Blog naukowy Mind the Graph

Data visualization is the graphical representation of data and is used for understanding and communication, including common graphics such as pie charts and plots. Data visualization helps convey complex data relationships in an easy-to-understand manner, making it a critical part of research. For example, if numerical data in a table requires considerable effort to conceptualize a trend, plotting the data visually makes the trend immediately clear to the reader.  

The way information is visually presented can influence human perception. Consider the pie chart, a visual representation designed to display how individual components contribute to a whole. It offers a simple way to organize data, allowing users to easily compare the size of each component with the others. Therefore, if a researcher needs to visualize proportions or relative contributions within a single category, a pie chart serves as a straightforward visualization tool. 

Let’s explore how to make pie charts, common pie chart uses, pie chart examples, and more! 

What is a Pie Chart? 

A simple pie chart definition is “a circular statistical graphic that is divided into radial slices to illustrate numerical proportion.” In this chart type, each categorical value corresponds to a single slice, and the size of that slice—measured by its area, arc length, and central angle—is designed to be proportional to the quantity it represents. Thus the main strength of the pie chart lies in how it helps convey data at a glance by instantly communicating the part-to-whole relationship.  

Uses of a Pie Chart in Research 

Did you know that the pioneer of data visualization, Florence Nightingale, used an adaptation of the pie chart, the polar area diagram or rose diagram as early as in 1858? She used this diagram to illustrate seasonal sources of patient mortality in military hospitals during the Crimean War, demonstrating that deaths due to disease (represented by a large segment) far outweighed deaths caused by wounds or other factors. 

Pie chart uses are limited to visualizing proportions where the primary objective is to show each group’s contribution relative to the total. Pie charts are especially useful when highlighting a particular slice whose proportion is close to a common fraction like 25% or 50%. They are highly effective in scenarios where simplicity and quick comprehension are paramount and where the chart needs to be compact. Note that pie charts are inappropriate for non-proportional data (data exceeding 100%) or when the goal is to compare groups to each other, rather than to the whole. 

In modern social science, pie charts or effective alternatives are employed for data where the components must sum to 100%. Relevant pie chart examples include distribution of aggregate income, population demographics, and election results. 

To handle complex data, researchers may turn to pie chart subtypes. For instance, treemaps can break down cumulative global CO₂ emissions by country, and sunburst diagrams may be used for visualizing hierarchical data, such as an employee directory of a company divided by country and department, where the size of the segment represents the number of employees. 

How to Calculate a Pie Chart Formula 

To create a pie chart, you need to first know the pie chart formula. The steps to calculate it are as follows: 

1. Add up the individual numbers to calculate the total number.  

2. Divide 360 (the total number of degrees in the pie chart) by the total number. The resulting value will tell you the angle, in degrees, that each category equates to in terms of a pie chart.  

3. Multiply each frequency by this angle value to get the angle for each segment of the pie chart. Remember, the segment angles should add up to 360°. 

Put simply, to calculate the angle of each slice of a pie chart, the pie chart formula is as follows: 

Segment angle = (360/total frequency) x category frequency

How to Create a Pie Chart? 

Now that you have understood how to calculate the pie chart formula, the next question is how to draw a pie chart? Suppose an ecologist has been tasked with understanding species distribution of various animal phyla in a forest understory. Here are the steps she will follow: 

Krok 1: Collect the data 

Krok 2: Tabulate the data: 

The data above can be represented by a pie chart as following and by using the circle graph formula, i.e. the pie chart formula explained above.  

Krok 3: Add up the individual numbers to calculate the total number. In this case: 50 + 7 + 10 + 5 = 72. 

Krok 4: Divide 360 (the total number of degrees in the pie chart) by the total number. In this case: 360 ÷ 72 = 5, so each species is equal to 5° in the pie chart. 

Krok 5: Multiply each frequency by this angle value to get the angle for each segment of the pie chart: 

Krok 6: Draw a circle and use a protractor to measure the degree of each sector (see Figure 1). You can also use online pie chart makers as well. 

Krok 7: Label the sections and provide annotations and a legend. 

Pie Chart Examples 

Beyond the basic standard pie chart, there are several variants based on the circular concept, which are often used as alternatives for specific visualization needs. 

1. Donut chart: This is a variant of the pie chart with a hole in the center. Its primary purpose is to visualize data as a percentage of the whole (Figure 2).  

2. 3D Pie chart: This variant gives an aesthetic, three-dimensional look (see Figure 3). However, the use of perspective distorts the proportional encoding of the data, making these plots difficult to interpret accurately. 

3. Exploded pie chart: This chart features one or more sectors “pulled out” from the rest of the disk to highlight a specific sector for emphasis. 

4. Square chart (waffle chart): This chart uses color-coded squares, typically arranged in a 10 × 10 grid, where each cell represents 1%, instead of a circle, to represent proportions.  

5. Polar area diagram (Nightingale diagram/rose diagram): This chart is similar to a pie chart, but the sectors all have equal angles and differ in how far they extend from the center (radius).  

6. Treemaps: This is a “square” version of the pie chart that uses a rectangular area divided into sections; the area of the sections is proportional to the corresponding value. Treemaps can handle hierarchies, and visualize many more categories than a traditional pie chart. 

Dos and Don’ts When Creating a Pie Chart 

When designing pie charts, careful consideration must be given to their specific limitations to ensure accurate and effective communication of data.  

Dos when creating a pie chart 

• Include annotations: Annotations, showing exact values or percentages, are a standard and critical inclusion for pie charts. 

• Limit the number of slices: Pie charts are generally most effective when displaying a small number of categories, typically around five or fewer.  

• Order slices strategically: A typical ordering is from the largest slice to the smallest slice, often starting at the 12 o’clock position and progressively decreasing in the clockwise direction. However, if categories have an inherent sequential order (such as age classes), plotting them in that established order is usually better. 

• Ensure high contrast and accessibility: For inclusive data visualization, ensure sufficient color contrast between pie segments, especially for users with visual impairments like color blindness. Provide text alternatives for screen readers and ensure keyboard compatibility for interactive elements. 

• Leverage familiar proportions: Pie charts are easiest for the human eye to read when the slices form right angles or multiples thereof (i.e., 25%, 50%, and 75%). 

Don’ts when creating a pie chart 

• Do not use 3D or distorting effects: Avoid using 3D pie charts or depth effects. The 3D perspective distorts the proportional representation. 

• Do not use non-proportional data: A common mistake is using a pie chart for data that does not represent a complete part-to-whole comparison, such as percentages that sum up to more than 100%. 

• Do not use pie charts for close comparisons: Avoid using pie charts when precise comparisons between categories are necessary, especially when slices are of similar size.  

• Do not overuse color: While color distinguishes slices, too much color can detract from the message and be overwhelming, especially when many slices force the use of similar or confusing color combinations. 

Advantages and Disadvantages of Pie Chart 

Pie charts have a specific, narrow use case, which contributes both to their enduring popularity and the intense criticism from data visualization experts. 

Pie chart advantages 

• Clarity in part-to-whole relationship: The major benefit and the pie chart’s principal purpose is to immediately fulfil the part-to-whole comparison objective. The circular shape naturally and intuitively encodes the concept of a total (100%), allowing readers to quickly perceive proportions. 

• Familiarity and simplicity: They are particularly effective when the goal is quick understanding of an insight rather than decimal-place accuracy. 

• Compactness: Pie chart advantages make them useful in contexts such as mobile applications (e.g., showing battery percentage or installation progress) because they are very compact. 

Pie chart disadvantages 

• Poor for comparisons: Pie charts are ineffective for tasks requiring the comparison of one group (slice) to another group (slice), or for comparing data across multiple instances of the chart. 

• Inefficient space utilization: Pie charts are space-inefficient because their circular nature and the necessary addition of labels or legends take up a larger amount of space than rectilinear charts, like bar charts, which provide the same or greater clarity. 

• Limited data capacity: They are ill-suited for large amounts of data. When slices become numerous or very small, the chart relies excessively on labels, arguably defeating the purpose of visualization and offering less value than a simple table. 

• Lack of flexibility: Pie charts are less flexible than alternatives, struggling to effectively handle complex data, sequential categories, or the addition of extra reference data like targets or ranges without becoming cluttered or distorted. 

In closing 

Pie charts are a simple yet powerful tool for visually representing how each component contributes to a whole, making them invaluable for part‑to‑whole comparisons in research papers. They excel in quick, intuitive communication of proportion and are most effective when used with a suitable pie chart maker and an accurate pie chart formula that ensures all segments sum to 100%. However, they are limited to scenarios where data segments represent parts of a single whole; therefore, researchers should use them sparingly, ideally with five or fewer categories, clear labels, and accessible annotations that align with recommended pie chart uses. While alternatives like donut charts, waffle charts, and treemaps can address specialized visualization needs and overcome several pie chart disadvantages, one should steer clear of pie charts when precise group comparisons, complex data structures, or large datasets are involved, where other visuals are more suitable. To maximize effectiveness and truly leverage pie chart advantages, researchers should avoid 3D distortions, unnecessary colors, and non‑proportional data; follow best practices for how to create a pie chart; and always prioritize clarity and accessibility in visual design. 

Mind the Graph is an easy-to-use visualization platform for researchers and scientists that enables fast creation of precise publication-ready graphical abstracts, infographics, posters, and slides. With 75,000+ scientifically accurate illustrations made by experts and hundreds of templates across 80+ major research fields, you can produce polished visuals in minutes — no design skills required.   

