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Hyper-realistic AI generated content, fabricated text and images now comprise a considerable chunk of flagged academic submissions. So, the buzz and search for the best AI detectors is hardly surprising. The implications of fabricated studies with synthetic data, hallucinated citations, and cooked up images and visuals are dangerous and far-reaching.  

As challenges from new tech arise, so do tailored remedies to restore balance. AI tools have the power to detect plagiarism, image manipulation, fabricated data, and other ethical concerns, including paper mills. Plagiarism checkers and AI checkers are therefore essential for maintaining scholarly standards, especially as submission volumes continue to grow. To avoid the proliferation of deceptive content in scholarly work, educators, researchers, and journal editors will want to use only the most reliable and best AI checkers available. At the same time, users have so many questions: How does an AI detector work? How accurate are AI detectors? And of course, which are the best AI content detectors out there? 

Best AI Checkers Compared 

The plethora of AI detection tools is sure to give anyone a case of analysis paralysis! Here is a handy table that can help compare the 7 best AI detectors at a glance (Table 1). 

Table 1: Best AI Checkers Compared 

What is an AI detector? 

An AI detector is a tool designed to determine whether a piece of content (text, images, video, or voice) was generated by AI or created organically by a human. These tools use sophisticated algorithms to identify certain telltale signs and inconsistencies that are characteristic of synthetic generation. 

The main goal of such tools is to ensure authenticity and uphold integrity. In academic research, reliable AI detectors can verify manuscripts, theses, and data visuals for originality before peer review or publication, helping editors flag fabricated content while minimizing false positives. 

How does an AI detector work? 

AI text detectors 

Text detectors analyze writing style through mathematical metrics to see if it mirrors the predictable nature of LLMs. One is a measure of how predictable the wording is. AI models are designed to “predict” the next most likely word, often resulting in text that has high predictability. The second measure is “burstiness” or variation in sentence length and structure. Natural human writing is “bursty,” with a mix of long, complex sentences and short ones (think one- or two-word sentences!). AI-generated text tends to have sentences of uniform length with consistent flow. In fact, if you read AI-generated text aloud, it seems to drone on flatly. 

AI image detectors 

How AI image detectors work is that they identify statistical patterns and visual inconsistencies that distinguish artificial content from human-created work. Image detectors use sophisticated algorithms to find “telltale signs” of synthetic generation that are often invisible to the human eye, e.g., pixel anomalies in visuals and lack of “noise.” Such tool are also good at “clone detection” to identify whether parts of an image have been duplicated from other online sources. 

Are AI detectors accurate? 

AI text checkers are highly accurate (95%–99% accuracy) at detecting pure AI text or “lazy” AI use (in other words, copy-pasted AI text). With human edited or hybrid text, however, detection accuracy drops to 80%–90%. When text is written by non-native English speakers, again, the reliability is lower. This is because non-native writing often follows simple, “predictable” patterns, i.e., with low perplexity and burstiness, appearing to AI detectors as machine logic. 

AI detectors flag text that follows a highly logical, structured pattern—qualities often found in certain types of human writing. In 2023, when researchers ran the U.S. Constitution through popular detectors like GPTZero and ZeroGPT, it was flagged as 92% to 97% AI-generated! Excerpts from the Bible have frequently returned high AI scores, often exceeding 88%. What’s more, many professional content writers have found their work written well before ChatGPT came to the scene flagged as AI! The reasons for these include an overlap in training data, formal structure (with naturally lower perplexity and burstiness). 

The best AI checkers like Paperpal use perplexity and burstiness metrics, hitting high (99%+) accuracy on benchmarks like “human-ChatGPT comparison corpus” or HC3. However, they cannot identify intellectual ownership or original ideas; they only assess the final output. Finally, because AI generators are constantly evolving, AI detectors must be frequently retrained to stay effective against newer models. Nevertheless, it is worth noting that Paperpal’s cross-verifying models are retrained every 90 days to ensure accuracy, reducing false positives by over 40%. 

You Might Like Reading: Why Paperpal Built Its Academic AI Detector (And Why Binary Scores Don’t Tell the Full Story) 

7 Most Accurate AI Detectors for Content and Image Detection 

With AI content detection, things are not really black and white. In 2026, the market for AI detection has shifted from simple “human vs. machine” scores to sophisticated and specialized tools. Here are the 7 most accurate detectors for content and images, categorized by their specific strengths. 

1. Paperpal: Best AI detector for academic and technical publishing 

Unlike general AI detectors, Paperpal AI Deetector doesn’t provide a blanket document score. Instead, it gives a contextual view of how AI may have influenced a piece of text, with sentence-level scoring to distinguish between AI written, AI-human blend, and human written text. Since Paperpal understands academic writing patterns, it doesn’t flag structured text, as often found in academic writing and tops the best AI detectors for academic content.   

2. GPTZero.me: Widely used for education and classroom integrity 

GPTZero.me is highly sensitive to ChatGPT and Claude outputs, making it a reliable text specialist. Its perplexity-based scans flag pure GPT outputs with a high accuracy of 98%, but it falters when it comes to hybrid text (82% accuracy).  

3. ZeroGPT: Lightweight tool suitable for bloggers 

ZeroGPT is a simple, no-frills AI detector for instant scans, detecting 90% unedited AI text with zero signup, making it good for quick, preliminary checks. It is known for its aggressive DeepAnalyze algorithm. 

4. Copyleaks: Great for multi-language detection 

Copyleaks is highly accurate for non-English text (30+ languages) and source code. It even integrates directly into platforms such as Canvas and learning management systems (LMSs) such as Moodle. 

 5. Originality.AI: Great for or web publishers and SEO  

Originality.AI includes a “Fact Checker” and “Readability” score alongside AI detection, allowing it to check for fabricated claims. It also detects content from latest models such as GPT-4o and Gemini Pro. 

6. Truthscan: Spots deep synthetic content 

This multimodal detector is designed to spot “deep” synthetic content. It is one of the few platforms that detects AI in images, voice, and video in addition to text. A veritable deepfake catcher, Truthscan is one of the best AI detection tools for fraud prevention and media verification. 

7. SynthID: Has a leading watermark-based detection technology 

SynthID (by Google DeepMind) looks for invisible digital watermarks embedded at the pixel or token level by Google’s own models (Gemini, Imagen, Veo). It is now available via a dedicated portal. 

