data presentation in a research

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

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

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

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

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How to Make a Successful Research Presentation

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

More is more

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

Less is more

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

Limit the scope of your presentation

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

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

Craft a compelling research narrative

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

• Introduction (exposition — rising action)

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

• Methods (rising action)

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

• Results (climax)

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

• Discussion (falling action)

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

• Conclusion (denouement)

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

Use your medium to enhance the narrative

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

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

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

— Alec Getraer, Natural Sciences Correspondent

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Presenting Research Data Effectively Through Tables and Figures

Presenting research data and key findings in an organized, visually attractive, and meaningful manner is a key part of a good research paper. This is particularly important in instances where complex data and information, which cannot be easily communicated through text alone, need to be presented engagingly. The best way to do this is through the use of tables and figures. They help to organize and summarize large amounts of data and present it in an easy-to-understand way.  

Tables are used to present numerical data, while figures are used to display non-numerical data, such as graphs, charts, and diagrams. There are different types of tables and figures, and choosing the appropriate format is essential to present the data effectively. This article provides some insights on how to present research data and findings using tables and figures.  

How to present research data in tables?

When complex data and statistical findings are too unwieldy or difficult to present either in text form or as figures, they can be presented through tables. Tables are best used where exact numerical values need to be analyzed and shared. It also aids in the comparison and contrast of various features or values among the different units. This allows swift and easy identification of patterns in the datasets. While presenting tables in a research paper, it is essential to incorporate certain core elements to ensure that readers are able to draw inferences and conclusions easily and quickly.  

• Title of the table :  The title should be concise and clear and communicate the purpose of the table. Tables must be referenced in the text through table numbers. Both the table number and the title are ideally mentioned just above the table. 
• Body of the table:  A crucial element in preparing the body of a table is to ensure uniformity in terms of units of measurement and the accurate use of decimal places. It is also important to format the table and ensure equal spacing between rows and columns.  
• Keep it simple and accurate:  It is important to ensure that only relevant information is presented in the table. One needs to be cautious not to populate tables with unnecessary information or design elements. Using plain fonts, in italics or bold, and the use of color or border styles help make the table visually appealing. Rows and columns must be labeled clearly and accurately to ensure that there is no ambiguity in analyzing the data presented. 

How to present research data in figures?

Figures are a powerful tool for visually presenting research data and key study findings. Figures are usually used to communicate trends or relationships and general patterns emerging from datasets. They are also used to present research data and complex information in a simpler form. Figures can take various forms like graphs, pie charts, scatter plots, line diagrams, drawings, maps, and photos. Early career researchers need to know how best to present figures in their research papers. The following are some core elements that should be incorporated.  

• Title:  Every figure must have a title that is clear and concise and must summarize the main point of the data being presented. It should be placed just below the figure. The numbering of the figures should be sequential and must correspond to the reference provided in the text. 
• Type of figure:  The type of figure to be used is usually dictated by the kind of information to be conveyed. Researchers need to decide which type of figure will enable readers to understand the information being shared easily. For example, scatter plots can be used to show relationships between two variables, pie charts can be used to illustrate relative proportions, and graphs can be used for the quantitative relationship between variables.  
• Use of Images:  When using figures, care should be taken to ensure that images are of a high resolution – sharp and clear. 
• Labeling:  Ensuring that all parts of the figures and the axes are labeled accurately is crucial if readers are to glean important details quickly. Use standard font sizes and styles. Experts also suggest the inclusion of scale bars in maps. 

Tips for Effectively Presenting Research Data through Tables and Figures

When presenting research data through tables or figures, it’s important to ensure that it is adding value to the text and not merely repeating values. This means taking care of certain vital aspects to ensure that the presentation is uniform, clear, and easy to read. Here are some tips to help you achieve that:

• Make sure that tables or figures add value to the text
• Ensure uniformity in numbering of tables, figures, and values both in the text and in the visual presentation
• Cite the source if tables and figures are used from a different source
• Use appropriate scales when creating tables and figures
• Use logarithmic scales if the data covers a wide range
• Use linear scales if the data is relatively small
• Check publication or style guide instructions of the target journal regarding the presentation of research data and findings, image resolution, presentation style, formatting, and so on
• Remember, tables and figures are only tools to convey information – using too many of them can overwhelm readers

In summary, presenting research data through tables and figures can be an effective way to convey information. However, it’s important to follow these tips to ensure that the presentation is clear and easy to read. By taking care of these vital aspects, researchers can effectively communicate their findings to their intended audience.

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The Ultimate Guide to Qualitative Research - Part 3: Presenting Qualitative Data

• Introduction

How do you present qualitative data?

Data visualization.

• Research paper writing
• Transparency and rigor in research
• How to publish a research paper

Table of contents

• Transparency and rigor

Navigate to other guide parts:

Part 1: The Basics or Part 2: Handling Qualitative Data

• Presenting qualitative data

In the end, presenting qualitative research findings is just as important a skill as mastery of qualitative research methods for the data collection and data analysis process . Simply uncovering insights is insufficient to the research process; presenting a qualitative analysis holds the challenge of persuading your audience of the value of your research. As a result, it's worth spending some time considering how best to report your research to facilitate its contribution to scientific knowledge.

When it comes to research, presenting data in a meaningful and accessible way is as important as gathering it. This is particularly true for qualitative research , where the richness and complexity of the data demand careful and thoughtful presentation. Poorly written research is taken less seriously and left undiscussed by the greater scholarly community; quality research reporting that persuades its audience stands a greater chance of being incorporated in discussions of scientific knowledge.

Qualitative data presentation differs fundamentally from that found in quantitative research. While quantitative data tend to be numerical and easily lend themselves to statistical analysis and graphical representation, qualitative data are often textual and unstructured, requiring an interpretive approach to bring out their inherent meanings. Regardless of the methodological approach , the ultimate goal of data presentation is to communicate research findings effectively to an audience so they can incorporate the generated knowledge into their research inquiry.

As the section on research rigor will suggest, an effective presentation of your research depends on a thorough scientific process that organizes raw data into a structure that allows for a thorough analysis for scientific understanding.

Preparing the data

The first step in presenting qualitative data is preparing the data. This preparation process often begins with cleaning and organizing the data. Cleaning involves checking the data for accuracy and completeness, removing any irrelevant information, and making corrections as needed. Organizing the data often entails arranging the data into categories or groups that make sense for your research framework.

Coding the data

Once the data are cleaned and organized, the next step is coding , a crucial part of qualitative data analysis. Coding involves assigning labels to segments of the data to summarize or categorize them. This process helps to identify patterns and themes in the data, laying the groundwork for subsequent data interpretation and presentation. Qualitative research often involves multiple iterations of coding, creating new and meaningful codes while discarding unnecessary ones , to generate a rich structure through which data analysis can occur.

Uncovering insights

As you navigate through these initial steps, keep in mind the broader aim of qualitative research, which is to provide rich, detailed, and nuanced understandings of people's experiences, behaviors, and social realities. These guiding principles will help to ensure that your data presentation is not only accurate and comprehensive but also meaningful and impactful.