Completing a thesis is a major milestone, but turning it into a publishable research paper is a different challenge altogether. Many researchers struggle with condensing a long, descriptive thesis into a clear, concise manuscript that meets journal standards.  

To help you bridge this gap, Mind the Graph along with Paperpal is thrilled to announce the next session in The AI Exchange Series – How to Transform your Thesis into a Research Paper 

Join Us for the Live Webinar: How to Transform your Thesis into a Research Paper 

Data: Thursday, November 27, 2025   
Time: 8:30 AM EDT | 1:30 PM GMT | 7:00 PM IST  
Language: English 
Duration: 1 hour 

What You’ll Learn 

In this session, we’ll break down the thesis-to-paper conversion process with clear, practical guidance on: 

• Key structural differences between a thesis and a research paper 
• How to identify which sections of your thesis are publishable 
• Common challenges and simple solutions during conversion 
• A clear 10-step method to shape a strong manuscript 
• How Paperpal helps you refine and speed up your path to publication 

Meet the Speaker 

Dr. Faheem Ullah
Assistant Professor and Academic Influencer 

Dr. Faheem Ullah is an award-winning academic and consultant specializing in AI and research methodologies. He holds a PhD and Postdoc in Computer Science from the University of Adelaide and has earned multiple distinctions, including two Gold Medals and a Bronze Medal. With over 40 publications in leading journals and conferences and experience supervising more than 40 undergraduate, master’s, and PhD researchers, Dr. Faheem brings deep insight into academic writing and ethical AI use. 

A respected voice in the global research community, he has attracted an audience of over 300,000 followers, sharing advice on research skills, publishing, and responsible AI. 

Why You Should Attend 

Publishing from your thesis can feel overwhelming, but the right guidance makes the process faster and more structured. This webinar will help you identify what’s publishable, avoid common mistakes, and reshape your thesis into a clear, journal-ready manuscript. Whether you’re a master’s or PhD researcher, you’ll walk away with practical steps and more confidence in your path to publication. 

Don’t miss this chance to learn how to turn your thesis into a clear, compelling manuscript and take the next big step in your research journey – Register for free today! 

Mind the Graph is an easy-to-use visualization platform for researchers and scientists that enables fast creation of precise publication-ready graphical abstracts, infographics, posters, and slides. With 75,000+ scientifically accurate illustrations made by experts and hundreds of templates across 80+ major research fields, you can produce polished visuals in minutes — no design skills required. 

AI has quietly become a co-author in modern research. From grammar checks to literature reviews, tools powered by artificial intelligence are helping academics write faster and smarter. But there’s a growing challenge — most researchers don’t disclose their use of AI, even when journals require it.

In fact, recent data shows that over 75% of authors using AI fail to mention it in their submissions. With journal policies tightening and transparency becoming a pillar of academic integrity, understanding how and when to disclose AI use has become essential.

To unpack this important topic, Mind the Graph along with Paperpal is hosting a free webinar as part of The AI Exchange Series.

Understanding AI Disclosures: Why, When, and How to Disclose AI Use

Data: Monday, November 10, 2025
Time: 1:00 PM EDT | 5:00 PM GMT | 10:30 PM IST
Language: English
Duration: 1 hour

What You’ll Learn

This session will help researchers and educators navigate the grey areas of AI use in academia. You’ll learn:

• Why AI disclosure matters for maintaining academic integrity
• Real examples of both effective and poor AI statements in papers
• When AI use must be disclosed during submission
• How to write clear, compliant disclosure statements aligned with journal policies

Plus, join the live Q&A with our expert to get answers to your own questions about ethical AI use in research.

Meet Our Expert 

Professor Tina Austin 
University of Southern California 

Professor Tina Austin is an AI ethics consultant, biomedical researcher, and educator who helps universities and organizations adopt AI responsibly. She has taught at UCLA, USC, CSU, and Caltech in subjects ranging from regenerative medicine and computational biology to AI ethics and communication. 

Tina is also the founder of GAInable.ai, a platform that empowers educators to use generative AI ethically and creatively. Recognized among ASU+GSV’s Top Leading Women in AI and as a Microsoft Innovative Educator Expert, she advises the California Department of Education and the Los Angeles AI Taskforce on responsible and inclusive AI adoption in education. 

Why You Should Join

If you’re a student, researcher, or educator exploring AI in your academic writing, this session is for you. Gain the clarity and confidence to use AI tools responsibly while ensuring your work meets the highest ethical and publication standards.

Don’t miss this chance to learn directly from a global AI ethics expert and strengthen your approach to AI use in academia – Register for free today!

Mind the Graph is an easy-to-use visualization platform for researchers and scientists that enables fast creation of precise publication-ready graphical abstracts, infographics, posters, and slides. With 75,000+ scientifically accurate illustrations made by experts and hundreds of templates across 80+ major research fields, you can produce polished visuals in minutes — no design skills required. 

Choosing the right color palette is one of the most important steps in creating clear, accurate, and engaging research visuals. A thoughtful color palette for data visualization helps your audience distinguish patterns, spot trends, and interpret results effortlessly. Whether you’re selecting colors for graph elements, deciding on a color for pie chart, or curating a consistent chart colors palette, your choices directly affect how your findings are understood. Understanding how to pick a color palette ensures that every shade serves a purpose rather than distracting from your message. 

In research graphics, your color palette for graph should enhance readability while maintaining aesthetic harmony. The pie chart color palette must balance contrast and consistency, while bar chart colors should remain distinct enough to compare values easily. By focusing on accessibility, consistency, and meaning, you can identify the best colors for graphs and build a cohesive color palette that strengthens your scientific storytelling. 

Types of a Color Palette 

Choosing the right color palette ensures that your visualizations communicate meaning effectively. Each palette type conveys a different kind of data relationship — whether distinguishing categories, showing progression, or highlighting deviation. The table below summarizes the main types and includes example color sets. 

• Qualitative palettes distinguish categories. 
• Sequential palettes show progression. 
• Diverging palettes highlight contrast from the center. 

Always keep accessibility in mind, ensuring adequate contrast and color-blind-friendly combinations. The right palette not only makes your data beautiful but also meaningful. 

How to Choose a Color Palette 

A color palette should be chosen with both clarity and accessibility in mind. When deciding how to choose a color palette, focus on contrast, consistency, and context—your colors should clearly distinguish categories without overwhelming the viewer.¹ Start by defining your data type: use qualitative palettes for categories, sequential ones for gradients, and diverging palettes for comparisons. A well-selected color palette enhances storytelling, making colors for graphs and bar charts both visually appealing and easy to interpret. Testing your color palette for color blindness and grayscale readability ensures that your visuals remain inclusive and scientifically sound. 

Table: Principles and Guidelines for Choosing Effective Color Palettes in Scientific Visualization 

Color Palette: Accessibility Matters 

Creating an inclusive and visually effective color palette is essential for ensuring that all viewers can interpret your research graphics accurately. From selecting colors for graph clarity to optimizing your pie chart color palette for accessibility, thoughtful design choices make your visuals both professional and user-friendly. 

Do’s for Accessible Color Palettes 

Don’ts for Accessible Color Palettes 

Designing with accessibility in mind transforms your color palette from a simple design choice into a strong scientific communication tool, helping your insights reach every viewer with equal impact. 

Learn h Mind the Graph’s PowerEdit tool to apply and fine-tune those colors directly to your research figures. PowerEdit helps you customize shades, adjust contrast, and maintain color consistency across multiple visuals effortlessly.

Best Practices for Choosing Colors for a Graph 

Selecting the right colors for graph elements is key to making your data both beautiful and meaningful. A well-thought-out color palette enhances clarity, readability, and consistency across all your visual materials. Whether you’re designing a color palette for data visualization, building a chart colors palette, or selecting the best colors for graphs, the following best practices will help you make confident, research-driven choices. 

Step 1: Understand Your Data 

• Identify whether your data is categorical, sequential, or diverging. 
• Match the palette accordingly: 
• Qualitative → Distinct colors for categories. 
• Sequential → Gradient for ordered or continuous data. 
• Diverging → Two hues showing deviation from a midpoint. 

Step 2: Choose an Appropriate Palette 

• Use trusted color sets (e.g., ColorBrewer, Viridis, Batlowlub Tableau 10). 
• Avoid rainbow scales for ordered data — they distort perception. 
• Opt for perceptually uniform palettes that display gradual changes accurately. 

Step 3: Consider Contrast and Legibility 

• Ensure adequate contrast between colors. 
• Check how your graph appears on both light and dark backgrounds. 
• Test for color-blind accessibility using tools such as Coblis or Color Oracle. 

Step 4: Keep the Palette Simple 

• Limit to 5–7 distinct colors to maintain visual clarity. 
• Use neutrals or desaturated tones for secondary elements (e.g., gridlines, axes). 
• Emphasize key data with accent colors sparingly. 

Step 5: Maintain Consistency 

• Use the same color for the same variable across multiple charts. 
• Develop a color hierarchy — main data, supporting data, and background. 

Step 6: Align Colors with Meaning 

• Choose colors that reinforce context or meaning: 
• Blue = cool/stable, Red = increase/warm, Green = growth. 
• Avoid misleading color associations (e.g., red for low values). 

