problems that can be solved by research

45 Research Problem Examples & Inspiration

A research problem is an issue of concern that is the catalyst for your research. It demonstrates why the research problem needs to take place in the first place.

Generally, you will write your research problem as a clear, concise, and focused statement that identifies an issue or gap in current knowledge that requires investigation.

The problem will likely also guide the direction and purpose of a study. Depending on the problem, you will identify a suitable methodology that will help address the problem and bring solutions to light.

Research Problem Examples

In the following examples, I’ll present some problems worth addressing, and some suggested theoretical frameworks and research methodologies that might fit with the study. Note, however, that these aren’t the only ways to approach the problems. Keep an open mind and consult with your dissertation supervisor!

Psychology Problems

1. Social Media and Self-Esteem: “How does prolonged exposure to social media platforms influence the self-esteem of adolescents?”

• Theoretical Framework : Social Comparison Theory
• Methodology : Longitudinal study tracking adolescents’ social media usage and self-esteem measures over time, combined with qualitative interviews.

2. Sleep and Cognitive Performance: “How does sleep quality and duration impact cognitive performance in adults?”

• Theoretical Framework : Cognitive Psychology
• Methodology : Experimental design with controlled sleep conditions, followed by cognitive tests. Participant sleep patterns can also be monitored using actigraphy.

3. Childhood Trauma and Adult Relationships: “How does unresolved childhood trauma influence attachment styles and relationship dynamics in adulthood?

• Theoretical Framework : Attachment Theory
• Methodology : Mixed methods, combining quantitative measures of attachment styles with qualitative in-depth interviews exploring past trauma and current relationship dynamics.

4. Mindfulness and Stress Reduction: “How effective is mindfulness meditation in reducing perceived stress and physiological markers of stress in working professionals?”

• Theoretical Framework : Humanist Psychology
• Methodology : Randomized controlled trial comparing a group practicing mindfulness meditation to a control group, measuring both self-reported stress and physiological markers (e.g., cortisol levels).

5. Implicit Bias and Decision Making: “To what extent do implicit biases influence decision-making processes in hiring practices?

• Theoretical Framework : Cognitive Dissonance Theory
• Methodology : Experimental design using Implicit Association Tests (IAT) to measure implicit biases, followed by simulated hiring tasks to observe decision-making behaviors.

6. Emotional Regulation and Academic Performance: “How does the ability to regulate emotions impact academic performance in college students?”

• Theoretical Framework : Cognitive Theory of Emotion
• Methodology : Quantitative surveys measuring emotional regulation strategies, combined with academic performance metrics (e.g., GPA).

7. Nature Exposure and Mental Well-being: “Does regular exposure to natural environments improve mental well-being and reduce symptoms of anxiety and depression?”

• Theoretical Framework : Biophilia Hypothesis
• Methodology : Longitudinal study comparing mental health measures of individuals with regular nature exposure to those without, possibly using ecological momentary assessment for real-time data collection.

8. Video Games and Cognitive Skills: “How do action video games influence cognitive skills such as attention, spatial reasoning, and problem-solving?”

• Theoretical Framework : Cognitive Load Theory
• Methodology : Experimental design with pre- and post-tests, comparing cognitive skills of participants before and after a period of action video game play.

9. Parenting Styles and Child Resilience: “How do different parenting styles influence the development of resilience in children facing adversities?”

• Theoretical Framework : Baumrind’s Parenting Styles Inventory
• Methodology : Mixed methods, combining quantitative measures of resilience and parenting styles with qualitative interviews exploring children’s experiences and perceptions.

10. Memory and Aging: “How does the aging process impact episodic memory , and what strategies can mitigate age-related memory decline?

• Theoretical Framework : Information Processing Theory
• Methodology : Cross-sectional study comparing episodic memory performance across different age groups, combined with interventions like memory training or mnemonic strategies to assess potential improvements.

Education Problems

11. Equity and Access : “How do socioeconomic factors influence students’ access to quality education, and what interventions can bridge the gap?

• Theoretical Framework : Critical Pedagogy
• Methodology : Mixed methods, combining quantitative data on student outcomes with qualitative interviews and focus groups with students, parents, and educators.

12. Digital Divide : How does the lack of access to technology and the internet affect remote learning outcomes, and how can this divide be addressed?

• Theoretical Framework : Social Construction of Technology Theory
• Methodology : Survey research to gather data on access to technology, followed by case studies in selected areas.

13. Teacher Efficacy : “What factors contribute to teacher self-efficacy, and how does it impact student achievement?”

• Theoretical Framework : Bandura’s Self-Efficacy Theory
• Methodology : Quantitative surveys to measure teacher self-efficacy, combined with qualitative interviews to explore factors affecting it.

14. Curriculum Relevance : “How can curricula be made more relevant to diverse student populations, incorporating cultural and local contexts?”

• Theoretical Framework : Sociocultural Theory
• Methodology : Content analysis of curricula, combined with focus groups with students and teachers.

15. Special Education : “What are the most effective instructional strategies for students with specific learning disabilities?

• Theoretical Framework : Social Learning Theory
• Methodology : Experimental design comparing different instructional strategies, with pre- and post-tests to measure student achievement.

16. Dropout Rates : “What factors contribute to high school dropout rates, and what interventions can help retain students?”

• Methodology : Longitudinal study tracking students over time, combined with interviews with dropouts.

17. Bilingual Education : “How does bilingual education impact cognitive development and academic achievement?

• Methodology : Comparative study of students in bilingual vs. monolingual programs, using standardized tests and qualitative interviews.

18. Classroom Management: “What reward strategies are most effective in managing diverse classrooms and promoting a positive learning environment?

• Theoretical Framework : Behaviorism (e.g., Skinner’s Operant Conditioning)
• Methodology : Observational research in classrooms , combined with teacher interviews.

19. Standardized Testing : “How do standardized tests affect student motivation, learning, and curriculum design?”

• Theoretical Framework : Critical Theory
• Methodology : Quantitative analysis of test scores and student outcomes, combined with qualitative interviews with educators and students.

20. STEM Education : “What methods can be employed to increase interest and proficiency in STEM (Science, Technology, Engineering, and Mathematics) fields among underrepresented student groups?”

• Theoretical Framework : Constructivist Learning Theory
• Methodology : Experimental design comparing different instructional methods, with pre- and post-tests.

21. Social-Emotional Learning : “How can social-emotional learning be effectively integrated into the curriculum, and what are its impacts on student well-being and academic outcomes?”

• Theoretical Framework : Goleman’s Emotional Intelligence Theory
• Methodology : Mixed methods, combining quantitative measures of student well-being with qualitative interviews.

22. Parental Involvement : “How does parental involvement influence student achievement, and what strategies can schools use to increase it?”

• Theoretical Framework : Reggio Emilia’s Model (Community Engagement Focus)
• Methodology : Survey research with parents and teachers, combined with case studies in selected schools.

23. Early Childhood Education : “What are the long-term impacts of quality early childhood education on academic and life outcomes?”

• Theoretical Framework : Erikson’s Stages of Psychosocial Development
• Methodology : Longitudinal study comparing students with and without early childhood education, combined with observational research.

24. Teacher Training and Professional Development : “How can teacher training programs be improved to address the evolving needs of the 21st-century classroom?”

• Theoretical Framework : Adult Learning Theory (Andragogy)
• Methodology : Pre- and post-assessments of teacher competencies, combined with focus groups.

25. Educational Technology : “How can technology be effectively integrated into the classroom to enhance learning, and what are the potential drawbacks or challenges?”

• Theoretical Framework : Technological Pedagogical Content Knowledge (TPACK)
• Methodology : Experimental design comparing classrooms with and without specific technologies, combined with teacher and student interviews.

Sociology Problems

26. Urbanization and Social Ties: “How does rapid urbanization impact the strength and nature of social ties in communities?”

• Theoretical Framework : Structural Functionalism
• Methodology : Mixed methods, combining quantitative surveys on social ties with qualitative interviews in urbanizing areas.

27. Gender Roles in Modern Families: “How have traditional gender roles evolved in families with dual-income households?”

• Theoretical Framework : Gender Schema Theory
• Methodology : Qualitative interviews with dual-income families, combined with historical data analysis.

28. Social Media and Collective Behavior: “How does social media influence collective behaviors and the formation of social movements?”

• Theoretical Framework : Emergent Norm Theory
• Methodology : Content analysis of social media platforms, combined with quantitative surveys on participation in social movements.

29. Education and Social Mobility: “To what extent does access to quality education influence social mobility in socioeconomically diverse settings?”

• Methodology : Longitudinal study tracking educational access and subsequent socioeconomic status, combined with qualitative interviews.

30. Religion and Social Cohesion: “How do religious beliefs and practices contribute to social cohesion in multicultural societies?”

• Methodology : Quantitative surveys on religious beliefs and perceptions of social cohesion, combined with ethnographic studies.

31. Consumer Culture and Identity Formation: “How does consumer culture influence individual identity formation and personal values?”

• Theoretical Framework : Social Identity Theory
• Methodology : Mixed methods, combining content analysis of advertising with qualitative interviews on identity and values.

32. Migration and Cultural Assimilation: “How do migrants negotiate cultural assimilation and preservation of their original cultural identities in their host countries?”

• Theoretical Framework : Post-Structuralism
• Methodology : Qualitative interviews with migrants, combined with observational studies in multicultural communities.

33. Social Networks and Mental Health: “How do social networks, both online and offline, impact mental health and well-being?”

• Theoretical Framework : Social Network Theory
• Methodology : Quantitative surveys assessing social network characteristics and mental health metrics, combined with qualitative interviews.

34. Crime, Deviance, and Social Control: “How do societal norms and values shape definitions of crime and deviance, and how are these definitions enforced?”

• Theoretical Framework : Labeling Theory
• Methodology : Content analysis of legal documents and media, combined with ethnographic studies in diverse communities.

35. Technology and Social Interaction: “How has the proliferation of digital technology influenced face-to-face social interactions and community building?”

• Theoretical Framework : Technological Determinism
• Methodology : Mixed methods, combining quantitative surveys on technology use with qualitative observations of social interactions in various settings.

Nursing Problems

36. Patient Communication and Recovery: “How does effective nurse-patient communication influence patient recovery rates and overall satisfaction with care?”

• Methodology : Quantitative surveys assessing patient satisfaction and recovery metrics, combined with observational studies on nurse-patient interactions.

37. Stress Management in Nursing: “What are the primary sources of occupational stress for nurses, and how can they be effectively managed to prevent burnout?”

• Methodology : Mixed methods, combining quantitative measures of stress and burnout with qualitative interviews exploring personal experiences and coping mechanisms.

38. Hand Hygiene Compliance: “How effective are different interventions in improving hand hygiene compliance among nursing staff, and what are the barriers to consistent hand hygiene?”

• Methodology : Experimental design comparing hand hygiene rates before and after specific interventions, combined with focus groups to understand barriers.

39. Nurse-Patient Ratios and Patient Outcomes: “How do nurse-patient ratios impact patient outcomes, including recovery rates, complications, and hospital readmissions?”

• Methodology : Quantitative study analyzing patient outcomes in relation to staffing levels, possibly using retrospective chart reviews.

40. Continuing Education and Clinical Competence: “How does regular continuing education influence clinical competence and confidence among nurses?”

• Methodology : Longitudinal study tracking nurses’ clinical skills and confidence over time as they engage in continuing education, combined with patient outcome measures to assess potential impacts on care quality.

Communication Studies Problems

41. Media Representation and Public Perception: “How does media representation of minority groups influence public perceptions and biases?”

• Theoretical Framework : Cultivation Theory
• Methodology : Content analysis of media representations combined with quantitative surveys assessing public perceptions and attitudes.

42. Digital Communication and Relationship Building: “How has the rise of digital communication platforms impacted the way individuals build and maintain personal relationships?”

• Theoretical Framework : Social Penetration Theory
• Methodology : Mixed methods, combining quantitative surveys on digital communication habits with qualitative interviews exploring personal relationship dynamics.

43. Crisis Communication Effectiveness: “What strategies are most effective in managing public relations during organizational crises, and how do they influence public trust?”

• Theoretical Framework : Situational Crisis Communication Theory (SCCT)
• Methodology : Case study analysis of past organizational crises, assessing communication strategies used and subsequent public trust metrics.

44. Nonverbal Cues in Virtual Communication: “How do nonverbal cues, such as facial expressions and gestures, influence message interpretation in virtual communication platforms?”

• Theoretical Framework : Social Semiotics
• Methodology : Experimental design using video conferencing tools, analyzing participants’ interpretations of messages with varying nonverbal cues.

45. Influence of Social Media on Political Engagement: “How does exposure to political content on social media platforms influence individuals’ political engagement and activism?”

• Theoretical Framework : Uses and Gratifications Theory
• Methodology : Quantitative surveys assessing social media habits and political engagement levels, combined with content analysis of political posts on popular platforms.

Before you Go: Tips and Tricks for Writing a Research Problem

This is an incredibly stressful time for research students. The research problem is going to lock you into a specific line of inquiry for the rest of your studies.

So, here’s what I tend to suggest to my students:

• Start with something you find intellectually stimulating – Too many students choose projects because they think it hasn’t been studies or they’ve found a research gap. Don’t over-estimate the importance of finding a research gap. There are gaps in every line of inquiry. For now, just find a topic you think you can really sink your teeth into and will enjoy learning about.
• Take 5 ideas to your supervisor – Approach your research supervisor, professor, lecturer, TA, our course leader with 5 research problem ideas and run each by them. The supervisor will have valuable insights that you didn’t consider that will help you narrow-down and refine your problem even more.
• Trust your supervisor – The supervisor-student relationship is often very strained and stressful. While of course this is your project, your supervisor knows the internal politics and conventions of academic research. The depth of knowledge about how to navigate academia and get you out the other end with your degree is invaluable. Don’t underestimate their advice.

I’ve got a full article on all my tips and tricks for doing research projects right here – I recommend reading it:

• 9 Tips on How to Choose a Dissertation Topic

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Animism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 10 Magical Thinking Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Social-Emotional Learning (Definition, Examples, Pros & Cons)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ What is Educational Psychology?

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35 Research Paper Problem Topics & Examples

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• Fighting drug selling in schools and campuses
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• Dehumanizing people in prisons
• Preventing human trafficking
• Making healthy lifestyle a desired choice for the people
• Improving the ecology of your hometown
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• Gender discrimination and sexism
• The problem of global hunger
• Underemployment and unemployment
• Balancing safety and the right to have private information
• Manipulative advertising
• Teaching children to spend more time offline
• Cheating at schools and colleges
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Academic Experience

How to identify and resolve research problems

Updated July 12, 2023

In this article, we’re going to take you through one of the most pertinent parts of conducting research: a research problem (also known as a research problem statement).

When trying to formulate a good research statement, and understand how to solve it for complex projects, it can be difficult to know where to start.

Not only are there multiple perspectives (from stakeholders to project marketers who want answers), you have to consider the particular context of the research topic: is it timely, is it relevant and most importantly of all, is it valuable?

In other words: are you looking at a research worthy problem?

The fact is, a well-defined, precise, and goal-centric research problem will keep your researchers, stakeholders, and business-focused and your results actionable.

And when it works well, it's a powerful tool to identify practical solutions that can drive change and secure buy-in from your workforce.

Free eBook: The ultimate guide to market research

What is a research problem?

In social research methodology and behavioral sciences , a research problem establishes the direction of research, often relating to a specific topic or opportunity for discussion.

For example: climate change and sustainability, analyzing moral dilemmas or wage disparity amongst classes could all be areas that the research problem focuses on.

As well as outlining the topic and/or opportunity, a research problem will explain:

• why the area/issue needs to be addressed,
• why the area/issue is of importance,
• the parameters of the research study
• the research objective
• the reporting framework for the results and
• what the overall benefit of doing so will provide (whether to society as a whole or other researchers and projects).

Having identified the main topic or opportunity for discussion, you can then narrow it down into one or several specific questions that can be scrutinized and answered through the research process.

What are research questions?

Generating research questions underpinning your study usually starts with problems that require further research and understanding while fulfilling the objectives of the study.

A good problem statement begins by asking deeper questions to gain insights about a specific topic.

For example, using the problems above, our questions could be:

"How will climate change policies influence sustainability standards across specific geographies?"

"What measures can be taken to address wage disparity without increasing inflation?"

Developing a research worthy problem is the first step - and one of the most important - in any kind of research.

It’s also a task that will come up again and again because any business research process is cyclical. New questions arise as you iterate and progress through discovering, refining, and improving your products and processes. A research question can also be referred to as a "problem statement".

Note: good research supports multiple perspectives through empirical data. It’s focused on key concepts rather than a broad area, providing readily actionable insight and areas for further research.

Research question or research problem?

As we've highlighted, the terms “research question” and “research problem” are often used interchangeably, becoming a vague or broad proposition for many.

The term "problem statement" is far more representative, but finds little use among academics.

Instead, some researchers think in terms of a single research problem and several research questions that arise from it.

As mentioned above, the questions are lines of inquiry to explore in trying to solve the overarching research problem.

Ultimately, this provides a more meaningful understanding of a topic area.

