become more cognizant of the varied aspects of data science through discussion, co-teaching, sharing of materials, short courses, and other forms of training.

Finding 5.3: The data science community would benefit from the creation of websites and journals that document and make available best

practices, curricula, education research findings, and other materials related to undergraduate data science education.

Finding 5.4: The evolution of undergraduate education in data science can be driven by data science. Exploiting administrative records, in conjunction with other data sources such as economic information and survey data, can enable effective transformation of programs to better serve their students.

Finding 5.5: Data science methods applied both to individual programs and comparatively across programs can be used for both evaluation and evolution of data science program components. It is essential that both processes are sustained as new pathways emerge at institutions.

Recommendation 5.3: Academic institutions should ensure that programs are continuously evaluated and should work together to develop professional approaches to evaluation. This should include developing and sharing measurement and evaluation frameworks, data sets, and a culture of evolution guided by high-quality evaluation. Efforts should be made to establish relationships with sector-specific professional societies to help align education evaluation with market impacts.

Finding 5.6: As professional societies adapt to data science, improved coordination could offer new opportunities for additional collaboration and cross-pollination. A group or conference with bridging capabilities would be helpful. Professional societies may find it useful to collaborate to offer such training and networking opportunities to their joint communities.

Recommendation 5.4: Existing professional societies should coordinate to enable regular convening sessions on data science among their members. Peer review and discussion are essential to share ideas, best practices, and data.

This page intentionally left blank.

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent.

Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Research paper

Writing a Research Paper Conclusion | Step-by-Step Guide

Published on October 30, 2022 by Jack Caulfield . Revised on April 13, 2023.

• Restate the problem statement addressed in the paper
• Summarize your overall arguments or findings
• Suggest the key takeaways from your paper

The content of the conclusion varies depending on whether your paper presents the results of original empirical research or constructs an argument through engagement with sources .

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

Step 1: restate the problem, step 2: sum up the paper, step 3: discuss the implications, research paper conclusion examples, frequently asked questions about research paper conclusions.

The first task of your conclusion is to remind the reader of your research problem . You will have discussed this problem in depth throughout the body, but now the point is to zoom back out from the details to the bigger picture.

While you are restating a problem you’ve already introduced, you should avoid phrasing it identically to how it appeared in the introduction . Ideally, you’ll find a novel way to circle back to the problem from the more detailed ideas discussed in the body.

For example, an argumentative paper advocating new measures to reduce the environmental impact of agriculture might restate its problem as follows:

Meanwhile, an empirical paper studying the relationship of Instagram use with body image issues might present its problem like this:

“In conclusion …”

Avoid starting your conclusion with phrases like “In conclusion” or “To conclude,” as this can come across as too obvious and make your writing seem unsophisticated. The content and placement of your conclusion should make its function clear without the need for additional signposting.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Having zoomed back in on the problem, it’s time to summarize how the body of the paper went about addressing it, and what conclusions this approach led to.

Depending on the nature of your research paper, this might mean restating your thesis and arguments, or summarizing your overall findings.

Argumentative paper: Restate your thesis and arguments

In an argumentative paper, you will have presented a thesis statement in your introduction, expressing the overall claim your paper argues for. In the conclusion, you should restate the thesis and show how it has been developed through the body of the paper.

Briefly summarize the key arguments made in the body, showing how each of them contributes to proving your thesis. You may also mention any counterarguments you addressed, emphasizing why your thesis holds up against them, particularly if your argument is a controversial one.

Don’t go into the details of your evidence or present new ideas; focus on outlining in broad strokes the argument you have made.

Empirical paper: Summarize your findings

In an empirical paper, this is the time to summarize your key findings. Don’t go into great detail here (you will have presented your in-depth results and discussion already), but do clearly express the answers to the research questions you investigated.

Describe your main findings, even if they weren’t necessarily the ones you expected or hoped for, and explain the overall conclusion they led you to.

Having summed up your key arguments or findings, the conclusion ends by considering the broader implications of your research. This means expressing the key takeaways, practical or theoretical, from your paper—often in the form of a call for action or suggestions for future research.

Argumentative paper: Strong closing statement

An argumentative paper generally ends with a strong closing statement. In the case of a practical argument, make a call for action: What actions do you think should be taken by the people or organizations concerned in response to your argument?

If your topic is more theoretical and unsuitable for a call for action, your closing statement should express the significance of your argument—for example, in proposing a new understanding of a topic or laying the groundwork for future research.

Empirical paper: Future research directions

In a more empirical paper, you can close by either making recommendations for practice (for example, in clinical or policy papers), or suggesting directions for future research.

Whatever the scope of your own research, there will always be room for further investigation of related topics, and you’ll often discover new questions and problems during the research process .

Finish your paper on a forward-looking note by suggesting how you or other researchers might build on this topic in the future and address any limitations of the current paper.

Full examples of research paper conclusions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

• Argumentative paper
• Empirical paper

While the role of cattle in climate change is by now common knowledge, countries like the Netherlands continually fail to confront this issue with the urgency it deserves. The evidence is clear: To create a truly futureproof agricultural sector, Dutch farmers must be incentivized to transition from livestock farming to sustainable vegetable farming. As well as dramatically lowering emissions, plant-based agriculture, if approached in the right way, can produce more food with less land, providing opportunities for nature regeneration areas that will themselves contribute to climate targets. Although this approach would have economic ramifications, from a long-term perspective, it would represent a significant step towards a more sustainable and resilient national economy. Transitioning to sustainable vegetable farming will make the Netherlands greener and healthier, setting an example for other European governments. Farmers, policymakers, and consumers must focus on the future, not just on their own short-term interests, and work to implement this transition now.

As social media becomes increasingly central to young people’s everyday lives, it is important to understand how different platforms affect their developing self-conception. By testing the effect of daily Instagram use among teenage girls, this study established that highly visual social media does indeed have a significant effect on body image concerns, with a strong correlation between the amount of time spent on the platform and participants’ self-reported dissatisfaction with their appearance. However, the strength of this effect was moderated by pre-test self-esteem ratings: Participants with higher self-esteem were less likely to experience an increase in body image concerns after using Instagram. This suggests that, while Instagram does impact body image, it is also important to consider the wider social and psychological context in which this usage occurs: Teenagers who are already predisposed to self-esteem issues may be at greater risk of experiencing negative effects. Future research into Instagram and other highly visual social media should focus on establishing a clearer picture of how self-esteem and related constructs influence young people’s experiences of these platforms. Furthermore, while this experiment measured Instagram usage in terms of time spent on the platform, observational studies are required to gain more insight into different patterns of usage—to investigate, for instance, whether active posting is associated with different effects than passive consumption of social media content.

If you’re unsure about the conclusion, it can be helpful to ask a friend or fellow student to read your conclusion and summarize the main takeaways.

• Do they understand from your conclusion what your research was about?
• Are they able to summarize the implications of your findings?
• Can they answer your research question based on your conclusion?

You can also get an expert to proofread and feedback your paper with a paper editing service .

Prevent plagiarism. Run a free check.

The conclusion of a research paper has several key elements you should make sure to include:

• A restatement of the research problem
• A summary of your key arguments and/or findings
• A short discussion of the implications of your research

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Caulfield, J. (2023, April 13). Writing a Research Paper Conclusion | Step-by-Step Guide. Scribbr. Retrieved April 1, 2024, from https://www.scribbr.com/research-paper/research-paper-conclusion/

Is this article helpful?

Jack Caulfield

Other students also liked, writing a research paper introduction | step-by-step guide, how to create a structured research paper outline | example, checklist: writing a great research paper, what is your plagiarism score.

Data Analysis, Interpretation, and Presentation Techniques: A Guide to Making Sense of Your Research Data

by Prince Kumar

Last updated: 27 February 2023

Table of Contents

Data analysis, interpretation, and presentation are crucial aspects of conducting high-quality research. Data analysis involves processing and analyzing the data to derive meaningful insights, while data interpretation involves making sense of the insights and drawing conclusions. Data presentation involves presenting the data in a clear and concise way to communicate the research findings. In this article, we will discuss the techniques for data analysis, interpretation, and presentation.

1. Data Analysis Techniques

Data analysis techniques involve processing and analyzing the data to derive meaningful insights. The choice of data analysis technique depends on the research question and objectives. Some common data analysis techniques are:

a. Descriptive Statistics

Descriptive statistics involves summarizing and describing the data using measures such as mean, median, and standard deviation.

b. Inferential Statistics

Inferential statistics involves making inferences about the population based on the sample data. This technique involves hypothesis testing, confidence intervals, and regression analysis.

c. Content Analysis

Content analysis involves analyzing the text, images, or videos to identify patterns and themes.

d. Data Mining

Data mining involves using statistical and machine learning techniques to analyze large datasets and identify patterns.

2. Data Interpretation Techniques

Data interpretation involves making sense of the insights derived from the data analysis. The choice of data interpretation technique depends on the research question and objectives. Some common data interpretation techniques are:

a. Data Visualization

Data visualization involves presenting the data in a visual format, such as charts, graphs, or tables, to communicate the insights effectively.

b. Storytelling

Storytelling involves presenting the data in a narrative format, such as a story, to make the insights more relatable and memorable.

c. Comparative Analysis

Comparative analysis involves comparing the research findings with the existing literature or benchmarks to draw conclusions.

3. Data Presentation Techniques

Data presentation involves presenting the data in a clear and concise way to communicate the research findings. The choice of data presentation technique depends on the research question and objectives. Some common data presentation techniques are:

a. Tables and Graphs

Tables and graphs are effective data presentation techniques for presenting numerical data.

b. Infographics

Infographics are effective data presentation techniques for presenting complex data in a visual and easy-to-understand format.

c. Data Storytelling

Data storytelling involves presenting the data in a narrative format to communicate the research findings effectively.

In conclusion, data analysis, interpretation, and presentation are crucial aspects of conducting high-quality research. By using the appropriate data analysis, interpretation, and presentation techniques, researchers can derive meaningful insights, make sense of the insights, and communicate the research findings effectively. By conducting high-quality data analysis, interpretation, and presentation in research, researchers can provide valuable insights into the research question and objectives.

How useful was this post?

5 star mean very useful & 1 star means not useful at all.

Average rating / 5. Vote count:

No votes so far! Be the first to rate this post.

We are sorry that this post was not useful for you! 😔

Let us improve this post!

Tell us how we can improve this post?

