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Emerging Research Methods – Types and Examples

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Emerging Research Methods

Definition:

Emerging research methods refer to innovative, unconventional, or newer approaches to research that challenge or augment traditional methodologies. They are typically developed or applied in response to changes in technology, society, or scientific understanding, or to address new types of questions or subjects that may be challenging to study using conventional methods.

Types of Emerging Research Methods

Types of Emerging Research Methods are as follows:

Data Science and Big Data Analysis

This involves sophisticated statistical techniques and artificial intelligence algorithms to examine large and complex data sets. This method can reveal patterns and correlations that may not be visible using traditional research methods.

Social Network Analysis

This method uses mathematical and visual models to study relationships within social networks, whether they be individuals, groups, computers, or even nations.

Digital Ethnography

This is a qualitative method that observes and analyzes the interaction of individuals and communities in digital spaces, like social media platforms, online communities, and more.

Blockchain Research

This relatively new field focuses on the study of blockchain technologies and their impact on society, economics, and the technical world.

Sentiment Analysis

Commonly used in marketing and social sciences, this method uses AI to analyze opinions, feelings, and attitudes in written language on digital platforms.

Mixed Methods

This research method combines quantitative and qualitative research techniques, which can provide a more comprehensive understanding of a research problem.

Machine Learning

This includes various techniques where computers learn from data. It’s used in numerous fields, including natural language processing, image recognition, and predictive modeling.

Citizen Science

This method involves the public in data collection processes, which has been particularly influential in environmental science and astronomy.

Virtual and Augmented Reality

These technologies are being increasingly used in experimental research to create simulated environments.

Neuroimaging Techniques

With advancements in technology, newer methods like functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) are being increasingly used in neuroscience to study brain function.

Role of Emerging Research Methods

Emerging research methods play several crucial roles in advancing our understanding across various disciplines. Here are some of their key roles:

Addressing Complex Problems : Emerging research methods often incorporate advanced computational techniques or innovative theoretical approaches that allow researchers to tackle complex problems that are not amenable to traditional methods.

Interdisciplinary Research : Emerging research methods can foster interdisciplinary research by enabling the integration of concepts, techniques, and perspectives from different fields. For instance, bioinformatics combines biology, computer science, and information engineering to analyze and interpret complex biological data.

Innovation : By challenging traditional methods and paradigms, emerging research methods can lead to innovative solutions, models, and theories. They allow researchers to explore new frontiers and can often open up entirely new fields of study.

Enhancing Accessibility and Participation : Methods like citizen science and crowdsourcing allow more people to participate in research, which can increase the diversity of perspectives, democratize scientific knowledge, and enhance the overall quality and quantity of data.

Improved Accuracy and Efficiency : Techniques such as machine learning and big data analysis can significantly increase the speed and accuracy of data analysis, allowing researchers to handle larger data sets and identify patterns that might not be discernible through manual analysis.

Real-time Analysis and Decision Making : Emerging research methods, particularly those involving real-time data collection and analysis, can provide immediate insights that inform decision-making processes in areas such as public health, environmental management, and policy development.

Ethical Research Practices : Some emerging research methods can address ethical issues by reducing the risk to participants (e.g., through anonymization techniques in digital research) or by increasing transparency and replicability (e.g., through open science practices).

In-depth Understanding : Certain emerging methods provide more comprehensive or nuanced understandings of phenomena. Mixed methods research, for instance, can provide both the broader trends from quantitative data and the detailed, personal insights from qualitative data.

Examples of Emerging Research Methods

Here are a few examples of how emerging research methods are being applied in real-world scenarios:

Big Data Analysis : Companies like Netflix and Amazon use big data analytics to understand user behavior and preferences. They analyze large datasets consisting of customer viewing habits, search histories, and reviews, among others. The insights derived from this analysis are used to personalize recommendations, enhance user experience, and develop new products.

Machine Learning : Google’s search engine uses machine learning algorithms to improve its search results and make them more relevant to the user’s query. Machine learning is also used in healthcare, where algorithms can help predict patient outcomes based on a wide array of variables, ranging from genetic information to lifestyle factors.

Digital Ethnography : Researchers use digital ethnography to understand the behavior and social interactions of users on platforms such as Twitter, Facebook, or Reddit. For instance, during the COVID-19 pandemic, digital ethnography was used to study how information about the virus spread online and how it influenced people’s behavior and attitudes.

Social Network Analysis : This method has been used to study the spread of information or misinformation in social networks. For instance, during political campaigns or public health crises, researchers can track how information spreads and who the key influencers are in these networks.

Citizen Science : The Zooniverse project is a platform that allows anyone with internet access to contribute to scientific research. It includes projects in a wide range of disciplines, from identifying galaxies to transcribing historical documents.

Mixed Methods : In education research, mixed methods approaches are often used to understand complex phenomena like learning outcomes. Researchers might combine quantitative data (like test scores) with qualitative data (like interviews or observations) to get a fuller picture of what influences student achievement.

Virtual and Augmented Reality : These methods are being used in psychological research to understand how people react to different environments or stimuli. For example, researchers can use VR to simulate phobic situations and study participants’ reactions, which can inform treatment for phobias.

Neuroimaging Techniques : In cognitive neuroscience, functional magnetic resonance imaging (fMRI) is used to study brain activity in response to various tasks. This helps scientists understand how different brain regions are involved in cognition and behavior.

Advantages and Disadvantages of Emerging Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Updated on November 12, 2023
• By Market Research Guy
• In Overviews

New Market Research Methods and Techniques

While traditional market research techniques such as surveys and focus groups are still widely used, there are many new market research methods and techniques to spice things up.  As technology and socioeconomic trends change, so will our means of gaining customer insights.  As you’ll notice, many of these are really just new technologies applied to traditional methods, as opposed to radically different methodologies.  In any case, here are a sampling of some of the new market research trends and techniques popular now, in no particular order:

1. A shift from data collection to data analysis :  Today, actual customer behavior data is collected with ease, to the point where analysis (or data mining) is much more challenging than obtaining the data.  For example, Google Analytics  provides webmasters with tons of information about website visitors, including languages, pages visited, screen resolutions, etc.  All of this information can be used to fine tune a website to the audience.  Another example of “ big data ” data mining of is Amazon’s predictive recommendations.  By carefully monitoring the products a user purchases/views and correlating that information with purchase histories of others, Amazon is able to very effectively present product recommendations.  All of this is done through data mining, without having to ask the user “what other products might you like?, which would be crazy.”  Twitter is another great source of readily available data that can be mined (text analytics).  

Jonathan Harris performed a great TED talk that beautifully demonstrates how readily available data can be visualized.

2. A shift from “how do think you will behave?” (self-reporting) to “I know how you actually behaved” (observational research) :  If you wanted to know what color cereal box would sell the most cereal, would you rather base your decision on a survey or an actual experiment where colors are tested?  Of course the experiment would be more valuable.  I want to know what customers actually do/want, not what they think they do/want.  It’s not that customers are trying to deceive researchers; it’s just that it’s difficult for users to predict their own future actions.  In any case, the world of new market research methods is shifting from self-reporting techniques (surveys, focus groups), to observational  research methods.  The data is much more reliable.

3. Mobile market research methods : Smart phones and tablets have taken the world by storm.  These devices are becoming a preferred platform for many applications and markets, including market research.  Examples of how these devices are being used in terms of new market research techniques include:

• Text messaging surveys and voting  (SMS Surveys) – One good example of this is a company called “ Poll Everywhere .”  They allow seminar attendees to vote and respond to poll questions via SMS (text messaging).
• Smartphone designed surveys – Good mobile surveys are ones designed specifically for the smartphone form factor.  There are many companies working on this such as  OpinionMeter .  These surveys can be web-based, optimized for phones, or they can be native applications built specifically for iOS, Android, or Windows mobile operating systems.  In today’s environment, it’s imperative for online surveys to be usable regardless of device (laptop, tablet, or mobile).
• Location Awareness – Advanced phone market research techniques can leverage smartphone location (GPS) information to trigger questions or simply track movement over time.  For example, you can imagine a survey question that only appears when the phone knows the user is at the gas station.
• Mobile Ethnography – Using information like location awareness, researchers are able to gather rich contextual data (using mobile phones) about behaviors, allowing them to really understand the habits and lifestyles of subjects.

4. Biometric Market Research Techniques : New biometric research methods that measure a subject’s physical response to stimuli (e.g., television commercial) provide valuable data that a subject might not be able or willing to express verbally.  Examples of biometric market research methods include heart rate monitoring, respiration monitoring, skin and muscle activity, brain activity (using functional MRI) and eye tracking.  A good article on the subject can be found here .   Campbell Soup has used such methods in their market research.

5. Prediction Markets:  A prediction market is like a mini stock market, where a group of people can buy and sell “predictions” of various events.  For example, one event might be “who will win the presidency?”  Participants could use their “currency” (fake or real) to buy or sell whoever they think will win.  Early on, the price of one candidate or the other might be $0.50, but as the election probability becomes more certain, a bid on one candidate will grow closer to $1.00.  At the end of an election, one candidate will be worth $1.00 and the other $0.00.  Participants can buy and sell their stake in a candidate along the way.  

The beauty of these prediction markets is that they tend to be good indications of reality.  So what does this have to do with market research?  Well, forward thinking companies are setting up these prediction markets to tap into the wisdom of their employees.  For example, a company could ask employees to bid on a prediction market that has to do with competitors, industry trends, or the success of product concepts in order to get an early read on those ideas.  If this is still foggy, check out PredictIt , a public prediction market. Consensus Point makes business to business software that has been used by companies like Best Buy.

6. Virtual Shopping:  This involves the use of  virtual store simulation to mimic a shopping experience for participants–a good way to test things retail issues like product placement, store layout, packaging, etc.  Once again, the idea is to replicate a real situation for research subjects and observe behavior, as opposed to asking them what they think they will do.  Virtual Reality is certainly a new market research method to keep an eye on. 

7. Live Audience Response:  In conferences or lectures, presenters often have difficulty engaging with the audience.  One tool to remedy this problem is live audience response systems.  These systems involve a handhold remote control for audience members to respond to questions that appear on-screen (usually in a PowerPoint slide).  You can imaging the applications for this: professors doing on-the-fly quizzes to see if students understand the concepts, presenters asking demographic questions to better understand their audience, polling, etc.

7. Online Collaboration Tools:  Tools like Skype (video calling), instant messaging, and shared whiteboarding allow researchers to conduct a variety of “traditional” market research techniques using new technology.  These technologies are often much cheaper than physically gathering people.  They also allow researchers to gather people from broader geographies much easier.

8. Social Media Market Research: Social media dominates the Web, so it is natural that market researchers are looking for ways to leverage this technology.  When people say “social media market research” they might mean several different things:

• Research of social media — Simply researching the market of social media.  For example, “X% of people use Facebook and the average age of a Google+ user is X.”
• Research using social media data — There is a lot of data that can be gleaned from social media sites.  Looking at how many times a certain news story or product is shared across sites can tell researchers a lot about what works and doesn’t work in journalism, product concepts, etc.  “Listening” to social media is like eavesdropping on a million conversations and can be a great place to pick up on trends.
• Research using social media as part of the methodology or delivery mechanism — Many companies have a large following on social media sites and can leverage that audience to ask questions.  Often, if a customer is willing to follow/friend/subscribe/whatever to a company on a social media site, they are a big fan of that company and one of the best customers (probably a “promoter” in NPS, or net promoter score language).  What a gold mine for companies to have instant access to their highly loyal and interested customers for market research purposes.  Twitter now allows polling as a native Twitter feature.  Very cool.

9.  QR Code Surveys : This overlaps with mobile phone market research.  A poster could ask a simple survey question and provide two QR codes, asking people to scan their choice.  Such an approach makes it very easy for someone to take a one-question survey without doing much more than pointing a phone.  A webmaster would then be able to gather the response data in aggregate.  Other companies are using QR codes as a simple launch point to a mobile survey.  A good example of this is Tiipz .

There you have it–an overview of new market research methods and techniques . This article will continue to evolve and update over time as new research methodologies and technologies emerge.  I hope this was informative.  If you have other examples of new market research, or if you have anything to add, please do so in the comments below.

10. AI Powered Research

Whether it’s AI to write surveys, interact as a chatbots , or analyze results, there is no doubt that AI will play a major role in market research from here until the end of time.

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Technology Advancements in Research Methodologies: Transforming the Research Landscape

by NotedSource

Technology is advancing at an unprecedented pace, with continuous innovations in various fields. These advancements are changing the way research is conducted, opening up new possibilities and enhancing the efficiency, accuracy, and outcomes of scientific endeavors.

Research directors must stay informed about the latest technological advancements in areas such as artificial intelligence (AI), machine learning, data analytics, and lab automation to ensure their organizations remain at the forefront of research excellence. Explore how these technologies are transforming the research landscape and discusses their implications for research directors and their teams.

Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the research process by automating complex tasks, analyzing massive data sets, and generating new insights. AI-powered tools can help researchers mine vast amounts of information from scientific literature, patents, and databases, enabling them to identify trends, patterns, and connections that might otherwise go unnoticed. Machine learning algorithms can be trained to recognize specific patterns, classify data, and make predictions based on historical data, allowing researchers to make more informed decisions and generate more accurate hypotheses.

In drug discovery, for example, AI and ML techniques are being used to screen millions of compounds for potential therapeutic targets, dramatically reducing the time and cost of identifying promising drug candidates. Furthermore, AI-driven computational models can simulate the behavior of biological systems, enabling researchers to better understand complex biological processes and develop more effective treatments for various diseases.

Data Analytics

Data analytics involves the use of advanced techniques and tools to analyze, interpret, and visualize large amounts of data, enabling researchers to extract valuable insights and make informed decisions. With the exponential growth of data generated by research activities, data analytics has become an essential component of modern research methodologies.

