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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on October 26, 2022 by Shona McCombes . Revised on November 21, 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, using sub-questions to strengthen your main research question, research questions quiz, other interesting articles, frequently asked questions about research questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

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Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

Chances are that your main research question likely can’t be answered all at once. That’s why sub-questions are important: they allow you to answer your main question in a step-by-step manner.

Good sub-questions should be:

• Less complex than the main question
• Focused only on 1 type of research
• Presented in a logical order

Here are a few examples of descriptive and framing questions:

• Descriptive: According to current government arguments, how should a European bank tax be implemented?
• Descriptive: Which countries have a bank tax/levy on financial transactions?
• Framing: How should a bank tax/levy on financial transactions look at a European level?

Keep in mind that sub-questions are by no means mandatory. They should only be asked if you need the findings to answer your main question. If your main question is simple enough to stand on its own, it’s okay to skip the sub-question part. As a rule of thumb, the more complex your subject, the more sub-questions you’ll need.

Try to limit yourself to 4 or 5 sub-questions, maximum. If you feel you need more than this, it may be indication that your main research question is not sufficiently specific. In this case, it’s is better to revisit your problem statement and try to tighten your main question up.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

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Educational resources and simple solutions for your research journey

How to Craft a Strong Research Question (With Research Question Examples)

A sound and effective research question is a key element that must be identified and pinned down before researchers can even begin their research study or work. A strong research question lays the foundation for your entire study, guiding your investigation and shaping your findings. Hence, it is critical that researchers spend considerable time assessing and refining the research question based on in-depth reading and comprehensive literature review. In this article, we will discuss how to write a strong research question and provide you with some good examples of research questions across various disciplines.

Table of Contents

The importance of a research question

A research question plays a crucial role in driving scientific inquiry, setting the direction and purpose of your study, and guiding your entire research process. By formulating a clear and focused research question, you lay the foundation for your investigation, ensuring that your research remains on track and aligned with your objectives so you can make meaningful contribution to the existing body of knowledge. A well-crafted research question also helps you define the scope of your study and identify the appropriate methodologies and data collection techniques to employ.

Key components of a strong research question

A good research question possesses several key components that contribute to the quality and impact of your study. Apart from providing a clear framework to generate meaningful results, a well-defined research question allows other researchers to understand the purpose and significance of your work. So, when working on your research question, incorporate the following elements:

• Specificity : A strong research question should be specific about the main focus of your study, enabling you to gather precise data and draw accurate conclusions. It clearly defines the variables, participants, and context involved, leaving no room for ambiguity.
• Clarity : A good research question is clear and easily understood, so articulate the purpose and intent of your study concisely without being generic or vague. Ensuring clarity in your research question helps both you and your readers grasp the research objective.
• Feasibility : While crafting a research question, consider the practicality of conducting the research and availability of necessary data or access to participants. Think whether your study is realistic and achievable within the constraints of time, resources, and ethical considerations.

How to craft a well-defined research question

A first step that will help save time and effort is knowing what your aims are and thinking about a few problem statements on the area or aspect one wants to study or do research on. Contemplating these statements as one undertakes more progressive reading can help the researcher in reassessing and fine-tuning the research question. This can be done over time as they read and learn more about the research topic, along with a broad literature review and parallel discussions with peer researchers and supervisors. In some cases, a researcher can have more than one research question if the research being undertaken is a PhD thesis or dissertation, but try not to cover multiple concerns on a topic.

A strong research question must be researchable, original, complex, and relevant. Here are five simple steps that can make the entire process easier.

• Identify a broad topic from your areas of interest, something that is relevant, and you are passionate about since you’ll be spending a lot of time conducting your research.
• Do a thorough literature review to weed out potential gaps in research and stay updated on what’s currently being done in your chosen topic and subject area.
• Shortlist possible research questions based on the research gaps or see how you can build on or refute previously published ideas and concepts.
• Assess your chosen research question using the FINER criteria that helps you evaluate whether the research is Feasible, Interesting, Novel, Ethical, and Relevant. 1
• Formulate the final research question, while ensuring it is clear, well-written, and addresses all the key elements of a strong research question.

Examples of research questions

Remember to adapt your research question to suit your purpose, whether it’s exploratory, descriptive, comparative, experimental, qualitative, or quantitative. Embrace the iterative nature of the research process, continually evaluating and refining your question as you progress. Here are some good examples of research questions across various disciplines.

Exploratory research question examples

• How does social media impact interpersonal relationships among teenagers?
• What are the potential benefits of incorporating mindfulness practices in the workplace?

Descriptive research question examples

• What factors influence customer loyalty in the e-commerce industry?
• Is there a relationship between socioeconomic status and academic performance among elementary school students?

Comparative research question examples

• How does the effectiveness of traditional teaching methods compare to online learning platforms in mathematics education?
• What is the impact of different healthcare policies on patient outcomes in various countries?

Experimental research question examples

• What are the effects of a new drug on reducing symptoms of a specific medical condition?
• Does a dietary intervention have an impact on weight loss among individuals with obesity?

Qualitative research question examples

• What are the lived experiences of immigrants adapting to a new culture?
• What factors influence job satisfaction among healthcare professionals?

Quantitative research question examples

• Is there a relationship between sleep duration and academic performance among college students?
• How effective is a specific intervention in reducing anxiety levels among individuals with phobias?

With these simple guidelines and inspiring examples of research questions, you are equipped to embark on your research journey with confidence and purpose. Here’s wishing you all the best for your future endeavors!

References:

• How to write a research question: Steps and examples. Indeed Career Guide. Available online at https://www.indeed.com/career-advice/career-development/how-to-write-research-questions

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For PhD students - how to formulate a research question

Different students enter the PhD program with different backgrounds. Some students take research-oriented modules (courses in US) at undergraduate level. Some other do a research masters before doing a PhD. However, the kind of research questions we address in a PhD are very new and requires a long period of deeper investigation. Therefore, it is important to know how to find a good question that gets you excited.

Direct encounter : Usually, a good question comes from an experience. In my case, I experienced how hard it is to derive the dynamics of a robot with high degrees of freedom (DoF). I actually tried to manually derive dynamics of a 4-DoF manipulator called Mark-II from Yasakawa Corporation, and then ran a Mathematica program to do a symbolic derivation for a 7-DoF robot manipulator called PA-10. I experienced how long the equations grew and thought how the brain might be dealing with a body of about 37 DoFs for model based predictive control. This direct encounter with the problem is very important, because it gives you a cause to work towards.

Look around : After finding a problem worthy of addressing, look around to see how others have approached to solve it. This is where you will see different schools of thought. Be careful. There are glaring band-waggons out there. It is so tempting to get in one of them. Don’t blindly follow them unless you have a good reason. Usually following is tiring. Think carefully trying out simple derivations and doing simulations or even doing simple physical experiments to see what kind of approaches get you excited. Some approaches appear very exciting, but direct usage will prove to be not so effective. At this point, it is very important to consult your supervisor. The supervisor may have a favorite approach. Most experienced supervisors are open for change and a good reasoned discussion will help you to benefit from their experience to polish up your research question and the method you want to address it. You should always check if there are quantifiable methods to address your research question. For instance, if you want to test whether there is a particular class of mechanisms available to minimise the size of collision force when a robot is dropped from a height, you should think about testing methods, candidate mechanisms, and the range of design paramaters to assess the scope of analysis. Sometimes, your laboratory may not have the full capacity to help you. This is where you can look for collaborations. Try to reach this level of planning logistics within the first 4-6 months in your PhD.

First experiment is important : Once you know your cause for the PhD and once the approach and collaborations are established, break your approach down to smaller steps. Don’t worry too much about how the last experiment will be done. Worry about your first experiment. Distill out a refined research question that needs a novel answer that you can reach in about 6 months. This is important to boost confidence. Temptations will be high to find the ultimate answer to bring your field to a conclusion, but even in that case, it is important to make a first firm step. In this first step, master the tools and techniques involved in your field. In my lab, students take this time to master robot design and fabrication skills, coding skills, data analysis skills, and cool math you can use to solve difficult problems. Develop the habit of reading at least one paper a week that empowers you with powerful tools to solve problems.

Documentation : It is important to develop the habit of keeping things in a well sorted file structure. Open a folder for each project. Have sub-folders for data, reports, codes, papers you read (using a repository like Madeley is also great), designs, and other resources. This is going to save time when you write a paper at some point. Now you have cloud resources like Box and Onedrive. Back up everything securely.

Writing the first paper : If everything works out, after about one year into the PhD, you will have some new results worthy of publishing. Sometimes, the first attempt doesn’t work out. But all failed attempts teach us lessons. Don’t get discouraged if the first experiment doesn’t work out. Develop the resilience to come back with a different approach or to formulate the question in a different way. Then when you write the first paper, you will have comparative results. The importance of reading papers at least one per week is that in 6 months, you would have read at least 25 papers. This is enough to write your first paper. Start writing why the question you addressed is new and important, and back it up with papers you read. Write down your methods very clearly keeping in mind that somebody should be able to read your paper and be able to replicate it for independent verification. Results and interpretations need to be as sharp and consistent as possible. Plan to go through several rounds of revisions with your supervisor and lab mates before any submission deadlines. I ask my PhD students to have the paper in a reasonable level for revision at least one month before the deadline. Have this as a ballpark period for revision in your first paper. This is the time where you develop the skills of articulating a concept clearly, present it to an audience, receive criticisms, and develop good habits of critical reflection.

Completing the cycle : You will of course get review feedback. Some suggestions I have  given in this note can be useful to go the rest of the journey. Once you get your first paper published, you will have your next research questions coming up easily. The advantage of taking an approach you are passionate about to serve the cause you selected is that it will naturally line up the next set of questions and methods you should be pursuing. My advise is to go through this full cycle of raising a question to publishing results at least 3 times during your PhD. It will give you a seasoned experience of the art of formulating good research questions.

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Creating a Good Research Question

• Advice & Growth
• Process in Practice

Successful translation of research begins with a strong question. How do you get started? How do good research questions evolve? And where do you find inspiration to generate good questions in the first place?  It’s helpful to understand existing frameworks, guidelines, and standards, as well as hear from researchers who utilize these strategies in their own work.

In the fall and winter of 2020, Naomi Fisher, MD, conducted 10 interviews with clinical and translational researchers at Harvard University and affiliated academic healthcare centers, with the purpose of capturing their experiences developing good research questions. The researchers featured in this project represent various specialties, drawn from every stage of their careers. Below you will find clips from their interviews and additional resources that highlight how to get started, as well as helpful frameworks and factors to consider. Additionally, visit the Advice & Growth section to hear candid advice and explore the Process in Practice section to hear how researchers have applied these recommendations to their published research.

• Naomi Fisher, MD , is associate professor of medicine at Harvard Medical School (HMS), and clinical staff at Brigham and Women’s Hospital (BWH). Fisher is founder and director of Hypertension Services and the Hypertension Specialty Clinic at the BWH, where she is a renowned endocrinologist. She serves as a faculty director for communication-related Boundary-Crossing Skills for Research Careers webinar sessions and the Writing and Communication Center .
• Christopher Gibbons, MD , is associate professor of neurology at HMS, and clinical staff at Beth Israel Deaconess Medical Center (BIDMC) and Joslin Diabetes Center. Gibbons’ research focus is on peripheral and autonomic neuropathies.
• Clare Tempany-Afdhal, MD , is professor of radiology at HMS and the Ferenc Jolesz Chair of Research, Radiology at BWH. Her major areas of research are MR imaging of the pelvis and image- guided therapy.
• David Sykes, MD, PhD , is assistant professor of medicine at Massachusetts General Hospital (MGH), he is also principal investigator at the Sykes Lab at MGH. His special interest area is rare hematologic conditions.
• Elliot Israel, MD , is professor of medicine at HMS, director of the Respiratory Therapy Department, the director of clinical research in the Pulmonary and Critical Care Medical Division and associate physician at BWH. Israel’s research interests include therapeutic interventions to alter asthmatic airway hyperactivity and the role of arachidonic acid metabolites in airway narrowing.
• Jonathan Williams, MD, MMSc , is assistant professor of medicine at HMS, and associate physician at BWH. He focuses on endocrinology, specifically unravelling the intricate relationship between genetics and environment with respect to susceptibility to cardiometabolic disease.
• Junichi Tokuda, PhD , is associate professor of radiology at HMS, and is a research scientist at the Department of Radiology, BWH. Tokuda is particularly interested in technologies to support image-guided “closed-loop” interventions. He also serves as a principal investigator leading several projects funded by the National Institutes of Health and industry.
• Osama Rahma, MD , is assistant professor of medicine at HMS and clinical staff member in medical oncology at Dana-Farber Cancer Institute (DFCI). Rhama is currently a principal investigator at the Center for Immuno-Oncology and Gastroenterology Cancer Center at DFCI. His research focus is on drug development of combinational immune therapeutics.
• Sharmila Dorbala, MD, MPH , is professor of radiology at HMS and clinical staff at BWH in cardiovascular medicine and radiology. She is also the president of the American Society of Nuclear Medicine. Dorbala’s specialty is using nuclear medicine for cardiovascular discoveries.
• Subha Ramani, PhD, MBBS, MMed , is associate professor of medicine at HMS, as well as associate physician in the Division of General Internal Medicine and Primary Care at BWH. Ramani’s scholarly interests focus on innovative approaches to teaching, learning and assessment of clinical trainees, faculty development in teaching, and qualitative research methods in medical education.
• Ursula Kaiser, MD , is professor at HMS and chief of the Division of Endocrinology, Diabetes and Hypertension, and senior physician at BWH. Kaiser’s research focuses on understanding the molecular mechanisms by which pulsatile gonadotropin-releasing hormone regulates the expression of luteinizing hormone and follicle-stimulating hormone genes.

Insights on Creating a Good Research Question

Play Junichi Tokuda video

Play Ursula Kaiser video

Start Successfully: Build the Foundation of a Good Research Question

Start Successfully Resources

Ideation in Device Development: Finding Clinical Need Josh Tolkoff, MS A lecture explaining the critical importance of identifying a compelling clinical need before embarking on a research project. Play Ideation in Device Development video .

Radical Innovation Jeff Karp, PhD This ThinkResearch podcast episode focuses on one researcher’s approach using radical simplicity to break down big problems and questions. Play Radical Innovation .

Using Healthcare Data: How can Researchers Come up with Interesting Questions? Anupam Jena, MD, PhD Another ThinkResearch podcast episode addresses how to discover good research questions by using a backward design approach which involves analyzing big data and allowing the research question to unfold from findings. Play Using Healthcare Data .

Important Factors: Consider Feasibility and Novelty

Refining Your Research Question 

Play video of Clare Tempany-Afdhal

Play Elliott Israel video

Frameworks and Structure: Evaluate Research Questions Using Tools and Techniques

Frameworks and Structure Resources

Designing Clinical Research Hulley et al. A comprehensive and practical guide to clinical research, including the FINER framework for evaluating research questions. Learn more about the book .

Translational Medicine Library Guide Queens University Library An introduction to popular frameworks for research questions, including FINER and PICO. Review translational medicine guide .

Asking a Good T3/T4 Question  Niteesh K. Choudhry, MD, PhD This video explains the PICO framework in practice as participants in a workshop propose research questions that compare interventions. Play Asking a Good T3/T4 Question video

Introduction to Designing & Conducting Mixed Methods Research An online course that provides a deeper dive into mixed methods’ research questions and methodologies. Learn more about the course

Network and Support: Find the Collaborators and Stakeholders to Help Evaluate Research Questions

Network & Support Resource

Bench-to-bedside, Bedside-to-bench Christopher Gibbons, MD In this lecture, Gibbons shares his experience of bringing research from bench to bedside, and from bedside to bench. His talk highlights the formation and evolution of research questions based on clinical need. Play Bench-to-bedside. 

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How to Write a Research Question

What is a research question? A research question is the question around which you center your research. It should be:

• clear : it provides enough specifics that one’s audience can easily understand its purpose without needing additional explanation.
• focused : it is narrow enough that it can be answered thoroughly in the space the writing task allows.
• concise : it is expressed in the fewest possible words.
• complex : it is not answerable with a simple “yes” or “no,” but rather requires synthesis and analysis of ideas and sources prior to composition of an answer.
• arguable : its potential answers are open to debate rather than accepted facts.

You should ask a question about an issue that you are genuinely curious and/or passionate about.

The question you ask should be developed for the discipline you are studying. A question appropriate for Biology, for instance, is different from an appropriate one in Political Science or Sociology. If you are developing your question for a course other than first-year composition, you may want to discuss your ideas for a research question with your professor.

Why is a research question essential to the research process? Research questions help writers focus their research by providing a path through the research and writing process. The specificity of a well-developed research question helps writers avoid the “all-about” paper and work toward supporting a specific, arguable thesis.

Steps to developing a research question:

• Choose an interesting general topic. Most professional researchers focus on topics they are genuinely interested in studying. Writers should choose a broad topic about which they genuinely would like to know more. An example of a general topic might be “Slavery in the American South” or “Films of the 1930s.”
• Do some preliminary research on your general topic. Do a few quick searches in current periodicals and journals on your topic to see what’s already been done and to help you narrow your focus. What issues are scholars and researchers discussing, when it comes to your topic? What questions occur to you as you read these articles?
• Consider your audience. For most college papers, your audience will be academic, but always keep your audience in mind when narrowing your topic and developing your question. Would that particular audience be interested in the question you are developing?
• Start asking questions. Taking into consideration all of the above, start asking yourself open-ended “how” and “why” questions about your general topic. For example, “Why were slave narratives effective tools in working toward the abolishment of slavery?” or “How did the films of the 1930s reflect or respond to the conditions of the Great Depression?”
• Is your research question clear? With so much research available on any given topic, research questions must be as clear as possible in order to be effective in helping the writer direct his or her research.
• Is your research question focused? Research questions must be specific enough to be well covered in the space available.
• Is your research question complex? Research questions should not be answerable with a simple “yes” or “no” or by easily-found facts.  They should, instead, require both research and analysis on the part of the writer. They often begin with “How” or “Why.”
• Begin your research . After you’ve come up with a question, think about the possible paths your research could take. What sources should you consult as you seek answers to your question? What research process will ensure that you find a variety of perspectives and responses to your question?