It may be useful to think of questions and problems as coming out of your business data – that’s the O-data (otherwise known as operational data) like sales figures and website metrics.

What's an example of a research problem?

Your overall research problem could be: "How do we improve sales across EMEA and reduce lost deals?"

This research problem then has a subset of questions, such as:

"Why do sales peak at certain times of the day?"

"Why are customers abandoning their online carts at the point of sale?"

As well as helping you to solve business problems, research problems (and associated questions) help you to think critically about topics and/or issues (business or otherwise). You can also use your old research to aid future research -- a good example is laying the foundation for comparative trend reports or a complex research project.

(Also, if you want to see the bigger picture when it comes to research problems, why not check out our ultimate guide to market research? In it you'll find out: what effective market research looks like, the use cases for market research, carrying out a research study, and how to examine and action research findings).

The research process: why are research problems important?

A research problem has two essential roles in setting your research project on a course for success.

1. They set the scope

The research problem defines what problem or opportunity you’re looking at and what your research goals are. It stops you from getting side-tracked or allowing the scope of research to creep off-course .

Without a strong research problem or problem statement, your team could end up spending resources unnecessarily, or coming up with results that aren’t actionable - or worse, harmful to your business - because the field of study is too broad.

2. They tie your work to business goals and actions

To formulate a research problem in terms of business decisions means you always have clarity on what’s needed to make those decisions. You can show the effects of what you’ve studied using real outcomes.

Then, by focusing your research problem statement on a series of questions tied to business objectives, you can reduce the risk of the research being unactionable or inaccurate.

It's also worth examining research or other scholarly literature (you’ll find plenty of similar, pertinent research online) to see how others have explored specific topics and noting implications that could have for your research.

Four steps to defining your research problem

Image credit: http://myfreeschooltanzania.blogspot.com/2014/11/defining-research-problem.html

1. Observe and identify

Businesses today have so much data that it can be difficult to know which problems to address first. Researchers also have business stakeholders who come to them with problems they would like to have explored. A researcher’s job is to sift through these inputs and discover exactly what higher-level trends and key concepts are worth investing in.

This often means asking questions and doing some initial investigation to decide which avenues to pursue. This could mean gathering interdisciplinary perspectives identifying additional expertise and contextual information.

Sometimes, a small-scale preliminary study might be worth doing to help get a more comprehensive understanding of the business context and needs, and to make sure your research problem addresses the most critical questions.

This could take the form of qualitative research using a few in-depth interviews , an environmental scan, or reviewing relevant literature.

The sales manager of a sportswear company has a problem: sales of trail running shoes are down year-on-year and she isn’t sure why. She approaches the company’s research team for input and they begin asking questions within the company and reviewing their knowledge of the wider market.

2. Review the key factors involved

As a marketing researcher, you must work closely with your team of researchers to define and test the influencing factors and the wider context involved in your study. These might include demographic and economic trends or the business environment affecting the question at hand. This is referred to as a relational research problem.

To do this, you have to identify the factors that will affect the research and begin formulating different methods to control them.

You also need to consider the relationships between factors and the degree of control you have over them. For example, you may be able to control the loading speed of your website but you can’t control the fluctuations of the stock market.

Doing this will help you determine whether the findings of your project will produce enough information to be worth the cost.

You need to determine:

• which factors affect the solution to the research proposal.
• which ones can be controlled and used for the purposes of the company, and to what extent.
• the functional relationships between the factors.
• which ones are critical to the solution of the research study.

The research team at the running shoe company is hard at work. They explore the factors involved and the context of why YoY sales are down for trail shoes, including things like what the company’s competitors are doing, what the weather has been like – affecting outdoor exercise – and the relative spend on marketing for the brand from year to year.

The final factor is within the company’s control, although the first two are not. They check the figures and determine marketing spend has a significant impact on the company.

3. Prioritize

Once you and your research team have a few observations, prioritize them based on their business impact and importance. It may be that you can answer more than one question with a single study, but don’t do it at the risk of losing focus on your overarching research problem.

Questions to ask:

• Who? Who are the people with the problem? Are they end-users, stakeholders, teams within your business? Have you validated the information to see what the scale of the problem is?
• What? What is its nature and what is the supporting evidence?
• Why? What is the business case for solving the problem? How will it help?
• Where? How does the problem manifest and where is it observed?

To help you understand all dimensions, you might want to consider focus groups or preliminary interviews with external (including consumers and existing customers) and internal (salespeople, managers, and other stakeholders) parties to provide what is sometimes much-needed insight into a particular set of questions or problems.

After observing and investigating, the running shoe researchers come up with a few candidate questions, including:

• What is the relationship between US average temperatures and sales of our products year on year?
• At present, how does our customer base rank Competitor X and Competitor Y’s trail running shoe compared to our brand?
• What is the relationship between marketing spend and trail shoe product sales over the last 12 months?

They opt for the final question, because the variables involved are fully within the company’s control, and based on their initial research and stakeholder input, seem the most likely cause of the dive in sales. The research question is specific enough to keep the work on course towards an actionable result, but it allows for a few different avenues to be explored, such as the different budget allocations of offline and online marketing and the kinds of messaging used.

Get feedback from the key teams within your business to make sure everyone is aligned and has the same understanding of the research problem and questions, and the actions you hope to take based on the results. Now is also a good time to demonstrate the ROI of your research and lay out its potential benefits to your stakeholders.

Different groups may have different goals and perspectives on the issue. This step is vital for getting the necessary buy-in and pushing the project forward.

The running shoe company researchers now have everything they need to begin. They call a meeting with the sales manager and consult with the product team, marketing team, and C-suite to make sure everyone is aligned and has bought into the direction of the research topic. They identify and agree that the likely course of action will be a rethink of how marketing resources are allocated, and potentially testing out some new channels and messaging strategies .

Can you explore a broad area and is it practical to do so?

A broader research problem or report can be a great way to bring attention to prevalent issues, societal or otherwise, but are often undertaken by those with the resources to do so.

Take a typical government cybersecurity breach survey, for example. Most of these reports raise awareness of cybercrime, from the day-to-day threats businesses face to what security measures some organizations are taking. What these reports don't do, however, is provide actionable advice - mostly because every organization is different.

The point here is that while some researchers will explore a very complex issue in detail, others will provide only a snapshot to maintain interest and encourage further investigation. The "value" of the data is wholly determined by the recipients of it - and what information you choose to include.

To summarize, it can be practical to undertake a broader research problem, certainly, but it may not be possible to cover everything or provide the detail your audience needs. Likewise, a more systematic investigation of an issue or topic will be more valuable, but you may also find that you cover far less ground.

It's important to think about your research objectives and expected findings before going ahead.

Ensuring your research project is a success

A complex research project can be made significantly easier with clear research objectives, a descriptive research problem, and a central focus. All of which we've outlined in this article.

If you have previous research, even better. Use it as a benchmark

Remember: what separates a good research paper from an average one is actually very simple: valuable, empirical data that explores a prevalent societal or business issue and provides actionable insights.

And we can help.

Sophisticated research made simple with Qualtrics

Trusted by the world's best brands, our platform enables researchers from academic to corporate to tackle the hardest challenges and deliver the results that matter.

Our CoreXM platform supports the methods that define superior research and delivers insights in real-time. It's easy to use (thanks to drag-and-drop functionality) and requires no coding, meaning you'll be capturing data and gleaning insights in no time.

It also excels in flexibility; you can track consumer behavior across segments , benchmark your company versus competitors , carry out complex academic research, and do much more, all from one system.

It's one platform with endless applications, so no matter your research problem, we've got the tools to help you solve it. And if you don't have a team of research experts in-house, our market research team has the practical knowledge and tools to help design the surveys and find the respondents you need.

Of course, you may want to know where to begin with your own market research . If you're struggling, make sure to download our ultimate guide using the link below.

It's got everything you need and there’s always information in our research methods knowledge base.

Scott Smith

Scott Smith, Ph.D. is a contributor to the Qualtrics blog.

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Organizing Your Social Sciences Research Paper

• The Research Problem/Question
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
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• Choosing a Title
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• Theoretical Framework
• Citation Tracking
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• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
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• Common Grammar Mistakes
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• Bibliography

A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. In the social and behavioral sciences, studies are most often framed around examining a problem that needs to be understood and resolved in order to improve society and the human condition.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

Importance of...

The purpose of a problem statement is to:

• Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study.
• Anchors the research questions, hypotheses, or assumptions to follow . It offers a concise statement about the purpose of your paper.
• Place the topic into a particular context that defines the parameters of what is to be investigated.
• Provide the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

• Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements; it also does include unspecific determinates like "very" or "giant"],
• Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood],
• Identification of what would be studied, while avoiding the use of value-laden words and terms,
• Identification of an overarching question or small set of questions accompanied by key factors or variables,
• Identification of key concepts and terms,
• Articulation of the study's conceptual boundaries or parameters or limitations,
• Some generalizability in regards to applicability and bringing results into general use,
• Conveyance of the study's importance, benefits, and justification [i.e., regardless of the type of research, it is important to demonstrate that the research is not trivial],
• Does not have unnecessary jargon or overly complex sentence constructions; and,
• Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

• Casuist Research Problem -- this type of problem relates to the determination of right and wrong in questions of conduct or conscience by analyzing moral dilemmas through the application of general rules and the careful distinction of special cases.
• Difference Research Problem -- typically asks the question, “Is there a difference between two or more groups or treatments?” This type of problem statement is used when the researcher compares or contrasts two or more phenomena. This a common approach to defining a problem in the clinical social sciences or behavioral sciences.
• Descriptive Research Problem -- typically asks the question, "what is...?" with the underlying purpose to describe the significance of a situation, state, or existence of a specific phenomenon. This problem is often associated with revealing hidden or understudied issues.
• Relational Research Problem -- suggests a relationship of some sort between two or more variables to be investigated. The underlying purpose is to investigate specific qualities or characteristics that may be connected in some way.

A problem statement in the social sciences should contain :

• A lead-in that helps ensure the reader will maintain interest over the study,
• A declaration of originality [e.g., mentioning a knowledge void or a lack of clarity about a topic that will be revealed in the literature review of prior research],
• An indication of the central focus of the study [establishing the boundaries of analysis], and
• An explanation of the study's significance or the benefits to be derived from investigating the research problem.

NOTE :   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

• Highlights a genuine dilemma, area of ambiguity, or point of confusion about a topic open to interpretation by your readers;
• Yields an answer that is unexpected and not obvious rather than inevitable and self-evident;
• Provokes meaningful thought or discussion;
• Raises the visibility of the key ideas or concepts that may be understudied or hidden;
• Suggests the need for complex analysis or argument rather than a basic description or summary; and,
• Offers a specific path of inquiry that avoids eliciting generalizations about the problem.

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

• The need is for a hospital
• The objective is to create a hospital
• The method is to plan for building a hospital, and
• The evaluation is to measure if there is a hospital or not.

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. Writing@CSU. Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

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The Research Problem & Statement

What they are & how to write them (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, you’re bound to encounter the concept of a “ research problem ” or “ problem statement ” fairly early in your learning journey. Having a good research problem is essential, as it provides a foundation for developing high-quality research, from relatively small research papers to a full-length PhD dissertations and theses.

In this post, we’ll unpack what a research problem is and how it’s related to a problem statement . We’ll also share some examples and provide a step-by-step process you can follow to identify and evaluate study-worthy research problems for your own project.

Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

Need a helping hand?

Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

Psst... there’s more!

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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Research Writing and Analysis

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Problem statement overview.

The dissertation problem needs to be very focused because everything else from the dissertation research logically flows from the problem. You may say that the problem statement is the very core of a dissertation research study. If the problem is too big or too vague, it will be difficult to scope out a purpose that is manageable for one person, given the time available to execute and finish the dissertation research study.

Through your research, your aim is to obtain information that helps address a problem so it can be resolved. Note that the researcher does not actually solve the problem themselves by conducting research but provides new knowledge that can be used toward a resolution. Typically, the problem is solved (or partially solved) by practitioners in the field, using input from researchers.

Given the above, the problem statement should do three things :

• Specify and describe the problem (with appropriate citations)
• Explain the consequences of NOT solving the problem
• Explain the knowledge needed to solve the problem (i.e., what is currently unknown about the problem and its resolution – also referred to as a gap )

What is a problem?

The world is full of problems! Not all problems make good dissertation research problems, however, because they are either too big, complex, or risky for doctorate candidates to solve. A proper research problem can be defined as a specific, evidence-based, real-life issue faced by certain people or organizations that have significant negative implications to the involved parties.

Example of a proper, specific, evidence-based, real-life dissertation research problem:

“Only 6% of CEOs in Fortune 500 companies are women” (Center for Leadership Studies, 2019).

Specific refers to the scope of the problem, which should be sufficiently manageable and focused to address with dissertation research. For example, the problem “terrorism kills thousands of people each year” is probably not specific enough in terms of who gets killed by which terrorists, to work for a doctorate candidate; or “Social media use among call-center employees may be problematic because it could reduce productivity,” which contains speculations about the magnitude of the problem and the possible negative effects.

Evidence-based here means that the problem is well-documented by recent research findings and/or statistics from credible sources. Anecdotal evidence does not qualify in this regard. Quantitative evidence is generally preferred over qualitative ditto when establishing a problem because quantitative evidence (from a credible source) usually reflects generalizable facts, whereas qualitative evidence in the form of research conclusions tend to only apply to the study sample and may not be generalizable to a larger population. Example of a problem that isn’t evidence-based: “Based on the researcher’s experience, the problem is that people don’t accept female leaders;” which is an opinion-based statement based on personal (anecdotal) experience.

Real-life means that a problem exists regardless of whether research is conducted or not. This means that “lack of knowledge” or “lack of research” cannot be used as the problem for a dissertation study because it’s an academic issue or a gap; and not a real-life problem experienced by people or organizations.  Example of a problem that doesn’t exist in real life: “There is not enough research on the reasons why people distrust minority healthcare workers.” This type of statement also reveals the assumption that people actually do mistrust minority healthcare workers; something that needs to be supported by actual, credible evidence to potentially work as an underlying research problem.

What are consequences?

Consequences are negative implications experienced by a group of people or organizations, as a result of the problem. The negative effects should be of a certain magnitude to warrant research. For example, if fewer than 1% of the stakeholders experience a negative consequence of a problem and that consequence only constitutes a minor inconvenience, research is probably not warranted. Negative consequences that can be measured weigh stronger than those that cannot be put on some kind of scale.

In the example above, a significant negative consequence is that women face much larger barriers than men when attempting to get promoted to executive jobs; or are 94% less likely than men to get to that level in Corporate America.

What is a gap?

To establish a complete basis for a dissertation research study, the problem has to be accompanied by a gap . A gap is missing knowledge or insights about a particular issue that contributes to the persistence of the problem. We use gaps to “situate” new research in the existing literature by adding to the knowledge base in the business research field, in a specific manner (determined by the purpose of the research). Identifying gaps requires you to review the literature in a thorough fashion, to establish a complete understanding of what is known and what isn’t known about a certain problem.  In the example from above about the underrepresentation of female CEOs, a gap may be that male-dominated boards have not been studied extensively in terms of their CEO hiring decisions, which might then warrant a study of such boards, to uncover implicit biases and discriminatory practices against female candidates.

How to Write a Problem Statement

How to write a problem statement.

• Here is one way to construct a problem section (keep in mind you have a 250-300 word limit, but you can write first and edit later):

It is helpful to begin the problem statement with a sentence :  “The problem to be addressed through this study is… ”  Then, fill out the rest of the paragraph with elaboration of that specific problem, making sure to “document” it, as NU reviewers will look for research-based evidence that it is indeed a problem (emphasis also on timeliness of the problem, supported by citations within the last 5 years).

Next, write a paragraph explaining the consequences of NOT solving the problem. Who will be affected? How will they be affected? How important is it to fix the problem? Again, NU reviewers will want to see research-based citations and statistics that indicate the negative implications are significant.

In the final paragraph, you will explain what information (research) is needed in order to fix the problem. This paragraph shows that the problem is worthy of doctoral-level research. What isn’t known about the problem? Ie, what is the gap? Presumably, if your problem and purpose are aligned, your research will try to close or minimize this gap by investigating the problem. Have other researchers investigated the issue? What has their research left unanswered?

• Another way to tackle the Statement of the Problem:

The Statement of the Problem section is a very clear, concise identification of the problem. It must stay within the template guidelines of 250-300 words but more importantly, must contain four elements as outlined below. A dissertation worthy problem should be able to address all of the following points:

-->identification of the problem itself--what is "going wrong" (Ellis & Levy, 2008)

-->who is affected by the problem

-->the consequences that will result from a continuation of the problem

-->a brief discussion of 1) at least 3 authors’ research related to the problem; and 2)   their stated suggestion/recommendation for further research related to the problem

Use the following to work on the Statement of the Problem by first outlining the section as follows:

1. One clear, concise statement that tells the reader what is not working, what is “going wrong”. Be specific and support it with current studies.