Researchers can employ sophisticated data analytics tools to analyze complex data sets, identify trends and patterns, and uncover hidden relationships between variables. These tools can also help researchers manage and process large volumes of data in real-time, facilitating more efficient and accurate data-driven decision-making. For example, in genomics research, data analytics techniques are used to analyze vast amounts of sequencing data, helping researchers identify genetic variants associated with specific diseases and understand the underlying molecular mechanisms.

Lab Automation

Lab automation involves the use of robotics, software, and other technologies to automate various tasks and processes in the laboratory, enhancing research efficiency and reproducibility. Automation technologies can perform repetitive tasks, such as pipetting, sample preparation, and data acquisition, more quickly and accurately than humans, reducing the potential for human error and freeing up researchers' time for more complex and creative tasks.

By automating routine tasks, research organizations can increase their throughput, reduce costs, and ensure greater consistency in experimental results. Lab automation is particularly useful in high-throughput screening, drug discovery, and genomics research, where large numbers of samples need to be processed and analyzed. In addition, lab automation can enable researchers to perform experiments in parallel and run experiments 24/7, significantly accelerating the research process.

Integration of Technologies

The integration of AI, machine learning, data analytics, and lab automation technologies can create synergies and amplify their impact on research methodologies. For instance, AI-driven data analysis can be combined with automated laboratory equipment to optimize experimental conditions, improving the quality and efficiency of research. Machine learning algorithms can be employed to analyze the vast amounts of data generated by automated experiments, enabling researchers to make more informed decisions and refine their experimental designs.

The integration of these technologies also facilitates the development of new research approaches, such as in silico experiments, where computational models and simulations can replace or complement traditional wet-lab experiments. These approaches can reduce the time, cost, and resources required for research while minimizing the use of animals or human subjects.

Challenges and Ethical Considerations

While these technological advancements offer numerous benefits, they also present challenges and ethical considerations that research directors must address. Some of the key challenges include:

Data privacy and security: As research methodologies become increasingly data-driven, ensuring the privacy and security of sensitive data is crucial. Research organizations must implement robust data protection measures, such as encryption and access controls, to safeguard the integrity and confidentiality of research data.

Skill development and training: The adoption of advanced technologies in research requires researchers to acquire new skills and knowledge in areas such as programming, data analysis, and machine learning. Research directors must invest in training and professional development programs to ensure their teams can effectively utilize these new tools and techniques.

Algorithmic bias and fairness: AI and machine learning algorithms can sometimes produce biased or unfair results, particularly if the training data used to develop the algorithms is biased or unrepresentative. Research directors must be vigilant in addressing potential biases in their algorithms and work to develop fair and unbiased models.

Ethical implications of AI-driven research: The use of AI in research can raise ethical questions, such as the potential for AI-generated discoveries to infringe on existing intellectual property rights or the responsibility for errors made by AI-driven systems. Research directors must be aware of these ethical issues and develop appropriate policies and guidelines to address them.

Infrastructure and resource requirements: The adoption of advanced technologies in research may require significant investments in infrastructure, such as high-performance computing systems, data storage, and network capabilities. Research directors must carefully plan and allocate resources to support the adoption and integration of these technologies into their research workflows.

Technological advancements in research methodologies, such as AI, machine learning, data analytics, and lab automation, are transforming the research landscape and offering new opportunities for innovation, efficiency, and accuracy. Research directors who stay informed about these advancements and proactively integrate them into their research processes can significantly enhance their organization's research capabilities and drive scientific breakthroughs.

However, the adoption of these technologies also presents challenges and ethical considerations that research directors must address to ensure responsible and effective use. By investing in training, infrastructure, and ethical guidelines, research directors can harness the power of these advanced technologies to drive their research forward while maintaining the highest standards of research integrity and professionalism.

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One way to know where a field is going? Take a look at what the sharpest new innovators are working on.

Good news for all of us: MIT Technology Review ’s list of 35 Innovators Under 35 just dropped. And a decent number of the people who made the list are working in fields that touch climate and energy in one way or another.

Looking through, I noticed a few trends that might provide some hints about the future of climate tech. Let’s dig into this year’s list and consider what these innovators’ work might mean for efforts to combat climate change.

Power to the people

Perhaps unsurprisingly, quite a few innovators on this list are working on energy— and many of them have an interest in making energy consistently available where and when it’s needed. Wind and solar are getting cheap, but we need solutions for when the sun isn’t shining and the wind isn’t blowing.

Tim Latimer cofounded Fervo Energy, a geothermal company hoping to provide consistently available, carbon-free energy using Earth’s heat. You may be familiar with his work, since Fervo was on our list of 15 Climate Tech Companies to Watch in 2023 .

Another energy-focused innovator on the list is Andrew Ponec of Antora Energy, a company working to build thermal energy storage systems. Basically, the company’s technology heats up blocks when cheap renewables are available, and then stores that heat and delivers it to industrial processes that need constant power. (You, the readers, named thermal energy storage the readers’ choice on this year’s 10 Breakthrough Technologies list.)

While new ways of generating electricity and storing energy can help cut our emissions in the future, other people are focused on how to clean up the greenhouse gases already in the atmosphere. At this point, removing carbon dioxide from the atmosphere is basically required for any scenario where we limit warming to 1.5 °C over preindustrial levels. A few of the new class of innovators are turning to rocks for help soaking up and locking away atmospheric carbon. 

Noah McQueen cofounded Heirloom Carbon Technologies, a carbon removal company. The technology works by tweaking the way minerals soak up carbon dioxide from the air (before releasing it under controlled conditions, so they can do it all again). The company has plans for facilities that could remove hundreds of thousands of tons of carbon dioxide each year. 

Another major area of research focuses on how we might store captured carbon dioxide. Claire Nelson is the cofounder of Cella Mineral Storage, a company working on storage methods to better trap carbon dioxide underground once it’s been mopped up.  

Material world

Finally, some of the most interesting work on our new list of innovators is in materials. Some people are finding new ones that could help us address our toughest problems, and others are trying to reinvent old ones to clean up their climate impacts.

Julia Carpenter found a way to make a foam-like material from metal. Its high surface area makes it a stellar heat sink, meaning it can help cool things down efficiently. It could be a huge help in data centers, where 40% of energy demand goes to cooling.

And I spoke with Cody Finke , cofounder and CEO of Brimstone, a company working on cleaner ways of making cement. Cement alone is responsible for nearly 7% of global greenhouse-gas emissions, and about half of those come from chemical reactions necessary to make it. Finke and Brimstone are working to wipe out the need for these reactions by using different starting materials to make this crucial infrastructural glue.

Addressing climate change is a sprawling challenge, but the researchers and founders on this list are tackling a few of the biggest issues I think about every day. 

Ensuring that we can power our grid, and all the industrial processes that we rely on for the stuff in our daily lives, is one of the most substantial remaining challenges. Removing carbon dioxide from the atmosphere in an efficient, cheap process could help limit future warming and buy us time to clean up the toughest sectors. And finding new materials, and new methods of producing old ones, could be a major key to unlocking new climate solutions. 

To read more about the folks I mentioned here and other innovators working in climate change and beyond, check out the full list .

Now read the rest of The Spark

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Fervo Energy (cofounded by 2024 innovator Tim Latimer) showed last year that its wells can be used like a giant underground battery .

A growing number of companies—including Antora Energy, whose CEO Andrew Ponec is a 2024 innovator—are working to bring thermal energy storage systems to heavy industry .

Cement is one of our toughest challenges, as Brimstone CEO and 2024 innovator Cody Finke will tell you. I wrote about Brimstone and other efforts to reinvent cement earlier this year .

Another thing

We need a whole lot of metals to address climate change, from the copper in transmission lines to the nickel in lithium-ion batteries that power electric vehicles. Some researchers think plants might be able to help. 

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6 ways technology will transform your market research.

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Data-driven market research

Times are changing fast. As CEO of Black Swan Data , I work with some of the biggest CPG brands in the world, and they all keep talking about it.

Why? Because these changes are not always in their favour. 

Consumer trends are changing faster than ever, CPG companies are losing sales to small, agile competitors. These small competitors are fast and close to the ground: they can spot trends before the big brands and bring new products to the market faster than anyone. In fact, many of these companies are dedicated to just following these trends.

So why are blue chip companies getting pipped by these young guns? Surely these giants of the industry can muster the resources to respond?

Unfortunately, a large part of the problem for them is their market research. CPGs lost an estimated $2.3 billion on failed innovation in 2019 alone. Traditional research methods can’t give these companies what they need to respond in today’s dynamic marketplace. Companies either bet on the wrong trend or cannot act fast enough to take advantage of what they know.

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Traditional market research is just not getting the job done in today’s world.

What are the limits of traditional market research?

Market research is experiencing a paradigm change. Born into the post-war economy of Mad Men and Madison Avenue, market research was an appeal to science to bring rigour to understanding what customers wanted and what they would pay for. 

Marketeers continued use of methods such as questionnaires, interviews and focus groups for the last 80 years pays testament to traditional research’s usefulness. However, instead of helping them win, the same methods that worked for large companies, even five years ago, are now starting to cost these companies sales and market share. 

T he 6 big problems of traditional market research :

1.     Overstatement bias (the ‘say/do’ gap)

2.     Limited number of questions you can ask

3.     Respondent engagement/quality

4.     Slow

5.     Expensive

6.     Ability to look forward and predict

Market research has always struggled with these problems, but until recently, companies had no other options to get the answers they needed. 

How digitisation and technology is transforming market research

The market research industry has not ignored the advent of the digital age.  ESOMAR ’s latest Global Research Report shows that spend on digital market research surpassed traditional methods back in 2018. However, not all digital market research has fixed the six big problems of the industry previously highlighted. This is worth a closer look.

Simply put, digital market research companies can be divided into two groups: 

• Tech- enabled  market research (e.g. online survey tools)
• Tech- driven   market research (e.g. Big Data and AI)

Can you guess where the real success story lies? Hint: the key doesn’t lie in just making old methods digital.

What is tech-enabled market research exactly? This is research performed by firms that apply more cost efficient, digital methods to gather the same data as before that used to require an army of researchers. 

Tech-enabled market research allows companies to speed up their traditional market research and to do it at a much lower cost (two of the six problems). However, tech-enabled market research doesn’t crack the biggest research problems in today’s fast-moving world.

It doesn’t solve the claimed vs actual behaviour issue (i.e. the say/do gap). It still relies on prompted questions to an unmotivated sample of respondents which means it will rarely uncover something you didn’t already know about (the unknowns). And it won’t allow you to predict future behaviour with any degree of statistical accuracy. 

How tech-driven market research can deliver success

Instead of tech-enabled firms that rely on old methods, let’s talk about tech- driven  market research that’s new and creating something transformative.

With the advent of the internet, smart phones, and advancement in data collection, there is more data available on customers, their habits, and their preferences than ever before. Tech-driven market research utilises and unlocks the value of these behavioural data sources.  

For example, UK market research company  Streetbees  is a tech-driven market research company that captures verbatim from 3.5M ‘bees’ across the globe who record product choices and consumption behaviour on their mobile phones. Streetbees’ AI and Natural Language Processing technology analyses and structures this vast collection of text and images into powerful consumer intelligence. Brands use it to gain a deeper understanding of natural, in-the-moment behaviour to identify how they can better serve their customers’ needs.  

Another example is analysing social media conversation trends to anticipate changes in consumer behaviour and attitude. Every day, millions of us share our ideas, needs, wants, frustrations, and desires on social media platforms. For almost any consumer related topic that you can think of, somebody somewhere will be discussing and posting about it. 

By analysing this vast and rich data source we can identify known and unknown consumer topics and understand how and why they are trending. Furthermore, by training data science models on historical social media data, we can scientifically predict which of today’s emerging trends will sustain growth and most impact consumer consumption and purchase behaviour in the future. This is incredibly useful information if you’re trying to create new product innovation ideas.  

What people say now is only important if it can tell you what they will buy next.

Tech-driven market research’s combination of new data sources and data science techniques, mean that for the first time companies can overcome the original critical flaws in market research. And critically, progress from looking only at past behaviour in the rear-view mirror, to looking forward with statistical confidence about what’s on the horizon. 

I know that my proposition sounds excellent, and you might be sceptical. How do we know this works? The proof is in the pudding.

PepsiCo pioneering tech-driven market research

PepsiCo is at the forefront of the market research paradigm change. Spearheaded by Chief Insights and Analytics Officer, Stephan Gans, PepsiCo has developed a  tech-driven analytics capability called ‘Ada’ .  

Ada, named after mathematician Ada Lovelace, integrates and applies an ecosystem of progressive, cutting-edge market research tools into PepsiCo’s core business processes, bringing new capabilities to their 600-strong global team of Insight professionals.  

In a recent  webinar hosted by the Yale School of Management , Stephan talked about creating a “faster, stronger, better” Consumer Insights function.  One that is “more predictive, more iterative, less sequential. More precise; end-to-end. More human, more empathetic, better connected with the everyday lives of our consumers.” 

For example, they use an AI-driven tool called  Tastewise  to analyse millions of menu data points and understand what food and cuisines people are eating. They combine this with Black Swan’s trend prediction technology:  Trendscope , to prioritise which emerging Beverage and Snacking trends are predicted to tip into the mainstream. Ideas created through this process are then rapidly tested and optimised through  Zappi’s  advanced new product concept testing platform.

Used alone, each of these tools offers a powerful, tech-driven solution that creates better answers to familiar questions. But integrated together through Ada, they combine to create an advantaged capability that sets PepsiCo apart from competitors in how they conceive new ideas and bring new products to market.  

Top-tier research agrees with PepsiCo

Industry research supports Gans’ vision. Looking at the CPG industry, BCG and Google saw that companies could improve their earnings by over  10% using AI and advanced analytics . 