Choosing the Right AI Detector for Text and Visual Outputs 

Not all AI detectors are built the same and choosing the right one depends on what you’re checking. Whether it’s a research manuscript, a conference poster, or a set of figures, the type of content you’re working with determines which AI detection tool will serve you best. 

For text-heavy academic work, you need an AI detector that understands the nuances of scholarly writing and doesn’t penalize legitimate editing. Paperpal emerges as the premier choice among the most cost-effective and reliable AI detectors for academic writing as it is tailored for academic rigor with its three-tier scoring (human, blended, AI), which minimizes false positives on edited drafts.  

For visuals, you need tools that can flag AI-generated images slipping into figures and presentations. AI checkers like SynthID can help here.  

AI detection tools are great at detecting raw, unedited LLM outputs. However, these cannot be used as the sole evidence to rate a scholar’s work. A human review must complement this check so that legitimately edited or paraphrased content isn’t unfairly flagged.  

Most journals now require that the use of LLMs be acknowledged. It is important to disclose AI use to uphold the integrity of the scientific record. Transparency allows readers to understand which parts of a study were synthesized by AI and which were the result of human analysis. By using reliable AI detectors such as Paperpal, authors can ensure their work remains compliant with these evolving standards.

Pertanyaan yang Sering Diajukan 

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.   

Thousands of new papers appear daily across disciplines, each competing for a few seconds of attention from editors, reviewers, and readers. Traditional abstracts, no matter how well written, are often skimmed or skipped entirely. 

A single, well-designed image is now becoming the first read of a research paper and journals are preferring papers with graphical abstracts. A 2016 study found that 47% of social science journals had already published at least one article with a graphical abstract.  

What Journals Mean by Visual Abstracts (and Why They Care) 

A visual abstract is a concise, visual summary of a research paper that highlights its core message at a glance. It uses a combination of icons, illustrations, short labels, and structured layouts to communicate the study’s objective.  

Why Journals Are Actively Pushing for Visual Abstracts 

• Research is now discovered in digital-first environments 
  Research today reaches its audience through journal websites, email alerts, social media feeds, and academic networking platforms. Text-heavy abstracts struggle to capture attention digitally. Visual abstracts are naturally platform-ready, designed to stand out in previews, thumbnails, and shared posts, attracting initial interest. 

• Information overload has changed how research is consumed 
  Editors, reviewers, and readers often scan content quickly to decide what deserves closer attention. Visual abstracts solve this by allowing research to be understood at a glance. Journals view infographics as tools that reduce cognitive load, helping readers efficiently skim through large volumes of content. 

• Science communication now extends beyond specialists 
  Research is increasingly read by interdisciplinary scholars, policymakers, practitioners, and the informed public. Journals want to encourage accessibility without compromising rigor, and visual abstracts offer a way to provide clear entry points into complex work without oversimplifying the science. 

• Visual summaries improve reach across language and discipline barriers 
  Text-heavy abstracts can be challenging for non-native English readers or those outside a specific field. Visual abstracts help bridge this gap by relying on visual cues, structure, and minimal text to convey meaning. Journals see this as a way to make research more inclusive and easier to interpret across disciplines and global audiences. 

How to Think About Designing a Visual Abstract 

• Start by deciding what you want to show: Clarify whether the visual abstract is meant to highlight a key result, explain the study design, or communicate the broader implication of the research. Journals value visuals with a clear objective. 

• Keep the intended audience in mind: Consider who is likely to encounter the visual abstract; specialists within the field, interdisciplinary researchers, or a broader audience. This influences the level of detail, terminology, and visual complexity used. 

• Use a clear structure to guide the reader: Organize the visual so the reader knows where to start and how to move through it. Most effective visual abstracts follow a simple flow, such as problem → method → outcome. Mind the Graph offers ready-made templates that help researchers maintain this structure without design experience. 

• Keep the design clean and focused: Avoid overcrowding the visual with too many elements or colors. Clean layouts improve readability and make the abstract suitable for journal pages, presentations, and social media sharing.  

• Design once, reuse everywhere: A well-designed visual abstract can be reused in conference slides, lectures, grant proposals, and online posts. With Mind the Graph, researchers can easily adapt and update visuals for different formats. 

The practice of journals encouraging, and in some cases mandating visual abstracts is expected to grow in the coming years. As more publishers adopt visual summaries as part of the submission process, it becomes important for researchers to get comfortable with simple and effective design approaches. 

Looking to make your research more visible and accessible? With Mind the Graph, researchers can create clear, publication-ready visual abstracts using scientifically accurate illustrations without any design experience.  

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.    
 

As AI becomes increasingly embedded in research and scholarly workflows, trust, transparency, and strong governance are more critical than ever. Cactus Communications (CACTUS), a leading technology company delivering AI-powered and expert-led solutions for the scholarly publishing ecosystem, has achieved ISO/IEC 42001:2023 certification for its Artificial Intelligence (AI) Management System, placing it among the early global adopters of this international standard. The certification confirms that CACTUS has established, implemented, and continues to maintain a robust AI governance framework aligned with global best practices.

ISO/IEC 42001:2023 sets an international benchmark for the responsible design, deployment, and management of AI systems. The standard emphasizes essential principles such as fairness, reliability, risk management, system oversight, and lifecycle governance, areas that are becoming increasingly vital as AI adoption accelerates across enterprises, institutions, and academia.

The certification applies across all CACTUS brands and solutions, including Paperpal, the AI writing and research assistant trusted by more than three million researchers worldwide. It validates that CACTUS operates a comprehensive, organization-wide AI governance framework aligned with international best practices and evolving regulatory expectations.

What this means for Paperpal users and partners

CACTUS’ AI Management System spans both expert services and AI-enabled product development, covering governance, legal, risk and compliance, responsible AI, engineering, product, and design functions. All processes operate in line with the controls defined under the ISO/IEC 42001:2023 Statement of Applicability, ensuring consistent oversight across the entire AI lifecycle from design and development to deployment and ongoing monitoring.

For researchers, institutions, and enterprise partners, this certification provides meaningful reassurance. It demonstrates that Paperpal’s AI is developed with strong safeguards for transparency, accountability, and risk management, while enabling continuous improvement as AI technologies and expectations continue to evolve.