2. Tell who is affected by the problem identified in #1. 

3. Briefly tell what will happen if the problem isn’t addressed.

4. Find at least 3 current studies and write a sentence or two for each study that

i. briefly discusses the author(s)’ work, what they studied, and

ii. state their recommendation for further research about the problem

• Finally, you can follow this simple 3-part outline when writing the statement of the problem section:

Your problem statement is a short (250-300 words), 3 paragraph section, in which you

• Explain context and state problem (“the problem is XYZ”), supported by statistics and/or recent research findings
• Explain the negative consequences of the problem to stakeholders, supported by statistics and/or recent research findings
• Explain the gap in the literature.

Example of a problem statement that follows the 3-part outline (295 words):

The problem to be addressed by this study is the decline of employee well-being for followers of novice mid-level managers and the corresponding rise in employee turnover faced by business leaders across the financial services industry (Oh et al., 2014).  Low levels of employee well-being are toxic for morale and result in expensive turnover costs, dysfunctional work environments, anemic corporate cultures, and poor customer service (Compdata, 2018; Oh et al., 2014).  According to Ufer (2017), the financial services industry suffers from one of the highest turnover rates among millennial-aged employees in all industries in the developed world, at 18.6% annually.  Starkman (2015) reported that 50% of those surveyed in financial services were not satisfied with a single one of the four key workplace aspects: job, firm, pay or career path. 

Low levels of employee well-being interrupt a financial services’ company’s ability to deliver outstanding customer service in a world increasingly dependent on that commodity (Wladawsky-Berger, 2018).Mid-level managers play an essential role in support of the success of many of top businesses today (Anicich & Hirsh, 2017). 

The current body of literature does not adequately address the well-being issue in the financial services industry from the follower’s perspective (Uhl-Bien, Riggio, Lowe, & Carsten, 2014). Strategic direction flows top-down from senior executives and passes through mid-level leadership to individual contributors at more junior grades.  The mid-level managers’ teams are tasked with the achievement of core tasks and the managers themselves are expected to maintain the workforce’s morale, motivation and welfare (Anicich & Hirsh, 2017).  Unless industry leaders better understand the phenomenon of employee well-being from the follower perspective and its role in positioning employees to provide a premium client experience, they may be handicapped from preserving their most significant principal market differentiator: customer service (Wladawsky-Berger, 2018). 

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Home » Research Problem – Examples, Types and Guide

Research Problem – Examples, Types and Guide

Table of Contents

Research Problem

Definition:

Research problem is a specific and well-defined issue or question that a researcher seeks to investigate through research. It is the starting point of any research project, as it sets the direction, scope, and purpose of the study.

Types of Research Problems

Types of Research Problems are as follows:

Descriptive problems

These problems involve describing or documenting a particular phenomenon, event, or situation. For example, a researcher might investigate the demographics of a particular population, such as their age, gender, income, and education.

Exploratory problems

These problems are designed to explore a particular topic or issue in depth, often with the goal of generating new ideas or hypotheses. For example, a researcher might explore the factors that contribute to job satisfaction among employees in a particular industry.

Explanatory Problems

These problems seek to explain why a particular phenomenon or event occurs, and they typically involve testing hypotheses or theories. For example, a researcher might investigate the relationship between exercise and mental health, with the goal of determining whether exercise has a causal effect on mental health.

Predictive Problems

These problems involve making predictions or forecasts about future events or trends. For example, a researcher might investigate the factors that predict future success in a particular field or industry.

Evaluative Problems

These problems involve assessing the effectiveness of a particular intervention, program, or policy. For example, a researcher might evaluate the impact of a new teaching method on student learning outcomes.

How to Define a Research Problem

Defining a research problem involves identifying a specific question or issue that a researcher seeks to address through a research study. Here are the steps to follow when defining a research problem:

• Identify a broad research topic : Start by identifying a broad topic that you are interested in researching. This could be based on your personal interests, observations, or gaps in the existing literature.
• Conduct a literature review : Once you have identified a broad topic, conduct a thorough literature review to identify the current state of knowledge in the field. This will help you identify gaps or inconsistencies in the existing research that can be addressed through your study.
• Refine the research question: Based on the gaps or inconsistencies identified in the literature review, refine your research question to a specific, clear, and well-defined problem statement. Your research question should be feasible, relevant, and important to the field of study.
• Develop a hypothesis: Based on the research question, develop a hypothesis that states the expected relationship between variables.
• Define the scope and limitations: Clearly define the scope and limitations of your research problem. This will help you focus your study and ensure that your research objectives are achievable.
• Get feedback: Get feedback from your advisor or colleagues to ensure that your research problem is clear, feasible, and relevant to the field of study.

Components of a Research Problem

The components of a research problem typically include the following:

• Topic : The general subject or area of interest that the research will explore.
• Research Question : A clear and specific question that the research seeks to answer or investigate.
• Objective : A statement that describes the purpose of the research, what it aims to achieve, and the expected outcomes.
• Hypothesis : An educated guess or prediction about the relationship between variables, which is tested during the research.
• Variables : The factors or elements that are being studied, measured, or manipulated in the research.
• Methodology : The overall approach and methods that will be used to conduct the research.
• Scope and Limitations : A description of the boundaries and parameters of the research, including what will be included and excluded, and any potential constraints or limitations.
• Significance: A statement that explains the potential value or impact of the research, its contribution to the field of study, and how it will add to the existing knowledge.

Research Problem Examples

Following are some Research Problem Examples:

Research Problem Examples in Psychology are as follows:

• Exploring the impact of social media on adolescent mental health.
• Investigating the effectiveness of cognitive-behavioral therapy for treating anxiety disorders.
• Studying the impact of prenatal stress on child development outcomes.
• Analyzing the factors that contribute to addiction and relapse in substance abuse treatment.
• Examining the impact of personality traits on romantic relationships.

Research Problem Examples in Sociology are as follows:

• Investigating the relationship between social support and mental health outcomes in marginalized communities.
• Studying the impact of globalization on labor markets and employment opportunities.
• Analyzing the causes and consequences of gentrification in urban neighborhoods.
• Investigating the impact of family structure on social mobility and economic outcomes.
• Examining the effects of social capital on community development and resilience.

Research Problem Examples in Economics are as follows:

• Studying the effects of trade policies on economic growth and development.
• Analyzing the impact of automation and artificial intelligence on labor markets and employment opportunities.
• Investigating the factors that contribute to economic inequality and poverty.
• Examining the impact of fiscal and monetary policies on inflation and economic stability.
• Studying the relationship between education and economic outcomes, such as income and employment.

Political Science

Research Problem Examples in Political Science are as follows:

• Analyzing the causes and consequences of political polarization and partisan behavior.
• Investigating the impact of social movements on political change and policymaking.
• Studying the role of media and communication in shaping public opinion and political discourse.
• Examining the effectiveness of electoral systems in promoting democratic governance and representation.
• Investigating the impact of international organizations and agreements on global governance and security.

Environmental Science

Research Problem Examples in Environmental Science are as follows:

• Studying the impact of air pollution on human health and well-being.
• Investigating the effects of deforestation on climate change and biodiversity loss.
• Analyzing the impact of ocean acidification on marine ecosystems and food webs.
• Studying the relationship between urban development and ecological resilience.
• Examining the effectiveness of environmental policies and regulations in promoting sustainability and conservation.

Research Problem Examples in Education are as follows:

• Investigating the impact of teacher training and professional development on student learning outcomes.
• Studying the effectiveness of technology-enhanced learning in promoting student engagement and achievement.
• Analyzing the factors that contribute to achievement gaps and educational inequality.
• Examining the impact of parental involvement on student motivation and achievement.
• Studying the effectiveness of alternative educational models, such as homeschooling and online learning.

Research Problem Examples in History are as follows:

• Analyzing the social and economic factors that contributed to the rise and fall of ancient civilizations.
• Investigating the impact of colonialism on indigenous societies and cultures.
• Studying the role of religion in shaping political and social movements throughout history.
• Analyzing the impact of the Industrial Revolution on economic and social structures.
• Examining the causes and consequences of global conflicts, such as World War I and II.

Research Problem Examples in Business are as follows:

• Studying the impact of corporate social responsibility on brand reputation and consumer behavior.
• Investigating the effectiveness of leadership development programs in improving organizational performance and employee satisfaction.
• Analyzing the factors that contribute to successful entrepreneurship and small business development.
• Examining the impact of mergers and acquisitions on market competition and consumer welfare.
• Studying the effectiveness of marketing strategies and advertising campaigns in promoting brand awareness and sales.

Research Problem Example for Students

An Example of a Research Problem for Students could be:

“How does social media usage affect the academic performance of high school students?”

This research problem is specific, measurable, and relevant. It is specific because it focuses on a particular area of interest, which is the impact of social media on academic performance. It is measurable because the researcher can collect data on social media usage and academic performance to evaluate the relationship between the two variables. It is relevant because it addresses a current and important issue that affects high school students.

To conduct research on this problem, the researcher could use various methods, such as surveys, interviews, and statistical analysis of academic records. The results of the study could provide insights into the relationship between social media usage and academic performance, which could help educators and parents develop effective strategies for managing social media use among students.

Another example of a research problem for students:

“Does participation in extracurricular activities impact the academic performance of middle school students?”

This research problem is also specific, measurable, and relevant. It is specific because it focuses on a particular type of activity, extracurricular activities, and its impact on academic performance. It is measurable because the researcher can collect data on students’ participation in extracurricular activities and their academic performance to evaluate the relationship between the two variables. It is relevant because extracurricular activities are an essential part of the middle school experience, and their impact on academic performance is a topic of interest to educators and parents.

To conduct research on this problem, the researcher could use surveys, interviews, and academic records analysis. The results of the study could provide insights into the relationship between extracurricular activities and academic performance, which could help educators and parents make informed decisions about the types of activities that are most beneficial for middle school students.

Applications of Research Problem

Applications of Research Problem are as follows:

• Academic research: Research problems are used to guide academic research in various fields, including social sciences, natural sciences, humanities, and engineering. Researchers use research problems to identify gaps in knowledge, address theoretical or practical problems, and explore new areas of study.
• Business research : Research problems are used to guide business research, including market research, consumer behavior research, and organizational research. Researchers use research problems to identify business challenges, explore opportunities, and develop strategies for business growth and success.
• Healthcare research : Research problems are used to guide healthcare research, including medical research, clinical research, and health services research. Researchers use research problems to identify healthcare challenges, develop new treatments and interventions, and improve healthcare delivery and outcomes.
• Public policy research : Research problems are used to guide public policy research, including policy analysis, program evaluation, and policy development. Researchers use research problems to identify social issues, assess the effectiveness of existing policies and programs, and develop new policies and programs to address societal challenges.
• Environmental research : Research problems are used to guide environmental research, including environmental science, ecology, and environmental management. Researchers use research problems to identify environmental challenges, assess the impact of human activities on the environment, and develop sustainable solutions to protect the environment.

Purpose of Research Problems

The purpose of research problems is to identify an area of study that requires further investigation and to formulate a clear, concise and specific research question. A research problem defines the specific issue or problem that needs to be addressed and serves as the foundation for the research project.

Identifying a research problem is important because it helps to establish the direction of the research and sets the stage for the research design, methods, and analysis. It also ensures that the research is relevant and contributes to the existing body of knowledge in the field.

A well-formulated research problem should:

• Clearly define the specific issue or problem that needs to be investigated
• Be specific and narrow enough to be manageable in terms of time, resources, and scope
• Be relevant to the field of study and contribute to the existing body of knowledge
• Be feasible and realistic in terms of available data, resources, and research methods
• Be interesting and intellectually stimulating for the researcher and potential readers or audiences.

Characteristics of Research Problem

The characteristics of a research problem refer to the specific features that a problem must possess to qualify as a suitable research topic. Some of the key characteristics of a research problem are:

• Clarity : A research problem should be clearly defined and stated in a way that it is easily understood by the researcher and other readers. The problem should be specific, unambiguous, and easy to comprehend.
• Relevance : A research problem should be relevant to the field of study, and it should contribute to the existing body of knowledge. The problem should address a gap in knowledge, a theoretical or practical problem, or a real-world issue that requires further investigation.
• Feasibility : A research problem should be feasible in terms of the availability of data, resources, and research methods. It should be realistic and practical to conduct the study within the available time, budget, and resources.
• Novelty : A research problem should be novel or original in some way. It should represent a new or innovative perspective on an existing problem, or it should explore a new area of study or apply an existing theory to a new context.
• Importance : A research problem should be important or significant in terms of its potential impact on the field or society. It should have the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Manageability : A research problem should be manageable in terms of its scope and complexity. It should be specific enough to be investigated within the available time and resources, and it should be broad enough to provide meaningful results.

Advantages of Research Problem

The advantages of a well-defined research problem are as follows:

• Focus : A research problem provides a clear and focused direction for the research study. It ensures that the study stays on track and does not deviate from the research question.
• Clarity : A research problem provides clarity and specificity to the research question. It ensures that the research is not too broad or too narrow and that the research objectives are clearly defined.
• Relevance : A research problem ensures that the research study is relevant to the field of study and contributes to the existing body of knowledge. It addresses gaps in knowledge, theoretical or practical problems, or real-world issues that require further investigation.
• Feasibility : A research problem ensures that the research study is feasible in terms of the availability of data, resources, and research methods. It ensures that the research is realistic and practical to conduct within the available time, budget, and resources.
• Novelty : A research problem ensures that the research study is original and innovative. It represents a new or unique perspective on an existing problem, explores a new area of study, or applies an existing theory to a new context.
• Importance : A research problem ensures that the research study is important and significant in terms of its potential impact on the field or society. It has the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Rigor : A research problem ensures that the research study is rigorous and follows established research methods and practices. It ensures that the research is conducted in a systematic, objective, and unbiased manner.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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October 1, 2018

To Solve Real-World Problems, We Need Interdisciplinary Science

Solving today’s complex, global problems will take interdisciplinary science

By Graham A. J. Worthy & Cherie L. Yestrebsky

T he Indian River Lagoon, a shallow estuary that stretches for 156 miles along Florida's eastern coast, is suffering from the activities of human society. Poor water quality and toxic algal blooms have resulted in fish kills, manatee and dolphin die-offs, and takeovers by invasive species. But the humans who live here have needs, too: the eastern side of the lagoon is buffered by a stretch of barrier islands that are critical to Florida's economy, tourism and agriculture, as well as for launching NASA missions into space.

As in Florida, many of the world's coastlines are in serious trouble as a result of population growth and the pollution it produces. Moreover, the effects of climate change are accelerating both environmental and economic decline. Given what is at risk, scientists like us—a biologist and a chemist at the University of Central Florida—feel an urgent need to do research that can inform policy that will increase the resiliency and sustainability of coastal communities. How can our research best help balance environmental and social needs within the confines of our political and economic systems? This is the level of complexity that scientists must enter into instead of shying away from.

Although new technologies will surely play a role in tackling issues such as climate change, rising seas and coastal flooding, we cannot rely on innovation alone. Technology generally does not take into consideration the complex interactions between people and the environment. That is why coming up with solutions will require scientists to engage in an interdisciplinary team approach—something that is common in the business world but is relatively rare in universities.

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Universities are a tremendous source of intellectual power, of course. But students and faculty are typically organized within departments, or academic silos. Scientists are trained in the tools and language of their respective disciplines and learn to communicate their findings to one another using specific jargon.

When the goal of research is a fundamental understanding of a physical or biological system within a niche community, this setup makes a lot of sense. But when the problem the research is trying to solve extends beyond a closed system and includes its effects on society, silos create a variety of barriers. They can limit creativity, flexibility and nimbleness and actually discourage scientists from working across disciplines. As professors, we tend to train our students in our own image, inadvertently producing specialists who have difficulty communicating with the scientist in the next building—let alone with the broader public. This makes research silos ineffective at responding to developing issues in policy and planning, such as how coastal communities and ecosystems worldwide will adapt to rising seas.

Science for the Bigger Picture

As scientists who live and work in Florida, we realized that we needed to play a bigger role in helping our state—and country—make evidence-based choices when it comes to vulnerable coastlines. We wanted to make a more comprehensive assessment of both natural and human-related impacts to the health, restoration and sustainability of our coastal systems and to conduct long-term, integrated research.

At first, we focused on expanding research capacity in our biology, chemistry and engineering programs because each already had a strong coastal research presence. Then, our university announced a Faculty Cluster Initiative, with a goal of developing interdisciplinary academic teams focused on solving tomorrow's most challenging societal problems. While putting together our proposal, we discovered that there were already 35 faculty members on the Orlando campus who studied coastal issues. They belonged to 12 departments in seven colleges, and many of them had never even met. It became clear that simply working on the same campus was insufficient for collaboration.

So we set out to build a team of people from a wide mix of backgrounds who would work in close proximity to one another on a daily basis. These core members would also serve as a link to the disciplinary strengths of their tenure home departments. Initially, finding experts who truly wanted to embrace the team aspect was more difficult than we thought. Although the notion of interdisciplinary research is not new, it has not always been encouraged in academia. Some faculty who go in that direction still worry about whether it will threaten their recognition when applying for grants, seeking promotions or submitting papers to high-impact journals. We are not suggesting that traditional academic departments should be disbanded. On the contrary, they give the required depth to the research, whereas the interdisciplinary team gives breadth to the overall effort.