Syllabus – Research Methodology

01 Introduction To Research Methodology

• Meaning and objectives of Research
• Types of Research
• Research Approaches
• Significance of Research
• Research methods vs Methodology
• Research Process
• Criteria of Good Research
• Problems faced by Researchers
• Techniques Involved in defining a problem

02 Research Design

• Meaning and Need for Research Design
• Features and important concepts relating to research design
• Different Research design
• Important Experimental Designs

03 Sample Design

• Introduction to Sample design
• Censure and sample survey
• Implications of Sample design
• Steps in sampling design
• Criteria for selecting a sampling procedure
• Characteristics of a good sample design
• Different types of Sample design
• Measurement Scales
• Important scaling Techniques

04 Methods of Data Collection

• Introduction
• Collection of Primary Data
• Collection through Questionnaire and schedule collection of secondary data
• Differences in Questionnaire and schedule
• Different methods to collect secondary data

05 Data Analysis Interpretation and Presentation Techniques

• Hypothesis Testing
• Basic concepts concerning Hypothesis Testing
• Procedure and flow diagram for Hypothesis Testing
• Test of Significance
• Chi-Square Analysis
• Report Presentation Techniques

We use essential cookies to make Venngage work. By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts.

Manage Cookies

Cookies and similar technologies collect certain information about how you’re using our website. Some of them are essential, and without them you wouldn’t be able to use Venngage. But others are optional, and you get to choose whether we use them or not.

Strictly Necessary Cookies

These cookies are always on, as they’re essential for making Venngage work, and making it safe. Without these cookies, services you’ve asked for can’t be provided.

Show cookie providers

• Google Login

Functionality Cookies

These cookies help us provide enhanced functionality and personalisation, and remember your settings. They may be set by us or by third party providers.

Performance Cookies

These cookies help us analyze how many people are using Venngage, where they come from and how they're using it. If you opt out of these cookies, we can’t get feedback to make Venngage better for you and all our users.

• Google Analytics

Targeting Cookies

These cookies are set by our advertising partners to track your activity and show you relevant Venngage ads on other sites as you browse the internet.

• Google Tag Manager
• Infographics
• Daily Infographics
• Graphic Design
• Graphs and Charts
• Data Visualization
• Human Resources
• Training and Development
• Beginner Guides

Blog Marketing

How To End A Presentation & Leave A Lasting Impression

By Krystle Wong , Aug 09, 2023

So you’ve got an exciting presentation ready to wow your audience and you’re left with the final brushstroke — how to end your presentation with a bang. 

Just as a captivating opening draws your audience in, creating a well-crafted presentation closing has the power to leave a profound and lasting impression that resonates long after the lights dim and the audience disperses.

In this article, I’ll walk you through the art of crafting an impactful conclusion that resonates with 10 effective techniques and ideas along with real-life examples to inspire your next presentation. Alternatively, you could always jump right into creating your slides by customizing our professionally designed presentation templates . They’re fully customizable and require no design experience at all! 

Click to jump ahead:

Why is it important to have an impactful ending for your presentation?

10 effective presentation closing techniques to leave a lasting impression, 7 things to put on a conclusion slide.

• 5 real-life exceptional examples of how to end a presentation

6 mistakes to avoid in concluding a presentation

Faqs on how to end a presentation, how to create a memorable presentation with venngage.

People tend to remember the beginning and end of a presentation more vividly than the middle, making the final moments your last chance to make a lasting impression. 

An ending that leaves a lasting impact doesn’t merely mark the end of a presentation; it opens doors to further exploration. A strong conclusion is vital because it:

• Leaves a lasting impression on the audience.
• Reinforces key points and takeaways.
• Motivates action and implementation of ideas.
• Creates an emotional connection with the audience.
• Fosters engagement, curiosity and reflection.

Just like the final scene of a movie, your presentation’s ending has the potential to linger in your audience’s minds long after they’ve left the room. From summarizing key points to engaging the audience in unexpected ways, make a lasting impression with these 10 ways to end a presentation:

1. The summary

Wrap up your entire presentation with a concise and impactful summary, recapping the key points and main takeaways. By doing so, you reinforce the essential aspects and ensure the audience leaves with a crystal-clear understanding of your core message.

2. The reverse story

Here’s a cool one: start with the end result and then surprise the audience with the journey that led you to where you are. Share the challenges you conquered and the lessons you learned, making it a memorable and unique conclusion that drives home your key takeaways.

Alternatively, customize one of our cool presentation templates to capture the attention of your audience and deliver your message in an engaging and memorable way

3. The metaphorical prop

For an added visual touch, bring a symbolic prop that represents your message. Explain its significance in relation to your content, leaving the audience with a tangible and unforgettable visual representation that reinforces your key concepts.

4. The audience engagement challenge

Get the audience involved by throwing them a challenge related to your informational presentation. Encourage active participation and promise to share the results later, fostering their involvement and motivating them to take action.

5. The memorable statistic showcase

Spice things up with a series of surprising or intriguing statistics, presented with attention-grabbing visual aids. Summarize your main points using these impactful stats to ensure the audience remembers and grasps the significance of your data, especially when delivering a business presentation or pitch deck presentation .

Transform your data-heavy presentations into engaging presentations using data visualization tools. Venngage’s chart and graph tools help you present information in a digestible and visually appealing manner. Infographics and diagrams can simplify complex concepts while images add a relatable dimension to your presentation. 

6. The interactive story creation

How about a collaborative story? Work with the audience to create an impromptu tale together. Let them contribute elements and build the story with you. Then, cleverly tie it back to your core message with a creative presentation conclusion.

7. The unexpected guest speaker

Introduce an unexpected guest who shares a unique perspective related to your presentation’s theme. If their story aligns with your message, it’ll surely amp up the audience’s interest and engagement.

8. The thought-provoking prompt

Leave your audience pondering with a thought-provoking question or prompt related to your topic. Encourage reflection and curiosity, sparking a desire to explore the subject further and dig deeper into your message.

9. The empowering call-to-action

Time to inspire action! Craft a powerful call to action that motivates the audience to make a difference. Provide practical steps and resources to support their involvement, empowering them to take part in something meaningful.

10. The heartfelt expression

End on a warm note by expressing genuine gratitude and appreciation for the audience’s time and attention. Acknowledge their presence and thank them sincerely, leaving a lasting impression of professionalism and warmth.

Not sure where to start? These 12 presentation software might come in handy for creating a good presentation that stands out. 

Remember, your closing slides for the presentation is your final opportunity to make a strong impact on your audience. However, the question remains — what exactly should be on the last slide of your presentation? Here are 7 conclusion slide examples to conclude with a high note:

1. Key takeaways

Highlight the main points or key takeaways from your presentation. This reinforces the essential information you want the audience to remember, ensuring they leave with a clear understanding of your message with a well summarized and simple presentation .

2. Closing statement

Craft a strong closing statement that summarizes the overall message of your presentation and leaves a positive final impression. This concluding remark should be impactful and memorable.

3. Call-to-action

Don’t forget to include a compelling call to action in your final message that motivates the audience to take specific steps after the presentation. Whether it’s signing up for a newsletter, trying a product or conducting further research, a clear call to action can encourage engagement.

4. Contact information

Provide your contact details, such as email address or social media handles. That way, the audience can easily reach out for further inquiries or discussions. Building connections with your audience enhances engagement and opens doors for future opportunities.

Use impactful visuals or graphics to deliver your presentation effectively and make the conclusion slide visually appealing. Engaging visuals can captivate the audience and help solidify your key points.

Visuals are powerful tools for retention. Use Venngage’s library of icons, images and charts to complement your text. You can easily upload and incorporate your own images or choose from Venngage’s library of stock photos to add depth and relevance to your visuals.

6. Next steps

Outline the recommended next steps for the audience to take after the presentation, guiding them on what actions to pursue. This can be a practical roadmap for implementing your ideas and recommendations.

7. Inspirational quote

To leave a lasting impression, consider including a powerful and relevant quote that resonates with the main message of your presentation. Thoughtful quotes can inspire and reinforce the significance of your key points.

Whether you’re giving an in-person or virtual presentation , a strong wrap-up can boost persuasiveness and ensure that your message resonates and motivates action effectively. Check out our gallery of professional presentation templates to get started.

5 real-life exceptional examples of how to end a presentation 

When we talk about crafting an exceptional closing for a presentation, I’m sure you’ll have a million questions — like how do you end a presentation, what do you say at the end of a presentation or even how to say thank you after a presentation. 

To get a better idea of how to end a presentation with style — let’s delve into five remarkable real-life examples that offer valuable insights into crafting a conclusion that truly seals the deal: 

1. Sheryl Sandberg 

In her TED Talk titled “Why We Have Too Few Women Leaders,” Sheryl Sandberg concluded with an impactful call to action, urging men and women to lean in and support gender equality in the workplace. This motivational ending inspired the audience to take action toward a more inclusive world.

2. Elon Musk

Elon Musk often concludes with his vision for the future and how his companies are working towards groundbreaking advancements. His passion and enthusiasm for pushing the boundaries of technology leave the audience inspired and eager to witness the future unfold.

3. Barack Obama

President Obama’s farewell address concluded with an emotional and heartfelt expression of gratitude to the American people. He thanked the audience for their support and encouraged them to stay engaged and uphold the values that define the nation.

4. Brené Brown 

In her TED Talk on vulnerability, Brené Brown ended with a powerful quote from Theodore Roosevelt: “It is not the critic who counts… The credit belongs to the man who is actually in the arena.” This quote reinforced her message about the importance of embracing vulnerability and taking risks in life.

5. Malala Yousafzai

In her Nobel Peace Prize acceptance speech, Malala Yousafzai ended with a moving call to action for education and girls’ rights. She inspired the audience to stand up against injustice and to work towards a world where every child has access to education.

For more innovative presentation ideas , turn ordinary slides into captivating experiences with these 15 interactive presentation ideas that will leave your audience begging for more.

So, we talked about how a good presentation usually ends. As you approach the conclusion of your presentation, let’s go through some of the common pitfalls you should avoid that will undermine the impact of your closing:

1. Abrupt endings

To deliver persuasive presentations, don’t leave your audience hanging with an abrupt conclusion. Instead, ensure a smooth transition by providing a clear closing statement or summarizing the key points to leave a lasting impression.

2. New information

You may be wondering — can I introduce new information or ideas in the closing? The answer is no. Resist the urge to introduce new data or facts in the conclusion and stick to reinforcing the main content presented earlier. By introducing new content at the end, you risk overshadowing your main message.

3. Ending with a Q&A session

While Q&A sessions are valuable, don’t conclude your presentation with them. Opt for a strong closing statement or call-to-action instead, leaving the audience with a clear takeaway.