Akhilesh Ayer, CEO, Cactus Communications, said, “Achieving ISO/IEC 42001:2023 reflects our commitment to building AI systems that stakeholders across academia, publishing, and research can trust. It reinforces our focus on delivering secure, accountable, and responsibly developed AI that users can rely on with confidence.”

“ISO/IEC 42001:2023 validates the discipline and safeguards that underpin our AI solutions—from how we govern data to how we evaluate, test, and monitor our models. As AI becomes increasingly central to research workflows, this certification assures our users and partners that our AI tools are built on strong foundations of safety, reliability, and responsible innovation,” Ayer added.

As AI continues to shape how research is written, reviewed, and shared, Paperpal’s ISO/IEC 42001:2023 certification further strengthens its position as one of the most rigorously governed AI writing tools available—built on a foundation of responsible innovation, safety, and trust.

Whether you’re preparing a manuscript, designing a conference poster, or sharing results on social platforms, the clarity and impact of your visuals influence how your work is perceived.  

For many early-career researchers and PhD students, the challenge lies in converting complex data into clean, engaging graphics, especially without dedicated design skills or extra time. That’s where Mind the Graph steps in: a platform designed from the ground up for scientists, to turn your visual communication from “nice to have” into a strategic advantage. 

What Impact Do Scientific Illustrations Have on Your Research? 

Articles with well-designed graphical abstracts or infographics often receive higher citations and stronger engagement. For a researcher or PhD student, this means a better chance their work gets noticed, remembered, and shared. However, templating, layout decisions, and scientific illustration sourcing often demand time and skill that you might prefer to spend on the science itself. Mind the Graph addresses that gap. 

How Does Mind the Graph Help You Design Compelling Scientific Visuals?  

• A Massive Library of Scientific Illustrations: Get the right visual elements for your science diagrams with access to 75,000+ accurate scientific illustrations across 80+ research fields, including medicine, biology, molecular sciences, and more.  That means you’re not Googling generic icons or redrawing diagrams, everything is built with science in mind. 

• Easy-to-Use Drag & Drop Editor: Use our simple drag-and-drop feature to quickly build scientific sketches, with powerful editing tools to help you produce perfectly tailored visuals that reflect your research—no design skills needed! 

• Templates Built by Scientists for Scientists: Save time with 300+ templates, vetted by domain experts to ensure your scientific diagrams, posters, and graphical abstracts are credible, meet journal standards, and maximize reader engagement. 

• Ready to Publish, Watermark-Free Export Formats:  Download scientific sketches in custom sizes as watermark-free PNG, PDF, SVG or TIFF (1200 DPI) without impacting quality, perfect for research paper drawings in manuscripts, conference posters, or grant proposals.   

• Collaboration & Institutional Support: If you’re working in a lab, research team or institution, Mind the Graph supports team workflows and offers tailor-made institutional plans. That means you can standardize your lab’s figure style, share templates among coworkers, and maintain consistent branding for your group’s visuals. 

How Researchers and PhD Students Can Use Mind the Graph? 

• Graphical abstract for your paper: Replace dense paragraphs with a one-page visual summary your editor and peers will appreciate. 

• Conference poster: Use a scientifically-styled poster template, plug in your sections, illustrations & charts, and export for print. 

• Infographic for outreach: Turn your research into a shareable image for social media or departmental newsletters, helping non-specialists understand your work. 

• Slide deck: Maintain consistency across slides with a template, use the scientific icons to highlight complex steps or processes, and finish in minutes. 

• Lab/team branding: Create a style guide (colours, icons, layouts) that ensures all team visuals look polished and professional, boosting credibility at conferences and publications. 

How Do Mind the Graph’s Science Diagrams Make a Difference to Your Research Communication? 

• Saves time and effort: Instead of fighting with PowerPoint or Illustrator, you focus on content as design is streamlined. 

• Elevates your communication: Better visuals = clearer messages = higher likelihood of engagement from reviewers, peers, funders. 

• Accessible even without design skills: You don’t need to be a designer, you just need your scientific idea and the platform does the heavy lifting. 

• Cost-effective: With free and affordable plans, you don’t need a big design budget just to level up your visuals. 

If you’re a researcher or PhD student serious about sharing your work effectively, Mind the Graph offers a powerful yet approachable solution. Sign up to Mind the Graph and explore the library, pick a template for your next project, customise it, and download it. Your science deserves to look as good as it is! 

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.    

Report writing involves creating documents to communicate with readers about specific topics and to present facts, findings, and recommendations for research. Report writing is commonly used in academic, research, and technical settings. A well-structured report is important to ensure clarity and accuracy and effective communication of the findings. 

In academia, report writing is essential for documenting details about research experiments, data analysis, and providing recommendations. Effective report writing requires clear, professional language and accurate information. Reports may need to be written in specific formats based on the guidelines provided and also need to be customized keeping the target audience in mind. For example, a budgetary report may provide the annual expenditures of the marketing department of a company and offer recommendations on how the expenditure can be reduced.¹ 

This article provides details and steps about report writing and also shares tips on how to create well-structured reports. 

What is Report Writing? 

Report writing is the process of presenting detailed information about a subject in a clear and accurate way using plain language. Report writing helps in communicating facts, ideas, research methods, recommendations, etc. with readers. Reports are useful across all industries such as academics, science, government, and business. 

Report writing follows a specific structure, mostly including sections such as the Introduction, Methodology, Results, Discussion, and sometimes a separate Conclusion (IMRaD) to give readers a complete understanding of the subject. Reports can be of different lengths depending on the type of report, requirement, and target audience. 

A good report has the following characteristics: 

• Informative and fact-based 
• Formally structured 
• Well-defined sections with headings 
• Visual aids like tables and graphs 
• Recommendations for further research 

What is the Difference Between Reports and Essays 

The following table describes the differences between reports and essays.^2,3 

Berbeda Types of Report Writing 

The following table lists a few different types of report writing and also provides examples from the educational setting.² 

How to Write a Report (Step-by-Step) 

Report writing is an organized and structured process. Here are some of the main steps you could use to write your report.² 

1. Identify the objective of the report and the target audience 

Before writing a report, you need to know the target audience for whom you are writing your report. The audience could be the general public, academicians, government, or any other specific group. The report should be written keeping your audience’s level of expertise and requirements in mind and including information that would be useful for them. 