There are demonstratable gains to be made when companies start focusing on tech-driven methods in their research. Despite this, only 6% of CPGs in the study were using AI and advanced analytics to empower functions and drive key decisions at scale. Clearly this is a potential area of strategic competitive advantage. I suspect that Stephan Gans has the same notion. 

Why do you need tech-driven market research?

Tech-driven market research can help you make more money, save more money and waste less money by helping you:

• Innovate great products that anticpate changing consumer needs 
• Reduce risk and make fewer duds that miss important trends entirely
• Increase agility and keep up with competitors of all sizes

When forward-thinking companies like PepsiCo, Mondelēz International, Proctor & Gamble, Johnson & Johnson, and others are using tech-driven market research, it’s clear that the ship is sailing. Times are moving on. 

If you are not on board yet, you are missing out. 

How do  you  think this technology will develop?

Tech-driven market research already seems like an extreme paradigm shift, but this is just the first drop in the bucket. The application of Big Data and AI powered tools is not only going to mature, it is going to accelerate and spin-off into multiple use-cases and applications. 

Can tech-driven research firms develop AI sophisticated enough to offer accurate, long term predictions of market size that can automate your entire new product innovation process? Can they offer a successful ‘Supply Chain as a Service’ outsourcing platform with accuracy in long term supply chain, and output with zero waste? I think so.

The opportunities are many, and the future is full of promise.

Do you have a good idea about the future of AI and market research? Do you think you are missing out? Get in touch, I would love to hear from you.

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Increasingly mobile: How new technologies can enhance qualitative research

Carrie ann moylan.

Binghamton University, Binghamton, NY, USA

Amelia Seraphia Derr

Seattle University, Seattle, WA, USA

Taryn Lindhorst

University of Washington, Seattle, WA, USA

Advances in technology, such as the growth of smart phones, tablet computing, and improved access to the internet have resulted in many new tools and applications designed to increase efficiency and improve workflow. Some of these tools will assist scholars using qualitative methods with their research processes. We describe emerging technologies for use in data collection, analysis, and dissemination that each offer enhancements to existing research processes. Suggestions for keeping pace with the ever-evolving technological landscape are also offered.

Introduction

Many technological advances in recent decades were quickly adopted by qualitative researchers. For example, most researchers now use digital audio recorders to document interviews because digital technology is more convenient than cassette tapes. Other technological advances have generated discussion and debate. Some researchers initially worried that the epistemological assumptions of computer software developers could subtly shape their own analytic process in ways that blurred methodological clarity ( Davidson and Di Gregorio, 2011 ). Early software programs were associated with a grounded theory approach and some worried that using the software would lead to a troubling homogenization of qualitative research methods ( Coffey et al., 1996 ). As researchers became more familiar with computer-assisted data analysis packages, these concerns largely dissipated and the use of computers in qualitative analysis has grown considerably ( Gilbert et al., 2013 ). New applications available on the internet, and more recently through smartphones, tablet computers, and cloud-based computing, are again changing the way we carry out our work as scholars ( Van Doorn, 2013 ).

In this manuscript, we describe technologies that can be used by qualitative researchers to enhance and improve the experience of conducting qualitative research. Our objective is to help researchers envision how such tools can increase our efficiency, enhance our interpretations, and further the reach of our scholarship. By illustrating how these tools fit within existing qualitative practices of data collection, analysis, and dissemination, we encourage scholars to experiment with these new technologies.

It would be a daunting task to review all the technologies that could be adopted by qualitative researchers, and such a review would certainly be outdated almost immediately. Instead, this paper provides examples of types of tools that can enhance the process of qualitative inquiry to stimulate further thinking among researchers about when and how to incorporate new technologies (see Table 1 for a summary and details on how to access each tool we describe). We have chosen to name products that we are familiar with as a starting place for readers who are interested in exploring technological options, but with the rapidity of change in technology we cannot guarantee the ongoing availability or utility of any specific tool. Often numerous programs offer similar functionality, so we encourage readers to spend some time researching programs before incorporating them into qualitative routines., To the best of our knowledge, all information about programs in this paper is accurate.

Detailed information about tools listed in the order mentioned in the text.

Before we begin, it is important to note that technologies have ramifications in terms of epistemology, methodology, and ethics. For example, ethical questions about data privacy, epistemological “fit” of technology tools, and access to technology are just a few of the questions that qualitative researchers must examine when considering new technologies. We do not have the space in this review of technological tools to wrestle deeply with these questions. Others have begun to discuss the implications of technological innovation, and we point readers to these resources for more extensive discussion of the issues (see Nind et al., 2012 ; Phillips and Shaw, 2011 ; Travers, 2009 ).

Data collection

Improved access to the internet and the increasingly “wireless” nature of many parts of the world has led to new options for gathering qualitative data. In this section, we highlight tools for data collection that function with new devices such as tablets and smart phones, and support a higher level of mobility in research. These tools can be useful not only for conducting interviews but also for collecting data through online focus groups, social media and online communities, and in the field. Despite the growing availability of access to the internet and mobile technology, some research participants may not have the financial resources or access to the equipment and services necessary for some of these tools. On the other hand, mobile technologies and internet-based data collection methods may facilitate access for communities that otherwise may have been geographically or socially inaccessible. As always, researchers should assess whether these data collection suggestions are appropriate for the research questions and populations being studied.

Programs such as Poll Everywhere make use of wireless technology to gather “live” data through internet-enabled devices. Poll Everywhere allows the researcher to ask questions, either multiple choice or open-ended, and get live responses from the participants via text or website interaction. The results can be viewed in real-time online or as an embedded image in a PowerPoint or Keynote presentation. This kind of technology can be useful in gauging a group's experiences or opinions. It could be especially useful in focus groups when there may be a sensitive topic where anonymity of responses would be beneficial to the participants. It could also be helpful in getting conversations started in focus groups by polling participants about the topic and using the results to prompt discussion.

Another online tool for gathering textual data is Voice over Internet Protocol applications such as Skype ( Hanna, 2012 ). Most people are aware of Skype for video and phone calls, virtual meetings or instant messaging, but it can also be used for data collection. For example, you can conduct interviews or focus groups in real time via the instant messaging feature, which allows multiple users to participate simultaneously by typing their comments in a common “room.” All conversations are saved in Skype and can then be searched for keywords or concepts. The conversation can also be exported in plain text format into programs such as Microsoft products (Word or Excel) or a specialized qualitative data analysis program. Programs such as Kudos Chat Search also allow the user to do more powerful and complex searches, backup data, and manage multiple Skype accounts and chats across multiple computers. Skype can also be useful for collecting audio and visual data. The researcher can conduct interviews or focus groups via the audio-only phone feature or the video chat feature that includes a live video stream of each participant. Programs such as Evaer video recorder for Skype allow you to record both audio and video content and convert it to MP4/AVI files that can be used in data analysis.

New applications for collecting audio data are readily available for the iPhone/ iPad and Android-based devices. Typical digital recorders allow the user to record audio and sync it to a computer where it can be listened to and/or transcribed. In addition, applications such as Circus Ponies or AudioNote allow you to record audio and take notes within the same interface, with the audio and notes synced. Users can either navigate through the audio file via automatically generated time stamps or by clicking on a note that then links directly to the audio that was recorded when the note was written. Notes can also be added after the initial recording is made, so researchers can sync added notes to the audio file while working on data analysis or during the writing process. Syncing notes and audio data enhances the utility of field notes and creates a seamless integration of normally unlinked sources of data.

Photographs are powerful ways of capturing qualitative data and there are many digital tools that offer opportunities to store and use visual data. For instance, photo-sharing programs such as Picasa and Flickr allow users to create closed groups of invited users to upload and share photographs from participatory visual methods projects. These photo-sharing sites can house data and also function as an online forum for reacting to and discussing the images. Other programs, such ass Ning, support more of a community-building process by allowing the researcher to create an interactive online community that includes customized forums, photos, videos, and blogs, all of which can be contributed to by invited users.

Lastly, there are several data collection tools that capture daily activities in a way that may be useful for a variety of research projects. Several applications will depict a person's daily routine and track specific behaviors of interest, such as health behaviors or social contacts. Daily Routine, for example, allows the user to customize the activities of interest and then track them via interacting with the application when they perform the specified behaviors. Summaries of behavioral data can be emailed as a PDF. This information could be used to prompt recall in interviews designed to understand the context or phenomenological experience of performing the tracked behaviors. Other programs, such as Path, include geo-tagging as part of the data collection process. Geo-tagging refers to the process of attaching geographical coordinates to other types of data. These programs create a digital history of place by using smartphones to track location as standalone data or to attach spatial information by “tagging” photos, videos, or written material. This function could be especially useful for ethnographies or other projects in which place is conceptually important.

Data analysis

Data analysis procedures vary widely based on the type of data, the research methods used, philosophical approaches, and numerous other factors. Here, we will explore common steps in preparing and analyzing qualitative data and technological tools that can enhance these processes.

Even though several computer-assisted qualitative data analysis software packages (CAQDAS) allow a researcher to directly upload audio files for analysis, most social science researchers transform audio data into a more usable format through transcription, the form of which is an analytical and epistemological choice ( Hammersley, 2010 ). As a pragmatic matter, transcription is a time-consuming process. Software (such as Transcriber AG) facilitates transcription by slowing down the audio and allowing pausing and rewinding of the audio using the keyboard, similar to the older foot pedal operated transcription machines. Advances in the accuracy of voice recognition software may further improve the experience of transcribing. Dragon™ is perhaps the most well-known of these products. Earlier versions of voice recognition software were riddled with transcription errors, taking significant effort on the part of the user to fix errors and “train” the software to recognize their voice ( Johnson, 2011 ). Newer versions are more accurate. A transcriber using Dragon might listen to the recording and repeat the audio back into a microphone linked to the transcription software ( Matheson, 2007 ). The researcher will still need to check the transcript for errors, but the bulk of the transcribing can be done with little to no typing using speech-to-text software.

Qualitative data analysis techniques range from using paper, highlighters, and index cards, to word processing ( LaPelle, 2004 ) or spreadsheet tools ( Meyer and Avery, 2009 ), and for the past decade, CAQDAS packages such as Atlas.ti and Nvivo. Advances in web-based technology are creating new opportunities for analyzing qualitative data, particularly in team-based projects.

Many qualitative researchers utilize some form of coding as their primary analytic technique. Various tools for visualizing data have been developed that can assist in the process of coding and discovering themes and patterns in the data. Word clouds, for example, use frequency counts to develop visual maps of the most commonly used words in a specified body of text. These diagrams can be helpful as an initial overview of common words and concepts in the data. However, the utility of these visualizations is limited because it is not possible to discern useful details like which respondents used the words and the contexts in which they were used. Other automated tools are somewhat better at providing context; for example, word trees search for a specified word or phrase in a body of text and display all of the words following the index phrase. Some of these tools are available in CAQDAS programs such as NVivo, while similar web-based tools often require the user to upload data into a public database, so they are more useful for analysis of texts, documents, public blogs, or other data that is not confidential.

While these visualizations are helpful, researchers will likely still need to use some system for coding and retrieving their data. Many researchers are familiar with a CAQDAS package, such as Atlas.ti, NVivo, or HyperRESEARCH (see Lewins and Silver, 2007 for a helpful guide). While these software packages offer many benefits to researchers, they were developed before cloud computing became readily available, and so have some limitations. For example, the software must be housed on a local machine or server, be updated often, and can be quite expensive to purchase especially for those who are not sure they will use the software repeatedly. Newer Web 2.0 (i.e. cloud-based) software, such as Dedoose, allow users to access their data and analysis from any computer with internet access. Dedoose also allows for multiple users to work on an analysis project simultaneously from distant locations, facilitating the increasingly common practice of team-based coding and analysis in qualitative research. Because the analysis software is housed on a remote server, it is updated continuously and without effort on the part of users allowing the developers to add new features without interrupting the user experience.

New technologies also help researchers to visually represent linkages between concepts and data. For instance, Atlas.ti has a well-developed “network” interface that allows the researcher to drag and drop codes, quotations, or other concepts in order to organize their connections and relationships. Similarly, mind mapping software is accessible and easy to use, allowing researchers to organize patterns and themes that emerge from data analysis into a conceptual framework. Advances in touch screen functionality on tablet computers make these applications particularly intuitive and flexible (MindNode is one example). Nodes, ideas, or concepts can be organized and moved much like the classic qualitative practice of using notecards to sort and arrange ideas, only with the ability to save a copy of the arranged ideas for later use or manipulation.

Dissemination

One of the essential elements of the research endeavor is the process of disseminating the results of that research, whether in journals, books, or conference presentations. In this section, we explore technological advances that can facilitate the dissemination of research in written and audio/visual formats, as well as exploring emerging dissemination media such as blogs and infographics.

Writing has moved in the past few decades from a pen and paper activity to one involving personal computers as technologies were developed that allowed for greater speed and efficiency. New software offers a means of increasing the ease by which qualitative researchers (and other writers) communicate their ideas. For example, Scrivener was designed to address an essential quandary that many writers have encountered: the process of writing does not lend itself to the linear format of traditional word-processing programs. In reality, the writing of research reports and manuscripts rarely happens in a linear fashion (starting with the abstract and continuing on smoothly through the conclusion). Researchers generally move back and forth between writing and revising sections of the manuscript in an order that makes intuitive sense (rather than in the order they appear in a finished manuscript). In qualitative research reports, in particular, writers often find that developing the best way to convey the findings takes multiple iterations and substantial revisions. Scrivener is designed to allow writers to compose in whatever order naturally makes sense, facilitating the organizing and reorganizing of content, and allowing for seamless interfacing with all related reference sources within the same program as the writing occurs. By utilizing a more flexible structure, Scrivener offers a simple but effective tweak on writing software.

