presenting a research article

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

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

More is more

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

Less is more

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

Limit the scope of your presentation

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

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

Craft a compelling research narrative

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

• Introduction (exposition — rising action)

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

• Methods (rising action)

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

• Results (climax)

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

• Discussion (falling action)

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

• Conclusion (denouement)

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

Use your medium to enhance the narrative

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

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

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

— Alec Getraer, Natural Sciences Correspondent

Share this:

• Share on Tumblr

Reference management. Clean and simple.

How to make a scientific presentation

Scientific presentation outlines

Questions to ask yourself before you write your talk, 1. how much time do you have, 2. who will you speak to, 3. what do you want the audience to learn from your talk, step 1: outline your presentation, step 2: plan your presentation slides, step 3: make the presentation slides, slide design, text elements, animations and transitions, step 4: practice your presentation, final thoughts, frequently asked questions about preparing scientific presentations, related articles.

A good scientific presentation achieves three things: you communicate the science clearly, your research leaves a lasting impression on your audience, and you enhance your reputation as a scientist.

But, what is the best way to prepare for a scientific presentation? How do you start writing a talk? What details do you include, and what do you leave out?

It’s tempting to launch into making lots of slides. But, starting with the slides can mean you neglect the narrative of your presentation, resulting in an overly detailed, boring talk.

The key to making an engaging scientific presentation is to prepare the narrative of your talk before beginning to construct your presentation slides. Planning your talk will ensure that you tell a clear, compelling scientific story that will engage the audience.

In this guide, you’ll find everything you need to know to make a good oral scientific presentation, including:

• The different types of oral scientific presentations and how they are delivered;
• How to outline a scientific presentation;
• How to make slides for a scientific presentation.

Our advice results from delving into the literature on writing scientific talks and from our own experiences as scientists in giving and listening to presentations. We provide tips and best practices for giving scientific talks in a separate post.

There are two main types of scientific talks:

• Your talk focuses on a single study . Typically, you tell the story of a single scientific paper. This format is common for short talks at contributed sessions in conferences.
• Your talk describes multiple studies. You tell the story of multiple scientific papers. It is crucial to have a theme that unites the studies, for example, an overarching question or problem statement, with each study representing specific but different variations of the same theme. Typically, PhD defenses, invited seminars, lectures, or talks for a prospective employer (i.e., “job talks”) fall into this category.

➡️ Learn how to prepare an excellent thesis defense

The length of time you are allotted for your talk will determine whether you will discuss a single study or multiple studies, and which details to include in your story.

The background and interests of your audience will determine the narrative direction of your talk, and what devices you will use to get their attention. Will you be speaking to people specializing in your field, or will the audience also contain people from disciplines other than your own? To reach non-specialists, you will need to discuss the broader implications of your study outside your field.

The needs of the audience will also determine what technical details you will include, and the language you will use. For example, an undergraduate audience will have different needs than an audience of seasoned academics. Students will require a more comprehensive overview of background information and explanations of jargon but will need less technical methodological details.

Your goal is to speak to the majority. But, make your talk accessible to the least knowledgeable person in the room.

This is called the thesis statement, or simply the “take-home message”. Having listened to your talk, what message do you want the audience to take away from your presentation? Describe the main idea in one or two sentences. You want this theme to be present throughout your presentation. Again, the thesis statement will depend on the audience and the type of talk you are giving.

Your thesis statement will drive the narrative for your talk. By deciding the take-home message you want to convince the audience of as a result of listening to your talk, you decide how the story of your talk will flow and how you will navigate its twists and turns. The thesis statement tells you the results you need to show, which subsequently tells you the methods or studies you need to describe, which decides the angle you take in your introduction.

➡️ Learn how to write a thesis statement

The goal of your talk is that the audience leaves afterward with a clear understanding of the key take-away message of your research. To achieve that goal, you need to tell a coherent, logical story that conveys your thesis statement throughout the presentation. You can tell your story through careful preparation of your talk.

Preparation of a scientific presentation involves three separate stages: outlining the scientific narrative, preparing slides, and practicing your delivery. Making the slides of your talk without first planning what you are going to say is inefficient.

Here, we provide a 4 step guide to writing your scientific presentation:

• Outline your presentation
• Plan your presentation slides
• Make the presentation slides
• Practice your presentation

Writing an outline helps you consider the key pieces of your talk and how they fit together from the beginning, preventing you from forgetting any important details. It also means you avoid changing the order of your slides multiple times, saving you time.

Plan your talk as discrete sections. In the table below, we describe the sections for a single study talk vs. a talk discussing multiple studies:

The following tips apply when writing the outline of a single study talk. You can easily adapt this framework if you are writing a talk discussing multiple studies.

Introduction: Writing the introduction can be the hardest part of writing a talk. And when giving it, it’s the point where you might be at your most nervous. But preparing a good, concise introduction will settle your nerves.

The introduction tells the audience the story of why you studied your topic. A good introduction succinctly achieves four things, in the following order.

• It gives a broad perspective on the problem or topic for people in the audience who may be outside your discipline (i.e., it explains the big-picture problem motivating your study).
• It describes why you did the study, and why the audience should care.
• It gives a brief indication of how your study addressed the problem and provides the necessary background information that the audience needs to understand your work.
• It indicates what the audience will learn from the talk, and prepares them for what will come next.

A good introduction not only gives the big picture and motivations behind your study but also concisely sets the stage for what the audience will learn from the talk (e.g., the questions your work answers, and/or the hypotheses that your work tests). The end of the introduction will lead to a natural transition to the methods.

Give a broad perspective on the problem. The easiest way to start with the big picture is to think of a hook for the first slide of your presentation. A hook is an opening that gets the audience’s attention and gets them interested in your story. In science, this might take the form of a why, or a how question, or it could be a statement about a major problem or open question in your field. Other examples of hooks include quotes, short anecdotes, or interesting statistics.

Why should the audience care? Next, decide on the angle you are going to take on your hook that links to the thesis of your talk. In other words, you need to set the context, i.e., explain why the audience should care. For example, you may introduce an observation from nature, a pattern in experimental data, or a theory that you want to test. The audience must understand your motivations for the study.

Supplementary details. Once you have established the hook and angle, you need to include supplementary details to support them. For example, you might state your hypothesis. Then go into previous work and the current state of knowledge. Include citations of these studies. If you need to introduce some technical methodological details, theory, or jargon, do it here.

Conclude your introduction. The motivation for the work and background information should set the stage for the conclusion of the introduction, where you describe the goals of your study, and any hypotheses or predictions. Let the audience know what they are going to learn.

Methods: The audience will use your description of the methods to assess the approach you took in your study and to decide whether your findings are credible. Tell the story of your methods in chronological order. Use visuals to describe your methods as much as possible. If you have equations, make sure to take the time to explain them. Decide what methods to include and how you will show them. You need enough detail so that your audience will understand what you did and therefore can evaluate your approach, but avoid including superfluous details that do not support your main idea. You want to avoid the common mistake of including too much data, as the audience can read the paper(s) later.

Results: This is the evidence you present for your thesis. The audience will use the results to evaluate the support for your main idea. Choose the most important and interesting results—those that support your thesis. You don’t need to present all the results from your study (indeed, you most likely won’t have time to present them all). Break down complex results into digestible pieces, e.g., comparisons over multiple slides (more tips in the next section).

Summary: Summarize your main findings. Displaying your main findings through visuals can be effective. Emphasize the new contributions to scientific knowledge that your work makes.

Conclusion: Complete the circle by relating your conclusions to the big picture topic in your introduction—and your hook, if possible. It’s important to describe any alternative explanations for your findings. You might also speculate on future directions arising from your research. The slides that comprise your conclusion do not need to state “conclusion”. Rather, the concluding slide title should be a declarative sentence linking back to the big picture problem and your main idea.

It’s important to end well by planning a strong closure to your talk, after which you will thank the audience. Your closing statement should relate to your thesis, perhaps by stating it differently or memorably. Avoid ending awkwardly by memorizing your closing sentence.

By now, you have an outline of the story of your talk, which you can use to plan your slides. Your slides should complement and enhance what you will say. Use the following steps to prepare your slides.

• Write the slide titles to match your talk outline. These should be clear and informative declarative sentences that succinctly give the main idea of the slide (e.g., don’t use “Methods” as a slide title). Have one major idea per slide. In a YouTube talk on designing effective slides , researcher Michael Alley shows examples of instructive slide titles.
• Decide how you will convey the main idea of the slide (e.g., what figures, photographs, equations, statistics, references, or other elements you will need). The body of the slide should support the slide’s main idea.
• Under each slide title, outline what you want to say, in bullet points.

In sum, for each slide, prepare a title that summarizes its major idea, a list of visual elements, and a summary of the points you will make. Ensure each slide connects to your thesis. If it doesn’t, then you don’t need the slide.

Slides for scientific presentations have three major components: text (including labels and legends), graphics, and equations. Here, we give tips on how to present each of these components.

• Have an informative title slide. Include the names of all coauthors and their affiliations. Include an attractive image relating to your study.
• Make the foreground content of your slides “pop” by using an appropriate background. Slides that have white backgrounds with black text work well for small rooms, whereas slides with black backgrounds and white text are suitable for large rooms.
• The layout of your slides should be simple. Pay attention to how and where you lay the visual and text elements on each slide. It’s tempting to cram information, but you need lots of empty space. Retain space at the sides and bottom of your slides.
• Use sans serif fonts with a font size of at least 20 for text, and up to 40 for slide titles. Citations can be in 14 font and should be included at the bottom of the slide.
• Use bold or italics to emphasize words, not underlines or caps. Keep these effects to a minimum.
• Use concise text . You don’t need full sentences. Convey the essence of your message in as few words as possible. Write down what you’d like to say, and then shorten it for the slide. Remove unnecessary filler words.
• Text blocks should be limited to two lines. This will prevent you from crowding too much information on the slide.
• Include names of technical terms in your talk slides, especially if they are not familiar to everyone in the audience.
• Proofread your slides. Typos and grammatical errors are distracting for your audience.
• Include citations for the hypotheses or observations of other scientists.
• Good figures and graphics are essential to sustain audience interest. Use graphics and photographs to show the experiment or study system in action and to explain abstract concepts.
• Don’t use figures straight from your paper as they may be too detailed for your talk, and details like axes may be too small. Make new versions if necessary. Make them large enough to be visible from the back of the room.
• Use graphs to show your results, not tables. Tables are difficult for your audience to digest! If you must present a table, keep it simple.
• Label the axes of graphs and indicate the units. Label important components of graphics and photographs and include captions. Include sources for graphics that are not your own.
• Explain all the elements of a graph. This includes the axes, what the colors and markers mean, and patterns in the data.
• Use colors in figures and text in a meaningful, not random, way. For example, contrasting colors can be effective for pointing out comparisons and/or differences. Don’t use neon colors or pastels.
• Use thick lines in figures, and use color to create contrasts in the figures you present. Don’t use red/green or red/blue combinations, as color-blind audience members can’t distinguish between them.
• Arrows or circles can be effective for drawing attention to key details in graphs and equations. Add some text annotations along with them.
• Write your summary and conclusion slides using graphics, rather than showing a slide with a list of bullet points. Showing some of your results again can be helpful to remind the audience of your message.
• If your talk has equations, take time to explain them. Include text boxes to explain variables and mathematical terms, and put them under each term in the equation.
• Combine equations with a graphic that shows the scientific principle, or include a diagram of the mathematical model.
• Use animations judiciously. They are helpful to reveal complex ideas gradually, for example, if you need to make a comparison or contrast or to build a complicated argument or figure. For lists, reveal one bullet point at a time. New ideas appearing sequentially will help your audience follow your logic.
• Slide transitions should be simple. Silly ones distract from your message.
• Decide how you will make the transition as you move from one section of your talk to the next. For example, if you spend time talking through details, provide a summary afterward, especially in a long talk. Another common tactic is to have a “home slide” that you return to multiple times during the talk that reinforces your main idea or message. In her YouTube talk on designing effective scientific presentations , Stanford biologist Susan McConnell suggests using the approach of home slides to build a cohesive narrative.

To deliver a polished presentation, it is essential to practice it. Here are some tips.

• For your first run-through, practice alone. Pay attention to your narrative. Does your story flow naturally? Do you know how you will start and end? Are there any awkward transitions? Do animations help you tell your story? Do your slides help to convey what you are saying or are they missing components?
• Next, practice in front of your advisor, and/or your peers (e.g., your lab group). Ask someone to time your talk. Take note of their feedback and the questions that they ask you (you might be asked similar questions during your real talk).
• Edit your talk, taking into account the feedback you’ve received. Eliminate superfluous slides that don’t contribute to your takeaway message.
• Practice as many times as needed to memorize the order of your slides and the key transition points of your talk. However, don’t try to learn your talk word for word. Instead, memorize opening and closing statements, and sentences at key junctures in the presentation. Your presentation should resemble a serious but spontaneous conversation with the audience.
• Practicing multiple times also helps you hone the delivery of your talk. While rehearsing, pay attention to your vocal intonations and speed. Make sure to take pauses while you speak, and make eye contact with your imaginary audience.
• Make sure your talk finishes within the allotted time, and remember to leave time for questions. Conferences are particularly strict on run time.
• Anticipate questions and challenges from the audience, and clarify ambiguities within your slides and/or speech in response.
• If you anticipate that you could be asked questions about details but you don’t have time to include them, or they detract from the main message of your talk, you can prepare slides that address these questions and place them after the final slide of your talk.

➡️ More tips for giving scientific presentations

An organized presentation with a clear narrative will help you communicate your ideas effectively, which is essential for engaging your audience and conveying the importance of your work. Taking time to plan and outline your scientific presentation before writing the slides will help you manage your nerves and feel more confident during the presentation, which will improve your overall performance.

A good scientific presentation has an engaging scientific narrative with a memorable take-home message. It has clear, informative slides that enhance what the speaker says. You need to practice your talk many times to ensure you deliver a polished presentation.

First, consider who will attend your presentation, and what you want the audience to learn about your research. Tailor your content to their level of knowledge and interests. Second, create an outline for your presentation, including the key points you want to make and the evidence you will use to support those points. Finally, practice your presentation several times to ensure that it flows smoothly and that you are comfortable with the material.

Prepare an opening that immediately gets the audience’s attention. A common device is a why or a how question, or a statement of a major open problem in your field, but you could also start with a quote, interesting statistic, or case study from your field.

Scientific presentations typically either focus on a single study (e.g., a 15-minute conference presentation) or tell the story of multiple studies (e.g., a PhD defense or 50-minute conference keynote talk). For a single study talk, the structure follows the scientific paper format: Introduction, Methods, Results, Summary, and Conclusion, whereas the format of a talk discussing multiple studies is more complex, but a theme unifies the studies.

Ensure you have one major idea per slide, and convey that idea clearly (through images, equations, statistics, citations, video, etc.). The slide should include a title that summarizes the major point of the slide, should not contain too much text or too many graphics, and color should be used meaningfully.

• Event Website Publish a modern and mobile friendly event website.
• Registration & Payments Collect registrations & online payments for your event.
• Abstract Management Collect and manage all your abstract submissions.
• Peer Reviews Easily distribute and manage your peer reviews.
• Conference Program Effortlessly build & publish your event program.
• Virtual Poster Sessions Host engaging virtual poster sessions.
• Customer Success Stories
• Wall of Love ❤️

How to Present Your Research (Guidelines and Tips)

Published on 01 Feb 2023

Presenting at a conference can be stressful, but can lead to many opportunities, which is why coming prepared is super beneficial.

The internet is full to the brim with tips for making a good presentation. From what you wear to how you stand to good slide design, there’s no shortage of advice to make any old presentation come to life. 

But, not all presentations are created equal. Research presentations, in particular, are unique. 

Communicating complex concepts to an audience with a varied range of awareness about your research topic can be tricky. A lack of guidance and preparation can ruin your chance to share important information with a conference community. This could mean lost opportunities in collaboration or funding or lost confidence in yourself and your work.

So, we’ve put together a list of tips with research presentations in mind. Here’s our top to-do’s when preparing to present your research.

Take every research presentation opportunity

The worst thing you could do for your research is to not present it at all. As intimidating as it can be to get up in front of an audience, you shouldn’t let that stop you from seizing a good opportunity to share your work with a wider community.

These contestants from the Vitae Three Minute Thesis Competition have some great advice to share on taking every possible chance to talk about your research. 

Double-check your research presentation guidelines

Before you get started on your presentation, double-check if you’ve been given guidelines for it. 

If you don’t have specific guidelines for the context of your presentation, we’ve put together a general outline to help you get started. It’s made with the assumption of a 10-15 minute presentation time. So, if you have longer to present, you can always extend important sections or talk longer on certain slides:

• Title Slide (1 slide) - This is a placeholder to give some visual interest and display the topic until your presentation begins.
• Short Introduction (2-3 slides) - This is where you pique the interest of your audience and establish the key questions your presentation covers. Give context to your study with a brief review of the literature (focus on key points, not a full review). If your study relates to any particularly relevant issues, mention it here to increase the audience's interest in the topic.
• Hypothesis (1 slide) - Clearly state your hypothesis.
• Description of Methods (2-3 slides) - Clearly, but briefly, summarize your study design including a clear description of the study population, the sample size and any instruments or manipulations to gather the data.
• Results and Data Interpretation (2-4 slides) - Illustrate your results through simple tables, graphs, and images. Remind the audience of your hypothesis and discuss your interpretation of the data/results.
• Conclusion (2-3 slides) - Further interpret your results. If you had any sources of error or difficulties with your methods, discuss them here and address how they could be (or were) improved. Discuss your findings as part of the bigger picture and connect them to potential further outcomes or areas of study.
• Closing (1 slide) - If anyone supported your research with guidance, awards, or funding, be sure to recognize their contribution. If your presentation includes a Q&A session, open the floor to questions.

Plan for about one minute for each slide of information that you have. Be sure that you don’t cram your slides with text (stick to bullet points and images to emphasize key points).

And, if you’re looking for more inspiration to help you in scripting an oral research presentation. University of Virginia has a helpful oral presentation outline script .

A PhD Student working on an upcoming oral presentation.

Put yourself in your listeners shoes

As mentioned in the intro, research presentations are unique because they deal with specialized topics and complicated concepts. There’s a good chance that a large section of your audience won’t have the same understanding of your topic area as you do. So, do your best to understand where your listeners are at and adapt your language/definitions to that.

There’s an increasing awareness around the importance of scientific communication. Comms experts have even started giving TED Talks on how to bridge the gap between science and the public (check out Talk Nerdy to Me ). A general communication tip is to find out what sort of audience will listen to your talk. Then, beware of using jargon and acronyms unless you're 100% certain that your audience knows what they mean. 

On the other end of the spectrum, you don’t want to underestimate your audience. Giving too much background or spending ages summarizing old work to a group of experts in the field would be a waste of valuable presentation time (and would put you at risk of losing your audience's interest). 

Finally, if you can, practice your presentation on someone with a similar level of topic knowledge to the audience you’ll be presenting to.

Use scientific storytelling in your presentation

In scenarios where it’s appropriate, crafting a story allows you to break free from the often rigid tone of scientific communications. It helps your brain hit the refresh button and observe your findings from a new perspective. Plus, it can be a lot of fun to do!

If you have a chance to use scientific storytelling in your presentation, take full advantage of it. The best way to weave a story for your audience into a presentation is by setting the scene during your introduction. As you set the context of your research, set the context of your story/example at the same time. Continue drawing those parallels as you present. Then, deliver the main message of the story (or the “Aha!”) moment during your presentation’s conclusion.

If delivered well, a good story will keep your audience on the edge of their seats and glued to your entire presentation.

Emphasize the “Why” (not the “How”) of your research

Along the same lines as using storytelling, it’s important to think of WHY your audience should care about your work. Find ways to connect your research to valuable outcomes in society. Take your individual points on each slide and bring things back to the bigger picture. Constantly remind your listeners how it’s all connected and why that’s important.

One helpful way to get in this mindset is to look back to the moment before you became an expert on your topic. What got you interested? What was the reason for asking your research question? And, what motivated you to power through all the hard work to come? Then, looking forward, think about what key takeaways were most interesting or surprised you the most. How can these be applied to impact positive change in your research field or the wider community?

Be picky about what you include

It’s tempting to discuss all the small details of your methods or findings. Instead, focus on the most important information and takeaways that you think your audience will connect with. Decide on these takeaways before you script your presentation so that you can set the scene properly and provide only the information that has an added value.

When it comes to choosing data to display in your presentation slides, keep it simple. Wherever possible, use visuals to communicate your findings as opposed to large tables filled with numbers. This article by Richard Chambers has some great tips on using visuals in your slides and graphs.

Hide your complex tables and data in additional slides

With the above tip in mind: Just because you don’t include data and tables in your main presentation slides, doesn’t mean you can’t keep them handy for reference. If there’s a Q&A session after your presentation (or if you’ll be sharing your slides to view on-demand after) one great trick is to include additional slides/materials after your closing slide. You can keep these in your metaphorical “back pocket” to refer to if a specific question is asked about a data set or method. They’re also handy for people viewing your presentation slides later that might want to do a deeper dive into your methods/results.