2. Conduct research to gather data 

Use appropriate sources to collect data for your research. Ensure that the sources are credible and authentic and provide accurate data. These sources should be cited correctly in both the text at the corresponding instances and also in the reference list at the end of the report. 

3. Create a structure to organize your report 

Although most reports have a common structure, make sure to plan and customize your sections and headings based on the information you have and which you need to present to the audience. You could use the typical sections such as Title page, Table of contents, Introduction, Methodology, Findings, Discussion, Conclusion, and References. 

4. Write your report 

Write each section of your report and ensure that you include all relevant information. For example, the Introduction should have the objective and background information to set the context to help readers understand your report clearly. In the Results section, use visual aids such as graphs, tables, etc., which help in clearly representing data. In the conclusion, summarize your research, note any implications and potential for further research, and also offer recommendations. 

5. Proofread your report 

Ensure that the language used is formal and free of any grammatical and typographical errors. Paperpal is an excellent tool to ensure consistent formatting and correct referencing. In addition, you could run Paperpal’s Plagiarism Check to ensure originality in your writing. 

Report Writing Elements, Format, and Structure 

Report writing has a few essential elements and a structure typically common to most reports. The common report writing elements are listed below, along with few examples:² 

1. Halaman judul: Title, author name, institution, date 

Baral, P., Larsen, M., & Archer, M. (2019). Does money grow on trees? Restoration financing in Southeast Asia. Atlantic Council. https://www.atlanticcouncil.org/in-depth-research-reports/report/does-money-grow-on-trees-restoring-financing-in-southeast-asia/  

2. Table of contents: Provides an overview of the report’s contents and lists entries using section headings and page numbers for easy retrieval.

3. Executive summary (or Abstract): This is a concise overview of the entire report, summarizing only the key aspects. 

Example: In 2024, the annual sales report for ABC Ltd. showed a 30% increase in overall sales compared to the previous year. Since majority of the customers were women aged 25 to 40 years, marketing efforts are recommended to be focused on this demographic in the next year. 

4. Pendahuluan: Provides the context and background of the research. 

Contoh: The inclusion of experiential and competency-based learning has benefited electronics engineering education. Industry partnerships provide an excellent alternative for students wanting to engage in solving real-world challenges. Industry-academia participation has grown in recent years due to the need for skilled engineers with practical training and specialized expertise. 

However, very few studies have considered using educational research methods for performance evaluation of competency-based higher engineering education. To remedy the current need for evaluating competencies in STEM fields and providing sustainable development goals in engineering education, in this study, a comparison was drawn between study groups without and with industry partners. 

5. Metodologi: Outlines the methods used for data collection, such as surveys, interviews, or experiments. 

Contoh: The study randomly allocated the 350 resultant patients to the treatment or control groups, which included life skills development and career training in an in-house workshop setting. The 350 participants were assessed upon admission and again after they reached the 90-day employment requirement. The psychological functioning and self-esteem assessments revealed considerable evidence of the impact of treatment on both measures, including results that contradicted our original premise. 

6. Results/Discussion/Analysis: Presents, discusses, and analyzes the key findings and data 

Contoh: A total of 5365 snakebites were reported to the CPCS from 1 September 1997 through 30 September 2017. All bites were reported from rattlesnakes. The majority of snakebite reports were called from health care facilities (4607, 85.9%) versus private residences (671, 12.6%), with the distribution of number of cases per county and incidence (number of cases per 1 million residents) shown in Figures 1 and 2. […] Males were significantly more likely than females to be injured (76.6%, 95% CI ¼ 75.6–77.9%, p < .01) […] 

7. Kesimpulan: Summarizes the main points, reiterates the report’s purpose, and suggests implications of the study. 

Contoh: In conclusion, our study has shown that increased usage of social media is significantly associated with higher levels of anxiety and depression among adolescents. These findings highlight the importance of understanding the complex relationship between social media and mental health to develop effective interventions and support systems for this vulnerable population.  

8. Recommendations: Offers feasible suggestions for improvements in future research. 

Contoh: E-learning platform developers should focus on e-learning platforms that are interactive and cater to different learning styles. They can also invest in features that promote learner autonomy and self-directed learning. 

Future researchers can further explore the long-term effects of e-learning on language acquisition to provide insights into whether e-learning can support sustained language development. 

9. Referensi: Cites all sources referred to in the research. 

10. Lampiran: Includes any supporting or additional information. 

Language and Style in Report Writing 

Report writing should use clear and concise language. The sentences are usually structured and use a formal tone and the paragraphs are typically shorter than those in an essay.  

Here are a few points to keep in mind:⁴ 

• Apostrophes and contractions should not be used; for example, use “it is” instead of “it’s” and “could not” instead of “couldn’t.” 
• Avoid including personal expressions or statements. The language should be impersonal to ensure formality. 
• Ensure correct tense usage. Usually, past tense is used to describe events that occurred before the report was written. Use past tense for the literature review and methodology sections. Use present tense for interpreting your results and conclusions. Use future tense while giving recommendations. 
• Use technical terms only if necessary. Do not overload your report with technical jargon. 

Tips for Creating Strong Reports 

Listed below are a few tips to help you in strong report writing: 

• Use active voice wherever possible. 
• Avoid unnecessary words, complex phrases, and excessive use of technical jargon. 
• Use bullet points, lists, tables, and other visual aids to ensure better readability. 
• Maintain objectivity in report writing and avoid any personal bias. 

To summarize, report writing involves writing a structured and organized document with clear sections and headings using formal and transparent language. The purpose of report writing is to clearly communicate required details about a topic to a target audience and include recommendations for future research. 

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.   