4. Overloading your final slide

Avoid cluttering your final slide with too much information or excessive visuals. Keep it clean, concise and impactful to reinforce your key messages effectively.

5. Forgetting the call-to-action

Most presentations fail to include a compelling call-to-action which can diminish the overall impact of your presentation. To deliver a persuasive presentation, encourage your audience to take specific steps after the talk, driving engagement and follow-through.

6. Ignoring the audience

Make your conclusion audience-centric by connecting with their needs and interests. Avoid making it solely about yourself or your achievements. Instead, focus on how your message benefits the audience.

What should be the last slide of a presentation?

The last slide of a presentation should be a conclusion slide, summarizing key takeaways, delivering a strong closing statement and possibly including a call to action.

How do I begin a presentation?

Grabbing the audience’s attention at the very beginning with a compelling opening such as a relevant story, surprising statistic or thought-provoking question. You can even create a game presentation to boost interactivity with your audience. Check out this blog for more ideas on how to start a presentation . 

How can I ensure a smooth transition from the body of the presentation to the closing? 

To ensure a smooth transition, summarize key points from the body, use transition phrases like “In conclusion,” and revisit the main message introduced at the beginning. Bridge the content discussed to the themes of the closing and consider adjusting tone and pace to signal the transition.

How long should the conclusion of a presentation be?

The conclusion of a presentation should typically be around 5-10% of the total presentation time, keeping it concise and impactful.

Should you say thank you at the end of a presentation?

Yes, saying thank you at the end of a PowerPoint presentation is a courteous way to show appreciation for the audience’s time and attention.

Should I use presentation slides in the concluding part of my talk? 

Yes, using presentation slides in the concluding part of your talk can be effective. Use concise slides to summarize key takeaways, reinforce your main points and deliver a strong closing statement. A final presentation slide can enhance the impact of your conclusion and help the audience remember your message.

Should I include a Q&A session at the end of the presentation?

Avoid Q&A sessions in certain situations to ensure a well-structured and impactful conclusion. It helps prevent potential time constraints and disruptions to your carefully crafted ending, ensuring your core message remains the focus without the risk of unanswered or off-topic questions diluting the presentation’s impact.

Is it appropriate to use humor in the closing of a presentation?

Using humor in the closing of a presentation can be appropriate if it aligns with your content and audience as it can leave a positive and memorable impression. However, it’s essential to use humor carefully and avoid inappropriate or offensive jokes.

How do I manage nervousness during the closing of a presentation?

To manage nervousness during the closing, focus on your key points and the main message you want to convey. Take deep breaths to calm your nerves, maintain eye contact and remind yourself that you’re sharing valuable insights to enhance your presentation skills.

Creating a memorable presentation is a blend of engaging content and visually captivating design. With Venngage, you can transform your ideas into a dynamic and unforgettable presentation in just 5 easy steps: 

• Choose a template from Venngage’s library: Pick a visually appealing template that fits your presentation’s theme and audience, making it easy to get started with a professional look.
• Craft a compelling story or outline: Organize your content into a clear and coherent narrative or outline the key points to engage your audience and make the information easy to follow.
• Customize design and visuals: Tailor the template with your brand colors, fonts and captivating visuals like images and icons, enhancing your presentation’s visual appeal and uniqueness. You can also use an eye-catching presentation background to elevate your visual content. 
• Incorporate impactful quotes or inspiring elements: Include powerful quotes or elements that resonate with your message, evoking emotions and leaving a lasting impression on your audience members
• Utilize data visualization for clarity: Present data and statistics effectively with Venngage’s charts, graphs and infographics, simplifying complex information for better comprehension.

Additionally, Venngage’s real-time collaboration tools allow you to seamlessly collaborate with team members to elevate your presentation creation process to a whole new level. Use comments and annotations to provide feedback on each other’s work and refine ideas as a group, ensuring a comprehensive and well-rounded presentation.

Well, there you have it—the secrets of how to conclude a presentation. From summarizing your key message to delivering a compelling call to action, you’re now armed with a toolkit of techniques that’ll leave your audience in awe.

Now go ahead, wrap it up like a pro and leave that lasting impression that sets you apart as a presenter who knows how to captivate, inspire and truly make a mark.

Researched by Consultants from Top-Tier Management Companies

Powerpoint Templates

Icon Bundle

Kpi Dashboard

Professional

Business Plans

Swot Analysis

Gantt Chart

Business Proposal

Marketing Plan

Project Management

Business Case

Business Model

Cyber Security

Business PPT

Digital Marketing

Digital Transformation

Human Resources

Product Management

Artificial Intelligence

Company Profile

Acknowledgement PPT

PPT Presentation

Reports Brochures

One Page Pitch

Interview PPT

All Categories

Top 10 Conclusion Slide Templates with Examples and Samples

Siranjeev Santhanam

The concluding moments of a presentation hold a unique power. They are the crescendo, the final brushstroke on the canvas, and the lingering resonance that shapes perceptions. Within the theatre of boardrooms and conference halls, creating impactful presentations with good and meaningful conclusions is not just a formality but a strategic necessity. These pivotal moments are what ultimately sway minds within boardrooms, leaving an indelible mark on the minds of the major decision-makers in business.

Are you having a hard time with SMS marketing? We’ve got something tailor made for you. Click here to check out our other blog on SMS marketing slides now. 

The Role of Presentation Conclusions

When a presentation draws to a close, the audience is most vulnerable to lasting impressions. The conclusion of a discussion is the critical juncture where the narrative converges, and the concepts and ideas crystallize into a final robust takeaway. A well-crafted conclusion can help a professional to etch his or her message into the collective minds of the audience. Whether it be unveiling a bold new venture, outlining crucial plans, or dissecting complicated data, the conclusion gives a presentation enough reason, and ripe ground, to take mundane information and transform it into insight. 

In this blog, we’re going to be taking a look at ten conclusion slides. These templates, like all others in SlideTeam’s curation, come with the distinct advantage of being easy to access and easy to customize, giving users optimal flexibility and convenience when deploying them. 

Let’s begin. 

Business is all about knowing your competition, and then working to get ahead of them. Click here and check out our other blog on competitor analysis slides to excel at this art

Template 1 Project Conclusion PPT Presentation Slides

The slides of this vibrant PPT can be configured to serve as the concluding piece in any large-scale presentation within the corporate medium. There are 30 slides in this deck, with headlines ranging from performance analysis and budget/cost analysis to open issues, project health card, project dashboard, and more. Take control of the narrative by synthesizing a cogent and impactful conclusion, all with the aid of this presentation deck that can assist you in this and more. 

Download now

Template 2 Conclusions Findings PPT Slides Rules

Unlock the true potential of your presentations with a concise and influential conclusion slide that can embolden the storytelling prowess of the medium. Do this and more with the aid of one-page conclusion slide, made to reflect professionalism and excellence. The slide gives you space for a set of subheadings with smaller content brackets where you can lay out the inherent content of your findings or conclusions in a structured and organized manner. 

Template 3 Conclusion Slides  Shown by Gears and Bulb connected

This animated slide is made to represent the creative process in its most age-old form, with a light bulb and gears depicted as an illustration of ideas and innovation. Integrated alongside this is a great breadth of space where you can summarize the main points of your presentation in a series of points with subheadings and smaller explanations alongside it. Use this one-page PPT as a canvas for your business insights, providing a holistic view of the crucial information you seek to present.

Template 4 - Conclusion slides future scope with human image and protruded arrows

Take your presentations to new heights with the aid of this one-page conclusion slide tailored to serve the corporate arena. Seamlessly weave together critical components of the presentation with this one slide, creating a memorable and impactful conclusion to the broader discussion. The slide has a clear white background and an array of smaller bullet points where you can list the major summarized points with ease. 

Template 5 Closing slide image with conclusion

This colorfully-rendered slide is sure to get audience’s attention, helping to cap off a complex and rewarding discussion staged during corporate hours. Use this one-page theme to empower your audiences, breaking down and conveying crucial complex data with a sense of cohesion and clarity. Seize control of the narrative and make a bolder impact with this one-page conclusion PPT Template. Get it now. 

Template 6 Conclusion Slides Image  with Magnifying Glass and Document

Crafting a meaningful and impactful conclusion becomes more effortless with the aid of a simple, elegant slide such as this one. Use this one-page theme as a business asset, giving you the capacity to assemble and synthesize key data points as you want to create a lasting impression on your audience. The slide is suited for business related situations, and comes with the added benefit of being easy to modify to fit any necessary circumstance as well. Get it now. 

Template 7 Conclusion Slides Reliability Efficiency Cost Size Usability Market

Introducing this slide, which is crafted to serve a forward-thinking professional. This one-page PowerPoint piece can help satiate the needs of any business meeting, caging your audience's attention at the conclusion of a meeting. The all-white color palette and intuitive design can help to amplify the message, giving you the tools to streamline complex data through visually cohesive narratives.

Template 8 - Conclusion and recommendations communication PPT Presentation Slides

Incorporate this conclusion slide into your presentation to transform the experience, ensuring that your audience is captivated by the unfolding discussion and the strategic journey that is being uncovered. It is divided into two segments - conclusion and recommendations. Both come with content brackets for additional information that you can insert at your own discretion. Get this slide now and nurture an optimistic, creative environment within your business spaces. 

Template 9 Conclusion PPT Slide Influencers

Don't let crucial discussions in the business medium become background noise featured as part of mundane presentations. Carve your mark with a more rousing conclusion, implemented with the aid of this one-page PPT. It features two separate segments within a broader canvas, allowing you to pose questions and to leave room for answers, as part of the conclusion of the presentation. Get it now and morph your presentation into a more fruitful experience.

Template 10 Conclusion Slides Pen Image  with Case Study Project Plan and Solutions

This one-page theme has been infused with a dynamic design and helps you illustrate critical data at the end of a discussion. Dissect the intricate details of your discussion with refined precision, all with the aid of this slide. It comes with a group of pre-loaded subheadings and space for additional information beneath all of them. Immerse your audience in a narrative that captivates as it informs, cementing your message in their consciousness, so that you are able to use it desired. 

THE END-GAME IS USUALLY FINE

In a broader discussion within the business sphere, the conclusion is the ripe moment to evoke emotional resonance from the audience. It’s the moment where you transcend the charts, graphs and data and instead bridge a deeper connection with the audience. A well-crafted conclusion can help synthesize vital data, while also giving the audience the tools to mould the insights into personal initiatives. It can empower your team members to take the next step, aligning their actions with the presented vision.

PS Don’t click away just yet, we’ve got more for you. Click here to read our other blog that delves into company profile templates now. 