While this process might seem intimidating at first, it's an essential part of any qualitative research project. It's also a skill that can be learned and refined over time, so don't be discouraged if you find it challenging at first. Remember, the goal of presenting qualitative data is to make your research findings accessible and understandable to others. This requires careful preparation, a clear understanding of your data, and a commitment to presenting your findings in a way that respects and honors the complexity of the phenomena you're studying.

In the following sections, we'll delve deeper into how to create a comprehensive narrative from your data, the visualization of qualitative data , and the writing and publication processes . Let's briefly excerpt some of the content in the articles in this part of the guide.

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How often do you read a research article and skip straight to the tables and figures? That's because data visualizations representing qualitative and quantitative data have the power to make large and complex research projects with thousands of data points comprehensible when authors present data to research audiences. Researchers create visual representations to help summarize the data generated from their study and make clear the pathways for actionable insights.

In everyday situations, a picture is always worth a thousand words. Illustrations, figures, and charts convey messages that words alone cannot. In research, data visualization can help explain scientific knowledge, evidence for data insights, and key performance indicators in an orderly manner based on data that is otherwise unstructured.

For all of the various data formats available to researchers, a significant portion of qualitative and social science research is still text-based. Essays, reports, and research articles still rely on writing practices aimed at repackaging research in prose form. This can create the impression that simply writing more will persuade research audiences. Instead, framing research in terms that are easy for your target readers to understand makes it easier for your research to become published in peer-reviewed scholarly journals or find engagement at scholarly conferences. Even in market or professional settings, data visualization is an essential concept when you need to convince others about the insights of your research and the recommendations you make based on the data.

Importance of data visualization

Data visualization is important because it makes it easy for your research audience to understand your data sets and your findings. Also, data visualization helps you organize your data more efficiently. As the explanation of ATLAS.ti's tools will illustrate in this section, data visualization might point you to research inquiries that you might not even be aware of, helping you get the most out of your data. Strictly speaking, the primary role of data visualization is to make the analysis of your data , if not the data itself, clear. Especially in social science research, data visualization makes it easy to see how data scientists collect and analyze data.

Prerequisites for generating data visualizations

Data visualization is effective in explaining research to others only if the researcher or data scientist can make sense of the data in front of them. Traditional research with unstructured data usually calls for coding the data with short, descriptive codes that can be analyzed later, whether statistically or thematically. These codes form the basic data points of a meaningful qualitative analysis. They represent the structure of qualitative data sets, without which a scientific visualization with research rigor would be extremely difficult to achieve. In most respects, data visualization of a qualitative research project requires coding the entire data set so that the codes adequately represent the collected data.

A successfully crafted research study culminates in the writing of the research paper . While a pilot study or preliminary research might guide the research design , a full research study leads to discussion that highlights avenues for further research. As such, the importance of the research paper cannot be overestimated in the overall generation of scientific knowledge.

The physical and natural sciences tend to have a clinical structure for a research paper that mirrors the scientific method: outline the background research, explain the materials and methods of the study, outline the research findings generated from data analysis, and discuss the implications. Qualitative research tends to preserve much of this structure, but there are notable and numerous variations from a traditional research paper that it's worth emphasizing the flexibility in the social sciences with respect to the writing process.

Requirements for research writing

While there aren't any hard and fast rules regarding what belongs in a qualitative research paper , readers expect to find a number of pieces of relevant information in a rigorously-written report. The best way to know what belongs in a full research paper is to look at articles in your target journal or articles that share a particular topic similar to yours and examine how successfully published papers are written.

It's important to emphasize the more mundane but equally important concerns of proofreading and formatting guidelines commonly found when you write a research paper. Research publication shouldn't strictly be a test of one's writing skills, but acknowledging the importance of convincing peer reviewers of the credibility of your research means accepting the responsibility of preparing your research manuscript to commonly accepted standards in research.

As a result, seemingly insignificant things such as spelling mistakes, page numbers, and proper grammar can make a difference with a particularly strict reviewer. Even when you expect to develop a paper through reviewer comments and peer feedback, your manuscript should be as close to a polished final draft as you can make it prior to submission.

Qualitative researchers face particular challenges in convincing their target audience of the value and credibility of their subsequent analysis. Numbers and quantifiable concepts in quantitative studies are relatively easier to understand than their counterparts associated with qualitative methods . Think about how easy it is to make conclusions about the value of items at a store based on their prices, then imagine trying to compare those items based on their design, function, and effectiveness.

Qualitative research involves and requires these sorts of discussions. The goal of qualitative data analysis is to allow a qualitative researcher and their audience to make such determinations, but before the audience can accept these determinations, the process of conducting research that produces the qualitative analysis must first be seen as trustworthy. As a result, it is on the researcher to persuade their audience that their data collection process and subsequent analysis is rigorous.

Qualitative rigor refers to the meticulousness, consistency, and transparency of the research. It is the application of systematic, disciplined, and stringent methods to ensure the credibility, dependability, confirmability, and transferability of research findings. In qualitative inquiry, these attributes ensure the research accurately reflects the phenomenon it is intended to represent, that its findings can be understood or used by others, and that its processes and results are open to scrutiny and validation.

Transparency

It is easier to believe the information presented to you if there is a rigorous analysis process behind that information, and if that process is explicitly detailed. The same is true for qualitative research results, making transparency a key element in qualitative research methodologies. Transparency is a fundamental aspect of rigor in qualitative research. It involves the clear, detailed, and explicit documentation of all stages of the research process. This allows other researchers to understand, evaluate, replicate, and build upon the study. Transparency in qualitative research is essential for maintaining rigor, trustworthiness, and ethical integrity. By being transparent, researchers allow their work to be scrutinized, critiqued, and improved upon, contributing to the ongoing development and refinement of knowledge in their field.

Research papers are only as useful as their audience in the scientific community is wide. To reach that audience, a paper needs to pass the peer review process of an academic journal. However, the idea of having research published in peer-reviewed journals may seem daunting to newer researchers, so it's important to provide a guide on how an academic journal looks at your research paper as well as how to determine what is the right journal for your research.

In simple terms, a research article is good if it is accepted as credible and rigorous by the scientific community. A study that isn't seen as a valid contribution to scientific knowledge shouldn't be published; ultimately, it is up to peers within the field in which the study is being considered to determine the study's value. In established academic research, this determination is manifest in the peer review process. Journal editors at a peer-reviewed journal assign papers to reviewers who will determine the credibility of the research. A peer-reviewed article that completed this process and is published in a reputable journal can be seen as credible with novel research that can make a profound contribution to scientific knowledge.

The process of research publication

The process has been codified and standardized within the scholarly community to include three main stages. These stages include the initial submission stage where the editor reviews the relevance of the paper, the review stage where experts in your field offer feedback, and, if reviewers approve your paper, the copyediting stage where you work with the journal to prepare the paper for inclusion in their journal.

Publishing a research paper may seem like an opaque process where those involved with academic journals make arbitrary decisions about the worthiness of research manuscripts. In reality, reputable publications assign a rubric or a set of guidelines that reviewers need to keep in mind when they review a submission. These guidelines will most likely differ depending on the journal, but they fall into a number of typical categories that are applicable regardless of the research area or the type of methods employed in a research study, including the strength of the literature review , rigor in research methodology , and novelty of findings.