However, just because you have these extra slides doesn’t mean you shouldn’t make the effort to make that information more accessible. A research conference platform like Fourwaves allows presenters to attach supplementary materials (figures, posters, slides, videos and more) that conference participants can access anytime.

Leave your audience with (a few) questions

Curiosity is a good thing. Whether you have a Q&A session or not, you should want to leave your audience with a few key questions. The most important one:

“Where can I find out more?”

Obviously, it’s important to answer basic questions about your research context, hypothesis, methods, results, and interpretation. If you answer these while focusing on the “Why?” and weaving a good story, you’ll be setting the stage for an engaging Q&A session and/or some great discussions in the halls after your presentation. Just be sure that you have further links or materials ready to provide to those who are curious. 

Conclusion: The true expert in your research presentation

Throughout the entire process of scripting, creating your slides, and presenting, it’s important to remember that no one knows your research better than you do. If you’re nervous, remind yourself that the people who come to listen to your presentation are most likely there due to a genuine interest in your work. The pressure isn’t to connect with an uninterested audience - it’s to make your research more accessible and relevant for an already curious audience.

Finally, to practice what we preached in our last tip: If you’re looking to learn more about preparing for a research presentation, check out our articles on how to dress for a scientific conference and general conference presentation tips .

5 Best Event Registration Platforms for Your Next Conference

By having one software to organize registrations and submissions, a pediatric health center runs aro...

5 Essential Conference Apps for Your Event

In today’s digital age, the success of any conference hinges not just on the content and speakers bu...

• Locations and Hours
• UCLA Library
• Research Guides
• Research Tips and Tools

Advanced Research Methods

• Presenting the Research Paper
• What Is Research?
• Library Research
• Writing a Research Proposal
• Writing the Research Paper

Writing an Abstract

Oral presentation, compiling a powerpoint.

Abstract : a short statement that describes a longer work.

• Indicate the subject.
• Describe the purpose of the investigation.
• Briefly discuss the method used.
• Make a statement about the result.

Oral presentations usually introduce a discussion of a topic or research paper. A good oral presentation is focused, concise, and interesting in order to trigger a discussion.

• Be well prepared; write a detailed outline.
• Introduce the subject.
• Talk about the sources and the method.
• Indicate if there are conflicting views about the subject (conflicting views trigger discussion).
• Make a statement about your new results (if this is your research paper).
• Use visual aids or handouts if appropriate.

An effective PowerPoint presentation is just an aid to the presentation, not the presentation itself .

• Be brief and concise.
• Focus on the subject.
• Attract attention; indicate interesting details.
• If possible, use relevant visual illustrations (pictures, maps, charts graphs, etc.).
• Use bullet points or numbers to structure the text.
• Make clear statements about the essence/results of the topic/research.
• Don't write down the whole outline of your paper and nothing else.
• Don't write long full sentences on the slides.
• Don't use distracting colors, patterns, pictures, decorations on the slides.
• Don't use too complicated charts, graphs; only those that are relatively easy to understand.
• << Previous: Writing the Research Paper
• Last Updated: Jan 4, 2024 12:24 PM
• URL: https://guides.library.ucla.edu/research-methods

• Research Guides

Reading for Research: Social Sciences

Structure of a research article.

• Structural Read

Guide Acknowledgements

How to Read a Scholarly Article from the Howard Tilton Memorial Library at Tulane University

Strategic Reading for Research   from the Howard Tilton Memorial Library at Tulane University

Bridging the Gap between Faculty Expectation and the Student Experience: Teaching Students toAnnotate and Synthesize Sources

Librarian for Sociology, Environmental Sociology, MHS and Public Policy Studies

Academic writing has features that vary only slightly across the different disciplines. Knowing these elements and the purpose of each serves help you to read and understand academic texts efficiently and effectively, and then apply what you read to your paper or project.

Social Science (and Science) original research articles generally follow IMRD: Introduction- Methods-Results-Discussion

Introduction

• Introduces topic of article
• Presents the "Research Gap"/Statement of Problem article will address
• How research presented in the article will solve the problem presented in research gap.
• Literature Review. presenting and evaluating previous scholarship on a topic.  Sometimes, this is separate section of the article. 

​Method & Results

• How research was done, including analysis and measurements.  
• Sometimes labeled as "Research Design"
• What answers were found
• Interpretation of Results (What Does It Mean? Why is it important?)
• Implications for the Field, how the study contributes to the existing field of knowledge
• Suggestions for further research
• Sometimes called Conclusion

You might also see IBC: Introduction - Body - Conclusion

• Identify the subject
• State the thesis 
• Describe why thesis is important to the field (this may be in the form of a literature review or general prose)

Body  

• Presents Evidence/Counter Evidence
• Integrate other writings (i.e. evidence) to support argument 
• Discuss why others may disagree (counter-evidence) and why argument is still valid
• Summary of argument
• Evaluation of argument by pointing out its implications and/or limitations 
• Anticipate and address possible counter-claims
• Suggest future directions of research
• Next: Structural Read >>
• Last Updated: Jan 19, 2024 10:44 AM
• URL: https://researchguides.library.vanderbilt.edu/readingforresearch

• Publication Recognition

How to Make a PowerPoint Presentation of Your Research Paper

• 4 minute read
• 120.5K views

Table of Contents

A research paper presentation is often used at conferences and in other settings where you have an opportunity to share your research, and get feedback from your colleagues. Although it may seem as simple as summarizing your research and sharing your knowledge, successful research paper PowerPoint presentation examples show us that there’s a little bit more than that involved.

In this article, we’ll highlight how to make a PowerPoint presentation from a research paper, and what to include (as well as what NOT to include). We’ll also touch on how to present a research paper at a conference.

Purpose of a Research Paper Presentation

The purpose of presenting your paper at a conference or forum is different from the purpose of conducting your research and writing up your paper. In this setting, you want to highlight your work instead of including every detail of your research. Likewise, a presentation is an excellent opportunity to get direct feedback from your colleagues in the field. But, perhaps the main reason for presenting your research is to spark interest in your work, and entice the audience to read your research paper.

So, yes, your presentation should summarize your work, but it needs to do so in a way that encourages your audience to seek out your work, and share their interest in your work with others. It’s not enough just to present your research dryly, to get information out there. More important is to encourage engagement with you, your research, and your work.

Tips for Creating Your Research Paper Presentation

In addition to basic PowerPoint presentation recommendations, which we’ll cover later in this article, think about the following when you’re putting together your research paper presentation:

• Know your audience : First and foremost, who are you presenting to? Students? Experts in your field? Potential funders? Non-experts? The truth is that your audience will probably have a bit of a mix of all of the above. So, make sure you keep that in mind as you prepare your presentation.

Know more about: Discover the Target Audience .

• Your audience is human : In other words, they may be tired, they might be wondering why they’re there, and they will, at some point, be tuning out. So, take steps to help them stay interested in your presentation. You can do that by utilizing effective visuals, summarize your conclusions early, and keep your research easy to understand.
• Running outline : It’s not IF your audience will drift off, or get lost…it’s WHEN. Keep a running outline, either within the presentation or via a handout. Use visual and verbal clues to highlight where you are in the presentation.
• Where does your research fit in? You should know of work related to your research, but you don’t have to cite every example. In addition, keep references in your presentation to the end, or in the handout. Your audience is there to hear about your work.
• Plan B : Anticipate possible questions for your presentation, and prepare slides that answer those specific questions in more detail, but have them at the END of your presentation. You can then jump to them, IF needed.

What Makes a PowerPoint Presentation Effective?

You’ve probably attended a presentation where the presenter reads off of their PowerPoint outline, word for word. Or where the presentation is busy, disorganized, or includes too much information. Here are some simple tips for creating an effective PowerPoint Presentation.

• Less is more: You want to give enough information to make your audience want to read your paper. So include details, but not too many, and avoid too many formulas and technical jargon.
• Clean and professional : Avoid excessive colors, distracting backgrounds, font changes, animations, and too many words. Instead of whole paragraphs, bullet points with just a few words to summarize and highlight are best.
• Know your real-estate : Each slide has a limited amount of space. Use it wisely. Typically one, no more than two points per slide. Balance each slide visually. Utilize illustrations when needed; not extraneously.
• Keep things visual : Remember, a PowerPoint presentation is a powerful tool to present things visually. Use visual graphs over tables and scientific illustrations over long text. Keep your visuals clean and professional, just like any text you include in your presentation.

Know more about our Scientific Illustrations Services .

Another key to an effective presentation is to practice, practice, and then practice some more. When you’re done with your PowerPoint, go through it with friends and colleagues to see if you need to add (or delete excessive) information. Double and triple check for typos and errors. Know the presentation inside and out, so when you’re in front of your audience, you’ll feel confident and comfortable.

How to Present a Research Paper

If your PowerPoint presentation is solid, and you’ve practiced your presentation, that’s half the battle. Follow the basic advice to keep your audience engaged and interested by making eye contact, encouraging questions, and presenting your information with enthusiasm.

We encourage you to read our articles on how to present a scientific journal article and tips on giving good scientific presentations .

Language Editing Plus

Improve the flow and writing of your research paper with Language Editing Plus. This service includes unlimited editing, manuscript formatting for the journal of your choice, reference check and even a customized cover letter. Learn more here , and get started today!

• Manuscript Preparation

Know How to Structure Your PhD Thesis

• Research Process

Systematic Literature Review or Literature Review?

You may also like.

What is a Good H-index?

What is a Corresponding Author?

How to Submit a Paper for Publication in a Journal

Input your search keywords and press Enter.

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

How to Create and Deliver a Research Presentation

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

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

Table of Contents

What is a Research Presentation

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

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

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

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

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

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

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

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

1.  Title Page

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

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

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

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

2. Executive Summary Slide

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

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

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

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

3. Introduction/ Project Description Slides

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

You want to answer these fundamental questions:

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

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

4. Data Presentation and Analyses Slides

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

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

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

5. Conclusions

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

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

6. Recommendations

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

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

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

7. Questions Slide

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

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

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

1. Focus on the Most Important Findings

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

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

2. Do Not Read Word-per-word

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

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

3. Don’t Go Without Handouts

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

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

4. Actively Listen

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

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

5. Be Confident

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

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

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

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

In-text citation

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

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

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

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

Image citation

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

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

Work cited page

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

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

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

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

1. Research Project Presentation PowerPoint Template

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

Use This Template

2. Research Presentation Scientific Method Diagram PowerPoint Template

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

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

3. Thesis Research Presentation PowerPoint Template

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

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

4. Animated Research Cards PowerPoint Template

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

5. Research Presentation Slide Deck for PowerPoint

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

By accessing this template, you get the following slides:

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

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

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

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

Like this article? Please share

Academics, Presentation Approaches, Research & Development Filed under Presentation Ideas

Related Articles

Filed under Design • March 27th, 2024

How to Make a Presentation Graph

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

Filed under Presentation Ideas • February 29th, 2024

How to Make a Fundraising Presentation (with Thermometer Templates & Slides)

Meet a new framework to design fundraising presentations by harnessing the power of fundraising thermometer templates. Detailed guide with examples.

Filed under Presentation Ideas • February 15th, 2024

How to Create a 5 Minutes Presentation

Master the art of short-format speeches like the 5 minutes presentation with this article. Insights on content structure, audience engagement and more.

Leave a Reply

How to Make an Effective Research Presentation

Presentation software programs have advanced to the point where you no longer need to be an experienced designer to put together a compelling piece of collateral that conveys your findings about academic research in exactly the right way. With the right materials, the right presentation software, and a little bit of time, you can visualize any data that you have in the form of a terrific presentation that sells your research better than numbers alone ever could. However, this does not mean that you shouldn’t keep in mind a few things. As both a marketing tool and a means to convey information, presentations are helpful because they are malleable—the format can essentially be anything you need it to be at any given time. The other side of this, however, is that there are certain traps that are all too easy for even experts to fall into that will harm your ultimate message, not help it. If you wish to learn how to make a professional research presentation as an author, or a researcher, then you should avoid some mistakes at all costs.

Mistakes to Avoid

As a researcher or a student, your number one goal isn’t just to provide insight into a topic—it’s to do so in a compelling way. It is important to communicate ideas in a way that is both easy to understand for people who haven’t completed the work you have and to do so in a compelling and engaging way. In many ways, it’s a lot like telling a story—albeit one that is heavily research-oriented. Every story has a beginning, middle, and end and you need to ensure that the content in the presentation has a proper narrative flow.

In many ways, your presentation will operate exactly along the same lines. To that end, always remember to make sure that the information is presented not only in the right manner but also in the right order to complement intent and maximize impact. If you have three subtopics within a presentation, all of which are related but are still different ideas, don’t mix and match the content. Don’t jump from one topic to the other and back again—you’re only going to lose focus and eventually, the attention of your reader.

If you start preparing your presentation and realize that you’re actually kind of covering two distinct and different topics, don’t be afraid to break one presentation into two. You’ll be able to devote more attention to promoting each idea and you’ll walk away with two great pieces of research presentations instead of one “okay” one.

Length of Your Presentation

Another element of your presentation that you need to pay extremely close attention to is the length. This goes back to another one of the old rules of storytelling: “Whatever you do, don’t overstay your welcome.” While it is true that presentations are naturally designed to be a longer form than something like an Infographic, it’s important to recognize when you’re asking too much of your reader/viewer. A presentation isn’t just a visualized form of something like a white paper. It’s a unique medium all unto itself.

When you start preparing your presentation for the first time, feel free to include as many slides or as much information as you want. Also, don’t forget that there are three versions of your presentation that will exist—the initial outline, the “first draft” of the presentation and the final edited version that you release. Make an effort to only include information that A) is needed to understand your research topic, and B) is necessary to contextualize your findings or the points you’re trying to make. Go through your presentation from start to finish and really try to experience it with fresh eyes—the same way your audience will.

Does it feel like the end of your presentation is getting a little sluggish? You feel that it should be over but there are ten slides to go still. Be precise in your editing process —rest assured that you’ll thank yourself when the end result is much more powerful than it would be if it had remained bloated.

The Power of Presentations

In many ways, presentations provide a unified experience where you can have text, images, video, and more. Remember that human beings are visual learners— visuals are processed up to 60,000 times faster than text and people have a much easier time understanding complex information when it is paired with relevant images as opposed to just text. As an author, researcher, or student, your job is to take complicated ideas and present them in a way that is appealing to a larger audience. Presentations are one of the most essential ways for you to do exactly that. The central message you are trying to convey—the thesis, if you will—needs to be strong enough to justify the creation of a presentation in the first place.

It needs to be a big enough topic to warrant a lengthy experience and a compelling enough story that demands to be told in this particular format above all others. If you start from that simple foundation and build outward, you’ll be left with the best type of marketing tool—one that promotes your research for you and one that people can’t wait to share with their friends and colleagues.

About the Author

Payman Taei is the founder of Visme , an easy-to-use online tool to create engaging presentations, infographics, and other forms of visual content. He is also the founder of HindSite Interactive , an award-winning Maryland based digital agency specializing in website design, user experience, and web app development.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Reporting Research

Beyond the Podium: Understanding the differences in conference and academic presentations

Conferences can be captivating as it where knowledge meets presentation skills. They serve as dynamic…

• Old Webinars
• Webinar Mobile App

How to Ace Your Next Virtual Academic Conference

Identifying the right conference Designing video presentations Handling Q&A professionally Tips for virtual networking

• Global Korea Webinars

국제회의를 위한 연구발표 준비하는 방법

올바른 컨퍼런스 식별하기 프레젠테이션 설계 전략 질의응답 확인 및 관리 네트워킹에 대한 효과적인 팁

• Career Corner
• PhDs & Postdocs

Tips to Present Your Scientific Poster Effectively

This article focuses on the prerequisites and tips on developing a poster/e-poster. Traditionally, scientific posters…

A Researcher’s Guide to Making the Most of Academic Conferences

Academics know the importance of attending conferences as part of their career. Conferences provide valuable…

6 Simple Ways to Handle a Q&A Session at a Conference

4 Quick Tips to Effectively Engage the Audience in Your Research Presentation

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

What should universities' stance be on AI tools in research and academic writing?

Have a language expert improve your writing

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

• Knowledge Base
• Dissertation
• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

The only proofreading tool specialized in correcting academic writing - try for free!

The academic proofreading tool has been trained on 1000s of academic texts and by native English editors. Making it the most accurate and reliable proofreading tool for students.

Try for free

I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Survivorship bias
• Self-serving bias
• Availability heuristic
• Halo effect
• Hindsight bias
• Deep learning
• Generative AI
• Machine learning
• Reinforcement learning
• Supervised vs. unsupervised learning

 (AI) Tools

• Grammar Checker
• Paraphrasing Tool
• Text Summarizer
• AI Detector
• Plagiarism Checker
• Citation Generator

The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Cite this Scribbr article

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

George, T. (2023, July 18). How to Write a Results Section | Tips & Examples. Scribbr. Retrieved April 18, 2024, from https://www.scribbr.com/dissertation/results/

Is this article helpful?

Tegan George

Other students also liked, what is a research methodology | steps & tips, how to write a discussion section | tips & examples, how to write a thesis or dissertation conclusion, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

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

How to Make a “Good” Presentation “Great”

• Guy Kawasaki

Remember: Less is more.

A strong presentation is so much more than information pasted onto a series of slides with fancy backgrounds. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others. Here are some unique elements that make a presentation stand out.

• Fonts: Sans Serif fonts such as Helvetica or Arial are preferred for their clean lines, which make them easy to digest at various sizes and distances. Limit the number of font styles to two: one for headings and another for body text, to avoid visual confusion or distractions.
• Colors: Colors can evoke emotions and highlight critical points, but their overuse can lead to a cluttered and confusing presentation. A limited palette of two to three main colors, complemented by a simple background, can help you draw attention to key elements without overwhelming the audience.
• Pictures: Pictures can communicate complex ideas quickly and memorably but choosing the right images is key. Images or pictures should be big (perhaps 20-25% of the page), bold, and have a clear purpose that complements the slide’s text.
• Layout: Don’t overcrowd your slides with too much information. When in doubt, adhere to the principle of simplicity, and aim for a clean and uncluttered layout with plenty of white space around text and images. Think phrases and bullets, not sentences.

As an intern or early career professional, chances are that you’ll be tasked with making or giving a presentation in the near future. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others.

• Guy Kawasaki is the chief evangelist at Canva and was the former chief evangelist at Apple. Guy is the author of 16 books including Think Remarkable : 9 Paths to Transform Your Life and Make a Difference.

Partner Center

How to Present a Research Paper? (Templates Included)

Last updated on December 25th, 2023

Research paper presentations are required in our academic progress. As a researcher, you must often present a research paper and convey your ideas to the audience. But research paper presentations have unique requirements that must be fulfilled for asserting the audience of your talk.

It can be quite challenging for a research presentation not to bore the audience and enthusiastically present the research paper, but if you follow the necessary steps, you can easily convey your ideas and grab the audience’s attention in no time . 

In this article, we go through how to present a research paper, why you would need to create a research presentation, and discuss the APA style of citing research papers. 

Lastly, we have also included research paper PowerPoint presentation examples at the end of this article to help you conveniently create the perfect research paper presentation in a short amount of time. 

How to Present a Research Paper?

A research presentation is typically 10 minutes long and needs to be formally presented in front of a panel of audience or, in the case of a seminar, a group of people. Here are some important points to help you understand how to present a research paper.

1. How Should the Title of a Research Paper be Presented?

An important part of a research paper is its title which needs to be presented directly and with an interesting take to catch the audience’s attention . It is the first impression that you leave on your audience and helps build the momentum for your upcoming talk. Keep it short and concise. Stretching it out will use your precious talk time, which should be for the crucial parts of the presentation. 

2. Introduction

Next is the introduction you need to cover by giving an overview of your research paper. See it like the abstract of any research paper that lets the readers or, in the research paper presentation’s case, the audience know what the presentation will be about. 

Take the opportunity to introduce yourself and offer a statement about your overall presentation. Explain why your research findings are useful for the audience and how they can help them achieve their goals or solve problems. Clearly define your hypothesis statement to indicate what is yet to come in your presentation. 

3. Main Body of the Presentation

The main body of the presentation should have the most important information your audience needs to know about. In most cases, the findings of the presentation are the most important. Still, in some cases, the methodology followed must also be highlighted and discussed to attract the audience’s attention. 

Ask yourself the most important part the audience needs to know about that signifies the benefit of your research and its findings. Identify the main points and record them to have a set of central points to plan your presentation. Keep the main issues short and to the point when talking about them. Going too much into detail will exceed your presentation time and will not be good for the audience’s attention span. 

4. Use Graphics, including Charts, Graphs, Infographics and Pictures

To clarify the concepts, support arguments, and offer a visual impact, use graphics including tables, flowcharts, graphs, charts, images, infographics and diagrams in your research paper presentation. Visual slides will help make your presentation appealing and memorable. 

Use flowcharts to explain a process flow and its outcomes. Graphs depict statistics in visual form and compare numerical data. Add supporting images and diagrams where necessary. And use charts to show the relationship between elements. 

Pro Tip: A research paper is sometimes presented with the help of a poster presentation . You can learn more on how to make a winning poster presentation in this article published by SlideModel.