Related posts:

• How to Design the Perfect Service Launch Presentation [Custom Launch Deck Included]
• Quarterly Business Review Presentation: All the Essential Slides You Need in Your Deck
• [Updated 2023] How to Design The Perfect Product Launch Presentation [Best Templates Included]
• 99% of the Pitches Fail! Find Out What Makes Any Startup a Success

Liked this blog? Please recommend us

Top 10 Attrition Dashboard Templates with Examples and Samples

Top 10 Demographics Icon PowerPoint Templates with Samples and Examples

This form is protected by reCAPTCHA - the Google Privacy Policy and Terms of Service apply.

Digital revolution powerpoint presentation slides

Sales funnel results presentation layouts

3d men joinning circular jigsaw puzzles ppt graphics icons

Business Strategic Planning Template For Organizations Powerpoint Presentation Slides

Future plan powerpoint template slide

Project Management Team Powerpoint Presentation Slides

Brand marketing powerpoint presentation slides

Launching a new service powerpoint presentation with slides go to market

Agenda powerpoint slide show

Four key metrics donut chart with percentage

Engineering and technology ppt inspiration example introduction continuous process improvement

Meet our team representing in circular format

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

National Academy of Medicine; The Learning Health System Series; Carman KL, Grossmann C, Zirkle M, et al., editors. Health Data Sharing to Support Better Outcomes: Building a Foundation of Stakeholder Trust. Washington (DC): National Academies Press (US); 2021.

Health Data Sharing to Support Better Outcomes: Building a Foundation of Stakeholder Trust.

• Hardcopy Version at National Academies Press

6 CONCLUSION

T o share and receive feedback on the vision statement (see Chapter 2 ), the definition of health data, and the stakeholder statements (see Chapters 3 , 4 , and 5 ), and to begin building support for implementing the short-term action steps identified, the National Academy of Medicine Leadership Consortium: Collaboration for a Value & Science-Driven Health System hosted a meeting on August 23, 2019, in Washington, DC, which brought together representatives of the steering committee and stakeholder groups that prepared the statements in Chapters 3 – 5 along with a broad array of other experts and health system leaders. The goal of the meeting was building partnerships and implementing principles and priorities to overcome the cultural, regulatory, financial, and ethical barriers identified by the stakeholder groups and achieve the vision described in Chapter 2 (see Appendix C ). Drawing on that discussion, this chapter describes the overlapping barriers and solutions from the stakeholder statements and a number of priorities for the nation that are critical to addressing the outstanding barriers to health data sharing, linkage, and use.

• OVERLAPPING BARRIERS AND SOLUTIONS

As was observed at the meeting, the statements produced by each of the stakeholder groups revealed a remarkable amount of overlap among the cultural, ethical, regulatory, and financial barriers to greater data sharing, linkage, and use (see Figure 6-1 ).

FIGURE 6-1 |

Cultural, ethical, regulatory, and financial barriers to data sharing, linkage, and use.

• The groups representing health care executives and patient and family leaders identified a misalignment of financial and other incentives as common barriers.
• The groups representing patient and family leaders and researchers and research oversight leaders identified a lack of agreed-upon practices and principles regarding patient data access, data control, and data ownership.
• The groups representing research and research oversight leaders and health care executives identified concerns regarding controversial uses of data, differing beliefs about whether data should be freely shared, and costs associated with data procurement.

A concern shared by all three stakeholder groups was a lack of trust in the intentions and actions of other groups. The members of the patient and family community lack trust that health care systems and researchers will make data and the conclusions based on those data available to them and will not misuse data they provide by rationing care and sharing those data with unauthorized third parties. Researchers share a similar mistrust in the intentions of third-party users. Health systems are concerned that patients will misinterpret data or use data inappropriately, such as allowing them to be combined with other elements so as to identify individuals. Specific examples of these problems were mentioned at the meeting, such as the dialysis clinic that refused to share a patient’s data that he wanted to share with researchers, or the lack of cooperation between two health systems that refused to even refer to each other by name because of the competition between them. Ultimately, such lack of trust stems from the diverging interests of each group and the lack of common cause for data sharing among the three groups.

The stakeholder statements reveal several imperatives for addressing data-sharing challenges that are common to one or more stakeholder groups, including

• developing a set of principles and commitments on data ownership, accessibility, and control;
• reframing the risk discussion or business case related to data sharing to highlight patient safety and evidence-based arguments about the risks of not sharing data;
• devising a national educational campaign tailored to specific stakeholder groups that highlights the benefits of bidirectional data exchange and prepares these stakeholder groups for using and contributing to shared data;
• conducting and disseminating research on stakeholder preferences and beliefs regarding whether and how data should be shared;
• standardizing data collections and use over the long term by identifying and implementing common data models and data standards, establishing data enclaves or safe harbors, and clarifying what kinds of data various groups are trying to access; and
• continuing efforts to address the misalignment of financial incentives by implementing payment models that reward value-based care and population health.

These common solutions formed the basis for much of the discussion at the August 23 meeting.

• PRIORITIES FOR THE FIELD

Over the course of the meeting, several priorities emerged as the most important to addressing the barriers identified by the stakeholder groups. Many of these priorities reinforced ideas generated in prior discussions of the stakeholder working groups.

ENGAGING IN A PUBLIC INFORMATION CAMPAIGN

Greater knowledge is essential to move data sharing forward. In particular, a widely disseminated public information campaign could help change attitudes and behaviors by showing people how the use of health data could improve their health and the health of people they know. Such a campaign could target not only the general public but also care providers, policy makers, health care administrators, and others in a position to influence data issues as described in the Statement of Patient and Family Leaders in Chapter 3 . Given the importance of this issue to the future of health care, reaching out to school-aged children and building data literacy early in life will be important.

An educational campaign could entail commercials, social media campaigns, public service announcements, webinars, newsletters, and many other forms of communication. Information from the campaign also could be extracted into PowerPoint presentations, infographics, narratives of personal experiences, accounts by early adopters of new approaches, and other flexible and usable forms. Such stories could emphasize that past instances of data sharing have not only created benefits but also not resulted in problems. An article in Science or another high-profile journal could address data sharing. Even movies and television shows could help make the case.

Several wedge issues could prove important in such a campaign. One is that the lack of data access is an issue of patient safety. Health care executives and organizations, insurers, clinicians, patients, families, and others all resonate with this issue. Another is the intersection of data sharing with privacy and security as subsets of health data are governed differently. In the absence of an overarching privacy law in the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects health data that flow among health systems, payers, and other covered entities but not consumer-generated data that originate from mHealth apps or wearable devices or data that are shared with third-party technology companies.. Therefore, guides to privacy and security could be disseminated as part of the campaign to educate consumers of health care about the issue.

Change will occur when there is sufficiently high demand by all of health care’s stakeholders, from the public to the clinical and research communities. Today, however, the public does not fully understand the benefits and value of data sharing, and the demand is not commensurate with the need for change.

CREATING AND PRIORITIZING USE CASES

Foundational to devising a national educational campaign, the development of use cases that promote various reasons for sharing and linking health data could help build demand for data sharing, demonstrate how to overcome the barriers identified by the stakeholder groups, and achieve the vision described in Chapter 2 . These use cases should highlight the benefits of sharing data and information not only with patients or for research but also for artificial intelligence applications, machine learning, performance improvement, best practice guidelines, and many other uses.

Use cases could highlight potential cost savings and analytics that drive value. They could reveal the interdependencies of complex systems and show how to build on success by extending successful innovations into new areas.

A matrix of all of the providers and users of data and their interconnections would reveal many potential use cases. This matrix could describe who provides data, who consumes data, what data elements need to be shared, and the benefits and risks of sharing data. Such a matrix could also identify gaps, both in the private and public sectors, and demonstrate the returns on investment of specific actions.

Use cases can focus on short-, medium-, and long-term objectives and on the critical levers for each. Within each use case category, it would also be helpful to identify exemplar organizations that have shared or linked data to positively impact health outcomes. By demonstrating how greater sharing and use of data can improve health, use cases and exemplars can drive the adoption of a variety of initiatives. For example, a use case that embodies value-based care or personalized medicine could be structured around the exchange and broader use of data. Use cases also could spur greater involvement by organizations and groups that are not yet heavily involved in data sharing, such as small health care systems or communities that have been disenfranchised.

Use cases can range widely across sectors and incorporate many types of data, including clinical, claims, social determinants, and consumer-generated data. To take just one example, standardized patient-reported outcomes could provide the basis for a use case, enabling robust comparison of these outcomes across health systems and care settings.

MAKING THE BUSINESS CASE

Each stakeholder in the health care system will need to see the advantages of moving toward greater data sharing. Particularly in the private sector, this will require the development of compelling business cases that clearly demonstrate these advantages and return on investment.

Developing a business case will require the involvement of different people and stakeholder groups. It also will require estimates of the costs and potential benefits of sharing data and of not sharing data. The business case should lay out not only the financial impact but also the other potential gains and losses (e.g., cost of patient harm, reputation/brand, market share). The existing market for health data, where organizations already attach value to curated databases, could help in understanding and deriving these costs.

A business case can demonstrate how data sharing is useful and brings benefits to multiple parties, including consumers, in preventing medical error and enhancing the care delivery experience. It can show how data sharing has the potential to produce competitive advantages rather than disadvantages. In these and other ways, a strong business case can inspire organizations to be early adopters of new procedures rather than waiting for others to go first. Discussions or partnerships with business groups on health or similar state-based alliances of employers and purchasers could help inform the development of the business case.

BASING PAYMENT ON VALUE

New payment models that incentivize paying for value and outcomes, rather than paying for the volume of care that is delivered, could have concurrent benefits to the imperative for more seamless data sharing. Population health management depends on shared knowledge of patients’ experiences of care across time and across setting. For example, value-based arrangements with care providers and health systems can result in tools that consumers can use to better navigate health care. Value-based health care also can support data exchange, infrastructure development, technology support, and research ( Kent, 2018 ).

There are already a number of initiatives under way to incentivize value-based payments. For example, the Centers for Medicare & Medicaid Services (CMS), an early adopter of value-based care delivery, has identified several alternative payment models that incentivize value over volume. The Medicare Access and CHIP (Children’s Health Insurance Program) Reauthorization Act of 2015 (MACRA) creates a new framework for rewarding Medicare patients’ physicians for providing higher quality care. One of the performance tracks in MACRA, Merit-Based Incentive Payments Systems, emphasizes promoting interoperability through focusing on patient engagement and the electronic exchange of health data ( Quality Payment Program, 2020 ). Additionally, new payment models, such as CMS’s Primary Care First Model—which offers an innovative payment structure to support primary care delivery for patients with complex chronic needs and high need, seriously ill patients—are being developed ( CMS, 2019b ). Private payers are also testing alternative payment approaches and are using approaches such as Shared Savings and Shared Losses Models, Bundled Payment Models, and Alternative Quality Contract to control spending growth and increase care quality ( Chernew et al., 2011 ).