Our cluster proposal was a success, and in 2019 the National Center for Integrated Coastal Research (UCF Coastal) was born. Our goal is to guide more effective economic development, environmental stewardship, hazard-mitigation planning and public policy for coastal communities. To better integrate science with societal needs, we have brought together biologists, chemists, engineers and biomedical researchers with anthropologists, sociologists, political scientists, planners, emergency managers and economists. It seems that the most creative perspectives on old problems have arisen when people with different training and life experiences are talking through issues over cups of coffee. After all, "interdisciplinary" must mean more than just different flavors of STEM. In academia, tackling the effects of climate change demands more rigorous inclusion of the social sciences—an area that has been frequently overlooked.

The National Science Foundation, as well as other groups, requires that all research proposals incorporate a social sciences component, as an attempt to assess the broader implications of projects. Unfortunately, in many cases, a social scientist is simply added to a proposal only to check a box rather than to make a true commitment to allowing that discipline to inform the project. Instead social, economic and policy needs must be considered at the outset of research design, not as an afterthought. Otherwise our work might fail at the implementation stage, which means we will not be as effective as we could be in solving real-world problems. As a result, the public might become skeptical about how much scientists can contribute toward solutions.

Connecting with the Public

The reality is that communicating research findings to the public is an increasingly critical responsibility of scientists. Doing so has a measurable effect on how politicians prioritize policy, funding and regulations. UCF Coastal was brought into a world where science is not always respected—sometimes it is even portrayed as the enemy. There has been a significant erosion of trust in science over recent years, and we must work more deliberately to regain it. The public, we have found, wants to see quality academic research that is grounded in the societal challenges we are facing. That is why we are melding pure academic research with applied research to focus on issues that are immediate—helping a town or business recovering from a hurricane, for example—as well as long term, such as directly advising a community on how to build resiliency as flooding becomes more frequent.

As scientists, we cannot expect to explain the implications of our research to the wider public if we cannot first understand one another. A benefit of regularly working side by side is that we are crafting a common language, reconciling the radically different meanings that the same words can have to a variety of specialists. Finally, we are learning to speak to one another with more clarity and understand more explicitly how our niches fit into the bigger picture. We are also more aware of culture and industry as driving forces in shaping consensus and policy. Rather than handing city planners a stack of research papers and walking away, UCF Coastal sees itself as a collaborator that listens instead of just lecturing.

This style of academic mission is not only relevant to issues around climate change. It relates to every aspect of modern society, including genetic engineering, automation, artificial intelligence, and so on. The launch of UCF Coastal garnered positive attention from industry, government agencies, local communities and academics. We think that is because people do want to come together to solve problems, but they need a better mechanism for doing so. We hope to be that conduit while inspiring other academic institutions to do the same.

After all, we have been told for years to "think globally, act locally" and that "all politics is local." Florida's Indian River Lagoon will be restored only if there is engagement among residents, local industries, academics, government agencies and nonprofit organizations. As scientists, it is our responsibility to help everyone involved understand that problems that took decades to create will take decades to fix. We need to present the most helpful solutions while explaining the intricacies of the trade-offs for each one. Doing so is possible only if we see ourselves as part of an interdisciplinary, whole-community approach. By listening and responding to fears and concerns, we can make a stronger case for why scientifically driven decisions will be more effective in the long run.

Graham A. J. Worthy is founder and director of the National Center for Integrated Coastal Research at the University of Central Florida (UCF Coastal) and chairs the university's department of biology. His research focuses on how marine ecosystems respond to natural and anthropogenic perturbations.

Cherie L. Yestrebsky is a professor in the University of Central Florida's department of chemistry. Her research expertise is in environmental chemistry and remediation of pollutants in the environment.

Research does solve real-world problems: experts must work together to make it happen

Deputy Vice Chancellor Research & Innovation, University of South Australia

Disclosure statement

Tanya Monro receives funding from the Australian Research Council. She is Deputy Vice Chancellor of the University of South Australia, a member of the Commonwealth Science Council, the CSIRO board, the SA Economic Development Board and Defence SA.

University of South Australia provides funding as a member of The Conversation AU.

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Generating knowledge is one of the most exciting aspects of being human. The inventiveness required to apply this knowledge to solve practical problems is perhaps our most distinctive attribute.

But right now we have before us some hairy challenges – whether that be figuring our how to save our coral reefs from warmer water , landing a human on Mars , eliminating the gap in life expectancy between the “haves” and “have-nots” or delivering reliable carbon-free energy .

It’s commonly said that an interdisciplinary approach is vital if we are to tackle such real world challenges. But what does this really mean?

Read more: It takes a community to raise a startup

Listen and read with care and you’ll start to notice that the words crossdisciplinary, multidisciplinary, interdisciplinary and transdisciplinary are used interchangeably. These words describe distinctly different ways of harnessing the power of disciplinary expertise to chart a course into the unknown.

In navigation, the tools and methods matter – choose differently and you’ll end up in a different spot. How we go about creating knowledge and solving problems really matters – it changes not only what questions can be asked and answered but fundamentally shapes what’s possible.

What is a discipline?

For centuries we have organised research within disciplines, and this has delivered extraordinary depths of knowledge.

But what is a discipline? It’s a shared language, an environment in which there’s no need to explain the motivation for one’s work, and in which people have a shared sense of what’s valuable.

For example, my background discipline is optical physics. I know what it’s like to be able to skip down the corridor and say,

“I’ve figured out how we can get broadband flat dispersion - we just need to tailor the radial profile!”

…and have people instantly not just know what I mean, but be able to add their own ideas and drive the work forward.

In long-established disciplines it’s often necessary to focus in a narrow area to be able to extend the limits of knowledge within the time-frame of a PhD. And while it’s rarely obvious at the time what benefits will flow from digging a little deeper, our way of life has been transformed over and over as result.

Disciplines focus talent and so can be amazingly efficient ways of generating knowledge. But they can also be extraordinarily difficult to penetrate from the outside without understanding that discipline’s particular language and shared values.

The current emphasis on real-world impact has sharpened awareness on the need to translate knowledge into outcomes. It has also brought attention to the critical role partnerships with industry and other end-users of research play in this process.

Creating impact across disciplines

Try to solve a problem with the tools of a single discipline alone, and it’s as if you have a hammer - everything starts to look like a nail. It’s usually obvious when expertise from more than one discipline is needed.

Consider a panel of experts drawn from different fields to each apply the tools of their field to a problem that’s been externally framed. This has traditionally been how expertise from the social sciences is brought to bear on challenges in public health or the environment.

This is a crossdisciplinary approach , which can produce powerful outcomes provided that those who posed the question are positioned to make decisions based on the knowledge generated. But the research fields themselves are rarely influenced by this glancing encounter with different approaches to knowledge generation.

Multidisciplinary research involves the application of tools from one discipline to questions from other fields. An example is the application of crystallography, discovered by the Braggs, to unravel the structure of proteins . This requires concepts to transfer across domains, sometimes in real time but usually with a lag of years or decades.

Read more: If we really want an ideas boom, we need more women at the top tiers of science

Interdisciplinary research happens when researchers from different fields come together to pose a challenge that wouldn’t be possible in isolation. One example is the highly transparent optical fibres that underpin intercontinental telecommunication networks.

The knowledge creation that made this possible involved glass chemists, optical physicists and communication engineers coming together to articulate the possible, and develop the shared language required to make it a reality. When fields go on this journey together over decades, new fields are born.

In this example the question itself was clear – how can we harness the transparency of silica glass to create optical transmission systems that can transport large volumes of data over long distances?

But what about the questions we don’t know how to pose because without knowledge of another field we don’t know what’s possible? This line of reasoning leads us into the domain of transdisciplinary research .

Transdisciplinary research requires a willingness to craft new questions – whether because they were considered intractable or because without the inspiration from left field they simply didn’t arise. An example of this is applying photonics to IVF incubators - the idea that it could be possible to “listen” to how embryos experience their environment is unlikely to have arisen without bringing these fields together.

Read more: National Science Statement a positive gesture but lacks policy solutions: experts

In my own field, physics, I discovered that when talking to people from other areas the simple question “what would you like to measure?” quickly led to uncharted territory.

Before long we were usually, together, posing fundamentally new questions and establishing teams to tackle them. This can be scary territory but it’s tremendously rewarding and creates space for creativity and the emergence of disruptive technologies.

Excellence, communication, co-location, funding

One of the best ways of getting out of a disciplinary silo is to take every opportunity to talk to others outside your field. Disciplinary excellence is the starting point to get to the table.

And while disciplinary collaborations can flourish over large distances because they share a language and values, it’s usually true that once you mix disciplines co-location becomes a real asset. Then of course there are the questions of how we fund and organise research concentrations to allow inter- and transdisciplinary research to flourish.

With the increased emphasis on impact, these questions are becoming ever more pressing. Organisations that get this right will thrive.

• Research impact
• cross-disciplinary

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By Rebecca Knight

Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.

In India, a country with a population of 1.2bn, fewer than 30 per cent of citizens have a passport, driver’s licence or other form of identification.

It may seem a minor point, except that the absence of these documents makes it difficult to apply for a bank account, obtain a mobile phone or even receive the government subsidies for education and food that individuals are entitled to.

But according to a new survey led by Arun Sundararajan, an associate professor of information, operations and management sciences at New York University’s Stern School of Business and a group of his students, that is changing. A government-sponsored project that began towards the end of 2010 to give every person in India a unique 12-digit ID number is showing signs of success. If enrolment continues according to projections, Prof Sundararajan reckons that about 300m citizens who previously did not have a portable ID will have one by the end of the year.

“It’s a moon-shot project,” says Prof Sundararajan. “It’s having a transformative impact on the lives of hundreds of millions of people.”

It is also, he hopes, a project that will have a transformative impact on the careers of the dozen MBA students who are working with him on the survey. The survey, which is analysing the impact of India’s Unique Identity project, is part of the Stern Consulting Corps programme.

International trips provide hands-on experience

To try to better prepare students to operate in the global economy, a number of leading business schools have introduced courses for MBAs to embark on international consulting projects.

The courses are designed to give students an applied learning experience that is very different from the one they receive on campus.

Harvard Business School , for instance, recently launched a year-long required course for its first-year students called “Field Immersion Experiences for Leadership Development”, or Field for short. The capstone of the course is a week-long trip to a developing country where student teams work closely with a company to develop an idea for a product or service.

Last year, projects were based in cities including Cape Town, Mumbai, Shanghai, Warsaw and Buenos Aires.

“Our aspiration is that it becomes so self-evident about how valuable this is that other schools do it too,” says Youngme Moon, who chairs HBS’s MBA programme.

Massachusetts Institute of Technology’s Sloan School , meanwhile, has expanded its G-Lab course in which students work with the management of overseas start-ups. Student teams work remotely from MIT for three months and full-time at their host companies for at least three weeks. Last year students worked on projects in Kenya, Colombia, Indonesia and other countries.

While some schools may view courses with an international consulting component as a way to “teach students how to be [a] consultant, it’s very much meant to be an interesting learning challenge,” says Michellana Jester, director of Sloan’s action learning programme.

According to her, business schools are using these courses to strike the right balance between academic rigour and relevance. “Scholarship is important and research is important, but how do you make it relevant for students in business schools today?

“It’s a transition for business schools right now in terms of how we navigate this,” she says.

SCC, an elective course now in its 10th year, began as a programme that placed students with local non-profits on 10-week project engagements. This year, for the first time, students worked on projects in emerging markets linked to faculty research. This new element provides students with vivid illustrations of how academic research can be used to solve real world problems.

“What [students] are gaining from this is an understanding of the potential of business to be an agent of social change,” says Prof Sundararajan. “It’s one thing to be exposed to examples of this in a textbook, it’s another to witness it first hand.”

As top schools strive to infuse their curricula with more hands-on learning experiences by adding overseas exchange programmes and class consulting projects in far-flung corners of the world, SCC stands out for its emphasis on research.

The new focus of the SCC programme reflects the increasing interest from MBA students in using their degrees to work on social policy issues. The programme is popular on campus: more than 100 students participated in the programme this past academic year and applications to the SCC rose 117 per cent this spring compared with last year.

Academic research is often accused of being ponderous, narrow and detached from the real world. But there is a new wave of research coming out of schools today that concerns how government and business can work together to solve big social problems, according to Professor Peter Henry, Stern’s dean.

There is also a growing recognition on the part of management faculty that the type of research they conduct about corporations has potentially broad applications for other kinds of organisations.

“There is a false dichotomy between research and the real world,” says Prof Henry. “Research can have a real impact.”

One group of students, for instance, worked on a business plan for a city that is being developed in Honduras. Because the city is a new concept, the business plan will have a direct effect on policy decisions in the country.

The students, under the supervision of Professor Paul Romer who heads the Urbanization Project, a research centre at Stern that focuses on urban growth and governance in the developing world, spent the semester devising potential scenarios for the city, such as population growth models and potential financial rules and regulations, as well as working up infrastructure estimates and writing policy briefs. Some of their findings were presented in a meeting Prof Romer had with Octavio Sánchez, chief of staff to the Honduran president.

“We’re getting students into the world through the lens of research,” says Prof Henry. “We’re giving students the chance to say, ‘I didn’t just take a set of classes. I built something’.”

Throughout the projects, students work closely with a faculty member and often develop the type of mentoring relationship that has typically been the province of PhD programmes. Teams also work with an outside mentor from a top-tier consulting company.

Students hone their analytical skills, but are also able to practise their professional responsibilities such as meeting a timeline and soliciting feedback from a client.

“The learning experience for the students was better than I expected,” says Prof Romer. “What they learnt was not just how to think abstractly about governance and fiscal policy – the kind of things you have to think about when you’re creating a new city – but also how will you work in teams, how will you divide tasks, what will you do when one sub-team gets stuck.”

The fact that students were working on a project with real world implications “lent more urgency to the group effort”, he says. “People are taking real decisions on [the students’ work]. A term paper doesn’t really matter. This goes out of the realm of a pretend exercise and makes it real.”

For students, the risk of working on such an engrossing project is that it can make other business school assignments seem dull or unworthy by comparison.

But Benjamin Wise, a student who worked on the Honduras project, says that it forced him to think about how to apply concepts learnt in the classroom.

“I’d be sitting in my corporate finance class on the edge of my seat because we were learning about a financial model that I couldn’t wait to plug in to [one of the models in] my project,” he says.

Prof Sundararajan says that spending a week in India was an eye-opening experience for his students.

“I could see them immersed in the context. They gained a clearer understanding of the breadth of vision and they could see what the problem was.

“This is the kind of immersive experiential learning that alters the worldview of students.”

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"Science, I had come to learn, is as political, competitive, and fierce a career as you can find, full of the temptation to find easy paths." — Paul Kalanithi, neurosurgeon and writer (1977–2015)

Science is in big trouble. Or so we’re told.

In the past several years, many scientists have become afflicted with a serious case of doubt — doubt in the very institution of science.

Explore the biggest challenges facing science, and how we can fix them:

• Academia has a huge money problem
• Too many studies are poorly designed
• Replicating results is crucial — and rare
• Peer review is broken
• Too much science is locked behind paywalls
• Science is poorly communicated
• Life as a young academic is incredibly stressful

Conclusion:

• Science is not doomed

As reporters covering medicine, psychology, climate change, and other areas of research, we wanted to understand this epidemic of doubt. So we sent scientists a survey asking this simple question: If you could change one thing about how science works today, what would it be and why?

We heard back from 270 scientists all over the world, including graduate students, senior professors, laboratory heads, and Fields Medalists . They told us that, in a variety of ways, their careers are being hijacked by perverse incentives. The result is bad science.

The scientific process, in its ideal form, is elegant: Ask a question, set up an objective test, and get an answer. Repeat. Science is rarely practiced to that ideal. But Copernicus believed in that ideal. So did the rocket scientists behind the moon landing.

But nowadays, our respondents told us, the process is riddled with conflict. Scientists say they’re forced to prioritize self-preservation over pursuing the best questions and uncovering meaningful truths.

"I feel torn between asking questions that I know will lead to statistical significance and asking questions that matter," says Kathryn Bradshaw, a 27-year-old graduate student of counseling at the University of North Dakota.

Today, scientists' success often isn't measured by the quality of their questions or the rigor of their methods. It's instead measured by how much grant money they win, the number of studies they publish, and how they spin their findings to appeal to the public.

Scientists often learn more from studies that fail. But failed studies can mean career death. So instead, they’re incentivized to generate positive results they can publish. And the phrase "publish or perish" hangs over nearly every decision. It’s a nagging whisper, like a Jedi’s path to the dark side.

"Over time the most successful people will be those who can best exploit the system," Paul Smaldino, a cognitive science professor at University of California Merced, says.

To Smaldino, the selection pressures in science have favored less-than-ideal research: "As long as things like publication quantity, and publishing flashy results in fancy journals are incentivized, and people who can do that are rewarded … they’ll be successful, and pass on their successful methods to others."