But do remember not to over do it. Keep the text short and concise with a reasonable number of graphics to make it attractive and enable you to convey your message better.

5. Offer Supporting Documents

A research presentation is just a narrowed-down version of your research paper with extensively detailed information on the topic, literature review, methodology, findings, and implications. Therefore, along with your research presentation, be sure to offer supporting documents as required. 

If you are applying for a job and giving a research presentation as a part of the interview process, then be sure to provide a CV with a list of your published articles and a cover letter. 

In case of a funding request, submit a proposal when presenting to let the investors and sponsors know your requirements. 

Seminar and conference presentations often do not need supporting documents, but handing a summary of your research paper and its significance can be good for those interested in your research. Poster presentations can also be part of the supporting documents to be included as part of your research project.

6. Prepare a Summary of the Presentation

When concluding your research paper presentation, offer a brief summary of the main points and highlight the significance of your findings . Make a specific request if any action is needed from the audience or the panel of professionals. 

Acknowledge the collaborators and mentors, tell the audience the next steps, and share your contact information with them so they can contact you when required.  

7. Answer Any Underlying Questions

In the end, you can hold a question-and-answer session to answer any underlying queries the audience might have. You can have some questions of your own to stimulate a discussion among the audience and take in questions to answer, making it interactive. 

To prepare for this, write a list of possible questions the audience might have and prepare short, clear-cut answers. Make sure to go through your research paper deeply, so you can answer any question the audience asks and maintain your authority on the matter.  

Reasons for Creating a Research Presentation

Research paper presentations have their unique purpose, and you need to identify your reason for creating a research presentation to be able to properly convey your message and fulfill the purpose of presenting . Here are a few reasons why you would need to create a research presentation.

1. Dissertation Defense

A dissertation defense is commonly required in academic settings where you must defend your research paper’s content in front of a panel of qualified professionals and professors. Also known as a thesis defense , the panel examines and evaluates the presenter’s work and cross-questions their ideas to determine the validity of the paper’s contents. The goal is to defend your thesis and have the examiners approve of your contribution to research. 

2. Academic Job Interview

If you are applying for an academic job, you might need to present a research paper to a panel of professionals and interviewers. Your purpose is to inform, influence, and summarize your research findings and present them coherently to pass the interview and land the job. The audience of an academic job interview may include the department heads, HR managers, and other experts that are knowledgeable in the field. 

Pro Tip: Check out our free Job Interview PowerPoint template to prepare a compelling job interview presentation.

3. Conference/Seminar

Research papers are often presented at conferences and seminars to inform, educate, or inform the audience about the topic. The presentation influences the audience of your point of view about a certain topic, creates a name for the presenter in the field, and increases his/her network circle, resulting in more opportunities for research collaborations, jobs, and partnerships.

The audience of a research conference or seminar typically includes professionals with similar interests or experts in the same field looking for further collaboration opportunities and building up their networks.

4. Funding Request

Research findings can also be presented to a panel of investors to seek funding and obtain opportunities for expansion in any project. The research paper presentation’s purpose is to influence the investors and convince them of your ideas and propositions to receive the necessary funding for starting or expanding any project or business or visualize the concept. 

The audience of a funding request research presentation could be commercial sponsors, grant-giving bodies, and investors looking to solve a business problem and help qualified and experienced professionals get the funding they require. 

What should I do if I have to use the APA style?

APA style is a format for documenting references and includes in-text citations and citing sources at the end of each research paper. 

What Is APA Style?

APA stands for American Psychological Association , a commonly used style of documenting sources in research papers . The standard is used for scientific writing to help authors cite their findings and avoid plagiarism by giving credit to the researcher. It follows the author-date method for in-text citation, as well as a complete list of references at the end of the paper. 

Popularly used in psychology, education, and social science, the APA style was first introduced in 1929, when the Psychological Bulletin laid out its basic guidelines to help authors standardize their citations. APA formatIt makes it easier for readers and the audience to understand the text by knowing their sources and simplifies the research paper structure. 

How to Cite a Research Paper in a PowerPoint Presentation?

When presenting a research paper, you can cite sources in-text on each slide to let the audience know the source of information. You can also cite crucial references verbally in correspondence with in-text citations. 

To verbally let the audience know the source, you can express the argument by stating who said it. The words claim, add, argues, illustrates, grants, notes, observes, suggests, etc. are often used to cite the reference. 

To cite a PowerPoint in text using APA format, you must include the author and date of the presentation.

Another way is to include a reference slide at the end of your presentation. The audience must know where you got the statistics, visuals, facts, and other information. There should be no room for questioning the validity or source of any data. 

How to Present a Research Paper in 5 Minutes?

If time is restricted in your research paper presentation, it is important to plan ahead and practice discussing each slide in 1 minute or less. Give more time to the most important slides, such as the problem, motivation, and its proposed approach or solution. Just stick to 5 to 7 slides and no more to keep your talk short and avoid any unnecessary delays on swiping slides back and forth.  

How to Convert a Research Paper Into a Presentation?

You can convert a research paper into a presentation by summarizing its main points and focusing on the crucial aspects of the subject matter . Research papers are often quite detailed, and compiling them in a 5 to 10-minute presentation can be challenging. Still, it is possible to gather only the most important data to support your arguments. You can also use ChatGPT with GPT 4 with a proper prompt to summarize the research paper and prepare the presentation outline and content. You can use GPT-4V alongside ChatGPT to merge the text and visual comprehension to make the presentation more appealing.

Make clear statements about the problem and its solutions, and convey the essence of the research paper in bullets to keep it short. Visualize your findings to make them appealing and easy to understand, and indicate the essential details through charts, graphs, and other graphics to make them interesting.  

Examples of PowerPoint Templates for Research Presentation

Powerpoint is a great tool to create your research paper presentation effectively, but one might not have the time to design and build the perfect theme for the research paper from scratch. Therefore, you can use research paper PowerPoint presentation examples to create your own research presentation within just a few minutes. 

• Free Scientific Research PowerPoint Template 
• Free Chemistry Presentation Template
• Free Science Research PowerPoint Template
• Free Internet Research PowerPoint Template
• Research Powerpoint Template
• Free Market Research PowerPoint Template
• Free Research Timeline PowerPoint Template

Alternatively, you can download a Free Research Presentation Template for PowerPoint & Google Slides from reputable PPT template providers, such as SlideModel. Here is a preview of the free Research PowerPoint template.

This presentation slide template for PowerPoint contains useful slides that you can use to present a Research Paper to an audience. For example, find out slides such as Introduction, Research Questions & Hypotheses, Literature Review & Theory, Methods & Data Collection, Data Presentation & Findings as well as slides with infographics & data charts. Finally, present your conclusions with a simple & neat conclusions slide as the last slide of the presentation.

An effective research paper presentation needs to be engaging, interactive, and memorable, and that can only be achieved by properly planning out the outline of your presentation and adding the necessary information . Combining text with graphics, asking relevant questions, and answering them clearly during the presentation will establish your authority on the matter and show you as an expert researcher.

Furthermore, having a decent research paper presentation design can help you achieve success in your presentation. The templates provided can help you design and create the perfect research presentation for your upcoming talk and help you attain your purpose.  

Leave a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Sign up to our newsletter

We will send you our curated collections to your email weekly. No spam, promise!

Got any suggestions?

We want to hear from you! Send us a message and help improve Slidesgo

Top searches

Trending searches

49 templates

18 templates

40 templates

american football

16 templates

41 templates

tropical rainforest

29 templates

Research Presentation templates

Customize our free themes and templates for google slides or powerpoint and explain what your research is about. these designs are easy to edit, so that will speed things up.

Formal Research Paper Slideshow

Have you seen these slides? They are perfect for presenting your research paper! First of all, because we have included all the necessary sections of this type of work, such as hypothesis, objectives, methodology, analysis and the conclusions of the paper. The second reason is that the formal style will...

Economics Thesis

If numbers, exchange rates, money and trading are your forte, odds are you’re already working on an economics thesis for your master’s degree. Defending your dissertation is the last step and the most difficult one, but Slidesgo can help you. Here’s our new free presentation template with a focus on...

AP Research Defense for High School

AP, or Advanced Placement, is a North American educational program that offers a rigorous course designed to challenge and prepare high school students for their future careers and academic pursuits. It requires students to conduct independent research, write a lengthy academic paper, and present their findings to a panel of...

Biosafety Breakthrough

Download the "Biosafety Breakthrough" presentation for PowerPoint or Google Slides.Treating diseases involves a lot of prior research and clinical trials. But whenever there’s a new discovery, a revolutionary finding that opens the door to new treatments, vaccines or ways to prevent illnesses, it’s great news. Should there be a medical...

Premium template

Unlock this template and gain unlimited access

Pregnancy Breakthrough

Giving birth to a baby is a beautiful occasion, a manifestation of love between two people. Obstetrics are key during pregnancy, so how about giving a presentation about the latest breakthrough in this field? Our free medical template will come in handy.

Medical Anatomy Poster

Download the "Medical Anatomy Poster" presentation for PowerPoint or Google Slides. Healthcare goes beyond curing patients and combating illnesses. Raising awareness about diseases, informing people about prevention methods, discussing some good practices, or even talking about a balanced diet—there are many topics related to medicine that you could be sharing...

Nursing Capstone

In medical contexts, a capstone is often the final course in a nursing degree, a project of vital importance. It’s very demanding, so if you need help with the presentation, use this free professional template. Leave the design to us and focus on your data!

Urinary Catheter Implantation Breakthrough

Download the "Urinary Catheter Implantation Breakthrough" presentation for PowerPoint or Google Slides.Treating diseases involves a lot of prior research and clinical trials. But whenever there’s a new discovery, a revolutionary finding that opens the door to new treatments, vaccines or ways to prevent illnesses, it’s great news. Should there be...

Research Methods Lesson

If you deal with Science, it’s important to learn more about research methods. Teach your students about them with this presentation full of illustrations and drawings related to labs. Use graphs, maps, tables and overview diagrams to support your lecture in a visual way!

Thesis Proposal and Writing - Master of Arts in History

Download the "Thesis Proposal and Writing - Master of Arts in History" presentation for PowerPoint or Google Slides. As university curricula increasingly incorporate digital tools and platforms, this template has been designed to integrate with presentation software, online learning management systems, or referencing software, enhancing the overall efficiency and effectiveness...

Project Research Infographics

Download the "Project Research Infographics" template for PowerPoint or Google Slides and discover the power of infographics. An infographic resource gives you the ability to showcase your content in a more visual way, which will make it easier for your audience to understand your topic. Slidesgo infographics like this set...

Research Project Proposal

Before embarking yourself on a new project, especially if it’s about research, you need to set out a proposal to explain its viability. Here at Slidesgo we’re offering this theme that you can actually use for any kind of project, regardless of the topic.

Elegant Black & White Thesis Defense

Present your research findings with grace and assertiveness through this template. Available for Google Slides and PowerPoint, this design set offers minimalistic charm with its simple, gray scale elegance. The template not only provides a polished platform to showcase your thesis but also ensures seamless and efficient delivery of your...

Cranioencephalic Trauma Case Report

Download the "Cranioencephalic Trauma Case Report" presentation for PowerPoint or Google Slides. A clinical case is more than just a set of symptoms and a diagnosis. It is a unique story of a patient, their experiences, and their journey towards healing. Each case is an opportunity for healthcare professionals to...

Cahuachi's Role in Nazca Culture Thesis Defense

Download the "Cahuachi's Role in Nazca Culture Thesis Defense" presentation for PowerPoint or Google Slides. Congratulations, you have finally finished your research and made it to the end of your thesis! But now comes the big moment: the thesis defense. You want to make sure you showcase your research in...

Data Analysis for Marketing Strategies

With the amount of data available through various digital platforms, it's easier than ever to determine the trends and preferences of your target audience. By collecting and analyzing data, marketers can create highly personalized campaigns that align with the exact needs and wants of their customers. If you're trying to...

Surgical Case Report

Download the "Surgical Case Report" presentation for PowerPoint or Google Slides. A clinical case is more than just a set of symptoms and a diagnosis. It is a unique story of a patient, their experiences, and their journey towards healing. Each case is an opportunity for healthcare professionals to exercise...

SWOT Analysis Infographics

Discover the strengths, weaknesses, opportunities and threats of your own company performing a SWOT analysis. Use this basic strategic planning to evaluate your position with these new infographics created by Slidesgo.

• Page 1 of 79

New! Make quick presentations with AI

Slidesgo AI presentation maker puts the power of design and creativity in your hands, so you can effortlessly craft stunning slideshows in minutes.

Register for free and start editing online

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

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 18 April 2024

Reducing supply risk of critical materials for clean energy via foreign direct investment

• Xin Sun 1 , 2 ,
• Han Hao   ORCID: orcid.org/0000-0001-7542-4746 1 , 2 , 3 ,
• Clara Galeazzi 4 , 5 ,
• Tomer Fishman   ORCID: orcid.org/0000-0003-4405-2382 6 ,
• Dengye Xun 1 , 2 ,
• Magnus Ericsson   ORCID: orcid.org/0000-0002-6395-1001 7 ,
• Gang Liu 8 , 9 ,
• I-Yun L. Hsieh   ORCID: orcid.org/0000-0002-1668-4094 10 ,
• Zongwei Liu 1 , 3 &
• Fuquan Zhao 1 , 3  

Nature Sustainability ( 2024 ) Cite this article

Metrics details

• Energy management
• Environmental impact

Existing research on the security of the supply of critical materials for clean energy generally aggregates information at the country level, a practice that obscures the extensive role of foreign direct investment (FDI) in the production of critical materials. FDI refers to an ownership stake in a company or project by an overseas investor. Here we establish a database for global mining of lithium, cobalt, nickel and platinum at company level, covering 240 countries and regions. We show that 47% of lithium, 71% of cobalt, 41% of nickel and 34% of platinum mined in 2019 were under FDI. We then explore how FDI may affect supply risks by proposing a supply risk index that allocates production of the critical materials to the country of origin of investors instead of the country where production is located. We present upper and lower bounds of the supply risk index that reflect scenarios where either all investors or only state investors prioritize the home-country demand, respectively. This study presents an approach for assessing the national supply risks of critical materials, considering the geographical allocation of FDI.

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

Access options

Access Nature and 54 other Nature Portfolio journals

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

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

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

Data availability

The information on country affiliation of companies is from the ORBIS database of Bureau van Dijk 58 . The ownership hierarchy of mine owners and stakeholders was collected from annual reports, public statements and news reports of relevant companies, consulting groups and information services platforms. Geographical production and international trade of four materials are from the United Nations Comtrade database 54 , United States Geological Survey Reports 14 and our previous studies 8 , 19 , 36 , 37 , 66 . Source data are available in Supplementary Information .

World Energy Outlook 2021 (International Energy Agency, 2021); https://www.iea.org/reports/world-energy-outlook-2021

2022 Final List of Critical Minerals (US Geological Survey, Department of the Interior, 2023); https://www.federalregister.gov/documents/2022/02/24/2022-04027/2022-final-list-of-critical-minerals

Study on the Critical Raw Materials for the EU 2023 – Final Report (European Commission, 2020); https://op.europa.eu/en/publication-detail/-/publication/57318397-fdd4-11ed-a05c-01aa75ed71a1

The Role of Critical Minerals in Clean Energy Transitions (International Energy Agency, 2021).

Schrijvers, D. et al. A review of methods and data to determine raw material criticality. Resour. Conserv. Recycl. 155 , 104617 (2020).

Article   Google Scholar  

Tian, X. et al. Features of critical resource trade networks of lithium-ion batteries. Resour. Policy https://doi.org/10.1016/j.resourpol.2021.102177 (2021).

Rui, X., Geng, Y., Sun, X., Hao, H. & Xiao, S. Dynamic material flow analysis of natural graphite in China for 2001–2018. Resour. Conserv. Recycl . https://doi.org/10.1016/j.resconrec.2021.105732 (2021).

Xun, D. et al. Mapping global fuel cell vehicle industry chain and assessing potential supply risks. Int. J. Hydrogen Energy 46 , 15097–15109 (2021).

Article   CAS   Google Scholar  

Geng, J. et al. Static material flow analysis of neodymium in China. J. Ind. Ecol. 25 , 114–124 (2020).

Ali, S. H. et al. Mineral supply for sustainable development requires resource governance. Nature 543 , 367–372 (2017).

Goldthau, A. & Hughes, L. Protect global supply chains for low-carbon technologies. Nature 585 , 28–30 (2020).

Pell, R. et al. Towards sustainable extraction of technology materials through integrated approaches. Nat. Rev. Earth Environ. 2 , 665–679 (2021).

Bauer, C. et al. Charging sustainable batteries. Nat. Sustain. 5 , 176–178 (2022).

Mineral Commodity Summaries 2023 (United States Geological Survey, 2023); https://minerals.usgs.gov/minerals/pubs

Sun, X., Liu, Z., Zhao, F. & Hao, H. Global competition in the lithium-ion battery supply chain: a novel perspective for criticality analysis. Environ. Sci. Technol. 55 , 12180–12190 (2021).

Hao, H., Liu, Z., Zhao, F., Geng, Y. & Sarkis, J. Material flow analysis of lithium in China. Resour. Policy 51 , 100–106 (2017).

Liu, G. & Muller, D. B. Mapping the global journey of anthropogenic aluminum: a trade-linked multilevel material flow analysis. Environ. Sci. Technol. 47 , 11873–11881 (2013).

Nansai, K. et al. Global flows of critical metals necessary for low-carbon technologies: the case of neodymium, cobalt, and platinum. Environ. Sci. Technol. 48 , 1391–1400 (2014).

Sun, X., Hao, H., Zhao, F. & Liu, Z. Global lithium flow 1994–2015: implications for improving resource efficiency and security. Environ. Sci. Technol. 52 , 2827–2834 (2018).

Nansai, K. et al. Global mining risk footprint of critical metals necessary for low-carbon technologies: the case of neodymium, cobalt, and platinum in Japan. Environ. Sci. Technol. 49 , 2022–2031 (2015).

Sun, X., Hao, H., Hartmann, P., Liu, Z. & Zhao, F. Supply risks of lithium-ion battery materials: an entire supply chain estimation. Mater. Today Energy 14 , 100347 (2019).

Yan, W. et al. Rethinking Chinese supply resilience of critical metals in lithium-ion batteries. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2020.120719 (2020).

Greenwood, M., Wentker, M. & Leker, J. A region-specific raw material and lithium-ion battery criticality methodology with an assessment of NMC cathode technology. Appl. Energy https://doi.org/10.1016/j.apenergy.2021.117512 (2021).

van den Brink, S., Kleijn, R., Sprecher, B. & Tukker, A. Identifying supply risks by mapping the cobalt supply chain. Resour. Conserv. Recycl. https://doi.org/10.1016/j.resconrec.2020.104743 (2020).

Zhong, W. et al. Structure of international iron flow: based on substance flow analysis and complex network. Resour. Conserv. Recycl. 136 , 345–354 (2018).

Li, X. & Gallagher, K. P. Assessing the climate change exposure of foreign direct investment. Nat. Commun. 13 , 1451 (2022).

Global Locations (Livent, 2020); https://livent.com/company-overview/global-locations/#

Trost, J. N. & Dunn, J. B. Assessing the feasibility of the Inflation Reduction Act’s EV critical mineral targets. Nat. Sustain. 6 , 639 (2023).

Jain, E. & Obayashi, Y. Lynas gets $134 million funding from Japan to boost output. mining.com https://www.mining.com/web/lynas-gets-134-million-funding-from-japan-to-boost-output/ (2023).

Ericsson, M., Löf, O. & Löf, A. Chinese control over African and global mining—past, present and future. Miner. Econ. 33 , 153–181 (2020).

Gulley, A. L., McCullough, E. A. & Shedd, K. B. China’s domestic and foreign influence in the global cobalt supply chain. Resour. Policy 62 , 317–323 (2019).

Kaplinsky, R. & Morris, M. Chinese FDI in sub-Saharan Africa: engaging with large dragons. Eur. J. Dev. Res. 21 , 551–569 (2009).

Koyama, K. & Krane, J. Energy security through FDI: the legacy of early Japanese investment in the oil sectors of the Persian Gulf. Resour. Policy https://doi.org/10.1016/j.resourpol.2021.102165 (2021).

Olivetti, E. A., Ceder, G., Gaustad, G. G. & Fu, X. Lithium-ion battery supply chain considerations: analysis of potential bottlenecks in critical metals. Joule 1 , 229–243 (2017).

Sun, X., Ouyang, M. & Hao, H. Surging lithium price will not impede the electric vehicle boom. Joule 6 , 1738–1742 (2022).

Sun, X., Hao, H., Zhao, F. & Liu, Z. Tracing global lithium flow: a trade-linked material flow analysis. Resour. Conserv. Recycl. 124 , 50–61 (2017).

Sun, X., Hao, H., Liu, Z., Zhao, F. & Song, J. Tracing global cobalt flow: 1995–2015. Resour. Conserv. Recycl. 149 , 45–55 (2019).

Sun, X., Hao, H., Liu, Z. & Zhao, F. Insights into the global flow pattern of manganese. Resour. Policy 65 , 101578 (2020).