A complication of basing payments on value is that the United States does not have a single health care system. Rather, it is comprised of multiple health care systems that sometimes cooperate and sometimes compete. Business priorities drive some of this competition, as do cultural factors. Nevertheless, increased adoption of value-based care can lead to potential returns on investment and engage communities in exploring and identifying related principles and actions that improve health.

INSTITUTING SUPPORTIVE GOVERNMENT POLICIES

Government policies can support data sharing in a variety of ways, as elaborated in the health care executives’ statement. Such policies can establish ground rules and standards for data exchange across networks, as well as support the development of technologies and systems that promote rather than impede data sharing. They can send clear signals about how existing technologies should be used and what new technologies are needed. One example is CMS’s Interoperability and Patient Access final rule, which mandates Medicare participants to share data via open, secure, and standardized application programming interfaces ( CMS, 2019b ; see Chapter 5 ). Another example is the 21st Century Cures Act, which includes provisions requiring data sharing and management plans among recipients of federal research funds ( ONC, 2020c ).

Direct financial incentives can also drive change—for example, by encouraging hospitals and clinicians to share data through interoperable systems. Negative incentives can have an effect as well, such as public identification or the levying of financial penalties on systems that are blocking or not doing enough to share data. The aforementioned 21st Century Cures Act sets limits for data blocking ( CMS, 2019a ; ONC, 2019 ).

All funders of research, not just government, can require researchers to make their data available to other researchers and to research participants. However, as acknowledged by the National Institutes of Health’s request for public comments on the Draft Policy for Data Management and Sharing, the complexity and cost of preparing, curating, and sharing research data for secondary analyses could complicate the ability of research entities to comply with this. Funders also can support data-gathering efforts among other groups and organizations to generate datasets and make them publicly available.

BUILDING TRUST

Greater trust and transparency among stakeholder groups can both foster and support data sharing. Standards of conduct can build trust, because people know what to expect. For example, making the return of research results to participants the norm rather than the exception could inform people about the value of research and help build trust. Collaborative efforts built on trust can convert zero-sum relationships into positive-sum relationships, where data sharing serves everyone’s interests.

Trust has to be built and sustained thoughtfully and intentionally, given that it is a fragile commodity and can easily be lost. Even if an organization’s mission statement says that the consumers of health care come first, business plans and actions of those in the organization need to reflect that mission. Patients and consumers are often unaware that HIPAA protections do not extend to third-party health data companies, and a well-publicized breach in this area could undermine public confidence and set back the move toward data sharing. Ensuring that non-HIPAA-covered entities are protected by robust and enforced laws for data privacy will be critical in maintaining public trust. Hoping for trust when the customers’ interests are not being served will not work, especially given that contextual factors can influence trust in an entire sector. Additionally, trust can be maintained even when things go wrong. For example, a compensation plan funded by the users of data would be one way to make amends for data breaches.

ALLOCATING RESOURCES FOR ACHIEVING PRIORITIES

At the August 23 meeting, participants discussed a wide array of resources that are both needed and available to achieve the goal of both improved and more frequent data sharing.

One point made repeatedly at the meeting is that health data extend far beyond the data most directly related to health care and health outcomes. Health data include such widely varied data sources as geospatial data; census data; consumer data from groceries, media companies, and smartphone applications; and fitness- and wellness-related data. Many of these forms of data, alone or in combination, can be used to infer health status or inform health care.

Though the stakeholder groups were directed not to address the technical barriers to greater data sharing, computational technologies are a major part of the resources needed to achieve the priorities the groups identified. These technologies are advancing rapidly, highlighting the need for flexibility in responding to new capabilities and circumstances. Technologies can provide solutions to some barriers to data sharing, such as enabling audit trails to enforce compliance with regulations or balancing access with security and privacy. Technology development also can involve innovators and entrepreneurs in the health care system, further speeding progress.

As an example of a technological advance, many health care providers and hospitals are on the verge of meeting requirements for meaningful use by enabling consumers to use third-party applications to access clinical data. By enabling the consumers of health care to interact much more directly with their health data, this advance could mark the beginning of a transformation in health care. For example, given that not enough providers are available to manage the chronic diseases of an aging population, the use of such applications could greatly increase the number and agency of people who are taking an active role in co-managing their health care.

Some data-sharing initiatives can begin with small groups, such as patients with particular diseases, and then spread more widely as exemplified by the creation of many of the Patient-Centered Outcomes Research Institute’s Patient-Powered Research Networks. Others may start with particular institutions and then be adopted or adapted in other settings. Other industries offer examples of data sharing that the health care system could adopt. For example, the August 23 meeting featured a presentation about the spread of data sharing within the airline industry. Convening information technology experts who work outside of the health care industry with health care experts could help to drive innovative solutions to addressing the barriers to health data sharing.

A consortium of organizations committed to stewarding progress on data linkage, sharing, and use could produce greater collaboration and faster progress. Lessons can be derived from the Clinical and Translational Science Awards’ Connecting Data to Health program ( NCATS, 2020 ). Similarly, a shared framework and commitment to data sharing could rally all stakeholders around the idea, and a website that compiles ongoing data-sharing initiatives and resources could be a source of information and momentum.

TOWARD A CONTINUOUS LEARNING HEALTH SYSTEM

Health data are the foundation for a continuously learning health system in which science, informatics, incentives, and culture are aligned to yield continuous improvement, innovation, and equity. In such a system, new knowledge is seamlessly embedded in the delivery of health care, individuals and families are active participants in all elements of care, and new knowledge is generated as an integral product of the delivery of care. Addressing the cultural, ethical, regulatory, and financial barriers to data sharing that currently exist, and building support and demand for data-sharing efforts among key stakeholders including patients and caregivers, clinicians, health care executives, and researchers is paramount to achieving the vision of a continuously learning health system and improving health and health care outcomes.

• Cite this Page National Academy of Medicine; The Learning Health System Series; Carman KL, Grossmann C, Zirkle M, et al., editors. Health Data Sharing to Support Better Outcomes: Building a Foundation of Stakeholder Trust. Washington (DC): National Academies Press (US); 2021. 6, CONCLUSION.
• PDF version of this title (2.0M)

In this Page

Recent activity.

• CONCLUSION - Health Data Sharing to Support Better Outcomes CONCLUSION - Health Data Sharing to Support Better Outcomes

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

There is unequivocal evidence that Earth is warming at an unprecedented rate. Human activity is the principal cause.

• While Earth’s climate has changed throughout its history , the current warming is happening at a rate not seen in the past 10,000 years.
• According to the Intergovernmental Panel on Climate Change ( IPCC ), "Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact." 1
• Scientific information taken from natural sources (such as ice cores, rocks, and tree rings) and from modern equipment (like satellites and instruments) all show the signs of a changing climate.
• From global temperature rise to melting ice sheets, the evidence of a warming planet abounds.

The rate of change since the mid-20th century is unprecedented over millennia.

Earth's climate has changed throughout history. Just in the last 800,000 years, there have been eight cycles of ice ages and warmer periods, with the end of the last ice age about 11,700 years ago marking the beginning of the modern climate era — and of human civilization. Most of these climate changes are attributed to very small variations in Earth’s orbit that change the amount of solar energy our planet receives.

The current warming trend is different because it is clearly the result of human activities since the mid-1800s, and is proceeding at a rate not seen over many recent millennia. 1 It is undeniable that human activities have produced the atmospheric gases that have trapped more of the Sun’s energy in the Earth system. This extra energy has warmed the atmosphere, ocean, and land, and widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere have occurred.

Earth-orbiting satellites and new technologies have helped scientists see the big picture, collecting many different types of information about our planet and its climate all over the world. These data, collected over many years, reveal the signs and patterns of a changing climate.

Scientists demonstrated the heat-trapping nature of carbon dioxide and other gases in the mid-19th century. 2 Many of the science instruments NASA uses to study our climate focus on how these gases affect the movement of infrared radiation through the atmosphere. From the measured impacts of increases in these gases, there is no question that increased greenhouse gas levels warm Earth in response.

Scientific evidence for warming of the climate system is unequivocal.

Intergovernmental Panel on Climate Change

Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that Earth’s climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly 10 times faster than the average rate of warming after an ice age. Carbon dioxide from human activities is increasing about 250 times faster than it did from natural sources after the last Ice Age. 3

The Evidence for Rapid Climate Change Is Compelling:

Global Temperature Is Rising

The planet's average surface temperature has risen about 2 degrees Fahrenheit (1 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide emissions into the atmosphere and other human activities. 4 Most of the warming occurred in the past 40 years, with the seven most recent years being the warmest. The years 2016 and 2020 are tied for the warmest year on record. 5 Image credit: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

The Ocean Is Getting Warmer

The ocean has absorbed much of this increased heat, with the top 100 meters (about 328 feet) of ocean showing warming of 0.67 degrees Fahrenheit (0.33 degrees Celsius) since 1969. 6 Earth stores 90% of the extra energy in the ocean. Image credit: Kelsey Roberts/USGS

The Ice Sheets Are Shrinking

The Greenland and Antarctic ice sheets have decreased in mass. Data from NASA's Gravity Recovery and Climate Experiment show Greenland lost an average of 279 billion tons of ice per year between 1993 and 2019, while Antarctica lost about 148 billion tons of ice per year. 7 Image: The Antarctic Peninsula, Credit: NASA

Glaciers Are Retreating

Glaciers are retreating almost everywhere around the world — including in the Alps, Himalayas, Andes, Rockies, Alaska, and Africa. 8 Image: Miles Glacier, Alaska Image credit: NASA

Snow Cover Is Decreasing

Satellite observations reveal that the amount of spring snow cover in the Northern Hemisphere has decreased over the past five decades and the snow is melting earlier. 9 Image credit: NASA/JPL-Caltech

Sea Level Is Rising

Global sea level rose about 8 inches (20 centimeters) in the last century. The rate in the last two decades, however, is nearly double that of the last century and accelerating slightly every year. 10 Image credit: U.S. Army Corps of Engineers Norfolk District

Arctic Sea Ice Is Declining

Both the extent and thickness of Arctic sea ice has declined rapidly over the last several decades. 11 Credit: NASA's Scientific Visualization Studio