Sample Research Questions

Unclear: How should social networking sites address the harm they cause? Clear: What action should social networking sites like MySpace and Facebook take to protect users’ personal information and privacy? The unclear version of this question doesn’t specify which social networking sites or suggest what kind of harm the sites might be causing. It also assumes that this “harm” is proven and/or accepted. The clearer version specifies sites (MySpace and Facebook), the type of potential harm (privacy issues), and who may be experiencing that harm (users). A strong research question should never leave room for ambiguity or interpretation. Unfocused: What is the effect on the environment from global warming? Focused: What is the most significant effect of glacial melting on the lives of penguins in Antarctica?

The unfocused research question is so broad that it couldn’t be adequately answered in a book-length piece, let alone a standard college-level paper. The focused version narrows down to a specific effect of global warming (glacial melting), a specific place (Antarctica), and a specific animal that is affected (penguins). It also requires the writer to take a stance on which effect has the greatest impact on the affected animal. When in doubt, make a research question as narrow and focused as possible.

Too simple: How are doctors addressing diabetes in the U.S.? Appropriately Complex:   What main environmental, behavioral, and genetic factors predict whether Americans will develop diabetes, and how can these commonalities be used to aid the medical community in prevention of the disease?

The simple version of this question can be looked up online and answered in a few factual sentences; it leaves no room for analysis. The more complex version is written in two parts; it is thought provoking and requires both significant investigation and evaluation from the writer. As a general rule of thumb, if a quick Google search can answer a research question, it’s likely not very effective.

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How to Write a Research Question: Types and Examples 

The first step in any research project is framing the research question. It can be considered the core of any systematic investigation as the research outcomes are tied to asking the right questions. Thus, this primary interrogation point sets the pace for your research as it helps collect relevant and insightful information that ultimately influences your work.   

Typically, the research question guides the stages of inquiry, analysis, and reporting. Depending on the use of quantifiable or quantitative data, research questions are broadly categorized into quantitative or qualitative research questions. Both types of research questions can be used independently or together, considering the overall focus and objectives of your research.  

What is a research question?

A research question is a clear, focused, concise, and arguable question on which your research and writing are centered. 1 It states various aspects of the study, including the population and variables to be studied and the problem the study addresses. These questions also set the boundaries of the study, ensuring cohesion. 

Designing the research question is a dynamic process where the researcher can change or refine the research question as they review related literature and develop a framework for the study. Depending on the scale of your research, the study can include single or multiple research questions. 

A good research question has the following features: 

• It is relevant to the chosen field of study. 
• The question posed is arguable and open for debate, requiring synthesizing and analysis of ideas. 
• It is focused and concisely framed. 
• A feasible solution is possible within the given practical constraint and timeframe. 

A poorly formulated research question poses several risks. 1   

• Researchers can adopt an erroneous design. 
• It can create confusion and hinder the thought process, including developing a clear protocol.  
• It can jeopardize publication efforts.  
• It causes difficulty in determining the relevance of the study findings.  
• It causes difficulty in whether the study fulfils the inclusion criteria for systematic review and meta-analysis. This creates challenges in determining whether additional studies or data collection is needed to answer the question.  
• Readers may fail to understand the objective of the study. This reduces the likelihood of the study being cited by others. 

Now that you know “What is a research question?”, let’s look at the different types of research questions. 

Types of research questions

Depending on the type of research to be done, research questions can be classified broadly into quantitative, qualitative, or mixed-methods studies. Knowing the type of research helps determine the best type of research question that reflects the direction and epistemological underpinnings of your research. 

The structure and wording of quantitative 2 and qualitative research 3 questions differ significantly. The quantitative study looks at causal relationships, whereas the qualitative study aims at exploring a phenomenon. 

• Quantitative research questions:  
• Seeks to investigate social, familial, or educational experiences or processes in a particular context and/or location.  
• Answers ‘how,’ ‘what,’ or ‘why’ questions. 
• Investigates connections, relations, or comparisons between independent and dependent variables. 

Quantitative research questions can be further categorized into descriptive, comparative, and relationship, as explained in the Table below. 

• Qualitative research questions  

Qualitative research questions are adaptable, non-directional, and more flexible. It concerns broad areas of research or more specific areas of study to discover, explain, or explore a phenomenon. These are further classified as follows: 

• Mixed-methods studies  

Mixed-methods studies use both quantitative and qualitative research questions to answer your research question. Mixed methods provide a complete picture than standalone quantitative or qualitative research, as it integrates the benefits of both methods. Mixed methods research is often used in multidisciplinary settings and complex situational or societal research, especially in the behavioral, health, and social science fields. 

What makes a good research question

A good research question should be clear and focused to guide your research. It should synthesize multiple sources to present your unique argument, and should ideally be something that you are interested in. But avoid questions that can be answered in a few factual statements. The following are the main attributes of a good research question. 

• Specific: The research question should not be a fishing expedition performed in the hopes that some new information will be found that will benefit the researcher. The central research question should work with your research problem to keep your work focused. If using multiple questions, they should all tie back to the central aim. 
• Measurable: The research question must be answerable using quantitative and/or qualitative data or from scholarly sources to develop your research question. If such data is impossible to access, it is better to rethink your question. 
• Attainable: Ensure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific. 
• You have the expertise 
• You have the equipment and resources 
• Realistic: Developing your research question should be based on initial reading about your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline. 
• Based on some sort of rational physics 
• Can be done in a reasonable time frame 
• Timely: The research question should contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on. 
• Novel 
• Based on current technologies. 
• Important to answer current problems or concerns. 
• Lead to new directions. 
• Important: Your question should have some aspect of originality. Incremental research is as important as exploring disruptive technologies. For example, you can focus on a specific location or explore a new angle. 
• Meaningful whether the answer is “Yes” or “No.” Closed-ended, yes/no questions are too simple to work as good research questions. Such questions do not provide enough scope for robust investigation and discussion. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation before providing an answer. 

Steps for developing a good research question

The importance of research questions cannot be understated. When drafting a research question, use the following frameworks to guide the components of your question to ease the process. 4  

• Determine the requirements: Before constructing a good research question, set your research requirements. What is the purpose? Is it descriptive, comparative, or explorative research? Determining the research aim will help you choose the most appropriate topic and word your question appropriately. 
• Select a broad research topic: Identify a broader subject area of interest that requires investigation. Techniques such as brainstorming or concept mapping can help identify relevant connections and themes within a broad research topic. For example, how to learn and help students learn. 
• Perform preliminary investigation: Preliminary research is needed to obtain up-to-date and relevant knowledge on your topic. It also helps identify issues currently being discussed from which information gaps can be identified. 
• Narrow your focus: Narrow the scope and focus of your research to a specific niche. This involves focusing on gaps in existing knowledge or recent literature or extending or complementing the findings of existing literature. Another approach involves constructing strong research questions that challenge your views or knowledge of the area of study (Example: Is learning consistent with the existing learning theory and research). 
• Identify the research problem: Once the research question has been framed, one should evaluate it. This is to realize the importance of the research questions and if there is a need for more revising (Example: How do your beliefs on learning theory and research impact your instructional practices). 

How to write a research question

Those struggling to understand how to write a research question, these simple steps can help you simplify the process of writing a research question. 

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 

• Example 2 

References:  

• Thabane, L., Thomas, T., Ye, C., & Paul, J. (2009). Posing the research question: not so simple.  Canadian Journal of Anesthesia/Journal canadien d’anesthésie ,  56 (1), 71-79. 
• Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research.  Nephrology Nursing Journal ,  45 (2), 209-213. 
• Kyngäs, H. (2020). Qualitative research and content analysis.  The application of content analysis in nursing science research , 3-11. 
• Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question.  The clinical teacher ,  15 (2), 104-108. 
• Fandino, W. (2019). Formulating a good research question: Pearls and pitfalls.  Indian Journal of Anaesthesia ,  63 (8), 611. 
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  ACP journal club ,  123 (3), A12-A13 

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Research Question 101 📖

Everything you need to know to write a high-quality research question

By: Derek Jansen (MBA) | Reviewed By: Dr. Eunice Rautenbach | October 2023

If you’ve landed on this page, you’re probably asking yourself, “ What is a research question? ”. Well, you’ve come to the right place. In this post, we’ll explain what a research question is , how it’s differen t from a research aim, and how to craft a high-quality research question that sets you up for success.

Research Question 101

What is a research question.

• Research questions vs research aims
• The 4 types of research questions
• How to write a research question
• Frequently asked questions
• Examples of research questions

As the name suggests, the research question is the core question (or set of questions) that your study will (attempt to) answer .

In many ways, a research question is akin to a target in archery . Without a clear target, you won’t know where to concentrate your efforts and focus. Essentially, your research question acts as the guiding light throughout your project and informs every choice you make along the way.

Let’s look at some examples:

What impact does social media usage have on the mental health of teenagers in New York?

How does the introduction of a minimum wage affect employment levels in small businesses in outer London?

How does the portrayal of women in 19th-century American literature reflect the societal attitudes of the time?

What are the long-term effects of intermittent fasting on heart health in adults?

As you can see in these examples, research questions are clear, specific questions that can be feasibly answered within a study. These are important attributes and we’ll discuss each of them in more detail a little later . If you’d like to see more examples of research questions, you can find our RQ mega-list here .

Research Questions vs Research Aims

At this point, you might be asking yourself, “ How is a research question different from a research aim? ”. Within any given study, the research aim and research question (or questions) are tightly intertwined , but they are separate things . Let’s unpack that a little.

A research aim is typically broader in nature and outlines what you hope to achieve with your research. It doesn’t ask a specific question but rather gives a summary of what you intend to explore.

The research question, on the other hand, is much more focused . It’s the specific query you’re setting out to answer. It narrows down the research aim into a detailed, researchable question that will guide your study’s methods and analysis.

Let’s look at an example:

Research Aim: To explore the effects of climate change on marine life in Southern Africa.

Research Question: How does ocean acidification caused by climate change affect the reproduction rates of coral reefs?

As you can see, the research aim gives you a general focus , while the research question details exactly what you want to find out.

Need a helping hand?

Types of research questions

Now that we’ve defined what a research question is, let’s look at the different types of research questions that you might come across. Broadly speaking, there are (at least) four different types of research questions – descriptive , comparative , relational , and explanatory . 

Descriptive questions ask what is happening. In other words, they seek to describe a phenomena or situation . An example of a descriptive research question could be something like “What types of exercise do high-performing UK executives engage in?”. This would likely be a bit too basic to form an interesting study, but as you can see, the research question is just focused on the what – in other words, it just describes the situation.

Comparative research questions , on the other hand, look to understand the way in which two or more things differ , or how they’re similar. An example of a comparative research question might be something like “How do exercise preferences vary between middle-aged men across three American cities?”. As you can see, this question seeks to compare the differences (or similarities) in behaviour between different groups.

Next up, we’ve got exploratory research questions , which ask why or how is something happening. While the other types of questions we looked at focused on the what, exploratory research questions are interested in the why and how . As an example, an exploratory research question might ask something like “Why have bee populations declined in Germany over the last 5 years?”. As you can, this question is aimed squarely at the why, rather than the what.

Last but not least, we have relational research questions . As the name suggests, these types of research questions seek to explore the relationships between variables . Here, an example could be something like “What is the relationship between X and Y” or “Does A have an impact on B”. As you can see, these types of research questions are interested in understanding how constructs or variables are connected , and perhaps, whether one thing causes another.

Of course, depending on how fine-grained you want to get, you can argue that there are many more types of research questions , but these four categories give you a broad idea of the different flavours that exist out there. It’s also worth pointing out that a research question doesn’t need to fit perfectly into one category – in many cases, a research question might overlap into more than just one category and that’s okay.

The key takeaway here is that research questions can take many different forms , and it’s useful to understand the nature of your research question so that you can align your research methodology accordingly.

How To Write A Research Question

As we alluded earlier, a well-crafted research question needs to possess very specific attributes, including focus , clarity and feasibility . But that’s not all – a rock-solid research question also needs to be rooted and aligned . Let’s look at each of these.

A strong research question typically has a single focus. So, don’t try to cram multiple questions into one research question; rather split them up into separate questions (or even subquestions), each with their own specific focus. As a rule of thumb, narrow beats broad when it comes to research questions.

Clear and specific

A good research question is clear and specific, not vague and broad. State clearly exactly what you want to find out so that any reader can quickly understand what you’re looking to achieve with your study. Along the same vein, try to avoid using bulky language and jargon – aim for clarity.

Unfortunately, even a super tantalising and thought-provoking research question has little value if you cannot feasibly answer it. So, think about the methodological implications of your research question while you’re crafting it. Most importantly, make sure that you know exactly what data you’ll need (primary or secondary) and how you’ll analyse that data.

A good research question (and a research topic, more broadly) should be rooted in a clear research gap and research problem . Without a well-defined research gap, you risk wasting your effort pursuing a question that’s already been adequately answered (and agreed upon) by the research community. A well-argued research gap lays at the heart of a valuable study, so make sure you have your gap clearly articulated and that your research question directly links to it.

As we mentioned earlier, your research aim and research question are (or at least, should be) tightly linked. So, make sure that your research question (or set of questions) aligns with your research aim . If not, you’ll need to revise one of the two to achieve this.

FAQ: Research Questions

Research question faqs, how many research questions should i have, what should i avoid when writing a research question, can a research question be a statement.

Typically, a research question is phrased as a question, not a statement. A question clearly indicates what you’re setting out to discover.

Can a research question be too broad or too narrow?

Yes. A question that’s too broad makes your research unfocused, while a question that’s too narrow limits the scope of your study.

Here’s an example of a research question that’s too broad:

“Why is mental health important?”

Conversely, here’s an example of a research question that’s likely too narrow:

“What is the impact of sleep deprivation on the exam scores of 19-year-old males in London studying maths at The Open University?”

Can I change my research question during the research process?

How do i know if my research question is good.

A good research question is focused, specific, practical, rooted in a research gap, and aligned with the research aim. If your question meets these criteria, it’s likely a strong question.

Is a research question similar to a hypothesis?

Not quite. A hypothesis is a testable statement that predicts an outcome, while a research question is a query that you’re trying to answer through your study. Naturally, there can be linkages between a study’s research questions and hypothesis, but they serve different functions.

How are research questions and research objectives related?

The research question is a focused and specific query that your study aims to answer. It’s the central issue you’re investigating. The research objective, on the other hand, outlines the steps you’ll take to answer your research question. Research objectives are often more action-oriented and can be broken down into smaller tasks that guide your research process. In a sense, they’re something of a roadmap that helps you answer your research question.

Need some inspiration?

If you’d like to see more examples of research questions, check out our research question mega list here .  Alternatively, if you’d like 1-on-1 help developing a high-quality research question, consider our private coaching service .

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Dissertations & projects: Research questions

• Research questions
• The process of reviewing
• Project management
• Literature-based projects

Jump to content on these pages:

“The central question that you ask or hypothesis you frame drives your research: it defines your purpose.” Bryan Greetham, How to Write Your Undergraduate Dissertation

This page gives some help and guidance in developing a realistic research question. It also considers the role of sub-questions and how these can influence your methodological choices. 

Choosing your research topic

You may have been provided with a list of potential topics or even specific questions to choose from. It is more common for you to have to come up with your own ideas and then refine them with the help of your tutor. This is a crucial decision as you will be immersing yourself in it for a long time.

Some students struggle to find a topic that is sufficiently significant and yet researchable within the limitations of an undergraduate project. You may feel overwhelmed by the freedom to choose your own topic but you could get ideas by considering the following:

Choose a topic that you find interesting . This may seem obvious but a lot of students go for what they think will be easy over what they think will be interesting - and regret it when they realise nothing is particularly easy and they are bored by the work. Think back over your lectures or talks from visiting speakers - was there anything you really enjoyed? Was there anything that left you with questions?

Choose something distinct . Whilst at undergraduate level you do not have to find something completely unique, if you find something a bit different you have more opportunity to come to some interesting conclusions. Have you some unique experiences that you can bring: personal biography, placements, study abroad etc?

Don't make your topic too wide . If your topic is too wide, it will be harder to develop research questions that you can actually answer in the context of a small research project.

Don't make your work too narrow . If your topic is too narrow, you will not be able to expand on the ideas sufficiently and make useful conclusions. You may also struggle to find enough literature to support it.

Scope out the field before deciding your topic . This is especially important if you have a few different options and are not sure which to pick. Spend a little time researching each one to get a feel for the amount of literature that exists and any particular avenues that could be worth exploring.

Think about your future . Some topics may fit better than others with your future plans, be they for further study or employment. Becoming more expert in something that you may have to be interviewed about is never a bad thing!

Once you have an idea (or even a few), speak to your tutor. They will advise on whether it is the right sort of topic for a dissertation or independent study. They have a lot of experience and will know if it is too much to take on, has enough material to build on etc.

Developing a research question or hypothesis

Research question vs hypothesis.