Choosing the right journal isn't simply a matter of which journal is the most famous or has the broadest reach. Many universities keep lists of prominent journals where graduate students and faculty members should publish a research paper , but oftentimes this list is determined by a journal's impact factor and their inclusion in major academic databases.

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This section is part of an entire guide. Use this table of contents to jump to any page in the guide.

Part 1: The Basics

• What is qualitative data?
• 10 examples of qualitative data
• Qualitative vs. quantitative research
• What is mixed methods research?
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research questions
• Conceptual framework
• Conceptual vs. theoretical framework
• Focus groups
• Observational research
• Case studies
• Survey research
• What is ethnographic research?
• Confidentiality and privacy in research
• Bias in research
• Power dynamics in research
• Reflexivity

Part 2: Handling Qualitative Data

• Research transcripts
• Field notes in research
• Research memos
• Survey data
• Images, audio, and video in qualitative research
• Coding qualitative data
• Coding frame
• Auto-coding and smart coding
• Organizing codes
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• What is inductive reasoning?
• Inductive vs. deductive reasoning
• What is data interpretation?
• Qualitative analysis software

Part 3: Presenting Qualitative Data

• Data visualization - What is it and why is it important?

Top tips for presenting your data according to research

What do we really know about how to present complex data in ways that are easy to understand and have impacts that might help address complex issues such as climate change? Dr Lucy Richardson explores some of the useful tips provided by data visualisation and communication research that can help you effectively communicate complex information.

This article is part of the ISC’s Transform21 series, which features resources from our network of scientists and change-makers to help inform the urgent transformations needed to achieve climate and biodiversity goals.

Over the last year or so, many people across the world have become used to seeing charts and graphs with COVID-19 statistics in their news feeds, but all charts are not created equal when it comes to effectively communicating a key message.

Researchers have been examining how different aspects of data presentation influence audiences for many years. They have looked at the issue from diverse angles such as which components are viewed in what order and why, and whether text, graphs or maps are more engaging and easily understood. These diverse research questions have been addressed using a wide variety of methods ranging from tracking audience eye movements to surveys and social media polls. From this collection of research, we have gained valuable insights that can help make data visuals more effective communication tools.

A useful framework to think about when designing data visualisations follows the broad process of audience interaction with the presented information: (a) first the audience perceives the information (b) then they think about the information, and (c) then some sort of change or impact occurs due to those thoughts.

Perceiving the information (Perception)

Assuming that your data visualisation is presented to your target audience in a time and place where they are likely to see it, your audience needs to be able to perceive and differentiate each of the key components of your visualisation in order to discern its meaning.

Perception tends to happen in sequence, following a visual hierarchy of attention based on the following characteristics of any object (including maps and graphs): size, colour, contrast, alignment, repetition, proximity, whitespace, and texture and styles. Within each of these elements are further sub-hierarchies. For example, people tend to notice large elements before smaller ones, and bright colours before muted ones. Similarly, dramatic contrasting components are noticed more than those with less contrast.

The effect of these hierarchical elements can be impacted by perception challenges and should be carefully considered to ensure that they promote your message rather than confusing or distracting your audience. There are a range of different perception challenges that can impact on the effectiveness of data visualisations, but did you know there are actually seven different forms of colour blindness ? You can even run your data visualisation through a colour blindness simulator to see how it might be viewed by someone with these challenges.

Thinking about the information (Cognition)

When your audience thinks about and derives meaning from information they perceive, this is known as cognitive processing. It includes thinking, knowing, remembering, judging, and problem-solving; any number of which may be used when processing information associated with visualised data.

Some things you can do to help encourage the desired interpretation of meaning from your data visualisation include providing chart titles that are the main message rather than just a description of the content. A title such as ‘Higher amounts of green vegetation in cities is associated with lower summer temperatures’ is much more effective at guiding meaning-making than titling the same chart as ‘Green vegetation and temperature in Australian cities’.

Some topic areas that may require data visualisations can also have underlying psycho-social (psychological, social and/or political) factors that should be considered. This is particularly the case for climate change, a heavily politicised issue that is quite polarising in some countries. When presenting data relating to climate change, some valuable tips include:

• Avoid catastrophic messaging that can cause people to shut down as a coping response to their fear.
• Include solutions-based information can help counteract fear by promoting a sense that climate change can be addressed.
• Provide locally relevant information where possible, as this will resonate more strongly. People are naturally most interested in what happens in their local area.
• Where possible, consider if there are other ways to cover the issue without mentioning ‘climate change’ if communicating to audiences who may not accept current scientific evidence of its existence and urgency. This is easier for messages relating to adapting to changes in climate than mitigation, as there are often diverse benefits beyond climate change that can be used to frame adaptation information.

It’s also important to recognize that people are generally more likely to remember meaning than detail. This means that people are more likely to remember a trend—such as it’s getting ‘worse’ or ‘better’, ‘increasing’ or ‘decreasing’—but may not remember the specific amount or rate of that increase or decrease.

Changes effected (Impact)

There are a range of possible impacts that might arise from audiences viewing your data visualisation. These could be changes in thought (for example, awareness, understanding, attitudes or concern), or changes in behaviour (for example, information seeking, discussion with others, or even adoption of climate-friendly behaviours). The likelihood of change being effected due to your data visualisation will be enhanced by ensuring your messages are clear and relevant, where clarity will come from effectively addressing perception and cognition considerations and relevance will come from appropriate message framing and consideration of psycho-social factors. Knowing the kind of change you want to achieve will be critical in determining how best to integrate these various factors into your work.

Alternative formats

While most people wishing to present complex scientific data tend to think of charts, graphs, maps, and infographics, it is also possible to present information for perception by other senses such as through sound. Some researchers have been testing data sonification as an alternative to visual data representation. Sonification takes each data point and applies a mix of sound elements that can allow trends to be distinguished—for example, pitch, volume, and choice of instrument—to provide an audio representation of the information. NASA has done this so that people can ‘listen’ to the Milky Way Galaxy , and researchers at the Monash University Climate Change Communication Research Hub have sonified cyclone Debbie ’s movements around Australia in 2017.

A free best practice guide has been developed based on a review of data visualisation research. Hopefully, it will help you decide how you can best present your data for effective perception, cognition and impact. You can access the Best practice data visualisation: Guidelines and case study on the Monash Climate Change Communication Research Hub website .

Lucy Richardson

Dr Lucy Richardson is based at the Monash Climate Change Communication Research Hub, Monash University, on the lands of the Kulin Nations, Melbourne, Australia, and a member of the  Commonwealth Futures Climate Research Cohort  established by The Association of Commonwealth Universities and the British Council to support 26 rising-star researchers to bring local knowledge to a global stage in the lead-up to COP26.

The header image was created by NASA’s Scientific Visualization Studio to support a series of talks from NASA scientists for COP26. It is a still from a video that shows the atmosphere in three dimensions and highlights the accumulation of CO 2  during a single calendar year. You can watch the visualisation and find out more about the data on which it’s based here .