Many scientists have had enough.  They want to break this cycle of perverse incentives and rewards. They are going through a period of introspection, hopeful that the end result will yield stronger scientific institutions . In our survey and interviews, they offered a wide variety of ideas for improving the scientific process and bringing it closer to its ideal form.

Before we jump in, some caveats to keep in mind: Our survey was not a scientific poll. For one, the respondents disproportionately hailed from the biomedical and social sciences and English-speaking communities.

Many of the responses did, however, vividly illustrate the challenges and perverse incentives that scientists across fields face. And they are a valuable starting point for a deeper look at dysfunction in science today.

The place to begin is right where the perverse incentives first start to creep in: the money.

(1) Academia has a huge money problem

To do most any kind of research, scientists need money: to run studies, to subsidize lab equipment, to pay their assistants and even their own salaries. Our respondents told us that getting — and sustaining — that funding is a perennial obstacle.

Their gripe isn’t just with the quantity, which, in many fields, is shrinking. It’s the way money is handed out that puts pressure on labs to publish a lot of papers, breeds conflicts of interest, and encourages scientists to overhype their work.

In the United States, academic researchers in the sciences generally cannot rely on university funding alone to pay for their salaries, assistants, and lab costs. Instead, they have to seek outside grants. "In many cases the expectations were and often still are that faculty should cover at least 75 percent of the salary on grants," writes John Chatham, a professor of medicine studying cardiovascular disease at University of Alabama at Birmingham.

Grants also usually expire after three or so years, which pushes scientists away from long-term projects. Yet as John Pooley, a neurobiology postdoc at the University of Bristol, points out, the biggest discoveries usually take decades to uncover and are unlikely to occur under short-term funding schemes.

Outside grants are also in increasingly short supply. In the US, the largest source of funding is the federal government, and that pool of money has been plateauing for years, while young scientists enter the workforce at a faster rate than older scientists retire.

Take the National Institutes of Health, a major funding source. Its budget rose at a fast clip through the 1990s, stalled in the 2000s, and then dipped with sequestration budget cuts in 2013. All the while, rising costs for conducting science meant that each NIH dollar purchased less and less. Last year, Congress approved the biggest NIH spending hike in a decade . But it won’t erase the shortfall.

The consequences are striking: In 2000, more than 30 percent of NIH grant applications got approved. Today, it’s closer to 17 percent. "It's because of what's happened in the last 12 years that young scientists in particular are feeling such a squeeze," NIH Director Francis Collins said at the Milken Global Conference in May.

Truly novel research takes longer to produce, and it doesn’t always pay off. A National Bureau of Economic Research working paper found that, on the whole, truly unconventional papers tend to be less consistently cited in the literature. So scientists and funders increasingly shy away from them, preferring short-turnaround, safer papers. But everyone suffers from that: the NBER report found that novel papers also occasionally lead to big hits that inspire high-impact, follow-up studies.

"I think because you have to publish to keep your job and keep funding agencies happy, there are a lot of (mediocre) scientific papers out there ... with not much new science presented," writes Kaitlyn Suski, a chemistry and atmospheric science postdoc at Colorado State University.

Another worry: When independent, government, or university funding sources dry up, scientists may feel compelled to turn to industry or interest groups eager to generate studies to support their agendas.

Finally, all of this grant writing is a huge time suck, taking resources away from the actual scientific work. Tyler Josephson, an engineering graduate student at the University of Delaware, writes that many professors he knows spend 50 percent of their time writing grant proposals. "Imagine," he asks, "what they could do with more time to devote to teaching and research?"

It’s easy to see how these problems in funding kick off a vicious cycle. To be more competitive for grants, scientists have to have published work. To have published work, they need positive (i.e.,  statistically significant ) results. That puts pressure on scientists to pick "safe" topics that will yield a publishable conclusion — or, worse, may bias their research toward significant results.

"When funding and pay structures are stacked against academic scientists," writes Alison Bernstein, a neuroscience postdoc at Emory University, "these problems are all exacerbated."

Fixes for science's funding woes

Right now there are arguably too many researchers chasing too few grants. Or, as a 2014 piece in the Proceedings of the National Academy of Sciences put it: "The current system is in perpetual disequilibrium, because it will inevitably generate an ever-increasing supply of scientists vying for a finite set of research resources and employment opportunities."

"As it stands, too much of the research funding is going to too few of the researchers," writes Gordon Pennycook, a PhD candidate in cognitive psychology at the University of Waterloo. "This creates a culture that rewards fast, sexy (and probably wrong) results."

One straightforward way to ameliorate these problems would be for governments to simply increase the amount of money available for science. (Or, more controversially, decrease the number of PhDs, but we’ll get to that later.) If Congress boosted funding for the NIH and National Science Foundation, that would take some of the competitive pressure off researchers.

But that only goes so far. Funding will always be finite, and researchers will never get blank checks to fund the risky science projects of their dreams. So other reforms will also prove necessary.

One suggestion: Bring more stability and predictability into the funding process. "The NIH and NSF budgets are subject to changing congressional whims that make it impossible for agencies (and researchers) to make long term plans and commitments," M. Paul Murphy, a neurobiology professor at the University of Kentucky, writes. "The obvious solution is to simply make [scientific funding] a stable program, with an annual rate of increase tied in some manner to inflation."

Another idea would be to change how grants are awarded: Foundations and agencies could fund specific people and labs for a period of time rather than individual project proposals. (The Howard Hughes Medical Institute already does this.) A system like this would give scientists greater freedom to take risks with their work.

Alternatively, researchers in the journal mBio recently called for a lottery-style system. Proposals would be measured on their merits, but then a computer would randomly choose which get funded.

"Although we recognize that some scientists will cringe at the thought of allocating funds by lottery," the authors of the mBio piece write, "the available evidence suggests that the system is already in essence a lottery without the benefits of being random." Pure randomness would at least reduce some of the perverse incentives at play in jockeying for money.

There are also some ideas out there to minimize conflicts of interest from industry funding. Recently, in PLOS Medicine , Stanford epidemiologist John Ioannidis suggested that pharmaceutical companies ought to pool the money they use to fund drug research, to be allocated to scientists who then have no exchange with industry during study design and execution. This way, scientists could still get funding for work crucial for drug approvals — but without the pressures that can skew results.

These solutions are by no means complete, and they may not make sense for every scientific discipline. The daily incentives facing biomedical scientists to bring new drugs to market are different from the incentives facing geologists trying to map out new rock layers. But based on our survey, funding appears to be at the root of many of the problems facing scientists, and it’s one that deserves more careful discussion.

(2) Too many studies are poorly designed. Blame bad incentives.

Scientists are ultimately judged by the research they publish. And the pressure to publish pushes scientists to come up with splashy results, of the sort that get them into prestigious journals. "Exciting, novel results are more publishable than other kinds," says Brian Nosek , who co-founded the Center for Open Science at the University of Virginia.

The problem here is that truly groundbreaking findings simply don’t occur very often, which means scientists face pressure to game their studies so they turn out to be a little more "revolutionary." (Caveat: Many of the respondents who focused on this particular issue hailed from the biomedical and social sciences.)

Some of this bias can creep into decisions that are made early on: choosing whether or not to randomize participants, including a control group for comparison, or controlling for certain confounding factors but not others. (Read more on study design particulars  here .)

Many of our survey respondents noted that perverse incentives can also push scientists to cut corners in how they analyze their data.

"I have incredible amounts of stress that maybe once I finish analyzing the data, it will not look significant enough for me to defend," writes Jess Kautz, a PhD student at the University of Arizona. "And if I get back mediocre results, there's going to be incredible pressure to present it as a good result so they can get me out the door. At this moment, with all this in my mind, it is making me wonder whether I could give an intellectually honest assessment of my own work."

Increasingly, meta-researchers (who conduct research on research) are realizing that scientists often do find little ways to hype up their own results — and they’re not always doing it consciously. Among the most famous examples is a technique called "p-hacking," in which researchers test their data against many hypotheses and only report those that have statistically significant results.

In a recent study , which tracked the misuse of p-values in biomedical journals, meta-researchers found "an epidemic" of statistical significance: 96 percent of the papers that included a p-value in their abstracts boasted statistically significant results.

That seems awfully suspicious. It suggests the biomedical community has been chasing statistical significance, potentially giving dubious results the appearance of validity through techniques like p-hacking — or simply suppressing important results that don't look significant enough. Fewer studies share effect sizes (which arguably gives a better indication of how meaningful a result might be) or discuss measures of uncertainty.

"The current system has done too much to reward results," says Joseph Hilgard, a postdoctoral research fellow at the Annenberg Public Policy Center. "This causes a conflict of interest: The scientist is in charge of evaluating the hypothesis, but the scientist also desperately wants the hypothesis to be true."

The consequences are staggering. An estimated $200 billion — or the equivalent of 85 percent of global spending on research — is routinely wasted on poorly designed and redundant studies, according to meta-researchers who have analyzed inefficiencies in research. We know that as much as 30 percent of the most influential original medical research papers later turn out to be wrong or exaggerated.

Fixes for poor study design

Our respondents suggested that the two key ways to encourage stronger study design — and discourage positive results chasing — would involve rethinking the rewards system and building more transparency into the research process.

"I would make rewards based on the rigor of the research methods, rather than the outcome of the research," writes Simine Vazire, a journal editor and a social psychology professor at UC Davis. "Grants, publications, jobs, awards, and even media coverage should be based more on how good the study design and methods were, rather than whether the result was significant or surprising."

Likewise, Cambridge mathematician Tim Gowers argues that researchers should get recognition for advancing science broadly through informal idea sharing — rather than only getting credit for what they publish.

"We’ve gotten used to working away in private and then producing a sort of polished document in the form of a journal article," Gowers said. "This tends to hide a lot of the thought process that went into making the discoveries. I'd like attitudes to change so people focus less on the race to be first to prove a particular theorem, or in science to make a particular discovery, and more on other ways of contributing to the furthering of the subject."

When it comes to published results, meanwhile, many of our respondents wanted to see more journals put a greater emphasis on rigorous methods and processes rather than splashy results.

"I think the one thing that would have the biggest impact is removing publication bias: judging papers by the quality of questions, quality of method, and soundness of analyses, but not on the results themselves," writes Michael Inzlicht , a University of Toronto psychology and neuroscience professor.

Some journals are already embracing this sort of research. PLOS One , for example, makes a point of accepting negative studies (in which a scientist conducts a careful experiment and finds nothing) for publication, as does the aptly named Journal of Negative Results in Biomedicine .

More transparency would also help, writes Daniel Simons, a professor of psychology at the University of Illinois. Here’s one example: ClinicalTrials.gov , a site run by the NIH, allows researchers to register their study design and methods ahead of time and then publicly record their progress. That makes it more difficult for scientists to hide experiments that didn’t produce the results they wanted. (The site now holds information for more than 180,000 studies in 180 countries.)

Similarly, the AllTrials campaign is pushing for every clinical trial (past, present, and future) around the world to be registered, with the full methods and results reported. Some drug companies and universities have created portals that allow researchers to access raw data from their trials.

The key is for this sort of transparency to become the norm rather than a laudable outlier.

(3) Replicating results is crucial. But scientists rarely do it.

Replication is another foundational concept in science. Researchers take an older study that they want to test and then try to reproduce it to see if the findings hold up.

Testing, validating, retesting — it's all part of a slow and grinding process to arrive at some semblance of scientific truth. But this doesn't happen as often as it should, our respondents said. Scientists face few incentives to engage in the slog of replication. And even when they attempt to replicate a study, they often find they can’t do so . Increasingly it’s being called a "crisis of irreproducibility."

The stats bear this out: A 2015 study looked at 83 highly cited studies that claimed to feature effective psychiatric treatments. Only 16 had ever been successfully replicated. Another 16 were contradicted by follow-up attempts, and 11 were found to have substantially smaller effects the second time around. Meanwhile, nearly half of the studies (40) had never been subject to replication at all.

More recently, a landmark study published in the journal Science demonstrated that only a fraction of recent findings in top psychology journals could be replicated. This is happening in other fields too, says Ivan Oransky, one of the founders of the blog Retraction Watch , which tracks scientific retractions.

As for the underlying causes, our survey respondents pointed to a couple of problems. First, scientists have very few incentives to even try replication. Jon-Patrick Allem, a social scientist at the Keck School of Medicine of USC, noted that funding agencies prefer to support projects that find new information instead of confirming old results.

Journals are also reluctant to publish replication studies unless "they contradict earlier findings or conclusions," Allem writes. The result is to discourage scientists from checking each other's work. "Novel information trumps stronger evidence, which sets the parameters for working scientists."

The second problem is that many studies can be difficult to replicate. Sometimes their methods are too opaque. Sometimes the original studies had too few participants to produce a replicable answer. And sometimes, as we saw in the previous section, the study is simply poorly designed or outright wrong.

Again, this goes back to incentives: When researchers have to publish frequently and chase positive results, there’s less time to conduct high-quality studies with well-articulated methods.

Fixes for underreplication

Scientists need more carrots to entice them to pursue replication in the first place. As it stands, researchers are encouraged to publish new and positive results and to allow negative results to linger in their laptops or file drawers.

This has plagued science with a problem called "publication bias" — not all studies that are conducted actually get published in journals, and the ones that do tend to have positive and dramatic conclusions.

If institutions started to reward tenure positions or make hires based on the quality of a researcher’s body of work, instead of quantity, this might encourage more replication and discourage positive results chasing.

"The key that needs to change is performance review," writes Christopher Wynder, a former assistant professor at McMaster University. "It affects reproducibility because there is little value in confirming another lab's results and trying to publish the findings."

The next step would be to make replication of studies easier. This could include more robust sharing of methods in published research papers. "It would be great to have stronger norms about being more detailed with the methods," says University of Virginia’s Brian Nosek.

He also suggested more regularly adding supplements at the end of papers that get into the procedural nitty-gritty, to help anyone wanting to repeat an experiment.  "If I can rapidly get up to speed, I have a much better chance of approximating the results," he said.

Nosek has detailed other potential fixes that might help with replication — all part of his work at the Center for Open Science .

A greater degree of transparency and data sharing would enable replications, said Stanford’s John Ioannidis. Too often, anyone trying to replicate a study must chase down the original investigators for details about how the experiment was conducted.

"It is better to do this in an organized fashion with buy-in from all leading investigators in a scientific discipline," he explained, "rather than have to try to find the investigator in each case and ask him or her in detective-work fashion about details, data, and methods that are otherwise unavailable."

Researchers could also make use of new tools , such as open source software that tracks every version of a data set, so that they can share their data more easily and have transparency built into their workflow.

Some of our respondents suggested that scientists engage in replication prior to publication. "Before you put an exploratory idea out in the literature and have people take the time to read it, you owe it to the field to try to replicate your own findings," says John Sakaluk, a social psychologist at the University of Victoria.

For example, he has argued, psychologists could conduct small experiments with a handful of participants to form ideas and generate hypotheses. But they would then need to conduct bigger experiments, with more participants, to replicate and confirm those hypotheses before releasing them into the world. "In doing so,"  Sakaluk says, "the rest of us can have more confidence that this is something we might want to [incorporate] into our own research."

(4) Peer review is broken

Peer review is meant to weed out junk science before it reaches publication. Yet over and over again in our survey, respondents told us this process fails. It was one of the parts of the scientific machinery to elicit the most rage among the researchers we heard from.

Normally, peer review works like this: A researcher submits an article for publication in a journal. If the journal accepts the article for review, it's sent off to peers in the same field for constructive criticism and eventual publication — or rejection. (The level of anonymity varies; some journals have double-blind reviews, while others have moved to triple-blind review, where the authors, editors, and reviewers don’t know who one another are.)

It sounds like a reasonable system. But numerous studies and systematic reviews have shown that peer review doesn’t reliably prevent poor-quality science from being published.

The process frequently fails to detect fraud or other problems with manuscripts, which isn't all that surprising when you consider researchers aren't paid or otherwise rewarded for the time they spend reviewing manuscripts. They do it out of a sense of duty — to contribute to their area of research and help advance science.

But this means it's not always easy to find the best people to peer-review manuscripts in their field, that harried researchers delay doing the work (leading to publication delays of up to two years), and that when they finally do sit down to peer-review an article they might be rushed and miss errors in studies.

"The issue is that most referees simply don't review papers carefully enough, which results in the publishing of incorrect papers, papers with gaps, and simply unreadable papers," says Joel Fish, an assistant professor of mathematics at the University of Massachusetts Boston. "This ends up being a large problem for younger researchers to enter the field, since that means they have to ask around to figure out which papers are solid and which are not."

That's not to mention the problem of peer review bullying. Since the default in the process is that editors and peer reviewers know who the authors are (but authors don’t know who the reviews are), biases against researchers or institutions can creep in, opening the opportunity for rude, rushed, and otherwise unhelpful comments. (Just check out the popular #SixWordPeerReview hashtag on Twitter).

These issues were not lost on our survey respondents, who said peer review amounts to a broken system, which punishes scientists and diminishes the quality of publications. They want to not only overhaul the peer review process but also change how it's conceptualized.