First, it may be useful to explain the difference between a research question and a hypothesis. A research question is simply a question that your research will address and hopefully answer (or give an explanation of why you couldn't answer it). A hypothesis is a statement that suggests how you expect something to function or behave (and which you would test to see if it actually happens or not).

Research question examples

• How significant is league table position when students choose their university?
• What impact can a diagnosis of depression have on physical health?

Note that these are open questions - i.e. they cannot be answered with a simple 'yes' or 'no'. This is the best form of question.

Hypotheses examples

• Students primarily choose their university based on league table position.
• A diagnosis of depression can impact physical health.

Note that these are things that you can test to see if they are true or false. This makes them more definite then research questions - but you can still answer them more fully than 'no they don't' or 'yes it does'. For example, in the above examples you would look to see how relevant other factors were when choosing universities and in what ways physical health may be impacted.

For more examples of the same topic formulated as hypotheses, research questions and paper titles see those given at the bottom of this document from Oakland University: Formulation of Research Hypothesis

Which do you need?

Generally, research questions are more common in the humanities, social sciences and business, whereas hypotheses are more common in the sciences. This is not a hard rule though, talk things through with your supervisor to see which they are expecting or which they think fits best with your topic.

What makes a good research question or hypothesis?

Unless you are undertaking a systematic review as your research method, you will develop your research question  as a result of reviewing the literature on your broader topic. After all, it is only by seeing what research has already been done (or not) that you can justify the need for your question or your approach to answering it. At the end of that process, you should be able to come up with a question or hypothesis that is:

• Clear (easily understandable)
• Focused (specific not vague or huge)
• Answerable (the data is available and analysable in the time frame)
• Relevant (to your area of study)
• Significant (it is worth answering)

You can try a few out, using a table like this (yours would all be in the same discipline):

A similar, though different table is available from the University of California: What makes a good research topic?   The completed table has some supervisor comments which may also be helpful.

Ultimately, your final research question will be mutually agreed between yourself and your supervisor - but you should always bring your own ideas to the conversation.

The role of sub-questions

Your main research question will probably still be too big to answer easily. This is where sub-questions come in. They are specific, narrower questions that you can answer directly from your data.

So, looking at the question " How much do online users know and care about how their self-images can be used by Apple, Google, Microsoft and Facebook? " from the table above, the sub-questions could be:

• What rights do the terms and conditions of signing up for Apple, Google, Microsoft and Facebook accounts give those companies regarding the use of self-images?
• What proportion of users read the terms and conditions when creating accounts with these companies?
• How aware are users of the rights they are giving away regarding their self-images when creating accounts with these companies?
• How comfortable are users with giving away these rights?

Together, the answers to your sub-questions should enable you to answer the overarching research question.

How do you answer your sub-questions?

Depending on the type of dissertation/project your are undertaking, some (or all) the questions may be answered with information collected from the literature and some (or none) may be answered by analysing data directly collected as part of your primary empirical research .

In the above example, the first question would be answered by documentary analysis of the relevant terms and conditions, the second by a mixture of reviewing the literature and analysing survey responses from participants and the last two also by analysing survey responses. Different projects will require different approaches.

Some sub-questions could be answered by reviewing the literature and others from empirical study.

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Developing research questions

As I mentioned in one of my recent blog posts, I have PhD students at all stages (about to defend their PhD proposal, about to go on to the field, about to finish) and therefore I have been reading a lot of books on the PhD journey . I’ve also been participating in several events associated with PhD students’ performance: dissertation proposal defense, comprehensive exams, etc., both my own students and those on whose doctoral committees I sit on.

What I have been finding is that there’s a lot of variation in how doctoral students are mentored and what they learn and how they approach their research. So I tweeted about this, and complained that we do a poor job of training students at all three levels (undergraduate, masters and doctoral) on how to craft and develop good research questions.

I had a meeting with a student yesterday and I was reminded that we don't seem to teach how to design and ask research questions. #PhDChat — Dr Raul Pacheco-Vega (@raulpacheco) June 22, 2018

Regardless of whether you’re writing a book-length, cohesive manuscript or 3 papers, you should have a driving/leading research question. I also think we do a poor job of reminding students and even early career scholars: our job is to EXPLAIN phenomena. We are looking for answers to puzzles . And to explain phenomena and look for answers we need to ask good questions: WHAT matters is different to WHETHER it matters to HOW it does.

One of the main problems we face is teaching our students how to find puzzles . What drives your work, what motivates your research, what makes what you’re studying important? What’s the puzzle you are seeking to solve? This is a big problem – a lot of students and researchers don’t seem to know strategies to find a puzzle worth examining.

With one of my recent graduates, I used this piece: “what is the point? teaching graduate students how to construct political science research puzzles” https://t.co/gmNE4Xfi67 to help him devise what the puzzle was. What is it that makes you wonder/ponder about a phenomenon? — Dr Raul Pacheco-Vega (@raulpacheco) June 26, 2018

The truth is that we need to help our students follow a systematic process for undertaking research. This is my own strategy:

The way I think about research is usually as follows – Reflecting on a Puzzle – Drafting a Research Question – Creating a Research Design – Choosing Methods and a Methodological Strategy – Implementation (Conducting the Research) – Reporting — Dr Raul Pacheco-Vega (@raulpacheco) June 26, 2018

To me, finding a puzzle to solve is about pondering. Why, instead of getting X response to Y variable, we are getting Z? . It’s about finding something head scratching. It’s about going “huh?”. I have been using the framework I posit below to help my students frame their own research based on a puzzle that drives the research, and also based on positing specific research questions for each paper.

I'm working with my doctoral students and testing this framework (3 papers model). Comments as always, welcome. pic.twitter.com/Eve9nTYgND — Dr Raul Pacheco-Vega (@raulpacheco) June 26, 2018

A FEW RESOURCES TO HELP STUDENTS AND RESEARCHERS DEVELOP RESEARCH QUESTIONS :

• Alvesson and Sandberg (2011) Generating Research Questions Through Problematization . Academy of Management Review
• Gustafsson and Hagström (2017) what is the point? teaching graduate students how to construct political science research puzzles . European Political Science
• Schafer — Questions about Causes . — this is a great handout on examining causality and research questions
• Vandenbroucke and Pearce (2018) From ideas to studies: how to get ideas and sharpen them into research questions Clinical Epidemiol. 2018; 10: 253–264.
• Lauren Horn Griffin (2015) Puzzling It Out: Teaching Marketable Skills in History Courses with the Jigsaw Technique Perspectives on History . American Historian Associations
• Agee (2009) Developing qualitative research questions: a reflective process International Journal of Qualitative Studies in Education
• Lipowski (2008) Developing great research questions. American Journal of Health System Pharmacy.

If you liked this blog post, you may also be interested in my Resources for Graduate Students page, and on my reading notes of books I’ve read on how to do a doctoral degree.

You can share this blog post on the following social networks by clicking on their icon.

Posted in academia .

Tagged with methodology , research methods , research questions , research strategy .

By Raul Pacheco-Vega – June 27, 2018

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How to Write a Good Research Question (w/ Examples)

What is a Research Question?

A research question is the main question that your study sought or is seeking to answer. A clear research question guides your research paper or thesis and states exactly what you want to find out, giving your work a focus and objective. Learning  how to write a hypothesis or research question is the start to composing any thesis, dissertation, or research paper. It is also one of the most important sections of a research proposal . 

A good research question not only clarifies the writing in your study; it provides your readers with a clear focus and facilitates their understanding of your research topic, as well as outlining your study’s objectives. Before drafting the paper and receiving research paper editing (and usually before performing your study), you should write a concise statement of what this study intends to accomplish or reveal.

Research Question Writing Tips

Listed below are the important characteristics of a good research question:

A good research question should:

• Be clear and provide specific information so readers can easily understand the purpose.
• Be focused in its scope and narrow enough to be addressed in the space allowed by your paper
• Be relevant and concise and express your main ideas in as few words as possible, like a hypothesis.
• Be precise and complex enough that it does not simply answer a closed “yes or no” question, but requires an analysis of arguments and literature prior to its being considered acceptable. 
• Be arguable or testable so that answers to the research question are open to scrutiny and specific questions and counterarguments.

Some of these characteristics might be difficult to understand in the form of a list. Let’s go into more detail about what a research question must do and look at some examples of research questions.

The research question should be specific and focused 

Research questions that are too broad are not suitable to be addressed in a single study. One reason for this can be if there are many factors or variables to consider. In addition, a sample data set that is too large or an experimental timeline that is too long may suggest that the research question is not focused enough.

A specific research question means that the collective data and observations come together to either confirm or deny the chosen hypothesis in a clear manner. If a research question is too vague, then the data might end up creating an alternate research problem or hypothesis that you haven’t addressed in your Introduction section .

The research question should be based on the literature 

An effective research question should be answerable and verifiable based on prior research because an effective scientific study must be placed in the context of a wider academic consensus. This means that conspiracy or fringe theories are not good research paper topics.

Instead, a good research question must extend, examine, and verify the context of your research field. It should fit naturally within the literature and be searchable by other research authors.

References to the literature can be in different citation styles and must be properly formatted according to the guidelines set forth by the publishing journal, university, or academic institution. This includes in-text citations as well as the Reference section . 

The research question should be realistic in time, scope, and budget

There are two main constraints to the research process: timeframe and budget.

A proper research question will include study or experimental procedures that can be executed within a feasible time frame, typically by a graduate doctoral or master’s student or lab technician. Research that requires future technology, expensive resources, or follow-up procedures is problematic.

A researcher’s budget is also a major constraint to performing timely research. Research at many large universities or institutions is publicly funded and is thus accountable to funding restrictions. 

The research question should be in-depth

Research papers, dissertations and theses , and academic journal articles are usually dozens if not hundreds of pages in length.

A good research question or thesis statement must be sufficiently complex to warrant such a length, as it must stand up to the scrutiny of peer review and be reproducible by other scientists and researchers.

Research Question Types

Qualitative and quantitative research are the two major types of research, and it is essential to develop research questions for each type of study. 

Quantitative Research Questions

Quantitative research questions are specific. A typical research question involves the population to be studied, dependent and independent variables, and the research design.

In addition, quantitative research questions connect the research question and the research design. In addition, it is not possible to answer these questions definitively with a “yes” or “no” response. For example, scientific fields such as biology, physics, and chemistry often deal with “states,” in which different quantities, amounts, or velocities drastically alter the relevance of the research.

As a consequence, quantitative research questions do not contain qualitative, categorical, or ordinal qualifiers such as “is,” “are,” “does,” or “does not.”

Categories of quantitative research questions

Qualitative Research Questions

In quantitative research, research questions have the potential to relate to broad research areas as well as more specific areas of study. Qualitative research questions are less directional, more flexible, and adaptable compared with their quantitative counterparts. Thus, studies based on these questions tend to focus on “discovering,” “explaining,” “elucidating,” and “exploring.”

Categories of qualitative research questions

Quantitative and Qualitative Research Question Examples

Good and Bad Research Question Examples

Below are some good (and not-so-good) examples of research questions that researchers can use to guide them in crafting their own research questions.

Research Question Example 1

The first research question is too vague in both its independent and dependent variables. There is no specific information on what “exposure” means. Does this refer to comments, likes, engagement, or just how much time is spent on the social media platform?

Second, there is no useful information on what exactly “affected” means. Does the subject’s behavior change in some measurable way? Or does this term refer to another factor such as the user’s emotions?

Research Question Example 2

In this research question, the first example is too simple and not sufficiently complex, making it difficult to assess whether the study answered the question. The author could really only answer this question with a simple “yes” or “no.” Further, the presence of data would not help answer this question more deeply, which is a sure sign of a poorly constructed research topic.

The second research question is specific, complex, and empirically verifiable. One can measure program effectiveness based on metrics such as attendance or grades. Further, “bullying” is made into an empirical, quantitative measurement in the form of recorded disciplinary actions.

Steps for Writing a Research Question

Good research questions are relevant, focused, and meaningful. It can be difficult to come up with a good research question, but there are a few steps you can follow to make it a bit easier.

1. Start with an interesting and relevant topic

Choose a research topic that is interesting but also relevant and aligned with your own country’s culture or your university’s capabilities. Popular academic topics include healthcare and medical-related research. However, if you are attending an engineering school or humanities program, you should obviously choose a research question that pertains to your specific study and major.

Below is an embedded graph of the most popular research fields of study based on publication output according to region. As you can see, healthcare and the basic sciences receive the most funding and earn the highest number of publications. 

2. Do preliminary research  

You can begin doing preliminary research once you have chosen a research topic. Two objectives should be accomplished during this first phase of research. First, you should undertake a preliminary review of related literature to discover issues that scholars and peers are currently discussing. With this method, you show that you are informed about the latest developments in the field.

Secondly, identify knowledge gaps or limitations in your topic by conducting a preliminary literature review . It is possible to later use these gaps to focus your research question after a certain amount of fine-tuning.

3. Narrow your research to determine specific research questions

You can focus on a more specific area of study once you have a good handle on the topic you want to explore. Focusing on recent literature or knowledge gaps is one good option. 

By identifying study limitations in the literature and overlooked areas of study, an author can carve out a good research question. The same is true for choosing research questions that extend or complement existing literature.

4. Evaluate your research question

Make sure you evaluate the research question by asking the following questions:

Is my research question clear?

The resulting data and observations that your study produces should be clear. For quantitative studies, data must be empirical and measurable. For qualitative, the observations should be clearly delineable across categories.

Is my research question focused and specific?

A strong research question should be specific enough that your methodology or testing procedure produces an objective result, not one left to subjective interpretation. Open-ended research questions or those relating to general topics can create ambiguous connections between the results and the aims of the study. 

Is my research question sufficiently complex?

The result of your research should be consequential and substantial (and fall sufficiently within the context of your field) to warrant an academic study. Simply reinforcing or supporting a scientific consensus is superfluous and will likely not be well received by most journal editors.  

Editing Your Research Question

Your research question should be fully formulated well before you begin drafting your research paper. However, you can receive English paper editing and proofreading services at any point in the drafting process. Language editors with expertise in your academic field can assist you with the content and language in your Introduction section or other manuscript sections. And if you need further assistance or information regarding paper compositions, in the meantime, check out our academic resources , which provide dozens of articles and videos on a variety of academic writing and publication topics.

• How to Choose a PhD Research Topic
• Finding a PhD

Introduction

Whilst there are plenty of resources available to help prospective PhD students find doctoral programmes, deciding on a research topic is a process students often find more difficult.

Some advertised PhD programmes have predefined titles, so the exact topic is decided already. Generally, these programmes exist mainly in STEM, though other fields also have them. Funded projects are more likely to have defined titles, and structured aims and objectives.

Self funded projects, and those in fields such as arts and humanities, are less likely to have defined titles. The flexibility of topic selection means more scope exists for applicants to propose research ideas and suit the topic of research to their interests.

A middle ground also exists where Universities advertise funded PhD programmes in subjects without a defined scope, for example: “PhD Studentship in Biomechanics”. The applicant can then liaise with the project supervisor to choose a particular title such as “A study of fatigue and impact resistance of biodegradable knee implants”.

If a predefined programme is not right for you, then you need to propose your own research topic. There are several factors to consider when choosing a good research topic, which will be outlined in this article.

How to Choose a Research Topic

Our first piece of advice is to PhD candidates is to stop thinking about ‘finding’ a research topic, as it is unlikely that you will. Instead, think about developing a research topic (from research and conversations with advisors).

Consider several ideas and critically appraise them:

• You must be able to explain to others why your chosen topic is worth studying.
• You must be genuinely interested in the subject area.
• You must be competent and equipped to answer the research question.
• You must set achievable and measurable aims and objectives.
• You need to be able to achieve your objectives within a given timeframe.
• Your research question must be original and contribute to the field of study.

We have outlined the key considerations you should use when developing possible topics. We explore these below:

Focus on your interests and career aspirations

It is important to choose a topic of research that you are genuinely interested in. The decision you make will shape the rest of your career. Remember, a full-time programme lasts 3-4 years, and there will be unforeseen challenges during this time. If you are not passionate about the study, you will struggle to find motivation during these difficult periods.

You should also look to your academic and professional background. If there are any modules you undertook as part of your Undergraduate/Master degree that you particularly enjoyed or excelled in? These could form part of your PhD research topic. Similarly, if you have professional work experience, this could lead to you asking questions which can only be answered through research.

When deciding on a PhD research topic you should always consider your long-term career aspirations. For example, as a physicist, if you wish to become an astrophysicist, a research project studying black holes would be more relevant to you than a research project studying nuclear fission.

Read dissertations and published journals

Reading dissertations and published journals is a great way to identify potential PhD topics. When reviewing existing research ask yourself:

• What has been done and what do existing results show?
• What did previous projects involve (e.g. lab-work or fieldwork)?
• How often are papers published in the field?
• Are your research ideas original?
• Is there value in your research question?
• Could I expand on or put my own spin on this research?

Reading dissertations will also give you an insight into the practical aspects of doctoral study, such as what methodology the author used, how much data analysis was required and how was information presented.

You can also think of this process as a miniature literature review . You are searching for gaps in knowledge and developing a PhD project to address them. Focus on recent publications (e.g. in the last five years). In particular, the literature review of recent publications will give an excellent summary of the state of existing knowledge, and what research questions remain unanswered.

If you have the opportunity to attend an academic conference, go for it! This is often an excellent way to find out current theories in the industry and the research direction. This knowledge could reveal a possible research idea or topic for further study.

Finding a PhD has never been this easy – search for a PhD by keyword, location or academic area of interest.