Xun, D., Hao, H., Sun, X., Liu, Z. & Zhao, F. End-of-life recycling rates of platinum group metals in the automotive industry: insight into regional disparities. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2020.121942 (2020).

Herfindahl–Hirschman Index (US Department of Justice, 2018); https://www.justice.gov/atr/herfindahl-hirschman-index

International Trade in Resources: A Biophysical Assessment (United Nations Environment Programme, 2015); https://wedocs.unep.org/20.500.11822/7427

Sustainable Trade in Resources: Global Material Flows, Circularity and Trade (United Nations Environment Programme, 2020); https://www.resourcepanel.org/reports/sustainable-trade-resources

World Investment Report 2023 (United Nations Conference on Trade and Development, 2023); https://unctad.org/system/files/official-document/wir2023_en.pdf

Wang, Q. et al. Urban mining of lithium: prospects, challenges and policy recommendations. Sci. Technol. Rev. 38 , 6–15 (2020).

Google Scholar  

Melin, H. E. et al. Global implications of the EU battery regulation. Science 373 , 384–387 (2021).

Bhargav, A., He, J., Gupta, A. & Manthiram, A. Lithium-sulfur batteries: attaining the critical metrics. Joule 4 , 285–291 (2020).

Beuse, M., Steffen, B. & Schmidt, T. S. Projecting the competition between energy-storage technologies in the electricity sector. Joule 4 , 2162–2184 (2020).

Sun, X., Liu, G., Hao, H., Liu, Z. & Zhao, F. Modeling potential impact of COVID-19 pandemic on global electric vehicle supply chain. iScience 25 , 103903 (2022).

Country Codes – ISO 3166 (International Organization for Standardization, 2013); https://www.iso.org/obp/ui/#search

2015 Minerals Yearbook Cobalt (United States Geological Survey, 2017); https://minerals.usgs.gov/minerals/pubs/commodity/cobalt

2015 Minerals Yearbook Nickel (United States Geological Survey, 2017); https://minerals.usgs.gov/minerals/pubs/commodity/nickel/

2016 Minerals Yearbook Lithium (United States Geological Survey, 2018); https://minerals.usgs.gov/minerals/pubs/commodity/lithium/

2017 Minerals Yearbook Platinum Group Metal (United States Geological Survey, 2020); https://www.usgs.gov/centers/national-minerals-information-center/platinum-group-metals-statistics-and-information

Trade Data (United Nations Comtrade, 2020); https://comtrade.un.org/data/

Trade Statistics (Korea Customs Service, 2020); http://www.customs.go.kr/kcshome/trade

Import and Export Data (China Customs Information Network, 2020); http://www.haiguan.info/

Customs Data Enquiry (Trade Social Networking Service, 2021); https://www.tradesns.com/en

ORBIS - Company Information Across The Globe (Bureau van Dijk, 2020).

Li, P., Liu, Q., Zhou, P. & Li, Y. Mapping global platinum supply chain and assessing potential supply risks. Front. Energy Res. https://doi.org/10.3389/fenrg.2023.1033220 (2023).

Helbig, C. et al. Extending the geopolitical supply risk indicator: application of life cycle sustainability assessment to the petrochemical supply chain of polyacrylonitrile-based carbon fibers. J. Clean. Prod. 137 , 1170–1178 (2016).

Gemechu, E. D., Helbig, C., Sonnemann, G., Thorenz, A. & Tuma, A. Import-based indicator for the geopolitical supply risk of raw materials in life cycle sustainability assessments. J. Ind. Ecol. 20 , 154–165 (2016).

Nassar, N. T. et al. Evaluating the mineral commodity supply risk of the U.S. manufacturing sector. Sci. Adv. 6 , eaay8647 (2020).

Szpiro, G. G. Hirschman versus Herfindahl: some topological properties for the use of concentration indexes. Math. Soc. Sci. 14 , 299–302 (1987).

Worldwide Governance Indicator (World Bank, 2019); http://info.worldbank.org/governance/wgi/index.aspx#home

Julio, M. & Elmira, A. Fraser Institute Annual: Survey of Mining Companies 2022 (Fraser Institute, 2023); https://www.fraserinstitute.org/sites/default/files/annual-survey-of-mining-companies-2022.pdf

Sun, X., Hao, H., Geng, Y., Liu, Z. & Zhao, F. Exploring the potential for improving material utilization efficiency to secure lithium supply for China’s battery supply chain. Fundam. Res. 4 , 167–177 (2024).

Download references

Acknowledgements

This study was sponsored by the National Natural Science Foundation of China (72122010 to H.H., 72334001 to G.L., 71774100 to H.H., 71991484 to G.L.) and the National Key R&D Program of China (2019YFC1908501 to H.H.).

Author information

Authors and affiliations.

State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China

Xin Sun, Han Hao, Dengye Xun, Zongwei Liu & Fuquan Zhao

Tsinghua-Rio Tinto Joint Research Center for Resources Energy and Sustainable Development, Tsinghua University, Beijing, China

Xin Sun, Han Hao & Dengye Xun

Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing, China

Han Hao, Zongwei Liu & Fuquan Zhao

Belfer Center for Science and International Affairs, Harvard University, Cambridge, MA, USA

Clara Galeazzi

Cambridge Centre for Environment, Energy and Natural Resource Governance (C-EENRG), University of Cambridge, Cambridge, UK

Institute of Environmental Sciences (CML), Leiden University, Leiden, the Netherlands

Tomer Fishman

Luleå University of Technology, Luleå, Sweden

Magnus Ericsson

College of Urban and Environmental Sciences, Peking University, Beijing, China

Institute of Carbon Neutrality, Peking University, Beijing, China

Department of Civil Engineering, National Taiwan University, Taipei, Taiwan

I-Yun L. Hsieh

You can also search for this author in PubMed   Google Scholar

Contributions

X.S. and H.H. conceived the idea, conducted the analysis and wrote the paper. C.G., T.F. and G.L. wrote sections of the paper and provided guidance on the paper. D.X. and M.E. provided data for the analysis. I-Y.L.H., Z.L. and F.Z. reviewed and edited the paper. H.H. and G.L. supervised the work and secured funding for the project.

Corresponding author

Correspondence to Han Hao .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Sustainability thanks Roderick Eggert, Maria Amélia Enriquez and Yosuke Shigetomi for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Supplementary information.

Supplementary Figs. 1–14, Discussion and Tables 1–11.

Reporting Summary

Rights and permissions.

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

Reprints and permissions

About this article

Cite this article.

Sun, X., Hao, H., Galeazzi, C. et al. Reducing supply risk of critical materials for clean energy via foreign direct investment. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01329-3

Download citation

Received : 04 October 2023

Accepted : 19 March 2024

Published : 18 April 2024

DOI : https://doi.org/10.1038/s41893-024-01329-3

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

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

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

UC experts present at national neurology conference

University of Cincinnati researchers will present abstracts at the American Academy of Neurology annual meeting 2024, April 13-18 in Denver, Colorado.

Two-component treatment leads to improvement for patients

Hani Kushlaf, MD. Photo/University of Cincinnati.

Late-onset Pompe disease (LOPD) is a rare, inherited genetic disease caused by the accumulation of glycogen, the body’s stored form of glucose, in muscles and other organs. Left untreated, the muscle weakness it causes can lead to the loss of the ability to walk and breathing impairment.

A research team led by UC’s Hani Kushlaf, MD, looked at the effect of a two-component enzyme replacement therapy (ERT) of drugs cipaglucosidase and miglustat (cipa+mig) compared to a single ERT drug, alglucosidase alfa (alg) and a placebo. UC researchers participated in the Phase 3 trials that led to the Food and Drug Administration approval of both ERT regimens.

“The research question was to look at the magnitude and practical significance of the effect of cip+mig versus alg using patient data from the PROPEL trial on outcomes that included motor function, pulmonary function, muscle strength, biomarkers and patient- and physician-reported quality of life,” said Kushlaf, associate professor and director of Neuromuscular Research and the Neuromuscular Disorders Division in UC’s Department of Neurology & Rehabilitation Medicine in the College of Medicine.

Patients who switched to the dual ERT regimen experienced improvement or stability across the measured outcomes with no worsening of outcomes. Those who remained on the single ERT drug plus placebo experienced worsening or stability across the measured outcomes.

“This analysis highlights the potential of cipaglucosidase+miglustat to become an important treatment option for patients with LOPD, including patients already on enzyme replacement therapy,” Kushlaf said.

This research was sponsored by Amicus Therapeutics, Inc.

Research team learns more about events following immunotherapy treatment

Luca Marsili, MD, PhD. Photo/University of Cincinnati.

Immune checkpoint inhibitors (ICIs), an immunotherapy that activates the body’s immune system to fight cancer cells, has revolutionized cancer treatment. But while boosting anti-tumor immunity, the treatments may cause severe neurological-immune related adverse events. 

“These neurological-immune-related adverse events include meningitis, encephalitis, demyelinating diseases, vasculitis, neuropathy, neuromuscular junction disorders and myopathy,” said Luca Marsili, MD, PhD, movement disorder fellow in the Department of Neurology and Rehabilitation Medicine in the University of Cincinnati College of Medicine.  

Marsili said the frequency of these adverse events, and the best way to manage them, is still largely unknown. 

A team led by Marsili and Alberto Espay, MD, reviewed reported neurological-immune-related adverse events in patients treated with immune checkpoint inhibitors at UC from 2011-2023. They found the adverse events are rare, affecting 28 patients out of 1,677 treated, or 1.66%. 

The adverse events were most often associated with melanoma treatment with pembrolizumab, a common immunotherapy treatment. 

“The adverse events were most expressed as peripheral neuropathies and encephalitis, manifesting early during treatment within a mean of 2.3 months after ICI initiation,” Marsili said. “Most ICIs, 68%, were discontinued, and in only 10.7% of cases they were restarted without complications.” 

Moving forward, the team said further research is needed to determine clinical susceptibility factors and appropriate timing of restarting ICI treatment after discontinuing due to an adverse event. They are also planning to do more detailed demographic and clinical comparisons of the 28 patients identified to have adverse events to see if there are any predictive factors like tumor type, age, sex or ethnicity. 

“This study is part of a broader project in collaboration with the University of Udine in Italy and with the Department of Internal Medicine at UC,” Marsili said. “We would like to gather a high number of participants to assess incidence/prevalence of these adverse events and also to raise awareness among neurologists on how to treat/manage them.”

Safe, effective treatment for Parkinson’s

Alberto Espay, MD. Photo/University of Cincinnati.

Alberto Espay, MD, will present findings recently published in the Lancet Neurology journal that found Parkinson’s disease medication delivered through an infusion pump is safe and effective at reducing symptoms for longer periods of time. 

Parkinson’s symptoms such as tremors, slowness and stiffness are caused by low levels of dopamine in the body. For decades, doctors have treated Parkinson’s by giving patients levodopa, the inactive substance in the brain that once converted makes dopamine.  

“Levodopa is a replacement strategy. We all make levodopa, but Parkinson's patients make less of it,” said Espay, co-principal investigator of the trial, the James J. and Joan A. Gardner Family Center for Parkinson’s Disease Research Endowed Chair in UC’s Department of Neurology and Rehabilitation Medicine and a physician at the UC Gardner Neuroscience Institute.  

Levodopa is most commonly administered orally, but this trial tested continuous, 24-hour levodopa delivery through a subcutaneous infusion pump. A total of 381 patients with Parkinson’s disease in 16 countries enrolled in the trial and were randomized to receive levodopa through the infusion pump or through traditional oral medication. 

The researchers found levodopa delivered through the infusion pump was safe and led to almost two hours a day (1.72) of additional “on time,” or the time when the medication is working and symptoms are lessened, compared to taking levodopa orally. 

“Once approved, this will become an important treatment strategy to consider for patients with Parkinson’s disease experiencing motor fluctuations not adequately controlled with medication,” he said. “Future studies will need to determine the durability of the long-term benefits and whether any safety issues could emerge, as well as how it might compare with deep brain stimulation.”

Impact Lives Here

The University of Cincinnati is leading public urban universities into a new era of innovation and impact. Our faculty, staff and students are saving lives, changing outcomes and bending the future in our city's direction.  Next Lives Here.

UC research being presented at AAN includes:

• Kushlaf presenting “ Effect Size Analysis of Cipaglucosidase Alfa Plus Miglustat Versus Alglucosidase Alfa in ERT-experienced Adults with Late-onset Pompe Disease in PROPEL .”
• Marsili and Espay presenting “ Neurological Immune-related Adverse Events of Immune Checkpoint Inhibitors: A Single-center Retrospective Study. ” 
• Espay presenting “ Efficacy of ND0612, a 24-hour Subcutaneous Levodopa/Carbidopa Infusion for People with Parkinson’s Disease Experiencing Motor Fluctuations: Subgroup-analyses from a Randomized, Controlled Phase 3 Study .”
• Stacie Demel, DO, PhD, a physician-researcher at the UC Gardner Neuroscience Institute and associate professor of clinical neurology and rehabilitation medicine in UC’s College of Medicine, presenting “ Methylation Patterns Differ Between ICH Cases and Controls .”
• Yang Yu, MD, UC medical resident/fellow, presenting “ Multiple Sclerosis in a Patient with Friedreich's Ataxia .”
• Rhonna Shatz, DO, adjunct associate professor, division director for behavioral neurology, and the Bob and Sandy Heimann Endowed Chair in Research and Education in Alzheimer’s Disease in the UC College of Medicine, presenting “ Identifying a Relationship Between Executive Dysfunction, Poor Sleep Hygiene/Sleep Apnea, and Ventriculomegaly in Cancer-related Cognitive Impairment (CRCI) ”

Featured illustration at top of brain. Photo/iStock/Onurdongel.

• Clinical Research
• Faculty Staff
• College of Medicine
• UC Gardner Neuroscience Institute
• Academic Health Center
• Neurology & Rehabilitation Medicine

Related Stories

Learning more about how cancer affects stroke risk.

October 16, 2023

A collaborative team led by University of Cincinnati, University of North Carolina and Duke University researchers is studying how specific cancers and treatments affect patients' risk of stroke.

UC trial tests tongue exercises to improve swallowing function after stroke

January 9, 2024

A new trial at the University of Cincinnati Gardner Neuroscience Institute, funded by a $660,000 National Institutes of Health (NIH) grant, will test an at-home tongue endurance exercise to improve patients’ swallowing function after a stroke.

Collaborative University of Cincinnati Cancer Center team opens Phase 2 brain tumor trial

March 26, 2024

A multidisciplinary team of University of Cincinnati Cancer Center researchers have opened a Phase 2 clinical trial to test a new combination treatment for glioblastomas, the most deadly form of brain tumors.

Recent Progress on Water-based Liquid Embolic Agents in Endovascular Treatment

• Published: 18 April 2024

Cite this article

• Yi Qi 1 &
• Hailong Fan 2  

Vascular embolization treatment, a minimally invasive surgery for various blood vessel-related conditions, has emerged as a crucial method in treating such as hemorrhage, arteriovenous malformation, aneurysms, and hypervascular tumors. Liquid embolic agents are gaining prominence due to their distinct advantage in infiltrating distal regions, expanding the scope of embolization beyond the reach of solid agents. Recent strides in biomaterials and technologies have spurred the development of novel liquid embolic agents, addressing challenges posed by traditional options. This mini-review provides a concise overview of the recent progress in water-based liquid embolic agents, highlighting their potential to overcome limitations associated with current embolic materials. By presenting selected research outcomes, we illuminate advancements that enhance the efficacy of liquid embolic agents. Furthermore, the review outlines essential properties for effective liquid embolic agents, offering insights for future developments in this field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Hu J., Albadawi H., Chong B. W., Deipolyi A. R., Sheth R. A., Khademhosseini A., Oklu R., Adv. Mater. , 2019 , 31 , 1901071.

Article   Google Scholar  

Altman R., Ferreira R., Barragan C., Bhojani N., Lajkosz K., Zorn K. C., Chughtai B., Annamalai G., Elterman D. S., BMC Urol. , 2024 , 24 , 22.

Article   PubMed   PubMed Central   Google Scholar  

Szmygin P., Szmygin M., Roman T., Jargietto T., Rola R., Neurol. Neurochir Pol. , 2023 , 57 , 305.

PubMed   Google Scholar  

Qi Y., Liu H., Jia J., Li J., Liu A., Jiang C., Clin. Neurol. Neurosurg. , 2020 , 195 , 105882.

Article   PubMed   Google Scholar  

Li Z., Di C., Li S., Yang X., Nie G., Acc. Chem. Res. , 2019 , 52 , 2703.

Article   CAS   PubMed   Google Scholar  

Bruno C. A., Jr., Meyers P. M., Interv. Neurol. , 2013 , 1 , 109.

Schimmel K., Ali M. K., Tan S. Y., Teng J., Do H. M., Steinberg G. K., Stevenson D. A., Spiekerkoetter E., Int. J. Mol. Sci. , 2021 , 22 , 9037.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Pan P., Weinsheimer S., Cooke D., Winkler E., Abla A., Kim H., Su H., J. Cereb. Blood Flow Metab. , 2021 , 41 , 3141.

Qi Y., Sun Y., Wang Y., Jia J., Zhong H., Yang H., Lv M., Liu H., BioMed Res. Int. , 2021 , 2021 , 9976541.

PubMed   PubMed Central   Google Scholar  

Liu H., Li C., Ren S., Li T., Zhong H., Jia J., Yang H., Qi Y., Feng J., Li Y., Wang Y., Stroke Vasc. Neurol. , 2023 , doi: https://doi.org/10.1136/svn-2022-002260 .

Wang C. Y., Hu J., Sheth R. A., Oklu R., Prog. Biomed. Eng. , 2020 , 2 , 012003.

Leyon J. J., Littlehales T., Rangarajan B., Hoey E. T., Ganeshan A., Curr. Probl. Diagn. Radiol. , 2014 , 43 , 35.

Qi Y., Xu T., Jiang C., Wang Y., Liu H., Neurosurg. Rev. , 2022 , 45 , 1513.

Ko G., Choi J. W., Lee N., Kim D., Hyeon T., Kim H.-C., Biomaterials , 2022 , 287 , 121634.

Piacentino F., Fontana F., Curti M., Macchi E., Coppola A., Ossola C., Giorgianni A., Marra P., Mosconi C., Ierardi A. M., Basile A., Golfieri R., Carrafiello G., Carcano G., Venturini M., J. Clin. Med. , 2021 , 10 , 4841.

Jiang Y., Zhang Y., Lu Z., Wang X., Bai S., Chen Y., Mao J., Liu G., ChemPhysMater , 2022 , 1 , 39.

Jin Z. P., Fan H. L., Osanai T., Nonoyama T., Kurokawa T., Hyodoh H., Matoba K., Takeuchi A., Gong J. P., Fujimura M., Proc. Natl. Acad. Sci. USA , 2022 , 119 , e2206685119.

Fan L., Duan M., Xie Z., Pan K., Wang X., Sun X., Wang Q., Rao W., Liu J., Small , 2020 , 16 , 1903421.

Article   CAS   Google Scholar  

Poursaid A., Jensen M. M., Huo E., Ghandehari H., J. Controlled Release , 2016 , 240 , 414.

Lord J., Britton H., Spain S. G., Lewis A. L., J. Mater. Chem. B , 2020 , 8 , 8207.

Pal A., Blanzy J., Gómez K. J. R., Preul M. C., Vernon B. L., Gels , 2023 , 9 , 378.

Li X., Ullah M. W., Li B., Chen H., Adv. Healthcare Mater. , 2023 , 12 , 2202787.

Froberg L., Helgstrand F., Clausen C., Steinmetz J., Eckardt H., J. Emerg. Trauma Shock , 2016 , 9 , 107.

Sasaki K., Okada T., Yamaguchi M., Ahmed M., Gentsu T., Ueshima E., Sofue K., Tanimura K., Yamada H., Sugimoto K., Murakami T., Emerg. Radiol. , 2021 , 28 , 1127.

Eriksson L.-G., Ljungdahl M., Sundbom M., Nyman R., J. Vasc. Interv. Radiol. , 2008 , 19 , 1413.

Fang Y., Han X., Liu L., Lou W., Int. Surg. J. , 2018 , 51 , 223.

Hickman D. A., Pawlowski C. L., Sekhon U. D. S., Marks J., Gupta A. S., Adv. Mater. , 2018 , 30 , 1700859.

Lawton M. T., Rutledge W. C., Kim H., Stapf C., Whitehead K. J., Li D. Y., Krings T., terBrugge K., Kondziolka D., Morgan M. K., Moon K., Spetzler R. F., Nat. Rev. Dis. Primers , 2015 , 1 , 15008.

Kumar A., Mittal M., Srivastava D., Jaetli V., Chaudhary S., Contemp. Clin. Dent. , 2017 , 8 , 482.

Behravesh S., Yakes W., Gupta N., Naidu S., Chong B. W., Khademhosseini A., Oklu R., Cardiovasc. Diagn. Ther. , 2016 , 6 , 557.

Nassiri N., Cirillo-Penn N. C., Thomas J., J. Vasc. Surg. , 2015 , 62 , 1667.

Zhao B., Fan Y., Xiong Y., Yin R., Zheng K., Li Z., Tan X., Yang H., Zhong M., J. Neurol. Sci. , 2016 , 371 , 62.