Referensi 

1. Report writing: A step-by-step guide for professionals. Indeed. Updated November 27, 2025. Accessed December 11, 2025. https://uk.indeed.com/career-advice/career-development/report-writing 

2. What is report writing: Format, types, and examples. Paperpal blog. Published July 16, 2025. Accessed December 12, 2025. https://paperpal.com/blog/academic-writing-guides/what-is-report-writing-format-types-examples 

3. Reports and essays: Key differences. University of Portsmouth. Accessed December 17, 2025. https://www.port.ac.uk/sites/default/files/2022-10/Reports%20and%20Essays%20key%20differences.pdf 

4. Report writing. Sheffield Hallam University. Accessed December 22, 2025. https://libguides.shu.ac.uk/reportwriting/language 

5. Types of reports. LibreTexts. Accessed December 23, 2025. https://biz.libretexts.org/Courses/Chabot_College/Mastering_Business_Communication/06%3A_Business_Reports/6.01%3A_Types_of_Reports 

6. Common mistakes in report writing: A guide to avoiding pitfalls. Reportql vis Linkedin. Published November 29, 2023. Accessed December 24, 2025. https://www.linkedin.com/pulse/common-mistakes-report-writing-guide-avoiding-pitfalls-%C3%A7elik-ksssf/ 

Scientific illustration has become a cornerstone of modern research communication. Clear, well-designed visuals help researchers explain complex concepts, improve comprehension, and present findings more effectively in journals, posters, and conferences. Studies show that academic papers incorporating more diagrams and visual content typically achieve higher impact than those relying on text alone. For researchers, PhD students, and broader academic professionals, choosing the right scientific illustration tool can make the difference between a figure that merely looks good and one that simplifies complex data and clearly supports the research narrative while meeting stringent publication standards. 

Whether you are creating a graphical abstract, a conference poster, or high-resolution figures for peer-reviewed journals, several factors matter when selecting the best scientific illustration tool. These include access to accurate scientific visuals and icons, vector-based export formats, ease of use, ready-made templates, customization options, and compatibility with existing research workflows. 

In this blog, we compare the five best scientific illustration software tools: Mind the Graph, BioRender, Adobe Illustrator, Affinity Designer, and Inkscape, to help you choose the right solution for your research and visualization needs. 

Quick Comparison: Top Scientific Illustration Tools for Researchers 

*Free plan available with export limitations. 

Mind the Graph 

Mind the Graph is an easy-to-use scientific illustration software designed specifically for researchers, PhD students, and academic teams who need clean, accurate, and publication-ready visuals quickly. As an online scientific illustration program, it is fully browser-based and intuitive, making it ideal for academics without any formal design experience.  

The platform offers an extensive library of professionally created scientific illustrations spanning 80+ research fields, including biology, medicine, chemistry, life sciences, and environmental sciences. It also offers 300+ ready-made templates for graphical abstracts, scientific posters, presentations, and infographics.  

Mind the Graph: Key Features 

• 75,000+ scientifically accurate illustrations across major research domains 
• Pre-designed templates for graphical abstracts, posters, slides, and infographics 
• Drag-and-drop visual editor with easy customization and color adjustments 
• PowerEdit mode for customization of illustrations to match your science 
• Auto-alignment and simple layout guides for clean, organized figure design 
• Cloud-based access, allows you to work from any device without installation  
• Consistent visual style, ensuring cohesive figures across research projects 

Pros of Mind the Graph 

• Beginner-friendly drag-and-drop tool for non-designers 
• Rich scientific library saves time and ensures accuracy 
• Ready-to-use templates that cover general usage sizes 
• Ideal for quick creation of graphical abstracts and posters 
• Consistent visual style, ideal for multi-figure publications 
• No fussy setup; multi-device access ensures flexibility 
• Download and share illustrations as PNG, TIFF or PDF 
• On-demand custom science illustration by design experts  
• Paid plans include full authorial rights and transfer option  

Cons of Mind the Graph 

• Not linked to Excel or Google spreadsheets, forcing academics to enter data manually. 
• Best for 2D scientific illustrations; limited vector editing compared to Illustrator or Affinity 
• Requires an active internet connection; not built to work online 

BioRender 

BioRender is one of the most widely used science illustration software tools in the life sciences. It is especially popular among biology and biomedical researchers, who need polished, publication-ready figures without designing illustrations from scratch.  

Although not as flexible as Adobe Illustrator for complex vector editing, BioRender is a great platform to help researchers create key pathway diagrams, experimental workflows, and research figures that maintain consistent visual standards, making it a popular choice among labs and academic teams.  

BioRender: Key Features 

• Large library of biology-focused icons (cell biology, immunology, molecular pathways) 
• Intuitive drag-and-drop interface for rapid creation of scientific illustrations 
• Pre-built templates for figures, posters, and graphical abstracts 
• Auto-sizing features that customizes layouts and orientation  
• High-resolution image export options (up to 600 DPI quality)  
• Browser-based platform with advanced team collaboration tools  

Pros of BioRender 

• Very easy to use for life science researchers visualizing their science 
• Accurate, ready-made scientific icons for impressive illustrations 
• Excellent to build pathway diagrams and experiment workflows 
• Great for collaboration; popular among lab groups, research teams 
• Professional, consistent visual style/output across research figures 

Cons of BioRender 

• Limited advanced vector editing tools  
• Most high-quality exports require paid plans 
• Less flexible for custom or highly specialized illustrations 
• Not ideal for disciplines outside life and biomedical sciences 

Adobe Illustrator 

Adobe Illustrator is a powerful vector graphics tool widely used for creating highly customized scientific figures, especially when researchers need full control over shapes, layers, typography, and precise adjustments for publication. Many academics use Adobe Illustrator for science visualization, using it to create complex diagrams, multi-panel figures, or fine-tuned edits for journal publication. This flexibility makes it popular among researchers looking for reliable scientific poster templates; Adobe Illustrator can help to quickly craft posters and conference visuals based on submission guidelines.  

Adobe Illustrator: Key Features 

• Advanced vector editing with complete control over shapes, lines, and layers 
• Supports high-resolution SVG, EPS, and PDF formats for scientific publication 
• Works seamlessly for building custom diagrams and editing imported artwork 
• Often used to design posters using Adobe Illustrator scientific poster layouts 
• Integrates well with microscopy images, graphs, and other scientific visuals 
• Provides users with extensive typography, color, and layout tools for accuracy 

Pros of Adobe Illustrator 

• Good precision and customization for scientific illustrations 
• Scalable, high-quality vector outputs for journal publication 
• Ideal for complex, data-heavy figures and multi-panel posters 
• Works offline and is suitable to use in secure lab environments 
• Highly versatile scientific illustration tool across disciplines 

Cons of Adobe Illustrator 

• Can be complex with a steep learning curve for beginners 
• Does not have a built-in scientific icon library or templates 
• Time-intensive visualization, even when creating routine figures 
• Subscription-based pricing can be expensive for students 

Affinity Designer 

Affinity Designer is a powerful alternative to Illustrator for researchers who want advanced vector editing without paying for a subscription. Although it isn’t specifically built as scientific illustration software, it is frequently used by academics to produce precise, scalable diagrams for publication, scientific posters, and conference presentations. Its smooth workflow, offline availability, and flexible vector-raster workspace make it a strong option for researchers comfortable with design tools.   

