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Developing a Research Topic: Concept Mapping

• Concept Mapping
• Developing Keywords for Searching
• Boolean, Truncation, and Wildcards
• Topic Ideas
• Writing a Research Question

Concept Map / Mind Mapping

What is a concept map.

A concept map is a visual representation of what you know about a topic. Concept maps help you organize your thoughts and explore the relationships in a topic.  Use a concept map to organize and represent what you know about a topic. Explore the connections between elements of the topic. 

Why use a concept map?

Concept maps can be used to develop a research topic. They are a useful brainstorming tool.

Concept maps can be used to study. Mapping what you know about a subject and examining the relationships between elements help you develop a greater understanding of the material.

How do I create a concept map?

• On a whiteboard
• Any way that works for you!

How do I organize the map?

Most of the time you start with the central idea, topic, or subject. Then you branch out from that central point and show how the main idea can be broken into specific subtopics. Each subtopic can also be broken into even more specific topics,

Make a Research Appointment

Click Make a Research Appointment to schedule a meeting with a librarian!

Organize what you know by subtopic in a topic map.

Use the topic map to define your research topic.

For example: geography - local travel - rail - variants - rail systems - designs & availability - emissions - research & evidence

Make a topic statement or research question. 

I am researching the environmental impact of using commuter rail systems in cities.

How does using commuter rail systems in cities affect the environment?

Concept Map, Mind Mapping

Example concept map.

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Concept Mapping for Research Projects

Introduction

What is concept mapping in research, what is the purpose of concept mapping, examples of concept maps, how are concept maps used in research, benefits of a concept map, what is the concept mapping process.

Concept mapping is a straightforward yet powerful technique that offers a bird's eye view of scientific knowledge and connections between ideas. Through concept mapping, research can be systematically arranged to allow researchers to analyze complex topics, making it easier to see how different concepts relate to one another.

It transforms abstract thoughts into a clear, visual representation of scientific knowledge, acting as a practical tool for bridging brainstorming and detailed data analysis . Concept mapping is especially beneficial for uncovering relationships between concepts that may not be immediately obvious.

This article outlines what the concept mapping process entails, its purposes, and its advantages. It also provides a step-by-step guide on creating concept maps for your research project, supplemented with examples.

Concept mapping for qualitative studies is a valuable tool that allows researchers to visualize the relationships between different ideas, concepts, or pieces of information. It involves creating a diagram that showcases how various elements are interconnected, often revealing patterns, hierarchies, and associations that might not be evident through text alone. The map starts with a central idea or question and branches out to show how subsidiary ideas connect to it and to each other.

This technique is grounded in the cognitive sciences, reflecting how the brain organizes and structures knowledge. By mapping out concepts, researchers can more easily comprehend the scope of a project, identify gaps in knowledge, and determine the direction of their inquiry. It serves not just as a method for organizing thoughts but also as a tool for critical thinking and analysis.

In the context of research, concept mapping can be used at various stages of a project. Initially, it can help in formulating a research question or questions by visually exploring the key themes and variables involved. Throughout the research process, concept maps can be adapted and expanded to incorporate new findings and insights, making them dynamic tools for understanding and communication.

Moreover, concept mapping fosters collaboration among research team members, providing a shared visual language that can bridge disciplinary divides. It can visually present complex ideas, making discussions more productive and helping to align the team’s understanding and approach.

A concept map shows scientific knowledge in an organized manner. Moreover, it serves as a multifaceted tool designed to enhance comprehension, communication, and collaboration in research. At its core, concept mapping aims to clarify complex ideas and relationships, making abstract concepts more accessible and understandable. This clarity is achieved by visually representing ideas, which helps researchers and stakeholders alike grasp the breadth and depth of a project more intuitively.

One primary objective of concept mapping is to foster critical thinking and analytical skills regarding a particular research topic . By visually laying out the connections between concepts, researchers are encouraged to explore and question the nature of these relationships, potentially uncovering new insights or identifying underlying assumptions. This process promotes a more thorough examination of the subject matter, encouraging a deeper engagement with the material.

Furthermore, using concept maps can aid in the identification of gaps in knowledge. By organizing concepts visually, it becomes easier to spot areas that are underexplored or lacking in evidence. This can guide future research directions, ensuring that efforts are focused on filling these gaps and advancing understanding of the topic.

Another significant purpose of concept mapping is to enhance collaboration among researchers. By providing a clear and shared visual representation of a project’s structure, concept maps facilitate communication among team members, regardless of their disciplinary backgrounds. This shared understanding helps to align research efforts, streamline decision-making processes, and encourage collaborative problem-solving.

Concept maps are versatile tools that can be applied across diverse research areas to clarify complex ideas and foster deeper understanding. Below are conceptual examples from different domains, showcasing how concept maps can be tailored to specific research needs.

Environmental science

Imagine a study that employs mind mapping to focus on the impacts of climate change on coastal ecosystems. Researchers could use concept mapping to illustrate the intricate relationships between species, habitats, and environmental stressors. The map might center on "Coastal Ecosystems," branching out to related concepts like "Sea Level Rise," "Salinity Changes," and "Species Migration." This visual representation could help in understanding the multifaceted effects of climate change, guiding conservation efforts and policy development.

In exploring factors that influence student motivation, educational researchers might create a concept map to visualize connections between the classroom environment, teaching methods, and student engagement. Key elements such as "Active Learning," "Feedback," and "Classroom Climate" could be linked to impact "Student Motivation." This concept map could serve as a foundation for strategies to boost engagement and learning outcomes, demonstrating concept mapping's applicability in educational interventions.

Health sciences

For a study on Alzheimer's disease progression, a concept map could delineate complex pathways leading to neuronal degeneration. Featuring "Alzheimer's Disease" at its center, branches could extend to "Genetic Factors," "Environmental Influences," and "Biochemical Processes." This approach might identify potential therapeutic targets and help address health challenges.

Technology and business

Researching how small businesses adopt new technologies could involve a concept map exploring decision-making factors. The central theme "Technology Adoption" might connect to "Cost-Benefit Analysis," "Organizational Culture," "Market Trends," and "Regulatory Environment." This method could capture the multifaceted nature of technology adoption decisions, aiding in the development of supportive strategies for small businesses.

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Concept maps are utilized in research to facilitate a range of critical activities, from the initial stages of project planning to the dissemination of research findings. Their use enhances the clarity, organization, and effectiveness of research efforts in several key ways.

At the outset of a research project, a concept map can help in defining the scope and objectives. Researchers can use a concept map to identify and visually represent the main themes and questions their project will address. This early visualization aids in pinpointing the focus areas and can highlight potential research questions or hypotheses that warrant further exploration.

Throughout the research process, concept maps serve as dynamic tools for organizing and synthesizing information. As new data is collected and analyzed, the concept map can be updated to reflect new insights, connections between concepts, and emerging patterns. This ongoing adjustment helps researchers maintain a clear overview of their project, ensuring that their data analysis remains structured and focused.

Concept maps also play a crucial role in identifying relationships between variables and in revealing gaps in existing knowledge. By laying out the known connections and highlighting areas with limited information, researchers can more easily identify where further investigation is needed, guiding the direction of subsequent research efforts.

In terms of collaboration and communication, concept maps are invaluable. They provide a visual language that can be shared among team members and stakeholders, facilitating discussions and ensuring that all parties have a common understanding of the project’s framework and findings.

Finally, when presenting research outcomes, concept maps can effectively summarize and convey complex information to diverse audiences. They can be used in reports, presentations, and publications to illustrate the study’s structure, findings, and implications clearly and concisely, making the research accessible to both academic and non-academic audiences.

Concept maps offer a range of benefits that enhance the research process, making complex information more manageable and comprehensible. These benefits can be broadly categorized into improving understanding, facilitating communication, and aiding in the planning and organization of research.

Enhancing understanding

Concept maps help distill complex ideas into visual formats, making abstract concepts more tangible. By laying out the relationships between different pieces of information, these maps enable researchers to see patterns and connections that might not be obvious in textual form. A concept map can lead to a deeper understanding of the subject matter, as it encourages the identification of relationships, hierarchies, and networks within the research topic .