Researchers also disseminate results through presentations, such as at academic conferences or as training for practitioners. Presentation software has become the standard means of providing visual aids at conference and other presentations. Most academics are familiar with at least one presentation software package, such as Microsoft's Powerpoint or Apple's Keynote. Several new software applications have developed that improve the user experience or final product. For example, Prezi eschews the standard linear format of presentation software and instead has users place all their presentation content on a single canvas so that a “camera” can then move around and zoom in and out to view the content. Done well, this feature of Prezi gives presenters an added way of telling the story through the use of visual metaphors (e.g. the phrase “if we dig down deeper into the interview data, we can see something interesting emerge” can be paired with the camera zooming in to show the interesting findings). However, the movement of the camera around the canvas can make audience feel motion sickness. One of the more useful features of new presentation applications is that they are web-based, rather than housed on a local computer. Having cloud-based presentations allows for easy collaborating with coauthors. More importantly, the presentation is housed on a server and can be shared as widely as the user wants. Presentations can also be paired with pre-recorded audio tracks in order to share a verbally annotated version of a presentation.

For many social work researchers, it is just as important to disseminate findings to audiences that do not typically have access to academic journals and do not attend academic conferences. Much of the research done by social workers has direct relevance to practitioners and finding ways to distribute results to those most likely to make use of them is a particular priority for community-engaged scholars. Technological advances open up new possibilities for qualitative researchers to share their findings with nonacademic audiences. For example, Twitter can be used to publicize and share brief updates about research findings with networks of colleagues and practitioners. Blogs offer a venue for engaging in discussion and dialog about issues related to research ( Vannini, 2013 ). Many free blogging sites are available (e.g. WordPress, EduBlog, and Blogger). Infographics, or visual and graphic depictions of research findings offer engaging and accessible ways for practitioners to quickly understand the gist of the results. Visual.ly has a large gallery of infographics and templates for creating your own graphic. Others have experimented with using cartoons and digital animation to disseminate research results to communities and stakeholders ( Bartlett, 2013 ; Vaughn et al., 2013 ).

In this article, we have outlined a variety of ways that qualitative researchers can incorporate new technological tools into their research process. The obvious disadvantage of any publication about technology is that the tools continue to advance while the publication remains static and quickly becomes dated. For this reason, we felt it was important to offer readers some suggestions about how to keep up with emerging trends in technology that are relevant to qualitative researchers. As of the writing of this article, there are several websites that are useful sources of information (see Table 1 for URLs). The ProfHacker blog on the Chronicle of Higher Education website regularly reviews technology of use to academics. While not specific to qualitative research, it is a great source of up-to-date information about tools that other academics, teachers, and researchers have found useful. A wiki-type site called Bamboo DiRT features an extensive collection of links to digital tools of use to researchers (not exclusively qualitative in focus). An additional webpage, Mobile, and Cloud Qualitative Research Apps features a long and useful list of software and applications specific to qualitative research. Finally, the American Historical Association maintains “A Digital Toolbox for Historians” that features descriptions and links to a number of useful tools.

In addition, we urge qualitative researchers to discuss and describe their use of technological tools in their manuscripts and conference presentations so that others can learn about useful resources. This journal accepts short pieces on technical applications relevant to qualitative research that would make an excellent venue for researchers to describe technological aspects of their research. There are also social media spaces for the discussion of qualitative research, most relevant to readers of this journal perhaps are spaces such as the International Qualitative Social Work group on Facebook. Of course, social work and qualitative research conferences also provide formal and informal venues for discussion and resource sharing.

Finally, we encourage qualitative researchers to think about their own research processes and identify ways that their routines could be improved by technology. Many of the tools we described here were developed for purposes unrelated to qualitative research. In the future, we hope qualitative researchers will be able to partner with programmers and others to develop technologies that fit more closely with the needs of researchers.

Contributor Information

Carrie Ann Moylan, Binghamton University, Binghamton, NY, USA.

Amelia Seraphia Derr, Seattle University, Seattle, WA, USA.

Taryn Lindhorst, University of Washington, Seattle, WA, USA.

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Top 8 Technology Trends & Innovations driving Scientific Research in 2023

Curious about which scientific research technology trends & startups will soon impact your business? Explore our in-depth industry research on 590 scientific research startups & scaleups and get data-driven insights into technology-based solutions in our Scientific Research Innovation Map!

Advances in science and technology and their adoption in the real world are channelized by scientific research and interpretation. Research methods and workplaces are also evolving with the raising need for novel tools and technologies. Startups and emerging scaleups from academia are working to develop these research requirements. This report provides an overview of scientific research technology trends ranging from open science and artificial intelligence (AI) to immersive technologies and advanced computing. They accelerate the research timeline and novel therapeutics discovery, as well as create sustainable, environment-friendly development. Read more to explore how they impact your business.

Innovation Map outlines the Top 8 Scientific Research Technology Trends & 16 Promising Startups

For this in-depth research on the Top Scientific Research Trends & Startups, we analyzed a sample of 590 global startups and scaleups. The result of this research is data-driven innovation intelligence that improves strategic decision-making by giving you an overview of emerging technologies & startups in the scientific research industry. These insights are derived by working with our Big Data & Artificial Intelligence-powered StartUs Insights Discovery Platform , covering 2 500 000+ startups & scaleups globally. As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant startups, emerging technologies & future industry trends quickly & exhaustively.

In the Innovation Map below, you get an overview of the Top 8 Scientific Research Technology Trends & Innovations that impact 590 companies worldwide. Moreover, the Scientific Research Innovation Map reveals 16 hand-picked startups, all working on emerging technologies that advance their field.

Top 8 Scientific Research Trends

• Open Science
• Artificial Intelligence
• Advanced Computing
• Materials for Research
• Sustainability
• Immersive Technologies
• Smart Devices
• Data Processing & Visualization

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Tree Map reveals the Impact of the Top 8 Scientific Research Trends

Based on the Scientific Research Innovation Map, the Tree Map below illustrates the impact of the Top 8 Scientific Research Trends in 2023. Open science and related research practices, including open source, open access, and open drafting, are the most impactful scientific research technology trends. AI plays a critical role in the research lifecycle of the hypothesis till interpretation. Further, advanced computing allows scientists to compile, collate, and analyze massive volumes of raw data. Other trends in scientific research include advanced materials for research, improving sustainability in research, and immersive technologies. Lastly, smart tools and devices, along with data processing and visualization, aid research investigations and hypothesis validation.

Global Startup Heat Map covers 590 Scientific Research Startups & Scaleups

The Global Startup Heat Map below highlights the global distribution of the 590 exemplary startups & scaleups that we analyzed for this research. Created through the StartUs Insights Discovery Platform, the Heat Map reveals that UK and US see the most startup activity.

Below, you get to meet 16 out of these 590 promising startups & scaleups as well as the solutions they develop. These 16 startups are hand-picked based on criteria such as founding year, location, funding raised, and more. Depending on your specific needs, your top picks might look entirely different.

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1. Open Science

The science community is dispersed across academia and industrial settings, causing data silos and limiting collaboration. Moreover, networking among scientific communities happens mostly through meetings, conferences, and workshops. There is also a need to establish a string-free, open collaboration among them during research and publication. Open science promotes shared drafting, peer reviews, and transparent open source publications and creates an inclusive environment for health research practices. This benefits researchers, affiliated institutions, and society as a whole to achieve a common goal, vaccine development for example.

In&Sight advances Open Research Publishing

French startup In&Sight builds an open research publishing platform. It allows researchers to publish in multidisciplinary journals with open access, open peer review, and indexed journals in search databases. The platform also features a portal to draft and submit the manuscript in the prescribed format for pre-publication quality check. The article gets published on their open-access platform once the manuscript is accepted. Afterward, the paper receives peer reviews and constant feedback to improve upon the research.

OaMetrix enables Open-Access Research Paper Publishing

UK-based startup OaMetrix develops a web application for researchers. It aids open-access publishing by accumulating the details of accepted articles from publishers’ systems. The startup simplifies online payments, self-service invoices, author eligibility for article processing charges (APC), and the article approval process. This enables open access (OA) APC workflow and also connects authors, universities, funders, and publishers through a single platform.

2. Artificial Intelligence

In recent years, various industries have adopted to automation of methods and practices. Artificial intelligence, machine learning, and related technologies fuel automation and thus allow researchers to accelerate research analysis. Smart algorithms aid conventional setups, reviews, hypothesis creation, testing, and data analysis for reduced costs, quick turnaround, and lower manual interventions.

The Mindkind makes Intelligent Algorithms

Spanish startup The Mindkind develops algorithmic artificial general intelligence (AAGI). The startup’s software uses ETR Cognitive Architecture, the Mindkind is a vehicle for transferring neuroscientific discoveries into a commercial AAGI software engine. The platform is easily integrated within already existing commercial devices, such as assistive robotics, personal assistants, video games, and chatbots, among many others. The Mindkind’s target is developing intelligent and autonomous avatars and NPC capable to work for us within any Metaverse, and even being mistaken for humans.

Scispot offers Automated Research Workflow

Canadian startup Scispot provides a workflow automation platform for life science research and industry. The startup’s proprietary products are the lab operating system, Labsheets, and other integration modules that support research management. Scispot’s no-code tool enables researchers to automate their daily tasks and projects. Scispot’s key features of the product include inventory management, integration with third-party apps, team collaboration, data security & governance management. The startup aids researchers in all stages of R&D from project planning, and lab execution to reporting.

3. Advanced Computing

With the ever-evolving innovations and rapidly updating technological tools, there is a need for high computing power. Advanced computing has become a necessity in the digitalized world and metaverse. Various types of advanced computing are studied by researchers to understand and analyze usage in real life, including quantum computing, cognitive computing, evolutionary computing, and more. With advanced computational power and data processing capabilities comes the responsibility of handling the data, and thus privacy-preserving computation techniques are also being developed.

QuantFi provides Quantum Computing Algorithms

French startup Quantfi offers financial solutions using quantum computing. The startup’s proprietary Qiwi is a powerful quantum emulator that allows us to assess, develop, and optimize quantum algorithms today rather than in 5-10 years. QuantFi creates intellectual property in quantum computing through “quantum-inspired” simulators and algorithms. The startup offers research in quantum computing financial algorithms such as derivatives pricing, machine learning, anticipating market fluctuations, and portfolio optimization. The company caters to banks, wealth managers, and computer hardware companies.

Huiyin builds Precision Medicine Platform

Chinese startup Huiyin develops an end-to-end precision medicine platform using cloud computing, artificial intelligence, genomics, and clinical medicine. The startup caters to sample collection, logistics, storage and management, laboratory process management, analysis, and interpretation. Huiyin offers genetic disease-assisted decision-making, tumor-targeted drug analysis, carrier screening, and next-gen sequencing platforms. These solutions aid researchers to conduct efficient scientific research analysis and reporting.

4. Materials for Research

One of the key factors in research advancement is having the right resource and tools. The improvement in the available devices/ techniques is with the method used or the material. R&D plays a vital role in the development of novel materials. In the current era, the deployment of a novel material in a setup or an industry requires satisfying multiple criteria. The lifecycle of the material, environmental impact, carbon neutrality, and other characteristics. Thus, researchers are keen on studying lightweight, bio-based, value-added, next-generation materials that are beneficial for research, business, and the environment.

Kiutra advances Magnetic Cooling Technology

German startup Kiutra develops cryogen-free cooling solutions. The startup’s solution is based on closed-cycle cryocoolers and magnetic refrigeration technology known as Continuous Adiabatic Demagnetization Refrigeration. Kiutra offers sub-Kelvin research cryostats and add-ons for low-temperature analysis and material properties. The startup’s proprietary product S-Type is a versatile rack-mountable cryogenic platform, while S-Type Optical is a compact sub-kelvin cryostat with free-beam optical access. L-Type is a top-loading cryostat for simple and fast low-temperature analysis.

Hanay Advanced Chemicals formulates Natural Hydrogels

Turkish startup Hanay Advanced Chemicals offers natural amino acid-based advanced materials. The startup’s proprietary technology enables the fabrication of amino acid-based hydrogels having various compositions in different sizes. Hanay Advanced Chemicals do not utilize any metal catalyst for the synthesis of monomers, polymers, or hydrogels. The startup’s technology is viable to produce hydrolytically and enzymatically degradable hydrogels in any amino-acid combination and caters to the biotechnology and pharmaceutical industry.

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5. Sustainability

The focus of any research project in recent years has the sustainability module inbuilt. This ensures that the research outcome has a positive or neutral impact on the environment. Sustainability in research is achieved by conservation, preservation practices, or studies that are conducted to understand and counteract climate change. Startups are gaining momentum in these fields and are developing innovative methods and technologies to combat climate change and more.

Greenlab preserves Cannabis Germplasm

New Zealand-based startup Greenlab focuses on research, cultivation, and development of pharmaceutical-grade medicinal cannabis compounds. The startup utilizes unique germplasm for the selection of elite/ premium quality medical cannabis clones. Greenlabs collaborates with its partner institutions to build a genetic and breeding library of cannabis, to preserve, evaluate, and conserve unique compounds and gene signatures. The startup works on upcycling cannabis waste into value-added products.

EarthShine advances Climate Control Technology

US-based startup EarthShine Geoengineering focuses on research and development services that are economically effective and have higher efficiency in counteracting the effects of climate change. EarthShine’s proprietary climate control technology incorporates panels that are axially mounted and provide programmable effects. The startup’s Global Climate Control System consists of a Global Panel control network, satellite measurement system, Global climate command and control center, and system oversight. The system moderates energy in three ways including reflection, shading, and improving thermal emissions.

6. Immersive Technologies

Science education and research have evolved well from being illustrated in 2D books to a level of 3 & 4-D visualizations. Virtual reality and mixed reality have made the analysis and visualization of a micron size particle to the naked eye and have made interactions possible. Immersive Technologies are contributing quite a lot to the scientists’ everyday work and also have made “remote research” a possibility. These technologies also aid researchers to network and solve a scenario, virtually as a team from anywhere.