Step 7: Adapt for Medium and Accessibility 

• Adjust brightness and saturation for print vs. digital outputs. 
• Verify readability on various screen types and in grayscale. 

Step 8: Provide Guidance 

• Always include a legend or annotations explaining the color logic. 
• If colors represent intensity, show the scale or gradient bar. 

A thoughtfully selected color palette ensures that every visual is not only appealing but also clear, consistent, and accessible. 

Color Palette for Data Visualization: Dos and Don’ts 

A carefully chosen color palette for data visualization ensures that your graphs, charts, and figures communicate data clearly and inclusively. Your choice impacts how effectively your audience can interpret your research. Use the following dos and don’ts as a quick guide to create a consistent, accessible, and visually appealing chart colors palette. 

In summary, following these simple dos and don’ts will help you design a color palette for graphs and other visuals that are professional, inclusive, and easy to interpret. A cohesive color palette not only improves aesthetics but also strengthens your research storytelling by keeping your chart colors palette clear, consistent, and accessible to all. 

Kluczowe wnioski 

• A well-designed color palette for data visualization helps communicate research findings clearly, improving both accuracy and engagement.  

• Always choose a color palette for graphs that balances contrast, accessibility, and consistency across your visuals.  

• Use limited, distinct hues in your chart color palette to avoid confusion and maintain clarity.  

• The best colors for graphs are those that remain legible to all viewers, including those with color-vision deficiencies.  

• Keep your pie chart color palette visually consistent with your overall design style.  

• Learning how to choose a color palette ensures your charts are both visually appealing and scientifically sound.   

• A cohesive graph color palette strengthens readability, reinforces patterns, and enhances storytelling in your research visuals.  

Frequently Asked Questions on Qualitative Research  

What is the difference between qualitative and sequential palettes? 

Qualitative palettes use distinct, unrelated colors to represent different categories or groups—ideal for comparing nominal data like species, regions, or departments. Sequential palettes, on the other hand, use variations in lightness or saturation of a single hue (or a gradient between hues) to represent ordered or continuous data, such as temperature or population density. In short, qualitative palettes highlight differences in category, while sequential palettes show differences in amount. 

How many colors should I use in a qualitative palette? 

When creating a qualitative color palette, it’s best to use between five and eight distinct colors. This range provides enough variety to differentiate categories without overwhelming the viewer or making colors too similar to distinguish. If you need to represent more groups, consider using variations in shape, pattern, or brightness to maintain clarity and visual balance. 

Why is color blindness important in data visualization? 

Color blindness is crucial to consider in data visualization because around one in twelve men and one in two hundred women experience some form of color vision deficiency.² When visualizations rely solely on color differences—like red and green—to convey meaning, key insights can become invisible to part of the audience. Designing with accessible color palettes ensures that every viewer can accurately interpret patterns and relationships, making your data both clearer and more inclusive. 

Mind the Graph is an easy-to-use visualization platform for researchers and scientists that enables fast creation of precise publication-ready graphical abstracts, infographics, posters, and slides. With 75,000+ scientifically accurate illustrations made by experts and hundreds of templates across 80+ major research fields, you can produce polished visuals in minutes — no design skills required. 

Referencje 

1. Crameri, F., Shephard, G. E., & Heron, P. J. (2020). The misuse of colour in science communication. Nature communications, 11(1), 5444. 

2. National Eye Institute. “Types of Color Vision Deficiency.” NIH, U.S. Department of Health & Human Services, August 7, 2023. Available at https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/color-blindness/types-color-vision-deficiency. 

AI tools have revolutionized the way researchers work, making academic writing and scientific communication more efficient than ever. But with these advantages comes a new challenge: AI hallucinations — confidently presented but factually incorrect outputs.

Our 2025 Paperpal survey revealed that 63% of academics using AI in their research workflows are concerned about accuracy. This growing anxiety around the reliability of AI-generated content isn’t unfounded. AI hallucinations can jeopardize the integrity of research, lead to unintentional plagiarism, and create credibility risks for authors.

To help you navigate this critical challenge, we’re excited to announce our upcoming webinar as part of The AI Exchange Series, where we’ll dive deep into understanding, recognizing, and avoiding AI hallucinations in both academic writing and research visuals with Paperpal oraz Mind the Graph.

Join Us for an Insightful Discussion on How to Identify and Prevent AI Hallucinations in your Research.

Data: Thursday, October 30, 2025
Time: 1:00 PM EDT | 5:00 PM GMT | 10:30 PM IST
Language: English
Duration: 1 hour

What You’ll Learn

This exclusive session will help you understand what AI hallucinations really are, why they happen, and how they can affect the credibility of your research. You’ll also learn practical strategies to identify and avoid them before they impact your writing or visuals.

Here’s what to expect:

• Understand what AI hallucinations are and why they occur.
• Learn the risks of using unverified AI-generated content.
• Discover how reliable tools like Mind the Graph ensure research integrity.
• Get a guiding framework for responsible and ethical AI use in research.

Plus, you’ll have a chance to interact with our expert during a live Q&A and walk away with actionable insights to enhance your writing and communication workflow.

Meet Our Expert 

Emmanuel Tsekleves
Professor, Lancaster University

Emmanuel is a globally recognized academic with 130+ published research articles and extensive expertise in research excellence and academic integrity. A former Director of the Future Cities Research Institute, he advocates for responsible AI use in academia and guides researchers worldwide on ethical AI integration. His work has inspired over 220,000 researchers, and he serves on the Executive Board of the Design Research Society. He has supervised 14 PhDs and brings rich experience in research supervision and academic writing standards.

Why You Should Attend 

This webinar is designed for students, researchers, and educators who use AI in their research writing. By joining, you’ll gain a clear understanding of how to use AI tools responsibly and ensure that your work reflects the highest standards of academic integrity. 

Don’t miss this opportunity to hear directly from a leading academic and walk away with a practical framework for reliable and ethical AI-assisted writing. 

Don’t miss this chance to gain the clarity you need to navigate the grey areas and leverage AI responsibly in your research and writing – Register for free today! 

Mind the Graph is an easy-to-use visualization platform for researchers and scientists that enables fast creation of precise publication-ready graphical abstracts, infographics, posters, and slides. With 75,000+ scientifically accurate illustrations made by experts and hundreds of templates across 80+ major research fields, you can produce polished visuals in minutes — no design skills required. 

In a world where attention spans are shrinking, visual learning is no longer a “nice-to-have” — it’s essential. Research papers, journal articles, and presentations are often packed with dense text that only a few readers fully digest. Icons and visuals can cut through that clutter, making research communication clear and memorable. Tools like Mind the Graph empower scientists and scholars to transform complex data into intuitive visuals, whether that’s through scientific icons, infographics for research, or full-blown visual abstracts. 

The Psychology Behind Visual Learning 

Why do visuals stick with us while text often fades away? The answer lies in the psychology of visual learning. Our brains are naturally wired to process images quickly and remember them for longer periods. 

A recent study by Social Media Today revealed that posts using visuals receive 37% higher interaction than those with only text. Similarly, research cited by EmailAudience suggests the human brain can process images approximately 6–600 times faster than words. This proves why infographics for research and visual abstracts are such powerful tools — they capture attention instantly and convey meaning with clarity. 

Według Dual Coding Theory, when we combine words with images, our brain encodes information through two channels, doubling the chances of recall. Meanwhile, working memory has strict limits. Too much text can overwhelm, but a single scientific icon or infographic for research reduces that cognitive load dramatically. 

This is why visual abstracts are gaining momentum in journals — they help readers absorb key findings in seconds. As highlighted in our article on Graphical Abstracts and Science Communication, researchers worldwide are embracing visuals not just as decoration, but as vital tools to convey science with speed and clarity. 

Why Icons Work Better Than Text 

When it comes to research communication, simplicity always wins. That’s where icons in research truly shine. Unlike text, icons transcend language barriers — a DNA helix or a microscope icon is instantly understood by scientists worldwide, regardless of the communication language. 

Icons also strip away the unnecessary noise. A well-designed scientific icon communicates the essence of an idea without demanding readers parse long descriptions. This speed of comprehension leads to stronger retention, making icons one of the most efficient forms of visual storytelling in research. 

Tools like Mind The Graph empowers researchers with thousands of professional icons specifically designed for science. Instead of spending hours creating visuals, scientists can focus on what truly matters: presenting their data in ways that capture attention and drive understanding. 

Turning Research Into Impact: Real-World Uses of Visuals 

The true power of visual learning lies in how it transforms real-world research communication. Here are some practical ways researchers are already applying visuals: 

• Conference Posters & Presentations: Posters and slides enhanced with illustrations, scientific icons, and infographics stand out in crowded spaces. These visuals simplify complex ideas, making key findings easier to absorb and remember. 

• Journal Submissions: Visual abstracts are becoming a standard across many journals. They give readers a quick, intuitive overview of a study’s purpose, methods, and results,  increasing visibility and citation potential. 

• Grant Proposals & Reports: Funding committees review countless documents — visuals and infographics for research can highlight your key points quickly. 

• Social Media Outreach: Platforms like LinkedIn, Twitter, and Instagram thrive on visuals. Sharing your work with icons and infographics drives higher engagement, as proven by the 37% boost in interaction. 

• Zaangażowanie publiczne: For non-specialist audiences, visuals bridge the gap between expert language and everyday understanding. Well-crafted illustrations or simplified visual abstracts make science relatable and accessible. 