Affinity Designer: Key Features 

• Advanced vector editing with precise pen, node, and boolean tools 
• Seamless switching between vector and raster workspaces 
• Supports high-resolution PDF, SVG, and EPS exports suitable for journals 
• Unlimited layers, grouping, and artboards for multi-panel scientific figures 
• Color consistency tools to help alignment with journal-compliant palettes  
• Desktop-based software works seamlessly offline; ideal for labs and institutions 

Pros of Affinity Designer 

• Requires a one-time purchase for use (no subscription) 
• Powerful vector editing toolkit comparable to Illustrator  
• Great for precise, fully customizable scientific illustrations 
• Ideal for large posters and complex multi-panel design layouts  
• Best for engineering, interdisciplinary researchers creating complex schematics  

Cons of Affinity Designer 

• Does not offer built-in scientific templates or icon libraries 
• Does not have an online scientific illustration program 
• Steeper learning curve for new users and non-designers 
• Limited collaboration features compared to cloud-based tools 
• Not ideal for quick scientific diagrams or graphical abstracts  

Inkscape 

Inkscape is a free, open-source vector editor that is widely used by academic looking for full control over research visuals. It is often chosen by researchers looking for a reliable scientific illustration software free of cost. While it isn’t designed specifically to be a scientific illustration software, it can help with creating scalable diagrams, editing vector artwork, and producing figures suitable for journals and conferences.  

Inkscape: Key Features 

• Complete vector editing capabilities (paths, nodes, curves, boolean) 
• Supports SVG, EPS, and PDF exports suitable for publication 
• Layer management and grouping for multi-panel scientific figures 
• Useful for editing graphs, schematics, and imported plotting outputs 
• Large extension ecosystem allowing for easy customization 
• Works offline on Windows, macOS, and Linux 

Pros of Inkscape 

• Completely free to use open-source vector editor  
• Powerful for detailed editing and custom scientific diagrams 
• Flexible for engineering, physics, and computational research visuals 
• Active community and plugin support offers added functionality 

Cons of Inkscape 

• No access to scientific templates or built-in icon libraries 
• Steeper learning curve than template-based visualization tools 
• Interface can feel clunky for users compared to other platforms 
• Limited collaboration features for research visualization 
• Time-consuming, not ideal for quick posters, graphical abstracts 

Why Mind the Graph Is the Best Scientific Illustration Software 

After comparing the leading tools, Mind the Graph stands out as the best scientific illustration software for most researchers because it is built specifically for scientific communication, not general design. It is ideal for researchers, PhD scholars, and academic teams who need quick publication-ready visuals without spending days learning complex design tools.  

What Makes Mind the Graph Better Than Other General-Purpose Tools 

• Purpose-built for scientists and integrates seamlessly into academic workflows 

• Extensive library of 75,000+ accurate scientific illustrations across research domains 

• Expertly designed 300+ ready-to-use templates for graphical abstracts and posters 

• Intuitive platform with simple drag-and-drop interface for non-designers 

• Cloud-based platform ensures multi-device accessibility and strong collaboration  

• Consistent visual styling across figures ensures polished, publication-ready outputs  

• Powerful editing toolkit for customization of colors, layers, sizes, layout, and more 

• High-resolution exports that can be shared in PNG, TIFF, PDF and SVG formats 

• Full publishing rights with transfer options included as part of paid subscription 

• Access to on-demand custom scientific illustrations by design experts in your field 

Mind the Graph goes beyond what other tools offer to better address researcher needs. By eliminating the friction between scientific thinking and visual communication, it empowers researchers to focus on the content of their work, while still producing accurate figures and illustrations that meet the clarity and quality expected by leading journals and conferences.  

Finding the Right Scientific Illustration Software for You 

Choosing the best scientific illustration tool depends on your research workflow, design experience, and the type of visuals you create. Template-driven platforms like Mind the Graph and BioRender are ideal for quick, accurate figures with minimal effort. Advanced vector tools such as Adobe Illustrator, Affinity Designer, and Inkscape are better suited for highly customized or complex illustrations. 

Consider whether you need an online platform or offline software, built-in scientific icon libraries, collaboration features, and how much time you can invest in learning design tools. Ultimately, the right choice is the one that helps you create clear, journal-ready visuals efficiently and accurately. 

If you want to simplify the process of creating scientific illustrations and enhance the impact of your research visuals, sign up to Mind the Graph and explore its scientific templates and vast illustration library. It’s one of the easiest ways to elevate your posters, graphical abstracts, and publications so you can communicate your findings effectively and regain time to focus on your research. 

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.     

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: 

Langkah 1: Collect the data 

Langkah 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.  

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

Langkah 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. 

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

Langkah 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. 

Langkah 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 

Tanggal: 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

Tanggal: 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. 

Over the past few years, AI tools for researchers have become crucial, simplifying everything from literature discovery to manuscript writing and manuscript editing. The challenge lies in choosing the best AI tools for research that align with specific scholarly needs. Some focus on improving clarity and grammar, others on fact-checking and citation accuracy, while a few are designed to assist with overall manuscript writing and preparation. Selecting the right tool can make the difference between a paper that struggles through revisions and one that is submission ready. 

In this blog, we’ll review the top 7 AI tools for research — Paperpal, Grammarly, R Discovery, Scholarcy, Scite, Claude, and ChatGPT, highlighting how they can support writing, editing, and manuscript preparation. 

1. Paperpal 

If you’re looking for an AI tool built exclusively for academics, Paperpal is hard to beat. Unlike generic AI research tools, it’s designed around the needs of researchers, helping you draft, rewrite, cite, and polish manuscripts with ease. Whether you’re working on a thesis, journal article, or grant proposal, Paperpal acts like an always-available mentor guiding you toward a submission-ready draft. 