Dynamerse makes Immersive Scientific Simulations

UK-based startup Dynamerse provides virtual reality-based solutions. The startup makes interactive scientific simulations. Dynamerse allows scientists to collaborate remotely in a live-streamed virtual environment to communicate and collaborate on research material. Dynamerse offers a gamified mode for tackling drug docking, thus allowing researchers to dock a drug with a simple wave of the wrist. The startup’s tool enables users to scale the atomic world of chemistry, fluid dynamics, and virus modeling for exploring different structures and drug docking.

mSuit offers AI-based Motion Capture

Israeli startup mSuit provides a non-optical motion capture system for animation, streaming, and research. The startup offers a suit that provides real-time and AI-powered solutions for tracking and positioning based on Adaptive Lattice Filter Algorithms (ALFA). ALFA is a multipurpose technology for the realization of Adaptive Digital Filter, Kalman filters, Edge Computing, edgified Machine Learning, inter-period signal processing, and spectral analysis which enables stellar compute performance with extreme precision.

7. Smart Devices

The major requirements for a research study are data collection, interpretation, and analysis. While conventionally these tasks are manually done, nowadays startups are developing innovative devices to aid researchers and students. These devices improve the quality, time, and effort of a project and in turn also aid to save project budgets. Smart devices including robots are designed for scientific lab purposes, and more. Lasers, advanced computer vision cameras, and drones are built to understand and interpret data for multiple research purposes.

Darving builds Lab Robots

Australian startup Darving builds robots for researchers. The startup’s Darving robot aids in research experiments and enables researchers to test concepts rapidly. Darving robot is equipped with computer vision and QR code recognition to identify substances, reducing physical contact. The startup’s robot also is capable of auto-dispensing substances up to 10 micro levels and improves the measurement accuracy, consistency, and precision, and in turn, reduces wastage of chemicals.

Flapper Drones design Bio-inspired Drones

Dutch startup Flapper Drones develops bioinspired drones. The startup’s drones are bird-like drones for events and research purposes. Flapper Drones offers Research Drones that are ready-to-fly drone platforms as well as customized solutions for research projects. The platform is open source and programmable, gathers live telemetry data, and has extra payload capacity for sensors and batteries. The startup’s drones are autonomous in operation, lightweight, and well suited for research data collection, analysis, algorithm testing, and more.

8. Data Processing & Visualization

There are two types of research, qualitative and quantitative research. Both these studies’ outcome is data, different types of data, and volumes of data. Successful research is determined by the quality of data produced and the level of accurate interpretation. While data analysis and interpretation is the final step, some studies initiate with a set of data, including real-world data to determine a hypothesis. Thus, a research study requires the right data analysis tools. Emerging startups are introducing autonomous data analysis, and processing, and also providing real-world data set for research purpose. This saves a lot of time that goes into data collection and limits the errors in analysis.

Elysium aggregates Anonymized Patient Data

Italian startup Elysium provides accurate health data for scientific research. The startup’s Medory is an app that enables patients to reconstruct their medical history by uploading all health information to a portal. The anonymized information from Medory is extracted and aggregated into datasets for scientific research ensuring patient privacy. Researchers access the desired data sets using the search tools provided in the Elysium data platform. This aids in obtaining quality real-world data sets for medical and scientific research.

FiglinQ connects Data Lifecycle to Manuscript

Dutch startup FiglinQ is a collaborative platform for data analysis. The startup’s platform is similar to Excel with interactive charting and additionally allows the users to share and publish the data in data-connected smart manuscripts. FiglinQ enables researchers to create and maintain a direct connection between publication, quantitative charts, figures, and underlying scientific data by making data accessible and reusable by others. This allows researchers to include the entire data lifecycle in manuscripts and also ensures the implementation of fair data practices.

Discover all Scientific Research Trends, Technologies & Startups

The Scientific Research practices and community are adopting advancements in novel technologies, materials, tools, and more. Many emerging technologies effectively contribute to making research practice and outcomes more sustainable. Gamified experiments and open networking of scientific faculty are a possibility in near future. A robotic technician to prepare reagents in every research lab and an AI-based hypothesis framing are not far to adopt in most research institutions. With the aid of technology on one hand and a mindful use to promote sustainable research on the other, the evolution from conventional to tech-driven research seems more positive. Open research culture would be a fuel to accelerate beneficial outcomes for the society and environment.

The Scientific Research Trends & Startups outlined in this report only scratch the surface of trends that we identified during our data-driven innovation and startup scouting process. Among others, open science & research, intelligent workflows, and advanced computing will transform the sector as we know it today. Identifying new opportunities and emerging technologies to implement into your business goes a long way in gaining a competitive advantage. Get in touch to easily and exhaustively scout startups, technologies & trends that matter to you!

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Q&A: How – and why – we’re changing the way we study tech adoption

What share of U.S. adults have high-speed internet at home ? Own a smartphone? Use social media ?

Pew Research Center has long studied tech adoption by interviewing Americans over the phone. But starting with the publications released today, we’ll be reporting on these topics using our National Public Opinion Reference Survey (NPORS) instead. The biggest difference: NPORS participants are invited by postal mail and can respond to the survey via a paper questionnaire or online, rather than by phone.

To explain the thinking behind this change and its implications for our future work, here’s a conversation with Managing Director of Internet and Technology Research Monica Anderson and Research Associate Colleen McClain. This interview has been condensed and edited for clarity.

Pew Research Center has been tracking tech adoption in the United States for decades. Why is this area of study so important?

Anderson: We see this research as foundational to understanding the broader impact that the internet, mobile technology and social media have on our society.

Americans have an array of digital tools that help them with everything from getting news to shopping to finding jobs. Studying how people are going online, which devices they own and which social media sites they use is crucial for understanding how they experience the world around them.

This research also anchors our ongoing work on the digital divide : the gap between those who have access to certain technologies and those who don’t. It shows us where demographic differences exist, if they’ve changed over time, and how factors like age, race and income may contribute.

Our surveys are an important reminder that some technologies, like high-speed internet, remain out of reach for some Americans, particularly those who are less affluent. In fact, our latest survey shows that about four-in-ten Americans living in lower-income households do not subscribe to home broadband.

Why is your team making the switch from phone surveys to the National Public Opinion Reference Survey (NPORS)?

McClain: The internet hasn’t just transformed Americans’ everyday lives – it’s also transformed the way researchers study its impact. The changes we’ve made this year set us up to continue studying tech adoption long into the future.

We began tracking Americans’ tech use back in 2000. At that point, about half of Americans were online, and just 1% had broadband at home. Like much of the survey research world, we relied on telephone polling for these studies, and this approach served us well for decades.

But in more recent years, the share of people who respond to phone polls has plummeted , and these types of polls have become more costly. At the same time, online surveys have become more popular and pollsters’ methods have become more diverse . This transformation in polling is reflected in our online American Trends Panel , which works well for the vast majority of the Center’s U.S. survey work.

But there’s a caveat: Online-only surveys aren’t always the best approach when it comes to measuring certain types of data points. That includes measuring how many people don’t use technology in the first place.

Enter the National Public Opinion Reference Survey, which the Center launched in 2020 to meet these kinds of challenges. By giving people the choice to take our survey on paper or online, it is especially well-suited for hearing from Americans who don’t use the internet, aren’t comfortable with technology or just don’t want to respond online. That makes it a good fit for studying the digital divide. And NPORS achieves a higher response rate than phone polls .  

Shifting our tech adoption studies to NPORS ensures we’re keeping up with the latest advances in the Center’s methods toolkit, with quality at the forefront of this important work.

The internet hasn’t just transformed Americans’ everyday lives – it’s also transformed the way researchers study its impact. The changes we’ve made this year set us up to continue studying tech adoption long into the future. Colleen McClain

Are the old and new approaches comparable?

McClain: We took several steps to make our NPORS findings as comparable as possible with our earlier phone surveys. We knew that it can be tricky, and sometimes impossible, to directly compare the results of surveys that use different modes – that is, methods of interviewing. How a survey is conducted can affect how people answer questions and who responds in the first place. These are known as “mode effects.”

To try to minimize the impact of this change, we started by doing what we do best: gathering data.

Around the same time that we fielded our phone polls about tech adoption in 2019 and 2021, we also fielded some surveys using alternate approaches. We didn’t want to change the mode right away, but rather understand how any changes in our approach might affect the data we were collecting about how Americans use technology.

These test runs helped narrow our options and tweak the NPORS design. Using the 2019 and 2021 phone data we collected as a comparison point, we worked over the next few years to make the respondent experience as similar as possible across modes.

What does your new approach mean for your ability to talk about changes over time?

McClain: We carefully considered the potential for mode effects as we decided how to talk about the changes we saw in our findings this year. Even with all the work we did to make the approaches as comparable as possible, we wanted to be cautious.

For instance, we paid close attention to the size of any changes we observed. In some cases, the figures were fairly similar between 2021 and 2023, and even without the mode shift, we wouldn’t make too much of them.

We gave a thorough look at more striking differences. For example, 21% of Americans said they used TikTok in our 2021 phone survey, and that’s risen to 33% now in our paper/online survey. Going back to our test runs from earlier years helped us conclude it’s unlikely this change was all due to mode. We believe it also reflects real change over time.

While the mode shift makes it trickier than usual to talk about trends, we believe the change in approach is a net positive for the quality of our work. NPORS sets us up well for the future.

How are you communicating this mode shift in your published work?

McClain: It’s important to us that readers can quickly and easily understand the shift and when it took place.

In some cases, we’ll be displaying the findings from our paper/online survey side by side with the data points from prior phone surveys. Trend charts in our reports signal the mode shift with a dotted line to draw attention to the change in approach. In our fact sheets , a vertical line conveys the same thing. In both cases, we also provide information in the footnotes below the chart itself.

In other places in our publications, we’re taking an even more cautious approach and focusing on the new data rather than on trends.

Did you have to change the way you asked survey questions?

McClain: Writing questions that keep up with the ever-changing nature of technology is always a challenge, and the mode shift complicated this further. For example, our previous phone surveys were conducted by interviewers, but taking surveys online or on paper doesn’t involve talking to someone. We needed to adapt our questions to keep the experience as consistent as possible on the new paper and online surveys.

Take who subscribes to home broadband, for example. Knowing we wouldn’t have an interviewer to probe and confirm someone’s response in the new modes, we tested out different options in advance to help us ensure we were collecting quality data.

In this case, we gave people a chance to say they were “not sure” or to write in a different type of internet connection, if the ones we offered didn’t quite fit their situation. We also updated the examples of internet connections in the question to be consistent with evolving technology.

Which findings from your latest survey stand out to you?

Anderson: There are several exciting things in our latest work, but two findings related to social media really stand out.

The first is the rise of TikTok. A third of U.S. adults – including about six-in-ten adults under 30 – use this video-based platform. These figures have significantly jumped since we last asked these questions in 2021. And separate surveys from the Center have found that TikTok is increasingly becoming a news source for Americans , especially young adults.

The second is how dominant Facebook remains. While its use has sharply declined among teens in the U.S. , most adults – about two-thirds – say they use the site. And this share has remained relatively stable over the past decade or so. YouTube is the only platform we asked about in our current survey that is more widely used than Facebook.

These findings reinforce why consistently tracking the use of technology, especially specific sites and apps, is so important. The online landscape can evolve quickly. As researchers who study these platforms, a forward-looking mindset is key. We’ll continue looking for new and emerging platforms while tracking longer-standing sites to see how use changes – or doesn’t – over time.

To learn more about the National Public Opinion Reference Survey, read our NPORS fact sheet . For more on Americans’ use of technology, read our new reports:

• Americans’ Use of Mobile Technology and Home Broadband
• Americans’ Social Media Use
• Internet & Technology
• Research Explainers
• Survey Methods
• Technology Adoption

Anna Jackson is an editorial assistant at Pew Research Center .

How U.S. Public Opinion Has Changed in 20 Years of Our Surveys

Support for a u.s. tiktok ban continues to decline, and half of adults doubt it will happen, why many parents and teens think it’s harder being a teen today, methodology: teens and parents survey, many israelis say social media content about the israel-hamas war should be censored, most popular.

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan, nonadvocacy fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, computational social science research and other data-driven research. Pew Research Center is a subsidiary of The Pew Charitable Trusts , its primary funder.

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Research methods--quantitative, qualitative, and more: overview.

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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Sep 6, 2024 8:59 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Open access
• Published: 13 September 2024

Understanding disciplinary perspectives: a framework to develop skills for interdisciplinary research collaborations of medical experts and engineers

• Sophie van Baalen   ORCID: orcid.org/0000-0002-1592-3276 1 , 2 &
• Mieke Boon   ORCID: orcid.org/0000-0003-2492-2854 1  

BMC Medical Education volume  24 , Article number:  1000 ( 2024 ) Cite this article

Metrics details

Health professionals need to be prepared for interdisciplinary research collaborations aimed at the development and implementation of medical technology. Expertise is highly domain-specific, and learned by being immersed in professional practice. Therefore, the approaches and results from one domain are not easily understood by experts from another domain. Interdisciplinary collaboration in medical research faces not only institutional, but also cognitive and epistemological barriers. This is one of the reasons why interdisciplinary and interprofessional research collaborations are so difficult. To explain the cognitive and epistemological barriers, we introduce the concept of disciplinary perspectives . Making explicit the disciplinary perspectives of experts participating in interdisciplinary collaborations helps to clarify the specific approach of each expert, thereby improving mutual understanding.

We developed a framework for making disciplinary perspectives of experts participating in an interdisciplinary research collaboration explicit. The applicability of the framework has been tested in an interdisciplinary medical research project aimed at the development and implementation of diffusion MRI for the diagnosis of kidney cancer, where the framework was applied to analyse and articulate the disciplinary perspectives of the experts involved.