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

How to Create and Deliver a Research Presentation

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

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

Table of Contents

What is a Research Presentation

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

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

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

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

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

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

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

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

1.  Title Page

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

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

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

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

2. Executive Summary Slide

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

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

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

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

3. Introduction/ Project Description Slides

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

You want to answer these fundamental questions:

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

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

4. Data Presentation and Analyses Slides

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

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

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

5. Conclusions

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

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

6. Recommendations

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

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

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

7. Questions Slide

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

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

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

1. Focus on the Most Important Findings

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

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

2. Do Not Read Word-per-word

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

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

3. Don’t Go Without Handouts

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

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

4. Actively Listen

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

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

5. Be Confident

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

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

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

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

In-text citation

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

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

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

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

Image citation

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

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

Work cited page

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

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

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

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

1. Research Project Presentation PowerPoint Template

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

Use This Template

2. Research Presentation Scientific Method Diagram PowerPoint Template

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

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

3. Thesis Research Presentation PowerPoint Template

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

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

4. Animated Research Cards PowerPoint Template

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

5. Research Presentation Slide Deck for PowerPoint

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

By accessing this template, you get the following slides:

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

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

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

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

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Skills for Learning : Research Skills

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

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

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

Quantitative data analysis

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

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

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

Tips for working with statistical data

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

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

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

Statistical software packages

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

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

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

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

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

Qualitative data analysis

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

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

        (Miles and Huberman, 1994.)

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

Presenting information

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

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

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

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

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

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

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

Primary, secondary and tertiary sources

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

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

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

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

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

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

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

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

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

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

Artificial intelligence tools

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

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

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

2. Column chart

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

3. Pie Charts

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

4. Bar charts

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

5. Histograms

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

6. Box plots

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

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

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

Importance of Data Presentation

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

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

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

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

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

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

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

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

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

Leah Nguyen • 05 Apr 2024 • 11 min read

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

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

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

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

Table of Contents

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

#2 – Text

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

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

More tips with ahaslides.

• Marketing Presentation
• Survey Result Presentation
• Types of Presentation

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Get any of the above examples as templates. Sign up for free and take what you want from the template library!

What are Methods of Data Presentation?

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

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

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

Good data presentation helps…

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

Methods of Data Presentation and Examples

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

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

#1 – Tabular 

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

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

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

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

(Source: CustomerThermometer )

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

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

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

Bonus example: A literal ‘pie’ chart! 🥧

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

They can be as simple as this:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

5 Data Presentation Mistakes to Avoid

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

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

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

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

#2 – Use the wrong type of chart

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

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

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

#3 – Make it 3D

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

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

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

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

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

#5 – Bombard the audience with too much information

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

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

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

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

For example:

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

What is chart presentation?

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

When can I use charts for presentation?

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

Why should use charts for presentation?

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

What are the 4 graphical methods of presenting data?

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

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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10 Data Presentation Examples For Strategic Communication

By Krystle Wong , Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

Future of evaluation: Charting a path in a changing development landscape

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Join us for a series of virtual conversations from April 8 to 10, 2024, on the future direction of the independent evaluation practice, the questions it will have to answer, and the impact of new technologies for ever greater data gathering and analysis.

To tackle these issues, the Future of Evaluation will bring together practitioners and scholars from across the evaluation field, together with users of evaluative evidence from both development and government circles.

The event will also feature the announcement and presentation of the winner of the Young and Emerging Evaluators essay competition on culturally responsive evaluation.

Hosted by the Independent Evaluation Group and the Global Evaluation Initiative, this will be the culminating event of the IEG@50 celebrations.

Please save the dates!

Monday, April 8, 2024

9:00 – 10:00 AM ET |  REGISTER Session 1: The Role of Evaluation in a Changing Global Context How can evaluation remain relevant and provide policymakers with reliable evidence on global issues such as climate change, fragility, and pandemics, among others?

• Welcome:  Sabine Bernabè , Vice President and Director-General, Evaluation, Independent Evaluation Group.
• Opening Remarks:  Raj Kumar , Founding President and Editor-in-Chief, Devex.
• Speakers:  Andrea Cook , Executive Director, Sustainable Development Goals System-Wide Evaluation Office, United Nations;  Juha Ilari Uitto , former Director, Independent Evaluation Office, Global Environment Facility;  Patricia Rogers , Founder, Better Evaluation, former Professor of Public Sector Evaluation, Royal Melbourne Institute of Technology.
• Moderator:  Marie Gaarder , Executive Director, 3ie.

Tuesday, April 9, 2024

9:00 AM – 10:00 AM ET |  REGISTER Session 2: How is evaluation evolving as a practice? How is evaluation evolving as a practice between its professionalization with global standards and its use in different cultural contexts?

• Welcome and announcement of winner of Young and Emerging Evaluators competition:  Sabine Bernabè , Vice President and Director-General, Evaluation, Independent Evaluation Group.
• Opening Remarks: Culturally Responsive Evaluation (presentation of the winning essay of the Young and Emerging Evaluator competition.)
• Speakers:  Elliot Stern , Editor, Evaluation - international journal of theory, research and practice, Emeritus Professor of Evaluation Research, Lancaster University;  Asela Kalugampitiya , former President, Asia Pacific Evaluation Association, Visiting Lecturer, University of Saarland and University of Sri Jayewardenepura;  Josephine Watera , Assistant Director, Research Services and former Head of Monitoring and Evaluation Division, Parliament of Uganda.
• Moderator:  Jozef Vaessen , Adviser, Independent Evaluation Group.

Wednesday, April 10, 2024

9:00 AM – 10:00 AM ET |  REGISTER Session 3: The Institutionalization of Evaluation What is the changing role of international organizations in fostering the institutionalization of evaluation across the globe?

• Opening Remarks:  Ian Goldman , President, International Evaluation Academy, Monitoring and Evaluation Advisor, JET-IP Programme Management Unit, Presidency of South Africa.
• Speakers:  Emmanuel Jimenez , Director General, Independent Evaluation Department, Asian Development Bank;  Geeta Batra , Director, Independent Evaluation Office, Global Environment Facility;  Howard White , Director, Evaluation and Evidence Synthesis, Global Development Network.
• Moderator:  Dugan Fraser , Program Manager, Global Evaluation Initiative.

10:15 – 11:15 AM ET |  REGISTER Session 4: The Impact of New Data and Technology on Evaluation A range of new technologies, from geospatial analysis to mining social media, offers new ways of gathering data. Advances in machine learning have increased the amount of data that can be processed for evaluations. What opportunities does this present for evaluators in an increasingly complex development landscape?

• Opening Remarks:  Veronica Olazabal , Chief Impact and Evaluation Officer, BHP Foundation, Lecturer, School of International and Public Affairs, Columbia University.
• Speakers:  Emmanuel Letouzé , Director, Data-Pop Alliance, Founder and Executive Director, Open Algorithms;  Nagaraja Rao Harshadeep , Disruptive Technology Global Lead, Sustainable Development Practice Group, World Bank;  Estelle Raimondo , Head, Methods, Independent Evaluation Group.
• Moderator:  Deon Filmer , Director, Research Group, World Bank.
• Closing Remarks:  Sabine Bernabè , Vice President and Director-General, Evaluation, Independent Evaluation Group.