Visuals don’t replace text; they enhance it. In the context of research communication, they ensure faster comprehension, higher retention, and wider reach. By appealing to both logic and emotion, visuals make science more accessible and impactful. 

Platformy takie jak Mind the Graph are democratizing this process by equipping researchers with intuitive tools for visual storytelling in research. In an era of information overload, those who master the art of visuals will stand out — their work not just read, but remembered. 

Great academics know that powerful visuals can transform research—but not all tools give you that creative freedom. That’s why we’re proud to unveil PowerEdit, our breakthrough figure editing tool for Mind the Graph. Now, you can craft publication-ready visuals that are as precise and compelling as your science. 

Why Visuals Matter in Research (and Why Editing Them Should Too) 

Clear, professionally designed research figures boost reader engagement, improve comprehensibility, and even influence citation rates. Według MDPI, well-crafted visuals help research papers stand out and resonate more with audiences. Separate badania shows that high-quality figures are also likely to increase the chance of acceptance in scientific journals—because clarity signals academic rigor. 

Creating publication-ready visuals is not just about presentation; it’s about revealing the unseen patterns, insights, and trends in complex datasets that text alone can’t properly convey. This makes creating powerful and accurate scientific illustrations that convey your research story an essential skill for academics to master.  

Yet academics looking to create research figures are frequently left struggling with rigid templates, inaccessible diagram elements, or generic visualization tools that are unable to adapt to scientific complexity. That’s the gap Mind the Graph’s new PowerEdit figure editing tool fills. 

Meet PowerEdit: Your Complete Figure Editing Toolkit 

With PowerEdit, you can transform any Mind the Graph illustration into a powerful research figure that reflects your work, your voice: 

• ✂ Edit without limits — Unlock icons, parts, and layers for full customization. 

• 🎨 Polish instantly — Adjust colors, styles, and scale with surgical precision. 

• ➕➖ Remix effortlessly — Add, remove, or recombine visual elements to suit your needs. 

• ⚡ Work faster than ever — Create publication-ready visuals in minutes, not hours. 

How to Use PowerEdit on Mind the Graph? 

When you add an illustration to your canvas on Mind the Graph, you’ll see a toolbar appear above it. In this toolbar, look for the PowerEdit button to edit your research figures. 

• Step 1: Click PowerEdit 
  Once you click on PowerEdit, the scientific illustration will automatically break down into its individual elements. 

• Step 2: Edit Elements Individually 
  You can now:
  • Change the colors of specific parts 
  • Adjust wyrównanie 
  • Modify the kształt 
  • Or even remove elements you don’t need 

• Step 3: Regroup Your Illustration 
  After making your edits, simply click the Group button to put the elements back together into a single scientific illustration. 

• Step 4: Place on Canvas 
  Finally, move and resize your edited illustration on the canvas as you like. 

With PowerEdit, you get complete control to customize research figures and illustrations and make them perfectly match your scientific story. 

See PowerEdit in Action  

▶️ Watch the demo to see how effortlessly PowerEdit turns standard illustrations into publication-ready visuals. 

How Can PowerEdit Benefit Researchers? 

Mind the Graph’s PowerEdit feature is more than a convenience—it’s a productivity multiplier and clarity enhancer za swoją pracę. 

• Speed and efficiency: Edit 5x faster oraz save up to 70% of time per figure with direct in-app customizations. No more recreating visuals from scratch or exporting to design tools. 

• Accuracy with impact: Unlock unlimited flexibility; retain details such as measurement units or layered elements with fidelity. Change colors, shapes, alignment, or remove elements to edit your research figures instantly. 

• Stronger publications: Well-designed scientific illustrations and research figures ensure clarity, help you convey ideas with precision, and build reader trust. 

This isn’t just another feature, it’s the creative leverage researchers have been waiting for! 
Try PowerEdit now and bring precision, agility, and professionalism to every research figure. 

We value your input. Let us know how PowerEdit improves your visual workflow or share examples of its impact in your latest papers. If you have any questions or ideas on how we can make Mind the Graph even better for you, write to us at contact@mindthegraph.com. We’d love to hear from you! 

Mind the Graph is an easy-to-use visualization platform for researchers and scientists that enables fast creation of precise publication-ready graphical abstracts, infographics, posters, and slides. With 75,000+ scientifically accurate illustrations made by experts and hundreds of templates across 80+ major research fields, you can produce polished visuals in minutes — no design skills required. 

A scientific poster is an important and widely used format for sharing research in academic settings, particularly at conferences, workshops, and symposia. Unlike journal articles or oral presentations, a poster is a highly visual medium for summarizing complex ideas in a concise, accessible way. It allows researchers to communicate their work beyond words by integrating text, figures, charts, and images into a single, engaging layout. 

Effective visualization is central to research communication. Visual elements help audiences process information faster and remember it longer. Well‑designed visuals can make otherwise dense data approachable. What is more, engaging visualizations spark discussions and invite useful feedback. 

A scientific poster condenses months or years of investigation into a format that foregrounds clarity and creativity, while encouraging direct interaction between researcher and audience. An engaging and dynamic conference poster presentation has the power to infuse life into static information, transforming your work into a conversation starter or even a networking tool! 

In this article, you will learn how to make a poster presentation, see actual research poster examples, and get to know about scientific poster software. 

What is a Poster? 

Simply put, a poster is a visual summary of a research project designed to communicate seminal findings, methods, and insights in an attractive and accessible way. During poster sessions, researchers stand by their displays to interact directly with attendees and answer questions, turning the poster into both an informational tool and a springboard for dialog and networking.  

What are Scientific and Research Posters? 

Although academic conferences can be traced back to 1860, with the first international scientific conference held in Germany, poster sessions did not emerge until a century later. The conference poster presentation concept appears to have begun in Europe before making its way across the Atlantic to North America at the 1974 Biochemistry/Biophysics Meeting in Minneapolis. The American Chemical Society adopted poster sessions for their fall national conference in Chicago in 1975. 

Scientific and research posters are visual representations of research or scientific work presented at scholarly conferences or symposiums. They are a handy and powerful means for knowledge dissemination and research networking within academic communities. 

Both scientific and research posters incorporate a mix of brief text with tables, graphs, figures, infographics, charts, and images to convey major findings, data, and the significance of the research. Figures and tables are often the main focus, as they can describe research in detail with minimal text. Further, both scientific and research posters are organized to present research as a logical, coherent story. Common sections include a title, authors, abstract, introduction, materials and methods, results, discussion, conclusions, citations, and acknowledgements.  

While both types of posters serve as communication tools, a scientific poster is usually focused on the visual presentation of scientific research or experiments, often with standardized sections and an emphasis on data visualization. Research posters, more broadly, may summarize studies across various fields and sometimes include theoretical or review work.  

What makes a great Scientific Poster? 

Let’s look at how to create a scientific poster that really stands out. A great scientific poster is visually engaging and clear, effectively communicating salient findings to a diverse audience. Think of it as a hybrid between a published paper and an oral presentation. Here are some ways to take a poster from good to great: 

• Have a clear and focused narrative: Make your main message(s) stand out. Structure and organize your content clearly and logically 

• Użycie minimal text: The fewer the words the better.  

• Describe the direction and magnitude of associations clearly: Show, don’t tell!  

• Użycie formatting, fonts, and white space to your advantage: Get creative!  

• Make it interactive: Adding interactive elements like hidden informational panels, attached objects, or props will make the poster engaging and memorable. Here is a scientific poster example with a cool interactive feature. 

Poster Design Basics 

Now, let’s get down to the design essentials of how to create a scientific poster. 

Układ 

A scientific poster has an organized and simple design with a consistent layout. Content should fall into logical sections that flow clearly from one to the next, creating a visual path for the reader. Direction of reading is typically top to bottom and left to right. A poster might therefore even have a visual shaped like an “M.” Place your content strategically, while utilizing symmetry and white space smartly. Using a grid can help structure your layout evenly. In this way, text and images can be aligned properly.  

Standard sizes 

The standard size of a scientific poster is 36 × 48 inches, typically in portrait orientation. The poster should use easy-to-read fonts, with sans serif fonts preferred. Use varying font sizes (24–48 pt) to differentiate between the title, body text, headings, and highlights. The title should be readable from about 10 feet away (at least 40 pt), section headings 30–40 pt, and body text/captions 24–30 pt. Avoid font sizes smaller than 20 pt for most sections, except for references and acknowledgements 

Color schemes 

Your poster should follow a visually appealing and easy-to-read color scheme, with high-contrast colors to highlight important information. A cohesive color scheme, perhaps inspired by a key image, can reduce clutter. Use clear, white, or pastel backgrounds. 

High-quality visuals 

Figures and tables should be the main focus, serving as informative visuals that help tell the story. For photographs or illustrations, use high-quality, high-resolution images (ideally 300 DPI, saved in PNG or TIFF formats). Avoid using blown-up photos as poster backgrounds. 

Parts of a Scientific Poster 

Common sections in a scientific poster are as follows:  

• Tytuł: The first part is a short, attention-grabbing title, which should be readable from a distance. 

• Introduction/Background: The background and a clear research question or hypothesis appears as the next part of the scientific poster. 

• Methods: A brief explanation of the methodology should be provided. Too much detail is not required. 

• Results: Important findings are presented visually, using graphs and tables with legends and titles. 