Discuss research topic ideas with a PhD supervisor

Discuss your research topic ideas with a supervisor. This could be your current undergraduate/masters supervisor, or potential supervisors of advertised PhD programmes at different institutions. Come to these meetings prepared with initial PhD topic ideas, and your findings from reading published journals. PhD supervisors will be more receptive to your ideas if you can demonstrate you have thought about them and are committed to your research.

You should discuss your research interests, what you have found through reading publications, and what you are proposing to research. Supervisors who have expertise in your chosen field will have insight into the gaps in knowledge that exist, what is being done to address them, and if there is any overlap between your proposed research ideas and ongoing research projects.

Talking to an expert in the field can shape your research topic to something more tangible, which has clear aims and objectives. It can also find potential shortfalls of your PhD ideas.

It is important to remember, however, that although it is good to develop your research topic based on feedback, you should not let the supervisor decide a topic for you. An interesting topic for a supervisor may not be interesting to you, and a supervisor is more likely to advise on a topic title which lends itself to a career in academia.

Another tip is to talk to a PhD student or researcher who is involved in a similar research project. Alternatively, you can usually find a relevant research group within your University to talk to. They can explain in more detail their experiences and suggest what your PhD programme could involve with respect to daily routines and challenges.

Look at advertised PhD Programmes

Use our Search tool , or look on University PhD listing pages to identify advertised PhD programmes for ideas.

• What kind of PhD research topics are available?
• Are these similar to your ideas?
• Are you interested in any of these topics?
• What do these programmes entail?

The popularity of similar PhD programmes to your proposed topic is a good indicator that universities see value in the research area. The final bullet point is perhaps the most valuable takeaway from looking at advertised listings. Review what similar programmes involve, and whether this is something you would like to do. If so, a similar research topic would allow you to do this.

Writing a Research Proposal

As part of the PhD application process , you may be asked to summarise your proposed research topic in a research proposal. This is a document which summarises your intended research and will include the title of your proposed project, an Abstract, Background and Rationale, Research Aims and Objectives, Research Methodology, Timetable, and a Bibliography. If you are required to submit this document then read our guidance on how to write a research proposal for your PhD application.

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Examples of Research Questions

Phd in nursing science program, examples of broad clinical research questions include:.

• Does the administration of pain medication at time of surgical incision reduce the need for pain medication twenty-four hours after surgery?
• What maternal factors are associated with obesity in toddlers?
• What elements of a peer support intervention prevent suicide in high school females?
• What is the most accurate and comprehensive way to determine men’s experience of physical assault?
• Is yoga as effective as traditional physical therapy in reducing lymphedema in patients who have had head and neck cancer treatment?
• In the third stage of labor, what is the effect of cord cutting within the first three minutes on placenta separation?
• Do teenagers with Type 1 diabetes who receive phone tweet reminders maintain lower blood sugars than those who do not?
• Do the elderly diagnosed with dementia experience pain?
•  How can siblings’ risk of depression be predicted after the death of a child?
•  How can cachexia be prevented in cancer patients receiving aggressive protocols involving radiation and chemotherapy?

Examples of some general health services research questions are:

• Does the organization of renal transplant nurse coordinators’ responsibilities influence live donor rates?
• What activities of nurse managers are associated with nurse turnover?  30 day readmission rates?
• What effect does the Nurse Faculty Loan program have on the nurse researcher workforce?  What effect would a 20% decrease in funds have?
• How do psychiatric hospital unit designs influence the incidence of patients’ aggression?
• What are Native American patient preferences regarding the timing, location and costs for weight management counseling and how will meeting these preferences influence participation?
•  What predicts registered nurse retention in the US Army?
• How, if at all, are the timing and location of suicide prevention appointments linked to veterans‘ suicide rates?
• What predicts the sustainability of quality improvement programs in operating rooms?
• Do integrated computerized nursing records across points of care improve patient outcomes?
• How many nurse practitioners will the US need in 2020?

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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on 30 October 2022 by Shona McCombes . Revised on 12 December 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, research questions quiz, frequently asked questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

Prevent plagiarism, run a free check.

Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis – a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

An essay isn’t just a loose collection of facts and ideas. Instead, it should be centered on an overarching argument (summarised in your thesis statement ) that every part of the essay relates to.

The way you structure your essay is crucial to presenting your argument coherently. A well-structured essay helps your reader follow the logic of your ideas and understand your overall point.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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How to Develop a Research Question?

A Complete Guide to Frame Research Questions

Formulation of the research questions is an important task before starting any research work. The main objective of framing the research question is to understand the existing research gaps in any field of research and to identify the need for extensive investigation. Therefore, it is significant to formulate good research questions.

The very first step in developing a research question is to identify the area of research and then carry out the preliminary study. The researcher needs to do an extensive literature survey to identify gaps in existing research, and based on this gap, research questions can be framed and investigated. Moreover, formulation of a research question is a systematic process and has to be done meticulously as these questions have more impact on the successful completion of your novel research.

Therefore, in this article, iLovePhD discussed how to frame a research question that is specific enough to explore the paths related to your research.

Characteristics of the ideal Research Question

A good research question:

• Elaborates the problem statement
• Describes the problem under investigation
• Improves focus on the problem statement
• Guides data collection and analysis
• Enhances the importance of research.

Ratan et al., 2019 listed ten characteristics of a good research question which is presented below. It is expressed by the acronym ‘FINERMAPS’.

 “Successful and novel research topics are carefully defined and focused but are parts of a broad-ranging, complex problem.” ilovephd.com

How to Formulate the Research Question (Step-by-Step Procedure)

Before framing a research question, the researcher needs to have a broad and deep understanding of the field of research. The formulation of the research question is the result of extensive reading, thinking, and discussing the ideas with the supervisor. It is an iterative process, so, the research question gets revised periodically.

Step 1: Start by identifying a wider area of research for investigation ( For example Wastewater treatment )

Step 2: Do a gap analysis on that topic to understand the existing research works and what is to be investigated further. ( Example: Novel technique for the treatment of wastewater ).

Step 3: What is to be understood still? ( Example: Advantages and limitations of other existing wastewater treatment technologies ).

Step 4: What are the implied questions? ( Example: How this novel technique is unique from other technologies with respect to efficiency and cost ).

Step 5: Narrow down the scope and focus of research ( Example: A pilot-scale study on the treatment of wastewater by a novel technique for reuse ).

Once the research question is framed, the researcher should evaluate it. This is done to understand whether the questions framed are meaningful or need further revision. Helen Kara, 2015 presented the technical requirements to assess the research question as follows:

• Is the research question clear and specific?
• Is the research question simple or complex?
• Is the research question researchable?
• Is the research question relevant?
• Is the research question have social importance?
• Is the research question narrow or broad?
• Is the research question measurable?

Examples of Good Research Questions:

The following questions fulfill the criteria of good research questions , that is, feasible, novel, interesting, ethical, relevant, and social importance.

• What are the novel, safe, and cost-effective methods to transport hydrogen gas to use it as fuel?
• What are the sustainable ways of producing green hydrogen?
• What are the novel methods to treat emerging contaminants in wastewater?
• How to mitigate active security attacks in the Internet of Medical Things?
• What are the economical methods to recycle used batteries?
• How to minimize the impact of macroeconomic conditions such as inflation on essential goods and services?
• What kind of framework is needed to dynamically update the existing university syllabus in-line with emerging technologies?
• How to design and develop pollution-free vehicles to minimize air pollution?

We hope, this article helped you to learn how to formulate your research questions based on the knowledge gained from the literature review. A good research question requires complete gap analysis and deep insight into the problem to be studied. Research work carried out by such questions can have a good impact in the field of social and health sectors for taking policy decisions for the benefit of our society.

Also Read: Top 38 Possible PhD Viva Questions

• Characteristics of Research Question
• Example Research Questions
• Research Methodology
• Research Question

What is a Research Design? Importance and Types

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Top 18 PhD Viva Questions | Examples

The PhD viva is an oral assessment held by a committee during the PhD defense. This evaluation involves the committee posing inquiries to the PhD candidate regarding their research work and dissertation.

This article explores the PhD viva questions, provides 18 sample questions, and offers advice on responding to them effectively.

Table of Contents

The questions asked during a PhD viva typically come from the candidate’s original work proposal and other submitted written materials.

Types of PhD Viva Questions

Preparing and practicing your responses to questions from these four fundamental categories will significantly help in your preparation efforts.

Frequently Asked Questions for PhD Vivas and How to Answer to Them

You should be ready to answer these common questions logically, despite the differences in each PhD viva.

 1. Tell me about yourself

I’m someone who’s deeply passionate about research, particularly in areas where I can make a meaningful impact. My main areas of interest revolve around [specific fields or topics], where I believe there’s immense potential for innovation and discovery. I approach research with a positive mindset, always looking for new insights and solutions to complex problems.

Overall, I approach research with professionalism, enthusiasm, and a commitment to excellence, and I’m eager to continue exploring new avenues of inquiry and making meaningful contributions to the academic community.

2. Why did you choose this research question?

I chose this research question because it’s really important and can make a big difference in dealing with [specific issue or gap] in [field or discipline]. After looking at different research options, I found this question to be very interesting because it could add a lot to what we already know and help solve real problems. Also, it fits well with what I’ve studied before and what I’m interested in, so I can use my skills and knowledge to explore and solve problems in this area.

3. How did you come up with the idea for this research?

4. what is your research’s main area of focus.

Keep in mind that your response should not summarize your research but instead discuss the primary focus area of your research. Crucially, to showcase the viability of your research, it’s important to highlight some of the key questions it tackles.

5. What methods will you use to evaluate the effectiveness of your research?

I will use [specific methods, such as surveys, experiments, interviews, data analysis, etc.] to evaluate the effectiveness of my research. These methods are chosen based on their ability to gather relevant data, analyze findings, and draw meaningful conclusions that address the research objectives and hypotheses.

6. Did the research process proceed as planned, or did you encounter any unexpected circumstances?

7. what is the future of your research.

When addressing the future of your research area in your viva, it’s crucial to go beyond the current state and consider upcoming developments. Simply focusing on the present might suggest a limited understanding. Instead, provide a comprehensive response by discussing your vision for the research area’s future, its connection to the present, and its significance.

8. What are some limitations of your thesis?

9. is this work original, or have others done something similar before.

This work is original in [specific aspects or contributions], as it builds upon existing literature and presents novel findings or approaches. While others may have explored related topics or methodologies, the unique combination of [key elements or innovations] distinguishes this research from previous efforts.

10. What benefit does this research provide to society?

11. what are the limitations of your research design.

When discussing limitations in your research design during your viva, acknowledge that every design has its constraints. Be transparent about these limitations and explain how you mitigated or addressed them in your study. If your design was particularly good, highlight how it contributed to your results. Conversely, if aspects of your study didn’t go as planned, use this as evidence to analyze potential flaws in your hypothesis.

12. How might your research have been impacted if there were more data available on your topic?

If more data were available on my research topic, it would have significantly impacted the depth of my study. Firstly, a larger dataset would have allowed for more comprehensive analyses, such as subgroup analyses and advanced statistical modeling techniques. This could have led to more robust findings and a better understanding of the nuances within the data.

Overall, the availability of more data would have enhanced the quality, reliability, and generalizability of my research outcomes, contributing to a more comprehensive understanding of the topic.

13. Has your research challenged or changed how we think about the topic?

My research has challenged existing perceptions by uncovering previously unexplored facets of the topic. Specifically, I focused on [mention specific concept or theory] and conducted [briefly describe your study]. The results revealed [key findings or insights], which have prompted a reevaluation of [mention the paradigm or conventional understanding]. This shift in perspective has significant implications for [explain the broader impact on the field or applications of the research]. Overall, my research has contributed to a nuanced understanding of the topic and has initiated discussions on revising established frameworks in the academic discourse.

14. Do you think other researchers could replicate the results of your study?

15. could there be other explanations for the results of your research.

This question is a method for the viva examiners to assess your ability to critically evaluate your own research. Begin by conducting a thorough review of the existing literature to identify any alternative explanations for your research findings. If such alternative explanations exist, explain them in detail. On the other hand, if there are no alternative explanations or they are not relevant to your findings, clarify why this is the case. It’s crucial to demonstrate consideration for these alternative perspectives as they contribute to the overall understanding of why your findings are significant.

Overall, my research process involved a critical evaluation of potential alternative explanations, ensuring that the conclusions drawn are well-supported and contribute meaningfully to the existing body of knowledge on the topic.

16. Given your research findings, what would be an appropriate course of action for another researcher to pursue in this field?

17. summarize your thesis..

Familiarize yourself with the entire project, beginning with the rationale behind selecting your thesis topic and concluding with an optimal solution to the problem. Prepare for three types of responses: a 1-minute, 3-5 minutes, and 10-minute summary. Tailor your answer based on the audience’s expectations at the viva.

For the 3-5 minutes summary:

“The topic of my thesis was chosen based on its critical importance in [specific field]. The problem I sought to tackle is [provide a comprehensive overview of the problem, including its significance]. I was drawn to this topic because [explain your personal interest or motivation]. To address this problem effectively, I conducted extensive literature reviews, data collection, and analysis, focusing on key areas such as [list key areas]. The optimal solution I propose involves [describe the solution or approach in detail, including any innovative methodologies or findings]. This solution not only addresses the immediate problem but also has broader implications for [mention broader implications or potential applications].”

18. What are the research’s strengths and weaknesses?

On the other hand, a potential weakness of my research is [identify a weakness, such as sample size limitations, data availability, etc.]. However, this limitation has provided opportunities for future research to explore [potential areas of improvement or expansion].

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Research Question Generator: Best Tool for Students

Stuck formulating a research question? Try the tool we’ve made! With our research question generator, you’ll get a list of ideas for an academic assignment of any level. All you need to do is add the keywords you’re interested in, push the button, and enjoy the result!

Now, here comes your inspiration 😃

Please try again with some different keywords.

Why Use Research Question Generator?

The choice of research topic is a vital step in the process of any academic task completion. Whether you’re working on a small essay or a large dissertation, your topic will make it fail or fly. The best way to cope with the naming task and proceed to the writing part is to use our free online tool for title generation. Its benefits are indisputable.

• The tool generates research questions, not just topics
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Research Question Generator: How to Use

Using our research question generator tool, you won’t need to crack your brains over this part of the writing assignment anymore. All you need to do is:

• Insert your study topic of interest in the relevant tab
• Choose a subject and click “Generate topics”
• Grab one of the offered options on the list

The results will be preliminary; you should use them as an initial reference point and refine them further for a workable, correctly formulated research question.

Research Questions: Types & Examples

Depending on your type of study (quantitative vs. qualitative), you might need to formulate different research question types. For instance, a typical quantitative research project would need a quantitative research question, which can be created with the following formula:

Variable(s) + object that possesses that variable + socio-demographic characteristics

You can choose among three quantitative research question types: descriptive, comparative, and relationship-based. Let's consider each type in more detail to clarify the practical side of question formulation.

Descriptive

As its name suggests, a descriptive research question inquires about the number, frequency, or intensity of something and aims to describe a quantitative issue. Some examples include:

• How often do people download personal finance apps in 2022?
• How regularly do Americans go on holidays abroad?
• How many subscriptions for paid learning resources do UK students make a year?

Comparative

Comparative research questions presuppose comparing and contrasting things within a research study. You should pick two or more objects, select a criterion for comparison, and discuss it in detail. Here are good examples:

• What is the difference in calorie intake between Japanese and American preschoolers?
• Does male and female social media use duration per day differ in the USA?
• What are the attitudes of Baby Boomers versus Millennials to freelance work?

Relationship-based

Relationship-based research is a bit more complex, so you'll need extra work to formulate a good research question. Here, you should single out:

• The independent variable
• The dependent variable
• The socio-demographics of your population of interest

Let’s illustrate how it works:

• How does the socio-economic status affect schoolchildren’s dropout rates in the UK?
• What is the relationship between screen time and obesity among American preschoolers?

Research Question Maker FAQ

In a nutshell, a research question is the one you set to answer by performing a specific academic study. Thus, for instance, if your research question is, “How did global warming affect bird migration in California?," you will study bird migration patterns concerning global warming dynamics.

You should think about the population affected by your topic, the specific aspect of your concern, and the timing/historical period you want to study. It’s also necessary to specify the location – a specific country, company, industry sector, the whole world, etc.

A great, effective research question should answer the "who, what, when, where" questions. In other words, you should define the subject of interest, the issue of your concern related to that subject, the timeframe, and the location of your study.

If you don’t know how to write a compelling research question, use our automated tool to complete the task in seconds. You only need to insert your subject of interest, and smart algorithms will do the rest, presenting a set of workable, interesting question suggestions.

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The Mechanics, Chemistry, and Materials (MCM) PhD program leads interdisciplinary exploration in civil and environmental engineering. Our accomplished researchers specialize in mechanics, chemistry, and materials, leveraging classical and quantum mechanics to address challenges through innovative modeling techniques, bench experiments, and fieldwork.

This program equips you to comprehend material behaviors, optimize them for diverse applications, and contribute to sustainable advancements in civil and environmental engineering while addressing contemporary issues.

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How many research questions is enough for a quality dissertation.

• The Research question is the Primary organizing principle guiding you to analyze further.
• A study should have a minimum of 3 questions and a maximum of 6 queries. Once the research question is determined, the researcher should plan what the method suitable is.
• The research question In a dissertation is entirely relying on the nature of the Topic and the Method It is necessary to have one major question followed by the number of minor queries related to the study.