Molyneux A. J., Kerr R. S. C., Yu L.-M., Clarke M., Sneade M., Yarnold J. A., Sandercock P., Lancet , 2005 , 366 , 809.

Loffroy R., Guiu B., Cercueil J.-P., Krause D., Curr. Vasc. Pharmacol. , 2009 , 7 , 250.

Osuga K., Maeda N., Higashihara H., Hori S., Nakazawa T., Tanaka K., Nakamura M., Kishimoto K., Ono Y., Tomiyama N., Int. J. Clin. Oncol. , 2012 , 17 , 306.

Wu B., Zhou J., Ling G., Zhu D., Long Q., World J. Surg. Oncol. , 2018 , 16 , 69.

Dubbelboer I. R., Sjögren E., Lennernäs H., AAPS J. , 2018 , 20 , 96.

Ravina J. H., Ciraru-Vigneron N., Bouret J. M., Herbreteau D., Houdart E., Aymard A., Merland J. J., Lancet , 1995 , 346 , 671.

Pisco J. M., Bilhim T., Pinheiro L. C., Fernandes L., Pereira J., Costa N. V., Duarte M., Oliveira A. G., J. Vasc. Interv. Radiol. , 2016 , 27 , 1115.

Malling B., Røder M. A., Brasso K., Forman J., Taudorf M., Lönn L., Eur. Radiol. , 2019 , 29 , 287.

Hu X.-E., Shi Y.-R., Zhu X., Tian K.-W., Xu X.-L., J. Drug Delivery Sci. Technol. , 2023 , 80 , 104107.

Lee B. H., West B., McLemore R., Pauken C., Vernon B. L., Biomacromolecules , 2006 , 7 , 2059.

Shi X., Gao H., Dai F., Feng X., Liu W., Biomater. Sci. , 2016 , 4 , 1673.

Li H., Qian K., Zhang H., Li L., Yan L., Geng S., Zhao H., Zhang H., Xiong B., Li Z., Zheng C., Zhao Y., Yang X., Chem. Eng. J. , 2021 , 418 , 129534.

Li X., Liu W., Ye G., Zhang B., Zhu D., Yao K., Liu Z., Sheng X., Biomaterials , 2005 , 26 , 7002.

Xie W., Li H., Yu H., Zhou H., Guo A., Yao Q., Zhang L., Zhao Y., Tian H., Li L., Nanoscale , 2023 , 15 , 1835.

Li L., Cao Y., Zhang H., Zheng M., Xing J., Zheng C., Zhao Y., Yang X., J. Nanobiotechnol. , 2023 , 21 , 413.

Zhou H., Xie W., Guo A., Chen B., Hu S., Zheng M., Yu H., Tian H., Li L., Des. Monomers Polym. , 2023 , 26 , 31.

Zhao H., Zheng C., Feng G., Zhao Y., Liang H., Wu H., Zhou G., Liang B., Wang Y., Xia X., AJNR Am. J. Neuroradiol. , 2013 , 34 , 169.

Zhao Y., Zheng C., Wang Q., Fang J., Zhou G., Zhao H., Yang Y., Xu H., Feng G., Yang X., Adv. Funct. Mater. , 2011 , 21 , 2035.

Li H., Sun H., Liu Y., Yuan B., Hu J., Jiang Y., Li Q., Cao S., Liu H., Xiao B., Shi P., Yang X., Wang S., Zhao Y., Environ. Sci.: Nano , 2023 , 10 , 3357.

CAS   Google Scholar  

Bearat H. H., Preul M. C., Vernon B. L., J. Biomed. Mater. Res., Part A , 2013 , 101A , 2515.

Huang W., Rollett A., Kaplan D. L., Expert Opin. Drug Delivery , 2015 , 12 , 779.

Cappello J., Crissman J. W., Crissman M., Ferrari F. A., Textor G., Wallis O., Whitledge J. R., Zhou X., Burman D., Aukerman L., Stedronsky E. R., J. Controlled Release , 1998 , 53 , 105.

Poursaid A., Price R., Tiede A., Olson E., Huo E., McGill L., Ghandehari H., Cappello J., Biomaterials , 2015 , 57 , 142.

Poursaid A., Jensen M. M., Nourbakhsh I., Weisenberger M., Hellgeth J. W., Sampath S., Cappello J., Ghandehari H., Mol. Pharmaceutics , 2016 , 13 , 2736.

Dumortier G., Grossiord J. L., Agnely F., Chaumeil J. C., Pharm. Res. , 2006 , 23 , 2709.

Russo E., Villa C., Pharmaceutics , 2019 , 11 , 671.

Raymond J., Metcalfe A., Salazkin I., Schwarz A., Biomaterials , 2004 , 25 , 3983.

San Norberto E. M., Taylor J. H., Carrera S., Vaquero C., J. Vasc. Surg. , 2012 , 56 , 1782.

Huang L., Shen M., Li R., Zhang X., Sun Y., Gao P., Fu H., Liu H., He Y., Du Y., Cao J., Duan Y., Oncotarget , 2016 , 7 , 73280

Yang H., Lei K., Zhou F., Yang X., An Q., Zhu W., Yu L., Ding J., Mater. Sci. Eng. C , 2019 , 102 , 606.

Kang S. I., Na K., Bae Y. H., Macromol. Symp. , 2001 , 172 , 149.

Han S. K., Na K., Bae Y. H., Colloids Surf. A , 2003 , 214 , 49.

Shim W. S., Yoo J. S., Bae Y. H., Lee D. S., Biomacromolecules , 2005 , 6 , 2930.

Lym J. S., Nguyen Q. V., Ahn D. W., Huynh C. T., Jae H. J., Kim Y. I., Lee D. S., Acta Biomater. , 2016 , 41 , 253.

Nguyen Q. V., Lee M. S., Lym J. S., Kim Y. I., Jae H. J., Lee D. S., J. Mater. Chem. B , 2016 , 4 , 6524.

Nguyen Q. V., Lym J. S., Huynh C. T., Kim B. S., Jae H. J., Kim Y. I., Lee D. S., Polym. Chem. , 2016 , 7 , 5805.

Huynh C. T., Nguyen M. K., Kim J. H., Kang S. W., Kim B. S., Lee D. S., Soft Matter , 2011 , 7 , 4974.

Huynh C. T., Nguyen Q. V., Lym J. S., Kim B. S., Huynh D. P., Jae H. J., Kim Y. I., Lee D. S., RSC Adv. , 2016 , 6 , 47687.

Lu D., Wang J., Li Y., Zhang Y., Yu L., Xu T., Guo H., Zhang Y., Wang X., Wang X., Teng G., Lei Z., ACS Cent. Sci. , 2020 , 6 , 1977.

Fries F., Tomori T., Schulz-Schaeffer W. J., Jones J., Yilmaz U., Kettner M., Simgen A., Reith W., Mühl-Benninghaus R., J. Neurointerv. Surg. , 2022 , 14 , 286.

Jones J. P., Sima M., O’Hara R. G., Stewart R. J., Adv. Healthcare Mater. , 2016 , 5 , 795.

Avery R. K., Albadawi H., Akbari M., Zhang Y. S., Duggan M. J., Sahani D. V., Olsen B. D., Khademhosseini A., Oklu R., Sci. Transl. Med. , 2016 , 8 , 365ra156.

Smith S. A., Travers R. J., Morrissey J. H., Crit. Rev. Biochem. Mol. Biol. , 2015 , 50 , 326.

Fan H. L., Guo H. L., Wang J. H., Gong J. P., Giant , 2020 , 1 , 100005.

Israelachvili J. N., London: Elsevier. 3rd edition , 2010 .

Fan H. L., Wang J. H., Tao Z., Huang J. C., Rao P., Kurokawa T., Gong J. P., Nat. Commun. , 2019 , 10 , 5127.

Fan H. L., Cai Y. R., Gong J. P., Sci. China: Chem. , 2021 , 64 , 1560.

Download references

Acknowledgements

This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (Nos. JP21K14676, JP24K17728) and the Project of the Institute for Chemical Reaction Design and Discovery (WPI-ICReDD) established by the World Premier International Research Initiative (WPI), MEXT, Japan.

Author information

Authors and affiliations.

Department of Neurosurgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, 060-8638, Japan

Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, 001-0021, Japan

Hailong Fan

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Hailong Fan .

Ethics declarations

The authors declare no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Qi, Y., Fan, H. Recent Progress on Water-based Liquid Embolic Agents in Endovascular Treatment. Chem. Res. Chin. Univ. (2024). https://doi.org/10.1007/s40242-024-4060-2

Download citation

Received : 13 March 2024

Accepted : 03 April 2024

Published : 18 April 2024

DOI : https://doi.org/10.1007/s40242-024-4060-2

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Endovascular treatment
• Lquid embolic agent
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

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

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

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Health Serv Res
• v.42(1 Pt 1); 2007 Feb

Preparing and Presenting Effective Research Posters

Associated data.

APPENDIX A.2. Comparison of Research Papers, Presentations, and Posters—Contents.

Posters are a common way to present results of a statistical analysis, program evaluation, or other project at professional conferences. Often, researchers fail to recognize the unique nature of the format, which is a hybrid of a published paper and an oral presentation. This methods note demonstrates how to design research posters to convey study objectives, methods, findings, and implications effectively to varied professional audiences.

A review of existing literature on research communication and poster design is used to identify and demonstrate important considerations for poster content and layout. Guidelines on how to write about statistical methods, results, and statistical significance are illustrated with samples of ineffective writing annotated to point out weaknesses, accompanied by concrete examples and explanations of improved presentation. A comparison of the content and format of papers, speeches, and posters is also provided.

Each component of a research poster about a quantitative analysis should be adapted to the audience and format, with complex statistical results translated into simplified charts, tables, and bulleted text to convey findings as part of a clear, focused story line.

Conclusions

Effective research posters should be designed around two or three key findings with accompanying handouts and narrative description to supply additional technical detail and encourage dialog with poster viewers.

An assortment of posters is a common way to present research results to viewers at a professional conference. Too often, however, researchers treat posters as poor cousins to oral presentations or published papers, failing to recognize the opportunity to convey their findings while interacting with individual viewers. By neglecting to adapt detailed paragraphs and statistical tables into text bullets and charts, they make it harder for their audience to quickly grasp the key points of the poster. By simply posting pages from the paper, they risk having people merely skim their work while standing in the conference hall. By failing to devise narrative descriptions of their poster, they overlook the chance to learn from conversations with their audience.

Even researchers who adapt their paper into a well-designed poster often forget to address the range of substantive and statistical training of their viewers. This step is essential for those presenting to nonresearchers but also pertains when addressing interdisciplinary research audiences. Studies of policymakers ( DiFranza and the Staff of the Advocacy Institute 1996 ; Sorian and Baugh 2002 ) have demonstrated the importance of making it readily apparent how research findings apply to real-world issues rather than imposing on readers to translate statistical findings themselves.

This methods note is intended to help researchers avoid such pitfalls as they create posters for professional conferences. The first section describes objectives of research posters. The second shows how to describe statistical results to viewers with varied levels of statistical training, and the third provides guidelines on the contents and organization of the poster. Later sections address how to prepare a narrative and handouts to accompany a research poster. Because researchers often present the same results as published research papers, spoken conference presentations, and posters, Appendix A compares similarities and differences in the content, format, and audience interaction of these three modes of presenting research results. Although the focus of this note is on presentation of quantitative research results, many of the guidelines about how to prepare and present posters apply equally well to qualitative studies.

WHAT IS A RESEARCH POSTER?

Preparing a poster involves not only creating pages to be mounted in a conference hall, but also writing an associated narrative and handouts, and anticipating the questions you are likely to encounter during the session. Each of these elements should be adapted to the audience, which may include people with different levels of familiarity with your topic and methods ( Nelson et al. 2002 ; Beilenson 2004 ). For example, the annual meeting of the American Public Health Association draws academics who conduct complex statistical analyses along with practitioners, program planners, policymakers, and journalists who typically do not.

Posters are a hybrid form—more detailed than a speech but less than a paper, more interactive than either ( Appendix A ). In a speech, you (the presenter) determine the focus of the presentation, but in a poster session, the viewers drive that focus. Different people will ask about different facets of your research. Some might do policy work or research on a similar topic or with related data or methods. Others will have ideas about how to apply or extend your work, raising new questions or suggesting different contrasts, ways of classifying data, or presenting results. Beilenson (2004) describes the experience of giving a poster as a dialogue between you and your viewers.

By the end of an active poster session, you may have learned as much from your viewers as they have from you, especially if the topic, methods, or audience are new to you. For instance, at David Snowdon's first poster presentation on educational attainment and longevity using data from The Nun Study, another researcher returned several times to talk with Snowdon, eventually suggesting that he extend his research to focus on Alzheimer's disease, which led to an important new direction in his research ( Snowdon 2001 ). In addition, presenting a poster provides excellent practice in explaining quickly and clearly why your project is important and what your findings mean—a useful skill to apply when revising a speech or paper on the same topic.

WRITING FOR A VARIED PROFESSIONAL AUDIENCE

Audiences at professional conferences vary considerably in their substantive and methodological backgrounds. Some will be experts on your topic but not your methods, some will be experts on your methods but not your topic, and most will fall somewhere in between. In addition, advances in research methods imply that even researchers who received cutting-edge methodological training 10 or 20 years ago might not be conversant with the latest approaches. As you design your poster, provide enough background on both the topic and the methods to convey the purpose, findings, and implications of your research to the expected range of readers.

Telling a Simple, Clear Story

Write so your audience can understand why your work is of interest to them, providing them with a clear take-home message that they can grasp in the few minutes they will spend at your poster. Experts in communications and poster design recommend planning your poster around two to three key points that you want your audience to walk away with, then designing the title, charts, and text to emphasize those points ( Briscoe 1996 ; Nelson et al. 2002 ; Beilenson 2004 ). Start by introducing the two or three key questions you have decided will be the focus of your poster, and then provide a brief overview of data and methods before presenting the evidence to answer those questions. Close with a summary of your findings and their implications for research and policy.

A 2001 survey of government policymakers showed that they prefer summaries of research to be written so they can immediately see how the findings relate to issues currently facing their constituencies, without wading through a formal research paper ( Sorian and Baugh 2002 ). Complaints that surfaced about many research reports included that they were “too long, dense, or detailed,” or “too theoretical, technical, or jargony.” On average, respondents said they read only about a quarter of the research material they receive for detail, skim about half of it, and never get to the rest.

To ensure that your poster is one viewers will read, understand, and remember, present your analyses to match the issues and questions of concern to them, rather than making readers translate your statistical results to fit their interests ( DiFranza and the Staff of the Advocacy Institute 1996 ; Nelson et al. 2002 ). Often, their questions will affect how you code your data, specify your model, or design your intervention and evaluation, so plan ahead by familiarizing yourself with your audience's interests and likely applications of your study findings. In an academic journal article, you might report parameter estimates and standard errors for each independent variable in your regression model. In the poster version, emphasize findings for specific program design features, demographic, or geographic groups, using straightforward means of presenting effect size and statistical significance; see “Describing Numeric Patterns and Contrasts” and “Presenting Statistical Test Results” below.

The following sections offer guidelines on how to present statistical findings on posters, accompanied by examples of “poor” and “better” descriptions—samples of ineffective writing annotated to point out weaknesses, accompanied by concrete examples and explanations of improved presentation. These ideas are illustrated with results from a multilevel analysis of disenrollment from the State Children's Health Insurance Program (SCHIP; Phillips et al. 2004 ). I chose that paper to show how to prepare a poster about a sophisticated quantitative analysis of a topic of interest to HSR readers, and because I was a collaborator in that study, which was presented in the three formats compared here—as a paper, a speech, and a poster.

Explaining Statistical Methods

Beilenson (2004) and Briscoe (1996) suggest keeping your description of data and methods brief, providing enough information for viewers to follow the story line and evaluate your approach. Avoid cluttering the poster with too much technical detail or obscuring key findings with excessive jargon. For readers interested in additional methodological information, provide a handout and a citation to the pertinent research paper.

As you write about statistical methods or other technical issues, relate them to the specific concepts you study. Provide synonyms for technical and statistical terminology, remembering that many conferences of interest to policy researchers draw people from a range of disciplines. Even with a quantitatively sophisticated audience, don't assume that people will know the equivalent vocabulary used in other fields. A few years ago, the journal Medical Care published an article whose sole purpose was to compare statistical terminology across various disciplines involved in health services research so that people could understand one another ( Maciejewski et al. 2002 ). After you define the term you plan to use, mention the synonyms from the various fields represented in your audience.

Consider whether acronyms are necessary on your poster. Avoid them if they are not familiar to the field or would be used only once or twice on your poster. If you use acronyms, spell them out at first usage, even those that are common in health services research such as “HEDIS®”(Health Plan Employer Data and Information Set) or “HLM”(hierarchical linear model).

Poor: “We use logistic regression and a discrete-time hazards specification to assess relative hazards of SCHIP disenrollment, with plan level as our key independent variable.” Comment: Terms like “discrete-time hazards specification” may be confusing to readers without training in those methods, which are relatively new on the scene. Also the meaning of “SCHIP” or “plan level” may be unfamiliar to some readers unless defined earlier on the poster.

Better: “Chances of disenrollment from the State Children's Health Insurance Program (SCHIP) vary by amount of time enrolled, so we used hazards models (also known as event history analysis or survival analysis) to correct for those differences when estimating disenrollment patterns for SCHIP plans for different income levels.” Comment: This version clarifies the terms and concepts, naming the statistical method and its synonyms, and providing a sense of why this type of analysis is needed.

To explain a statistical method or assumption, paraphrase technical terms and illustrate how the analytic approach applies to your particular research question and data:

Poor : “The data structure can be formulated as a two-level hierarchical linear model, with families (the level-1 unit of analysis) nested within counties (the level-2 unit of analysis).” Comment: Although this description would be fine for readers used to working with this type of statistical model, those who aren't conversant with those methods may be confused by terminology such as “level-1” and “unit of analysis.”

Better: “The data have a hierarchical (or multilevel) structure, with families clustered within counties.” Comment: By replacing “nested” with the more familiar “clustered,” identifying the specific concepts for the two levels of analysis, and mentioning that “hierarchical” and “multilevel” refer to the same type of analytic structure, this description relates the generic class of statistical model to this particular study.

Presenting Results with Charts

Charts are often the preferred way to convey numeric patterns, quickly revealing the relative sizes of groups, comparative levels of some outcome, or directions of trends ( Briscoe 1996 ; Tufte 2001 ; Nelson et al. 2002 ). As Beilenson puts it, “let your figures do the talking,” reducing the need for long text descriptions or complex tables with lots of tiny numbers. For example, create a pie chart to present sample composition, use a simple bar chart to show how the dependent variable varies across subgroups, or use line charts or clustered bar charts to illustrate the net effects of nonlinear specifications or interactions among independent variables ( Miller 2005 ). Charts that include confidence intervals around point estimates are a quick and effective way to present effect size, direction, and statistical significance. For multivariate analyses, consider presenting only the results for the main variables of interest, listing the other variables in the model in a footnote and including complex statistical tables in a handout.

Provide each chart with a title (in large type) that explains the topic of that chart. A rhetorical question or summary of the main finding can be very effective. Accompany each chart with a few annotations that succinctly describe the patterns in that chart. Although each chart page should be self-explanatory, be judicious: Tufte (2001) cautions against encumbering your charts with too much “nondata ink”—excessive labeling or superfluous features such as arrows and labels on individual data points. Strive for a balance between guiding your readers through the findings and maintaining a clean, uncluttered poster. Use chart types that are familiar to your expected audience. Finally, remember that you can flesh out descriptions of charts and tables in your script rather than including all the details on the poster itself; see “Narrative to Accompany a Poster.”

Describing Numeric Patterns and Contrasts

As you describe patterns or numeric contrasts, whether from simple calculations or complex statistical models, explain both the direction and magnitude of the association. Incorporate the concepts under study and the units of measurement rather than simply reporting coefficients (β's) ( Friedman 1990 ; Miller 2005 ).

Poor: “Number of enrolled children in the family is correlated with disenrollment.” Comment: Neither the direction nor the size of the association is apparent.

Poor [version #2]: “The log-hazard of disenrollment for one-child families was 0.316.” Comment: Most readers find it easier to assess the size and direction from hazards ratios (a form of relative risk) instead of log-hazards (log-relative risks, the β's from a hazards model).

Better: “Families with only one child enrolled in the program were about 1.4 times as likely as larger families to disenroll.” Comment: This version explains the association between number of children and disenrollment without requiring viewers to exponentiate the log-hazard in their heads to assess the size and direction of that association. It also explicitly identifies the group against which one-child families are compared in the model.

Presenting Statistical Test Results

On your poster, use an approach to presenting statistical significance that keeps the focus on your results, not on the arithmetic needed to conduct inferential statistical tests. Replace standard errors or test statistics with confidence intervals, p- values, or symbols, or use formatting such as boldface, italics, or a contrasting color to denote statistically significant findings ( Davis 1997 ; Miller 2005 ). Include the detailed statistical results in handouts for later perusal.