Kelebihan: 

• Covers the full academic workflow: drafting, rewriting, editing, and journal checks 
• Specialized language checks designed for research writing, not just everyday grammar 
• Built-in plagiarism checker with similarity reports 
• Access to 250M+ research articles and 10,000+ citation styles 
• Integrates with Word, Google Docs, and Overleaf 
• Strong security—manuscripts are never used to train AI models 

Kekurangan: 

• Free plan limits daily AI usage 
• Mostly useful for academic and technical writing, not creative content 

For students, PhD scholars, or journal authors, Paperpal acts as a reliable academic assistant helping you write clearly, check compliance, and submit polished manuscripts with confidence. 

And if you want to pair polished writing with compelling visuals, Mind the Graph helps create professional infographics, figures, and graphical abstracts to make your research stand out.

2. Grammarly  

Grammarly is an AI writing assistant that has been helping students and professionals produce clear, polished writing for over a decade. It combines grammar checking, proofreading, and style enhancements with generative AI features through Grammarly Go, which can create outlines, rewrite sentences, and even generate thesis statements. For academics, its plagiarism checker scans text against 16B+ web pages and academic databases, providing originality scores. 

Kelebihan: 

• Easy-to-use interface with real-time suggestions across platforms 
• Strong grammar, spelling, tone, and clarity checks 
• Generative AI support for brainstorming, rewriting, and thesis statements 
• Plagiarism detection and basic citation generator (APA, MLA, Chicago) 
• Works seamlessly across browsers, email, Google Docs, and desktop apps 
• Free plan makes it accessible for everyday users 

Kekurangan: 

• Less precise for academic jargon and discipline-specific writing 
• No multilingual support (English only) 
• Inconsistent with technical checks in equations, tables, and figures 
• More general-purpose compared to academic-focused tools like Paperpal 

Grammarly is one of the best AI tools for research when you need a solution for everyday writing, grammar checks, and readability. However, for researchers seeking deeper academic precision, it pairs best with specialized tools. 

3. R Discovery 

R Discovery is an AI-powered literature discovery tool that helps researchers quickly find and understand relevant academic papers. With access to 250M+ research papers, it cuts down hours of searching by offering personalized feeds, summaries, and topic alerts. Its AI Research Assistant also helps answer questions directly from papers, making literature reviews faster and more efficient. 

Kelebihan: 

• Personalized research feeds based on your interests and reading patterns 
• Access to 250M+ papers, including open-access and publisher-hosted content 
• AI-powered article summaries to speed up literature understanding 
• Topic alerts to stay updated with the latest studies in your field 
• Research Assistant feature answers queries directly from full papers 
• Integrated reading library with highlights, notes, and saved papers 
• Works on web and mobile, making it easy to read anytime 

Kekurangan: 

• Does not support manuscript drafting, rewriting, or editing 
• AI summaries may sometimes miss niche technical nuances 
• Premium features like deeper summaries and offline reading require subscription 

R Discovery is ideal for students, PhD researchers, and academics who need to stay updated with recent literature, discover new papers, and manage their reading list efficiently. It doesn’t write for you, but it ensures you always have the most relevant research at your fingertips—making literature reviews significantly faster and more organized. 

4. Scholarcy 

Scholarcy is an AI-powered summarization tool that helps researchers quickly understand dense academic papers, articles, reports, and book chapters. Instead of reading page after page, Scholarcy breaks documents into clear, digestible flashcards that highlight key points, references, and even figures or tables. It’s not designed to write for you but to save time by making complex research easier to navigate. 

Kelebihan: 

• Generates concise summaries and flashcards from PDFs and Word files 
• Automatically extracts references and formats bibliographies 
• Can pull figures, tables, and images for quicker understanding 
• Chrome and Edge extensions connect directly to open-access repositories 
• Great for literature reviews and staying updated with the latest research 

Kekurangan: 

• Not a writing or editing tool, only helps with summarization 
• Free plan is limited to a few summaries per day 
• Struggles with very complex or technical papers 

Scholarcy won’t draft your manuscript, but it’s an excellent AI tool for researchers who want to speed up reading, simplify literature reviews, and keep track of references. 

5. Scite 

Scite.ai is a research evaluation tool that helps you go beyond counting citations. Instead of just showing how often a paper is cited, Scite tells you why by showing whether other researchers support, question, or simply mention the paper’s findings. This makes it an invaluable aid for critical literature reviews, identifying influential studies, and avoiding unreliable references. 

Kelebihan: 

• Shows the context of each citation (supporting, contradicting, or mentioning) 
• Access to 200M+ scholarly sources, including preprints and open-access works 
• Summarizes research and answers questions based on the literature 
• Helps visualize how studies are connected and build on each other 
• Instantly see how articles are cited while reading online 
• Integrates with Zotero and Mendeley for seamless reference management 

Kekurangan: 

• Free plan is restrictive with limited searches 
• Interface can feel overwhelming at first for new users 
• Premium subscription may be costly for students on a budget 

Scite.ai acts like a quality filter for research, giving you the context behind citations so you can decide which papers to trust and which to question. For anyone conducting a systematic review or building a strong reference list, it’s one of the best AI tools for researchers today. 

6. ChatGPT 

ChatGPT is one of the most powerful and widely used AI research tools today. While it isn’t built specifically for manuscript writing or editing, it excels at brainstorming ideas, creating outlines, simplifying complex topics, and providing instant explanations. For researchers, it can act as a 24/7 assistant helping with definitions, structuring content, or clarifying concepts. 

Kelebihan: 

• Supports idea generation, brainstorming, and outlining 
• Explains complex theories and concepts in simple terms 
• Multi-language support for global researchers 
• Offers advanced features like Projects, DALL·E image generation, custom GPTs, and voice mode 
• Flexible for both academic and non-academic tasks 

Kekurangan: 

• Not designed for academic precision, fact-checking is always required 
• Doesn’t generate citations or reference lists directly 
• Outputs can sometimes be overly verbose or lack natural flow 
• Risk of outdated information since knowledge may not always be up to date 

ChatGPT isn’t the best choice for drafting research papers, but it’s an excellent brainstorming and learning partner. Use it to clarify concepts, generate outlines, or get quick perspectives but always verify the information with trusted academic sources. 

7. Claude 

Claude is one of the newer but fast-rising AI assistants. While it isn’t as feature-heavy, it excels at handling long, complex documents and producing natural, human-like prose. With its 200K token context window, Claude can process entire books, research papers, or lengthy reports, making it particularly valuable for academics working with large datasets or manuscripts. 