PhD Dissertation Writing is academic writing based on your research. A thesis is an explanation of the scholarly method. New researchers are expected to do more and better reading and research to talk about their idea. It means that your research should be accurate and novel in its investigation and discussion of a subject. It means that your design will give evidence of critical analysis of the study. There are a lot more new terms and policies to consider before writing the research question. It is difficult for students and new researchers to accept all the criteria and give a quality research question, our experts in PhD Dissertation Writing Help assist you in writing a good research question.

PhD Dissertation Writing Services experts know the importance of the research question to carry out the research. They help you to pinpoint what you find in your research and explain the purpose of the study. The research question is a handy tool for defining what the researcher is trying to tell the readers. Research questions will help you to decide the research area effectively and develop an idea.

PhD Writing Services – Experts in Thesis Help you to write various types of research questions, three common types of research questions are,

• Descriptive: This type of study carries a primary purpose to report what is going on or what is already exists. Public opinion that used only to describe the proportion of people who hold various views are descriptive. To represent the opinion or explain the facts that already exist is a descriptive type research question.
• Relational: This type of study to concentrate on the relationships between two or more variables that exist. It is the comparison of the old version and new updated version and describing by picking the one by explaining their advantages and disadvantages.
• Causal:  Explaining their concept and choosing the thing based on the cause and effect. Why the change happened to study the reason for every problem, causal type research question states the cause and effect of one or more variables.

Steps in writing a research question:

Dissertation writing services expert mentor you to find the topic that interests you and help you in finding a research topic and gives the result that adds purpose to take Quality Dissertation Writing Services.

• Find the general area of interest and choose a topic.
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• Search for extensive information about the topic. Gain in-depth knowledge.
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• Begin your research after you come up with a research question.

The research question in the quality outcome:

PhD Dissertation Assistance helps to find question depends on the quality approach. PhD Thesis Writing Help your research to initiate with just one research question, then increases the question when the research development. The more the question need for analysis is more, and it is impossible to manage too many inquiries in a short period.

The research question helps to frame a hypothesis that contributes to research without committing any errors during the study. Research question helps to get an outcome without doing any rework on particular research which is helpful to make a standardized outcome. It is the question that allows the writer to make the audience understand the research. Best Custom Dissertation Writing Service focuses on a single problem that will help to address the issue.

• Doctoral Dissertation Help   you find a novel research question and address the problem relevant to your field.
• The research question you choose that focuses on the issue or problem should be in the format to answer—this knowledge gained by reading scholarly articles and the research work of other researchers.
• The way of question should address the issue, not the opinion of whether the research is good or bad.
• The quality of the dissertation completely depends on the question, so give the research question answerable within the practical constraints.
• Use specific and well-defined meaning for the concept to answer the question.
• The research question should not contain easy facts and figure found on the internet.
• Press books. (2020). The Purpose of Research Questions.
• USC Libraries. (2020). Research Guides.
• William M.K. Trochim. (2020). Types of Research Questions.

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The Scholars’ Lab staff is offering a mix of remote and in-person consultations, workshops, and events. Visit our Makerspace or contact us at [email protected] to schedule a GIS, VR, or digital project consultation.

A Co-authored Series of Posts ‘About 1919,’ that is, about English-language books published from 1914 to 1921, according to the online bibliography and database, Collective Biographies of Women .

Graduate and undergraduate students and I worked as a research team in 2022-2024.

Is war good for women? It’s an absurd question—no, war has always meant a terrible fate for women. The theaters of war around the world today are blighted by “conflict-related sexual violence,” CRSV, as it is too well known. The war dead too often are women and children. Refugees or survivors suffer all the more because of codes, doctrines, and religious or political laws concerning women’s rights, along with economic, racial, ethnic, and national inequalities. So let’s rephrase the question. Which war, which women, what is their social status and location during it? What have some historical women gained from military conflict?

I wouldn’t attempt to answer this question for all eras and wars, nor would I quibble about a cost/benefit analysis. We noticed that collections of chapter-length biographies of women show the impact of wars across centuries, even though it is widely assumed that politics and the military are exclusively male. (Feminist studies have gone further into historical gender analysis than biographies can go, for example Carol Cohn, ed. Women and Wars: Contested Histories, Uncertain Futures [Polity, 2012].) Within the horizon of a digital humanities project on English-language books published 1914-1921, we considered the effects of World War I on this genre of biographical record about women’s lives. Versions of women’s life stories published during and after World War I suggest that some women gain recognition for their war efforts, but also that this war called new attention to historical women of many times and occupations. Books published during the war and its aftermath years opened up pathways to becoming noteworthy that still seem pioneering or defiant of gender norms of that time.

What We Did and Who We Were

“A Gallery of World War I Women” was a rewarding collaboration in 2022-2024 of graduate, undergraduate, and faculty researchers supported by a library staff and infrastructure. Our nickname for the team’s focus on a set of books from these years “the 1919 project,” was a humble, distant echo of the famous 1619 Project , the New York Times Magazine production by Nikole Hannah-Jones (also a major book by many hands). This series of blog posts is no controversial transformation of women’s history as The 1619 Project is of U.S. history. But we found surprising reconfigurations of women’s nationalities and collective histories in this period.

We were Alison Booth , Director of Collective Biographies of Women, Professor of English and Faculty Director of the DH Center, UVA Library; Lloyd Sy , project manager for CBW, PhD (’23) English, now assistant professor at Yale; Isabel Bielat , research assistant, PhD candidate in history; Mackenzie Daly , research assistant, MA (’24) in English, soon to enter the doctoral program at Boston College; Yichu Wang , research assistant, MA (’23) in English, now a PhD candidate at Cornell; Anna Seungyeon Lee , research assistant, BA (’23) in English and statistics. We met, usually weekly during semesters, in my English-department office to coordinate our parallel research on the books listed in Isabel’s guide to the CBW books 1914-1921 . Find these texts in CBW through the hyperlinks, e.g. a844 .

A meticulous bibliography underlies the database, so we have a ready-made timeline of publication dates. Some books on this chronological sample are conspicuously about World War I, as a844 is; others belong to perennial types of collection: biblical, regional, religious, beauty, high status, arts, mothers. CBW researchers have identified collections by tagging with terms for the kinds of subjects/roles depicted in them. Although Yet many biographies showcasing women’s lives are liberal, advocating Abolition or education. CBW includes volumes dedicated to African American women’s lives; many Irishwomen, adventuresses, writers and artists, and figures who seem to have superpowers desired today.

It was a good guess that volumes published in and around World War I would reflect greater internationalism and wider vocational range. As you will see in the series of posts, each researcher focused at a different angle and scale on texts in this project. Perhaps the books that seem to have least to do with the trenches of European power struggle reveal the most surprises for readers today, as some books feature women of nationalities, religions, or races at margins of Empire.

Lloyd, Isabel, Mackenzie, Yichu, and Anna have each come up with their own contributions, peer reviewed them, and shared them with members of the Scholars’ Lab staff for further vetting. This series of blog posts gives an idea of our explorations of a varied set of volumes as they appear in CBW’s records.

On Collective Biographies of Women

Collective Biographies of Women has seen decades of development with support of both the Scholars’ Lab and the Institute for Advanced Technology in the Humanities (IATH). The “1919” effort began before the DH Center joined these two groups upon IATH’s migration to the Library’s budgetary and HR organization. The database and schema for narrative analysis were greatly indebted to Worthy Martin and Daniel Pitti of IATH along with Doug Ross, Cindy Girard, and Shayne Brandon. Rennie Mapp served as project manager until 2016, followed by Lloyd Sy relying on Rennie’s documentation.

See CBW About . The project helps users access information about (with digitized text where available) 1274 books, some issued centuries before and after the project’s focus dates, 1830-1940. These are not encyclopedias, not researched full-length biographies, but appealing books for general readers with several chapters of documentary entertainment about an assortment of women. These books were often written by men, and inevitably have a Eurocentric and upper-class bias.

Beyond the queens, writers, and celebrities who predominate in such books, many more ordinary women were deemed significant enough to be placed among Notable Women in History . A closer look across the spectrum of the books in CBW (not solely 1914-1921) shows that the reason a woman made a name is related to upheavals of war. War, of course, often relates to race and religion as well as territory and resources. Of approximately 8,000 women identified in CBW’s texts, 140 appear in a search for any of four of our terms for persona types: “soldier,” “military,” “heroine of war,” or “role in revolution.” Searching by other person types–“adventure, physical feat or survival,” “assassin,” “expatriated, exiled person,” “pacifist,” “patriot,” “nationalist,” “model of race,” or “representative of nationality” turns up 604 names. There are 34 female subjects of short biographies in these collections identified as “spy,” while 399 are labeled “nurse.” In short, this genre helps to dislodge the assumption that women are simply the victims of war and that they typically eschew politics. Women as agents of history do not necessarily frequent courts or theaters or salons of Europe or North America.

This series of blog posts gives an idea of our exploration from different angles of a varied set of volumes as they appear in CBW’s records. Each book, with its bibliographical data and its chapters and their human subjects, is organized in a relational database that offers us varied kinds of comparative data.

Users can search persons by various criteria including type from the “backend” pages of CBW: Persons . Email me, Alison Booth if curious to learn more ways to search and sort by person or collection type, publication data, and so on. For more on this genre, see my book, How to Make It as a Woman , University of Chicago Press, 2004.

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Human health, tourism, and nature

Studying the Relationship Between Tourism and Mosquito-Borne Diseases

 Interviewing Elizabeth Pellecer Rivera, PhD candidate at the University of Maine

[ Español ]

1. Can you tell us a bit about your personal background and how you started your PhD at the University of Maine?

Prior to starting my PhD, I studied sustainable tourism and development studies. Most of my professional experience focused on applied social science research with the Center for Health Studies, at the Universidad del Valle de Guatemala. I worked on researching vector-borne diseases from ecological, biological, and social approaches. This experience made me realize how complex and multifactorial problems are, and how interdisciplinary research can help inform and seek potential solutions. I had the privilege of working on a project related to the Zika virus during its initial emergence in the Americas.

When I learned about the opportunity to work on an interdisciplinary research project about the relationship between tourism and mosquito-borne diseases at the University of Maine, I was thrilled because it touched on many aspects of my personal and academic background. Hopefully this research project will have a positive impact in the public health and tourism sectors.

2. What is your project about and why is it important?

The project aims to provide insight into the dynamics between tourism and emerging diseases, as the world is more interconnected than ever, and tourism and travel represent a large part of human mobility. This project aims to better understand how travel can serve as a driver to spread diseases, how humans perceive the risk of emerging diseases, and what kind of actions and responses they take when facing this kind of risk. Originally the project focused on two mosquito-borne diseases, Zika and chikungunya, but given the extent of impact that the COVID-19 pandemic had, we have included it in the research.

The trends show that international tourism was steadily growing before 2020, and it is expected that outbreaks of emerging diseases will continue occurring. Understanding past experiences can only allow us to better prepare for future events. 

3. What questions did you set out to answer?

My central research questions are:

• What preventative and travel behaviors are being suggested in newsprint media targeting visitors?
• What business strategies, if any, have stakeholders used to respond to the threat that Zika and/or chikungunya, and COVID-19 pose to their business, and their clients?
• How do travelers perceive the potential risk of exposure to Zika, chikungunya or COVID-19 when traveling? What factors influence their risk perception?

With all the data we have gathered, most of our questions are being answered, and new questions have emerged. The challenge is to bring it all together and transform it into useful information for both academics and practitioners.

4. Could you share insights into your research methodology?

The social science part of the research focuses on doing a case study in Guatemala to understand the topic, from diverse perspectives: (1) what is being communicated in the news; (2) what are the travelers’ risk perceptions towards the diseases and what kind of travel-related and preventive actions they tend to adopt; and (3) how the tourism industry has been impacted and what coping and response strategies have they implemented. I am using mixed methods (quantitative and qualitative), including content analysis, surveys, and phenomenological interviews. 

5. What have you discovered so far?

Although my analysis is ongoing, my research has already revealed some interesting findings.We found that news media don’t necessarily balance the messages being communicated. More news included risk-elevating messages, focusing on the “scary” side of the diseases, but did not include as many messages that inform people what to do and how to prevent the diseases. From the tourism stakeholders’ perspective, we have identified some of the main strategies used to cope with mosquito-borne diseases and COVID-19, as well as some of the enabling and limiting factors to overcome the impact of these health-related risks.

6. What were some key challenges and highlights that you have encountered while conducting research?

One of the major challenges has been doing a PhD during the COVID-19 pandemic. The impact of this situation was twofold — it not only induced stress and isolation but also disrupted our initial research plans. We had intended to conduct international research, but the imposition of travel restrictions made it unfeasible. Much of my research had to be conducted virtually.

My favorite part of doing research is interacting with people and being able to listen and learn from their experiences. Therefore, I really enjoyed conducting qualitative interviews, and hearing from the tourism sector about their resilience and capacity to adapt when facing threats like mosquito-borne diseases and COVID-19, especially as the latter impacted the sector as never before. I found it surprising that some individuals pointed out positive outcomes resulting from the pandemic, such as the catalyzation of local organization among stakeholders at destinations and an enhanced appreciation for nature and green spaces among visitors. In conclusion, I was amazed by the diversity of responses and the high degree of optimism and hope.

7. How do you envision the practical applications or real-world impact of your research?

I think the COVID-19 pandemic opened everyone’s eyes to the extent of impact and spread of an emerging disease, both as a public health issue and because it halted human mobility. An integral practical application of my project is to promote intersectoral and interinstitutional relationships, public-private partnerships, academia-practitioner collaborations, and tourism-public health interactions. Overall, my investigation can improve understanding of the intricate relationship between tourism and disease.  

To learn more about my work, read my recent publication :

Elizabeth Pellecer Rivera, Sandra De Urioste-Stone, Laura N. Rickard, Andrea Caprara & Lorena N. Estrada (2024) Tourists and epidemics: how news media cover the risks of Zika virus and chikungunya outbreaks in the Americas, Current Issues in Tourism , DOI: 10.1080/13683500.2024.2309164

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How AI Revolutionized Protein Science, but Didn’t End It

June 26, 2024

How does a one-dimensional string of molecules fold correctly into its innate three-dimensional shape? This question, known as the protein folding problem, was recently solved by artificial intelligence.

Fran Pulido for  Quanta Magazine

Introduction

In December 2020, when pandemic lockdowns made in-person meetings impossible, hundreds of computational scientists gathered in front of their screens to watch a new era of science unfold.

They were assembled for a conference, a friendly competition some of them had attended in person for almost three decades where they could all get together and obsess over the same question. Known as the protein folding problem, it was simple to state: Could they accurately predict the three-dimensional shape of a protein molecule from the barest of information — its one-dimensional molecular code? Proteins keep our cells and bodies alive and running. Because the shape of a protein determines its behavior, successfully solving this problem would have profound implications for our understanding of diseases, production of new medicines and insight into how life works.

At the conference, held every other year, the scientists put their latest protein-folding tools to the test. But a solution always loomed beyond reach. Some of them had spent their entire careers trying to get just incrementally better at such predictions. These competitions were marked by baby steps, and the researchers had little reason to think that 2020 would be any different.

They were wrong about that.

That week, a relative newcomer to the protein science community named John Jumper had presented a new artificial intelligence tool, AlphaFold2, which had emerged from the offices of Google DeepMind, the tech company’s artificial intelligence arm in London. Over Zoom, he presented data showing that AlphaFold2’s predictive models of 3D protein structures were over 90% accurate — five times better than those of its closest competitor.

In an instant, the protein folding problem had gone from impossible to painless. The success of artificial intelligence where the human mind had floundered rocked the community of biologists. “I was in shock,” said Mohammed AlQuraishi , a systems biologist at Columbia University’s Program for Mathematical Genomics, who attended the meeting. “A lot of people were in denial.”

But in the conference’s concluding remarks, its organizer John Moult left little room for doubt: AlphaFold2 had “largely solved” the protein folding problem — and shifted protein science forever. Sitting in front of a bookshelf in his home office in a black turtleneck, clicking through his slides on Zoom, Moult spoke in tones that were excited but also ominous. “This is not an end but a beginning,” he said.

Quanta Magazine ; source: RCSB PDB

Proteins are molecules that come in hundreds of millions of different shapes. Each one serves a particular biological function, from carrying oxygen through the blood to sparking chemical reactions. The function is typically defined by its shape or structure.

When Google’s public relations machine churned the news out to the world, the media went wild. Headlines claimed that AlphaFold2 “ will change everything .” Protein biologists who had spent their entire careers investigating the structures of single proteins feared that they would lose their jobs. Some claimed that AlphaFold2 would revolutionize drug development; now that biologists could quickly learn proteins’ shapes, they could create new medicines that could target them. Others pushed back, arguing that the results were mostly hype and little would change.

Moult could barely comprehend the news himself. He ended the conference with the question on everyone’s mind: “What now?”

That was three and a half years ago. It’s finally possible to start answering his question.

AlphaFold2 has undeniably shifted the way biologists study proteins. However, while AlphaFold2 is a powerful prediction tool, it’s not an omniscient machine. It has solved one part of the protein folding problem very cleverly, but not the way a scientist would. It has not replaced biological experiments but rather emphasized the need for them.

Perhaps AlphaFold2’s biggest impact has been drawing biologists’ attention to the power of artificial intelligence. It has already inspired new algorithms, including ones that design new proteins not found in nature; new biotech companies; and new ways to practice science. And its successor, AlphaFold3, which was announced in May 2024 , has moved to the next phase of biological prediction by modeling the structures of proteins in combination with other molecules like DNA or RNA.

“It’s the biggest ‘machine learning in science’ story that there has been,” AlQuraishi said.