We propose a general framework, in the form of a series of questions, based on new insights from the philosophy of science into the epistemology of interdisciplinary research. We explain these philosophical underpinnings in order to clarify the cognitive and epistemological barriers of interdisciplinary research collaborations. In addition, we present a detailed example of the use of the framework in a concrete interdisciplinary research project aimed at developing a diagnostic technology. This case study demonstrates the applicability of the framework in interdisciplinary research projects.

Interdisciplinary research collaborations can be facilitated by a better understanding of how an expert’s disciplinary perspectives enables and guides their specific approach to a problem. Implicit disciplinary perspectives can and should be made explicit in a systematic manner, for which we propose a framework that can be used by disciplinary experts participating in interdisciplinary research project. Furthermore, we suggest that educators can explore how the framework and philosophical underpinning can be implemented in HPE to support the development of students’ interdisciplinary expertise.

Peer Review reports

Expertise is highly domain-specific, and learned by being immersed in professional practice [ 1 ]. However, today’s rapidly evolving health care systems require clinicians who are capable of meeting complex challenges [ 2 ], which often requires interdisciplinary and interprofessional collaborations between experts from distinct disciplines. Footnote 1 With the increasingly central role of innovative medical technologies in many medical specialties [ 3 ], health professionals will presumable participate in interdisciplinary and interprofessional research collaborations. But interprofessional and interdisciplinary research collaborations are notoriously difficult (e.g., [ 4 , 5 , 6 , 7 ]). Boon et al. (2019) argue that the complexity of current medical practices requires interdisciplinary expertise , which is an extension of adaptive expertise [ 8 ]. Interdisciplinary expertise involves the ability to understand the role of disciplinary perspectives .

In this paper, we combine insights from the philosophy of science on disciplinary perspectives and practice experience from an interdisciplinary medical research project aimed at the development and implementation of diffusion MRI for the diagnosis of kidney cancer. Based on these insights and practice experience, we propose a framework for mitigating cognitive and epistemological barriers caused by different disciplinary perspectives. In addition, we present a detailed example of the use of the framework to analyse and explain the experts’ disciplinary perspectives in the aforementioned interdisciplinary research project aimed at developing a diagnostic technology. This case study demonstrates the use of the framework in interdisciplinary research projects. The framework can be used by health professionals to facilitate their interdisciplinary research projects, by analysing and explaining their disciplinary perspectives.

Interdisciplinary research

To address the barriers to interdisciplinary research, various authors have developed analytical frameworks to guide the research process and help disciplinary experts understand what it takes to execute projects together with experts from other disciplines [ 9 , 10 , 11 , 12 ]. Menken et al. (2016), for example, provide a method for interdisciplinary research that is much similar to the traditional empirical cycle, including steps such as “identify problem or topic,” “formulate preliminary research questions,” “data collection” and “draw conclusions” [ 11 ]. Other frameworks describe which steps need to be taken in the interdisciplinary research process . In the literature on team science , several authors also aim to provide a better understanding of the process of interdisciplinary research. For example, Hasan et al. (2023) focuses on the ‘micro’ layers of the team science ecosystem proposed by Stokols et al. (2019) – the layer of individual team members collaborating in interdisciplinary research projects [ 13 , 14 ]. From their analysis of an online collaborations between early academics from different fields, they provide insights into common issues in interdisciplinary research and methods for dealing with them. By applying their framework from the start of the interdisciplinary research process, they argue, interdisciplinary capture [ 15 ] can be avoided.

Although the aforementioned frameworks provide valuable guidance on the process of interdisciplinary collaboration, they do not address the deeper cognitive and epistemological challenges of interdisciplinary research collaboration [ 5 , 16 ], which is the objective of our contribution. A crucial assumption in current frameworks seems to be that interdisciplinary research collaboration is learned by doing, and that the integration of different disciplines will automatically follow. Footnote 2 In our view, however, the integration of different disciplines is both crucial and one of the most challenging aspects of interdisciplinary research collaboration. In previous work we have argued that the inherent cognitive and epistemological (knowledge-theoretical) challenges of integration have been neglected by most authors providing models for interdisciplinary research [ 8 ]. In this paper, our focus is therefore on challenges of using and producing knowledge in interdisciplinary research collaborations that aim at solving complex real-world problems. Examples are collaborations between distinct medical specialists in the diagnosis and treatment of a specific patient (e.g., an oncologist and radiologist), but also collaborations between medical experts and biomedical engineers aimed at innovative medical technology for clinical uses. In this paper, we focus on inter disciplinary research projects, in which two or more academic fields are integrated to solve real-world problems, and not on trans disciplinary projects in which one or more academic fields are integrated with expertise from outside of academia such as policy-making or practice. Footnote 3

The challenge of interdisciplinary research collaborations aimed at solving a shared problem is that each expert is guided by his/her own disciplinary perspective. However, the results produced by experts from different disciplines, although internally coherent, are not mutually coherent, so that they are not easily integrated. Furthermore, approaches and results understood within a contributing disciplinary perspective are not easily understood by experts specialised in other disciplinary perspectives, even though each expert aims to contribute to the same problem.

In short, the way in which experts use and produce knowledge is guided by the disciplinary perspective typical of their own practice. But experts are often unaware of having a disciplinary perspective. We argue that this is an obstacle to participating in interdisciplinary research collaborations focused on using and producing knowledge for complex problem-solving . Moreover, disciplinary perspectives are often considered impenetrable —as they are acquired by doing — which makes dealing with the disciplinary perspective of other experts a difficult learning objective. In this paper, we defend that disciplinary perspectives can be made explicit in a systematic manner, and that their role in ‘how experts in a specific discipline use and produce knowledge’ can thus be made understandable for experts and students in both their own and other disciplines.

To this end, we have developed a framework, based on new insights in the philosophy of science and on practice experience of interdisciplinary research collaboration aimed at the development of a medical technology, which can be used by experts in a particular discipline to analyse different elements of their discipline and, together with collaborators, to analyse the same elements from other disciplines. We believe that this systematic approach to understanding disciplinary perspectives will facilitate interdisciplinary research collaborations between experts from different fields. It will create awareness of one’s own disciplinary perspective and the ability to understand the disciplinary perspective of other experts at a sufficient level. Our framework thus aims to alleviate the challenge of integration in a collaborative research project by providing a tool for analysing disciplinary perspectives . We suggest that the concrete descriptions of disciplinary perspectives that result from the application of the framework, clarify the approaches of experts in a multi-disciplinary team. It thus enables effective communication through improved understanding of how each discipline contributes. Once researchers sufficiently understand each other’s discipline, they will be able to construct so-called conceptual models that integrate content relevant to the problems at hand. Footnote 4

Education in interdisciplinary research

In addition to professionals using our framework to facilitate collaboration in interdisciplinary research projects, we suggest that this framework can also be implemented in medical education. It can be used to teach students what it means to have a disciplinary perspective, and to explicate the role of disciplinary perspectives of disciplinary experts participating in an interdisciplinary research collaboration. We have implemented this framework in an innovative, challenge-based educational design that explicitly aims to support and promote the development of interdisciplinary research skills [ 22 ]. Research into the intended learning objectives has not yet been completed, but our initial findings indicate that the proposed framework effectively supports students in their ability to develop crucial skills for conducting interdisciplinary research projects. We suggest therefore that the framework can also be implemented in HPE as a scaffold for teaching and learning metacognitive skills needed in interdisciplinary research collaborations, for example between medical experts and engineers.

Research has shown that interprofessional education courses for healthcare students can have a positive effect on the knowledge, skills and attitudes required for interprofessional collaboration, but that organising such interventions is challenging [ 23 , 24 ]. In the HPE literature, it is generally assumed that the limitations of interprofessional and interdisciplinary teamwork are due to problems of communication, collaboration and cooperation [ 25 , 26 ], which are linked to barriers and enablers at institutional, organizational, infrastructural, professional and individual levels (e.g., [ 27 , 28 ]). Therefore, interprofessional and interdisciplinary collaborations are discussed extensively in the HPE literature – our focus is challenges of interdisciplinary research collaboration.

The ability to use and produce knowledge and methods in solving (novel) problems is covered in the HPE literature by the notion of adaptive expertise , which encompasses clinical reasoning, integrating basic and clinical sciences, and the transfer of previously learned knowledge, concepts and methods to solve new problems in another context (e.g., [ 1 , 29 , 30 , 31 , 32 , 33 , 34 ]). In previous work, we introduced the concept of interdisciplinary expertise, which expands on the notion of adaptive expertise by including the ability to understand, analyse and communicate disciplinary perspectives [ 8 ]. In this paper, we address the challenge posed by how this ability to understand, analyse and communicate disciplinary perspectives can be learned. The framework that we propose can be implemented in HPE to function as a tool to scaffold metacognitive skills of health professions students, facilitating the development of interdisciplinary expertise.

Aims and contributions of this paper

Our first objective is to show that interdisciplinary collaboration in (medical) research faces not only institutional, but also cognitive and epistemological barriers. Therefore, we first provide a theoretical explanation of the concept of ‘disciplinary perspective’ as developed in the philosophy of science, in order to make it plausible that the cognitive barriers experienced by experts in interdisciplinary collaboration are the result of different disciplinary perspectives on a problem and its solution.

Our second objective is to provide a systematic approach to improve interdisciplinary research, for which we propose a framework, in the form of a series of questions, based on new insights from the philosophy of science into the epistemology of interdisciplinary research. We provide a detailed explanation of the application of the proposed framework in an interdisciplinary medical research project to illustrate its applicability in a multidisciplinary research collaborations, by showing that the different disciplinary perspectives that inform researchers and technicians within a multidisciplinary research team can be made transparent in a systematic way.

In short, our intended contribution is (i) to explain cognitive and epistemological barriers by introducing the concept of disciplinary perspectives in medical research collaborations, (ii) to offer a framework that enables the mitigation of these barriers within interdisciplinary research projects that are caused by different disciplinary perspectives, and (iii) to illustrate the applicability of this framework by a concrete case of an interdisciplinary research collaboration in a medical-technical research setting.

We developed a framework for making disciplinary perspectives of experts participating in an interdisciplinary research collaboration explicit, by combining insights from the philosophy of science with practical experience from a medical research project. Philosophy of science provided the theoretical basis for our concept of disciplinary perspectives. Our detailed case-description stems from an interdisciplinary medical research project to develop and implement a new imaging tool for the diagnosis of kidney cancer, in which the first author participated. We then applied the framework to analyze and articulate the disciplinary perspectives of experts involved in this interdisciplinary medical research project.

The usefulness and applicability of the proposed framework was tested by the first author who, in her role as PI, was able to use it successfully in coordinating an interdisciplinary research project aimed at developing a biomedical technology for clinical practice [ 35 , 36 ]. Below, we illustrate how the framework was systematically applied to this specific case, providing initial evidence of its applicability. However, to test whether the proposed framework reduces the cognitive and epistemological barriers caused by different disciplinary perspectives, experts need to be trained in its use. We suggest that training in the use of this framework requires, among other things, some insight into the philosophical underpinnings of the concept of ‘disciplinary perspective’. Our explanation of the so-called epistemology of disciplinary perspectives in this paper aims to provide such insight.

Developing a framework for analysing and articulating a disciplinary perspective

The framework proposed here is based on insights about disciplinary perspectives in the philosophy of science. These insights concern an epistemology (a theory of knowledge) of scientific disciplines. In other words, the framework is based on an account of the knowledge-theoretical (epistemic) and pragmatic aspects that guide the production of knowledge and scientific understanding by a discipline [ 21 ].

The epistemology of scientific disciplines developed in our previous work is based on the philosophical work of Thomas Kuhn [ 37 ]. Building on his seminal ideas, we understand disciplinary perspectives as analysable in terms of a coherent set of epistemic and pragmatic aspects related to the way in which experts trained in the discipline (and who have thus, albeit implicitly, acquired the disciplinary perspective) apply and produce knowledge [ 38 ]. In our approach, the epistemic and pragmatic aspects that generally characterize a discipline, are made explicit through a set of questions that form the basis of the proposed framework (see Table 1 , and the first column of Table  2 ). The disciplinary perspective can thus be revealed through this framework. In turn, when used in educational settings, this framework can be used to foster interdisciplinary expertise by acting as a scaffold for teaching and learning metacognitive skills for interdisciplinary research collaborations. Footnote 5

The general aspects indicated by italics in each question in Table 1 are interdependent, so that analysis using this framework results in a coherent description of the disciplinary perspective in terms of these aspects. The framework can be used by experts in an interdisciplinary research project not only to make explicit their disciplinary perspective in a general sense, but to also to specify in a systematic way how these aspects relate to the interdisciplinary research problem from their disciplinary discipline (see Table  2 , which contains both the general and problem-specific descriptions for each aspect per discipline). In our view, this approach is productive in overcoming the cognitive and epistemological barriers. It thus contributes to productive interdisciplinary collaboration.

Applying the framework in an interdisciplinary medical research project

To test the applicability of this framework, we applied it to an interdisciplinary medical research project. The interdisciplinary medical research project aimed at developing a new clinical imaging tool, namely, diffusion magnetic resonance imaging (i.e., diffusion MRI) to characterize the micro-structural makeup of kidney tumours, running from early 2014 to mid-2018. The first author was involved in this project as a principle investigator (PI). As an interdisciplinary expert with a background in technical medicine , which combines medical training with technological expertise [ 41 ], she coordinated and integrated contributions from experts with medical and engineering backgrounds. In her role as PI, she applied the proposed framework to analyse and articulate the disciplinary perspectives of other experts involved in the medical research project.