Extreme Events Are Increasing in Frequency

The number of record high temperature events in the United States has been increasing, while the number of record low temperature events has been decreasing, since 1950. The U.S. has also witnessed increasing numbers of intense rainfall events. 12 Image credit: Régine Fabri,  CC BY-SA 4.0 , via Wikimedia Commons

Ocean Acidification Is Increasing

Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30%. 13 , 14 This increase is due to humans emitting more carbon dioxide into the atmosphere and hence more being absorbed into the ocean. The ocean has absorbed between 20% and 30% of total anthropogenic carbon dioxide emissions in recent decades (7.2 to 10.8 billion metric tons per year). 1 5 , 16 Image credit: NOAA

1. IPCC Sixth Assessment Report, WGI, Technical Summary . B.D. Santer et.al., “A search for human influences on the thermal structure of the atmosphere.” Nature 382 (04 July 1996): 39-46. https://doi.org/10.1038/382039a0. Gabriele C. Hegerl et al., “Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method.” Journal of Climate 9 (October 1996): 2281-2306. https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2. V. Ramaswamy, et al., “Anthropogenic and Natural Influences in the Evolution of Lower Stratospheric Cooling.” Science 311 (24 February 2006): 1138-1141. https://doi.org/10.1126/science.1122587. B.D. Santer et al., “Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes.” Science 301 (25 July 2003): 479-483. https://doi.org/10.1126/science.1084123. T. Westerhold et al., "An astronomically dated record of Earth’s climate and its predictability over the last 66 million years." Science 369 (11 Sept. 2020): 1383-1387. https://doi.org/10.1126/science.1094123

2. In 1824, Joseph Fourier calculated that an Earth-sized planet, at our distance from the Sun, ought to be much colder. He suggested something in the atmosphere must be acting like an insulating blanket. In 1856, Eunice Foote discovered that blanket, showing that carbon dioxide and water vapor in Earth's atmosphere trap escaping infrared (heat) radiation. In the 1860s, physicist John Tyndall recognized Earth's natural greenhouse effect and suggested that slight changes in the atmospheric composition could bring about climatic variations. In 1896, a seminal paper by Swedish scientist Svante Arrhenius first predicted that changes in atmospheric carbon dioxide levels could substantially alter the surface temperature through the greenhouse effect. In 1938, Guy Callendar connected carbon dioxide increases in Earth’s atmosphere to global warming. In 1941, Milutin Milankovic linked ice ages to Earth’s orbital characteristics. Gilbert Plass formulated the Carbon Dioxide Theory of Climate Change in 1956.

3. IPCC Sixth Assessment Report, WG1, Chapter 2 Vostok ice core data; NOAA Mauna Loa CO2 record O. Gaffney, W. Steffen, "The Anthropocene Equation." The Anthropocene Review 4, issue 1 (April 2017): 53-61. https://doi.org/abs/10.1177/2053019616688022.

4. https://www.ncei.noaa.gov/monitoring https://crudata.uea.ac.uk/cru/data/temperature/ http://data.giss.nasa.gov/gistemp

5. https://www.giss.nasa.gov/research/news/20170118/

6. S. Levitus, J. Antonov, T. Boyer, O Baranova, H. Garcia, R. Locarnini, A. Mishonov, J. Reagan, D. Seidov, E. Yarosh, M. Zweng, " NCEI ocean heat content, temperature anomalies, salinity anomalies, thermosteric sea level anomalies, halosteric sea level anomalies, and total steric sea level anomalies from 1955 to present calculated from in situ oceanographic subsurface profile data (NCEI Accession 0164586), Version 4.4. (2017) NOAA National Centers for Environmental Information. https://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/index3.html K. von Schuckmann, L. Cheng, L,. D. Palmer, J. Hansen, C. Tassone, V. Aich, S. Adusumilli, H. Beltrami, H., T. Boyer, F. Cuesta-Valero, D. Desbruyeres, C. Domingues, A. Garcia-Garcia, P. Gentine, J. Gilson, M. Gorfer, L. Haimberger, M. Ishii, M., G. Johnson, R. Killick, B. King, G. Kirchengast, N. Kolodziejczyk, J. Lyman, B. Marzeion, M. Mayer, M. Monier, D. Monselesan, S. Purkey, D. Roemmich, A. Schweiger, S. Seneviratne, A. Shepherd, D. Slater, A. Steiner, F. Straneo, M.L. Timmermans, S. Wijffels. "Heat stored in the Earth system: where does the energy go?" Earth System Science Data 12, Issue 3 (07 September 2020): 2013-2041. https://doi.org/10.5194/essd-12-2013-2020.

7. I. Velicogna, Yara Mohajerani, A. Geruo, F. Landerer, J. Mouginot, B. Noel, E. Rignot, T. Sutterly, M. van den Broeke, M. Wessem, D. Wiese, "Continuity of Ice Sheet Mass Loss in Greenland and Antarctica From the GRACE and GRACE Follow-On Missions." Geophysical Research Letters 47, Issue 8 (28 April 2020): e2020GL087291. https://doi.org/10.1029/2020GL087291.

8. National Snow and Ice Data Center World Glacier Monitoring Service

9. National Snow and Ice Data Center D.A. Robinson, D. K. Hall, and T. L. Mote, "MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0, Version 1 (2017). Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0530.001 . http://nsidc.org/cryosphere/sotc/snow_extent.html Rutgers University Global Snow Lab. Data History

10. R.S. Nerem, B.D. Beckley, J. T. Fasullo, B.D. Hamlington, D. Masters, and G.T. Mitchum, "Climate-change–driven accelerated sea-level rise detected in the altimeter era." PNAS 15, no. 9 (12 Feb. 2018): 2022-2025. https://doi.org/10.1073/pnas.1717312115.

11. https://nsidc.org/cryosphere/sotc/sea_ice.html Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock, 2003) http://psc.apl.washington.edu/research/projects/arctic-sea-ice-volume-anomaly/ http://psc.apl.uw.edu/research/projects/projections-of-an-ice-diminished-arctic-ocean/

12. USGCRP, 2017: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 470 pp, https://doi.org/10.7930/j0j964j6 .

13. http://www.pmel.noaa.gov/co2/story/What+is+Ocean+Acidification%3F

14. http://www.pmel.noaa.gov/co2/story/Ocean+Acidification

15. C.L. Sabine, et al., “The Oceanic Sink for Anthropogenic CO2.” Science 305 (16 July 2004): 367-371. https://doi.org/10.1126/science.1097403.

16. Special Report on the Ocean and Cryosphere in a Changing Climate , Technical Summary, Chapter TS.5, Changing Ocean, Marine Ecosystems, and Dependent Communities, Section 5.2.2.3. https://www.ipcc.ch/srocc/chapter/technical-summary/

Header image shows clouds imitating mountains as the sun sets after midnight as seen from Denali's backcountry Unit 13 on June 14, 2019. Credit: NPS/Emily Mesner Image credit in list of evidence: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

Discover More Topics From NASA

Explore Earth Science

Earth Science in Action

Earth Science Data

Facts About Earth

Welcome to the MIT CISR website!

This site uses cookies. Review our Privacy Statement.

Data Is Everybody's Business—Session Presentation

Why can't i view this research.

This presentation from MIT CISR's 2024 European International Executive Forum event is available only to logged-in members and attendees of the event.

See the list of MIT CISR members

Related publications.

Research Briefing

What is data monetization.

Data Is Everybody’s Business

Working Paper: Vignette

Ge’s environment, health, and safety team creates value using machine learning.

Data Is Everybody’s Business: The Fundamentals of Data Monetization

The Australian Taxation Office: Creating Value with Advanced Analytics

Satellogic: moving from ai solutions to ai products, about the authors.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Leslie Owens, Industry Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

MIT CISR Associate Members

MIT CISR wishes to thank all of our associate members for their support and contributions.

MIT CISR's Mission Expand

MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 15 March 2024

Fine tuning of CpG spatial distribution with DNA origami for improved cancer vaccination

• Yang C. Zeng   ORCID: orcid.org/0000-0003-0566-4976 1 , 2 , 3 ,
• Olivia J. Young 1 , 2 , 3 , 4 ,
• Christopher M. Wintersinger   ORCID: orcid.org/0000-0003-4633-9651 1 , 3 ,
• Frances M. Anastassacos 2 , 3 ,
• James I. MacDonald 2 ,
• Giorgia Isinelli   ORCID: orcid.org/0000-0002-1798-1576 2 , 5 ,
• Maxence O. Dellacherie 6 ,
• Miguel Sobral 6 ,
• Haiqing Bai 2 ,
• Amanda R. Graveline 2 ,
• Andyna Vernet 2 ,
• Melinda Sanchez 2 ,
• Kathleen Mulligan 1 ,
• Youngjin Choi 7 ,
• Thomas C. Ferrante 2 ,
• Derin B. Keskin 8 ,
• Geoffrey G. Fell 9 ,
• Donna Neuberg   ORCID: orcid.org/0000-0003-2566-3145 9 ,
• Catherine J. Wu   ORCID: orcid.org/0000-0002-3348-5054 8 ,
• David J. Mooney   ORCID: orcid.org/0000-0001-6299-1194 2 , 6 ,
• Ick Chan Kwon   ORCID: orcid.org/0000-0003-1272-7074 1 , 7 , 10 ,
• Ju Hee Ryu   ORCID: orcid.org/0000-0003-3422-3272 1 , 7 &
• William M. Shih   ORCID: orcid.org/0000-0002-1395-9267 1 , 2 , 3  

Nature Nanotechnology ( 2024 ) Cite this article

3071 Accesses

109 Altmetric

Metrics details

• Drug delivery
• Nanostructures

Multivalent presentation of ligands often enhances receptor activation and downstream signalling. DNA origami offers a precise nanoscale spacing of ligands, a potentially useful feature for therapeutic nanoparticles. Here we use a square-block DNA origami platform to explore the importance of the spacing of CpG oligonucleotides. CpG engages Toll-like receptors and therefore acts to activate dendritic cells. Through in vitro cell culture studies and in vivo tumour treatment models, we demonstrate that square blocks induce Th1 immune polarization when CpG is spaced at 3.5 nm. We observe that this DNA origami vaccine enhances DC activation, antigen cross-presentation, CD8 T-cell activation, Th1-polarized CD4 activation and natural-killer-cell activation. The vaccine also effectively synergizes with anti-PD-L1 for improved cancer immunotherapy in melanoma and lymphoma models and induces long-term T-cell memory. Our results suggest that DNA origami may serve as a platform for controlling adjuvant spacing and co-delivering antigens in vaccines.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 print issues and online access

251,40 € per year

only 20,95 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

A DNA nanodevice-based vaccine for cancer immunotherapy

Shaoli Liu, Qiao Jiang, … Baoquan Ding

Role of nanoscale antigen organization on B-cell activation probed using DNA origami

Rémi Veneziano, Tyson J. Moyer, … Mark Bathe

In vivo assembly of epitope-coated biopolymer particles that induce anti-tumor responses

Devi Jenika, Saranya Pounraj, … Justine D. Mintern

Data availability

Data supporting the findings of this study are available in the Article and its Supplementary Information . The RNAseq data for the stimulated BMDCs generated and analysed in this Article have been deposited in NCBI’s Gene Expression Omnibus under accession no. GSE251850 . Source data are provided with this paper.