Facilitating communication

One of the key advantages of concept maps is their ability to act as a communication tool among research team members and with external stakeholders. They provide a shared visual language that can help in explaining ideas clearly and succinctly, bridging knowledge gaps between individuals with different expertise or backgrounds. This common ground is especially valuable in multidisciplinary research teams, where understanding each others' perspectives is crucial for collaborative success. Moreover, concept maps can be effective in presentations or publications by conveying findings and theories in a more engaging and understandable way.

Aiding planning and organization

In the context of research planning and organization, concept maps serve as an invaluable tool for structuring projects and outlining research strategies. They allow researchers to visually map out the scope of their study, identify key components and variables, and organize their approach to data collection and analysis. This can support a more efficient use of resources and time, as potential overlaps or gaps in research can be identified early in the process. Furthermore, concept maps can be used to track progress over time, providing a clear overview of how individual pieces of research contribute to the overall project goals.

The process of creating a concept map involves several structured steps that guide researchers from the initial exploration of a topic to the development of a comprehensive visual display of their ideas and findings. This process can be divided into key phases: starting with a literature review , drafting an initial mind map, and refining the concept map into a final version.

Conducting a literature review

The first step to creating a concept map is to conduct a thorough literature review. This stage is crucial for gathering existing knowledge on the topic, identifying key concepts, theories, and relationships that have been previously established. The literature review helps in framing the main idea or question that will be at the center of the concept map. It provides a solid foundation of information from which researchers can begin to build their map, ensuring that it is grounded in established research and theory.

Drafting an initial map

With a clear understanding of the topic from the literature review, the next step is to start drafting an initial concept map. This can begin as a simple mind map, with the main idea at the center and primary concepts branching out from it. Researchers can then add layers of detail, linking related concepts and indicating the nature of their relationships. This initial draft is a flexible tool, allowing for easy adjustments as new ideas emerge or as the structure of the map evolves.

Refining the concept map

The last phase involves refining the initial draft into a final version of the concept map. This step may require multiple revisions, as researchers review their concept map for clarity, coherence, and completeness. It's an opportunity to ensure that all relevant concepts are included and accurately represented. The final version should clearly convey the main idea, its supporting concepts, and their interconnections. This polished concept map can then be used for various purposes, such as the basis for a research project, a visual aid in a blog post, or a tool for communication within a research team.

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6 ways to use concept mapping in your research

Joseph Novak developed concept mapping in the 1970s and ever since, it has been used to present the construction of knowledge. A concept map is a great way to present all the moving parts of your research project in one visually appealing figure. I recommend using this technique when you start thinking about your new research topic all the way through to the end product, and once you submitted your thesis, dissertation or research article, you can use concept mapping to plan your next project. If you prefer to watch the video explaining the 6 steps, scroll down.

What is the purpose of concept mapping?

You may wonder what the purpose of a concept map is. A concept map shows the different “ideas” which form part of your research project, as well as the relationships between them. A concept map is a visual presentation of concepts as shapes, circles, ovals, triangles or rectangles, and the relationships between these concepts are presented by arrows. Your concept map will show the concept in words inside a shape, and the relationship is then presented in words next to each arrow, so that each branch reads like a sentence. What is the difference between a mind map and a concept map? A mind map is different from a concept map in that a mind map puts much less emphasis on the relationship between concepts.

How to use a concept map in your research

Don’t wait to put your concept map together until only after you have, what you consider, “all the knowledge” and have read “all the literature” (anyway, with two million research articles published each year, will that day ever come?). In the very early stages, when you start thinking about your research project, draw your concept map to get your thoughts organised. Then, as you become more and more abreast with the research out there, modify your concept map.

The process of creating a concept map is an iterative one and you will find that it feels like you have drawn and redrawn the map over and over so many times that you wonder if you are ever going to get to a final version. This process in itself is a learning experience and is vital to sort the concepts out for yourself. If you have clarity in your own head, it is easier to explain what your research is all about to someone else. In addition, including a concept map into a dissertation, thesis, or research article (where relevant) makes it easier for the reader (including the examiner or reviewer) to understand what your research project is all about. There are several instances in your research journey where a concept map will come in handy.

#1 Use a concept map to brainstorm your research topic

When you are conceptualising your research topic, create a concept map to put all the different aspects related to your research topic onto paper and to show the relationships between them. This will give you a bird’s eye view of all the moving parts associated with the chosen research topic. You will also, most probably, realise that the topic is too broad, and you’ll be able to zoom in a bit more to focus your research question better. But before you settle on a specific research question, do a bit of reading around the topic area. Your concept map will show you which keywords to search for.

#2 Use a concept map when planning the search strategy for your literature review

Jumping right into those databases to do a search for articles to include in your literature review can really take you down the deepest darkest rabbit hole. One of those where you find an appropriate article, then gets suggested a few related articles and then you find another few related articles to the related articles, and after 4 hours you can’t even remember what your actual focus was. To avoid this situation, draw your concept map first. You can use the concept map you drew when you brainstormed your research topic to give you guidance in terms of the keywords to search for. Planning your search strategy before you jump in will ensure that you remain on the well-lit path.

#3 Add a concept map to your completed literature review chapter

As you read more about your research topic, you’ll get a better idea of the relationships between the current concepts, and you’ll find more concepts to add to your concept map. Adapt your concept map as you go along, and once you have the final version of your literature review, add your concept map as a figure to your literature review chapter. This will give the reader a good overview of your literature review and it will make their hearts happy because we all know how nice it is to be rewarded with a picture after reading pages and pages of text.

#4 Use a concept map to plan your discussion

Once you completed your data analysis and interpretation, developing a concept map for your discussion will give you clarity on what to include in your discussion chapter or section.

#5 Add a concept map to your completed research project

Once you have completed your entire research project and you want to show how your findings filled a gap in the literature, you can indicate this by modifying the concept map which you created for our literature review. This is a great way to show how your research findings have added to the existing concepts related to your research topic.

#6 Use a concept map to show your research niche area

You can use a concept map to visually present your own research niche area and as your career progresses and you create more knowledge in a specific niche, you can add to your concept map.

How to make a concept map for research

Go to a place where there are very few distractions, a place that is conducive to letting those creative juices flow freely. Seeing that we all function differently, shall I rather say, a place which you perceive as having few distractions. It may be in a park, in your garden, at a restaurant, in the library or in your own study.

Take out a blank piece of paper and start thinking about your research project. Of course, you can do it on a blank page on your laptop as well. One of my students used sticky notes with each sticky note presenting a concept, and with smaller strips of sticky notes showing the relationships between concepts. You can even get all fancy and use concept mapping software. But as a start, a blank piece of paper is more than enough.

Jot down all the ideas that come to mind while you answer the following questions: What is your research about? Why is your research important? What gap does your research fill? What problem will your research solve? What influences your research outcome? Just jot all your thoughts down. Then, once you have all your thoughts on paper, see if you can identify some relationships between the concepts which you noted down. What comes before what? What is a consequence of what? What is associated with what?

Once you are happy with what you have put together, present it to a friend, preferably at a time when both of you are not in a hurry to get somewhere. At a bar with loud music may not work well, and on a first date may also not be a good idea. Explain what is going on in the concept map and give your friend a chance to ask some questions. As you explain it to someone, as well as through fielding your friend’s questions, it will start to make more sense to you, and you will most probably move some concepts around and add new ones. Repeat this process with someone else when you feel you need some more input.

If you are planning to feature your concept map in your thesis, dissertation or research article, now is the time to turn your rough concept map into something more presentable. One can easily get totally lost when it comes to choosing software to create a concept map. Some of the software out there is paid for while others give you a free version for some basic concept mapping. Be careful of that software which only gives you free access for 30 days, remember, you are going to change your concept map quite a few times as time goes on. If you prefer to use software which you are already familiar with, why not just do it in PowerPoint or Word? On the other hand, Lucidchart is really user-friendly. Watch the video below to see how easy it is to create a concept map with Lucidchart. Explore a few options and see what works for you, but be careful, this exploration can take you down that 4-hour rabbit hole and when a proposal submission deadline is looming, that rabbit hole is a dark place to be in.

We'd like to acknowledge Coffee Machine Cleaning  for the image of the coffee cup and notebook used in this blog post. 

Examples of concept maps

Here are a few examples of concept maps that show the concepts and the relationship between the concepts well. Click on the image to visit the original source. Go and enjoy developing a concept map for your research!