The aim of the interdisciplinary medical research project was to develop a new imaging tool for the characterization of renal tumours, i.e., diffusion MRI. Diffusion MRI allows for visualization and quantification of water diffusion without administration of exogenous contrast materials and is, therefore, a promising technique for imaging kidney tumours. In earlier studies, several parameters derived from diffusion MRI studies were found to differentiate between different tumour types in the kidney [ 42 , 43 , 44 ]. Existing imaging methods in clinical practice can detect the size and location of kidney tumours, but the tumour type and malignancy can only be determined histologically after surgery. The purpose of the medical research project was to assess whether more advanced parameters that can be obtained from diffusion MRI [ 35 , 45 ] can differentiate between malignant and benign kidney tumours [ 36 ]. Being able to make this distinction could potentially prevent unnecessary surgery in patients with non-malignant tumours.

The interdisciplinary medical research project needed to bring together expertise (knowledge and skills) from different professionals, academic researchers as well as clinicians. Therefore, the research team consisted of a physicist, a biomedical engineer, a radiologist, a urologist and the principle investigator. The complex, interdisciplinary research object can be thought of as a system that encompasses several elements: the MRI-machine, the software necessary to produce images, the patient with a (suspected) kidney tumour, and the wider practice of care in which the clinical tool should function. In developing the clinical tool, these elements must be considered interrelated, whereas usually each expert focuses on one of these elements.

The PI utilized the framework to coordinate and integrate the contributions from different experts in the following manner. Throughout the project, she had meetings with each of the team members, where she probed them to explain their specific expertise in regard of the research object, as well as their expert contribution to the development of the imaging tool. Her approach in these meetings was guided by the general questions of the framework (Table 1 ). In this manner, she succeeded in getting a clear insight in aspects of each discipline relevant to the research object, and also in the specific contribution that needed to be made by each expert (as illustrated in Table  2 below). The level of understanding gained by this approach enabled her to, firstly, facilitate interdisciplinary team meetings in which disciplinary interpretations and questions from the experts about the target system could be aligned, and secondly, integrate their contributions towards the development of the new imaging tool [ 36 ].

In the presented approach, the framework was exclusively used by the PI, enabling her to acquire relevant information and understanding about the contributions of the disciplines involved. The other team members in the medical research project were not explicitly involved in applying the framework, nor in articulating their own disciplinary perspective or that of others. Hence, the resulting articulation of the disciplinary perspectives and of the contributions per discipline to the research object (in Table  2 ) is crafted by the PI. The level of understanding of the role of each discipline that the PI has acquired thereby appears to be sufficient to enable her coordinating task in this complex medical research project. Our suggestion for other research and educational practices, though, is that clinicians (as well as) other medical experts can develop this metacognitive skill by using the scaffold (in Table  1 ) in order to participate more effectively in these kinds of complex medical research projects.

In the results  section we will first present our explanation and justification of the idea that disciplinary perspectives determine the specific approaches of experts (who have been trained in a specific discipline in using and producing knowledge) when faced with a complex problem. In this explanation and justification, we will use insights from the philosophy of science. Next, we will explain and illustrate the systematic use of the proposed framework (Table 1 ) by showing the results of applying it to the interdisciplinary medical research project.

The insights from philosophy of science on which the proposed framework for the explication of disciplinary perspectives is rooted in insights of the philosophers Immanuel Kant (1794–1804) and Thomas Kuhn (1922–1996). Their important epistemological insight was that ‘objective’ knowledge of reality does not arise from some kind of imprint in the mind, such as on a photographic plate, but is partly formed by the concepts and theories that scientists hold. These concepts and theories therefore shape the way they perceive the world and produce knowledge about reality. This philosophical insight provides an important explanation for the cognitive and epistemological barriers between disciplines. After all, scientific experts learn these concepts and theories by being trained within a certain discipline. In this way, they develop a disciplinary perspective that determines their view and understanding of reality. Based on this philosophical insight, we can imagine how these barriers can be bridged, namely by developing the metacognitive ability to think about their own cognition and how their scientific view of reality is shaped by their specific disciplinary perspective. In order to facilitate this ability, we develop a framework that can be used as a metacognitive scaffold. Finally, we apply this framework to an example interdisciplinary medical-technical research project, to illustrate it’s use in practice.

Insights from the philosophy of science: disciplinary perspectives

Boon et al. (2019) refer to the notion of disciplinary perspectives and their indelible role in how experts approach problems —in particular, the ways in which experts use and produce knowledge in regard of the problem they aim to solve— and provide a philosophical account of this notion based on so-called constructivist (Kantian) epistemology (i.e., knowledge-theory, [ 38 , 46 ]). On a Kantian view, ‘the world does not speak for itself,’ i.e., knowledge of (aspects of) the external world is not acquired passively on the basis of impressions in the mind (physically) caused by the external world (e.g., similar to how pictures of the world are physically imprinted on a photographic plate). Instead, the way in which people produce and use knowledge results from an interaction between the external world, the human senses and the human cognitive system. Crucially, neither our concepts nor our perceptions stem from passive impressions. Instead, ‘pre-given’ concepts ‘in the mind’ are needed in order to be able to perceive something at all and thus to produce knowledge about reality. Conversely, according to Kant, the imaginative (i.e. creative) capacity of the mind is then able to generate new concepts and to draw new connections of which the adequacy and usability must be tested against our experiences of reality. When new concepts (invented by the creative capacity of the human mind) have been tested against experience, they allow us to see new things in the external world, which we would not see without those concepts. This theoretical insight by Kant is crucial to get past naïve conceptions of knowledge, in particular, by understanding the indelible role of concepts in generating knowledge from observations and experiences.

This philosophical insight already makes it clear, for instance, that ‘descriptions of facts’ in a research project involve discipline-specific concepts, making these descriptions not easy to understand for someone who is not trained in that discipline. After Kant, this role of concepts has been expanded to the role of perspectives . For, Kuhn [ 37 ] created awareness that the human mind plays ‘unconsciously’ and ‘unintentionally’ a much greater role in the way scientific knowledge is created than usually assumed in the view that scientific knowledge is objective . Kuhn has introduced the concept of scientific paradigm to indicate in what sense the mind contributes. His idea was revolutionary because the notion of true and objective knowledge, which is the aim of science, became deeply problematic, as knowledge is only true and objective within the scientific paradigm, whereas it may even be meaningless in another.

Our notion of disciplinary perspectives is in many respects comparable to Kuhn’s idea of scientific paradigm, and is certainly indebted to Kuhn’s invention, particularly, with regard to the idea that it is a more or less coherent, usually implicit ‘background picture’ or ‘conceptual framework,’ which constitutes an inherent part of the cognitive system of an expert, and which forms the basis from which an expert thinks, sees and investigates in a scientific or professional practice. Furthermore, the scientific paradigm is not ‘innate,’ nor individually acquired, but maintained and transferred in scientific or professional practices, usually by being immersed in it. The same can be said about disciplinary perspectives. Yet, there are also important differences.

First, Kuhn believed that the paradigm is so deeply rooted in the cognitive structure of individual scientists, and, moreover, is embedded in how the scientific community functions, that it takes a scientific revolution and a new generation of scientists to shift into another paradigm, which is called a paradigm-shift (sometimes explained as a Gestalt-switch ). Kuhn’s belief suggests that humans lack the capacity to reflect on their own paradigm. Footnote 6 Conversely, we argue that humans can develop the metacognitive ability to perform this kind of reflection by which the structure and content of the paradigm or disciplinary perspective is made explicit. We take this as an important part of interdisciplinary expertise . Our suggestion, however, should not be confused with the idea that we can think without any paradigm or disciplinary perspective – we can’t, but we can explicate its workings (and adapt it), which is what we will illustrate in the case-description below.

Second, Kuhn’s focus was science , i.e., the production of objectively true scientific knowledge, in particular, theories. Instead, our focus is on experts trained in specific disciplines, who use and produce knowledge with regard to (practical) problems that have to be solved. Nonetheless, the Kuhnean insight explains why knowledge generated in distinct disciplines often cannot be combined in a straightforward manner (e.g., as in a jigsaw puzzle), which is due to the fact that knowledge is only fully meaningful and understandable relative to the disciplinary perspective in which it has been produced.

Our notion of disciplinary perspectives is similar to Kuhn’s idea of paradigm (which he specified later on as disciplinary matrices ) in the sense that a paradigm functions as a perspective or a conceptual framework , i.e., a background picture within which a scientific or professional practice of a specific discipline is embedded and which guides and enables this practice. But instead of considering them as replacing each other in a serial historical order as Kuhn did, we assume that disciplinary perspectives co-exist, that is, exist in parallel instead of serial. This view on disciplinary perspectives can be elaborated somewhat further by harking back to Ludwik Fleck [ 47 ], a microbiologist, who already in the 1930s developed a historical philosophy and sociology of science that is very similar to Kuhn’s (also see [ 48 ]). Footnote 7 Similar to and deeply affected by Kant, Fleck draws a close connection between human knowledge (e.g., facts) and cognition. Hence, Fleck disputes that facts are descriptions of things in reality discovered through properly passive observation of aspects in reality – which is why, according to Fleck, facts are invented , not discovered . Similar to Kuhn, Fleck expands on Kant by also including the role of the community in which scientists and experts are trained. Instead of paradigms , however, Fleck uses the terms thought styles and thought collectives to describe how experts in a certain professional or academic community adopt similar ways of perceiving and thinking that differ between disciplines: “The expert [trained in the discipline] is already a specially moulded individual who can no longer escape the bonds of tradition and of the collective; otherwise he would not be an expert” ([ 47 ], p. 54). But while Kuhn strove to explain radical changes in science, Fleck’s focus is on ‘normal science,’ that is, on communities ( thought collectives each having their own thought style ) that co-exist and gradually, rather than radically, change, which is closer to our take on disciplines. Importantly, according to Fleck, the community guides which problems members of that communities find relevant and how they approach these problems. Translated to our vocabulary, in scientific and professional practices, experts trained in different disciplines each have different disciplinary perspective, by means of which they recognize different aspects and problems of the same so-called research object , which they approach in accordance with their own discipline.

We propose that disciplinary perspectives can be analysed and made explicit, which we consider a crucial metacognitive skill of interdisciplinary experts. Our proposal for the framework to analyse disciplinary perspectives (in Table 1 ) takes its cue in Kuhn’s notion of disciplinary matrices. Kuhn’s original notion presents a matrix by which historians and philosophers can analyse the paradigm in hindsight, specifying aspects such as the metaphysical background beliefs and basic concepts, core theories, epistemic values, and methods, which all play a role in how knowledge is generated (also see [ 8 , 50 ]). Our framework includes some of these aspects, but also adds others, thereby generating a scaffold that facilitates interdisciplinary collaborations aimed at applying and producing knowledge for complex problem-solving in professional research practices aimed at ‘real-world’ practices, such as medical research practice. Below, we will illustrate the application of this framework in a concrete case.

Interdisciplinary research project: diffusion MRI for the diagnosis of kidney tumour

We will illustrate the applicability of the proposed framework (Table 1 ) for the analysis of disciplinary perspectives using the example of a research project that aims to develop a new clinical imaging tool, namely, diffusion MRI to characterize the microstructure of renal tumours. In our analysis, we focus on experts from four different disciplines: (I) clinical practice, (II) medical biology, (III) MRI physics, and (IV) signal and image processing. As indicated in the methods section, the complex, interdisciplinary research object that these experts have to deal with concerns a system consisting of the MRI-machine, the software necessary to produce images, and the patient with a (suspected) renal tumour, including the broader care practice in which the clinical tool should function.

In the following paragraphs we will first present a general explanation of the four disciplines involved in the project, and next, illustrate how the proposed framework can be applied to analyse and articulate each disciplinary perspective as well as the specific contribution of each discipline to the research object (in Table  2 ). It is not our intention to provide comprehensive descriptions of the fields that are involved, but rather to provide insight into how the fields differ from each other across the elements of our framework. In addition, we do not believe that all (disciplinary) experts only adhere to one disciplinary perspective. For example, clinicians usually combine both a clinical and biomedical perspective to fit together a complete picture of a patient for clinical decision-making concerning diagnosis and treatment [ 51 , 52 , 53 ]. Moreover, MRI engineers will usually need to combine insights from MRI physics and signal processing.

I. Clinical practice concerning patients with renal tumours

Clinical practice concerns the patient with a renal tumour. This practice differs from the other disciplines in our example, because it is not primarily a scientific discipline. Nonetheless, to develop a diagnostic tool, the disciplinary perspective of clinicians specialized in patients with kidney tumours is crucial, for example, to determine the conditions that the technology needs to meet in order to be useful for their clinical practice. The knowledge-base of clinical experts is rooted in biomedical sciences, which means that clinical experts often understand their patient’s signs and symptoms from a biomedical perspective (i.e., in terms of tumour formation of healthy renal physiology). Yet, clinicians will usually focus on their patient’s clinical presentation and possible diagnostic and clinical pathways. In clinical practice, several kidney tumour types are distinguished, each with its own histological presentation (visible under the microscope), tumour growth rate and chance of metastases. Unfortunately, all kidney tumour types, including non-malignant types, appear the same on standard imaging modalities, namely, as solid lesions. When the tumour is not metastasized, treatment consists of surgery removing the whole kidney or the part of the kidney that contains the tumour (i.e., ‘radical’ or ‘partial’ nephrectomy). If surgery is not possible, other treatments include chemotherapy or radiation. After surgery, a pathologist examines the tumour tissue to determine the tumour type. Occasionally, the pathologist concludes that the removed tumour was non-malignant, which is a situation that may be prevented if diffusion MRI can be used to distinguish between malignant and non-malignant tumours prior to surgery.