Sahin, U. & Tureci, O. Personalized vaccines for cancer immunotherapy. Science 359 , 1355–1360 (2018).

Article   ADS   CAS   PubMed   Google Scholar  

Bauer, S. et al. Human TLR9 confers responsiveness to bacterial DNA via species-specific CpG motif recognition. Proc. Natl Acad. Sci. USA 98 , 9237–9242 (2001).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Bode, C., Zhao, G., Steinhagen, F., Kinjo, T. & Klinman, D. M. CpG DNA as a vaccine adjuvant. Expert Rev. Vaccines 10 , 499–511 (2011).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Klinman, D. M., Sato, T. & Shimosato, T. Use of nanoparticles to deliver immunomodulatory oligonucleotides. WIREs Nanomed. Nanobiotechnol. 8 , 631–637 (2016).

Article   CAS   Google Scholar  

Schuller, V. J. et al. Cellular immunostimulation by CpG-sequence-coated DNA origami structures. ACS Nano 5 , 9696–9702 (2011).

Article   PubMed   Google Scholar  

Casaletto, J. B. & McClatchey, A. I. Spatial regulation of receptor tyrosine kinases in development and cancer. Nat. Rev. Cancer 12 , 387–400 (2012).

Shaw, A. et al. Spatial control of membrane receptor function using ligand nanocalipers. Nat. Methods 11 , 841–846 (2014).

Article   CAS   PubMed   Google Scholar  

Kwon, P. S. et al. Designer DNA architecture offers precise and multivalent spatial pattern-recognition for viral sensing and inhibition. Nat. Chem. 12 , 26–35 (2020).

Pulendran, B. & Ahmed, R. Translating innate immunity into immunological memory: implications for vaccine development. Cell 124 , 849–863 (2006).

Ohto, U. et al. Structural basis of CpG and inhibitory DNA recognition by Toll-like receptor 9. Nature 520 , 702–705 (2015).

Leleux, J. A., Pradhan, P. & Roy, K. Biophysical attributes of CpG presentation control TLR9 signaling to differentially polarize systemic immune responses. Cell Rep. 18 , 700–710 (2017).

Schmidt, N. W. et al. Liquid-crystalline ordering of antimicrobial peptide-DNA complexes controls TLR9 activation. Nat. Mater. 14 , 696–700 (2015).

Lee, E. Y. et al. A review of immune amplification via ligand clustering by self-assembled liquid-crystalline DNA complexes. Adv. Colloid Interface Sci. 232 , 17–24 (2016).

Comberlato, A., Koga, M. M., Nussing, S., Parish, I. A. & Bastings, M. M. C. Spatially controlled activation of Toll-like receptor 9 with DNA-based nanomaterials. Nano Lett. 22 , 2506–2513 (2022).

Du, R. R. et al. Innate immune stimulation using 3D wireframe DNA origami. ACS Nano 16 , 20340–20352 (2022).

Johansson, M., Denardo, D. G. & Coussens, L. M. Polarized immune responses differentially regulate cancer development. Immunol. Rev. 222 , 145–154 (2008).

Yew, N. S. et al. CpG-depleted plasmid DNA vectors with enhanced safety and long-term gene expression in vivo. Mol. Ther. 5 , 731–738 (2002).

Kumar, V. et al. DNA nanotechnology for cancer therapy. Theranostics 6 , 710–725 (2016).

Udomprasert, A. & Kangsamaksin, T. DNA origami applications in cancer therapy. Cancer Sci. 108 , 1535–1543 (2017).

Li, S. et al. A DNA nanorobot functions as a cancer therapeutic in response to a molecular trigger in vivo. Nat. Biotechnol. 36 , 258–264 (2018).

Liu, S. et al. A DNA nanodevice-based vaccine for cancer immunotherapy. Nat. Mater. 20 , 421–430 (2021).

Kern, N., Dong, R., Douglas, S. M., Vale, R. D. & Morrissey, M. A. Tight nanoscale clustering of Fcγ receptors using DNA origami promotes phagocytosis. eLife 10 , e68311 (2021).

Berger, R. M. L. et al. Nanoscale FasL organization on DNA origami to decipher apoptosis signal activation in cells. Small 17 , e2101678 (2021).

Rothemund, P. W. Folding DNA to create nanoscale shapes and patterns. Nature 440 , 297–302 (2006).

Douglas, S. M. et al. Self-assembly of DNA into nanoscale three-dimensional shapes. Nature 459 , 414–418 (2009).

Liedl, T., Hogberg, B., Tytell, J., Ingber, D. E. & Shih, W. M. Self-assembly of three-dimensional prestressed tensegrity structures from DNA. Nat. Nanotechnol. 5 , 520–524 (2010).

Shih, W. M. Exploiting weak interactions in DNA self-assembly. Science 347 , 1417–1418 (2015).

Dietz, H., Douglas, S. M. & Shih, W. M. Folding DNA into twisted and curved nanoscale shapes. Science 325 , 725–730 (2009).

Shen, H. et al. Enhanced and prolonged cross-presentation following endosomal escape of exogenous antigens encapsulated in biodegradable nanoparticles. Immunology 117 , 78–88 (2006).

Min, Y. et al. Antigen-capturing nanoparticles improve the abscopal effect and cancer immunotherapy. Nat. Nanotechnol. 12 , 877–882 (2017).

Chesson, C. B. & Zloza, A. Nanoparticles: augmenting tumor antigen presentation for vaccine and immunotherapy treatments of cancer. Nanomedicine 12 , 2693–2706 (2017).

Ponnuswamy, N. et al. Oligolysine-based coating protects DNA nanostructures from low-salt denaturation and nuclease degradation. Nat. Commun. 8 , 15654 (2017).

Anastassacos, F. M., Zhao, Z., Zeng, Y. & Shih, W. M. Glutaraldehyde cross-linking of oligolysines coating DNA origami greatly reduces susceptibility to nuclease degradation. J. Am. Chem. Soc. 142 , 3311–3315 (2020).

Lucas, C. R. et al. DNA origami nanostructures elicit dose-dependent immunogenicity and are nontoxic up to high doses in vivo. Small 18 , e2108063 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Wamhoff, E. C. et al. Evaluation of nonmodified wireframe DNA origami for acute toxicity and biodistribution in mice. ACS Appl. Bio. Mater. 6 , 1960–1969 (2023).

Douglas, S. M. et al. Rapid prototyping of 3D DNA-origami shapes with caDNAno. Nucleic Acids Res. 37 , 5001–5006 (2009).

Njongmeta, L. M. et al. CD205 antigen targeting combined with dendritic cell recruitment factors and antigen-linked CD40L activation primes and expands significant antigen-specific antibody and CD4(+) T cell responses following DNA vaccination of outbred animals. Vaccine 30 , 1624–1635 (2012).

Lahoud, M. H. et al. DEC-205 is a cell surface receptor for CpG oligonucleotides. Proc. Natl Acad. Sci. USA 109 , 16270–16275 (2012).

You, C. X. et al. AAV2/IL-12 gene delivery into dendritic cells (DC) enhances CTL stimulation above other IL-12 applications: evidence for IL-12 intracrine activity in DC. Oncoimmunology 1 , 847–855 (2012).

Heo, M. B., Kim, S. Y., Yun, W. S. & Lim, Y. T. Sequential delivery of an anticancer drug and combined immunomodulatory nanoparticles for efficient chemoimmunotherapy. Int J. Nanomed. 10 , 5981–5992 (2015).

CAS   Google Scholar  

Scheuerpflug, A. et al. The role of dendritic cells for therapy of B-cell lymphoma with immune checkpoint inhibitors. Cancer Immunol. Immunother. 70 , 1343–1350 (2020).

Keestra, A. M., de Zoete, M. R., Bouwman, L. I. & van Putten, J. P. Chicken TLR21 is an innate CpG DNA receptor distinct from mammalian TLR9. J. Immunol. 185 , 460–467 (2010).

Oldenburg, M. et al. TLR13 recognizes bacterial 23S rRNA devoid of erythromycin resistance-forming modification. Science 337 , 1111–1115 (2012).

Spies, B. et al. Vaccination with plasmid DNA activates dendritic cells via Toll-like receptor 9 (TLR9) but functions in TLR9-deficient mice. J. Immunol. 171 , 5908–5912 (2003).

Yu, D. et al. ‘Immunomers’–novel 3′-3′-linked CpG oligodeoxyribonucleotides as potent immunomodulatory agents. Nucleic Acids Res. 30 , 4460–4469 (2002).

Minari, J., Mochizuki, S. & Sakurai, K. Enhanced cytokine secretion owing to multiple CpG side chains of DNA duplex. Oligonucleotides 18 , 337–344 (2008).

Smith, L. K. et al. Interleukin-10 directly inhibits CD8(+) T cell function by enhancing N-glycan branching to decrease antigen sensitivity. Immunity 48 , 299–312 e295 (2018).

Li, A. W. et al. A facile approach to enhance antigen response for personalized cancer vaccination. Nat. Mater. 17 , 528–534 (2018).

Kreiter, S. et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature 520 , 692–696 (2015).

Toubi, E. & Shoenfeld, Y. Protective autoimmunity in cancer (review). Oncol. Rep. 17 , 245–251 (2007).

CAS   PubMed   Google Scholar  

Ke, Y., Voigt, N. V., Gothelf, K. V. & Shih, W. M. Multilayer DNA origami packed on hexagonal and hybrid lattices. J. Am. Chem. Soc. 134 , 1770–1774 (2012).

Douglas, S. M., Chou, J. J. & Shih, W. M. DNA-nanotube-induced alignment of membrane proteins for NMR structure determination. Proc. Natl Acad. Sci. USA 104 , 6644–6648 (2007).

Hahn, J., Wickham, S. F., Shih, W. M. & Perrault, S. D. Addressing the instability of DNA nanostructures in tissue culture. ACS Nano 8 , 8765–8775 (2014).