• Discussion: This is where the findings are concisely interpreted, and implications are highlighted. 

• Wnioski: This can be combined with the discussion. This section summarizes the research and its significance. 

• Referencje: A list of the cited literature should be provided, but it should not be too long. 

• Acknowledgments: If applicable, this is where authors can thank non-author contributors. 

An additional, optional part of scientific posters might be a resource link or QR code to access additional materials. 

Research Poster Examples 

Compelling scientific posters have the power to grab the attention of academic stalwarts and industry leaders, leading to collaboration opportunities and career progression. “Best Poster Awards” often carry cash prizes too!  

With a plethora of tools and resources available today, you can amp up the quality and impact of your posters. For example, a university research lab used Mind the Graph (MTG) to create eye-catching conference posters that effectively presented complex data and drew significant attention. In another case, independent researchers used the platform’s scientific illustrations for a science fair poster that successfully communicated their findings to audiences ranging from scientists to the general public, ultimately winning the Best Poster Award.  

Let’s look at some scientific poster examples (templates and layouts) that might inspire your next poster! 

Browse tutaj for more research poster examples. 

How to Create a Scientific Poster

Wondering how to make a scientific poster or where to get started? Here’s a handy checklist with key steps: 

1. Plan and brainstorm 

Spend time narrowing your focus down to the main message and main findings of your research. Think of the pertinent data, stories, or visuals that will make your poster engaging.  

2. Put your data and information together 

Organize all your research outputs (figures, tables, methods, results, and conclusions) and ensure that visuals and summaries are complete before outlining the final. 

3. Outline the content 

Draft a logical structure: group related information and plan which elements will be visual versus narrative. 

4. Put it all together 

Start designing your scientific poster on your chosen software or template. You can use tools like Mind the Graph, which offer numerous template options and galleries with professional and customizable illustrations. Insert the content into well-defined blocks, add figures/tables, and use coherent color schemes, fonts, and layouts.  

5. Review and feedback 

Share your draft with colleagues or mentors for input on clarity, flow, and impact. Collect suggestions for improving scientific accuracy, accessibility, and aesthetics, and edit your draft accordingly. 

6. Test print 

Do a trial print (at a reduced size) to check if all elements are legible and well formatted. Assess color quality, scaling, and possible layout issues.  

7. Proofread! 

Scrutinize every part of your poster for typos, grammatical errors, and factual inconsistencies. Double-check names, affiliations, and reference formatting for professional presentation. 

8. Final print 

After final adjustments, print the poster at full scale following the conference specifications for size and format. Use high-resolution files and quality printing materials for a polished result. 

Key Considerations when Creating a Scientific Poster 

Here are some important tips academics should keep in mind when creating posters: 

• Focus on the core message and maintain a clear narrative in your research poster. 

• Tailor your poster to the expected audience’s level of knowledge about your topic. Provide sufficient background and translate complex information appropriately. 

• Use minimal text. Summarize sections with bullet points and short sentences. Aim for approximately 300-800 words, allowing an average person to read it in about 5 minutes. 

• Check the specific requirements of the event or conference. 

• Pay careful attention to design elements, color scheme, fonts, figures, and tables. 

• Test your poster with your peers and supervisor and incorporate the feedback received. 

Tips for a Winning Poster Design 

Did you know that many conferences have “Best Poster Awards”? Now, wouldn’t you like your research poster to win a prize? Here are some practical tips that will make your poster shine! 

1. Balance visuals with text: Aim for a visually engaging poster by maintaining generous white space and balancing striking images or graphics with concise, supporting text. Avoid dense blocks of writing. 

2. Avoid excessive jargon: Limit technical jargon or acronyms to ensure your work is accessible to both specialists and non-specialists.  

3. Use tables and charts smartly: Charts are preferred for conveying numeric patterns, relative sizes, comparative outcomes, or trends, reducing the need for extensive text. Provide charts with clear, large titles and succinct annotations explaining patterns. Simplified tables can replace large, detailed statistical tables. 

4. Use high-resolution images: All images, diagrams, and icons should be crisp and high-resolution (at least 300 DPI) to prevent pixelation or blurriness on large posters.  

5. Avoid clutter: Use margins and padding between sections. Eliminate redundant text, unnecessary logos, or decorative elements that do not support your main message. 

6. Get creative: Consider experimenting with design elements and layouts and adding interactive elements. 

Tools for Making Scientific Posters 

You can choose from a wide and ever-growing list of tools to create a scientific poster: Mind the Graph (suitable for scholarly posters), Microsoft PowerPoint, Google Slides (often free), Adobe Illustrator (for high-quality graphics), Canva (user-friendly web-based), LaTeX (for scientific publications), Mac Pages, and Inkscape (free vector design).  

Let’s break down the pros and cons of some of these scientific poster software: 

Mind the Graph 

Plusy 

• Largest gallery of accurate scientific illustrations 

• Easy-to-use interface, with drag-and-drop feature 

• Templates well suited for academic research posters 

• Editable graphics, including color, scale, position, etc. 

• High-quality, accurate graphics as final output 

Wady 

• Limited free version 

• Subscription required for full features 

Canva 

Plusy 

• User-friendly 

• Collaborative features for team projects 

Wady 

• Premium features require subscription 

• Limited scientific-specific templates 

• Too generic for academic purposes 

PowerPoint 

Plusy 

• Widely available and familiar for most users 

• No additional cost if one has MS Office 

• Offline functionality 

Wady 

• Limited design capability for posters 

• Challenging to create professional-looking posters for academic purposes 

Adobe Illustrator 

Plusy 

• Professional-grade design capabilities 

• High-quality vector graphics 

Wady 

• Expensive subscription 

• Steep learning curve 

How to ace your Scientific Poster Presentation 

We have carefully deciphered all the dos and don’ts of scientific poster making. But acing poster-making is not enough. You also need to know how to make a poster presentation impressive and memorable. 

You should be prepared to verbally explain your work with a brief oral synopsis of your objectives, main findings, and implications. You could even prepare a few questions to ask viewers to encourage dialog. A good practice is to provide handouts with additional technical details, full results, and contact information.  

Your scientific poster might be your first presentation or publication. And so, it could well be the first step into the academic spotlight. Make the most of the opportunity to leave a lasting impression! 

Często zadawane pytania 

Q. What are the different types of scientific posters? 

A. Scientific posters generally fall into two main formats: physical posters (printed and pinned at events) and e-posters (digital versions displayed on screens). Both can be static (single-page or slides) or dynamic (including animations and videos). Within posters, common styles include traditional research posters and infographic-style posters for outreach. 

Q. How to make a poster presentation for conferences? 

A. For your conference poster presentation, (i) focus on clarity; (ii) highlight major findings; (iii) prepare your verbal summary (practice a 3–5-minute overview for conversation and questions); and (iv) stand by your poster, invite questions, and remain enthusiastic about your research. 

Q. How to decide the layout of your plakat naukowy? 

A. When deciding the layout of your poster, ensure logical flow (use columns and group information into sections—title, authors, introduction, methods, results, and conclusion), maintain visual hierarchy (keep important findings prominent, and maintain white space for readability), while maintaining consistency and adhering to the conference guidelines. 

Mind the Graph is an easy-to-use visualization platform for researchers and scientists that enables fast creation of precise publication-ready graphical abstracts, infographics, posters, and slides. With 75,000+ scientifically accurate illustrations made by experts and hundreds of templates across 80+ major research fields, you can produce polished visuals in minutes — no design skills required. 

Creating compelling scientific illustrations shouldn’t require a PhD in graphic design or drain your research budget. Yet, here you are, staring at complex data that needs to become a clear, publication-ready figure by tomorrow’s deadline.  

Sound familiar? 

Whether you’re preparing a manuscript, grant proposal, or conference presentation, the right scientific illustration tool can transform hours of frustration into minutes of creative flow. In this article, we’ll explore the capabilities of two popular scientific illustration tools Mind the Graph vs BioRender and pit them against each other. 

Mind the Graph vs BioRender: Quick Comparison 

What is BioRender and Why is it Popular? 

BioRender is a comprehensive scientific illustration platform that helps researchers and scientists create professional, publication-quality figures without needing artistic skills.  

BioRender’s scientific illustration library hosts over 40,000 icons and illustrations, and 5,000 templates specifically designed for diverse life science fields, including molecular biology, neuroscience, microbiology, immunology, and clinical research. Extending its capabilities to data visualizations, graph integration, and AI-powered features, BioRender has established itself as the premium choice for biological illustrations.  

Here’s an overview of BioRender’s core capabilities:  

• Extensive scientific icon and template library for life sciences  

• Intuitive drag-and-drop editor to assemble complex scientific figures in minutes  

• Creates publication-ready data visualizations, runs statistical tests (t-tests, ANOVAs, regressions), and integrates results with illustrations all within one platform 

• Exports in multiple formats (PNG, JPG, PDF, PowerPoint) in up to 600 DPI quality to meet journal standards for print and web 

• Customizes layouts and orientation in a click with auto-sizing features 

• AI-powered figure generation (Timeline Generator, Cell-Cell Interaction Generator) 

• Advanced collaboration tools (shared folders, commenting, team management) 

• Brand kits for organizational consistency 

Why Researchers and Scientists Seek BioRender Alternatives? 

While BioRender is a great scientific illustration tool, many scientists and researchers are seeking alternatives to BioRender because its restrictive copyright policies create barriers in academic publishing and collaboration.  