• WHEN:  8-10 April, 2024
• TIME:  9am to 11:15am
• WHERE:  Online

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Case report article, case report: iatrogenic tattoos caused by skin marking pen in a postoperative patient.

• 1 Department of Burn and Plastic Surgery, West China Hospital, Sichuan University, Chengdu, China
• 2 Department of Dermatology, West China Fourth Hospital, Sichuan University, Chengdu, China

In this report, a female patient suffering from pigment retention caused by a skin marking pen was elucidated. The patient underwent blepharoplasty 6 months ago and presented with blue-black linear marks at the upper eyelid incision 2 weeks after surgery. Under dermoscopy, scattered pigments were observed to accumulate in the epidermis of the upper eyelid. The patient was diagnosed with iatrogenic tattoo by a surgical marking pen. We chose surgical excision of the skin with the pigmentation. Previous studies have established that the risk of bacterial contamination, contact dermatitis, and allergies may increase with the surgical marking pens, while pigment retention has not yet been mentioned yet. Here, we present a case with a pigment retention in the incision. The selection of the surgical labelling methods and the management of the pigmentation were also addressed. According to our clinical findings, the risk of pigment retention by marking pens needs to be mentioned in the patient’s informed consent. Therefore, the practitioner should ensure that the ink is cleaned by the end of each invasive procedure.

Introduction

In dermatosurgery, oil-based surgical marking pens are involved in preoperative marking to define the surgical area. Optimally, the marker should withstand disinfectants and surgery wash without leaving a permanent stain on the skin. Increasing persistence and visibility, however, also increases the risk of colorant residue. Previous complications related to marking pens were mainly bacterial contamination, contact dermatitis, and allergies. No relevant literature has yet been reported on pigment residue. In our clinical practice, however, we have confirmed the possibility of oil-based markers leading to an iatrogenic tattoo at the time of surgery or injection.

Case presentation

A 47-year-old female patient presented to the outpatient clinic with blue-black linear marks on the upper eyelid ( Figure 1A ). She had a blepharoplasty surgery 6 months prior. Two weeks after surgery, she found a blue-black linear mark on the bilateral upper eyelid. Conservative treatment was taken to monitor the changes in the marks. One month later, part of the pigmentation had faded, leaving some scattered pigments in linear arrangements around the incision.

Figure 1 . (A) Photograph taken 6 months after surgery showed an inhomogeneous blue-black pigment in the double eyelid incision. (B) The evaluation with a dermoscopy showed the exogenous blue-black pigments in the skin. (C) Histology of the upper eyelid skin tattooed with blue-black ink (H&E stain, magnification 20×). (D) Pigment particles are presented in the basal layer of the squamous epithelium (arrows), visible at 40× magnification. (E) Highly refractive brown pigment granules (triangle arrows) and chromophages (asterism) could be seen scattered in the superficial dermis and the perivascular area, visible at 40× magnification.

On physical examination, the marks were approximately 7 mm from the supraciliary line hidden in the upper eyelid crease. The patient had no dermatitis over the pigment or keloid hyperplasia, hyperpigmentation, or depigmented changes in the surgical site involvement. On palpation, no foreign body was detected in the upper eyelid. Under dermoscopy, scattered pigments were observed to accumulate in the skin ( Figure 1B ).

The diagnosis of exogenous pigment residue was suspected, given her surgical history, physical examination, and dermatology examination. We speculated that the origin of the pigmentation was a surgical marker used in preoperative preparation. Our teams chose surgical removal of bilateral upper eyelid marks. The postoperative histological examination of the skin tissue confirmed that brown pigments accumulated in the squamous epithelium and the connective tissue ( Figure 1C ).

In preoperative preparation, marking pens are used to define the surgical field, design surgical incisions, mark landmarks, and others. Optimally, the markers should withstand disinfectants and surgical scrubbing without leaving a permanent stain on the skin. For visibility, the marker ink contains water-insoluble pigments that the body cannot degrade. Which means that increasing persistence and visibility also increases the risk of colorant residue.

Preoperative skin marking is an essential safety practice to prevent serious reportable events (SPE), as defined by the National Quality Forum (NQF) ( 1 ). However, unintended retention of a foreign object, including marking pen pigments in a patient after surgery or other invasive procedures, is also classified as an SRE. Previous studies have established that the risk of bacterial contamination, contact dermatitis, and allergies may increase with surgical marking pens ( 2 – 5 ). The terminology in the literature needs to be more consistent, and we refer to this complication as iatrogenic tattoo by marking pen.

The requirements for a skin marking pen are visibility and durability on the skin. Although oil-based pens are common, convenient, and inexpensive, the ink may contain carcinogenic substances such as crystal violet and xylene ( 6 ). Since conventional skin markers contain harmful substances, organic colorants have become increasingly used in skin marking. Studies on water-based ink have shown superior durability compared to the conventional oil-based pen ( 5 ). Moreover, Goto et al. ( 7 ) reported an organic-based marker using Inkbox ® (Inkbox, Toronto, Canada) with a duration of 16 days compared to 4 days for an oil-based pen. However, although the organic-based markers do not contain carcinogenic substances, some patients have experienced severe contact allergies or been provoked to granulomatous reactions ( 5 , 8 ). The labeling approach differs with respect to skin types and the purpose of the labeling. For areas with hair growth, marking pens are more suitable than stickers. Similarly, marking pens are more suitable for marking the surgical field and incisions. Therefore, a balance should be struck between safety and effectiveness when choosing a proper marker.

Our patient’s presentation was colorant retention in the epidermis layer of the upper eyelids. In such patients with a sense of cosmetic issues, removing colorants needs to be addressed. The techniques listed in the published work include dermabrasion, cryotherapy, laser, and surgical excision ( 3 ). Dermabrasion and cryotherapy completely remove the epidermis to induce re-epithelialization, which is technique-dependent and time-consuming, with potential complications, including pigment changes, hypertrophic scarring, and infection. The introduction of newer therapies, such as lasers, allowed for precise determination of percent surface area and clinical recovery time and efficacy, providing more significant clinical outcomes. However, such procedures can hardly achieve removal effectiveness with a single treatment. Moreover, the safety of using lasers in the periorbital area is yet to be discussed. As aesthetics is the primary endpoint, surgical excision seems to be the preferable option for this patient.

In conclusion, according to this case report the marking pen may lead to iatrogenic tattoos. Physicians should inform patients of the risk of pigment residue before invasive procedures and avoid any pigment retention by the end of the procedure.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving human participant was reviewed and approved by the Medical Ethics Committee of West China Hospital of Sichuan University. All procedures performed in this study involving the human participant were in accordance with the 1964 Helsinki declaration. Written informed consent to participate in this study was provided by the participant. Written informed consent was obtained from the participant for the publication of any potentially identifiable images or data included in this article.

Author contributions

HZ: Visualization, Writing – original draft, Writing – review & editing. XW: Conceptualization, Writing – original draft, Writing – review & editing. YY: Data curation, Writing – original draft, Writing – review & editing. CC: Supervision, Validation, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Namburi, N, and Lee, LS. National Quality Forum In: StatPearls . Treasure Island, FL: StatPearls Publishing (2023).