Fixes for peer review

On the question of editorial bias and transparency, our respondents were surprisingly divided. Several suggested that all journals should move toward double-blinded peer review, whereby reviewers can't see the names or affiliations of the person they're reviewing and publication authors don't know who reviewed them. The main goal here was to reduce bias.

"We know that scientists make biased decisions based on unconscious stereotyping," writes Pacific Northwest National Lab postdoc Timothy Duignan. "So rather than judging a paper by the gender, ethnicity, country, or institutional status of an author — which I believe happens a lot at the moment — it should be judged by its quality independent of those things."

Yet others thought that more transparency, rather than less, was the answer: "While we correctly advocate for the highest level of transparency in publishing, we still have most reviews that are blinded, and I cannot know who is reviewing me," writes Lamberto Manzoli, a professor of epidemiology and public health at the University of Chieti, in Italy. "Too many times we see very low quality reviews, and we cannot understand whether it is a problem of scarce knowledge or conflict of interest."

Perhaps there is a middle ground. For example,  e Life , a new  open access journal that is rapidly rising in impact factor, runs a collaborative peer review process. Editors and peer reviewers work together on each submission to create a consolidated list of comments about a paper. The author can then reply to what the group saw as the most important issues, rather than facing the biases and whims of individual reviewers. (Oddly, this process is faster — eLife takes less time to accept papers than Nature or Cell.)

Still, those are mostly incremental fixes. Other respondents argued that we might need to radically rethink the entire process of peer review from the ground up.

"The current peer review process embraces a concept that a paper is final," says Nosek. "The review process is [a form of] certification, and that a paper is done." But science doesn't work that way. Science is an evolving process, and truth is provisional. So, Nosek said, science must "move away from the embrace of definitiveness of publication."

Some respondents wanted to think of peer review as more of a continuous process, in which studies are repeatedly and transparently updated and republished as new feedback changes them — much like Wikipedia entries. This would require some sort of expert crowdsourcing.

"The scientific publishing field — particularly in the biological sciences — acts like there is no internet," says Lakshmi Jayashankar, a senior scientific reviewer with the federal government. "The paper peer review takes forever, and this hurts the scientists who are trying to put their results quickly into the public domain."

One possible model already exists in mathematics and physics, where there is a long tradition of "pre-printing" articles. Studies are posted on an open website called  arXiv.org , often before being peer-reviewed and published in journals. There, the articles are sorted and commented on by a community of moderators, providing another chance to filter problems before they make it to peer review.

"Posting preprints would allow scientific crowdsourcing to increase the number of errors that are caught, since traditional peer-reviewers cannot be expected to be experts in every sub-discipline," writes Scott Hartman, a paleobiology PhD student at the University of Wisconsin.

And even after an article is published, researchers think the peer review process shouldn't stop. They want to see more "post-publication" peer review on the web, so that academics can critique and comment on articles after they've been published. Sites like PubPeer and F1000Research have already popped up to facilitate that kind of post-publication feedback.

"We do this a couple of times a year at conferences," writes Becky Clarkson, a geriatric medicine researcher at the University of Pittsburgh. "We could do this every day on the internet."

The bottom line is that traditional peer review has never worked as well as we imagine it to — and it’s ripe for serious disruption.

(5) Too much science is locked behind paywalls

After a study has been funded, conducted, and peer-reviewed, there's still the question of getting it out so that others can read and understand its results.

Over and over, our respondents expressed dissatisfaction with how scientific research gets disseminated. Too much is locked away in paywalled journals, difficult and costly to access, they said. Some respondents also criticized the publication process itself for being too slow, bogging down the pace of research.

On the access question, a number of scientists argued that academic research should be free for all to read. They chafed against the current model, in which for-profit publishers put journals behind pricey paywalls.

A single article in Science will set you back $30; a year-long subscription to Cell will cost $279. Elsevier publishes 2,000 journals that can cost up to $10,000 or $20,000 a year for a subscription.

Many US institutions pay those journal fees for their employees, but not all scientists (or other curious readers) are so lucky. In a recent issue of Science , journalist John Bohannon described the plight of a PhD candidate at a top university in Iran. He calculated that the student would have to spend $1,000 a week just to read the papers he needed.

As Michael Eisen, a biologist at UC Berkeley and co-founder of the Public Library of Science (or PLOS ) , put it , scientific journals are trying to hold on to the profits of the print era in the age of the internet.  Subscription prices have continued to climb, as a handful of big publishers (like Elsevier) have bought up more and more journals, creating mini knowledge fiefdoms.

"Large, publicly owned publishing companies make huge profits off of scientists by publishing our science and then selling it back to the university libraries at a massive profit (which primarily benefits stockholders)," Corina Logan, an animal behavior researcher at the University of Cambridge, noted. "It is not in the best interest of the society, the scientists, the public, or the research." (In 2014, Elsevier reported a profit margin of nearly 40 percent and revenues close to $3 billion.)

"It seems wrong to me that taxpayers pay for research at government labs and universities but do not usually have access to the results of these studies, since they are behind paywalls of peer-reviewed journals," added Melinda Simon, a postdoc microfluidics researcher at Lawrence Livermore National Lab.

Fixes for closed science

Many of our respondents urged their peers to publish in open access journals (along the lines of PeerJ or PLOS Biology ). But there’s an inherent tension here. Career advancement can often depend on publishing in the most prestigious journals, like Science or Nature , which still have paywalls.

There's also the question of how best to finance a wholesale transition to open access. After all, journals can never be entirely free. Someone has to pay for the editorial staff, maintaining the website, and so on. Right now, open access journals typically charge fees to those submitting papers, putting the burden on scientists who are already struggling for funding.

One radical step would be to abolish for-profit publishers altogether and move toward a nonprofit model. "For journals I could imagine that scientific associations run those themselves," suggested Johannes Breuer, a postdoctoral researcher in media psychology at the University of Cologne. "If they go for online only, the costs for web hosting, copy-editing, and advertising (if needed) can be easily paid out of membership fees."

As a model, Cambridge’s Tim Gowers has launched an online mathematics journal called Discrete Analysis . The nonprofit venture is owned and published by a team of scholars, it has no publisher middlemen, and access will be completely free for all.

Until wholesale reform happens, however, many scientists are going a much simpler route: illegally pirating papers.

Bohannon reported that millions of researchers around the world now use Sci-Hub , a site set up by Alexandra Elbakyan, a Russia-based neuroscientist, that illegally hosts more than 50 million academic papers. "As a devout pirate," Elbakyan told us, "I think that copyright should be abolished."

One respondent had an even more radical suggestion: that we abolish the existing peer-reviewed journal system altogether and simply publish everything online as soon as it’s done.

"Research should be made available online immediately, and be judged by peers online rather than having to go through the whole formatting, submitting, reviewing, rewriting, reformatting, resubmitting, etc etc etc that can takes years," writes Bruno Dagnino, formerly of the Netherlands Institute for Neuroscience. "One format, one platform. Judge by the whole community, with no delays."

A few scientists have been taking steps in this direction. Rachel Harding, a genetic researcher at the University of Toronto, has set up a website called Lab Scribbles , where she publishes her lab notes on the structure of huntingtin proteins in real time, posting data as well as summaries of her breakthroughs and failures. The idea is to help share information with other researchers working on similar issues, so that labs can avoid needless overlap and learn from each other's mistakes.

Not everyone might agree with approaches this radical; critics worry that too much sharing might encourage scientific free riding. Still, the common theme in our survey was transparency. Science is currently too opaque, research too difficult to share. That needs to change.

(6) Science is poorly communicated to the public

"If I could change one thing about science, I would change the way it is communicated to the public by scientists, by journalists, and by celebrities," writes Clare Malone, a postdoctoral researcher in a cancer genetics lab at Brigham and Women's Hospital.

She wasn't alone. Quite a few respondents in our survey expressed frustration at how science gets relayed to the public. They were distressed by the fact that so many laypeople hold on to completely unscientific ideas or have a crude view of how science works.

They have a point. Science journalism is often full of exaggerated, conflicting, or outright misleading claims. If you ever want to see a perfect example of this, check out "Kill or Cure," a site where Paul Battley meticulously documents all the times the Daily Mail reported that various items — from antacids to yogurt — either cause cancer, prevent cancer, or sometimes do both.

Sometimes bad stories are peddled by university press shops. In 2015, the University of Maryland issued a press release claiming that a single brand of chocolate milk could improve concussion recovery. It was an absurd case of science hype.

Indeed, one review in BMJ found that one-third of university press releases contained either exaggerated claims of causation (when the study itself only suggested correlation), unwarranted implications about animal studies for people, or unfounded health advice.

But not everyone blamed the media and publicists alone. Other respondents pointed out that scientists themselves often oversell their work, even if it's preliminary, because funding is competitive and everyone wants to portray their work as big and important and game-changing.

"You have this toxic dynamic where journalists and scientists enable each other in a way that massively inflates the certainty and generality of how scientific findings are communicated and the promises that are made to the public," writes Daniel Molden, an associate professor of psychology at Northwestern University. "When these findings prove to be less certain and the promises are not realized, this just further erodes the respect that scientists get and further fuels scientists desire for appreciation."

Fixes for better science communication

Opinions differed on how to improve this sorry state of affairs — some pointed to the media, some to press offices, others to scientists themselves.

Plenty of our respondents wished that more science journalists would move away from hyping single studies. Instead, they said, reporters ought to put new research findings in context, and pay more attention to the rigor of a study's methodology than to the splashiness of the end results.

"On a given subject, there are often dozens of studies that examine the issue," writes Brian Stacy of the US Department of Agriculture. "It is very rare for a single study to conclusively resolve an important research question, but many times the results of a study are reported as if they do."

But it’s not just reporters who will need to shape up. The "toxic dynamic" of journalists, academic press offices, and scientists enabling one another to hype research can be tough to change, and many of our respondents pointed out that there were no easy fixes — though recognition was an important first step.

Some suggested the creation of credible referees that could rigorously distill the strengths and weaknesses of research. (Some variations of this are starting to pop up: The Genetic Expert News Service solicits outside experts to weigh in on big new studies in genetics and biotechnology.) Other respondents suggested that making research free to all might help tamp down media misrepresentations.

Still other respondents noted that scientists themselves should spend more time learning how to communicate with the public — a skill that tends to be under-rewarded in the current system.

"Being able to explain your work to a non-scientific audience is just as important as publishing in a peer-reviewed journal, in my opinion, but currently the incentive structure has no place for engaging the public," writes Crystal Steltenpohl, a graduate assistant at DePaul University.

Reducing the perverse incentives around scientific research itself could also help reduce overhype.  "If we reward research based on how noteworthy the results are, this will create pressure to exaggerate the results (through exploiting flexibility in data analysis, misrepresenting results, or outright fraud)," writes UC Davis's Simine Vazire. "We should reward research based on how rigorous the methods and design are."

Or perhaps we should focus on improving science literacy. Jeremy Johnson, a project coordinator at the Broad Institute, argued that bolstering science education could help ameliorate a lot of these problems. "Science literacy should be a top priority for our educational policy," he said, "not an elective."

(7) Life as a young academic is incredibly stressful

When we asked researchers what they’d fix about science, many talked about the scientific process itself, about study design or peer review. These responses often came from tenured scientists who loved their jobs but wanted to make the broader scientific project even better.

But on the flip side, we heard from a number of researchers — many of them graduate students or postdocs — who were genuinely passionate about research but found the day-to-day experience of being a scientist grueling and unrewarding. Their comments deserve a section of their own.

Today, many tenured scientists and research labs depend on small armies of graduate students and postdoctoral researchers to perform their experiments and conduct data analysis.

These grad students and postdocs are often the primary authors on many studies. In a number of fields, such as the biomedical sciences, a postdoc position is a prerequisite before a researcher can get a faculty-level position at a university.

This entire system sits at the heart of modern-day science. (A new card game called Lab Wars pokes fun at these dynamics.)

But these low-level research jobs can be a grind. Postdocs typically work long hours and are relatively low-paid for their level of education — salaries are frequently pegged to stipends set by NIH National Research Service Award grants, which start at $43,692 and rise to $47,268 in year three.

Postdocs tend to be hired on for one to three years at a time, and in many institutions they are considered contractors, limiting their workplace protections. We heard repeatedly about extremely long hours and limited family leave benefits.

"Oftentimes this is problematic for individuals in their late 20s and early to mid-30s who have PhDs and who may be starting families while also balancing a demanding job that pays poorly," wrote one postdoc, who asked for anonymity.

This lack of flexibility tends to disproportionately affect women — especially women planning to have families — which helps contribute to gender inequalities in research. ( A 2012 paper found that female job applicants in academia are judged more harshly and are offered less money than males.) "There is very little support for female scientists and early-career scientists," noted another postdoc.

"There is very little long-term financial security in today's climate, very little assurance where the next paycheck will come from," wrote William Kenkel, a postdoctoral researcher in neuroendocrinology at Indiana University. "Since receiving my PhD in 2012, I left Chicago and moved to Boston for a post-doc, then in 2015 I left Boston for a second post-doc in Indiana. In a year or two, I will move again for a faculty job, and that's if I'm lucky. Imagine trying to build a life like that."

This strain can also adversely affect the research that young scientists do. "Contracts are too short term," noted another researcher. "It discourages rigorous research as it is difficult to obtain enough results for a paper (and hence progress) in two to three years. The constant stress drives otherwise talented and intelligent people out of science also."

Because universities produce so many PhDs but have way fewer faculty jobs available, many of these postdoc researchers have limited career prospects. Some of them end up staying stuck in postdoc positions for five or 10 years or more.

"In the biomedical sciences," wrote the first postdoc quoted above, "each available faculty position receives applications from hundreds or thousands of applicants, putting immense pressure on postdocs to publish frequently and in high impact journals to be competitive enough to attain those positions."

Many young researchers pointed out that PhD programs do fairly little to train people for careers outside of academia. "Too many [PhD] students are graduating for a limited number of professor positions with minimal training for careers outside of academic research," noted Don Gibson, a PhD candidate studying plant genetics at UC Davis.

Laura Weingartner, a graduate researcher in evolutionary ecology at Indiana University, agreed: "Few universities (specifically the faculty advisors) know how to train students for anything other than academia, which leaves many students hopeless when, inevitably, there are no jobs in academia for them."

Add it up and it's not surprising that we heard plenty of comments about anxiety and depression among both graduate students and postdocs. "There is a high level of depression among PhD students," writes Gibson. "Long hours, limited career prospects, and low wages contribute to this emotion."

A 2015 study at the University of California Berkeley found that 47 percent of PhD students surveyed could be considered depressed. The reasons for this are complex and can't be solved overnight. Pursuing academic research is already an arduous, anxiety-ridden task that's bound to take a toll on mental health.

But as Jennifer Walker explored recently at Quartz, many PhD students also feel isolated and unsupported, exacerbating those issues.

Fixes to keep young scientists in science

We heard plenty of concrete suggestions. Graduate schools could offer more generous family leave policies and child care for graduate students. They could also increase the number of female applicants they accept in order to balance out the gender disparity.

But some respondents also noted that workplace issues for grad students and postdocs were inseparable from some of the fundamental issues facing science that we discussed earlier. The fact that university faculty and research labs face immense pressure to publish — but have limited funding — makes it highly attractive to rely on low-paid postdocs.

"There is little incentive for universities to create jobs for their graduates or to cap the number of PhDs that are produced," writes Weingartner. "Young researchers are highly trained but relatively inexpensive sources of labor for faculty."

Some respondents also pointed to the mismatch between the number of PhDs produced each year and the number of academic jobs available.

A recent feature by Julie Gould in Nature explored a number of ideas for revamping the PhD system. One idea is to split the PhD into two programs: one for vocational careers and one for academic careers. The former would better train and equip graduates to find jobs outside academia.

This is hardly an exhaustive list. The core point underlying all these suggestions, however, was that universities and research labs need to do a better job of supporting the next generation of researchers. Indeed, that's arguably just as important as addressing problems with the scientific process itself. Young scientists, after all, are by definition the future of science.

Weingartner concluded with a sentiment we saw all too frequently: "Many creative, hard-working, and/or underrepresented scientists are edged out of science because of these issues. Not every student or university will have all of these unfortunate experiences, but they’re pretty common. There are a lot of young, disillusioned scientists out there now who are expecting to leave research."

Science needs to correct its greatest weaknesses

Science is not doomed.

For better or worse, it still works. Look no further than the novel vaccines to prevent Ebola, the discovery of gravitational waves , or new treatments for stubborn diseases. And it’s getting better in many ways. See the work of meta -researchers who study and evaluate research — a field that has gained prominence over the past 20 years.

More from this feature

We asked hundreds of scientists what they’d change about science. Here are 33 of our favorite responses.

But science is conducted by fallible humans, and it hasn’t been human-proofed to protect against all our foibles. The scientific revolution began just 500 years ago. Only over the past 100 has science become professionalized. There is still room to figure out how best to remove biases and align incentives.

To that end, here are some broad suggestions:

One: Science has to acknowledge and address its money problem. Science is enormously valuable and deserves ample funding. But the way incentives are set up can distort research.