One last thing before you go, for more valuable content related to academic research, subscribe to the Research Masterminds YouTube Channel  and hit the bell so that you get notified when I upload a new video. If you are a (post)graduate student working on a masters or doctoral research project, and you are passionate about life, adamant about completing your studies successfully and ready to get a head-start on your academic career, this opportunity is for you! Join our awesome membership site - a safe haven offering you coaching, community and content to boost your research experience and productivity. Check it out!  https://www.researchmasterminds.com/academy . 

Example 1 Little Red Riding Hood

Example 2 Nursing Management

Example 3 Operations Management

Example 4 Cup of Coffee

Example 5 Flexibility

Example 6 Human Body Systems

Example 7 Simple Concept Map Template

If you prefer to watch the video, here it is:

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• 1. Concept Mapping

Concept Mapping

A concept map (also called a mind map) is a tool that you can use to help brainstorm a research topic or help you narrow down a general idea into a more focused idea. Concept maps can also be used to help you come up with a thesis statement for your assignment or to help you develop keywords that you can use in your database searching. 

Concept maps also help you see connections between some of your ideas and show you how many different directions you can take your research based on a single idea.

How to create a Concept Map

The great thing about concept maps is that they can look however you'd like and can be done either on a piece of paper or even done online. Some free online concept mapping tools are MindMup , Creately , and Lucidchart , although there are many other tools available.

Start with your main idea in the center of your map. Then to start building out your map, use the 5Ws + How (Who, What, When, Where, Why, and How) to start thinking about related concepts. For example, why is this topic so important? Who is influenced by your topic? Where did your topic take place?

Example Concept Map

The general topic of your paper, the All Community Colleges Should be Tuition Free  goes in the center of your page.

Using the 5Ws + How, you have built out a concept map, to give you a better sense of which directions your paper can go. For example, based on your concept map, you might decide to narrow down your topic to who is impacted by community colleges being tuition free or how free tuition makes education more accessible.

• Community members
• Strengthens the workforce.
• Makes education more accessible.
• Current issue.
• Spartanburg Community College
• More community members have the ability to go to college.
• Free tuition helps ease the financial burden of going to college.
• State funding.

Helpful Resources

• Concept Mapping Handout This printable handout can help you get started on a concept map.
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• What Makes a Good Research Topic?
• 2. Background Research
• 3. Narrow Your Topic / Thesis Statements

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Organizing Knowledge: How to Create an Effective Concept Map for Your Research

In the realm of research, concept mapping emerges as a powerful tool for organizing and synthesizing knowledge. This article delves into the art and science of creating effective concept maps, which serve as visual representations of the relationships between ideas, concepts, and data. By providing a structured approach, concept maps can enhance understanding, foster critical thinking, and streamline the research process. Whether you're a seasoned researcher or embarking on your first project, mastering concept mapping can transform the way you handle complex information.

Key Takeaways

• Concept maps are essential for visualizing the relationships between ideas, aiding in knowledge integration and research clarity.
• Creating an effective concept map involves identifying core concepts, establishing relationships, and choosing the appropriate format and tools.
• Concept maps can be instrumental in generating research questions, refining objectives, and expanding the scope of research.
• In literature reviews, concept maps help organize sources, identify patterns, and support critical analysis of existing research.
• Concept maps enhance data analysis and interpretation by providing a visual framework to examine qualitative and quantitative relationships.

The Fundamentals of Concept Mapping

Defining concept maps.

Concept maps are visual representations that organize and structure knowledge. They illustrate relationships between concepts through connecting lines and linking words, forming a hierarchical framework that reflects your understanding of a subject. Concept maps are particularly effective for making internal conceptual structures visible , which is essential for communicating complex ideas clearly and effectively.

Comparing Concept Maps with Other Visual Tools

While concept maps share similarities with other visual tools like mind maps and flowcharts, they are distinct in their focus on relationships and hierarchy. Mind maps tend to center around a single idea, branching out without a strict hierarchy, whereas flowcharts depict sequential processes. Concept maps, however, emphasize the connections and interdependencies among a set of concepts, which can be particularly useful in educational settings to assess understanding.

The Role of Concept Maps in Knowledge Integration

Concept maps serve as a meta-level knowledge tool that aids in integrating new and existing knowledge. By visually organizing concepts, you can identify how new information fits within your current understanding, revealing gaps and patterns. This process not only enhances learning but also supports the creation of comprehensive research frameworks. Concept maps can help bridge the gap between academic jargon and everyday language, simplifying complex procedures into clear, simple steps that are accessible to all researchers.

Designing a Concept Map for Research Purposes

When embarking on your research journey, an effective concept map can be a powerful tool to visually organize and synthesize information. Identifying core concepts and relationships is the first step in this process. It involves pinpointing the main ideas and how they interconnect, which will form the backbone of your map. To ensure clarity and effectiveness, choosing the right format and tools is crucial. Whether you opt for digital software or traditional pen and paper, your choice should facilitate easy modifications and a clear presentation of your research landscape.

Adhering to best practices for concept map creation will enhance the utility of your map. These include:

• Keeping the design simple and readable
• Using hierarchical structuring for complex information
• Incorporating cross-links to show interrelated concepts
• Employing consistent symbols and colors for easy recognition

Remember, a well-designed concept map is not just a static image; it's a dynamic and evolving representation of your research that can guide you through the maze of information and keep your work on track.

Utilizing Concept Maps to Generate Research Questions

From concepts to questions.

When you begin your research, the first step is often to understand the landscape of knowledge surrounding your topic. By crafting a concept map , you can visualize the connections between various concepts, which can lead to the formulation of specific research questions. This process involves identifying the main concepts related to your study and exploring how they interact, which can reveal areas that require further investigation.

Highlight: Based on a preliminary investigation of your topic, create a concept map of potential research questions or topics.

Expanding the Research Horizon

Concept maps are not just tools for organization; they are catalysts for creativity. As you delve into the relationships and hierarchies within your map, you'll find yourself expanding the research horizon . This means looking beyond the obvious connections and considering alternative perspectives or less explored areas that could yield valuable insights.

Refining and Focusing Research Objectives

The ultimate goal of using concept maps is to refine and focus your research objectives. Through the iterative process of mapping and re-mapping, you can distill broad concepts into more targeted questions. This helps in creating a research agenda that is both manageable and aligned with your academic goals. Remember, a well-defined question is the cornerstone of any successful research project.

Concept Maps in Literature Review

Organizing sources and themes.

When embarking on a literature review, the sheer volume of sources can be overwhelming. Concept maps serve as a powerful organizational tool, allowing you to visually structure the relationships between various sources and themes. By graphically representing ideas , concept maps can simplify complex information, making it easier to see how different pieces of literature connect to each other and to your central thesis. This visual approach can be particularly helpful in alleviating thesis anxiety , as it provides a clear overview of your research landscape.

Identifying Gaps and Patterns

A well-constructed concept map can reveal patterns and gaps within the literature, guiding you on where to focus further research. It's a strategic method for expanding the research horizon by pinpointing areas that are underexplored or overrepresented. This insight is invaluable when determining how to write a thesis proposal that is both innovative and grounded in existing scholarship.

Facilitating Critical Analysis

The final stage of utilizing concept maps in literature reviews involves critical analysis. By visually mapping out the literature, you can more easily assess the strengths and weaknesses of existing research. This process aids in refining and focusing research objectives, ensuring that your work contributes meaningfully to the field. Concept maps can transform a daunting task into a manageable one, providing a clear path on how to write a thesis that is both comprehensive and critically engaged.

Data Analysis and Interpretation through Concept Maps

Visualizing data relationships.

Concept maps offer a unique way to visualize the intricate relationships between data points, which can be especially beneficial in revealing hidden patterns and connections. By representing variables as nodes and their interrelations as lines, you can create a dynamic framework that not only simplifies complex data sets but also highlights trends and outliers. This visual approach can be particularly useful when dealing with large volumes of data, where traditional methods like spreadsheets may fall short.

Enhancing Clarity in Qualitative Analysis

In qualitative research, concept mapping is a powerful tool to elicit key findings from data, such as interview transcripts or observational notes. It aids in organizing themes and identifying relationships that might not be immediately apparent. By grouping similar concepts and using visual cues like colors and shapes, you can enhance the clarity of your analysis, making it easier to communicate your findings to others.