1. BioRender’s mandatory attribution requirements require all figures to display “Created with BioRender.com” as credits, even when these are heavily modified or customized from the original assets.  

2. BioRender also limits publication rights only to premium customers, which means you need to be a premium subscriber to use its visuals in your work. This adds recurring costs that many academic budgets cannot accommodate. 

3. BioRender’s licensing terms often clash with Creative Commons Atrribution (CC-BY) licenses used by many scientific journals. This conflict can prevent researchers from publishing their work in open-access venues or meeting their funding requirements for open science. 

4. BioRender’s limited content ownership means researchers cannot freely share, modify, or redistribute their figures without maintaining BioRender’s licensing obligations, hampering collaborative research and educational use. 

5. BioRender’s illustration library is highly focused on the life sciences. While this provides impressive depth for fields like molecular biology, researchers in other scientific fields may find the variety of illustrations to be limited, often leading them to seek BioRender alternatives. 

Mind the Graph vs BioRender: Licensing & Usage Rights Comparison  

These constraints have driven researchers to seek BioRender alternatives that offer more flexible licensing, full ownership rights, or open-source solutions that better align with academic publishing standards and collaborative research practices. 

What Makes Mind the Graph the Perfect BioRender Alternative? 

Mind the Graph stands out as the leading BioRender alternative, as the “Canva for Scientists and Researchers” with an extensive library of over 75,000 scientific illustrations spanning 80+ specialized fields.  

With Mind the Graph, researchers can access and customize everything from quantum mechanics and geological processes to cellular pathways and neuroscience illustrations without needing any design expertise.  

The platform’s intuitive drag-and-drop editor allows you to create professional-quality scientific visuals effortlessly, while its advanced editing capabilities let you modify even the smallest SVG elements, adjusting layers, colors, and styles directly within the platform. This eliminates the need to export illustrations to edit on Adobe Illustrator or be forced to manage multiple tools.  

With transparent licensing and clear usage rights, this BioRender alternative provides researchers with a hassle-free, all-in-one solution for scientific illustrations that delivers both the quantity of resources and the precision needed for academic publications, presentations, and educational materials across any scientific discipline. 

Let’s talk about what you can achieve with Mind the Graph: 

1. Create professional scientific visuals in minutes: Mind the Graph’s easy-to-use interface eliminates the frustration of complex designing. Simply search for what you need, drag it onto your canvas, and customize it to match your data. No tutorials needed. No learning curve. 

2. Start with 300+ publication-ready templates: No more designing from scratch with this BioRender alternative. Choose from 300+ pre-made templates on Mind the Graph, spanning 80+ scientific fields, and tweak them as needed to fit your specific requirements.   

3. Get complete control on illustration editing: With Mind the Graph’s PowerEdit, you can edit individual components in an illustration without affecting other objects. Change colors, adjust shapes, or add and remove elements to create exactly what you need with this BioRender alternative. 

4. Download print-ready scientific visuals: Download illustrations in the perfect format and quality for any use, from a website icon to a high-quality print. Export in common formats like PNG, JPEG, and PDF or SVG, with high-resolution options up to 1200 DPI available for professional prints. 

5. On-Demand Custom Illustrations: Can’t find the perfect illustration? With the Researcher Plan, you can get up to five custom illustrations created by our team of scientific illustrators each month with a 7-day delivery guarantee. 

6. Collaborate with Ease: Built as a web-based application, this BioRender alternative allows you to add multiple collaborators to your projects. Edit and make changes, check version history, and revert back to the previous versions with ease using Mind the Graph.  

Mind the Graph vs BioRender: Which Scientific Illustration Tool Offers Better Value for Researchers? 

BioRender and Mind the Graph both offer unique capabilities to design scientific illustrations, but Mind the Graph is a far more affordable alternative to BioRender.  

Mind the Graph: $72/year 

• 75,000+ expert-curated scientific illustrations 
• 300+ professionally designed templates  
• Fully editable illustrations and SVGs  
• Unlimited AI generation 
• Unlimited vector downloads with infinite zoom 
• Request for custom illustrations from experts 
• Export in PDF, PNG, JPEG, SVG up to 1200 DPI and infinite zoom 
• Add unlimited collaborators  
• Own complete publishing rights  
• On-demand customer support 

BioRender : $420/year 

• Unlimited life sciences illustrations 
• 50,000+ icons (life sciences only)  
• Unlimited data visualization and graph integration  
• Exports in PNG, JPG, PDF, PowerPoint in up to 600 DPI quality   
• AI-powered figure generation (Timeline Generator, Cell-Cell Interaction Generator)  
• Advanced collaboration tools (shared folders, commenting, team management)  
• Brand kits for organizational consistency 
• Complex licensing restrictions and usage rules 

Mind the Graph stands out as the ideal BioRender alternative, helping academics create accurate scientific illustrations without requiring exceptional design skills or following an extensive learning curve. The platform’s scientific illustrations come with Creative Commons licensing that seamlessly supports open access publishing, eliminating the compliance issues that plague BioRender users. 

With Mind the Graph’s Researcher or Prime subscription plan, academics gain complete ownership of all visual assets with zero attribution requirements, providing true freedom to use, share, or modify your illustrations however needed. You can unlock Mind the Graph’s full capabilities at an unbeatable price of just $15/month or $72/year—making professional scientific visualization accessible to every researcher. 

Create Scientific Illustrations Faster, without any hassle. Get Mind the Graph Prime dzisiaj! 

Mind the Graph vs BioRender: Which Scientific Illustration Tool to Choose & Why? 

Still unsure which scientific illustration tool to choose? Here’s a quick guide tailored to your specific needs to help you decide between Mind the Graph and BioRender:  

Choose Mind the Graph when: 

• Your research requires open access publishing compatibility that Mind the Graph’s Creative Commons licensing provides 

• You require complete ownership of visual assets without mandatory attribution  

• You need freedom to modify, share, and redistribute illustrations 

• You want to access an interdisciplinary illustration library of 75,000+ visuals and 300+ templates  

• You need visuals beyond life sciences; Mind the Graph supports 80+ subject areas 

• You need custom illustrations designed by experts in just 7 days  

• You need secure file storage and access controls to protect sensitive research data while working in teams 

Choose BioRender when:  

• You want to access curated life-science specific illustrations  
• You have existing premium subscriptions with full publication rights 
• Work primarily with closed-access journals that accept BioRender licensing 
• Your institution has enterprise BioRender licenses covering copyright concerns 
• Publishing pipeline is already optimized for BioRender’s attribution requirements 

Ready to Transform Your Scientific Communication? 

Stop letting limited libraries and restrictive usage terms hold back your visual communication. Join the 75,000+ researchers who’ve discovered that professional scientific illustration doesn’t require a premium budget. 

Create your first 4 illustrations at no cost. Start with a free Mind the Graph account today! Create Your Free Account and start illustrating your science in minutes, not hours. 

Research design is the backbone of any successful study—it’s the structured plan that guides how you collect, analyze, and interpret data. Think of it as the blueprint of your project: without it, your research risks becoming unfocused or unreliable. Whether you’re a student working on a dissertation, a professional conducting a survey, or a researcher designing a clinical trial, choosing the right research design ensures your study stays on track and produces meaningful results. 

A good research design doesn’t just tell you what methods to use—it shapes the entire process, from defining your research question to analyzing the data. It helps you avoid common errors, reduces bias, and improves reliability and validity.¹ In other words, it ensures that the findings you present are trustworthy and can be applied with confidence in real-world situations. 

This article covers everything you need to know about research design: its definition, key steps in creating a design, different types of research design, and the benefits of choosing the right one. We’ll also look at the differences between qualitative and quantitative research design, complete with examples to make things easy to understand. 

What is Research Design? 

Research design is the structured framework that outlines how a study will be conducted, guiding everything from formulating the research question to collecting and analyzing data. It acts as a blueprint, ensuring that the research process is logical, systematic, and aligned with the study’s objectives.^1,2 Without it, research can become unfocused, unreliable, or even invalid. 

A good research design clearly defines whether a study will use a qualitative approach (to explore experiences and meanings), a quantitative approach (to measure variables and test hypotheses), or a mixed-methods approach that combines both. It also determines important elements such as the target population, sampling methods, data collection tools, and analysis techniques. 

What makes research design essential is its role in ensuring reliability, validity, and accuracy. By setting a clear structure, it reduces bias, minimizes errors, and increases the credibility of findings. For example, in clinical trials or social research, a strong design is what makes the results trustworthy and applicable to real-world contexts. 

In short, research design is more than just planning—it’s the foundation that connects research questions to meaningful, actionable conclusions. 

Why is Research Design Important? 

Getting research design right is not optional—it’s the foundation of any successful study. A strong design shapes every stage of research, ensuring that the process is efficient, focused, and trustworthy. Let’s look at the core reasons why it matters so much: 

• Provides structure and clarity. Keeps your study organized with well-defined objectives. 
• Improves reliability and validity. Ensures accurate and trustworthy findings. 
• Minimizes errors and bias. Strengthens the credibility of your conclusions. 
• Saves time and resources. Streamlines the entire process, preventing wasted effort. 
• Enhances analysis. Equips you with the right framework to interpret data effectively. 
• Boosts real-world impact. Transforms insights into practical and actionable knowledge. 

In essence, good research design doesn’t just support the process—it drives research success by transforming ideas into actionable knowledge. 