To illustrate these recommendations, Figures 1 and ​ and2 2 demonstrate how to divide results from a complex, multilevel model across several poster pages, using charts and bullets in lieu of the detailed statistical table from the scientific paper ( Table 1 ; Phillips et al. 2004 ). Following experts' advice to focus on one or two key points, these charts emphasize the findings from the final model (Model 5) rather than also discussing each of the fixed- and random-effects specifications from the paper.

Presenting Complex Statistical Results Graphically

Text Summary of Additional Statistical Results

Multilevel Discrete-Time Hazards Models of Disenrollment from SCHIP, New Jersey, January 1998–April 2000

Source : Phillips et al. (2004) .

SCHIP, State Children's Health Insurance Program; LRH, log relative-hazard; SE, standard error.

Figure 1 uses a chart (also from the paper) to present the net effects of a complicated set of interactions between two family-level traits (race and SCHIP plan) and a cross-level interaction between race of the family and county physician racial composition. The title is a rhetorical question that identifies the issue addressed in the chart, and the annotations explain the pattern. The chart version substantially reduces the amount of time viewers need to understand the main take-home point, averting the need to mentally sum and exponentiate several coefficients from the table.

Figure 2 uses bulleted text to summarize other key results from the model, translating log-relative hazards into hazards ratios and interpreting them with minimal reliance on jargon. The results for family race, SCHIP plan, and county physician racial composition are not repeated in Figure 2 , averting the common problem of interpreting main effect coefficients and interaction coefficients without reference to one another.

Alternatively, replace the text summary shown in Figure 2 with Table 2 —a simplified version of Table 1 which presents only the results for Model 5, replaces log-relative hazards with hazards ratios, reports associated confidence intervals in lieu of standard errors, and uses boldface to denote statistical significance. (On a color slide, use a contrasting color in lieu of bold.)

Relative Risks of SCHIP Disenrollment for Other * Family and County Characteristics, New Jersey, January 1998–April 2000

Statistically significant associations are shown in bold.

Based on hierarchical linear model controlling for months enrolled, months-squared, race, SCHIP plan, county physician racial composition, and all variables shown here. Scaled deviance =30,895. Random effects estimate for between-county variance =0.005 (standard error =0.006). SCHIP, State Children's Health Insurance Program; 95% CI, 95% confidence interval.

CONTENTS AND ORGANIZATION OF A POSTER

Research posters are organized like scientific papers, with separate pages devoted to the objectives and background, data and methods, results, and conclusions ( Briscoe 1996 ). Readers view the posters at their own pace and at close range; thus you can include more detail than in slides for a speech (see Appendix A for a detailed comparison of content and format of papers, speeches, and posters). Don't simply post pages from the scientific paper, which are far too text-heavy for a poster. Adapt them, replacing long paragraphs and complex tables with bulleted text, charts, and simple tables ( Briscoe 1996 ; Beilenson 2004 ). Fink (1995) provides useful guidelines for writing text bullets to convey research results. Use presentation software such as PowerPoint to create your pages or adapt them from related slides, facilitating good page layout with generous type size, bullets, and page titles. Such software also makes it easy to create matching handouts (see “Handouts”).

The “W's” (who, what, when, where, why) are an effective way to organize the elements of a poster.

• In the introductory section, describe what you are studying, why it is important, and how your analysis will add to the existing literature in the field.
• In the data and methods section of a statistical analysis, list when, where, who, and how the data were collected, how many cases were involved, and how the data were analyzed. For other types of interventions or program evaluations, list who, when, where, and how many, along with how the project was implemented and assessed.
• In the results section, present what you found.
• In the conclusion, return to what you found and how it can be used to inform programs or policies related to the issue.

Number and Layout of Pages

To determine how many pages you have to work with, find out the dimensions of your assigned space. A 4′ × 8′ bulletin board accommodates the equivalent of about twenty 8.5″ × 11″ pages, but be selective—no poster can capture the full detail of a large series of multivariate models. A trifold presentation board (3′ high by 4′ wide) will hold roughly a dozen pages, organized into three panels ( Appendix B ). Breaking the arrangement into vertical sections allows viewers to read each section standing in one place while following the conventions of reading left-to-right and top-to-bottom ( Briscoe 1996 ).

• At the top of the poster, put an informative title in a large, readable type size. On a 4′ × 8′ bulletin board, there should also be room for an institutional logo.

Suggested Layout for a 4′ × 8′ poster.

• In the left-hand panel, set the stage for the research question, conveying why the topic is of policy interest, summarizing major empirical or theoretical work on related topics, and stating your hypotheses or project aims, and explaining how your work fills in gaps in previous analyses.
• In the middle panel, briefly describe your data source, variables, and methods, then present results in tables or charts accompanied by text annotations. Diagrams, maps, and photographs are very effective for conveying issues difficult to capture succinctly in words ( Miller 2005 ), and to help readers envision the context. A schematic diagram of relationships among variables can be useful for illustrating causal order. Likewise, a diagram can be a succinct way to convey timing of different components of a longitudinal study or the nested structure of a multilevel dataset.
• In the right-hand panel, summarize your findings and relate them back to the research question or project aims, discuss strengths and limitations of your approach, identify research, practice, or policy implications, and suggest directions for future research.

Figure 3 (adapted from Beilenson 2004 ) shows a suggested layout for a 4′ × 8′ bulletin board, designed to be created using software such as Pagemaker that generates a single-sheet presentation; Appendix C shows a complete poster version of the Phillips et al. (2004) multilevel analysis of SCHIP disenrollment. If hardware or budget constraints preclude making a single-sheet poster, a similar configuration can be created using standard 8.5″ × 11″ pages in place of the individual tables, charts, or blocks of text shown in Figure 3 .

Find out well in advance how the posters are to be mounted so you can bring the appropriate supplies. If the room is set up for table-top presentations, tri-fold poster boards are essential because you won't have anything to attach a flat poster board or pages to. If you have been assigned a bulletin board, bring push-pins or a staple gun.

Regardless of whether you will be mounting your poster at the conference or ahead of time, plan how the pages are to be arranged. Experiment with different page arrangements on a table marked with the dimensions of your overall poster. Once you have a final layout, number the backs of the pages or draw a rough sketch to work from as you arrange the pages on the board. If you must pin pages to a bulletin board at the conference venue, allow ample time to make them level and evenly spaced.

Other Design Considerations

A few other issues to keep in mind as you design your poster. Write a short, specific title that fits in large type size on the title banner of your poster. The title will be potential readers' first glimpse of your poster, so make it inviting and easy to read from a distance—at least 40-point type, ideally larger. Beilenson (2004) advises embedding your key finding in the title so viewers don't have to dig through the abstract or concluding page to understand the purpose and conclusions of your work. A caution: If you report a numeric finding in your title, keep in mind that readers may latch onto it as a “factoid” to summarize your conclusions, so select and phrase it carefully ( McDonough 2000 ).

Use at least 14-point type for the body of the poster text. As Briscoe (1996) points out, “many in your audience have reached the bifocal age” and all of them will read your poster while standing, hence long paragraphs in small type will not be appreciated! Make judicious use of color. Use a clear, white, or pastel for the background, with black or another dark color for most text, and a bright, contrasting shade to emphasize key points or to identify statistically significant results ( Davis 1997 ).

NARRATIVE TO ACCOMPANY A POSTER

Prepare a brief oral synopsis of the purpose, findings, and implications of your work to say to interested parties as they pause to read your poster. Keep it short—a few sentences that highlight what you are studying, a couple of key findings, and why they are important. Design your overview as a “sound byte” that captures your main points in a succinct and compelling fashion ( Beilenson 2004 ). After hearing your introduction, listeners will either nod and move along or comment on some aspect of your work that intrigues them. You can then tailor additional discussion to individual listeners, adjusting the focus and amount of detail to suit their interests. Gesture at the relevant pages as you make each point, stating the purpose of each chart or table and explaining its layout before describing the numeric findings; see Miller (2005) for guidelines on how to explain tables and charts to a live audience. Briscoe (1996) points out that these mini-scripts are opportunities for you to fill in details of your story line, allowing you to keep the pages themselves simple and uncluttered.

Prepare short answers to likely questions about various aspects of your work, such as why it is important from a policy or research perspective, or descriptions of data, methods, and specific results. Think of these as little modules from an overall speech—concise descriptions of particular elements of your study that you can choose among in response to questions that arise. Beilenson (2004) also recommends developing a few questions to ask your viewers, inquiring about their reactions to your findings, ideas for additional questions, or names of others working on the topic.

Practice your poster presentation in front of a test audience acquainted with the interests and statistical proficiency of your expected viewers. Ideally, your critic should not be too familiar with your work: A fresh set of eyes and ears is more likely to identify potential points of confusion than someone who is jaded from working closely with the material while writing the paper or drafting the poster ( Beilenson 2004 ). Ask your reviewer to identify elements that are unclear, flag jargon to be paraphrased or defined, and recommend changes to improve clarity ( Miller 2005 ). Have them critique your oral presentation as well as the contents and layout of the poster.

Prepare handouts to distribute to interested viewers. These can be produced from slides created in presentation software, printed several to a page along with a cover page containing the abstract and your contact information. Or package an executive summary or abstract with a few key tables or charts. Handouts provide access to the more detailed literature review, data and methods, full set of results, and citations without requiring viewers to read all of that information from the poster ( Beilenson 2004 ; Miller 2005 ). Although you also can bring copies of the complete paper, it is easier on both you and your viewers if you collect business cards or addresses and mail the paper later.

The quality and effectiveness of research posters at professional conferences is often compromised by authors' failure to take into account the unique nature of such presentations. One common error is posting numerous statistical tables and long paragraphs from a research paper—an approach that overwhelms viewers with too much detail for this type of format and presumes familiarity with advanced statistical techniques. Following recommendations from the literature on research communication and poster design, this paper shows how to focus each poster on a few key points, using charts and text bullets to convey results as part of a clear, straightforward story line, and supplementing with handouts and an oral overview.

Another frequent mistake is treating posters as a one-way means of communication. Unlike published papers, poster sessions are live presentations; unlike speeches, they allow for extended conversation with viewers. This note explains how to create an oral synopsis of the project, short modular descriptions of poster elements, and questions to encourage dialog. By following these guidelines, researchers can substantially improve their conference posters as vehicles to disseminate findings to varied research and policy audiences.

CHECKLIST FOR PREPARING AND PRESENTING AN EFFECTIVE RESEARCH POSTERS

• Design poster to focus on two or three key points.
• Adapt materials to suit expected viewers' knowledge of your topic and methods.
• Design questions to meet their interests and expected applications of your work.
• Paraphrase descriptions of complex statistical methods.
• Spell out acronyms if used.
• Replace large detailed tables with charts or small, simplified tables.
• Accompany tables or charts with bulleted annotations of major findings.
• Describe direction and magnitude of associations.
• Use confidence intervals, p -values, symbols, or formatting to denote statistical significance.

Layout and Format

• Organize the poster into background, data and methods, results, and study implications.
• Divide the material into vertical sections on the poster.
• Use at least 14-point type in the body of your poster, at least 40-point for the title.

Narrative Description

• Rehearse a three to four sentence overview of your research objectives and main findings.
• Summary of key studies and gaps in existing literature
• Data and methods
• Each table, chart, or set of bulleted results
• Research, policy, and practice implications
• Solicit their input on your findings
• Develop additional questions for later analysis
• Identify other researchers in the field
• Prepare handouts to distribute to interested viewers.
• Print slides from presentation software, several to a page.
• Or package an executive summary or abstract with a few key tables or charts.
• Include an abstract and contact information.

Acknowledgments

I would like to thank Ellen Idler, Julie Phillips, Deborah Carr, Diane (Deedee) Davis, and two anonymous reviewers for helpful comments on earlier drafts of this work.

Supplementary Material

The following supplementary material for this article is available online:

APPENDIX A.1. Comparison of Research Papers, Presentations, and Posters—Materials and Audience Interaction.

Suggested Layout for a Tri-Fold Presentation Board.

Example Research Poster of Phillips et al. 2004 Study.

• Beilenson J. Developing Effective Poster Presentations. Gerontology News. 2004; 32 (9):6–9. [ Google Scholar ]
• Briscoe MH. Preparing Scientific Illustrations: A Guide to Better Posters, Presentations, and Publications. 2. New York: Springer-Verlag; 1996. [ Google Scholar ]
• Davis M. Scientific Papers and Presentations. New York: Academic Press; 1997. [ Google Scholar ]
• DiFranza JR. A Researcher's Guide to Effective Dissemination of Policy-Related Research. Princeton, NJ: The Robert Wood Johnson Foundation; 1996. the Staff of the Advocacy Institute, with Assistance from the Center for Strategic Communications. [ Google Scholar ]
• Fink A. How to Report on Surveys. Thousand Oaks, CA: Sage Publications; 1995. [ Google Scholar ]
• Friedman GD. Be Kind to Your Reader. American Journal of Epidemiology. 1990; 132 (4):591–3. [ PubMed ] [ Google Scholar ]
• Maciejewski ML, Diehr P, Smith MA, Hebert P. Common Methodological Terms in Health Services Research and Their Symptoms. Medical Care. 2002; 40 :477–84. [ PubMed ] [ Google Scholar ]
• McDonough J. Experiencing Politics: A Legislator's Stories of Government and Health Care. Berkeley: University of California Press; 2000. [ Google Scholar ]
• Miller JE. The Chicago Guide to Writing about Multivariate Analysis. Chicago Guides to Writing, Editing and Publishing. Chicago: University of Chicago Press; 2005. [ Google Scholar ]
• Nelson DE, Brownson RC, Remington PL, Parvanta C, editors. Communicating Public Health Information Effectively: A Guide for Practitioners. Washington, DC: American Public Health Association; 2002. [ Google Scholar ]
• Phillips JA, Miller JE, Cantor JC, Gaboda D. Context or Composition. What Explains Variation in SCHIP Disenrollment? Health Services Research. 2004; 39 (4, part I):865–8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Snowdon D. Aging with Grace: What the Nun Study Teaches Us about Leading Longer, Healthier, and More Meaningful Lives. New York: Bantam Books; 2001. [ Google Scholar ]
• Sorian R, Baugh T. Power of Information Closing the Gap between Research and Policy. Health Affairs. 2002; 21 (2):264–73. [ PubMed ] [ Google Scholar ]
• Tufte ER. The Visual Display of Quantitative Information. 2. Cheshire, CT: Graphics Press; 2001. [ Google Scholar ]

This paper is in the following e-collection/theme issue:

Published on 16.4.2024 in Vol 26 (2024)

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Authors of this article:

Original Paper

• Satoshi Nishioka 1 , PhD   ; 
• Satoshi Watabe 1 , BSc   ; 
• Yuki Yanagisawa 1 , PhD   ; 
• Kyoko Sayama 1 , MSc   ; 
• Hayato Kizaki 1 , MSc   ; 
• Shungo Imai 1 , PhD   ; 
• Mitsuhiro Someya 2 , BSc   ; 
• Ryoo Taniguchi 2 , PhD   ; 
• Shuntaro Yada 3 , PhD   ; 
• Eiji Aramaki 3 , PhD   ; 
• Satoko Hori 1 , PhD  

1 Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan

2 Nakajima Pharmacy, Hokkaido, Japan

3 Nara Institute of Science and Technology, Nara, Japan

Corresponding Author:

Satoko Hori, PhD

Division of Drug Informatics

Keio University Faculty of Pharmacy

1-5-30 Shibakoen

Tokyo, 105-8512

Phone: 81 3 5400 2650

Email: [email protected]

Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients.

Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed.

Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs.

Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives.

Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work.

Introduction

Increasing numbers of people are expected to develop cancers in our aging society [ 1 - 3 ]. Thus, there is increasing interest in how to detect and manage the side effects of anticancer therapies in order to improve treatment regimens and patients’ quality of life [ 4 - 8 ]. The primary approaches for side effect management are “early signal detection and early intervention” [ 9 - 11 ]. Thus, more efficient approaches for this purpose are needed.

It has been recognized that patients’ voices concerning adverse events represent an important source of information. Several studies have indicated that the number, severity, and time of occurrence of adverse events might be underevaluated by physicians [ 12 - 15 ]. Thus, patient-reported outcomes (PROs) have recently received more attention in the drug evaluation process, reflecting patients’ real voices. Various kinds of PRO measures have been developed and investigated in different disease populations [ 16 , 17 ]. Health care authorities have also encouraged the pharmaceutical industry to use PROs for drug evaluation [ 18 , 19 ], and it is becoming more common to take PRO assessment results into consideration for drug marketing approval [ 20 , 21 ]. Similar trends can be seen in the clinical management of individual patients. Thus, health care professionals have an interest in understanding how to appropriately gather patients’ concerns in order to improve safety management and clinical decisions [ 22 - 24 ].

The applications of deep learning for natural language processing have expanded dramatically in recent years [ 25 ]. Since the development of a high-performance deep learning model in 2018 [ 26 ], attempts to apply cutting-edge deep learning models to various kinds of patient-generated text data for the evaluation of safety events or the analysis of unscalable subjective information from patients have been accelerating [ 27 - 31 ]. Most studies have been conducted to use patients’ narrative data for pharmacovigilance [ 27 , 32 - 35 ], while few have been aimed at improvement of real-time safety monitoring for individual patients. In addition, there have been some studies on adverse event severity grading based on health care records [ 36 - 39 ], but none has yet aimed to extract clinically important adverse event signals that require medical intervention from patients’ narratives. It is important to know whether deep learning models could contribute to the detection of such important adverse event signals from concern texts generated by individual patients.

To address this question, we have developed deep learning models to detect adverse event signals from individual patients with cancer based on patients’ blog articles in online communities, following other types of natural language processing–related previous work [ 40 , 41 ]. One deep learning model focused on the specific symptom of hand-foot syndrome (HFS), which is one of the typical side effects of anticancer treatments [ 42 ], and another focused on a broad range of adverse events that impact patients’ activities of daily living [ 43 ]. We showed that our models can provide good performance scores in targeting adverse event signals. However, the evaluation relied on patients’ narratives from the patients’ blog data used for deep learning model training, so further evaluation is needed to ensure the validity and applicability of the models to other texts regarding patients’ concerns. In addition, the blog data source did not contain medical information, so it was not feasible to assess whether the models could contribute to the extraction of clinically important adverse event signals.

To address these challenges, we focused on pharmaceutical care records written by pharmacists at community pharmacies. The gold standard format for pharmaceutical care records in Japan is the SOAP (subjective, objective, assessment, plan)-based document that follows the “problem-oriented system” concept proposed by Weed [ 44 ] in 1968. Pharmacists track patients’ subjective concerns in the S column, provide objective information or observations in the O column, give their assessment from the pharmacist perspective in the A column, and suggest a plan for moving forward in the P column [ 45 , 46 ]. We considered that SOAP-based pharmaceutical care records could be a unique data source suitable for further evaluation of our deep learning models because they contain both patients’ concerns and professional health care records by pharmacists, including the medication prescription history with time stamps. Therefore, this study was designed to assess whether our deep learning models could extract clinically important adverse event signals that require intervention by medical professionals from these records. We also aimed to evaluate the characteristics of the models when applied to patients’ subjective information noted in the pharmaceutical care records, as there have been only a few studies on the application of deep learning models to patients’ concerns recorded during pharmacists’ daily work [ 47 - 49 ].

Here, we report the results of applying our deep learning models to patients’ concern text data in pharmaceutical care records, focusing on patients receiving anticancer treatment.

Data Source

The original data source was 2,276,494 pharmaceutical care records for 303,179 patients, created from April 2020 to December 2021 at community pharmacies belonging to the Nakajima Pharmacy Group in Japan [ 50 ]. To focus on patients with cancer, records of patients with at least 1 prescription for an anticancer drug were retrieved by sorting individual drug codes (YJ codes) used in Japan (YJ codes starting with 42 refer to anticancer drugs). Records in the S column (ie, S records) were collected from the patients with cancer as the text data of patients’ concerns for deep learning model analysis.

Deep Learning Models

The deep learning models used for this research were those that we constructed based on patients’ narratives in blog articles posted in an online community and that showed the best performance score in each task in our previous work (ie, a Bidirectional Encoder Representations From Transformers [BERT]–based model for HFS signal extraction [ 42 ] and a T5-based model for adverse event signal extraction [ 43 ]). BERT [ 26 ] and T5 [ 51 ] both belong to a type of deep learning model that has recently shown high performance in several studies [ 29 , 52 ]. Hereafter, we refer to the deep learning model for HFS signals as the HFS model, the model for any adverse event signals as All AE (ie, all or any adverse events) model, and the model for adverse event signals limited to patients’ activities of daily living as the AE-L (adverse events limiting patients’ daily lives) model. It was also confirmed that these deep learning models showed similar or higher performance scores for the HFS, All AE, or AE-L identification tasks using 1000 S records randomly extracted from the data source of this study compared to the values obtained in our previous work [ 42 , 43 ] (the performance scores of sentence-level tasks from our previous work are comparable, as the mean number of words in the sentences in the data source in our previous work was 32.7 [SD 33.9], which is close to that of the S records used in this study, 38.8 [SD 29.4]). The method and results of the performance-level check are described in detail in Multimedia Appendix 1 [ 42 , 43 ]. We applied the deep learning models to all text data in this study without any adjustment in setting parameters from those used in constructing them based on patient-authored texts in our previous work [ 42 , 43 ].