Streamlining Quantitative Data Interpretation

Quantitative data interpretation often involves statistical analysis, which can be daunting. Concept maps can streamline this process by providing a clear visual outline of the data's structure. For example, you can use a table to present structured quantitative data succinctly:

By mapping out the relationships between different variables, you can more easily identify which factors are most influential and how they interact with one another.

Concept Maps as a Tool for Academic Collaboration

Fostering communication among researchers.

Concept maps serve as a dynamic interface for academic dialogue, enabling you to visualize and share complex ideas with clarity. By mapping out the connections between various concepts, researchers can more effectively communicate their thoughts and findings, leading to enhanced understanding and synergy within the team. Effective collaboration in research projects is achieved through standardized file naming, logical folder structure, data management software, and security measures. Organized data enhances productivity and teamwork.

Coordinating Interdisciplinary Research Efforts

Interdisciplinary research often involves integrating knowledge from diverse fields, which can be challenging. Concept maps facilitate this integration by providing a common visual language that all participants can understand and contribute to. They help in identifying overlapping areas of expertise and fostering a collaborative environment where ideas from different disciplines can be combined to form innovative solutions.

Sharing Knowledge Across Academic Communities

The dissemination of research findings is crucial for the advancement of knowledge. Concept maps can be used to summarize and present research in a way that is accessible to a broader academic audience. They support the creation of a shared knowledge base that can be built upon by others, promoting a culture of open information exchange and continuous learning within the academic community.

Evaluating the Effectiveness of Concept Maps in Research

Assessment criteria for concept maps.

When assessing the effectiveness of concept maps in research, it's crucial to establish clear criteria. Consider the clarity of the visual representation , the accuracy of the relationships depicted, and the map's ability to facilitate understanding. A well-constructed concept map should serve as a cognitive tool that enhances the researcher's ability to see connections and draw insights.

• Clarity of presentation
• Accuracy of depicted relationships
• Enhancement of understanding

Improving Concept Maps through Feedback

Feedback is an essential component in refining concept maps. Engage with peers and mentors to gain perspectives that can reveal oversights and introduce new connections. Iterative revisions based on constructive criticism ensure that your concept map remains a dynamic and evolving guide throughout your research process.

• Seek peer and mentor feedback
• Incorporate suggestions for improvement
• Revise and refine iteratively

Case Studies of Successful Concept Map Applications

To truly appreciate the value of concept maps, examine case studies where they have been instrumental in advancing research. These examples highlight the practical benefits of concept mapping, from comprehending the scope of a project to identifying knowledge gaps and shaping the direction of inquiry. Analyze the strategies employed and the outcomes achieved to inform your own concept mapping endeavors.

• Analysis of successful applications
• Strategies employed in case studies
• Outcomes and benefits realized

Concept maps are a powerful tool for organizing and visualizing complex information, making them invaluable in research. If you're looking to enhance your research skills or need guidance on your thesis, our step-by-step Thesis Action Plan at Research Rebels can help. We translate academic instructions into everyday language, ensuring you grasp every concept with ease. Don't let anxiety and stress hinder your academic journey. Visit our website to learn more and claim your special offer today!

In conclusion, the creation of an effective concept map is a pivotal step in organizing and synthesizing knowledge for research purposes. It serves as a visual representation that can clarify complex relationships between ideas, streamline the research process, and foster a deeper understanding of the subject matter. By integrating concept maps into their research strategy, scholars can navigate the intricate web of information with greater ease, generate pertinent research questions, and maintain a structured approach to their scholarly inquiries. Ultimately, concept maps are not just tools for organization; they are catalysts for intellectual exploration and innovation in the academic realm.

Frequently Asked Questions

What is a concept map, and how does it aid in research.

A concept map is a visual tool that organizes and structures knowledge on a research topic. It helps researchers navigate the relationships between concepts, generate research questions, and keep track of vast amounts of data.

How do concept maps differ from other visual tools like mind maps?

Concept maps focus on the relationships and hierarchies between concepts, often with labeled arrows connecting them. Mind maps are more free-form and radial, emphasizing ideas branching out from a central concept without explicit relationships.

Can concept maps be used during a literature review?

Yes, concept maps are useful in literature reviews to organize sources and themes, identify gaps in research, and facilitate critical analysis, helping researchers to visualize and structure their findings.

What are some best practices for creating an effective concept map?

Best practices include starting with core concepts, using clear and concise labels, connecting related concepts with labeled arrows, and maintaining a hierarchical structure to reflect the complexity and relationships of the information.

How can concept maps improve academic collaboration?

Concept maps foster communication among researchers by providing a clear visual representation of shared knowledge. They help coordinate interdisciplinary efforts and promote knowledge sharing across academic communities.

What criteria should be used to evaluate the effectiveness of a concept map in research?

Effectiveness can be assessed by the map's clarity, comprehensiveness, ease of navigation, ability to reveal insights or gaps, and its utility in advancing understanding or facilitating communication among researchers.

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• Literature Review: The What, Why and How-to Guide
• How to Pick a Topic

Literature Review: The What, Why and How-to Guide — How to Pick a Topic

• Getting Started
• Introduction
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

Picking a Topic and Keywords to Research your Topic

Whether you are writing a literature review as a standalone work or as part of a paper, choosing a topic is an important part of the process. If you haven't select a topic yet for your literature view or you feel that your topic is too broad, this page is for you!

The key to successfully choosing a topic is to find one that is not too broad (impossible to adequately cover) but also not too narrow (not enough has been written about it). Use the tools below to help you brainstorm a topic and keywords that then you can use to search our many databases. Feel free to explore these different options or  contact a Subject Specialist if you need more help!

Concept Mapping

• [REMOVE] Mind Mapping (also known as Concept Mapping) A helpful handout to show step by step how to create a concept map to map out a topic.

Picking a topic and search terms

Below are some useful links and handouts that help you develop your topic. Check the handouts in this page. They are useful to help you develop keywords/search terms or to learn the best way to search databases to find articles and books for your research.

• UVA Thinking Tool: Choosing a Topic and Search Terms Provides a template for focusing a research assignment through the brainstorming of ideas, keywords, and other terminology related to a topic.
• UW How to Improve Database Search Results Suggested strategies for retrieving relevant search results
• UCLA: Narrowing a Topic Useful tips on how to narrow a topic when you are getting too many results in your search. From the librarians at UCLA.
• UCLA: Broadering a Topic Useful tips on how to broaden your topic when you are getting few results in your search. From the librarians at UCLA.
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• Next: Strategies to Find Sources >>
• Last Updated: Sep 21, 2022 2:16 PM
• URL: https://guides.lib.uconn.edu/literaturereview

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Concept mapping.

• What is Concept Mapping?
• Concept Mapping Tools
• Concept Mapping How-to
• More Resources

Concept Mapping for Research Development -- Belk Library

Check out this video for an overview of how to use concept mapping in developing your research from the Belk Library. 

Mapping Your Research Ideas -- UCLA Library

Check out this video from the UCLA Library to learn about how to map your research ideas.

Concept Mapping for Research

Concept mapping can be very useful in the research process, for brainstorming topics, understanding your topic in detail, reading the literature, etc. Check out the videos below for information on how to use concept mapping in the research process. 

• << Previous: Concept Mapping Tools
• Next: More Resources >>
• Last Updated: Jul 30, 2024 1:28 PM
• URL: https://library.framingham.edu/conceptmapping

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Introduction to the Research Process

Choose your topic.

• Find Background Information
• Find Information
• Evaluate Sources
• Writing and Citing

Choose an interesting and relevant research topic using these tips and exercises.

Concept Mapping / Mind Mapping

Concept mapping (or mind mapping) is a way to visually organize a topic in order to identify relevant themes and connections. A concept map can be made using sketching software, a whiteboard or just a pen and scratch paper - whichever you prefer.

Download the worksheet below for more information on concept mapping.

• Brainstorm and Concept Map Worksheet Examples of questions that could be asked to advance exploration of a research topic

Getting Started With Topics

A good research topic...