Beyond planning and execution, it’s equally important to present your research effectively. Mind the Graph enables researchers to create professional, visually appealing graphics that make complex results easy to understand.

What are the Characteristics of Good Research Design? 

A good research design goes beyond just structuring a study—it ensures that research instruments, methods, and processes all work together to produce reliable and meaningful results. When done right, it not only enhances the credibility of findings but also makes the research process smoother and more efficient. 

Here are the key characteristics of good research design that every researcher should aim for: 

• Clarity of Purpose: A strong research design starts with well-defined research objectives and clear research questions, ensuring the study stays focused. 
• Validity and Reliability: It provides accurate measurements (validity) and consistent results when repeated (reliability), strengthening trust in the findings. 
• Elastyczność: While structured, a good research design also adapts to real-world challenges, allowing modifications without compromising quality. 
• Obiektywizm: Research instruments should minimize bias, ensuring that outcomes are based on evidence rather than assumptions. 
• Wydajność: A good design uses time, resources, and data collection methods wisely, avoiding unnecessary duplication or waste. 
• Ethical Soundness: It respects participant rights and upholds integrity, making the research both credible and responsible. 

In essence, the characteristics of good research design act as guiding principles that transform a study from just “collecting data” into producing knowledge that is trustworthy, impactful, and actionable. 

Key Elements in Research Design 

Think of research design as the blueprint that shapes your study—it tells you what to focus on, how to collect data, and how to analyze it. Without a clear design, even the best ideas can lose direction. 

Here are the key elements every strong research design should include: 

1. Research Problem: The heart of your study. A well-defined problem statement explains co you’re investigating and dlaczego it matters. 

2. Objectives of the Study: These act like a compass, outlining the goals you hope to achieve. Objectives can be broad or very specific, depending on your research scope. 

3. Hypothesis (if applicable): A testable prediction about the relationship between variables. While not always required, hypotheses are especially important in experimental and analytical studies. 

4. Metodologia badań: The overall strategy of your study. This includes: 

• Type of research (qualitative, quantitative, or mixed) 
• Research design (descriptive, experimental, exploratory, etc.) 
• Techniques used for data collection and analysis 

5. Zmienne: The building blocks of your research. These can be: 

• Independent variables (factors you change) 
• Dependent variables (factors you measure) 
• Control variables (factors you keep constant) 

6. Population and Sampling: Since studying an entire population is often impossible, researchers select a representative próbka. Choosing the right sampling method ensures accuracy and credibility. 

7. Metody gromadzenia danych: The “how” of your study. Depending on your problem, this could include surveys, interviews, observations, experiments, or secondary data sources. To present this data clearly, researchers often use tools like Mind the Graph to design infographics and figures that highlight key insights.

8. Data Analysis Plan: Once data is collected, it needs to be organized and interpreted. This step outlines the tools, techniques, or statistical methods that will be used. 

9. Rozważania etyczne: Strong research respects ethics, including obtaining informed consent, protecting participants’ privacy, and reporting findings honestly. 

10. Limitations of the Study: Every study has constraints. Being upfront about limitations (like time, resources, or sample size) adds transparency and builds trust. 

Qualitative vs Qualitative Research Design 

When designing a study, one key decision is whether to adopt a qualitative or quantitative research design. Both have unique strengths and suit different research questions.² 

In practice, researchers often combine both in a mixed-methods research design to capture the best of both worlds—deep insights from qualitative data and measurable evidence from quantitative analysis. 

What are Different Types of Research Design? 

Research design is not one-size-fits-all. Depending on the goals of your research—whether it’s to explore new ideas, describe trends, or establish cause-and-effect relationships—different designs are more suitable than others. 

Here are the main types of research design, summarized in a clear comparison table. 

Each type of research design serves a unique purpose. Exploratory research is best for uncovering new insights, descriptive designs help map out details of a phenomenon, correlational studies reveal patterns, while causal and quasi-experimental designs test cause-and-effect. Mixed methods, on the other hand, bring together the strengths of both qualitative and quantitative approaches. By understanding these options, researchers can choose the design that best matches their objectives and ensures reliable, meaningful results. 

Research Design: Step-by-Step Guide 

Crafting the right research design ensures your research question, methods, and analysis all align to produce meaningful and reliable results. Here’s a step-by-step guide to get it right: 

1. Define your research problem and objectives: Start by clearly stating what you want to investigate. A well-defined research question sets the direction for the entire design. 

2. Choose your research approach: Decide whether your study requires a qualitative, a quantitative, or a mixed-methods approach. 

3. Wybierz the type of research design: Based on your goals, pick the right design—causal, descriptive, exploratory, or correlational. Each design offers a different way to answer your research problem. 

4. Identify your target population and sampling method: Define who you want to study and how you will select them. Proper sampling techniques ensure your results are representative and minimize bias. 

5. Plan data collection methods: Decide whether you’ll use surveys, interviews, observations, or experiments. Align your data collection strategy with your design to gather accurate and reliable information. 

6. Choose data analysis techniques: Select the tools and methods (statistical tests, thematic coding, etc.) that will best help you interpret your results and then use platforms like Mind the Graph to present those results visually for maximum clarity.

7. Ensure reliability, validity, and ethical considerations: Double-check that your design minimizes errors, respects participants, and strengthens the trustworthiness of your findings. 

8. Write and refine your projekt badawczy: Document the entire plan clearly, ensuring it acts as a blueprint for carrying out your study smoothly. 

With these steps, your research design becomes a structured roadmap that keeps your study organized, minimizes bias, and leads to results that are credible and impactful. 

Benefits of Good Research Design 

With the right research design in place, researchers can unlock several key benefits that make their studies more effective and impactful—here are some of the most important benefits. 

1. Keeps you organized: Think of research design as your roadmap—it lays out the steps from start to finish so you don’t get lost along the way. For example, if you’re studying the effects of social media on student productivity, a clear design will tell you whether to run surveys, experiments, or interviews, instead of trying random methods that don’t connect. 

2. Helps answer your research question: A good design makes sure your methods align with your goals. If your question is “Does daily exercise improve focus?”, then an experimental design with a control and test group will help you actually measure the impact—rather than just asking people what they think. 

3. Reduces mistakes and bias: Without a solid design, it’s easy to fall into traps like sampling the wrong group or asking leading questions. For instance, interviewing only your friends about social media habits could skew results. A well-thought-out design ensures your participants and questions stay fair and representative. 

4. Boosts reliability and validity: Strong research design makes your results more accurate and trustworthy. If you repeat the same study with different groups and still get similar results, that shows reliability. Validity means you’re measuring what you set out to measure—like testing focus with actual tasks instead of just self-reported feelings. 

5. Saves time and resources: A clear plan prevents wasted effort. Imagine starting interviews only to realize later that a simple survey could have answered your question faster. Good design avoids backtracking and helps you use your time, budget, and participants wisely. 

6. Improves impact: When your research is well-structured, the findings are meaningful and applicable. For example, a study on study techniques that’s designed properly could give schools actionable insights to improve teaching methods—rather than vague results that don’t change anything. 

In short, good research design is your secret weapon—it keeps your project focused, reliable, and impactful, while making the process smoother and more efficient. 

Kluczowe wnioski 

A strong research design acts as the framework of a study, ensuring clarity, consistency, and credibility from start to finish. By carefully aligning your research questions with the right methodology—whether qualitative, quantitative, or mixed-methods—you can choose the most suitable research design, such as experimental, survey, case study, or ethnographic, to guide sampling, data collection, and analysis. Selecting the right design minimizes bias, strengthens validity and reliability, and ultimately makes findings more meaningful.¹ A well-planned design provides structure and ensures that every stage—from formulating objectives to interpreting results—works in harmony to produce impactful research.  

Remember, how you communicate findings is as important as how you design your study. With Mind the Graph, you can create clear, professional visuals that make your research more engaging and accessible.

Często zadawane pytania 

How does research design affect the entire research process? 

Choosing the right research design shapes everything from defining your research questions and choosing methods, to collecting data, analyzing findings, and interpreting results. It provides structure and ensures that each step supports your objectives, and helps you align your tools and approach to what you’re actually trying to discover. The chosen design sets the tone for every phase of the process. It is the logical plan that ensures consistency and coherence across all stages, from defining aims to drawing final conclusions. 

What are common biases or errors in research design, and how to avoid them? 

Even the most well-intentioned studies can stumble into common pitfalls—from sampling and selection errors, where unrepresentative or self-selected participants skew findings, to measurement issues like ambiguous questions or inconsistent scales that confuse respondents. Response biases—such as social desirability or acquiescence—can nudge participants toward answers they think are expected, rather than what they truly believe. Meanwhile, observer or confirmation biases may lead researchers to unintentionally favor data that aligns with their expectations. To prevent these issues, clearly define your population and use representative sampling, craft neutral and unambiguous questions (ideally pre-tested), ensure consistent measurement scales, anonymize responses where appropriate, and employ strategies like blinding or structured protocols to counteract researcher expectations. 

How to choose the right research design for a study? 

Choosing the right research design starts with clarifying your research question and whether you need a qualitative, quantitative, or mixed-methods approach. From there, select a design—such as experimental, survey, case study, or ethnographic—that aligns with your goals, resources, and ethical considerations. It’s also critical to define your target population, sampling method, and data collection strategy while ensuring reliability and validity to strengthen your findings and ensure your design matches your research objectives. 