Evaluation of Extracted S Records by the Deep Learning Models

In this study, we focused on the evaluation of S records that our deep learning models extracted as HFS or AE-L positive. Each positive S record was assessed as if it was a true adverse event signal, a sort of adverse event symptom, whether or not an intervention was made by health care professionals. We also investigated the kind of anticancer treatment prescription in connection with each adverse event signal identified in S records.

To assess whether an extracted positive S record was a true adverse event signal, we used the same annotation guidelines as in our previous work [ 43 ]. In brief, each S record was treated as an “adverse event signal” if any untoward medical occurrence happened to the patient, regardless of the cause. For the AE-L model only, if a positive S record was confirmed as an adverse event signal, it was further categorized into 1 or more of the following adverse event symptoms: “fatigue,” “nausea,” “vomiting,” “diarrhea,” “constipation,” “appetite loss,” “pain or numbness,” “rash or itchy,” “hair loss,” “menstrual irregularity,” “fever,” “taste disorder,” “dizziness,” “sleep disorder,” “edema,” or “others.”

For the assessment of interventions by health care professionals and anticancer treatment prescriptions, information from the O, A, and P columns and drug prescription history in the data source were investigated for the extracted positive S records. The interventions by health care professionals were categorized in any of the following: “adding symptomatic treatment for the adverse event signal,” “dose reduction or discontinuation of causative anticancer treatment,” “consultation with physician,” “others,” or “no intervention (ie, just following up the adverse event signal).” The actions categorized in “others” were further evaluated individually. For this assessment, we also randomly extracted 200 S records and evaluated them in the same way for comparison with the results from the deep learning model. Prescription history of anticancer treatment was analyzed by primary category of mechanism of action (MoA) with subcategories if applicable (eg, target molecule for kinase inhibitors).

Applicability Check to Other Text Data Including Patients’ Concerns

To check the applicability of our deep learning models to data from a different source, interview transcripts from patients with cancer were also evaluated. The interview transcripts were created by the Database of Individual Patient Experiences-Japan (DIPEx-Japan) [ 53 ]. DIPEx-Japan divides the interview transcripts into sections for each topic, such as “onset of disease” and “treatment,” and posts the processed texts on its website. Processing is conducted by accredited researchers based on qualitative research methods established by the University of Oxford [ 54 ]. In this study, interview text data created from interviews with 52 patients with breast cancer conducted from January 2008 to October 2018 were used to assess whether our deep learning models can extract adverse event signals from this source. In total, 508 interview transcripts were included with the approval of DIPEx-Japan.

Ethical Considerations

This study was conducted with anonymized data following approval by the ethics committee of the Keio University Faculty of Pharmacy (210914-1 and 230217-1) and in accordance with relevant guidelines and regulations and the Declaration of Helsinki. Informed consent specific to this study was waived due to the retrospective observational design of the study with the approval of the ethics committee of the Keio University Faculty of Pharmacy. To respect the will of each individual stakeholder, however, we provided patients and pharmacists of the pharmacy group with an opportunity to refuse the sharing of their pharmaceutical care records by posting an overview of this study at each pharmacy store or on their web page regarding the analysis using pharmaceutical care records. Interview transcripts from DIPEx-Japan were provided through a data sharing arrangement for using narrative data for research and education. Consent for interview transcription and its sharing from DIPEx-Japan was obtained from the participants when the interviews were recorded.

From the original data source of 2,180,902 pharmaceutical care records for 291,150 patients, S records written by pharmacists for patients with a history of at least 1 prescription of an anticancer drug were extracted. This yielded 30,784 S records for 2479 patients with cancer ( Table 1 ). The mean and median number of words in the S records were 38.8 (SD 29.4) and 32 (IQR 20-50), respectively. We applied our deep learning models, HFS, All AE, and AE-L, to these 30,784 S records for the evaluation of the deep learning models for adverse event signal detection.

For interview transcripts created by DIPEx-Japan, the mean and median number of words were 428.9 (SD 160.9) and 416 (IQR 308-526), respectively, in the 508 transcripts for 52 patients with breast cancer.

a SOAP: subjective, objective, assessment, plan.

b S: subjective.

Application of the HFS Model

First, we applied the HFS model to the S records for patients with cancer. The BERT-based model was used for this research as it showed the best performance score in our previous work [ 42 ].

S Records Extracted as HFS Positive

The S records extracted as HFS positive by the HFS model ( Table 2 ) amounted to 167 (0.5%) records for 119 (4.8%) patients. A majority of the patients had 1 HFS-positive record in their S records (n=91, 76.5%), while 2 patients had as many as 6 (1.7%) HFS-positive records. When we examined whether the extracted S records were true adverse event signals or not, 152 records were confirmed to be adverse event signals, while the other 15 records were false-positives. All the false-positive S records were descriptions about the absence of symptoms or confirmation of improving condition (eg, “no diarrhea, mouth ulcers, or limb pain so far” or “the skin on the soles of my feet has calmed down a lot with this ointment”). Some examples of S records that were predicted as HFS positive by the model are shown in Table S1 in Multimedia Appendix 2 .

The same examination was conducted with interview transcripts from DIPEx-Japan. Only 1 (0.2%) transcript was extracted as HFS positive by the HFS model, and it was a true adverse event signal (100%). The actual transcript extracted as HFS positive is shown in Table S2 in Multimedia Appendix 2 .

a S: subjective.

b HFS: hand-foot syndrome.

c All false-positive S records were denial of symptoms or confirmation of improving condition.

Interventions by Health Care Professionals

The 167 S records extracted as HFS positive as well as 200 randomly selected records were checked for interventions by health care professionals ( Figure 1 ). The proportion showing any action by health care professionals was 64.1% for 167 HFS-positive S records compared to 13% for the 200 random S records. Among the actions taken for HFS positives, “adding symptomatic treatment” was the most common, accounting for around half (n=79, 47.3%), followed by “other” (n=18, 10.8%). Most “other” actions were educational guidance from pharmacists, such as instructions on moisturizing, nail care, or application of ointment and advice on daily living (eg, “avoid tight socks”).

Anticancer Drugs Prescribed

The types of anticancer drugs prescribed for HFS-positive patients are summarized based on the prescription histories in Table 3 . For the 152 adverse event signals identified by the HFS model in the previous section, the most common MoA class of anticancer drugs used for the patients was antimetabolite (n=62, 40.8%), specifically fluoropyrimidines (n=59, 38.8%). Kinase inhibitors were next (n=49, 32.2%), with epidermal growth factor receptor (EGFR) inhibitors and multikinase inhibitors as major subgroups (n=28, 18.4% and n=14, 9.2%, respectively). The third and fourth most common MoAs were aromatase inhibitors (n=24, 15.8%) and antiandrogen or estrogen drugs (n=7, 4.6% each) for hormone therapy.

a EGFR: epidermal growth factor receptor.

b VEGF: vascular endothelial growth factor.

c HER2: human epidermal growth factor receptor-2.

d CDK4/6: cyclin-dependent kinase 4/6.

Application of the All AE or AE-L model

The All AE and AE-L models were also applied to the same S records for patients with cancer. The T5-based model was used for this research as it gave the best performance score in our previous work [ 43 ].

S Records Extracted as All AE or AE-L positive

The numbers of S records extracted as positive were 7604 (24.7%) for 1797 patients and 196 (0.6%) for 142 patients for All AE and AE-L, respectively. In the case of All AE, patients tended to have multiple adverse event positives in their S records (n=1315, 73.2% of patients had at least 2 positives). In the case of AE-L, most patients had only 1 AE-L positive (n=104, 73.2%), and the largest number of AE-L positives for 1 patient was 4 (2.8%; Table 4 ).

We focused on AE-L evaluation due to its greater importance from a medical viewpoint and lower workload for manual assessment, considering the number of positive S records. Of the 197 AE-L–positive S records, it was confirmed that 157 (80.1%) records accurately extracted adverse event signals, while 39 (19.9%) records were false-positives that did not include any adverse event signals ( Table 4 ). The contents of the 39 false-positives were all descriptions about the absence of symptoms or confirmation of improving condition, showing a similar tendency to the HFS false-positives (eg, “The diarrhea has calmed down so far. Symptoms in hands and feet are currently fine” and “No symptoms for the following: upset in stomach, diarrhea, nausea, abdominal pain, abdominal pain or stomach cramps, constipation”). Examples of S records that were predicted as AE-L positive are shown in Table S3 in Multimedia Appendix 2 .

The deep learning models were also applied to interview transcripts from DIPEx-Japan in the same manner. The deep learning models identified 84 (16.5%) and 18 (3.5%) transcripts as All AE or AE-L positive, respectively. Of the 84 All AE–positive transcripts, 73 (86.9%) were true adverse event signals. The false-positives of All AE (n=11, 13.1%) were categorized into any of the following 3 types: explanations about the disease or its prognosis, stories when their cancer was discovered, or emotional changes that did not include clear adverse event mentions. With regard to AE-L, all the 18 (100%) positives were true adverse event signals (Table S4 in Multimedia Appendix 2 ). Examples of actual transcripts extracted as All AE or AE-L positive are shown in Table S5 in Multimedia Appendix 2 .

b All AE: all (or any of) adverse event.

c AE-L: adverse events limiting patients’ daily lives.

d All false-positive S records were denial of symptoms or confirmation of improving condition.

Whether or not interventions were made by health care professionals was investigated for the 196 AE-L–positive S records. As in the HFS model evaluation, data from 200 randomly selected S records were used for comparison ( Figure 2 ). In total, 91 (46.4%) records in the 196 AE-L–positive records were accompanied by an intervention, while the corresponding figure in the 200 random records was 26 (13%) records. The most common action in response to adverse event signals identified by the AE-L model was “adding symptomatic treatment” (n=71, 36.2%), followed by “other” (n=11, 5.6%). “Other” included educational guidance from pharmacists, inquiries from pharmacists to physicians, or recommendations for patients to visit a doctor.

The types of anticancer drugs prescribed for patients with adverse event signals identified by the AE-L model were summarized based on the prescription histories ( Table 5 ). In connection with the 157 adverse event signals, the most common MoA of the prescribed anticancer drug was antimetabolite (n=62, 39.5%) and fluoropyrimidine (n=53, 33.8%), which accounted for the majority. Kinase inhibitor (n=31, 19.7%) was the next largest category with multikinase inhibitor (n=14, 8.9%) as the major subgroup. These were followed by antiandrogen (n=27, 17.2%), antiestrogen (n=10, 6.4%), and aromatase inhibitor (n=10, 6.4%) for hormone therapy.

b JAK: janus kinase.

c VEGF: vascular endothelial growth factor.

d BTK: bruton tyrosine kinase.

e FLT3: FMS-like tyrosine kinase-3.

f PARP: poly-ADP ribose polymerase.

g CDK4/6: cyclin-dependent kinase 4/6.

h CD20: cluster of differentiation 20.

Adverse Event Symptoms

For the 157 adverse event signals identified by the AE-L model, the symptoms were categorized according to the predefined guideline in our previous work [ 43 ]. “Pain or numbness” (n=57, 36.3%) accounted for the largest proportion followed by “fever” (n=46, 29.3%) and “nausea” (n=40, 25.5%; Table 6 ). Symptoms classified as “others” included chills, tinnitus, running tears, dry or peeling skin, and frequent urination. When comparing the proportion of the symptoms associated with or without interventions by health care professionals, a trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). On the other hand, a smaller proportion was observed in “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes).

This study was designed to evaluate our deep learning models, previously constructed based on patient-authored texts posted in an online community, by applying them to pharmaceutical care records that contain both patients’ subjective concerns and medical information created by pharmacists. Based on the results, we discuss whether these deep learning models can extract clinically important adverse event signals that require medical intervention, and what characteristics they show when applied to data on patients’ concerns in pharmaceutical care records.

Performance for Adverse Event Signal Extraction

The first requirement for the deep learning models is to extract adverse event signals from patients’ narratives precisely. In this study, we evaluated the proportion of true adverse event signals in positive S records extracted by the HFS or AE-L model. True adverse event signals amounted to 152 (91%) and 157 (80.1%) for the HFS and AE-L models, respectively ( Tables 2 and 4 ). Given that the proportion of true adverse event signals in 200 randomly extracted S records without deep learning models was 54 (27%; categories other than “no adverse event” in Figures 1 and 2 ), the HFS and AE-L models were able to concentrate S records with adverse event mentions. Although 15 (9%) for the HFS model and 39 (19.9%) for the AE-L model were false-positives, it was confirmed all of the false-positive records described a lack of symptoms or confirmation of improving condition. We considered that such false-positives are due to the unique feature of pharmaceutical care records, where pharmacists might proactively interview patients about potential side effects of their medications. As the data set of blog articles we used to construct the deep learning models included few such cases (especially comments on lack of symptoms), our models seemed unable to exclude them correctly. Even though we confirmed that the proportion of true “adverse event” signals extracted from the S records by the HFS or AE-L model was more than 80%, the performance scores to extract true “HFS” or “AE-L” signals were not so high based on the performance check using 1000 randomly extracted S records ( F 1 -scores were 0.50 and 0.22 for true HFS and AE-L signals, respectively; Table S1 in Multimedia Appendix 1 ). It is considered that the performance to extract true HFS and AE-L signals was relatively low due to the short length of texts in the S records, providing less context to judge the impact on patients’ daily lives, especially for the AE-L model (the mean word number of the S records was 38.8 [SD 29.4; Table 1 ], similar to the sentence-level tasks in our previous work [ 42 , 43 ]). However, we consider a true adverse event signal proportion of more than 80% in this study represents a promising outcome, as this is the first attempt to apply our deep learning models to a different source of patients’ concern data, and the extracted positive cases would be worthy of evaluation by a medical professional, as the potential adverse events could be caused by drugs taken by the patients.

When the deep learning models were applied to DIPEx-Japan interview transcripts, including patients’ concerns, the proportion of true adverse event signals was also more than 80% (for All AE: n=73, 86.9% and for HFS and AE-L: n=18, 100%). The difference in the results between pharmaceutical care S records and DIPEx-Japan interview transcripts was the features of false-positives, descriptions about lack of symptoms or confirmation of improving condition in S records versus explanations about disease or its prognosis, stories about when their cancer was discovered, or emotional changes in interview transcripts. This is considered due to the difference in the nature of the data source; the pharmaceutical care records were generated in a real-time manner by pharmacists through their daily work, where adverse event signals are proactively monitored, while the interview transcripts were purely based on patients’ retrospective memories. Our deep learning models were able to extract true adverse event signals with an accuracy of more than 80% from both text data sources in spite of the difference in their nature. When looking at future implementation of the deep learning models in society (discussed in the Potential for Deep Learning Model Implementation in Society section), it may be desirable to further adjust deep learning models to reduce false-positives depending upon the features of the data source.

Identification of Important Adverse Events Requiring Medical Intervention

To assess whether the models could extract clinically important adverse event signals, we investigated interventions by health care professionals connected with the adverse event signals that are identified by our deep learning models. In the 200 randomly extracted S records, only 26 (13%) consisted of adverse event signals, leading to any intervention by health care professionals. On the other hand, the proportion of signals associated with interventions was increased to 107 (64.1%) and 91 (46.4%) in the S records extracted as positive by the HFS and AE-L models, respectively ( Figures 1 and 2 ). These results suggest that both deep learning models can screen clinically important adverse event signals that require intervention from health care professionals. The performance level in screening adverse event signals requiring medical intervention was higher in the HFS model than in the AE-L model (n=107, 64.1% vs n=91, 46.4%; Figures 1 and 2 ). Since the target events were specific and adverse event signals of HFS were narrowly defined, which is one of the typical side effects of some anticancer drugs, we consider that health care providers paid special attention to HFS-related signals and took action proactively. In both deep learning models, similar trends were observed in actions taken by health care professionals in response to extracted adverse event signals; common actions were attempts to manage adverse event symptoms by symptomatic treatment or other mild interventions, including educational guidance from pharmacists or recommendations for patients to visit a doctor. More direct interventions focused on the causative drugs (ie, “dose reduction or discontinuation of anticancer treatment”) amounted to less than 5%; 7 (4.2%) for the HFS model and 6 (3.1%) for the AE-L model ( Figures 1 and 2 ). Thus, it appears that our deep learning models can contribute to screening mild to moderate adverse event signals that require preventive actions such as symptomatic treatments or professional advice from health care providers, especially for patients with less sensitivity to adverse event signals or who have few opportunities to visit clinics and pharmacies.

Ability to Catch Real Side Effect Signals of Anticancer Drugs

Based on the drug prescription history associated with S records extracted as HFS or AE-L positive, the type and duration of anticancer drugs taken by patients experiencing the adverse event signals were investigated. For the HFS model, the most common MoA of anticancer drug was antimetabolite (fluoropyrimidine: n=59, 38.8%), followed by kinase inhibitors (n=49, 32.2%, of which EGFR inhibitors and multikinase inhibitors accounted for n=28, 18.4% and n=14, 9.2%, respectively) and aromatase inhibitors (n=24, 15.8%; Table 3 ). It is known that fluoropyrimidine and multikinase inhibitors are typical HFS-inducing drugs [ 55 - 58 ], suggesting that the HFS model accurately extracted HFS side effect signals derived from these drugs. Note that symptoms such as acneiform rash, xerosis, eczema, paronychia, changes in the nails, arthralgia, or stiffness of limb joints, which are common side effects of EGFR inhibitors or aromatase inhibitors [ 59 , 60 ], might be extracted as closely related expressions to those of HFS signals. When looking at the MoA of anticancer drugs for patients with adverse event signals identified by the AE-L model, antimetabolite (fluoropyrimidine) was the most common one (n=53, 33.8%), as in the case of those identified by the HFS model, followed by kinase inhibitors (n=31, 19.7%) and antiandrogens (n=27, 17.2%; Table 5 ). Since the AE-L model targets a broad range of adverse event symptoms, it is difficult to rationalize the relationship between the adverse event signals and types of anticancer drugs. However, the type of anticancer drugs would presumably closely correspond to the standard treatments of the cancer types of the patients. Based on the prescribed anticancer drugs, we can infer that a large percentage of the patients had breast or lung cancer, indicating that our study results were based on data from such a population. Thus, a possible direction for the expansion of this research would be adjusting the deep learning models by additional training with expressions for typical side effects associated with standard treatments of other cancer types. To interpret these results correctly, it should be noted that we could not investigate anticancer treatments conducted outside of the pharmacies (eg, the time-course relationship with intravenously administered drugs would be missed, as the administration will be done at hospitals). To further evaluate how useful this model is in side effect signal monitoring for patients with cancer, comprehensive medical information for the eligible patients would be required.

Suitability of the Deep Learning Models for Specific Adverse Event Symptoms

Among the adverse event signals identified by the AE-L model, the type of symptom was categorized according to a predefined annotation guideline that we previously developed [ 43 ]. The most frequently recorded adverse event signals identified by the AE-L model were “pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%; Table 6 ). Since the pharmaceutical care records had information about interventions by health care professionals, the frequency of the presence or absence of the interventions for each symptom was examined. A trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). There seem to be 2 possible explanations for this: these symptoms are of high importance and require early medical intervention or effective symptomatic treatments are available for these symptoms in clinical practice so that medical intervention is an easy option. On the other hand, a trend for a smaller proportion of adverse event signals to result in interventions was observed for “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes). The reason for this may be the lack of effective symptomatic treatments or the difficulty of judging whether the severity of these symptoms justifies medical intervention by health care providers. In either case, there may be room for improvement in the quality of medical care for these symptoms. We expect that our research will contribute to a quality improvement in safety monitoring in clinical practice by supporting adverse event signal detection in a cost-effective manner.

Potential for Deep Learning Model Implementation in Society

Although we evaluated our deep learning models using pharmaceutical care records in this study, the main target of future implementation of our deep learning models in society would be narrative texts that patients directly write to record their daily experiences. For example, the application of these deep learning models to electronic media where patients record their daily experiences in their lives with disease (eg, health care–related e-communities and disease diary applications) could enable information about adverse event signal onset that patients experience to be provided to health care providers in a timely manner. Adverse event signals can automatically be identified and shared with health care providers based on the concern texts that patients post to any platform. This system will have the advantage that health care providers can efficiently grasp safety-related events that patients experience outside of clinic visits so that they can conduct more focused or personalized interactions with patients at their clinic visits. However, consideration should be given to avoid an excessive burden on health care providers. For instance, limiting the sharing of adverse event signals to those of high severity or summarizing adverse event signals over a week rather than sharing each one in a real-time manner may be reasonable approaches for medical staff. We also need to think about how to encourage patients to record their daily experiences using electronic tools. Not only technical progress and support but also the establishment of an ecosystem where both patients and medical staff can feel benefit will be required. Prospective studies with deep learning models to follow up patients in the long term and evaluate outcomes will be needed. We primarily looked at patient-authored texts as targets of implementation, but our deep learning models may also be worth using medical data including patients’ subjective concerns, such as pharmaceutical care S records. As this study confirmed that our deep learning models are applicable to patients’ concern texts tracked by pharmacists, it should be possible to use them to analyze other “patient voice-like” medical text data that have not been actively investigated so far.