Right now, small studies with bold results that can be quickly turned around and published in journals are disproportionately rewarded. By contrast, there are fewer incentives to conduct research that tackles important questions with robustly designed studies over long periods of time. Solving this won’t be easy, but it is at the root of many of the issues discussed above.

Two: Science needs to celebrate and reward failure. Accepting that we can learn more from dead ends in research and studies that failed would alleviate the "publish or perish" cycle. It would make scientists more confident in designing robust tests and not just convenient ones, in sharing their data and explaining their failed tests to peers, and in using those null results to form the basis of a career (instead of chasing those all-too-rare breakthroughs).

Three: Science has to be more transparent. Scientists need to publish the methods and findings more fully, and share their raw data in ways that are easily accessible and digestible for those who may want to reanalyze or replicate their findings.

There will always be waste and mediocre research, but as Stanford’s Ioannidis explains in a recent paper , a lack of transparency creates excess waste and diminishes the usefulness of too much research.

Again and again, we also heard from researchers, particularly in social sciences, who felt that their cognitive biases in their own work, influenced by pressures to publish and advance their careers, caused science to go off the rails. If more human-proofing and de-biasing were built into the process — through stronger peer review, cleaner and more consistent funding, and more transparency and data sharing — some of these biases could be mitigated.

These fixes will take time, grinding along incrementally — much like the scientific process itself. But the gains humans have made so far using even imperfect scientific methods would have been unimaginable 500 years ago. The gains from improving the process could prove just as staggering, if not more so.

Correction: An earlier version of this story misstated Noah Grand's title. At the time of the survey he was a lecturer in sociology at UCLA, not a professor.

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Science leads the response to COVID-19. These 25 scientists are tackling the other global challenges

Introducing the Class of 2020 Young Scientists Image:  Photo by Drew Hays on Unsplash

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Martha chahary.

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Stay up to date:, global health.

• Scientists have maximum visibility in the COVID-19 response, while proposing solutions to other global challenges, from climate change to cybersecurity, poverty to pandemics, and food technologies to fracking.
• The World Economic Forum created the Young Scientists Community in 2008, to engage leaders with science and the role it plays in society. The class of 2020 represents 25 researchers at the forefront of scientific discovery from 14 countries across the world.

The COVID-19 crisis has highlighted science’s vital role in society. Science will provide us with an “exit strategy” from the pandemic when a vaccine is finally developed but until then, scientists are helping to understand the origins of the virus, how it spreads, what treatment(s) are most effective and indeed if a cure is possible.

Scientists have maximum visibility right now as different groups of people turn to them looking for answers. COVID-19 aside, science proposes solutions to the myriad of other global challenges facing society, from climate change to cybersecurity, poverty to pandemics, and food technologies to fracking.

Have you read?

Here’s how ‘science diplomacy’ can help us contain covid-19, bill gates explains how the world can use science to tackle the crisis.

That’s part of the reason why the World Economic Forum created the Young Scientists Community in 2008, to engage leaders with science and the role it plays in society. Science is no longer a specialist concern. It is the driving force behind the highest-level decisions on global governance and policy-making, while also informing the individual choices people make about how they want to live and what changes they want to make.

Today we announce our Class of 2020 Young Scientists, representing 25 exceptional researchers at the forefront of scientific discovery from 14 countries across the world.

From chemical oceanography to child psychology and artificial intelligence, these brilliant young academics are joining a community whose aims are to:

• Communicate cutting-edge research and position science discourse within the context of scientific evidence.
• Develop leadership skills and a fuller understanding of global, regional and industry agendas.
• Build a diverse global community of next-generation scientific leaders, committed to engaging in collaborations related to collectively identified issues.

Responding to the COVID-19 pandemic requires global cooperation among governments, international organizations and the business community , which is at the centre of the World Economic Forum’s mission as the International Organization for Public-Private Cooperation.

Since its launch on 11 March, the Forum’s COVID Action Platform has brought together 1,667 stakeholders from 1,106 businesses and organizations to mitigate the risk and impact of the unprecedented global health emergency that is COVID-19.

The platform is created with the support of the World Health Organization and is open to all businesses and industry groups, as well as other stakeholders, aiming to integrate and inform joint action.

As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched – bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the coronavirus.

By joining Forum events, engaging in personal and professional learning modules and sharing experiences with each other, we’re looking forward to working with the Class of 2020 Young Scientists to help leaders from the public and private sector engage more meaningfully with science and in doing so, help these amazing young researchers become stronger ambassadors for science.

Here are the World Economic Forum’s Young Scientists of 2020:

From Africa:

Sarah Fawcett (University of Cape Town, South Africa, South African): Sarah researches the role of ocean chemistry and biology in climate, as well as the impacts of human activities on marine environments.

Salome Maswime (University of Cape Town, South Africa, South African): Salome seeks to understand surgical health systems and causes of maternal death during caesarean section in poorly resourced areas to improve surgical care across populations.

From the Americas:

Gao Wei ( California Institute of Technology, USA, Chinese): Gao Wei develops skin-interfaced wearable biosensors that will enable analytics through sweat rather than blood, leading to non-invasive and real-time analysis and timely medical intervention.

Francisca Garay (Pontificia Universidad Católica de Chile, Chile, Chilean): Francisca is studying what are the most basic building blocks of the universe by developing technologies to accelerate and enhance the capabilities of particle accelerators.

Diego Garcia-Huidobro (Pontificia Universidad Católica de Chile, Chile, Chilean): Diego uses human-centred design methods to develop sustainable and scalable community-level health interventions in Chile.

Jennifer Ronholm (McGill University, Canada, Canadian): Jennifer is working to strengthen the microbiome of agricultural animals to resist infections in the absence of antibiotics, with the aim of reducing the spread of antimicrobial resistance.

Stefanie Sydlik (Carnegie Mellon University, USA, American): Stefanie designs new materials that stimulate the body's healing response to enable the regeneration of natural bone as an alternative to metal implants currently used to heal bone injuries.

Fatma Zeynep Temel (Carnegie Mellon University, USA, Turkish): Fatma uses mathematical models and physical prototypes to test and explore biologically inspired designs, leading to the development of small-scale robots and sensors

Lee Sue-Hyun (Korea Advanced Institute of Science and Technology, South Korea, Korean): Sue-Hyun researches how memories are recalled and updated, and how emotional processes affect human memory, to inform therapeutic interventions for mental disorders.

Meng Ke (Tsinghua University, China, Chinese): Meng Ke seeks to understand the socio-economic causes of population ageing and declining fertility rates to suggest what public policy measures and innovations can be used to address them.

Shi Ling (Hong Kong University of Science and Technology, China, Chinese): Shi Ling researches the vulnerability of cyber-physical systems to protect safety-critical infrastructures – such as power utilities and water transportation systems – from attacks.

Sho Tsuji (University of Tokyo, Japan, Japanese): Sho Tsuji seeks to understand how an infant’s social environment affects language acquisition – a key predictor of future literacy – to inform culturally sensitive, science-based, societal interventions.

Wu Dan (Zhejiang University, China, Chinese): Wu Dan is researching technological advances in MRI techniques to improve its ability to detect tumours and stroke, as well as monitor foetal brain development.

Yi Li (Peking University, China, Chinese): Yi Li researches social-communicative impairments in children with autism in China to develop more precise screening and diagnosis, as well as innovative treatment approaches in the country.

Ying Xu (Chinese Academy of Sciences, China, Chinese): Ying Xu’s research focuses on enhancing China's low-orbit Beidou navigation satellite system, which could lead to advances in the commercial aerospace industry

From Europe:

Celeste Carruth (ETH Zurich, Switzerland, American): Celeste is developing a new 2D ion trap experiment for quantum information processing that is expected to be more reliable and cheaper to scale up than competing technologies and aims to lead to breakthrough quantum computing results.

Nicola Gasparini (Imperial College London, United Kingdom, Italian): Nicola is developing novel technologies to treat severe and incurable vision problems caused by degeneration of the retina, which affects almost 200 million people worldwide.

Joe Grove (University College London, United Kingdom, British): Joe investigates how viruses enter human cells and evade the immune system to reveal new biology and inform the design of future vaccines.

Philip Moll (Ecole Polytechnique Fédérale de Lausanne, Switzerland, German): Philip is developing new methods to make micro-scale modifications to material structures with the potential to improve quantum computing.

Mine Orlu (University College London, United Kingdom, British): Mine is designing patient-tailored pharmaceutical and healthcare technologies that contribute to healthy and independent ageing across the life course.

Michael Saliba (University of Stuttgart, Germany, German): Michael is developing inexpensive, stable and highly efficient perovskite solar cells that will enable the acceleration of sustainable energy technology.

Andy Tay (Imperial College London, United Kingdom, Singaporean): Andy is developing new technology and materials to engineer immune cells, tissues and systems, with the aim of preventing and treating cancer.

Jan Dirk Wegner (ETH Zurich, Switzerland, German): Jan develops novel artificial intelligence methods to analyse large-scale environmental data and accelerate humanity’s ability to solve ecological problems

From the Middle East:

Joseph Costantine (American University of Beirut, Lebanon, Lebanese): Joseph’s research leverages electromagnetism to design a new generation of wireless communication systems, biomedical sensors and wirelessly powered devices through radio frequency energy harvesting.

Joanna Doummar (American University of Beirut, Lebanon, Lebanese): Joanna seeks to better understand complex underground drainage systems, known as karst aquifers, to better address and solve national water quality and quantity challenges.

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January 6, 2020

The Most Important Scientific Problems Have Yet to Be Solved

If certain areas of science appear to be quite mature, others are in the process of development, and yet others remain to be born

By Santiago Ramón y Cajal

Santiago Ramón y Cajal in the 1880s.

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American

Santiago Ramón y Cajal (1852-1934) was a neuroscientist and pathologist, and Spain’s first Nobel laureate. This excerpt from his book Advice for a Young Investigator was first posted on the MIT Press Reader on January 6, 2020. An essay on his remarkable scientific drawings appeared in Scientific American in 2015.

Here is a false concept often heard from the lips of the newly graduated: “Everything of major importance in the various areas of science has already been clarified. What difference does it make if I add some minor detail or gather up what is left in some field where more diligent observers have already collected the abundant, ripe grain. Science won’t change its perspective because of my work, and my name will never emerge from obscurity.”

This is often indolence masquerading as modesty. However, it is also expressed by worthy young men reflecting on the first pangs of dismay experienced when undertaking some major project. This superficial concept of science must be eradicated by the young investigator who does not wish to fail, hopelessly overcome by the struggle developing in his mind between the utilitarian suggestions that are part and parcel of his ethical environment (which may soon convert him to an ordinary and financially successful general practitioner), and those nobler impulses of duty and loyalty urging him on to achievement and honor.

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Wanting to earn the trust placed in him by his mentors, the inexperienced observer hopes to discover a new lode at the earth’s surface, where easy exploration will build his reputation quickly. Unfortunately, with his first excursions into the literature hardly begun, he is shocked to find that the metal lies deep within the ground—surface deposits have been virtually exhausted by observers fortunate enough to arrive earlier and exercise their simple right of eminent domain.

It is nevertheless true that if we arrived on the scene too late for certain problems, we were also born too early to help solve others. Within a century we shall come, by the natural course of events, to monopolize science, plunder its major assets, and harvest its vast fields of data.

Yet we must recognize that there are times when, on the heels of a chance discovery or the development of an important new technique, magnificent scientific discoveries occur one after another as if by spontaneous generation. This happened during the Renaissance when Descartes, Pascal, Galileo, Bacon, Boyle, Newton, our own Sanchez, and others revealed clearly the errors of the ancients and spread the belief that the Greeks, far from exhausting the field of science, had scarcely taken the first steps in understanding the universe. It is a wonderful and fortunate thing for a scientist to be born during one of these great decisive moments in the history of ideas, when much of what has been done in the past is invalidated. Under these circumstances, it could not be easier to choose a fertile area of investigation.

However, let us not exaggerate the importance of such events. Instead, bear in mind that even in our own time science is often built on the ruins of theories once thought to be indestructible. It is important to realize that if certain areas of science appear to be quite mature, others are in the process of development, and yet others remain to be born. Especially in biology, where immense amounts of work have been carried out during the last century, the most essential problems remain unsolved—the origin of life, the problems of heredity and development, the structure and chemical composition of the cell, and so on.

It is fair to say that, in general, no problems have been exhausted; instead, men have been exhausted by the problems. Soil that appears impoverished to one researcher reveals its fertility to another. Fresh talent approaching the analysis of a problem without prejudice will always see new possibilities—some aspect not considered by those who believe that a subject is fully understood. Our knowledge is so fragmentary that unexpected findings appear in even the most fully explored topics. Who, a few short years ago, would have suspected that light and heat still held scientific secrets in reserve? Nevertheless, we now have  argon  in the atmosphere, the  x-rays  of Roentgen, and the  radium  of the Curies, all of which illustrate the inadequacy of our former methods, and the prematurity of our former syntheses.

The best application of the following beautiful dictum of Geoffroy Saint-Hilaire is in biology: “The infinite is always before us.” And the same applies to Carnoy’s no less graphic thought: “Science is a perpetual creative process.” Not everyone is destined to venture into the forest and by sheer determination carve out a serviceable road. However, even the most humble among us can take advantage of the path opened by genius and by traveling along it extract one or another secret from the unknown.

If the beginner is willing to accept the role of gathering details that escaped the wise discoverer, he can be assured that those searching for minutiae eventually acquire an analytical sense so discriminating, and powers of observation so keen, that they are able to solve important problems successfully.

So many apparently trivial observations have led investigators with a thorough knowledge of methods to great scientific conquests! Furthermore, we must bear in mind that because science relentlessly differentiates, the minutiae of today often become important principles tomorrow.

It is also essential to remember that our appreciation of what is important and what is minor, what is great and what is small, is based on false wisdom, on a true anthropomorphic error. Superior and inferior do not exist in nature, nor do primary and secondary relationships. The hierarchies that our minds take pleasure in assigning to natural phenomena arise from the fact that instead of considering things individually, and how they are interrelated, we view them strictly from the perspective of their usefulness or the pleasure they give us. In the chain of life all links are equally valuable because all prove equally necessary.

Things that we see from a distance or do not know how to evaluate are considered small. Even assuming the perspective of human egotism, think how many issues of profound importance to humanity lie within the protoplasm of the simplest microbe! Nothing seems more important in bacteriology than a knowledge of infectious bacteria, and nothing more secondary than the inoffensive microbes that grow abundantly in decomposing organic material. Nevertheless, if these humble fungi—whose mission is to return to the general circulation of matter those substances incorporated by the higher plants and animals—were to disappear, humans could not inhabit the planet.

The far-reaching importance of attention to detail in technical methodology is perhaps demonstrated more clearly in biology than in any other sphere. To cite but one example, recall that Koch, the great German bacteriologist, thought of adding a little alkali to a basic aniline dye, and this allowed him to stain and thus discover the tubercle bacillus—revealing the etiology of a disease that had until then remained uncontrolled by the wisdom of the most illustrious pathologists.

Even the most prominent of the great geniuses have demonstrated a lack of intellectual perspective in the appraisal of scientific insights. Today, we can find many seeds of great discoveries that were mentioned as curiosities of little importance in the writings of the ancients, and even in those of the wise men of the Renaissance. Lost in the pages of a confused theological treatise ( Christianismi restitutio ) are three apparently disdainful lines written by Servetus referring to the pulmonary circulation, which now constitute his major claim to fame. The Aragonese philosopher would be surprised indeed if he were to rise from the dead today. He would find his laborious metaphysical disquisitions totally forgotten, whereas the observation he used simply to argue for the residence of the soul in the blood is widely praised! Or again, it has been inferred from a passage of Seneca’s that the ancients knew the magnifying powers of a crystal sphere filled with water. Who would have suspected that in this phenomenon of magnification, disregarded for centuries, slumbered the embryo of two powerful analytical instruments, the microscope and telescope—and two equally great sciences, biology and astronomy!

In summary, there are no small problems. Problems that appear small are large problems that are not understood. Instead of tiny details unworthy of the intellectual, we have men whose tiny intellects cannot rise to penetrate the infinitesimal. Nature is a harmonious mechanism where all parts, including those appearing to play a secondary role, cooperate in the functional whole. In contemplating this mechanism, shallow men arbitrarily divide its parts into essential and secondary, whereas the insightful thinker is content with classifying them as understood and poorly understood, ignoring for the moment their size and immediately useful properties. No one can predict their importance in the future.

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• How to Define a Research Problem | Ideas & Examples

How to Define a Research Problem | Ideas & Examples

Published on 8 November 2022 by Shona McCombes and Tegan George.

A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge.

Some research will do both of these things, but usually the research problem focuses on one or the other. The type of research problem you choose depends on your broad topic of interest and the type of research you think will fit best.

This article helps you identify and refine a research problem. When writing your research proposal or introduction , formulate it as a problem statement and/or research questions .

Table of contents

Why is the research problem important, step 1: identify a broad problem area, step 2: learn more about the problem, frequently asked questions about research problems.

Having an interesting topic isn’t a strong enough basis for academic research. Without a well-defined research problem, you are likely to end up with an unfocused and unmanageable project.