However, there are still massive gaps that artificial intelligence hasn’t filled. These tools can’t simulate how proteins change through time or model them in the context in which they exist: within cells. “AlphaFold changed everything and nothing,” said Paul Adams , a structural biologist who develops algorithms to model the structures of biomolecules at Lawrence Berkeley National Laboratory.

This is the story of how Jumper’s team at Google DeepMind pulled off their coup in protein science, and what it means for the future of artificial intelligence in biology.

Formulating the Problem

A piece of origami paper is little more than pressed wood pulp until it’s folded in specific ways; then it becomes something new. A few precise crimps and flips, and it’s a fortune teller, a paper device that can predict your future. Take the same piece of paper, change a few of the folding steps, and now it’s a winged crane, granting good fortune to its recipient.

Similarly, a long string of amino acid molecules has no function until it spontaneously folds into its innate shape, which biologists call its structure. A protein’s structure determines how it binds to or otherwise interacts with other molecules, and therefore defines its role in a cell.

Mark Belan for  Quanta Magazine

There are a couple hundred million known proteins on the planet and many more unknown ones. They do it all: Hemoglobin and myoglobin ferry oxygen around the muscles and body. Keratin gives structure to hair, nails and skin. Insulin enables glucose to move into cells to be converted into energy. Proteins can take on a seemingly infinite number of shapes to match the seemingly infinite number of jobs they do in life.

“Right from the atom all the way to ecosystems, [protein structure] is kind of a lingua franca,” AlQuraishi said. “It’s where everything happens.”

A cell makes proteins by daisy-chaining small molecules called amino acids into long polypeptide strings. The amino acids it chooses depends on the underlying set of instructions provided to it by DNA. Within a fraction of a second of its creation, a polypeptide string bends, buckles and folds precisely into the protein’s final three-dimensional shape. Once off the molecular assembly line, it scurries along to do its biological work.

If proteins didn’t perform this folding process exceedingly well, cascades of disasters would tumble through the body. An incorrectly folded or unraveled protein can lead to toxicity and cell death. Many diseases and disorders, such as sickle cell anemia, are caused by misfolded proteins. And misfolded proteins can aggregate into clumps that are hallmarks of neurodegenerative diseases like Alzheimer’s and Parkinson’s.

Yet no one really knows specifically how protein folding happens. How does the sequence information in these simple molecular chains encode a protein’s complex shape? This is the “most profound question that we can ask,” said George Rose , a biophysics professor emeritus at Johns Hopkins University.

In the 1950s, the biochemist Christian Anfinsen conducted experiments which suggested that a string of amino acids contains an internal code telling it how to fold into a protein — and that there should be a way to predict the protein’s shape from that code. This hypothesis is known as Anfinsen’s dogma.

PBH Images/Alamy

Scientists were probing this question as far back as the 1930s. But efforts really took off in the mid-1950s when the biochemist Christian Anfinsen added proteins to chemical solutions that either unfolded them by breaking their bonds or caused them to fold incorrectly. Then he observed what they did next. Anfinsen saw that the unfolded or misfolded proteins could spontaneously refold into their correct structures. His findings, which later won him a Nobel Prize , demonstrated that proteins form their 3D shapes from an internal code — the one written by their string of amino acids.

Anfinsen hypothesized that there should, therefore, be a way to predict a protein’s shape from the sequence of its amino acids. This became generally known as the protein folding problem.

Once its polypeptide chain is assembled, a protein can fold into its structure within a thousandth of a second — a timescale that perplexed the molecular biologist Cyrus Levinthal. In his 1969 paper “ How to Fold Graciously ,” Levinthal calculated that if a protein were to try out every possible folding option, it would take an impossibly long time to assemble. Clearly, he mused, something must send the protein down the right folding pathway more directly.

Over time, the protein folding problem has forked into new kinds of problems. Now three main questions are posed: Can a protein’s structure be predicted from its amino acid sequence? What is the folding code? And what is the folding mechanism?

These questions started to burrow into scientists’ brains in the early 1960s when the first experimentally determined protein structures became available. Max Perutz and John Kendrew, two biologists at the University of Cambridge, grew proteins into crystals, bombarded them with X-rays and measured how the rays bent — a technique known as X-ray crystallography. By doing so, they determined the 3D structures of hemoglobin and myoglobin. It took them more than two decades. They shared a Nobel Prize for their discoveries.

John Kendrew (left) and Max Perutz (right) meticulously uncovered the structures of hemoglobin and myoglobin using X-ray crystallography. Then they built physical models using balls (atoms) and sticks (chemical bonds).

MRC Laboratory of Molecular Biology

Since then, innumerable researchers have strived to understand not just what different proteins look like, but how they come to look that way. “It’s a very human thing to want to know what things look like, because then you can understand how they function,” said Helen Walden , a structural biologist at the University of Glasgow. Some looked into the chemistry of the problem, others into the physics. Experimentalists reconstructed protein shapes through painstaking lab work. Computational biologists hunted for clues with models and simulations, which they programmed and reprogrammed with different combinations of algorithmic rules.

As more structures came along, the protein science community needed a way to organize and share them. In 1971, the Protein Data Bank was founded as an archive for protein structures. Freely available, the data bank became a dependable tool for anyone who needed to know the structure of a protein to probe a biological question.

When the Protein Data Bank opened, it held the structures of seven proteins. By the time Google DeepMind used it to train AlphaFold2 nearly 50 years later, it held more than 140,000 — each laboriously decoded by the scientists known as structural biologists.

The Experimentalists’ Agony

Starting in the mid-1970s, every few months Janet Thornton reliably received a package in the mail. Inside was a 12-inch magnetic tape containing data about new protein structures deposited in the Protein Data Bank. A biophysicist at the University of Oxford, Thornton would eagerly rip open the package so she could analyze the new structures nearly as soon as they were discovered. The first tape Thornton received had only 20 structures on it.

“I had many students who said, ‘I want to come and solve the protein folding problem,’” said Janet Thornton, a structural biologist who retired from the European Molecular Biology Laboratory last year. “But I didn’t have any new ideas, frankly, about how to do that.”

Jeff Dowling, EMBL-EBI

Every one of those proteins represented years of work. Often a doctoral candidate would spend their four or more years in graduate school crystallizing a single protein, collecting data from it or interpreting that data to figure out the folded structure.

Oxford’s department of biophysics was, at the time, one of the world’s centers for X-ray crystallography. There, in 1965, David Phillips, one of the pioneers of protein crystallography, first determined the structure of an enzyme: lysozyme, which the immune system uses to attack bacteria. Using X-ray crystallography, Oxford biophysicists created maps of proteins’ electron density; the areas in which electrons congregated were likely to contain an atom. Thornton and her colleagues printed these electron density maps onto plastic sheets and stacked them one on top of another to create a “contour map” of the protein’s geography, she said.

X-ray crystallography helps scientists build electron density maps, which visualize where electrons congregate and therefore where atoms likely sit in a molecule. By stacking the maps on top of one another (left), scientists can deduce the structure of a protein or another molecule like penicillin (right).

Science Museum Group

Then they converted the maps into physical models. They placed their plastic maps into a Richards box, named for the Oxford biophysicist Frederic Richards, who invented the device in 1968. Inside a Richards box, an angled mirror reflected the maps into a workspace, allowing the scientists to see exactly where each atom was located relative to others. Then they physically built their model out of balls and sticks.

This method was cumbersome and restrictive. In 1971, Louise Johnson, who would go on to become an eminent crystallographer, was modeling phosphorylase, which at 842 amino acids was at the time the largest protein anyone had worked on. To model it, Johnson had to climb a ladder into a two-story Richards box, which Oxford constructed especially for her project.

Once a model was complete, scientists used a ruler to measure the distances between atoms to come up with coordinates for the protein structure. “It was archaic,” Thornton said. Then they fed the coordinates into a computer. The computerized version looked like a dense forest, she said, with atoms clumped together in a jumble. Only when Thornton looked at the structure through 3D glasses could she start to see the protein’s topology.

“It was a very torturous process,” Thornton said. “It’s amazing that it got done.”

Year by agonizing year, they did it. Once researchers were confident in their protein structure, they submitted it to the Protein Data Bank. By 1984, 152 proteins had been deposited. In 1992, that number climbed to 747.

While the experimentalists toiled on with their physical models, another faction of protein biologists — the computational scientists — took a different approach. But as they pondered Anfinsen’s insight that a protein’s structure should be predictable from its amino acid sequence, they got a bit overconfident.

Writing Their Own Rules

As an undergraduate in the early 1960s, John Moult planned to become a physicist. Then he learned about the protein folding problem. “Somebody came and gave a lecture about biology being too important to leave to the biologists,” he said, “which I arrogantly took seriously.” Captivated, he took his career in a different direction.

After he graduated, Moult went into protein crystallography. He decoded the structures of several proteins, including beta-lactamase, a bacterial enzyme that destroys penicillin, and received his doctorate in molecular biophysics at Oxford in 1970. But as he started his postdoc, he tired of the experimentalist approach and began to drift toward the growing field of protein computation. Computational biologists, as opposed to experimentalists, wrote computer algorithms to try and prove that Anfinsen was right: that they could feed a program a string of amino acids to generate a correct protein structure.

John Moult co-founded the Critical Assessment of Structure Prediction (CASP) experiment to force himself and other computational biologists to test their computer models of proteins against experimentally determined protein structures.

Umit Gulsen for Quanta Magazine

The transition from biological experiments to computation was an uneasy one. Moult was used to the slow, careful work of solving a single protein structure. In his new field, computational papers regularly claimed to have solved the protein folding problem and related sub-problems.

Moult was dubious. “The things that were being published in that area were not as rigorous as I was used to,” he said. “This is not because we’re all a load of crooks in this field. It’s because if you’re doing this sort of computational work, you’re doing it in a virtual world.”

In a virtual world, computationalists wrote their own rules when the rules of the natural world didn’t work. They designed their algorithms so that atoms stuck together in a certain way or the protein always folded to the right or the left. Over time, the models drifted farther away from reality. It’s hard to maintain rigor in a world where you have complete control, Moult said.

Still, he could see the value of both sides. Experimentalists worked precisely but slowly; computationalists worked quickly but were so removed from biophysical realities that they were often wrong.

There must be a way, he thought, to bring the best of both approaches together.

The Stamping Begins

In the early 1990s, Moult and his colleague Krzysztof Fidelis had an idea for bringing discipline to the field’s chaos. They set up a community science experiment that they called the Critical Assessment of Structure Prediction, or CASP.

The idea was simple. As CASP’s organizers, Moult and Fidelis would publish a list of amino acid sequences for proteins whose structures had been recently solved and supplied to them by experimentalists, but for which the results hadn’t yet been published. Then computational groups around the world would try their best to predict the protein’s structure using whatever method they wanted. An independent group of scientists would assess the models by comparing their answers to the experimentally confirmed structures.

The idea took off. CASP soon became a proving ground for computational approaches to the protein folding problem. These were the days before artificial intelligence, when computational approaches involved simulating molecular physics. It was a chance for scientists to put their thinking to the test in a public trial against their peers. “It wasn’t supposed to be a competition,” Thornton said. “But it actually has turned out to be a competition.”

Every two years, scientists gathered at the Asilomar conference center, an old chapel near Monterey, California, that used to be a Methodist retreat. During these conferences, the organizers announced the competition’s results and the computationalists gave talks about their methods and approaches. Moult encouraged attendees to stamp their feet on the wooden floors if they didn’t like what they were hearing.

“There was, at the beginning, quite a lot of stamping,” he said.

It was “almost like a drum,” recalled David Jones , a professor of bioinformatics at University College London who studied under Thornton. The biologists stamped if the talks got bogged down in details. They stamped if claims were overblown. They stamped if speakers were repetitive or too much in the weeds. But it was friendly stamping, Jones said: “It wasn’t nasty.”

The early CASP conferences were held at the Asilomar conference center in Monterey, California. When attendees stamped on the wooden floors, it sounded like a drum.

Aramark Destinations

Whatever the reason, when the echoes of stamping started ringing in a speaker’s ears, it was embarrassing. “Thank God I never got stamped on,” Jones said. One year, he and his colleagues presented a computational method called threading, in which amino acid sequences were woven through known protein structures to search for a fit. They didn’t do too badly. “We were quite pleased. … It was all downhill after that,” Jones recalled, laughing. “No, it was fun.”

There was a lot of excitement back then, said Silvio Tosatto , a professor of bioinformatics at the University of Padua. “People thought that they could become millionaires because they had the right algorithm, and some other people thought that they would immediately win the Nobel Prize.”

Neither of those things happened during the early years. When asked what the CASP submissions were like during that time, Moult paused. “‘Random’ is a good word,” he said.

Some methods performed better than expected, such as “homology modeling,” which compared the structures of known proteins to deduce the structures of unknown ones. Others were a dead loss. Most structure predictions were “tortured-looking objects,” Moult said.

“I was loving seeing them fail,” joked Anastassis Perrakis , a structural biologist at the Netherlands Cancer Institute and Utrecht University who gave experimentally determined structures to CASP organizers for use in the competition. “It’s not rivalry, but we like to tease each other in science.”

Through this process, clear leaders emerged. In 1996, after the second CASP wrapped up, a young man named David Baker asked Jones to share a taxi to the airport. Baker had seen Jones’ talk and was working on his own computational model. He didn’t have it ready for this CASP, but he wanted to chat about it. Jones listened to his ideas in the cab and never expected to see him again.

At the next competition in 1998, Baker blew the doors wide open with his algorithm Rosetta. He became “the man to beat,” Jones said.

David Baker, who is now one of the world’s leading protein design experts, was the man to beat at CASP with his high-performing algorithm named Rosetta.

BBVA Foundation

Algorithms like Rosetta modeled interactions between the atoms of amino acid molecules to predict how they would fold. They “showed that you actually could predict protein structure,” Baker said. “But it wasn’t good enough or accurate enough to be useful.”

In 2008, humans were still beating the computers. Baker , who by then was running his own lab at the University of Washington, created a free online computer game called Foldit , in which players folded strings of amino acids into protein structures. In a paper published in Nature , his team reported that human Foldit players outperformed Rosetta in modeling proteins.

But the human lead wouldn’t last long. In the early 2010s, important breakthroughs in a concept known as co-evolution propelled the field forward and would later turn out to be critical for artificial intelligence. The idea, which had been around for decades, was straightforward: By comparing closely related sequences of amino acids in hundreds to thousands of proteins, scientists could identify the amino acids that had mutated — and, importantly, determine whether they’d mutated in step with others. If two amino acids changed together, they were likely linked in some way. “You start to be able to say, ‘Well, these two things are probably close together in space,’” said Adams, the structural biologist at Berkeley Lab.

But until the early 2010s, such predictions of which amino acids were in contact were dismal. Their accuracy hovered between 20% and 24%. Then scientists noticed that their statistical methods were introducing errors, suggesting that some amino acids were in contact when they weren’t. Later, Moult learned that statisticians had been keenly aware of this kind of error for decades. When you looked back, he said, you’d think, “How could I be so stupid?”

Computational biologists cleaned up the statistical tools . By 2016, the accuracy of contact prediction had shot up to 47%. Two years later, it reached 70%. Baker’s algorithm built on this success: In 2014, Rosetta produced two protein structures so accurate that a CASP assessor thought Baker might have solved the protein folding problem.

The co-evolution insights were “fantastic,” Adams said. Without using machine learning, co-evolution was “one of the big things that came that really pushed that field forward.”

However, it got the field only so far. Co-evolution required an abundance of similar proteins to compare, and experimentalists weren’t solving protein structures fast enough to supply computationalists’ needs.

The years flowed by in a punctuated equilibrium, Moult said, using a term from evolutionary biology. Sometimes it felt as though no good ideas had evolved for a billion years — and then something exciting would happen.

Off the Deep End

In 2016, David Jones caught a glimpse of the future in a new paper in Nature . Researchers from Google DeepMind, an artificial intelligence team based in London, detailed how their algorithm, which used a method known as deep learning, had beaten a human champion at an ancient board game called Go.

Jones was amazed. “Things are happening,” he recalled thinking at the time. “I’m really going to have to get into this deep learning.”

Deep learning is a flavor of artificial intelligence loosely inspired by the human brain. In your brain, molecular information is sent across an interconnected web of brain cells called neurons. Neurons have little arms called dendrites that grab molecules dispatched by neighboring neurons which tell the receiving neuron either to fire and propagate a signal or not to fire.

“If enough activity comes at that neuron, then that neuron is going to fire,” said Michael Littman , a computer science professor at Brown University. That results in another wave of molecules being released to the next neuron.

In the 1950s, computer scientists realized that they could wire electronic bits together to create “neural networks.” Every unit in the neural network is a node, which researchers likened to a neuron: A neuron receives information from other neurons, then calculates whether to fire toward the next ones. In neural networks, information propagates across multiple layers of neurons to produce a particular outcome, like recognizing a dog in an image.

The more layers of neurons you have, the more intricate calculations you can perform. But early neural networks were made up of only two layers. In the 1990s, that number increased to three, and it stayed there for two decades. “We could not figure out how to reliably create networks that were deeper than that,” Littman said.

Structural biologists, including Jones and Moult, had tried using neural networks in protein science since the 1990s, but the limitations of shallow networks and sparse data held them back. Then, in the early 2010s, computer scientists learned how to better structure neural networks to allow reliable training of more layers. Networks deepened to 20, 50, 100 and then thousands of layers. “To distinguish that from the way we were doing it in the ’90s, people started to call it ‘deep learning,’” Littman said. “Because if machine learning people are good at one thing, it’s making up sexy names.”