• Fits the assignment requirements
• Can be supported by research materials available at SCAD Libraries
• Is interesting to you - it's something you want to learn more about
• Has a unique angle or explores a key issue in the field

Can’t think of a topic to research? Get ideas from:

• Your class textbook(s) and required reading(s)
• Notes from class discussions and lectures
• Up-to-date industry magazines and news sources - try Lexis Nexis
• A current issues database such as CQ Researcher
• Online or printed encyclopedias
• Your instructor or a librarian

See the Background Information tab for more starter sources.

Picking A Topic Video

This 3 minute video explains how to test and revise a topic as part of the research process. (Via North Carolina State University )

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• Last Updated: Aug 28, 2024 10:00 AM
• URL: https://scad.libguides.com/researchintroduction

How To Write a Research Paper

• 1. Understand the Assignment
• 2. Choose Topic & Write Thesis Statement
• 3. Create Concept Map & Keyword List

Create a Concept Map

Make a list of keywords / search terms.

• 4. Research Your Topic
• 5. Create an Outline
• 6. Write the Paper
• Assignment Calculator

Create a concept map of your topic. A concept map is a visual diagram that shows the relationship between different ideas related to your topic.

To create a concept map:

• Who is affected?
• Who is affected? -- Children, Men, Women
• Causes -- Genetics, Poor diet, Sedentary lifestyle
• Effects -- Poor mental health, Poor physical health, Low self-esteem
• Prevention -- Better diet/nutrition, More exercise/physical fitness, Better medical interventions
• The sub-sub topic Poor Diet be broken further into -- More fast food, Bigger portion sizes, Higher caloric intake, etc.

Selecting the right keywords is important when you begin searching. Keywords are the most important parts of your topic and are necessary to properly communicate with the different research tools you'll be using. Here are some tips to choosing keywords:

• Never use whole sentences or long statements
• Nouns usually make the best keywords
• Keep adding, subtracting, and replacing keywords you use to find a variety of results
• Expand your list by using synonyms, subject terms, and more specific concepts

Below are some examples of education-related keywords to help you get started:

After you have chosen your keywords, you'll want to combine them to form a search statement . You can combine words using "AND" in between. Have more than one word as a keyword? Put quotations around it. This will tell the database or search engine that you using a phrase and not just individual words. Here are few examples drawn from the above keyword list:

→ "academic achievement" AND "class size"

→  education AND children AND "parental involvement"

→  "teacher-student ratios" AND "public schools"

• Choosing Search Terms

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• Next: 4. Research Your Topic >>
• Last Updated: Jul 24, 2024 1:38 PM
• URL: https://libguides.seminolestate.edu/researchpaper

Research Skills

Creating a research question, mapping your research ideas.

For many students, having to start with a research question is the biggest difference between how they did research in high school and how they are required to carry out their college research projects. Developing a research question is a process of working from the outside in: you start with the world of all possible topics (or your assigned topic) and narrow your ideas down until you’ve focused your interest enough to be able to tell precisely what you want to find out instead of only what you want to “write about.”

All Possible Topics – You’ll need to narrow your topic in order to do research effectively. Without specific areas of focus, it will be hard to even know where to begin.

Assigned Topics – Ideas about a narrower topic can come from anywhere, and even when you are assigned a topic you typically will need to further define the focus for your project. Often, a narrower topic boils down to deciding what’s interesting to you (e.g., why do you care about the topic, how does it affect you now and/or how may it affect you in the future, etc.). One way to get ideas is to read background information in a source like Wikipedia.

Topic Narrowed by Initial Exploration – It’s helpful to do some more reading about that narrower topic to both learn more about it and learn specialized terms used by professionals and scholars who study it. You may be able to use those specialized terms to help you look for further source material to use in your project.

Topic Narrowed to Research Question(s) – A research question defines exactly what you are trying to find out. It will influence most of the steps you take to conduct the research.

Most of us look for information to answer questions every day, and we often act on the answers to those questions. You may be wondering then, are research questions any different from most of the questions for which we seek information? Yes.

See the chart below for examples of regular questions and research questions that are based on them.

Research questions cannot be answered by a quick web search. Answering them involves using more critical thinking than answering everyday questions because they seem more debatable. Research questions require more sources of information to answer and, consequently, take more time to answer. They, more often than regular questions, start with the word “How” or “Why.”

Check your understanding

• Choosing & Using Sources: A Guide to Academic Research: Narrowing your Sources. Provided by : Ohio State University Libraries. Located at : https://osu.pb.unizin.org/choosingsources/chapter/narrowing-a-topic/ . Project : Ohio State University Libraries Teaching and Learning. License : CC BY: Attribution
• Mapping your Research Ideas. Provided by : UCLA Library. Located at : https://www.youtube.com/watch?v=jj-F6YVtsxI . License : CC BY: Attribution

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Develop your Topic

• Develop a Topic

Mapping Your Research Ideas

Using limiters to narrow your topic into a question.

• The Thesis Statement

Are you a visual thinker? Try this method of brainstorming to develop your assignment subject into a focused, researchable topic statement or question!

UCLA Library. (2014, September 9). Mapping your research ideas. YouTube. https://www.youtube.com/watch?v=jj-F6YVtsxI

Are you a verbal thinker? Try this method of brainstorming to develop your assignment subject into a focused, researchable topic statement or question!

U of G Library. (2018, August 30). Four steps to narrow your research topic . YouTube. https://www.youtube.com/watch?v=rpCbSjIdXlM&t=3s

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• v.2(3); 2021 Mar 12

Mapping research topics at multiple levels of detail

1 ICPSR, University of Michigan, Ann Arbor, MI 48109, USA

Werner Kuhn

2 Department of Geography, University of California, Santa Barbara, CA 93106, USA

Kelly Caylor

Libby hemphill, associated data.

The data and code supporting our analysis for the institutional review is available in our public Github repository: https://github.com/saralafia/ERI-5-year-review . More information about ERI's review process is available on its website: https://www.eri.ucsb.edu/2014-external-review .

The institutional review of interdisciplinary bodies of research lacks methods to systematically produce higher-level abstractions. Abstraction methods, like the “distant reading” of corpora, are increasingly important for knowledge discovery in the sciences and humanities. We demonstrate how abstraction methods complement the metrics on which research reviews currently rely. We model cross-disciplinary topics of research publications and projects emerging at multiple levels of detail in the context of an institutional review of the Earth Research Institute (ERI) at the University of California at Santa Barbara. From these, we design science maps that reveal the latent thematic structure of ERI's interdisciplinary research and enable reviewers to “read” a body of research at multiple levels of detail. We find that our approach provides decision support and reveals trends that strengthen the institutional review process by exposing regions of thematic expertise, distributions and clusters of work, and the evolution of these aspects.

• • A method for modeling and mapping topics from bibliometric data
• • A web application dashboard with interactive, multilevel topic maps
• • A user evaluation of topic mapping's utility in institutional reviews

The bigger picture

Research institutes and organizations are interested in communicating the impact of their work and its value to a broader audience. However, quantifying impact and providing high-level views of interdisciplinary research trends are challenging. To address this, we leverage distant reading methods from the digital humanities to model the topics of a large body of interdisciplinary research products and visualize them in maps. We analyze 3,770 academic publications and grants affiliated with an interdisciplinary earth science research institute over a 10-year period and model its research topics. We then map the topics at two distinct levels of detail and evaluate the interpretation of the maps through a survey of leading researchers. We show that the topic maps reveal insights including the emergence of interdisciplinary collaboration areas and evolving areas of expertise over time.

Many institutions use metrics to evaluate their research productivity; however, it is challenging to effectively summarize and evaluate research across academic disciplines. We use topic modeling to develop maps of science at multiple levels of detail. The maps were used in an institutional review and evaluated by leading researchers at an earth science institute. We demonstrate that mapping research topics supports the review process by offering insights into interdisciplinary research collaborations and areas of expertise at the institute.

Introduction

Universities and funding agencies request that organized research units (ORUs) summarize and report on their research, collaboration, and growth as part of periodic institutional reviews. These reviews typically ask questions about trends in research quality, significance, research specialties, areas of influence or prominence, and interdisciplinarity collaborations. The review process is not unique to universities or research institutes; many kinds of organizations, including those in non-governmental, governmental, and industry settings, regularly conduct “meta-research” 1 on their activities in order to provide a high-level view of their impact and productivity. Yet, it remains unclear how best to summarize and present interdisciplinary bodies of work in ways that generate useful insights and can support effective reviews.