You might end up repeating what other people have already said, trying to say too much, or doing research without a clear purpose and justification. You need a clear problem in order to do research that contributes new and relevant insights.

Whether you’re planning your thesis , starting a research paper , or writing a research proposal , the research problem is the first step towards knowing exactly what you’ll do and why.

Prevent plagiarism, run a free check.

As you read about your topic, look for under-explored aspects or areas of concern, conflict, or controversy. Your goal is to find a gap that your research project can fill.

Practical research problems

If you are doing practical research, you can identify a problem by reading reports, following up on previous research, or talking to people who work in the relevant field or organisation. You might look for:

• Issues with performance or efficiency
• Processes that could be improved
• Areas of concern among practitioners
• Difficulties faced by specific groups of people

Examples of practical research problems

Voter turnout in New England has been decreasing, in contrast to the rest of the country.

The HR department of a local chain of restaurants has a high staff turnover rate.

A non-profit organisation faces a funding gap that means some of its programs will have to be cut.

Theoretical research problems

If you are doing theoretical research, you can identify a research problem by reading existing research, theory, and debates on your topic to find a gap in what is currently known about it. You might look for:

• A phenomenon or context that has not been closely studied
• A contradiction between two or more perspectives
• A situation or relationship that is not well understood
• A troubling question that has yet to be resolved

Examples of theoretical research problems

The effects of long-term Vitamin D deficiency on cardiovascular health are not well understood.

The relationship between gender, race, and income inequality has yet to be closely studied in the context of the millennial gig economy.

Historians of Scottish nationalism disagree about the role of the British Empire in the development of Scotland’s national identity.

Next, you have to find out what is already known about the problem, and pinpoint the exact aspect that your research will address.

Context and background

• Who does the problem affect?
• Is it a newly-discovered problem, or a well-established one?
• What research has already been done?
• What, if any, solutions have been proposed?
• What are the current debates about the problem? What is missing from these debates?

Specificity and relevance

• What particular place, time, and/or group of people will you focus on?
• What aspects will you not be able to tackle?
• What will the consequences be if the problem is not resolved?

Example of a specific research problem

A local non-profit organisation focused on alleviating food insecurity has always fundraised from its existing support base. It lacks understanding of how best to target potential new donors. To be able to continue its work, the organisation requires research into more effective fundraising strategies.

Once you have narrowed down your research problem, the next step is to formulate a problem statement , as well as your research questions or hypotheses .

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement.

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

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.

Research objectives describe what you intend your research project to accomplish.

They summarise the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Cite this Scribbr article

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

McCombes, S. & George, T. (2022, November 08). How to Define a Research Problem | Ideas & Examples. Scribbr. Retrieved 14 May 2024, from https://www.scribbr.co.uk/the-research-process/define-research-problem/

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Artificial intelligence and the future of surgery

by University of Auckland

You may not think artificial intelligence could have a role in surgery, but new research shows AI can help solve problems for patients, doctors and the health system. A group of researchers led by surgery researcher Dr. Chris Varghese at Waipapa Taumata Rau, University of Auckland has published an article on artificial intelligence in surgery in Nature Medicine .

"I think AI has a role in every part of a patient's surgical journey, before surgery, during surgery and, most interestingly, after surgery," Varghese says.

"Each time that we leave hospital, we are at increased risk of having complications from surgery.

"AI has got a real potential to provide monitoring and safety-netting to ensure that we can mitigate and prevent some of these complications and enhance the recovery that you're able to achieve at home."

Another application for AI is already being used in Aotearoa, New Zealand, where automated algorithms can process very long waiting lists and prioritize them based on need, so the right patients are seen at the right time.

An emerging area is the use of AI during surgery using "computer vision."

"AI is trying to learn what surgeons see, what the surgical instruments look like, what different organs look like," Dr. Varghese says.

"And the potential there is to identify abnormal anatomy and [determine] what the safest approach to an operation might be.

"Using virtual reality and augmented reality to plan ahead of surgeries can be really useful for cutting out cancers and more."

However, there are limitations, especially in overcoming issues of data privacy and ethics.

"AI is based on building models from lots and lots of data and ensuring that the data we feed into these algorithms are unbiased and are not perpetuating existing inequities in our data sets and our research is essential.

"So, really ensuring that what we feed into these models and train these models on, is really robust and achieving the best outcomes for our patients.

"In terms of what's next for New Zealand, I think there needs to be a big focus on investing in our digital infrastructure.

"Right now we have hospitals across the country using different health care systems that don't communicate with each other.

"It is a real potential to unify our health care data systems with Te Whatu Ora and bring in everyone's data in a safe and robust manner to ensure that we can keep abreast and be leaders in the field as we integrate AI technologies into health care."

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Wavefunction matching for solving quantum many-body problems

Strongly interacting systems play an important role in quantum physics and quantum chemistry. Stochastic methods such as Monte Carlo simulations are a proven method for investigating such systems. However, these methods reach their limits when so-called sign oscillations occur. This problem has now been solved by an international team of researchers from Germany, Turkey, the USA, China, South Korea and France using the new method of wavefunction matching. As an example, the masses and radii of all nuclei up to mass number 50 were calculated using this method. The results agree with the measurements, the researchers now report in the journal " Nature ."

All matter on Earth consists of tiny particles known as atoms. Each atom contains even smaller particles: protons, neutrons and electrons. Each of these particles follows the rules of quantum mechanics. Quantum mechanics forms the basis of quantum many-body theory, which describes systems with many particles, such as atomic nuclei.

One class of methods used by nuclear physicists to study atomic nuclei is the ab initio approach. It describes complex systems by starting from a description of their elementary components and their interactions. In the case of nuclear physics, the elementary components are protons and neutrons. Some key questions that ab initio calculations can help answer are the binding energies and properties of atomic nuclei and the link between nuclear structure and the underlying interactions between protons and neutrons.

However, these ab initio methods have difficulties in performing reliable calculations for systems with complex interactions. One of these methods is quantum Monte Carlo simulations. Here, quantities are calculated using random or stochastic processes. Although quantum Monte Carlo simulations can be efficient and powerful, they have a significant weakness: the sign problem. It arises in processes with positive and negative weights, which cancel each other. This cancellation leads to inaccurate final predictions.

A new approach, known as wavefunction matching, is intended to help solve such calculation problems for ab initio methods. "This problem is solved by the new method of wavefunction matching by mapping the complicated problem in a first approximation to a simple model system that does not have such sign oscillations and then treating the differences in perturbation theory," says Prof. Ulf-G. Meißner from the Helmholtz Institute for Radiation and Nuclear Physics at the University of Bonn and from the Institute of Nuclear Physics and the Center for Advanced Simulation and Analytics at Forschungszentrum Jülich. "As an example, the masses and radii of all nuclei up to mass number 50 were calculated -- and the results agree with the measurements," reports Meißner, who is also a member of the Transdisciplinary Research Areas "Modeling" and "Matter" at the University of Bonn.

"In quantum many-body theory, we are often faced with the situation that we can perform calculations using a simple approximate interaction, but realistic high-fidelity interactions cause severe computational problems," says Dean Lee, Professor of Physics from the Facility for Rare Istope Beams and Department of Physics and Astronomy (FRIB) at Michigan State University and head of the Department of Theoretical Nuclear Sciences.

Wavefunction matching solves this problem by removing the short-distance part of the high-fidelity interaction and replacing it with the short-distance part of an easily calculable interaction. This transformation is done in a way that preserves all the important properties of the original realistic interaction. Since the new wavefunctions are similar to those of the easily computable interaction, the researchers can now perform calculations with the easily computable interaction and apply a standard procedure for handling small corrections -- called perturbation theory.

The research team applied this new method to lattice quantum Monte Carlo simulations for light nuclei, medium-mass nuclei, neutron matter and nuclear matter. Using precise ab initio calculations, the results closely matched real-world data on nuclear properties such as size, structure and binding energy. Calculations that were once impossible due to the sign problem can now be performed with wavefunction matching.

While the research team focused exclusively on quantum Monte Carlo simulations, wavefunction matching should be useful for many different ab initio approaches. "This method can be used in both classical computing and quantum computing, for example to better predict the properties of so-called topological materials, which are important for quantum computing," says Meißner.

The first author is Prof. Dr. Serdar Elhatisari, who worked for two years as a Fellow in Prof. Meißner's ERC Advanced Grant EXOTIC. According to Meißner, a large part of the work was carried out during this time. Part of the computing time on supercomputers at Forschungszentrum Jülich was provided by the IAS-4 institute, which Meißner heads.

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Story Source:

Materials provided by University of Bonn . Note: Content may be edited for style and length.

Journal Reference :

• Serdar Elhatisari, Lukas Bovermann, Yuan-Zhuo Ma, Evgeny Epelbaum, Dillon Frame, Fabian Hildenbrand, Myungkuk Kim, Youngman Kim, Hermann Krebs, Timo A. Lähde, Dean Lee, Ning Li, Bing-Nan Lu, Ulf-G. Meißner, Gautam Rupak, Shihang Shen, Young-Ho Song, Gianluca Stellin. Wavefunction matching for solving quantum many-body problems . Nature , 2024; DOI: 10.1038/s41586-024-07422-z

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Facility for Rare Isotope Beams

At michigan state university, international research team uses wavefunction matching to solve quantum many-body problems, new approach makes calculations with realistic interactions possible.

FRIB researchers are part of an international research team solving challenging computational problems in quantum physics using a new method called wavefunction matching. The new approach has applications to fields such as nuclear physics, where it is enabling theoretical calculations of atomic nuclei that were previously not possible. The details are published in Nature (“Wavefunction matching for solving quantum many-body problems”) .

Ab initio methods and their computational challenges

An ab initio method describes a complex system by starting from a description of its elementary components and their interactions. For the case of nuclear physics, the elementary components are protons and neutrons. Some key questions that ab initio calculations can help address are the binding energies and properties of atomic nuclei not yet observed and linking nuclear structure to the underlying interactions among protons and neutrons.

Yet, some ab initio methods struggle to produce reliable calculations for systems with complex interactions. One such method is quantum Monte Carlo simulations. In quantum Monte Carlo simulations, quantities are computed using random or stochastic processes. While quantum Monte Carlo simulations can be efficient and powerful, they have a significant weakness: the sign problem. The sign problem develops when positive and negative weight contributions cancel each other out. This cancellation results in inaccurate final predictions. It is often the case that quantum Monte Carlo simulations can be performed for an approximate or simplified interaction, but the corresponding simulations for realistic interactions produce severe sign problems and are therefore not possible.

Using ‘plastic surgery’ to make calculations possible

The new wavefunction-matching approach is designed to solve such computational problems. The research team—from Gaziantep Islam Science and Technology University in Turkey; University of Bonn, Ruhr University Bochum, and Forschungszentrum Jülich in Germany; Institute for Basic Science in South Korea; South China Normal University, Sun Yat-Sen University, and Graduate School of China Academy of Engineering Physics in China; Tbilisi State University in Georgia; CEA Paris-Saclay and Université Paris-Saclay in France; and Mississippi State University and the Facility for Rare Isotope Beams (FRIB) at Michigan State University (MSU)—includes  Dean Lee , professor of physics at FRIB and in MSU’s Department of Physics and Astronomy and head of the Theoretical Nuclear Science department at FRIB, and  Yuan-Zhuo Ma , postdoctoral research associate at FRIB.

“We are often faced with the situation that we can perform calculations using a simple approximate interaction, but realistic high-fidelity interactions cause severe computational problems,” said Lee. “Wavefunction matching solves this problem by doing plastic surgery. It removes the short-distance part of the high-fidelity interaction, and replaces it with the short-distance part of an easily computable interaction.”

This transformation is done in a way that preserves all of the important properties of the original realistic interaction. Since the new wavefunctions look similar to that of the easily computable interaction, researchers can now perform calculations using the easily computable interaction and apply a standard procedure for handling small corrections called perturbation theory.  A team effort

The research team applied this new method to lattice quantum Monte Carlo simulations for light nuclei, medium-mass nuclei, neutron matter, and nuclear matter. Using precise ab initio calculations, the results closely matched real-world data on nuclear properties such as size, structure, and binding energies. Calculations that were once impossible due to the sign problem can now be performed using wavefunction matching.

“It is a fantastic project and an excellent opportunity to work with the brightest nuclear scientist s in FRIB and around the globe,” said Ma. “As a theorist , I'm also very excited about programming and conducting research on the world's most powerful exascale supercomputers, such as Frontier , which allows us to implement wavefunction matching to explore the mysteries of nuclear physics.”

While the research team focused solely on quantum Monte Carlo simulations, wavefunction matching should be useful for many different ab initio approaches, including both classical and  quantum computing calculations. The researchers at FRIB worked with collaborators at institutions in China, France, Germany, South Korea, Turkey, and United States.

“The work is the culmination of effort over many years to handle the computational problems associated with realistic high-fidelity nuclear interactions,” said Lee. “It is very satisfying to see that the computational problems are cleanly resolved with this new approach. We are grateful to all of the collaboration members who contributed to this project, in particular, the lead author, Serdar Elhatisari.”

This material is based upon work supported by the U.S. Department of Energy, the U.S. National Science Foundation, the German Research Foundation, the National Natural Science Foundation of China, the Chinese Academy of Sciences President’s International Fellowship Initiative, Volkswagen Stiftung, the European Research Council, the Scientific and Technological Research Council of Turkey, the National Natural Science Foundation of China, the National Security Academic Fund, the Rare Isotope Science Project of the Institute for Basic Science, the National Research Foundation of Korea, the Institute for Basic Science, and the Espace de Structure et de réactions Nucléaires Théorique.

Michigan State University operates the Facility for Rare Isotope Beams (FRIB) as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics. Hosting what is designed to be the most powerful heavy-ion accelerator, FRIB enables scientists to make discoveries about the properties of rare isotopes in order to better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society, including in medicine, homeland security, and industry.

The U.S. Department of Energy Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit energy.gov/science.

Advertisement

Frozen human brain tissue can now be revived without damage

Using a new approach, scientists have successfully frozen and thawed brain organoids and cubes of brain tissue from someone with epilepsy, which could enable better research into neurological conditions

By Christa Lesté-Lasserre

15 May 2024

Thawed brain organoids shown via an imaging technique called immunofluorescence staining

Weiwei Xue et al.

A new technique has allowed scientists to freeze human brain tissue so that it regains normal function after thawing, potentially opening the door to improved ways of studying neurological conditions.

Brain tissue doesn’t usually survive freezing and thawing, a problem that has significantly hindered medical research. In an effort to overcome this, Zhicheng Shao at Fudan University in Shanghai, China, and his colleagues used human embryonic stem cells to grow self-organising brain samples, known as organoids, for three weeks — long enough for the development of neurons and neural stem cells that can become different kinds of functional brain cells.

The researchers then placed these organoids — which measured 4 millimetres across on average — in different chemical compounds, such as sugars and antifreeze, that they suspected might help keep the brain cells alive while frozen and able to grow after being thawed.

Restoring the brain’s mitochondria could slow ageing and end dementia

After storing these organoids in liquid nitrogen for at least 24 hours, the team thawed them and looked for cell death or the growth of neurites — the “branches” of nerve cells — over the following two weeks.

Based on the rates of cell death and growth associated with each compound, the researchers chose their top compound candidates, trying different combinations during freezing and thawing tests on a new set of organoids.

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The combination that led to the least cell death and most growth was a blend of chemical compounds called methylcellulose, ethylene glycol, DMSO and Y27632 — which the scientists named “MEDY”. They suspect MEDY interferes with a pathway that otherwise programs cellular death.

Shao and his colleagues tested MEDY through a series of experiments involving brain organoids ranging from 28 days old to more than 100 days old. The team placed the organoids in MEDY, before freezing — usually for 48 hours — and thawing them. The researchers then observed their growth in the laboratory for up to 150 days post-thawing.

They found that the thawed organoids’ appearance, growth and function were highly similar to those of organoids of the same age that had never been frozen, even among those that had been frozen in MEDY for 18 months. The team also observed similar results for organoids representing different regions of the brain.

Finally, the researchers took 3-millimetre cubes of brain tissue from a 9-month-old girl with epilepsy and placed them in MEDY, before freezing and thawing them. The tissue maintained its pre-freezing structure and continued to remain active in a laboratory culture for at least two weeks after thawing.

We are finally starting to understand brain fog and how to treat it

Being able to freeze human brain tissues could lead to better investigations of brain development in the lab for health research, says Roman Bauer at the University of Surrey in the UK.

João Pedro Magalhães at the University of Birmingham in the UK says he is impressed that the team’s method successfully prevented cell death and preserved function. “We know brain cells are very fragile and sensitive to stress,” he says.

With significantly more research and the use of larger tissues, the work could one day lead to freezing entire brains, says Magalhães. “Thinking decades or centuries ahead, we can imagine patients being cryopreserved when they have a terminal condition or astronauts being cryopreserved in order to travel to other star systems,” he says. MEDY may represent “one small step” towards that goal, says Magalhães.

Journal reference:

Cell Reports Methods DOI: 10.1016/j.crmeth.2024.100777

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