II. Medical biology

In biology, the structure and working of the body is studied at several levels, from the interaction of proteins and other macromolecules within cells to the functioning of organs. In the case at hand, the organ of interest is the kidney. Functions of the kidneys are excretion of waste materials, control of blood pressure via hormone excretion, balancing the body fluid, acid-base balance and balancing salts by excretion or resorption of ions. Understanding these functions requires insights into the anatomy, tissue architecture and physiology of the kidneys. The main functional structures of the kidney are: (1) the nephron, consisting of a tuft of capillaries (the glomerulus) surrounded by membranes that are shaped like a cup (Bowman’s capsule), responsible for the first filtration of water and small ions, and (2) the renal tubule that is responsible for more specific resorption and excretion of ions and water. The arrangement of small tubes that fan from the centre towards the outside (or cortex) of the kidneys allows maintaining variation in concentrations of ions, which helps to regulate resorption and excretion. The contribution of medical biology to the development of the diagnostic tool is important because knowledge about kidneys such as just sketched provides an understanding of the properties (i.e., microstructural of physiological properties) by which different tumour types can be distinguished from each other, which is crucial to interpreting the novel diagnostic imaging technology.

III. MRI physics & diffusion MRI

Magnetic resonance imaging is based on the physics of magnetism and the interaction of tissue components with radio magnetic fields. The main component of the human body that clinical MRI machines are sensitive to is (the amount of) water molecules or, more specifically, hydrogen nuclei (protons). These protons can be thought of as rotating or spinning , producing (tiny) magnetic fields. By placing tissue in a relatively strong magnetic field (usually 1.5 or 3 Tesla emitted by a large coil that surrounds the body), the tiny magnetic fields of protons (in the water-phase of the tissue) will align themselves with the direction of the strong magnetic field. By then applying a series of radiofrequency pulses, protons will be pushed out of balance and rotate back to their original state, causing a magnetic flux that causes a change in voltage which is picked up by receiver coils in the MRI machine. The rate with which protons return to their original state, the relaxation time, is influenced by the makeup of their environment, and will, therefore, differ for different tissues, resulting in image contrasts between tissues. To be able to form images of the signal, magnetic field gradients are applied, spatially varying the field which enables to differentiate between signals from different locations. Computer software using mathematical formulas ‘translate’ the signal into a series of images.

Diffusion MRI is a subfield of MR imaging, that is based on a contrast between ‘diffusion rates’ of water molecules in different tissues. Diffusion is based on the random (‘Brownian’) motion of water molecules in tissue. This motion is restricted by tissue components such as membranes and macromolecules and therefore water molecules move (or ‘diffuse’) at different rates in different tissues, depending on the microstructure of tissues. To measure this, additional magnetic field gradients are applied, which results in a signal attenuation proportional to the diffusion rate, as water molecules move (‘or diffuse’) out of their original voxel due to diffusion.

The method for acquiring diffusion-weighted images with an MRI machine (i.e., the ‘acquisition sequence’ of applying radiofrequency pulses and switching gradients on and off) is designed to gain sensitivity to the water molecules diffusing from their original location. The measured diffusion coefficient is considered to be related to microstructural properties of the tissue, namely the density of tissue structures such as macromolecules and membranes that restrict water diffusion. Together with other diffusion parameters that can be obtained by fitting the signal to other functions or ‘models’, the diffusion coefficient can be used to characterise and distinguish between different (tumour) tissue types, which is the aim of this new imaging tool.

IV. Signal and image processing

The signal acquired by MRI machines undergoes many processing steps before they appear as images on the screen. Some of these steps are performed automatically by the MRI system while others require standardized operations in the software package supplied by the manufacturer, and yet other, more advanced, manipulations are performed in custom-made programs or software packages developed for specific research purposes. In the field of diffusion MRI, software packages that perform the most common fitting procedures are available but often custom-made algorithms are required. The reason for this is that diffusion MRI is originally developed for brain imaging, while investigating its feasibility in other organs has started more recently and only makes up a small part of the field. New applications generate new challenges. For example, unlike the brain, kidneys (and other abdominal organs) move up and down as a consequence of breathing. Therefore, specific algorithms manipulating the scan to correct for this respiratory motion are required for diffusion MRI of the kidneys. Furthermore, as tissue structure and physiology in the kidneys differ from that in the brain, existing models need to be adjusted to that of the kidney.

In this paper, we have argued that interdisciplinary collaboration is difficult because of the role of experts’ disciplinary perspective, which shapes their view and approach to a problem and creates cognitive and epistemological barriers when collaborating with other disciplines. To overcome these barriers, disciplinary experts involved in interdisciplinary research projects need to be able to explicate their own disciplinary perspective. This ability is part of what is known as interdisciplinary expertise [ 8 ]. We defend that interdisciplinary expertise begins with creating awareness of the role of disciplinary perspectives in how experts view a problem, interpret it, formulate questions and develop solutions.

Analytical frameworks to guide interdisciplinary research processes previously developed by other authors typically focus on the process of interdisciplinary collaboration [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. The approach we propose here contributes to this literature by addressing the deeper cognitive and epistemological challenges of interdisciplinary research collaboration on the role of the disciplinary perspective as an inherent part of one’s expertise [ 5 , 16 ]. Several authors have already used the concept of ‘disciplinary perspectives’ to point out the challenges of interdisciplinary research (e.g., [ 9 , 15 ]). Our contribution to this literature is the idea, based on philosophical insights into the epistemology of interdisciplinary research, that disciplinary perspectives can be made explicit, and next, to provide an analytical framework with which disciplinary perspectives within an interdisciplinary research context can be systematically described (as in Table 1 ) with the aim of facilitating interdisciplinary communication within such research projects.

Our further contribution is that we have applied this framework to a concrete case, thereby demonstrating that disciplinary perspectives within a concrete interdisciplinary research project can actually be analyzed and explicated in terms of a coherent set of elements that make up the proposed framework. The result of this analysis (in Table  2 ) shows a coherent description of the discipline in question per column, with an explanation per aspect of what this aspect means for the interdisciplinary research project. It can also be seen that the horizontal comparison (in Table  2 ) results in very different descriptions per aspect for each discipline. We believe that this example demonstrates that it is possible to explain the nature of a specific discipline in a way that is accessible to experts from other disciplines. We do not claim, therefore, that this table is an exhaustive description of the four disciplines involved. Instead, our aim is to show that the approach outlined in this table reduces cognitive and epistemological barriers in interdisciplinary research by enabling communication about the content and nature of the disciplines involved.

We suggest that educators can explore how the framework and philosophical underpinning can be implemented in HPE to support the development of students’ interdisciplinary expertise. Much has been written, especially in the engineering education literature, about the importance of interdisciplinarity and how to teach it. A recent systematic review article shows that the focus of education aimed at interdisciplinarity is on so-called soft skills such as communication and teamwork. Project-based learning is often used to teach the necessary skills, but without specific support to promote these skills [ 7 ]. In our literature review on education for interdisciplinarity [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ], we did not find any authors who specifically address the cognitive and epistemological barriers to interdisciplinary collaboration as described in our article. One possible reason for this is that current epistemological views on the application of science in real-world problem-solving contexts, such as the research project presented here, do not recognise the inherent cognitive and epistemological barriers philosophically explained in this article [ 78 ]. The novelty of our approach is therefore our emphasis on the epistemological and cognitive barriers between disciplines that result from the ineradicable role of disciplinary perspectives in the discipline-bound way in which researchers frame and interpret the common problem. This makes interdisciplinary communication and integration particularly difficult. Specific scaffolds are needed to overcome these barriers. The framework proposed here, which systematically makes the disciplinary perspective explicit, aims to be such a scaffold. We therefore argue that much more attention should be paid to this specific challenge of interdisciplinary collaboration in academic HPE education. This requires both an in-depth philosophical explanation that offers a new view of scientific knowledge that makes clear why interdisciplinary research is difficult, and learning how to make disciplinary perspectives explicit, for which the proposed framework provides a metacognitive scaffold.

We have implemented this framework in a newly designed minor programme that uses challenge-based learning and aims to develop interdisciplinary research skills. In this minor, small groups of students from different disciplines work on the (interdisciplinary) analysis and solution of a complex real-world problem. A number of other scaffolds focused on the overarching learning objective have been included in the educational design, which means that the framework proposed here cannot be tested in isolation. Although our research into whether this new educational design achieves the intended learning goal is not yet complete, our initial experience of using the framework is positive. Students, guided by the teacher, are able to use the framework in their interdisciplinary communication - first in a general sense to get to know each other’s disciplines and then within their research project. This implies that the framework is useful in education aimed at learning to conduct interdisciplinary research.

This example, where the framework has been implemented in education aimed at developing interdisciplinary research skills, also shows that although it was developed in the context of a medical-technical research project, it is in fact very general and well suited for any interdisciplinary research.

A critical comment should be made regarding our preliminary evidence of the framework’s usefulness. The first author, who was PI of the interdisciplinary medical research project, in which she applied this framework in her role as coordinator, was also involved in the development of the framework [ 35 , 36 ]. She, therefore has a detailed insight into the theoretical underpinnings of the framework in relation to its intended application. The lack of such a theoretical background may make it more difficult to apply the framework in interdisciplinary research. Footnote 8 Which is why we have provided an extensive elaboration of these underpinnings in this paper.

Further research should address the question of whether this scaffold can facilitate interdisciplinary collaboration between disciplinary experts.

Further research is also needed to systematically analyse the value of this framework in HPE education. This starts with the question of what type of educational design it can be successfully implemented in. Other important questions are: Can interdisciplinary expertise be acquired without knowledge of the other discipline (e.g., biomedical engineering)? In other words, how much education in other disciplines should HPE provide to prepare experts to participate in specific interdisciplinary collaborations?

Furthermore, we emphasize that in addition to learning to use this framework as a metacognitive scaffold to gain a deeper understanding of the epistemological and cognitive barriers, students also need to develop other skills necessary for interdisciplinary research collaboration and working in interdisciplinary teams. The frameworks discussed in our introduction that analyse and guide the interdisciplinary research process provide insights into these skills (e.g. [ 9 , 10 , 11 , 12 ] and [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ]).

We suggest that the article as a whole can be used in such educational settings to achieve several goals, provided that students are guided and coached by educators. First, to foster student’s understanding of the epistemological challenges of interdisciplinary collaboration and to recognize that these challenges are usually underestimated and not addressed in most approaches. Second, by providing insights into the epistemological challenges by outlining the philosophical underpinnings, students will be made aware of having a disciplinary perspective and how it guides their work. Finally, by providing a framework that can be used to analyse these disciplinary perspectives and by providing an example from the case description. When successful, this approach encourages students to developing transferrable skills that can be used in research projects beyond the initial educational project.

Conclusions

Interdisciplinary research collaborations can be facilitated by a better understanding of how an expert’s disciplinary perspectives enables and guides their specific approach to a problem. Implicit disciplinary perspectives can and should be made explicit in a systematic manner, for which we propose a framework that can be used by disciplinary experts participating in interdisciplinary research projects. With this framework, and its philosophical underpinning, we contribute to a fundamental aspect of interdisciplinary collaborations.

Availability of data and materials

All data generated or analysed during this study are included in this published.

In this article, we use ‘disciplines,’ ‘fields’ and ‘specialisms’ interchangeably.

Bridle (2013), Klein (1990), Newell (2007) and Szostak (2002) provide activities that are important for interdisciplinary collaborations, such as communication, negotiation and evaluating assumptions. In order to be able to perform such activities, students need to develop the appropriate skills [ 9 , 17 , 18 , 19 ].

Roux et al. (2017) provide a clear characterization of transdisciplinary research: “A key aim of transdisciplinary research is for actors from science, policy and practice to co-evolve their understanding of a social–ecological issue, reconcile their diverse perspectives and co-produce appropriate knowledge to serve a common purpose.” ([ 20 ], p. 1).

Boon (2020, 2023) explains the notion of conceptual modelling in application oriented research [ 21 , 22 ].

i.e., a framework that enables us to think analytically and systematically about our cognitive processes when we use and produce knowledge [ 39 , 40 ].

Yet, we recognize that this belief was plausible in Kuhn’s era, where the idea that humans (including scientists) are inevitably and indelibly guided by paradigms and perspectives was revolutionary and devastating with regard to the rational view of man. But nowadays we have become familiar with this idea, which offers an opening for the metacognitive abilities that we suggest.

To scholars in HPE, we recommend the entry on Ludwik Fleck in the Stanford Encyclopedia of Philosophy [ 49 ].

The point made here touches on a more fundamental issue that is beyond the scope of this article. Namely, that resistance of students, but also of teachers, to the described approach may have to do with more traditional epistemological beliefs about science that do not fit well with the way scientific research works in practice [ 78 , 79 ]. The philosophical underpinnings of the proposed framework explained in this article suggest alternative epistemological beliefs that are more appropriate for interdisciplinary research aimed at (complex) ‘real-world’ problems.

Abbreviations

Health professions education

Magnetic Resonance Imaging

Principle investigator

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We are very grateful to three anonymous reviewers who have provided valuable feedback and suggestions that have helped us improve the paper.

This work is financed by an Aspasia grant (409.40216) of the Dutch National Science Foundation (NWO) for the project Philosophy of Science for the Engineering Sciences , and by the work package Interdisciplinary Engineering Education at the 4TU-CEE (Centre Engineering Education https://www.4tu.nl/cee/en/ ) in The Netherlands.

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Mieke Boon (PhD) graduated in chemical engineering (cum laude) and is a full professor in philosophy of science in practice . Her research aims at a philosophy of science for the engineering sciences , addressing topics such as methodology, technological instruments, scientific modeling, paradigms of science, interdisciplinarity, and science teaching. Sophie van Baalen (PhD) graduated in technical medicine and in philosophy of science technology and society , both cum laude. She recently finished her PhD project in which she aimed to understand epistemological aspects of technical medicine from a philosophy of science perspective, such as evidence-based medicine, expertise, interdisciplinarity and technological instruments.

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van Baalen, S., Boon, M. Understanding disciplinary perspectives: a framework to develop skills for interdisciplinary research collaborations of medical experts and engineers. BMC Med Educ 24 , 1000 (2024). https://doi.org/10.1186/s12909-024-05913-1

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