Bibliometrics and scientometrics support the quantitative study of published documentation and academic disciplines; 2 they have become cornerstones of institutional research assessments. Research administrators and funding agencies often use metrics, like the Hirsch index (h-index) and the journal impact factor (JIF), to assess the impact and performance of departments or individual researchers and to monitor collaborators or competitors. 3 Such metrics are trusted due in part to their perceived scientific legitimacy and because they offer indicators, which, if appropriately selected and applied, can yield data to support performance monitoring and the selection of research priorities. 4

Quantitative metrics like impact factors, however, have been recognized as poor choices for assessing or comparing research output of scholars and journals. They are often not comparable across academic disciplines 5 and have been found to be vulnerable to manipulation. 6 A study of the relationship between journals and citation rates has demonstrated evidence of a cumulative advantage for publications in “high-impact” journals. 7 The single numbers these metrics produce also obscure differences between disciplines and outlets over time.

Alternative quantitative metrics have been developed in response to these limitations. The Eigenfactor metrics 8 consider author centrality in citation networks, while the SCImago index 9 considers the flow of prestige between thematically related journals. Altmetrics 10 capture a more comprehensive picture of the ecosystem of scientific products and activity, like discourse about scientific software, that goes beyond the partial view from formal citations. These metrics are reshaping how scientists value research products and assess impact. 11

In this vein, there is a growing desire for interdisciplinary research evaluation that can more adequately capture impact and quality. One strategy has been to complement quantitative metrics with high-level characterizations and narratives. 12 Another has been to develop maps that chart the structure of knowledge domains and show the development of research areas, their interconnections, and evolution within them. 13 These approaches offer more contextual information than single-measure quantitative metrics. Science maps are examples of spatializations, 14 which use space as a metaphor to map abstract domains to thematic spaces in which nearby elements are similar. They can help evaluators process more information 15 than can be effectively communicated by a single quantitative metric; they also make patterns and trends more apparent.

In this article, we examine the utility and benefits of spatialization to produce maps of research that support an institutional review, specifically by revealing trends and providing decision support. To develop and test our ideas, we situate our study in the context of an ORU at the University of California at Santa Barbara (UCSB): the Earth Research Institute (ERI) ( https://www.eri.ucsb.edu/ ). ERI's stated mission is to “support research and education in the sciences of the solid, fluid, and living Earth.” Core areas of research within the institute consist of natural hazards, human impacts, earth system science, and earth evolution. ERI's faculty and researchers are supported by 145 different funding agencies covering the full breadth of earth and environmental sciences.

To date, ERI has taken an ad hoc approach to characterizing its research. For example, anecdotal observations based on faculty hires from ERI's last institutional review indicated that its expertise had broadened from traditional earth science and crustal studies to include conservation and biodiversity topics. To formally capture and verify this kind of institutional knowledge about ERI's evolving research expertise, we propose a data-driven approach for eliciting cross-cutting research topics. Our approach demonstrates how science mapping can complement current quantitative or ad hoc approaches to institutional reviews by uncovering trends and relationships obscured by other metrics. We produced research maps that capture the latent thematic structure of an interdisciplinary body of research at multiple levels of detail. To do this, we analyzed research publications and funded projects from 240 researchers spanning 24 academic departments affiliated with ERI between 2009 and 2019. We then evaluated the insights that the maps can support by surveying researchers within the institution whose work is represented in the maps.

In the remainder of this article, we situate our work in relation to existing approaches for abstracting and mapping information. Specifically, we discuss science mapping as a method for domain analysis and knowledge representation. We then describe our approach to produce maps of a body of research at two levels of detail. Finally, we report how leading ERI researchers evaluate the potential for our maps to support an institutional review. A delay in the actual institutional review (resulting from COVID-19) precluded feedback from external reviewers in time for our research project. We find that our approach complements the review process by exposing and relating thematic expertise, highlighting relationships between academic departments or teams of authors, analyzing topical distributions and clusters of work, and tracking the evolution of these aspects over time.

The interpretation of interdisciplinary research trends and impact is an important task for many research institutions, and single-value quantitative metrics are insufficient. We review methods that facilitate trend and impact analysis by abstracting and visually summarizing large collections of research documents. To situate our contribution, we first review science mapping applications in scientometrics and knowledge domain visualizations. We then describe dimensionality reduction and data visualization techniques used to design science maps, namely topic modeling and clustering techniques.

Science mapping

Mapping is indispensable in many monitoring and planning contexts; without maps of the physical territory, it would be challenging to plan and manage the development of cities, landscapes, and infrastructure. Cadastral maps, for example, document ownership and other rights to the land; they also inform and communicate numerous planning interventions, including strategic land use decisions, economic investment, and mitigation measures. 16

Science mapping charts the structure and evolution of knowledge in a domain or discipline by using maps as visual communication metaphors. 13 Science maps are based on bodies of scientific literature analyzed using computational tools and visualized to highlight trends, which can be interpreted using theories of scientific change. 17 Scientometric applications use quantitative metrics, including author co-citation, 18 document or journal co-citation, 19 co-word analysis, 20 and other bibliometrics extracted from documents. Many applications configure bibliometric elements using multidimensional scaling, network analysis, tree maps, or other visualization techniques. 21 Similarity measures are constructed and applied along with dimensionality reduction to visualize scientific documents. 13

A number of recent applications combine topic modeling with interactive visualizations to provide decision support. A visual topic modeling system called UTOPIAN 22 combines several dimensionality reduction techniques, including topic modeling and clustering, to merge or split topics based on user input. A related system called Termite 23 presents salient terms discovered from each topic, which can be used to explore documents. Other systems for visualizing and interpreting topics include LDAvis, 24 TopicLens, 25 and VISTopic. 26 Like Termite, LDAvis supports interpretation of relevant relationships between terms and discovered topics; topics are presented in a low-dimensional view, showing their correspondence with terms. Like UTOPIAN, TopicLens responds dynamically to user input by regenerating multilevel topic models and embeddings based on user specifications. Similarly, VISTopic supports multilevel topic representation but partitions the corpus of input documents hierarchically.

Although our work bears similarities to these systems, we distinguish our contribution as follows. First, several of these existing systems allow users to adjust the level of detail in the visualizations, which is handled hierarchically. Strict hierarchies may not offer the best knowledge representation, however, especially in applications like institutional reviews where topical overlap is of interest. For example, a coarse representation of a corpus may have a topic about “ecology,” while a more detailed representation may have topics about “nutrient cycling” and “predation”; while related, these topics can also be independent of the more general “ecology” topic. Alternative tree-like structures, like semilattices or sets of partially overlapping concepts, might be more adequate for knowledge organization. 27 We chose not to take a hierarchical approach when modeling topics. Instead, we handle level of detail by selecting numbers of topics in advance.

Second, we chose not to exploit the potential of network visualizations based on quantitative metrics like co-citation. Network-based measures are well established 13 and support specific kinds of questions; in previous work, we found that embedding research objects based on their topical similarity revealed their distribution and the coverage of their corpus, while linking them revealed their topical connectivity and centrality. 28 As ERI is an interdisciplinary institution, however, we did not want to use metrics or create visual representations that would draw imbalanced comparisons between the contributions of individual researchers from different disciplines. Instead, we treat research documents as objects embedded in a continuous topic space, which form regions of research that change over time and vary by level of detail.

Finally, while many prior systems offer use cases with real data, few involve usability testing. We demonstrate the utility of our application, which is situated in a real institutional review. This allows us to collect valuable insights about science map interactions and interpretations as reported under Evaluation .

In the following sections, we focus on dimensionality reduction and data visualization techniques that underpin science mapping and support the exploration and discovery of research documents at multiple levels of detail.

Dimensionality reduction

Dimensionality reduction is a key step in producing science maps, as it addresses the problem of displaying complex, high-dimensional data in a low-dimensional space like a two-dimensional map. 13 This is analogous to cartographic generalization, where computational and cognitive issues of complexity are addressed by deliberately reducing the level of detail in the representation. 21 To reduce the level of detail in our corpus of research documents, we use topic modeling to identify major themes shared by research documents. Topic modeling offers a way to identify research topics latent in articles and projects that are not bounded by traditional silos, like academic departments and their terminologies.