Referencje 

1. Moissenko, F., Braicu, C., Tomuleasa, C., & Berindan-Neagoe, I. (2015). Types of research designs. In Cancer Research and Clinical Trials in Developing Countries: A Practical Guide (pp. 29-39). Cham: Springer International Publishing. 

2. Asenahabi, B. M. (2019). Basics of research design: A guide to selecting appropriate research design. International Journal of Contemporary Applied Researches, 6(5), 76-89. 

Generative AI tools are transforming how researchers and students write, yet many still face uncertainty about what’s ethical, what’s allowed, and how to avoid unintentional mistakes. Paperpal’s recent survey of 1,400+ academics revealed that while over 80% use AI tools for their research and writing, 44% of the respondents were unclear on institutional policies and AI disclosure requirements. 

AI is quickly reshaping academia but where do the boundaries lie? As part of The AI Exchange initiative by Paperpal, this exclusive panel discussion will deliver clear answers on AI detection, disclosures, and responsible use. Mind the Graph and Paperpal, interconnected tools that help researchers communicate their work more effectively—one through visuals and the other through writing—come together here to extend this important conversation. 

Join us for an exclusive panel discussion with top academic experts! 

AI for Academic Writing: Separating Fact from Fiction 

Data: Thursday, August 28, 2025 
Time: 9:00 AM EDT | 1:00 PM GMT | 6:30 PM IST 
Duration: 60 minutes 

Registration is FREE - Click here to block your spot now! 

What You Will Get 

In this interactive 60-minute session, our expert panel will: 

• Discuss the right ways to use AI for academic writing without crossing ethical lines. 
• Clear common myths and misconceptions around AI use in academia. 
• Offer advice on AI disclosure, AI detection, and the risks of concealing AI use. 
• Share real-world perspectives and editorial expectations from journal insiders. 
• Provide practical tips to ensure responsible AI use in your research and writing. 

You’ll also have the opportunity to engage directly with the speakers during a live Q&A. It’s your chance to clear the air on some of academia’s most debated topics. 

Sign up now for FREE - Click here to block your spot now! 

Why This Session Matters 

The debate around AI in academia is often filled with fear and misinformation. Paperpal’s latest survey uncovered some eye-opening insights into how academics perceive AI, revealing just how widespread the uncertainty still is: 

• Nearly 87% worry about being flagged for plagiarism. 
• 63% are concerned about accuracy of AI-generated content in their work. 
• 52% feel the absence of clear institutional guidelines leaves them unsure about the right way forward. 

Be a part of the conversation. Seats are limited, so take a minute now – Register for FREE! 

Meet Our Speakers 

Christopher Ostro 

Assistant Teaching Professor and AI Strategist, CU Boulder 

Chris is an experienced educator with 7+ years in teaching, writing consulting, and curriculum development across traditional, hybrid, and online formats. He mentors new and experienced teachers, helping them enhance accessibility, adapt content for diverse learners, and innovate course design to meet the evolving needs of students. 

Joris van Rossum                                                                                                                                  

Program Director, STM Integrity Hub 

Joris van Rossum leads initiatives at STM Solutions focusing on research integrity and the application of AI in scholarly publishing. His work aims to enhance the reliability and trustworthiness of scientific literature. 

Dr. Hong Zhou                                                                                                                      

Senior Director of AI Products Management, Wiley 

Hong Zhou brings a strong track record of four years in AI research and 16 years of experience in technology, product, and business development. Passionate about digital transformation, he designs innovative products using emerging technologies and strategic approaches to enhance business performance and improve people’s lives. His expertise lies in defining effective processes and business models that drive growth in the digital era. 

Don’t miss this chance to gain the clarity you need to navigate the grey areas and leverage AI responsibly in your research and writing – Register for free today! 

As social media grows noisier, infographics in social media have become one of the most powerful ways to captivate audiences. Science communication infographics have become crucial for students and researchers aiming to break through the digital clutter and get their work seen and recognized. 

A recent study by Social Media Today found that posts using visuals received 37% higher interaction than those with only text. Another interesting article by EmailAudience showed that the human brain processes images approximately 6 – 600 times faster than words. This underscores the effectiveness of social media infographics as a tool for capturing attention and delivering information instantly.  

In this blog article, we’ll explore how to create impactful visuals for scientific illustrations for social media, cover which platforms work best for different types of content, and offer practical tips to get you started.  

Simplifying Complex Scientific Data for Public Engagement 

Science is complex. And while that may be necessary for publishing in journals, it’s a barrier on social media. Scientific insights are often wrapped in layers of technical language, equations, or dense datasets. The goal of science communication infographics or designing infographics for social media is to make these findings more digestible without losing the meaning.  

To visualize your research effectively, start by replacing technical jargon with simple relatable metaphors. Instead of long blocks of text, convert data and tables into well-designed charts and flow diagrams using recognizable icons. When designing infographics for social media, remember that the aim is to help your audience understand “Why it matters” more than just focusing on “What it is.” 

Focus on conveying your findings in digestible chunks by organizing information into smaller sections, which make it easier to read and retain. When designing infographics for social media:  

• Use color and layout to direct attention where needed 
• Limit yourself to one core message for each slide or frame 
• Avoid overloading your visuals with data, text, or numbers. 

Mind the Graph jest really great tool to use here. This accurate scientific illustration tool boasts of the largest library of visuals and templates across top subject areas. With detailed templates designed by experts and a simple drag-and-drop functionality, it helps you create scientific illustrations, graphical abstracts, infographics, posters, and more in minutes. Whether you’re building a CRISPR explainer or presenting climate data and trends, Mind the Graph can help you blend storytelling with powerful visuals that communicate your research effectively.  

Understanding Platform-Specific Design Requirements 

Not all platforms are created equal. To drive engagement and ensure clarity on social platforms, it’s important to be aware of their respective design formats. Here’s what you need to know when designing visuals for the top three social media for science and research. 

• Twitter (now X) thrives on immediacy. Users scroll feeds in seconds, which means your visuals need to be clear and interesting. With strict character count limits and 2:1 image ratio, your visuals must be bold, simple, and easy to grasp at a glance. Teaser graphics are a great choice for the platform, allowing academics to share quick ideas or updates and engage meaningfully with the audience without overwhelming the feed. 

• Instagram is ideal for visual storytelling. It’s one of the most popular social media for scientists looking to share glimpses from the lab, snapshots from the research journey, or science communication infographics. The platform favors square (1:1) and vertical (4:5 or 9:16) formats. Here, Stories and Carousel posts that allow for quick updates and step-by-step storytelling are ideal formats to break down scientific concepts for users. 

• LinkedIn caters to a more technical, professional audience. If you’re using social media in science to build credibility and showcase your expertise, LinkedIn is where detailed visuals shine. Here, you can share longer scientific infographics that summarize research insights, conference takeaways, and other academic works. Use it to experiment with timelines, “how-it-works” infographics or explainers around your field of research.  

Pro tip: Mind the Graph provides editable templates, accurate illustrations, and drag-and-drop elements making it easy to craft powerful scientific infographics. You can then quickly adjust layouts, aspect ratios, and visual emphasis as needed for different platforms, without starting from scratch. 

Science Communication Best Practices for Visual Aesthetics 

Once your content is simplified, the next step is to present it beautifully. A strong visual aesthetic increases shareability and ensures your scientific infographic works for everyone. Consistency, clarity, and inclusivity is what differentiates a good infographic from one that actually stands out in cluttered feeds with visuals and infographics in social media. 

• Visual Consistency: Use a minimal color palette and stick to 1–2 fonts to create a unified visual style that matches your research or brand. If you’re creating a series of infographics on different aspects of your study — keep layout and color themes consistent. This makes your content feel cohesive and builds recognition and recall on social media for science.  

• Improved Shareability: Designing infographics for social media that evoke emotions (humor, curiosity, or concern) or rouse curiosity is likely to get your content shared more. Use catchy headlines, relatable hooks, and maintain balance in every frame. Make your visuals scroll-stopping but not cluttered, leave white spaces so viewers can better absorb your content. 

• Accessibility First: Choose color combinations that are accessible to users with color blindness. Maintain high contrast between text and background and use fonts and typography that are optimized for mobile devices. A good infographic that is difficult to read is as good as one that’s never seen. 

Common Pitfalls to Avoid When Designing Scientific Infographics 

Even the most well-structured infographic can fall flat if you make these critical blunders. The most common slip-up? Trying to say too much, too fast. Here are some mistakes to watch out for when creating science communication infographics: 

• Cramming in too much information 
• Using complex or unlabeled graphs 
• Oversimplifying at the cost of accuracy 
• Ignoring platform-specific image dimensions 
• Poor contrast or hard-to-read text 
• Not optimizing for viewing on mobile devices 
• Neglecting to add a call to action or next steps 

Designing infographics for social media isn’t just a creative task anymore, it’s a crucial science communication skill every academic should master. Whether you’re a student showcasing their findings or a scientist presenting their work at a conference, you can amplify your message and make your work more accessible with the right visuals.  

Social media for scientists is not about dumbing down concepts — it’s about making your work more visible, relatable, and understood. And with tools like Mind the Graph, which provides pre-designed templates, scientific icons, and social-media-ready formats, it’s easier and faster than ever to convey your research and boost your engagement through accurately designed scientific illustrations.