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2. Pesapane, F, Nazzaro, G, Gianotti, R, and Coggi, A. A short history of tattoo. JAMA Dermatol . (2014) 150:145. doi: 10.1001/jamadermatol.2013.8860

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3. Laux, P, Tralau, T, Tentschert, J, Blume, A, Dahouk, SA, Bäumler, W, et al. A medical-toxicological view of tattooing. Lancet . (2016) 387:395–402. doi: 10.1016/S0140-6736(15)60215-X

4. Huff, ML, Jacobs, AM, Huang, E, and Miles, MG. Surgical marking pen contamination: writing a postoperative infection into your preoperative plan. Cureus . (2023) 15:e40007. doi: 10.7759/cureus.40007

5. Nakayama, S, Hirose, M, Kaneshige, S, Nakamura, K, Matsuo, Y, and Monzen, H. Development and evaluation of a novel water-based pigment marker for radiation therapy skin marking. Radiol Phys Technol . (2023) 16:543–51. doi: 10.1007/s12194-023-00743-0

6. Canada, H Important safety information. Available at: https://recalls-rappels.canada.ca/en/alert-recall/health-canada-warns-canadians-potential-cancer-risk-associated-gentian-violet . (Accessed June 12, 2019).

7. Goto, M, Oshiro, Y, Tamaki, Y, Ishida, T, Kato, Y, Shinoda, K, et al. A novel method for skin marking in radiotherapy: first clinical use of temporary organic tattoo seal. J Radiat Res . (2022) 63:314–8. doi: 10.1093/jrr/rrab126

8. Bircher, AJ, Sigg, R, Scherer Hofmeier, K, Schlegel, U, and Hauri, U. Allergic contact dermatitis caused by a new temporary blue-black tattoo dye—sensitization to genipin from jagua ( Genipa americana L.) fruit extract. Contact Derm . (2017) 77:374–8. doi: 10.1111/cod.12844

Keywords: surgical complication, pigmentation and colour, dermatosurgery, tattoo, iatrogenic accident

Citation: Zhao H, Wu X, Yu Y and Cao C (2024) Case report: Iatrogenic tattoos caused by skin marking pen in a postoperative patient. Front. Med . 11:1387773. doi: 10.3389/fmed.2024.1387773

Received: 18 February 2024; Accepted: 25 March 2024; Published: 11 April 2024.

Reviewed by:

Copyright © 2024 Zhao, Wu, Yu and Cao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chang Cao, [email protected]

† These authors have contributed equally to this work and share first authorship

‡ Guarantor of the clinical content of this submission

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Zymeworks Presents New Data from Multiple Preclinical Development Programs at 2024 American Association for Cancer Research Annual Meeting

VANCOUVER, British Columbia , April 08, 2024 (GLOBE NEWSWIRE) -- Zymeworks Inc. (Nasdaq: ZYME), a clinical-stage biotechnology company developing a diverse pipeline of novel, multifunctional biotherapeutics to improve the standard of care for difficult-to-treat diseases, today announced five presentations including new data from its preclinical development-stage programs at the 2024 American Association for Cancer Research (AACR) Annual Meeting being held in San Diego, California April 5-10, 2024 .

“Our team is looking forward to share new data from our strong portfolio of multispecific antibody therapeutics and antibody-drug conjugates including ZW191, an anticipated 2024 IND candidate,” said Paul Moore , Ph.D., Chief Scientific Officer at Zymeworks . “These data provide important new insights highlighting the potential of our candidates to represent major advances in the treatment of cancer while also demonstrating the potential to develop a new generation of novel antibody-drug conjugates and multispecific antibody therapeutics.”

Presentation Highlights

ZW191 - a FRα-targeting antibody-drug conjugate with strong preclinical activity across multiple FRα-expressing indications Abstract: 1862Session Category: Experimental and Molecular TherapeuticsSession Title: Antibody-Based Technologies and New Inhibitors

ZW191 is a FRα-targeting antibody-drug conjugate (ADC) differentiated by its novel antibody and novel topoisomerase I inhibitor payload. The compelling preclinical activity profile supports ZW191 development across multiple tumor types, including FRα-high/mid/low ovarian cancers and other FRα-expressing indications, including non-small cell lung cancer, endometrial cancer, and triple-negative breast cancer. An investigational new drug (IND) submission or foreign equivalent is planned for 2024.

Key Results:

• Superior internalization, payload delivery, and spheroid penetration to other FRα-targeted multi-specific antibodies.
• Bystander active payload drives activity in settings with heterogeneous FRα expression.
• Well-tolerated, with a highest non-severely toxic dose of 60 mg/kg in non-human primates.

Screening novel format antibodies to design bispecific ADCs that address target heterogeneity Abstract: 2052Session Category: Experimental and Molecular TherapeuticsSession Title: New Technologies

Inter-patient and intra-tumoral target expression heterogeneity is an obstacle in the design of ADCs that target a single tumor associated antigen (TAA). While bystander active payloads mitigate intra-tumoral target heterogeneity, bispecific ADCs that target two different TAAs independently provide an additional approach to overcome limitations associated with the expression profile of any single target antigen. Critical to this bispecific ADC approach however is identification of the optimal bispecific antibody format suited for payload delivery to two independent tumor antigens. To address this challenge a novel design and screening approach of bispecific ADCs was employed, using FRα and NaPi2b as an exemplary target pair, independently expressed across various cancer types.

• Leveraging Azymetric™, bioconjugation, analytical mass spectrometry, and high throughput functional screening, a workflow for the rapid generation and characterization of bispecific ADCs was established to identify optimal format, paratope, and valency.
• A library of 48 bispecific ADC molecules co-targeting FRα and NaPi2b was rapidly generated covering multiple paratopes, antibody formats, and valency bins (1+1, 2+1, 2+2). Functional screening of the library across multiple cancer cell lines expressing FRα and/or NaPi2b revealed ranges in binding, internalization, and cytotoxicity that were dependent on epitope, valency, and format geometry.

Development of three-dimensional cancer cell line spheroid models for the in vitro functional characterization of cytotoxic antibody-drug conjugates Abstract: 3127Session Category: Experimental and Molecular TherapeuticsSession Title: Antibody-Drug Conjugates

Antibody-drug conjugates are an effective class of cancer therapeutics comprised of a linker-payload conjugated to a monoclonal antibody targeting a TAA, to enable the delivery of the cytotoxic payload to cancer cells. Current standard in vitro monolayer models do not sufficiently reflect in vivo tumor tissue complexity, particularly in consideration of the interaction between protein-based therapeutics such as antibodies in a three-dimensional (3D) environment. To address this, methodology to yield in vitro 3D spheroid models from cancer cell lines in a rapid, robust, and uniform manner was developed. Subsequently methods were integrated to evaluate the tissue penetration capability and cytotoxic activity of structurally distinct antibodies or ADCs bearing various payload classes targeting multiple TAAs.

• A readily implementable method for the rapid generation of cancer cell line spheroids was established and applied to over 50 distinct immortalized cancer cell lines derived from more than 10 tissue types, demonstrating varying morphological features with a success rate of >95%.
• In vitro assays were developed to evaluate the spheroid penetration capability and 3D cytotoxic activity of multispecific antibodies and ADCs, enabling the interrogation of various antibody formats and payload classes.
• These 3D models and assays serve as valuable functional tools that provide improved translation between in vitro and in vivo activity, supporting the characterization of therapeutic ADC candidates and their pipeline advancement.