Download references

Acknowledgements

We thank Q. Yan, Z. Zhao, T. Zhang, P. Lill, P. Prabhala, L. Chou, J. Han, D. Minev, B. Everhart, J. Deng, D. Zhang, K. Adu-Berchie, H. Dembele, A. Rajwar and K. Simpson for aiding in labour support, experimental design, exploring experiments and manuscript proofreading. We also thank M. Perez, M. Carr and E. Zigon for their assistance in lab management and facility usage. We thank M. Bastings and A. Li for exploration—before the current project initiated—of CpG-functionalized DNA origami barrels for immune stimulation. This work was funded by the Barr Award granted by the Claudia Adams Barr Program (Y.C.Z.) in the Dana-Farber Cancer Institute and by Wyss validation funding (Y.C.Z.) at the Wyss Institute for Biologically Inspired Engineering at Harvard. This study was supported by the Korean Fund for Regenerative Medicine (J.H.R.; 21A0504L1) funded by the Korean government (the Ministry of Science and ICT and the Korean Ministry of Health & Welfare) and the Intramural Research Program of KIST (J.H.R.; no. 2E30840). This project was also supported by an NIH U54 grant (W.M.S.; CA244726-01).

Author information

Authors and affiliations.

Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

Yang C. Zeng, Olivia J. Young, Christopher M. Wintersinger, Kathleen Mulligan, Ick Chan Kwon, Ju Hee Ryu & William M. Shih

Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA

Yang C. Zeng, Olivia J. Young, Frances M. Anastassacos, James I. MacDonald, Giorgia Isinelli, Haiqing Bai, Amanda R. Graveline, Andyna Vernet, Melinda Sanchez, Thomas C. Ferrante, David J. Mooney & William M. Shih

Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

Yang C. Zeng, Olivia J. Young, Christopher M. Wintersinger, Frances M. Anastassacos & William M. Shih

Harvard-Massachusetts Institute of Technology (MIT) Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA

Olivia J. Young

Department of Drug and Health Sciences, University of Catania, Catania, Italy

Giorgia Isinelli

Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

Maxence O. Dellacherie, Miguel Sobral & David J. Mooney

Center for Theragnosis, Biomedical Research Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea

Youngjin Choi, Ick Chan Kwon & Ju Hee Ryu

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

Derin B. Keskin & Catherine J. Wu

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

Geoffrey G. Fell & Donna Neuberg

KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, Republic of Korea

Ick Chan Kwon

You can also search for this author in PubMed   Google Scholar

Contributions

Y.C.Z. developed and planned the experiments, carried out the vaccine fabrication and validation, and wrote the manuscript. J.H.R. and W.M.S. provided the experimental and theoretical guidance and edited the manuscript. O.J.Y. assisted Y.C.Z. in performing the experiments, analysing the data and manuscript editing. C.M.W., F.M.A., J.I.M. and G.I. assisted with the DNA origami design, modelling and fabrication. H.B. performed the RNAseq analysis. M.O.D., M. Sobral, M. Sanchez, A.R.G. and A.V. helped with the animal study design, modelling and sampling. T.C.F. assisted with the confocal experiment. Y.C. assisted with the 3D modelling and manuscript editing. G.G.F. and D.N. guided the statistical analysis. C.J.W. and D.B.K. guided the experiment design and offered manuscript editing. D.J.M. and I.C.K. provided project support and manuscript editing.

Corresponding authors

Correspondence to Ju Hee Ryu or William M. Shih .

Ethics declarations

Competing interests.

W.M.S., J.H.R. and Y.C.Z. are inventors on US patent application PCT/US2020/036281 held by Dana-Farber Cancer Institute, Korea Institute of Science and Technology and Wyss Institute (filed on 5 June 2020) partially based on this work. All other authors declare no competing interests.

Peer review

Peer review information.

Nature Nanotechnology thanks Mark Bathe, Diana Morzy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Figs. 1–34, Tables 1–6, Discussion and Methods.

Reporting Summary

Supplementary Video 1 This video demonstrates the effective uptake of DNA origami nanoparticles coated with K10-PEG5k by DCs within a time frame of 2 h and reveals the presence of DNA origami nanoparticles (pink) conjugated with OVA antigen (green) within the DCs, along with late endosomes (cyan).

Supplementary Data 1

Statistical source data.

Supplementary Data 2

Supplementary data 3, supplementary data 4, supplementary data 5, supplementary data 6, supplementary data 7, supplementary data 8, supplementary data 9, supplementary data 10, supplementary data 11, supplementary data 12, supplementary data 13, supplementary data 14, supplementary data 15, supplementary data 16, supplementary data 17, supplementary data 18, supplementary data 19, supplementary data 20, source data, source data fig. 1, source data fig. 2, source data fig. 3, source data fig. 4, source data fig. 5, source data fig. 6, rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Zeng, Y.C., Young, O.J., Wintersinger, C.M. et al. Fine tuning of CpG spatial distribution with DNA origami for improved cancer vaccination. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-024-01615-3

Download citation

Received : 19 December 2022

Accepted : 18 January 2024

Published : 15 March 2024

DOI : https://doi.org/10.1038/s41565-024-01615-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

The Federal Register

The daily journal of the united states government, request access.

Due to aggressive automated scraping of FederalRegister.gov and eCFR.gov, programmatic access to these sites is limited to access to our extensive developer APIs.

If you are human user receiving this message, we can add your IP address to a set of IPs that can access FederalRegister.gov & eCFR.gov; complete the CAPTCHA (bot test) below and click "Request Access". This process will be necessary for each IP address you wish to access the site from, requests are valid for approximately one quarter (three months) after which the process may need to be repeated.

An official website of the United States government.

If you want to request a wider IP range, first request access for your current IP, and then use the "Site Feedback" button found in the lower left-hand side to make the request.

Precipitating factors, presentation and outcomes of diabetic ketoacidosis among patients seen at Moi Teaching and Referral Hospital (MTRH), Eldoret Kenya.

• Find this author on Google Scholar
• Find this author on PubMed
• Search for this author on this site
• ORCID record for Clemence Mwahe Msagha
• For correspondence: [email protected]
• Info/History
• Preview PDF

PRECIPITATING FACTORS, PRESENTATION AND OUTCOMES OF DIABETIC KETOACIDOSIS AMONG PATIENTS SEEN AT MOI TEACHING AND REFERRAL HOSPITAL, ELDORET KENYA. Clemence Msagha1, Jemima Kamano2, Paul Ayuo3 Background: Diabetes Ketoacidosis (DKA) is a major complication of Diabetes Mellitus (DM) with a likelihood of high mortality if not managed appropriately. It is diagnosed with a triad of hyperglycemia, ketonemia and metabolic acidosis. Objectives: To describe the precipitating factors, clinical presentation and outcomes of DKA among patients attending Moi Teaching and Referral Hospital (MTRH). Methods: This prospective study involved 120 consecutively recruited participants diagnosed with DKA. Participants were drawn from the Emergency department and Diabetes Outpatient clinic and followed up in the wards and intensive care unit (ICU) in MTRH for up to 10 days. Focused history and physical examination was done. Blood sugar was measured daily; blood ketones and blood gases were measured on days 1,2,3 and 5. Precipitating factors, presentation and outcomes were summarised as frequencies and their corresponding percentages and presented in tables and charts. Results: The median age of participants was 33 years (IQR 23, 44.5). Type 1 DM represented 63.3% and type 2 DM 34.2% of the patients. The most common precipitating factors for DKA were; new onset undiagnosed DM (37.5%), missed medication (36.7%) and infection (35.8%). The most common presentation was dehydration (97.5%) with 49.2% of the patients having severe DKA while 22.5% had mild DKA. Urine and blood ketones for diagnosis of DKA were present in 46.4% and 100% of patients respectively. The median length of hospital stay was 6 days (IQR 5,7) with infection being a significant determinant (aOR 2.63). The number of days taken for DKA to resolve ranged from 1 to 5 days with a median period of 3 days (IQR 2,3). DKA in-hospital mortality was 9.2% with new onset DM being a significant determinant (uOR 5.19). Conclusion: Some of the identified DKA precipitants in the study are preventable. The impact of DKA in MTRH is notable given the significant hospital stay and mortality. Recommendation: We recommend implementation research studies that would develop and test different strategies to address the precipitants to prevent DKA. For the hospital to undertake an audit of current DKA management process with the aim of improving outcomes in terms of hospital stay and mortality.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

The author(s) received no specific funding for this work

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Moi University Instituitonal Research and Ethics Commitee. Approval garnted

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data has been availed in the manuscript

View the discussion thread.

Thank you for your interest in spreading the word about medRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Citation Manager Formats

• EndNote (tagged)
• EndNote 8 (xml)
• RefWorks Tagged
• Ref Manager
• Tweet Widget
• Facebook Like
• Google Plus One

Subject Area

• Endocrinology (including Diabetes Mellitus and Metabolic Disease)
• Addiction Medicine (314)
• Allergy and Immunology (617)
• Anesthesia (159)
• Cardiovascular Medicine (2268)
• Dentistry and Oral Medicine (279)
• Dermatology (201)
• Emergency Medicine (369)
• Endocrinology (including Diabetes Mellitus and Metabolic Disease) (797)
• Epidemiology (11558)
• Forensic Medicine (10)
• Gastroenterology (676)
• Genetic and Genomic Medicine (3557)
• Geriatric Medicine (336)
• Health Economics (614)
• Health Informatics (2292)
• Health Policy (912)
• Health Systems and Quality Improvement (859)
• Hematology (333)
• HIV/AIDS (745)
• Infectious Diseases (except HIV/AIDS) (13132)
• Intensive Care and Critical Care Medicine (755)
• Medical Education (358)
• Medical Ethics (100)
• Nephrology (388)
• Neurology (3339)
• Nursing (191)
• Nutrition (505)
• Obstetrics and Gynecology (650)
• Occupational and Environmental Health (643)
• Oncology (1750)
• Ophthalmology (524)
• Orthopedics (208)
• Otolaryngology (284)
• Pain Medicine (223)
• Palliative Medicine (66)
• Pathology (437)
• Pediatrics (999)
• Pharmacology and Therapeutics (419)
• Primary Care Research (402)
• Psychiatry and Clinical Psychology (3048)
• Public and Global Health (5979)
• Radiology and Imaging (1217)
• Rehabilitation Medicine and Physical Therapy (712)
• Respiratory Medicine (808)
• Rheumatology (367)
• Sexual and Reproductive Health (347)
• Sports Medicine (313)
• Surgery (385)
• Toxicology (50)
• Transplantation (170)
• Urology (142)