Deep learning transformed artificial intelligence, leading to algorithms that excelled at recognizing features in photos or voices — and, it turns out, at beating humans in games.

In March 2016, when DeepMind co-founder Demis Hassabis was in Seoul watching his AI system AlphaGo beat a human world champion in the ancient game of Go, he flashed back to playing Foldit as an undergraduate. He wondered: If DeepMind researchers could write an algorithm to mimic the intuition of Go masters , couldn’t they write one to mimic the intuition of Foldit gamers, who knew nothing about biology but could fold proteins?

In 2016, AlphaGo, an AI system by Google DeepMind, defeated Lee Sedol (right), the world champion in the ancient game of Go. Its ability to mimic human intuition drew biologists’ attention to the potential power of deep learning in protein science.

Google DeepMind

Jinbo Xu , a professor at the Toyota Technological Institute at Chicago, also recognized the potential of using deep learning to attack the protein folding problem. He was inspired by what these networks were doing in image recognition. By then, computer scientists had had great success with convolutional networks, which program deep learning algorithms to break images into pieces and identify patterns between them. Xu brought this technique to protein folding. He used a mathematical object called a matrix to represent which amino acids were close together in space, then fed the data into a convolutional network as an image. The algorithm looked for patterns among these images to predict the 3D coordinates of the atoms that make up a protein.

In 2016, Xu posted a preprint of this work on arxiv.org (it was later published in PLOS Computational Biology ) that “was quite influential” for the field, Moult said. It showed people “the sort of things you might do with deep learning.”

Before long, protein structure groups started dabbling in deep learning. AlQuraishi and his team were the first to develop an approach that could directly predict protein structure exclusively with neural networks, in what’s called an “end-to-end” method — it just didn’t work very well. Others wondered how they could dip their toes into a new approach that felt so momentous.

“I didn’t know exactly what I wanted to do with deep learning, but I realized I needed to be doing deep learning,” Jones said.

He had started to write grant applications to find his way in when he received an email from Google DeepMind. They asked Jones about the CASP competition and offered help. “I just assumed they meant: We’ve got lots of computer power,” Jones said.

After he met them, it became obvious that Google had larger ambitions. But to pull them off, the tech giant would need more scientific brainpower.

A New Player on the Field

In 2016, when Jones started working as a consultant for Google DeepMind on a project that would later be known as AlphaFold, John Jumper was completing his doctorate in theoretical chemistry at the University of Chicago.

As a teenager, Jumper had taught himself how to program computers. He also had a knack for physics. So when it came time to go to college, he decided to study math and physics even though his parents, both engineers, were worried he’d never be able to find a job.

“I thought I was going to be a ‘laws of the universe’ physicist all the way through,” Jumper said. “I’ve always loved this notion of discovering something true of the universe.”

As an undergraduate at Vanderbilt University, he joined a collaboration with researchers at the Fermi National Accelerator Laboratory to study the strange properties of subatomic particles called quarks . One day, when he was sitting at a lunch table with the researchers, he got some sour news. “So, this experiment that we’re working on — when’s it going to turn on?” Jumper recalled asking them. One of the professors said that he’d probably retire first. The other, a bit older, said he might not live to see it.

“I wanted to do science in a little shorter timeframe than that,” Jumper said. After finishing undergrad, he started a doctoral program in theoretical condensed matter physics — and quickly dropped out. He had gotten a job at D.E. Shaw Research, a New York company that, at the time, was creating basic simulations of proteins. By understanding how proteins move and change, they hoped to better understand the mechanisms of various ailments like lung cancer.

It was the first time Jumper grasped the potential significance of his work. “It’s about health and extending people’s lives,” he said. For the next three years, Jumper modeled protein movements on the company’s supercomputers, which they had built specifically to simulate molecules faster. “I was doing more simulation on a Tuesday of some weeks than I was going to do in my entire Ph.D.,” he said.

In 2011, he gave graduate school another shot, this time studying theoretical chemistry at the University of Chicago. He was still interested in protein structure and movement. But he was frustrated by the slow pace of academia. “I no longer had this access to this custom computer hardware” he had used at D.E. Shaw, Jumper said. He wondered if he could use artificial intelligence — “at the time we called it statistical physics” — to reach a level of quick simulation that otherwise required advanced machines. He began dabbling in machine learning and neural networks.

During this time, he also started thinking about the protein folding problem. He suspected that the problem should be solvable with the training data available in the Protein Data Bank — by 2012, it contained more than 76,000 protein structures.

“I believed that the data were sufficient,” Jumper said. But “the ideas weren’t.”

In 2017, Jumper heard a rumor that Google DeepMind was getting into protein structure prediction. He had just completed his doctorate, using machine learning to simulate protein folding and dynamics. He applied for a job as a research scientist.

“The project was still secret,” he said. If he raised the subject of protein folding in an interview, the DeepMind team changed the subject. “You can only do that so many times before I’m pretty sure what you’re doing,” Jumper said.

In October 2017, he arrived at DeepMind’s London office. With Jones’ help as a consultant, the team was already deep into the development of AlphaFold. “It was a great fun time where we were just throwing ideas at the wall,” Jones said. “Eventually, a good core idea emerged, and they ran with it.”

To train their algorithm, the DeepMind team used more than 140,000 structures from the Protein Data Bank. They fed this information into a convolutional network, but didn’t change much about the AI architecture itself. It was “standard machine learning,” Jumper said.

By the spring of 2018, AlphaFold was ready to join CASP and compete against bona fide protein scientists. “It’s a bit like Formula One racing,” Jones reflected. “You think you’ve built the best car, but you just don’t know what the other teams have built.” The stakes felt high. The DeepMind team debated whether they should compete anonymously; they didn’t want to risk humiliation.

“Nobody wants to fail,” Jones said. In academia, it’s part of the job; you fail, and you move on because you don’t have a choice. “But obviously if you’re a multibillion-dollar tech company, it’s not a good look if you tried to do something and failed.”

They ultimately decided to submit their results under the Google DeepMind name. A few months before the December meeting, Jones heard from CASP’s organizers. They suggested that the DeepMind team come along to the meeting because AlphaFold had performed really well.

Their victory wasn’t massive — they were about 2.5 times better at predicting protein structures compared to the next-best team — but their win made an impression. “It was clear something interesting had happened,” Moult said.

Rebooting the Algorithm

The win should have energized the DeepMind team. But they knew they weren’t close to solving the protein folding problem. Hassabis had gathered them a few months earlier. “Are we going to go after solving this or not?” Jumper recalled him saying. “If not, let’s find problems that we can have this really, really big impact on.”

“We had this moment where we really decided: We are going to go after solving it,” Jumper said. They went back to the drawing board.

John Jumper suspected that biologists had studied enough protein structures to solve the protein folding problem. “I believed that the data were sufficient,” said Jumper, who started working at Google DeepMind in 2017. But “the ideas weren’t.”

With his diverse background in physics, chemistry, biology and computation, Jumper brought original insights to brainstorming sessions. Soon, he was leading the team, which had grown from six to 15 people. “There was something very unique going on,” said Raphael Townshend, who interned at Google DeepMind in 2019 and later founded Atomic AI, an AI-driven biotech company.

In academia, experts are often siloed from one another, each pursuing independent projects with little collaboration. At DeepMind, experts in statistics, structural biology, computational chemistry, software engineering and more worked together on the protein folding problem. They also had the massive financial and computational resources of Google behind them. “Things that would have taken me months to do as a Ph.D. student, I was doing in a single day,” Townshend said.

The London DeepMind office was high-energy, he said, and much of that energy was generated by Jumper. “He’s a true genius, I would say, and also a very humble person,” said the computer scientist Ellen Zhong , who interned at DeepMind in 2021 and is now an assistant professor at Princeton University. “He was beloved by the team.”

Under Jumper’s leadership, AlphaFold was reconstructed. DeepMind designed a new type of transformer architecture — a type of deep learning that has “powered basically every single machine learning breakthrough that’s happened in the last five years,” Townshend said. The neural network tweaked the strength of its connections to create more accurate representations of the data, in this case protein evolutionary and structure data. It ran that data through a second transformer to predict the 3D structure of a protein. The algorithm then honed the structure further by running it, together with some of the revised data, back through its transformers a few more times.

When they first started working on AlphaFold2, their algorithm was “terrible, but not as terrible as we expected,” Jumper said. “[It] made helices that kind of vaguely looked like a protein.” But as they honed it further, they noticed enormous increases in the efficiency and accuracy of their predictions.

“It was actually terrifying,” Jumper said. If it’s working too well, that usually means “you’re doing the wrong thing.” They checked, but there wasn’t a problem. It was simply working.

The team decided to run an internal experiment to see whether their system would be helpful to biologists. They identified roughly 50 papers published in high-end journals like Science , Nature and Cell that not only described a new protein structure but also generated insights about the protein’s function from the structure. They wanted to see if AlphaFold2 would stand up to the experimentalists’ laborious approach.

They entered the amino acid sequences. AlphaFold2 ran its prediction engine. For each sequence, it spat out a prediction close to the experimental structure presented in the papers. However, in the team’s view, it wasn’t accurate enough. The structures were missing key details that the experimentalists learned about their proteins. “You feel like you’ve finished the race and it’s like finding out that you’ve got the second half,” Jumper said.

The team further honed the system over the next six months, minor improvement by minor improvement. A few weeks before the protein candidates were released for the 2020 CASP competition, they performed another usefulness test. Jumper was satisfied. Google DeepMind submitted their predictions to CASP in the spring of 2020. And then they waited.

The Earthquake

In early summer, Moult received an email from a CASP assessor: “Look at this, it’s pretty impressive.” Attached to the email was a protein structure solved by Google DeepMind. Moult was indeed impressed, but he thought it was a one-off.

Then he got another email, and another. “That’s strange,” he recalled thinking. There were three, four, a whole slew of near-perfect protein predictions — and all from DeepMind. By the end of the summer, “we rapidly realized … something very, very extraordinary had happened,” Moult said.

CASP assessors score each submission by comparing the predicted protein structure to its proven experimental structure. A perfect score, in which the model and reality match atom by atom, is 100. Moult had always believed that anything above 90 would indicate that an algorithm had effectively solved a protein’s structure. Most of AlphaFold’s structures hit or surpassed the 90 mark.

A few months before the meeting, Moult called Jumper with the news. “I cursed out loud,” Jumper recalled. “My wife asked if I was OK.”

In December 2020, less than a year into the Covid-19 pandemic, Jumper presented AlphaFold2 over Zoom at the virtual CASP meeting.

Like the rest of the attendees, Jones watched from home. “I was just stuck … watching this unfold,” he said. “There’s no outlet because your colleagues aren’t nearby. … We’re all under lockdown so we can’t go anywhere.”

For anyone who wasn’t a neural network expert, the ideas were complex. Even so, the conclusions were clear. DeepMind had solved the structure prediction part of the protein folding problem. AlphaFold2 could accurately predict the structure of a protein from its amino acid sequence.

“Ugh, my favorite subject is dead,” Jones recalled thinking. “DeepMind shot it, and it’s the end.”

For years, Anastassis Perrakis had contributed unpublished experimental results to CASP for the competition. When he saw AlphaFold2’s results for a protein his team had sweated over, he thought, “Uh-oh.” AlphaFold2 had gotten it perfectly right.

Alone at home in lockdown, the scientists were united in thinking that the world of protein science had changed forever. As its inhabitants looked out across the new landscape, they had one question in mind: What now?

Shock and Awe

Structural biology suddenly became unstructured.

At first there was “a lot of soul searching,” said Silvio Tosatto, who had competed in CASP since its earliest days. Some structural biologists feared their jobs would become obsolete. Others grew defensive and claimed that AlphaFold2 wasn’t accurate.

The computational biologists who had been trying to solve this problem, some for decades, found the moment bittersweet. In a blog post he wrote after CASP, AlQuraishi cited an attendee who described feeling like someone whose child had left home for the first time.

Mohammed AlQuraishi, a systems biologist at Columbia University’s Program for Mathematical Genomics, hopes that deep learning will be able to simulate an entire cell and all the structures and dynamics within it by 2040.

Nicole Pereira

But even amid their trepidation around this shiny new tool, many scientists were ecstatic. Those who didn’t do structural work used to have to collaborate with structural biologists to determine protein structures for their broader experiments. Now, they could just press a few buttons and get the structure on their own.

In the media, AlphaFold2 became the shiny new artificial intelligence breakthrough that would “ change everything .” But it took months and years for scientists to tease apart what AlphaFold2 could and couldn’t do. Around six months after Jumper’s talk, Google DeepMind published their results and shared AlphaFold2’s underlying code. “When AlphaFold2 was out, the next day we were trying to install it in our GPU servers,” Perrakis said. Biologists began to play.

“I expected [AlphaFold2] to fall down,” Thornton said. “But actually my impression is that it’s been remarkably successful.”

It started to become clear that rather than being a threat, AlphaFold2 might be a catalyst for accelerating research. Rather than put structural biologists out of a job, it gave them a new tool to do their jobs better. “If you view a structural biologist simply as a technical specialist who works out the structure of proteins, then yes, of course structural biologists are out of the job,” Walden said. But that would be like saying that the Human Genome Project made genomicists obsolete because they could no longer publish a paper describing the sequence of a single gene.

In many cases, a structural biologist’s goal is to discover the function of a protein. With AlphaFold2, they could create a hypothesis within minutes rather than wait for months or years to work out a structure through experiments.

“This changes structural biology in many good ways, and not bad ways,” Adams said. “This only makes this a more exciting field to work in.”

However, it didn’t immediately result in all kinds of new drugs as some people had predicted — and researchers soon learned that the tool has its limitations. AlphaFold2 predictions aren’t perfect. They require experimental validation, Perrakis said. But “you can move much quicker to the actual study of the structures.” Now when his students start a new project, they first use AlphaFold2 to predict the structure of a particular protein. Then they conduct experiments to validate it.

Perrakis suspects that he and other researchers will continue to use X-ray crystallography to a degree. But to develop initial protein structures, many are starting to combine deep learning predictions with advanced electron microscope techniques such as cryo-EM, which involves flash-freezing biological samples and bombarding them with electrons. Then they can get to the interesting questions about what their proteins do. AlphaFold2 has “turbo-boosted” cryo-EM, AlQuraishi said.

That shift has already begun. In June 2022, a special issue of Science revealed the near-atomic structure of a human nuclear pore complex. This massive, complicated structure — built of 30 different proteins — had been a biological quandary for decades. The scientists used AlphaFold2 predictions to fill in gaps in the proteins’ structures left unsolved by cryo-EM.

Seeing that paper, in which other scientists used AlphaFold2 to make a biological breakthrough, was the “moment that I knew that [AlphaFold] really, really mattered,” Jumper said.

Discoveries like the nuclear pore complex dot the timeline of the last three years of protein science. Already, AlphaFold2 has predicted protein structures that have been used to study diseases and create new tools for drug delivery. “It’s been hugely helpful for us,” said Feng Zhang , a molecular biologist at the Broad Institute who used AlphaFold2 to engineer a molecular syringe to deliver drugs into human cells. Knowing a protein’s structure can also help develop drugs if researchers can identify molecules to latch onto a protein’s shape, for example, and change its behavior. While some studies have suggested that AlphaFold2 predictions aren’t as useful as experimental structures in this realm, others have shown that they work just as well . The full impact of AI tools on drug discovery is still unfolding.

Some biologists, however, are already looking beyond AlphaFold2’s use in discerning the structures and functions of known proteins and toward designing ones that don’t exist in nature — a technique pivotal for designing novel medicines.

The Next Frontier

Almost immediately after seeing Jumper’s talk at the 2020 CASP conference, Baker got back to work on his Rosetta algorithm. Google hadn’t yet shared AlphaFold2’s underlying source code. Still, “we started playing with some of the ideas that they introduced,” Baker said. On the same day that Google DeepMind published AlphaFold2 in Nature , he and his team announced RoseTTAFold , a highly accurate rival to AlphaFold. RoseTTAFold also uses deep learning to predict protein structures but has a very different underlying architecture than AlphaFold2.

“Once the scientific idea is out there, it’s possible for people, at least the ones who have enough resources, to reverse engineer it and try to build on top of that,” Tosatto said.

RoseTTAFold wasn’t alone. Other AlphaFold competitors, including Meta, crafted their own algorithms to address protein structure prediction or related problems. Some, including Townshend’s biotech startup Atomic AI, have expanded beyond proteins to use deep learning to understand RNA structures. However, in the realm of single-structure predictions, no one has been able to match AlphaFold’s accuracy so far, Thornton said. “I’m sure they will, but I think getting another … AlphaFold moment like that will be very difficult.”

Last year, David Baker (pictured here), with John Jumper and Demis Hassabis, received the Frontiers of Knowledge Award in Biology and Biomedicine for their work revolutionizing the study and design of proteins with artificial intelligence.

At least in public, Baker and Jumper have continued the tradition of productive competition established by CASP. “They might feel that I compete with them, but I feel like they’ve just been inspirational for us,” Baker said.

Jumper welcomes it. “It’s really important that people build on this science,” he said. “It would be sad to me if there was no intellectual lineage of AlphaFold.”

Baker is already evolving his program’s lineage to focus on a new frontier in protein science: protein design. Right now, biologists are confined to studying the proteins already invented by nature. Baker envisions a science in which they could design novel proteins — ones crafted specifically to harness sunlight, break down plastic, or form the basis of drugs or vaccines.