TriTCE Co-Stim: A next generation trispecific T cell engager platform with integrated CD28 costimulation, engineered to improve responses in the treatment of solid tumors  Abstract: 6719Session Category: ImmunologySession Title: Targeted ICEs

Low T cell infiltration and T cell anergy are challenges associated with the treatment of solid tumors with conventional CD3-engaging bispecific T cell engagers (TCEs)1. By optimizing “Signal 1” (CD3) and “Signal 2” (CD28) within the context of a single molecule, co-stimulatory trispecific TCEs (TriTCE Co-Stim) have the potential to increase therapeutic responses beyond that achievable by conventional CD3-based TCEs by stimulating T cell proliferation in patients with poorly infiltrated tumors and providing more durable anti-tumor control by enhancing T cell activation.

• Relative to comparator bispecific TCEs, the lead CLDN18.2 TriTCE Co-Stim molecule mediates enhanced T cell-mediated killing of tumor cells at low E:T ratios, exhibits sustained T cell mediated activity in serial challenge assays and supports superior antitumor activity in humanized models of gastric cancer.
• TriTCE Co-stim design facilitates obligate cis T cell binding and activation of CD28 that requires co-engagement of CD3 with no detectable cytokine levels observed upon incubation with human peripheral blood mononuclear cells in the absence of tumor target engagement.
• CLDN18.2 TriTCE Co-stim was well tolerated in non-human primates upon repeat dosing, with minimal peripheral cytokine elevations and no on-target histopathological changes observed.

DLL3 TriTCE Co-Stim: A next generation trispecific T cell engager with integrated CD28 costimulation for the treatment of DLL3-expressing cancers Abstract: 6716Session Category: ImmunologySession Title: Targeted ICEs

Small cell lung cancer (SCLC) is an aggressive neuroendocrine cancer with a poor prognosis and high unmet medical need2. DLL3 is a therapeutic target that is selectively expressed in SCLC and other neuroendocrine tumors. Bispecific TCEs targeting DLL3 have entered the clinic and demonstrated encouraging anti-tumor activity in SCLC patients3,4; however, SCLC is frequently characterized by an immunosuppressive microenvironment and poor T cell infiltration, which may limit clinical activity of CD3 engagers5. DLL3 TriTCE Co-Stim is a TriTCE designed to optimally engage CD3 and CD28 to redirect and enhance cytotoxic T cell responses to DLL3-expressing tumor cells while maintaining a desired safety profile. This approach has the potential to improve outcomes for patients, especially those with poorly infiltrated tumors, by increasing the depth and durability of response.

Using Zymeworks’ TriTCE Co-stim platform in combination with our Azymetric™️ and EFECT™️ technologies, we generated a panel of DLL3 TriTCE Co-Stim antibody formats and evaluated multiple formats, geometries, and paratope affinities, which allowed for optimization of selectivity and activity to promote a widened therapeutic index with enhanced anti-tumor activity.

• Induces greater in vitro cytotoxicity and improves T cell proliferation and survival compared to bispecific TCEs.
• Displays no cross-linking of T cells and exhibits obligate cis T cell binding of CD28, requiring co-engagement of CD3.
• Mediates improved in vivo tumor regression in an established SCLC humanized xenograft model relative to a clinical benchmark TCE.

Posters will be available at the time of presentation at the conference on the Company's website located at www.zymeworks.com .

About Zymeworks Inc.

Zymeworks is a global biotechnology company committed to the discovery, development, and commercialization of novel, multifunctional biotherapeutics. Zymeworks’ mission is to make a meaningful difference in the lives of people impacted by difficult-to-treat cancers and other diseases. The Company’s complementary therapeutic platforms and fully integrated drug development engine provide the flexibility and compatibility to precisely engineer and develop highly differentiated antibody-based therapeutic candidates. Zymeworks engineered and developed zanidatamab, a HER2-targeted bispecific antibody using the Company’s proprietary Azymetric™ technology. Zymeworks has entered into separate agreements with BeiGene, Ltd. (BeiGene) and Jazz Pharmaceuticals Ireland Limited (Jazz), granting each exclusive rights to develop and commercialize zanidatamab in different territories. Zanidatamab is currently being evaluated in multiple global clinical trials as a potential best-in-class treatment for patients with HER2-expressing cancers. A Biologics License Application (BLA) to the U.S. Food and Drug Administration (FDA) seeking accelerated approval for the HER2-targeted bispecific antibody zanidatamab as a treatment for previously treated, unresectable, locally advanced, or metastatic HER2-positive biliary tract cancer (BTC) has been submitted. If approved, zanidatamab would be the first HER2-targeted treatment specifically approved for BTC in the U.S. Zymeworks is rapidly advancing a deep pipeline of product candidates based on its experience and capabilities in both antibody-drug conjugates and multispecific antibody therapeutics across multiple novel targets in indications that represent areas of significant unmet medical need. In addition to Zymeworks’ wholly owned pipeline, its therapeutic platforms have been further leveraged through strategic partnerships with global biopharmaceutical companies. For information about Zymeworks , visit www.zymeworks.com and follow @ZymeworksInc on X.

Cautionary Note Regarding Forward-Looking Statements

This press release includes “forward-looking statements” or information within the meaning of the applicable securities legislation, including Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Forward-looking statements in this press release include, but are not limited to, statements that relate to the anticipated data presentations; Zymeworks’ preclinical pipeline; the potential therapeutic effects of zanidatamab and Zymeworks’ other product candidates; anticipated regulatory submissions and the timing thereof; Zymeworks’ clinical development of its product candidates and enrollment in its clinical trials; the ability to advance product candidates into later stages of development; and other information that is not historical information. When used herein, words such as “plan”, “believe”, “expect”, “may”, “anticipate”, “potential”, “will”, “continues”, and similar expressions are intended to identify forward-looking statements. In addition, any statements or information that refer to expectations, beliefs, plans, projections, objectives, performance or other characterizations of future events or circumstances, including any underlying assumptions, are forward-looking. All forward-looking statements are based upon Zymeworks’ current expectations and various assumptions. Zymeworks believes there is a reasonable basis for its expectations and beliefs, but they are inherently uncertain. Zymeworks may not realize its expectations, and its beliefs may not prove correct. Actual results could differ materially from those described or implied by such forward-looking statements as a result of various factors, including, without limitation: future clinical trials may not demonstrate safety and efficacy of any of Zymeworks’ or its collaborators’ product candidates; clinical trials may not demonstrate safety and efficacy of any of Zymeworks’ or its collaborators’ product candidates; any of Zymeworks’ or its partners’ product candidates may fail in development, may not receive required regulatory approvals, or may be delayed to a point where they are not commercially viable; regulatory agencies may impose additional requirements or delay the initiation of clinical trials; the impact of new or changing laws and regulations; market conditions; inability to maintain or enter into new partnerships or strategic collaborations; and the factors described under “Risk Factors” in Zymeworks’ quarterly and annual reports filed with the Securities and Exchange Commission from time to time (copies of which may be obtained at www.sec.gov and www.sedar.com ).