Topic models are statistical machine learning techniques that can uncover structures in collections of documents, for example, by grouping documents in which similar terms co-occur. 29 Topic models have been applied to classify and summarize large collections of documents, as well as solving similarity judgment problems. 30 Topics themselves can also be of interest; for example, the National Institutes of Health and the National Science Foundation have developed topic-based search interfaces to explore trends across related research projects. 29

We consider two main kinds of topic modeling approaches: latent Dirichlet allocation (LDA) and non-negative matrix factorization (NMF). LDA represents documents as mixtures of topics composed of words with certain probabilities. 30 It assumes that similar words occur in similar contexts and aims to discover latent topics in the documents. LDA offers insights “into inter- or intra-document statistical structure” 30 and has been positioned as an improvement over other measures used in information-retrieval applications like term frequency-inverse document frequency, or tf-idf, 31 which is used to determine the relative importance of terms in a given document or corpus.

In matrix factorization approaches, a document-term matrix is decomposed into a smaller set of matrices, which can be interpreted as a topic model. 32 NMF is a dimensionality reduction technique for decomposing samples, which are documents in topic modeling. Similar to LDA, documents are represented as term vectors, which can be combined into a document term matrix. However, documents are represented as combinations of co-occurring terms rather than likelihoods. In NMF, term weighting using tf-idf, for example, 31 can also be used to boost distinctive terms.

A central challenge in topic modeling is the selection of an appropriate number of topics; selecting too few leads to overly broad topics, while selecting too many leads to redundancy. 33 Best practices recommend a combination of human evaluation strategies and topic coherence measures. 29 Coherence measures quantify the degree to which statements in a set support one another; in topic modeling, coherence measures evaluate sets of words that compose topics. 34

Data visualization

Data visualization controls the transformation and layout of data into a map. 13 To visualize research documents, we use clustering methods to further abstract the topic models and give a visual impression of their underlying structure, in particular, the similarity between concepts. Broadly, the outputs of these clustering methods can be interpreted as spatializations, which offer high-level views of content through the familiar visual modality of maps. 15

In general, space and time are fundamental ordering relations for knowledge representation. 35 The “spatial turn” observed in the social sciences and humanities has exploited the idea of spatial organization to facilitate cross-disciplinary exchange, allowing many lines of thought to converge. 36 In cognitive science, it has been claimed that conceptual spaces in which nearby concepts are similar underlie human thinking and learning. 37 The first law of cognitive geography, or distance-similarity metaphor, references the first law of geography, which states that “everything is related to everything else, but nearby things are more related than distant things.” 38 The distance-similarity metaphor treats distance in abstract spaces as metaphorically equivalent to dissimilarity. 39 These powers of spatial representation underpin the idea of spatialization, which maps abstract domains to spaces in which nearby elements are similar. 14 Spatialization has been applied to organize multidimensional and thematically diverse collections.

Previous studies have shown that levels of detail in spatialized displays, such as hierarchical regions, shape viewers' interpretation of the similarity of elements like news articles. 39 Spatialization relies on generalization methods for merging individual features into groups. This is analogous to cartographic generalization, which performs hierarchical clustering based on feature similarity and results in changing representations and labels for the features at each level of detail. 21

Spatialization methods are related to a broader suite of “macroscopic research” devices, 40 including science maps 13 and “distant reading” diagrams 41 that enable the study of patterns at multiple levels of detail over time. Distant reading, 41 in the digital humanities, provides methods for deliberately abstracting and visualizing text; to analyze hundreds of novels, for example, it is necessary to render fewer elements in order to offer a sharper sense of high-level themes and their interconnection. Distant reading uses graphs, maps, and trees to spatially configure units, like genres and novels, and reveal latent structures in their source material. These methods are generic enough to guide abstraction over many kinds of texts, which in our case are large numbers of abstracts from publications and grant proposals. They support a broader understanding of latent trends, such as the emergence and evolution of shared research topics.

To further systematize our spatialization methods, we apply the theory of core concepts of spatial information. 42 The concepts summarized in Table 1 provide a high-level vocabulary with which to ask and answer questions about phenomena in space and time. They capture distinct ways of computing with spatial information; thus, they are applicable to geographic as well as other kinds of spaces. They provide us with a set of interchangeable lenses through which research data can be spatialized and viewed. 43

Summary of core concepts of spatial information

To produce maps, we first produce a field of continuous topic values from the texts of research documents with a topic value at each position. This can be thought of as a landscape or surface of topic values. Research documents conceptualized as objects are then located in this continuous two-dimensional topic space according to their topic mixtures using two embedding techniques: t-distributed stochastic neighbor embedding, or t-SNE, 44 and uniform manifold approximation and projection, or UMAP. 45 Both t-SNE and UMAP model high-dimensional research objects as points in a low-dimensional map space while clustering similar objects and spacing apart dissimilar ones. This embedding results in regions of documents in which events , like changes in the configurations of individual or departmental research, can be detected over time.

We produce maps that support the distant reading of the ERI's activities at distinct levels of detail. These maps show research topics and their evolution over time. The input to these maps are the descriptions of two kinds of research documents: publications and funded projects. We take the titles and abstracts from their metadata and model topics from them at two distinct levels of detail. We then feed the resulting document topic models into spatialization algorithms to output maps of the research topics.

Data sources

We analyzed publications and funded projects from ERI's 240 researchers active from 2009 to 2019. We gathered publication metadata using the Dimensions API, which is available for non-commercial use. We retrieved publications for each active researcher at ERI during the study period. These publications were then hand-curated by ERI staff to verify that they were associated with the correct researcher and sponsored by ERI during the period of analysis. This yielded 3,108 publications. We retained the title, abstract, year, digital object identifier (DOI) (if available), and authors. Examples of publication outlets include PLOS One , Proceedings of the National Academy of Sciences , and Environmental Science & Technology . Field-of-research codes assigned to publications in Dimensions ( https://app.dimensions.ai/browse/categories/publication/for ) characterize major research areas and include earth sciences, biological sciences, environmental sciences, and engineering.

We also used ERI's internal data on funded proposals, grants, and contracts. Similarly, we retained only the title, abstract, year, and identifier (if available). This yielded 662 funded projects. The majority of funding for projects came from federal agencies like the National Science Foundation, National Aeronautics and Space Administration, and National Oceanic and Atmospheric Administration. Partnerships with municipal and state agencies along with other universities also provided substantial funding. Figure 1 summarizes the numbers of research publications and projects per year over the period of analysis.

Distribution of research documents per year for analysis period

Text pre-processing

We combined the metadata of the 3,770 research documents and performed text pre-processing by removing records with identical identifiers (DOIs or grant numbers), removing HTML tags, and reformatting ASCII extended characters. To determine whether to set a document length threshold, we checked the document distribution. Figure 2 shows a normal distribution of lengths, which are relatively concise; the average document is 1,678 characters long.

Distribution of research documents by word count

Next, we followed a standard natural language processing pipeline to reformat the titles and abstracts of the research documents. 46 We first determined distinct document terms using tf-idf. 31 This measure reflects the relative importance of a term to a document in a corpus and is often used as a weighting factor in information retrieval applications; we use this measure to balance specific terms that show up frequently in relatively few documents (e.g., “polymerase”) with those that show up frequently across many documents (e.g., “sample”). Many frequent terms describe research methods (e.g., “estimate”) rather than subject matter (e.g., “snow”).

We removed the following frequent and generic terms, which had low tf-idf scores: “data,” “study,” “project,” “research,” “collaborative,” “include,” “result,” “increase,” “high,” “low,” “large,” “include,” and “based.” We then constructed unigram and bigram models to preserve contiguous sequences of terms (e.g., “climate_change”). We did not lemmatize the input text because we did not want to lose the variation of domain-specific terms (e.g., “hydrology” and “hydrological”). We created a normalized document term matrix composed of 3,770 documents and 80,152 distinct terms. We set the minimum document frequency to 2 and we considered both unigrams and bigrams. This resulted in a corpus of documents and term frequencies to use in topic modeling.

Topic modeling

We applied LDA 30 and NMF 32 to the normalized document term matrix. Our goal was to model a range of topics for the documents and to generate coherent topics at multiple levels of detail that describe major research themes at ERI.