“The number of different types of protein structures or shapes in nature at the moment is quite limited,” said Danny Sahtoe , a structural biologist at the Hubrecht Institute in the Netherlands who did his postdoc under Baker. “In theory, more should be possible, and if you can have more shapes, that also means that you can have more functions.”

Protein design is essentially the “inverse protein folding problem,” said Baker, who directs the Institute for Protein Design at the University of Washington. Rather than feed an amino acid sequence to a deep learning algorithm and ask it to spit out a protein structure, a protein designer feeds a structure into an algorithm and asks it to spit out a sequence. Then, using that amino acid sequence, the designer builds the protein in the lab.

AlphaFold and RoseTTAFold by themselves can’t spit out these sequences; they are programmed to do the opposite. But Baker created a design-specific iteration of RoseTTAFold, known as RoseTTAFold diffusion or RF diffusion , based on its neural architecture.

The field of protein design has existed for a long time, but deep learning has accelerated it, Sahtoe said. It makes the process of designing realistic computer models of proteins “incredibly fast.” It used to take weeks or months for trained protein designers to create the backbone of a new protein. Now they can make one in days, sometimes even overnight.

Foldit , an online game developed by Baker’s lab, has players predict protein structures.

Baker also updated Foldit to incorporate his obsession: Instead of building protein structures, players design proteins. It’s been productive. Baker’s lab has written papers on several of the player-designed proteins. One of the world’s top Foldit players is now a graduate student working with one of Baker’s colleagues at the University of Washington.

“Do we understand protein folding? Well, if we can design new sequences that fold up to new structures, then that shows we understand quite a lot about protein folding,” Baker said. “That, in a sense, you could view as a solution to the protein folding problem too.”

Trust Exercise

AlphaFold2’s success has undeniably shifted biologists’ attitudes toward artificial intelligence. For a long time, many experimental biologists distrusted computation. They understood that some machine learning approaches can make data appear better than it is. Then Google DeepMind demonstrated “unequivocally that you could do serious work with this,” AlQuraishi said. Any skepticism is now matched with: “Well, what about AlphaFold?”

“Biologists now believe our prediction results,” said Xu, the computational biologist who advanced convolutional networks. “Before, biologists always suspected if our prediction is reliable.”

Playing into this trust is a feature of the AlphaFold2 platform: It not only generates a 3D model of a protein, but also self-assesses the accuracy of its prediction by grading different parts of the structure on a confidence scale from zero to 100.

In July 2022, after Google DeepMind released the structure predictions of 218 million proteins — nearly all those known in the world — Adams decided to analyze AlphaFold2’s self-reports. He compared the predictions to the proteins’ solved structures and independently assessed their accuracy.

The “good news is that when AlphaFold thinks that it’s right, it often is very right,” Adams said. “When it thinks it’s not right, it generally isn’t.” However, in about 10% of the instances in which AlphaFold2 was “very confident” about its prediction (a score of at least 90 out of 100 on the confidence scale), it shouldn’t have been, he reported: The predictions didn’t match what was seen experimentally.

That the AI system seems to have some self-skepticism may inspire an overreliance on its conclusions. Most biologists see AlphaFold2 for what it is: a prediction tool. But others are taking it too far. Some cell biologists and biochemists who used to work with structural biologists have replaced them with AlphaFold2 — and take its predictions as truth. Sometimes scientists publish papers featuring protein structures that, to any structural biologist, are obviously incorrect, Perrakis said. “And they say: ‘Well, that’s the AlphaFold structure.’”

“Some people are overconfident — like, way overconfident — in what these deep learning models can do,” said Lauren Porter , an investigator at the National Institutes of Health. “We should use these deep learning models for as much as we can, but we also need to approach them with caution and humility.”

Jones has heard of scientists struggling to get funding to determine structures computationally. “The general perception is that DeepMind did it, you know, and why are you still doing it?” Jones said. But that work is still necessary, he argues, because AlphaFold2 is fallible.

“There are very large gaps,” Jones said. “There are things that it can’t do quite clearly.”

While AlphaFold2 is excellent at predicting the structures of small, simple proteins, it’s less accurate at predicting those containing multiple parts. It also can’t account for the protein’s environment or bonds with other molecules, which alter a protein’s shape in the wild. Sometimes a protein needs to be surrounded by certain ions, salts or metals to fold properly.

“At the moment, AlphaFold is a little bit of a ways away from being able to determine context,” Walden said. Her group has determined several structures experimentally that AlphaFold2 couldn’t predict.

There are also several types of dynamic proteins that AlphaFold2 predicts poorly but that are no less important in function. Shape-shifting proteins, also known as fold-switching proteins, are not static: Their shapes change as they interact with other molecules. Some fold into dramatically different shapes, despite having the same amino acid sequence. Fold-switching proteins “challenge the paradigm that sequences encode one structure,” Porter said, “because clearly they don’t.”

Fold-switching proteins like RfaH, shown here, can change conformations to perform different tasks. When in its alpha helix form, the RfAH protein can’t bind to its target — but when it switches its form to a beta sheet, it can.

Courtesy of Lauren Porter

Compared to the hundreds of thousands of static, single-structure proteins that the DeepMind algorithm trained on, there are only about 100 examples of fold-switching proteins — although more surely exist. It’s perhaps no surprise, Porter said, that “generally speaking, these algorithms were made to predict a single fold.”

And then there are the proteins that flail about like an air dancer outside a car dealership. Intrinsically disordered proteins or protein regions lack a stable structure . They wiggle and re-form constantly. “They’ve been in many ways ignored simply because they were a little bit annoying,” said Kresten Lindorff-Larsen , a professor of computational protein biophysics at the University of Copenhagen. Around 44% of human proteins have a disordered region made up of at least 30 amino acids. “It’s a relatively large fraction of them,” Lindorff-Larsen said.

AlphaFold2 can predict when a region is likely to be intrinsically disordered — but it can’t tell you what that disorder looks like.

For his part, Jumper’s biggest frustration is that AlphaFold2 doesn’t register the difference between two proteins that vary by a single amino acid, known as a point mutation. Point mutations can “have quite dramatic effects, sometimes on structure and often on function of proteins,” he said. “AlphaFold is relatively blind” to them, in that it will produce the same structure for both sequences.

In September 2023 DeepMind released AlphaMissense , a deep learning algorithm that predicts the effects of such mutations. It can’t show the change to the structure, but it informs the user if the mutation might turn the protein pathogenic or introduce dysfunction based on similar mutations in known pathogenic proteins.

However, even if AlphaFold2 could predict all proteins perfectly, it would be far from modeling biological reality. That’s because in a cell, proteins never act alone.

Cellular Complexity

The insides of cells are complicated and chaotic. A cell’s external membrane envelops a biochemical environment densely crowded with molecular parts — proteins, signaling molecules, messenger RNA, organelles and more. Proteins bind to each other and to other molecules, which alters their forms and functions.

Proteins don’t work alone: They interact constantly with other molecules. This rendering of a cellular landscape is made of models of real proteins and other molecules in “an attempt to visualize the great complexity and beauty of the cell’s molecular choreography,” wrote its creator, Gael McGill .

Evan Ingersoll and Gaël McGill, PhD/Digizyme Inc

AlphaFold2’s ability to predict the structure of a single protein doesn’t get biologists close to understanding proteins in this intricate native environment. But that’s the direction the field is now heading. Protein science’s artificial intelligence giants, Google DeepMind and David Baker’s Institute for Protein Design, are now evolving their deep learning algorithms to predict the structures that proteins assume while interacting with other molecules.

In spring 2024, they both published papers describing similar developments in this area. Updates to their algorithms — launched with the new names AlphaFold3 and RoseTTAFold All-Atom — enable them to predict the structures of proteins bound to each other, DNA, RNA and other small molecules.

Biologists are just starting to test out these updates. So far, AlphaFold3 is much more accurate than RoseTTAFold All-Atom, AlQuraishi said — but it’s not as big of a leap as an “AlphaFold2 moment.” For some macromolecules, such as RNA structures, its accuracy remains below that of other physics-based systems and experiments.

AlphaFold3 predicts the structures of molecular complexes, such as this enzyme found in a plant-damaging fungus. In this model structure, the protein (blue) is linked to simple sugars (yellow) and an ion (yellow sphere).

Even so, the new algorithms are a step in the right direction. The interactions between proteins and other molecules are critical to their functioning in cells. To develop drugs that can dock onto proteins and alter their activity as desired, researchers need to understand what those complexes look like. It’s unlikely, though, that either algorithm will lead to new medicines anytime soon, Adams said. “Both methods are still limited in their accuracy, [but] both are dramatic improvements on what was possible.”

There is one other major change in DeepMind’s new product. AlphaFold2’s underlying code was open-source so that other researchers could study the algorithm and remake it for their own projects. However, rather than share AlphaFold3’s source code, Google has so far opted to protect it as a trade secret. “For the time being, at least, no one can run and use it like they did with [AlphaFold2],” AlQuraishi said.

Even before the release of AlphaFold3, researchers had been testing AlphaFold2 to see if it could provide useful information on proteins in different conformations. Brenda Rubenstein , an associate professor of chemistry and physics at Brown University, was interested in kinases, a type of protein that activates other proteins. Specifically, she wanted to understand the mechanism of a kinase that causes cancer so that she could develop more precise drugs against it. Her lab modeled the kinase’s structure using a physics-based approach, which maps the 3D coordinates of atoms using Newton’s laws. It took two and a half years.

“About a year ago, we said: Can we do this faster?” Rubenstein said. They tried using AlphaFold2 in a novel way. By feeding data about related proteins to the algorithm, she found that it could predict her kinase in different conformations with more than 80% accuracy.

Rubenstein’s is one of several labs finding that “if you poke AlphaFold in the right way, you get it to kind of spit out alternate conformations,” AlQuraishi said. “That’s been encouraging.”

AlQuraishi hopes that by 2040, deep learning will be able to simulate an entire cell and all the structures and dynamics within it. Getting there, however, will require leaps on both the experimental and computational sides.

An Outsider’s Take

For many biologists, AlphaFold2 was the breakthrough they had been waiting for. The goal of CASP had been to create computing tools that predict protein structure from sequence. Still, many can’t help but ask: Why was a relative newcomer able to crack the protein code when so many experts had struggled for decades?

The insights that Google DeepMind’s team of computer and protein scientists brought to the problem are undeniable. At the same time, the ground of protein science was fertile and ready to yield a deep learning revolution, AlQuraishi said. “These things don’t appear out of nowhere.”

By the time CASP 2020 came around, many researchers expected a breakthrough in structure prediction to come through artificial intelligence. “It was all heading in that direction,” Townshend said. But they didn’t expect it to come from a multibillion-dollar technology company, and they didn’t expect it so soon. Some said AlphaFold2 wasn’t a feat of new science but rather clever engineering. Some were surprised that David Baker’s algorithms didn’t take the trophy. Others were less surprised because of Google DeepMind’s unmatched resources.

Around 100 labs participate in CASP every year, and though they had begun to adopt AI technologies, they “probably didn’t have the expertise that DeepMind had, nor the computing power,” Thornton said. DeepMind “had access to basically unlimited computing power.”

She also speculated that Google’s lack of expertise in protein science may have freed them creatively. “They were single-minded,” Thornton said, and focused on building a great neural network. Protein biologists had baggage. As they worked on their AI tools, they wanted to capture the atomic-level molecular physics and chemistry involved in protein folding. DeepMind had a different approach: We will transform sequence data into a 3D structure, and it doesn’t matter how we get there.

“Rather than trying to solve the protein folding problem, which I think a lot of previous predictions tried to do, they actually just went with the brute force” of mapping out the atoms’ final positions in space, Walden said. “Rather interestingly, they have therefore probably solved the problem.”

The Princeton University computer scientist Ellen Zhong, who was an intern on Google DeepMind’s AlphaFold2 team in 2021, pioneered the use of deep learning in combination with cryo-EM to study protein dynamics.

Tori Repp/Fotobuddy for Princeton University

To some biologists, that approach leaves the protein folding problem incomplete. From the earliest days of structural biology, researchers hoped to learn the rules of how an amino acid string folds into a protein. With AlphaFold2, most biologists agree that the structure prediction problem is solved. However, the protein folding problem is not. “Right now, you just have this black box that can somehow tell you the folded states, but not actually how you get there,” Zhong said.

“It’s not solved the way a scientist would solve it,” said Littman, the Brown University computer scientist.

This might sound like “semantic quibbling,” said George Rose, the biophysics professor emeritus at Johns Hopkins. “But of course it isn’t.” AlphaFold2 can recognize patterns in how a given amino acid sequence might fold up based on its analysis of hundreds of thousands of protein structures. But it can’t tell scientists anything about the protein folding process.

“For many people, you don’t need to know. They don’t care,” Rose said. “But science, at least for the past 500 years or so … has been involved with trying to understand the process by which things occur.” To understand the dynamics, mechanisms, functions and nature of protein-based life, Rose argued, you need the full story — one that deep learning algorithms can’t tell us.

To Moult, it doesn’t matter that the machine does something he doesn’t understand. “We’re all used to machines doing things we can’t. You know, I can’t run as fast as my car,” he said. To molecular biologists who are trying to study a protein and just need to know roughly what it looks like, how they get there doesn’t really matter.

But “until we know really how it works, we’re never going to have a 100% reliable predictor,” Porter said. “We have to understand the fundamental physics to be able to make the most informed predictions we can.”

“We keep moving the goalpost,” AlQuraishi said. “I do think that core problem has been solved, so now it’s very much about what comes next.”

Even as biologists continue to debate these topics, others are looking forward to a field that’s undeniably changed — and backward toward its recent past.

Sometimes Perrakis is hit by a wave of nostalgia for the old ways of doing things. In 2022, his team described an enzyme involved in modifying microtubules (giant, rod-shaped molecules that provide structures to cells) that they had determined using X-ray crystallography. “I realized that I’m never going to do that [again],” he said. “Having the first structure appearing after months of work was a very particular satisfaction.”

AlphaFold2 hasn’t made those experiments obsolete. On the contrary, it’s illuminated just how necessary they are. It has stitched together two historically disparate disciplines, launching a new and stimulating conversation.

The New World

Seventy years ago, proteins were thought to be a gelatinous substance, Porter said. “Now look at what we can see”: structure after structure of a vast world of proteins, whether they exist in nature or were designed.

The field of protein biology is “more exciting right now than it was before AlphaFold,” Perrakis said. The excitement comes from the promise of reviving structure-based drug discovery, the acceleration in creating hypotheses and the hope of understanding complex interactions happening within cells.

“It [feels] like the genomics revolution,” AlQuraishi said. There is so much data, and biologists, whether in their wet labs or in front of their computers, are just starting to figure out what to do with it all.

But like other artificial intelligence breakthroughs sparking across the world, this one might have a ceiling.

AlphaFold2’s success was founded on the availability of training data — hundreds of thousands of protein structures meticulously determined by the hands of patient experimentalists. While AlphaFold3 and related algorithms have shown some success in determining the structures of molecular compounds, their accuracy lags behind that of their single-protein predecessors. That’s in part because there is significantly less training data available.

The protein folding problem was “almost a perfect example for an AI solution,” Thornton said, because the algorithm could train on hundreds of thousands of protein structures collected in a uniform way. However, the Protein Data Bank may be an unusual example of organized data sharing in biology. Without high-quality data to train algorithms, they won’t make accurate predictions.

“We got lucky,” Jumper said. “We met the problem at the time it was ready to be solved.”

No one knows if deep learning’s success at addressing the protein folding problem will carry over to other fields of science, or even other areas of biology. But some, like AlQuraishi, are optimistic. “Protein folding is really just the tip of the iceberg,” he said. Chemists, for example, need to perform computationally expensive calculations. With deep learning, these calculations are already being computed up to a million times faster than before, AlQuraishi said.

Artificial intelligence can clearly advance specific kinds of scientific questions. But it may get scientists only so far in advancing knowledge. “Historically, science has been about understanding nature,” AlQuraishi said — the processes that underlie life and the universe. If science moves forward with deep learning tools that reveal solutions and no process, is it really science?

“If you can cure cancer, do you care about how it really works?” AlQuraishi said. “It is a question that we’re going to wrestle with for years to come.”

If many researchers decide to give up on understanding nature’s processes, then artificial intelligence will not just have changed science — it will have changed the scientists too.

Meanwhile, the CASP organizers are wrestling with a different question: how to continue their competition and conference. AlphaFold2 is a product of CASP, and it solved the main problem the conference was organized to address. “It was a big shock for us in terms of: Just what is CASP anymore?” Moult said.

In 2022, the CASP meeting was held in Antalya, Turkey. Google DeepMind didn’t enter, but the team’s presence was felt. “It was more or less just people using AlphaFold,” Jones said. In that sense, he said, Google won anyway.

Some researchers are now less keen on attending. “Once I saw that result, I switched my research,” Xu said. Others continue to hone their algorithms. Jones still dabbles in structure prediction, but it’s more of a hobby for him now. Others, like AlQuraishi and Baker, continue on by developing new algorithms for structure prediction and design, undaunted by the prospect of competing against a multibillion-dollar company.

Moult and the conference organizers are trying to evolve. The next round of CASP opened for entries in May. He is hoping that deep learning will conquer more areas of structural biology, like RNA or biomolecular complexes. “This method worked on this one problem,” Moult said. “There are lots of other related problems in structural biology.”

The next meeting will be held in December 2024 by the aqua waters of the Caribbean Sea. The winds are cordial, as the conversation will probably be. The stamping has long since died down — at least out loud. What this year’s competition will look like is anyone’s guess. But if the past few CASPs are any indication, Moult knows to expect only one thing: “surprises.”

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