Kelebihan: 

• Exceptional prose quality—more natural and readable than many GPT models 
• Handles very large documents (up to 150,000 words) with ease 
• Minimalist, distraction-free interface that’s easy to use 
• Useful for analyzing lengthy texts, generating summaries, or rewriting drafts 
• Integrates with tools like Google Drive and supports Claude Projects for organization 

Kekurangan: 

• Fewer advanced features compared to ChatGPT or Gemini (e.g., no image generation, limited voice integration) 
• Heavily censored content filters may restrict some types of queries 
• Still developing its ecosystem, so fewer third-party integrations 

Claude may not replace specialized AI tools for research writing but it shines when you need to analyze long documents, summarize large texts, or rewrite content with clarity. For researchers dealing with massive academic papers or thesis drafts, its extended context window is a game-changer. 

While the 7 AI tools for research excel in literature search, academic writing, and editing, toools like Perplexity, Wordtune and Yomu AI are also quite popular AI tools for research among students and researchers.  

• Wordtune – Ideal for quick sentence rewrites and improving readability. It’s great for rephrasing sections or enhancing fluency, though it’s less tailored for academic tone compared to Paperpal. 

• Yomu AI – An academic writing assistant with distraction-free UI and content structuring features. Perfect for students who prefer minimalistic interfaces with smart feedback on coherence. 

• Perplexity AI – Excellent for real-time academic research. It combines conversational AI with live web search, offering citations for every response. A solid companion for fact-checking and finding the latest studies or policy papers. 

Each of these tools complements the seven reviewed above, especially when used for brainstorming ideas, refining phrasing, or validating information quickly. 

Enhance Your Research Drafts with Mind the Graph’s Scientific Illustrations 

While AI research tools streamline writing, reviewing, and referencing, impactful research communication extends beyond text. Visuals play an equally powerful role in how your findings are understood and remembered and that’s where Mind the Graph excels. 

Whether you’re creating a graphical abstract, conference poster, scientific figure, or slide deck, Mind the Graph empowers you to design visually consistent, professional-quality graphics — no design background needed. 

With over 75,000 scientifically accurate illustrations, discipline-specific templates, and drag-and-drop editing, Mind the Graph helps researchers transform complex data into visuals that reviewers and readers instantly grasp. You can create everything from schematic diagrams and research workflows to molecular structures and infographics — all while maintaining journal-ready formatting standards. 

How to Choose the Right AI Tool for Your Research Needs 

With so many AI tools for researchers available today, the smartest approach is to start with clarity: what do you want the tool to do for you? If your main goal is manuscript writing or manuscript editing, an academic-first platform like Paperpal is ideal. If you need quick fact-checks, literature searches, or citation insights, options like R Discovery, Scholarcy or Scite might serve you better. 

1. Define your goal: Are you drafting, editing, summarizing, or fact-checking? Match the tool to the task—Paperpal for manuscript writing and editing, Scholarcy for summaries, Scite for citation analysis, etc. 

2. Start with a base tool: Many researchers begin with general purpose AI tools for researchers such as ChatGPT or Claude. These are flexible but less precise for academic work, so pair them with specialized tools like Paperpal for journal-ready writing. 

3. Check features and security: Ensure tools that protect your data and integrate with your preferred workflow (MS Word, Google Docs, Overleaf). For academics, the security and confidentiality of manuscripts are non-negotiable. 

4. Consider budget and value: Free plans are great for testing, but advanced AI research tools require richer features that are often available in paid tiers. Investing in Prime or Premium plans often pays off in higher-quality manuscripts and saved time. 

5. Evaluate the learning curve: Some tools (like Overleaf) require more technical knowledge, while others (like Paperpal or Grammarly) are intuitive. Select one that fits your comfort and expertise. 

6. Test before you commit: Almost all top AI tools for research provide a free trial or free plan that allow you to explore. Test them side by side to see which handles your writing style the best, whether their suggestions and corrections are contextually accurate, and how smoothly the interface fits your workflow. 

In short: Don’t just pick the most popular AI research tool, select the one that aligns with your research stage, writing goals, technical requirements, and integrity needs. For most academics, combining a general AI tool with a specialized academic tool like Paperpal offers the best AI for research, providing productivity, precision, and trust. 

Best Practices for Using AI Research Tools  

• Acknowledge usage: Clearly state in your manuscript if you used an AI tool for research, including its name and version. 

• Follow guidelines: Always check and comply with your journal’s or institution’s policies on AI use. 

• Avoid plagiarism: Treat AI outputs as drafts. Verify, edit, and ensure originality before submission. 

• Prioritize integrity: Don’t use AI research tools to replace your own analysis, reasoning, or academic voice. 

• Combine AI with human review: Use AI as an assistant, but always proofread, fact-check, and finalize manually. 

• Ensure data security: Only use tools that protect your manuscripts and research data. 

Paperpal is built on these principles, offering academic-focused manuscript writing and academic editing features while keeping your work secure and original.  

The Future of Research with AI 

AI is reshaping how researchers write, edit, and prepare manuscripts. The best AI tools for research save time, elevate writing quality, and enhance productivity across every stage of the research process. Yet their value lies in how responsibly they are used—with transparency, human oversight, and a strong commitment to academic integrity. The takeaway is simple: Explore, test, and integrate AI thoughtfully into your workflow. When used wisely, AI research tools can become trusted partners, helping you focus on what matters most: advancing knowledge, achieving excellence, and driving scientific innovation. 

Among the many AI tools for researchers, Paperpal stands out as an academic-first platform for manuscript writing and editing. It also offers reliable support with literature discovery, citation formatting, and submission readiness checks, which makes it a trusted all-in-one AI research assistant. R Discovery is perfect for staying updated with tailored reading recommendations and advanced features that boost productivity, while Mind the Graph empowers researchers to communicate their research findings visually. Together, these tools provide end-to-end support for researchers on the path to success; the best part is that they are available in one best-in-class AI research bundle when you buy the Mind the Graph Prime plan. 

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, Batlowatau 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. 

Hal-hal Penting yang Dapat Dipetik 

• 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. 

Referensi 

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 dan Mind the Graph.

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

Tanggal: 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.