Although Zymeworks believes that such forward-looking statements are reasonable, there can be no assurance they will prove to be correct. Investors should not place undue reliance on forward-looking statements. The above assumptions, risks and uncertainties are not exhaustive. Forward-looking statements are made as of the date hereof and, except as may be required by law, Zymeworks undertakes no obligation to update, republish, or revise any forward-looking statements to reflect new information, future events or circumstances, or to reflect the occurrences of unanticipated events.

Investor Inquiries:

Shrinal Inamdar Director, Investor Relations(604) 678-1388 [email protected]    

Media Inquiries:

Diana Papove Senior Director, Corporate Communications(604) 678-1388 [email protected]

___________________

1 Arvedson T., et al. 2022. Targeting Solid Tumors with Bispecific T Cell Engager Immune Therapy (Vol. 6, pp.17-34).2 Saltos, A. et al . 2020. Update in the biology, management, and treatment of Small Cell Lung Cancer (SCLC). Front. Oncol. 10, 10743 Eastland, J. DLL3 market opportunity and KOL discussion of HPN328. Harpoon Therapeutics. Corporate slide deck Sept 15 2023 .4 Paz-Ares. et al. 2023. Tarlatamab, a first-in-class DLL3-targeted bispecific T-cell engager, in recurrent small-cell lung cancer: an open-label, phase I study. J. Clin . Oncol. 41:2898-29035 Tian, Y. et al. 2019. Potential immune escape mechanisms underlying the distinct clinical outcome of immune checkpoint blockades in small cell lung cancer. J Hematol Oncol. 12:67

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Tempus Announces 18 Abstracts Accepted For Presentation at the American Association for Cancer Research Annual Meeting 2024

Tempus, a leader in artificial intelligence and precision medicine, today announced 18 abstracts were accepted for presentation at the 2024 American Association for Cancer Research (AACR) Annual Meeting, which convenes from April 5-10, in San Diego, California. Tempus researchers will demonstrate how the company’s AI-enabled precision medicine platform collects and analyzes high-quality, multimodal datasets to advance cancer research.

“We look forward to presenting 18 abstracts at AACR this year, demonstrating the breadth and depth of our research both at Tempus and with our biopharma collaborators,” said Ezra Cohen, MD, Chief Medical Officer, Oncology at Tempus. “Our research showcases the power of combining our molecular profiling offerings with our multimodal database to better understand cancer biology, treatment response, and patient outcomes.”

Research highlights include:

• Session Date & Time : Monday, April 8, 2024; 1:30 p.m. - 5:00 p.m. PT
• Location : Poster Section 41
• Overview : Tempus xM used for treatment response monitoring (TRM) is an algorithm that quantifies changes in circulating DNA tumor fraction (TF) and can be simultaneously used to detect the emergence of ESR1 mutation (ESR1m) variants. In a heterogeneous real-world cohort of ER+ HER2- metastatic breast cancer patients, we showed that the combined effect of molecular response and ESR1 mutation status, a mutation associated with resistance to aromatase inhibitors (AIs), was associated with real-world overall survival outcomes. These preliminary findings suggest that xM used for TRM can identify patients with ESR1m and poor response on AI who may benefit from switching therapy.
• Session Date & Time : Monday, April 8, 2024; 9:00 a.m. - 12:30 p.m. PT
• Location : Poster Section 37
• Overview : To more efficiently filter out clonal hematopoiesis (CH) variants from tumor-derived variants, the team trained a random forest classifier on advanced, pan-solid tumor cancer samples sequenced using liquid biopsy and solid-tumor NGS with matched buffy coat assays (n=660). On a held-out validation set of samples (n=661), the classifier could reliably distinguish between CH and other tumor-derived variants with high accuracy, sensitivity, and specificity using only liquid biopsy data for predictions, providing an operationally simpler alternative to combined liquid and solid-based methodologies.
• Session Date & Time : Tuesday, April 9, 2024; 9:00 a.m. - 12:30 p.m. PT
• Location : Poster Section 44
• Overview : By analyzing changes in circulating DNA tumor fraction (ctDNA TF) in a real-world pan-cancer cohort of patients treated with Tyrosine kinase inhibitors (TKIs), the research team found that patients classified as molecular responders (defined as ≥ 50% in ctDNA tumor fraction) had significantly improved real-world overall survival and progression-free survival compared to molecular non-responders. Based on these findings, xM used for TRM may be used to optimize treatment decision making for patients treated with TKIs, sparing patients who do not respond to TKIs from ineffective therapy.

To learn more, visit www.tempus.com/events/aacr-annual-meeting-2024 .

About Tempus

Tempus is a technology company advancing precision medicine through the practical application of artificial intelligence in healthcare. With one of the world’s largest libraries of multimodal data, and an operating system to make that data accessible and useful, Tempus provides AI-enabled precision medicine solutions to physicians to deliver personalized patient care and in parallel facilitates discovery, development and delivery of optimal therapeutics. The goal is for each patient to benefit from the treatment of others who came before by providing physicians with tools that learn as the company gathers more data. For more information, visit tempus.com.

Erin Carron, [email protected] , 773-612-4414

View source version on businesswire.com: https://www.businesswire.com/news/home/20240405982000/en/

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• v.74(8); 2010 Oct 11

Presenting and Evaluating Qualitative Research

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education . It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

• Audio recordings and transcripts from in-depth or semi-structured interviews
• Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
• Audio recordings and transcripts from focus group sessions.
• Field notes (notes taken by the researcher while in the field [setting] being studied)
• Video recordings (eg, lecture delivery, class assignments, laboratory performance)
• Case study notes
• Documents (reports, meeting minutes, e-mails)
• Diaries, video diaries
• Observation notes
• Press clippings
• Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

• Issues can be examined in detail and in depth.
• Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
• The research framework and direction can be quickly revised as new information emerges.
• The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
• Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
• Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

• Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
• Rigor is more difficult to maintain, assess, and demonstrate.
• The volume of data makes analysis and interpretation time consuming.
• It is sometimes not as well understood and accepted as quantitative research within the scientific community
• The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
• Issues of anonymity and confidentiality can present problems when presenting findings
• Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

• □ Research question is clearly stated.
• □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
• □ Any specific research or educational terminology used later in manuscript is defined.
• □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
• □ Reason for choosing particular research method is stated.
• □ Criteria for selecting study participants are explained and justified.
• □ Recruitment methods are explicitly stated.
• □ Details of who chose not to participate and why are given.
• □ Study sample and research setting used are described.
• □ Method for gaining informed consent from the participants is described.
• □ Maintenance/Preservation of subject anonymity and confidentiality is described.
• □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
• □ Methods are outlined and examples given (eg, interview guide).
• □ Decision to stop data collection is described and justified.
• □ Data analysis and verification are described, including by whom they were performed.
• □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
• □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
• □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
• □ Transferability of research findings to other settings is discussed.
• □ Findings are presented in the context of any similar previous research and social theories.
• □ Discussion often is incorporated into the results in qualitative papers.
• □ A discussion of the existing literature and how this present research contributes to the area is included.
• □ Any particular strengths and limitations of the research are discussed.
• □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

• □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.