Limitations

First, the major limitation of this study was that we were not able to collect complete medical information of the patients. Although we designed this study to analyze patients’ concerns extracted by the deep learning models and their relationship with medical information contained in the pharmaceutical care records, some information could not be tracked (eg, missing history of medical interventions or anticancer treatment at hospitals as well as diagnosis of patients’ primary cancers). Second, there might be a data creation bias in S records for patients’ concerns by pharmacists. For example, symptoms that have little impact on intervention decisions might less likely be recorded by them. It should be also noted that the characteristics of S records may not be consistent at different community pharmacies.

Conclusions

Our deep learning models were able to screen clinically important adverse event signals that require intervention by health care professionals from patients’ concerns in pharmaceutical care records. Thus, these models have the potential to support real-time adverse event monitoring of individual patients taking anticancer treatments in an efficient manner. We also confirmed that these deep learning models constructed based on patient-authored texts could be applied to patients’ subjective information recorded by pharmacists through their daily work. Further research may help to expand the applicability of the deep learning models for implementation in society or for analysis of data on patients’ concerns accumulated in professional records at pharmacies or hospitals.

Acknowledgments

This work was supported by Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (KAKENHI; grant 21H03170) and Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (CREST; grant JPMJCR22N1), Japan. Mr Yuki Yokokawa and Ms Sakura Yokoyama at our laboratory advised SN about the structure of pharmaceutical care records. This study would not have been feasible without the high quality of pharmaceutical care records created by many individual pharmacists at Nakajima Pharmacy Group through their daily work.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

SN and SH designed the study. SN retrieved the subjective records of patients with cancer from the data source for the application of deep learning models and organized other data for subsequent evaluations. SN ran the deep learning models with the support of SW. SN, YY, and KS checked the adverse event signals for each subjective record that was extracted as positive by the models for hand-foot syndrome or adverse events limiting patients’ daily lives and evaluated the adverse event signal symptoms, details of interventions taken by health care professionals, and types of anticancer drugs prescribed for patients based on available data from the data source. HK and SI advised on the study concept and process. MS and RT provided pharmaceutical records at their community pharmacies along with advice on how to use and interpret them. SY and EA supervised the natural language processing research as specialists. SH supervised the study overall. SN drafted and finalized the paper. All authors reviewed and approved the paper.

Conflicts of Interest

SN is an employee of Daiichi Sankyo Co, Ltd. All other authors declare no conflicts of interest.

Performance evaluation of deep learning models.

Examples of S records and sample interview transcripts.

• Global cancer observatory: cancer over time. World Health Organization. URL: https://gco.iarc.fr/overtime/en [accessed 2023-07-02]
• Mattiuzzi C, Lippi G. Current cancer epidemiology. J Epidemiol Glob Health. 2019;9(4):217-222. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Montazeri F, Komaki H, Mohebi F, Mohajer B, Mansournia MA, Shahraz S, et al. Editorial: disparities in cancer prevention and epidemiology. Front Oncol. 2022;12:872051. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lasala R, Santoleri F. Association between adherence to oral therapies in cancer patients and clinical outcome: a systematic review of the literature. Br J Clin Pharmacol. 2022;88(5):1999-2018. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pudkasam S, Polman R, Pitcher M, Fisher M, Chinlumprasert N, Stojanovska L, et al. Physical activity and breast cancer survivors: importance of adherence, motivational interviewing and psychological health. Maturitas. 2018;116:66-72. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Markman M. Chemotherapy-associated neurotoxicity: an important side effect-impacting on quality, rather than quantity, of life. J Cancer Res Clin Oncol. 1996;122(9):511-512. [ CrossRef ] [ Medline ]
• Jitender S, Mahajan R, Rathore V, Choudhary R. Quality of life of cancer patients. J Exp Ther Oncol. 2018;12(3):217-221. [ Medline ]
• Di Nardo P, Lisanti C, Garutti M, Buriolla S, Alberti M, Mazzeo R, et al. Chemotherapy in patients with early breast cancer: clinical overview and management of long-term side effects. Expert Opin Drug Saf. 2022;21(11):1341-1355. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cuomo RE. Improving cancer patient outcomes and cost-effectiveness: a Markov simulation of improved early detection, side effect management, and palliative care. Cancer Invest. 2023;41(10):858-862. [ CrossRef ] [ Medline ]
• Pulito C, Cristaudo A, La Porta C, Zapperi S, Blandino G, Morrone A, et al. Oral mucositis: the hidden side of cancer therapy. J Exp Clin Cancer Res. 2020;39(1):210. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bartal A, Mátrai Z, Szûcs A, Liszkay G. Main treatment and preventive measures for hand-foot syndrome, a dermatologic side effect of cancer therapy. Magy Onkol. 2011;55(2):91-98. [ FREE Full text ] [ Medline ]
• Basch E, Jia X, Heller G, Barz A, Sit L, Fruscione M, et al. Adverse symptom event reporting by patients vs clinicians: relationships with clinical outcomes. J Natl Cancer Inst. 2009;101(23):1624-1632. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Basch E. The missing voice of patients in drug-safety reporting. N Engl J Med. 2010;362(10):865-869. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fromme EK, Eilers KM, Mori M, Hsieh YC, Beer TM. How accurate is clinician reporting of chemotherapy adverse effects? A comparison with patient-reported symptoms from the Quality-of-Life Questionnaire C30. J Clin Oncol. 2004;22(17):3485-3490. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Liu L, Suo T, Shen Y, Geng C, Song Z, Liu F, et al. Clinicians versus patients subjective adverse events assessment: based on patient-reported outcomes version of the common terminology criteria for adverse events (PRO-CTCAE). Qual Life Res. 2020;29(11):3009-3015. [ CrossRef ] [ Medline ]
• Churruca K, Pomare C, Ellis LA, Long JC, Henderson SB, Murphy LED, et al. Patient-reported outcome measures (PROMs): a review of generic and condition-specific measures and a discussion of trends and issues. Health Expect. 2021;24(4):1015-1024. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pérez-Alfonso KE, Sánchez-Martínez V. Electronic patient-reported outcome measures evaluating cancer symptoms: a systematic review. Semin Oncol Nurs. 2021;37(2):151145. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Patient-reported outcome measures: use in medical product development to support labeling claims. U.S. Food & Drug Administration. 2009. URL: https:/​/www.​fda.gov/​regulatory-information/​search-fda-guidance-documents/​patient-reported-outcome-measures-use-medical-product-development-support-labeling-claims [accessed 2023-11-26]
• Appendix 2 to the guideline on the evaluation of anticancer medicinal products in man—the use of patient-reported outcome (PRO) measures in oncology studies—scientific guideline. European Medicines Agency. 2016. URL: https:/​/www.​ema.europa.eu/​en/​appendix-2-guideline-evaluation-anticancer-medicinal-products-man-use-patient-reported-outcome-pro [accessed 2023-11-26]
• Weber SC. The evolution and use of patient-reported outcomes in regulatory decision making. RF Q. 2023;3(1):4-9. [ FREE Full text ]
• Teixeira MM, Borges FC, Ferreira PS, Rocha J, Sepodes B, Torre C. A review of patient-reported outcomes used for regulatory approval of oncology medicinal products in the European Union between 2017 and 2020. Front Med (Lausanne). 2022;9:968272. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Newell S, Jordan Z. The patient experience of patient-centered communication with nurses in the hospital setting: a qualitative systematic review protocol. JBI Database System Rev Implement Rep. 2015;13(1):76-87. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Yagasaki K, Takahashi H, Ouchi T, Yamagami J, Hamamoto Y, Amagai M, et al. Patient voice on management of facial dermatological adverse events with targeted therapies: a qualitative study. J Patient Rep Outcomes. 2019;3(1):27. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Giardina TD, Korukonda S, Shahid U, Vaghani V, Upadhyay DK, Burke GF, et al. Use of patient complaints to identify diagnosis-related safety concerns: a mixed-method evaluation. BMJ Qual Saf. 2021;30(12):996-1001. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lauriola I, Lavelli A, Aiolli F. An introduction to deep learning in natural language processing: models, techniques, and tools. Neurocomputing. 2022;470:443-456. [ CrossRef ]
• Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. 2019. Presented at: 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; June 2-7, 2019;4171-4186; Minneapolis, MN, USA.
• Dreisbach C, Koleck TA, Bourne PE, Bakken S. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. Int J Med Inform. 2019;125:37-46. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sim JA, Huang X, Horan MR, Stewart CM, Robison LL, Hudson MM, et al. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: a systematic review. Artif Intell Med. 2023;146:102701. [ CrossRef ] [ Medline ]
• Weissenbacher D, Banda JM, Davydova V, Estrada-Zavala D, Gascó Sánchez L, Ge Y, et al. Overview of the seventh social media mining for health applications (#SMM4H) shared tasks at COLING 2022. 2022. Presented at: Proceedings of the Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task; October 12-17, 2022;221-241; Gyeongju, Republic of Korea. URL: https://aclanthology.org/2022.smm4h-1.54/
• Matsuda S, Ohtomo T, Okuyama M, Miyake H, Aoki K. Estimating patient satisfaction through a language processing model: model development and evaluation. JMIR Form Res. 2023;7(1):e48534. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Yu D, Vydiswaran VGV. An assessment of mentions of adverse drug events on social media with natural language processing: model development and analysis. JMIR Med Inform. 2022;10(9):e38140. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Liu X, Chen H. A research framework for pharmacovigilance in health social media: identification and evaluation of patient adverse drug event reports. J Biomed Inform. 2015;58:268-279. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nikfarjam A, Sarker A, O'Connor K, Ginn R, Gonzalez G. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc. 2015;22(3):671-681. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kakalou C, Dimitsaki S, Dimitriadis VK, Natsiavas P. Exploiting social media for active pharmacovigilance: the PVClinical social media workspace. Stud Health Technol Inform. 2022;290:739-743. [ CrossRef ] [ Medline ]
• Bousquet C, Dahamna B, Guillemin-Lanne S, Darmoni SJ, Faviez C, Huot C, et al. The adverse drug reactions from patient reports in social media project: five major challenges to overcome to operationalize analysis and efficiently support pharmacovigilance process. JMIR Res Protoc. 2017;6(9):e179. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Young IJB, Luz S, Lone N. A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis. Int J Med Inform. 2019;132:103971. [ CrossRef ] [ Medline ]
• Jacobsson R, Bergvall T, Sandberg L, Ellenius J. Extraction of adverse event severity information from clinical narratives using natural language processing. Pharmacoepidemiol Drug Saf. 2017;26(S2):37. [ FREE Full text ]
• Liang C, Gong Y. Predicting harm scores from patient safety event reports. Stud Health Technol Inform. 2017;245:1075-1079. [ CrossRef ] [ Medline ]
• Jiang G, Wang L, Liu H, Solbrig HR, Chute CG. Building a knowledge base of severe adverse drug events based on AERS reporting data using semantic web technologies. Stud Health Technol Inform. 2013;192(1-2):496-500. [ CrossRef ] [ Medline ]
• Usui M, Aramaki E, Iwao T, Wakamiya S, Sakamoto T, Mochizuki M. Extraction and standardization of patient complaints from electronic medication histories for pharmacovigilance: natural language processing analysis in Japanese. JMIR Med Inform. 2018;6(3):e11021. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Watanabe T, Yada S, Aramaki E, Yajima H, Kizaki H, Hori S. Extracting multiple worries from breast cancer patient blogs using multilabel classification with the natural language processing model bidirectional encoder representations from transformers: infodemiology study of blogs. JMIR Cancer. 2022;8(2):e37840. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nishioka S, Watanabe T, Asano M, Yamamoto T, Kawakami K, Yada S, et al. Identification of hand-foot syndrome from cancer patients' blog posts: BERT-based deep-learning approach to detect potential adverse drug reaction symptoms. PLoS One. 2022;17(5):e0267901. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nishioka S, Asano M, Yada S, Aramaki E, Yajima H, Yanagisawa Y, et al. Adverse event signal extraction from cancer patients' narratives focusing on impact on their daily-life activities. Sci Rep. 2023;13(1):15516. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Weed LL. Medical records that guide and teach. N Engl J Med. 1968;278(11):593-600. [ CrossRef ] [ Medline ]
• Podder V, Lew V, Ghassemzadeh S. SOAP Notes. Treasure Island, FL. StatPearls Publishing; 2023.
• Shenavar Masooleh I, Ramezanzadeh E, Yaseri M, Sahere Mortazavi Khatibani S, Sadat Fayazi H, Ali Balou H, et al. The effectiveness of training on daily progress note writing by medical interns. J Adv Med Educ Prof. 2021;9(3):168-175. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Grothen AE, Tennant B, Wang C, Torres A, Sheppard BB, Abastillas G, et al. Application of artificial intelligence methods to pharmacy data for cancer surveillance and epidemiology research: a systematic review. JCO Clin Cancer Inform. 2020;4:1051-1058. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ohno Y, Kato R, Ishikawa H, Nishiyama T, Isawa M, Mochizuki M, et al. Using the natural language processing system MedNER-J to analyze pharmaceutical care records. medRxiv. Preprint posted online on October 2, 2023. [ CrossRef ]
• Ranchon F, Chanoine S, Lambert-Lacroix S, Bosson JL, Moreau-Gaudry A, Bedouch P. Development of artificial intelligence powered apps and tools for clinical pharmacy services: a systematic review. Int J Med Inform. 2023;172:104983. [ CrossRef ] [ Medline ]
• Nakajima Pharmacy. URL: https://www.nakajima-phar.co.jp/ [accessed 2023-12-07]
• Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res. 2020;21(140):1-67. [ FREE Full text ]
• Pathak A. Comparative analysis of transformer based language models. Comput Sci Inf Technol. 2021.:165-176. [ FREE Full text ] [ CrossRef ]
• DIPEx Japan. URL: https://www.dipex-j.org/ [accessed 2024-02-04]
• Herxheimer A, McPherson A, Miller R, Shepperd S, Yaphe J, Ziebland S. Database of patients' experiences (DIPEx): a multi-media approach to sharing experiences and information. Lancet. 2000;355(9214):1540-1543. [ CrossRef ] [ Medline ]
• Lara PE, Muiño CB, de Spéville BD, Reyes JJ. Hand-foot skin reaction to regorafenib. Actas Dermosifiliogr. 2016;107(1):71-73. [ CrossRef ]
• Zaiem A, Hammamia SB, Aouinti I, Charfi O, Ladhari W, Kastalli S, et al. Hand-foot syndrome induced by chemotherapy drug: case series study and literature review. Indian J Pharmacol. 2022;54(3):208-215. [ FREE Full text ] [ CrossRef ] [ Medline ]
• McLellan B, Ciardiello F, Lacouture ME, Segaert S, Van Cutsem E. Regorafenib-associated hand-foot skin reaction: practical advice on diagnosis, prevention, and management. Ann Oncol. 2015;26(10):2017-2026. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ai L, Xu Z, Yang B, He Q, Luo P. Sorafenib-associated hand-foot skin reaction: practical advice on diagnosis, mechanism, prevention, and management. Expert Rev Clin Pharmacol. 2019;12(12):1121-1127. [ CrossRef ] [ Medline ]
• Tenti S, Correale P, Cheleschi S, Fioravanti A, Pirtoli L. Aromatase inhibitors-induced musculoskeletal disorders: current knowledge on clinical and molecular aspects. Int J Mol Sci. 2020;21(16):5625. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lacouture ME, Melosky BL. Cutaneous reactions to anticancer agents targeting the epidermal growth factor receptor: a dermatology-oncology perspective. Skin Therapy Lett. 2007;12(6):1-5. [ FREE Full text ] [ Medline ]

Abbreviations

Edited by G Eysenbach; submitted 25.12.23; peer-reviewed by CY Wang, L Guo; comments to author 24.01.24; revised version received 14.02.24; accepted 09.03.24; published 16.04.24.

©Satoshi Nishioka, Satoshi Watabe, Yuki Yanagisawa, Kyoko Sayama, Hayato Kizaki, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Outstanding Master’s Student Ayden Cohn: presenting a better future for nuclear reactors

Ayden Cohn has excelled in the field of nuclear engineering, and his efforts have earned him the title of Outstanding Master's Student in the College of Engineering.

• Alex Parrish

18 Apr 2024

• Share on Facebook
• Share on Twitter
• Copy address link to clipboard

Ayden Cohn came to Virginia Tech in 2018, after graduating from Granby High School in Norfolk, Virginia. He was the valedictorian of a class of more than 400 peers. He chose to major in Mechanical Engineering and minor in both green engineering and nuclear engineering, blending those disciplines into his interest in the field of nuclear reactors.

Two years after completing his undergraduate studies, Cohn is graduating with a pair of master’s degrees, one from the Pamplin College of Business and another from the College of Engineering , where he has been named the Outstanding Master’s Student. The award was formally presented on March 28 at a reception hosted by the Virginia Tech Graduate School following a nomination and review through the college, reflecting Cohn's excellence in research.

Cohn won a scholarship from the Department of Energy during his junior year at Virginia Tech, which is only given to a small group of students pursuing careers in the nuclear engineering field. He also completed two internships with the Nuclear Regulatory Commission (NRC) in 2022 and 2023, and he gained hands-on experience in Blacksburg nuclear engineering labs as well. 

“When I was a junior, I started getting involved in research,” said Cohn. “I worked with Mark Pierson on small fusion devices that produce neutron output, which first got me involved in nuclear research.” 

He also found his way to the Multi-Phase Flow and Thermal-Hydraulics Lab (MFTL) of Yang Liu during his senior year, eventually becoming a group member for a project funded by the NRC in partnership with Rensselaer Polytechnic Institute (RPI). The work included a study of how fuel rods in nuclear reactors are cooled by water during different boiling conditions. The data will be used to create virtual simulations so future nuclear reactors can be tested for safety before any physical structures are even built.

“The research I conducted in Dr. Liu’s lab produces data that is sent directly to the NRC,” said Cohn. “It will be used to analyze future nuclear reactor designs. I believe it will have a big impact on new reactors and help ensure that any approved reactor is safe. That’s the biggest obstacle to phasing out fossil fuels in favor of nuclear technology, assuring the public that reactors are safe.” 

Liu commented on the value added to his team by Cohn's work.

"Ayden joined our lab with a strong background in reactor thermal hydraulics, which makes him an ideal fit for the project funded by the NRC," Liu said. "Having him on the project has been a tremendous asset.”

In pursuit of his nuclear engineering master’s, Cohn finished his academic studies during his first year of graduate school thanks in large part to the accelerated undergraduate/graduate program in mechanical engineering . His second year included a deep dive into the research he had started with Liu. To make the most of his time in graduate school, he chose to additionally enroll in the Master of Business Administration – Business Analytics through the Pamplin College of Business. The studies in that area gave him a new way to view data, but also taught him how to communicate his data more effectively.

“I’ve learned how to explain my engineering results in ways that are more meaningful,” said Cohn. “Some of the classes in the business program taught me effective strategies to present visuals, and draw your audience’s attention to what’s really important.”

Cohn packed his gear in March and journeyed to the RPI Thermal-Hydraulics Lab’s test facility to conduct a larger-scale version of the tests he performed in Liu’s lab.

He has been active in the Virginia Tech chapter of the American Nuclear Society, and will present his research at their annual conference in June. In July, he starts a job in North Carolina with General Electric where he will work on a team designing nuclear reactors.  

Chelsea Seeber

540-231-2108

• Class of 2024
• College of Engineering
• Graduate Education
• Graduate Students
• Mechanical Engineering
• News directly from around campus
• Nuclear Engineering
• Pamplin College of Business
• Undergraduate Research
• University Awards

Related Content

Cookie Acceptance Needed

This website would like to use cookies to collect information to improve your browsing experience. Please review our Privacy Statement for more information. Do you accept?

accept deny

College of Sciences and Mathematics Homepage

• Toggle Search
• Find People

COSAM News Articles 2024 01 Sean Grate will present "Bootstrapping Computations in Topological Data Analysis" at COSAM Graduate Student Research Forum (GSRF) on April 17.

Sean Grate will present "Bootstrapping Computations in Topological Data Analysis" at COSAM Graduate Student Research Forum (GSRF) on April 17.

Published: 04/17/2024

Sean Grate  will present "Bootstrapping Computations in Topological Data Analysis" at COSAM Graduate Student Research Forum (GSRF) on April 17 th  at 5 PM in CASIC Building Room 109/110 (559 Devall Drive AU Research Park).

FREE PARKING and FREE FOOD! Dinner from Taco Mama will be catered before the forum.

Latest Headlines

• Sean Grate will present "Bootstrapping Computations in Topological Data Analysis" at COSAM Graduate Student Research Forum (GSRF) on April 17. 04/17/2024
• DMS Drs. Overtoun Jenda and Peter Johnson have their grant that supports the REU program renewed by the NSF.  04/12/2024
• John F. Hartwig receives 2024 Kosolapoff Award sharing insight of catalysis and complex molecules 04/02/2024
• Emily Roarty shares her scientific journey at this year’s Anne Phillips Sassaman Lecture 04/02/2024
• STEM Outreach hosts State Science Olympiad 04/02/2024

Stay Connected