To determine a range of topic values to model, we used Miller's law 47 as a heuristic. It proposes that the average person can hold approximately 7 ± 2 “chunks” of information in working memory (e.g., 7 digits, 6 letters, 5 words), limiting the simultaneous perception and processing of information by humans. Miller's law, applied to our topic models, suggests a coarse level of detail (7 ± 2 topics) that reviewers should be able to consider at once. For a suitable number of topics at a more detailed level, we reapplied Miller's law to each chunk of the coarse level, resulting in bounds of (5 × 5) and (9 × 9), or a range of 25–81 chunks, or in our case topics, to generate.

To compare the models and evaluate their quality, we use coherence as an interpretability measure. It is based on the fundamental idea in classification that the members of a class should be more similar to one another than to members of other classes and measures the extent to which top terms representing a topic are semantically related, relative to other terms in the corpus. 48 Coherence is considered to be more human interpretable for evaluating topic model quality than other measures, including perplexity and log likelihood. 33 Specifically, we use the topic coherence Word2Vec metric, which generates word embeddings to evaluate the similarity of term level descriptors from topics. 49

We generated LDA and NMF models across a range of topic numbers (2–100) and calculated their coherence scores. Figure 3 shows a comparison between coherence scores for the LDA and NMF topic models. We generated LDA models using Gensim's Mallet wrapper ( https://radimrehurek.com/gensim/models/wrappers/ldamallet.html ) and NMF models using Scikit-learn ( https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html ). The NMF model was initialized with non-negative double-singular value decomposition (“nndsvd”), which is optimized for sparse data.

Coherence scores for NMF and LDA topic models with 2–100 topics

We found NMF to be a more suitable topic modeling approach for our purposes than LDA. It produced topic models with higher coherence scores than our LDA models by about 17% on average. This may be because NMF is better suited to modeling smaller or sparser datasets, like titles and abstracts, rather than full text. 50 We also found that NMF produced topics that were more indicative of subject matter, rather than methods. This may be due to term weighting with tf-idf, unlike LDA, which operates on raw term frequency. 33

Although the addition of topics increases the coherence of the models, we wanted to select models that followed the Miller's law heuristic we previously established; the NMF model with 100 topics has the highest coherence score, but this value is out of range. To select topic models, we relied on human evaluation 51 of the most coherent models within a first range of 5–9 topics and a second range of 25–81 topics. Specifically, ERI's director, Kelly Caylor, evaluated the topic descriptors for models within each range and selected two topic models to develop into maps: a coarse-grained model with 9 topics and a fine-grained model with 36 topics. This choice was important because we wanted to ensure that the themes emerging from the topic models were interpretable, in addition to being coherent, and could support institutional reporting.

Table 2 shows samples of topics and topic descriptors as a list of top terms for each of the NMF models we generated. Whereas most of the terms are unigrams, some bigrams, like “species_richness,” also capture scientific concepts that are compound terms. NMF results in a document-topic matrix in which each document is described by a mixture of topics with different strengths of association. The document-topic matrix forms the input to the subsequent spatializations, while the topic-term matrix is used to reference topics and term descriptors.

Topics and descriptors discovered for NMF topic models

Spatialization

The inputs to the spatializations are the document-topic matrices resulting from the coarse (9) and detailed (36) NMF topic models. We first mapped research documents with t-SNE using manifold learning in Scikit-learn ( https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html ). The t-SNE algorithm transforms the high-dimensional document-topic matrix into a low-dimensional coordinate representation. Each document is assigned a position based on its topic mixture, resulting in the placement of topically similar documents near one another and dissimilar documents farther apart. The UMAP process for assigning locations to research documents is similar to that of t-SNE; a key difference is the assumption that documents are uniformly distributed on a complex surface, resulting in a distinct spatial configuration. We produced these with UMAP learn ( https://umap-learn.readthedocs.io/en/latest/ ). The axes in both t-SNE and UMAP are left unlabeled, as they describe complex curved paths in the original high-dimensional space and do not have human-interpretable meaning. 44 , 45

We interactively explored the maps to interpret the effects of the map parameters, which balance local, pairwise similarity with global, intercluster similarity. 52 The first parameter influencing the size, distance, and shape of clusters is perplexity, which controls the number of nearest neighbors. Perplexity describes how well a probability distribution predicts a sample. In our maps, low perplexity values produce clearly delineated clusters, while high values allow for more global connectivity and less clearly delineated clusters. Typical values fall between 5 and 50 34 . The second parameter is early exaggeration, which determines the compactness of clusters. This optimization method creates empty space between clusters so they can achieve better global and local organization. 44

To select the map parameters, we used a visual inspection method. The director of ERI, Kelly Caylor, evaluated the topic regions resulting from the t-SNE and UMAP configurations against the benchmark of the previous institutional review report. Based on his familiarity with the institute's research, the director confirmed that the results from t-SNE with an early exaggeration value of 5 and a perplexity value of 7 were easiest to interpret and supported his reporting needs. Furthermore, Figure 4 shows that t-SNE produces local clusters of similar objects that are visually distinct, while UMAP allows for more outliers and preserves compact clusters; for instance, all red documents clustered and labeled with “fault (seismic motion)” are concentrated in UMAP, while they are split into three distinct regions in the t-SNE map. The effects of uniform spacing are also visible in UMAP; the red and blue clusters are disjoint in UMAP but are partial neighbors in t-SNE. The arrangement of individual documents and clusters of documents in t-SNE conveys topical similarity well. Based on these observations, the director deemed t-SNE to be a more compelling technique for reporting purposes.

ERI research documents clustered by 9 topics with t-SNE (left) and UMAP (right); each color corresponds to the document's main topic, labeled with three term descriptors

Our methods address the question of how to systematically elicit and represent the major topics of a complex, interdisciplinary body of research at multiple levels of detail that show their similarities and evolution over time. We produce maps of research documents located in a continuous topic space, which exhibit topical proximity in regions and capture multiple levels of detail over periods of time. We explore whether and how these maps of research topics support the institutional assessment of an interdisciplinary body of research.

Reading maps of research documents

The maps produced with t-SNE show research documents with similar topics forming regions at two distinct levels of detail. Documents are assigned to topic clusters, which are labeled with the first three terms from their topic descriptor. Topic modeling does not produce labels for the resulting topics, so assigning labels is a pragmatic choice that allows us to reference and interpret the topic clusters. The categorical colormap ( https://colorcet.holoviz.org/ ) offers perceptually distinct categories for visualizing the relatively large number of topics in the detailed topic model.

In the coarse map with 9 topics shown in Figure 5 , we observe patterns related to the centrality, size, contiguity, and proximity of clusters. Documents assigned to the large “ocean” cluster are in the center of the map, while smaller clusters like “snow” are on the periphery. This suggests that the documents described by the “ocean” topic are similar to more documents in the corpus than those assigned to the “snow” cluster, which may be more niche.

Coarse (9 topic) map of research documents (2009–2019)

The cluster labeled “rocks” is small and discrete compared with the “species” cluster, suggesting that more of ERI's research is ecological rather than geological in nature; however, these disciplinary identities are not mutually exclusive. Documents can be characterized by more than one research topic in the map.

Documents in the “soil moisture” cluster are uniformly located in a similar region of the map, while others, like those in the “climate change” cluster, are dispersed and non-contiguous. This suggests a lack of internal conformity within this cluster. Lower document dispersion in the “soil moisture” cluster suggests topical homogeneity, while higher dispersion in the “climate change” cluster suggests more heterogeneous documents.

The adjacency of the “sediment” cluster with the “rocks,” “climate change,” and “ocean” clusters suggests that its documents straddle, and sometimes bridge, these research areas, particularly those on the clusters' edges. Clusters located farther apart are also dissimilar. The “snow” and the “soil moisture” clusters are found on opposite sides of the map; however, other documents described by these topics are neighboring at the bottom of the map, converging around an edge of the “climate change” cluster. Indeed, the documents found there bridge these areas; they address snowmelt, surface temperature in forests, biomass accumulation, streamflow changes, and other related ideas.

Whereas the coarse map presents a distant overview of ERI's research topics, the detailed map shown in Figure 6 reveals intricate patterns. The center “population” cluster borders other research areas, including the “species,” “ocean,” and “fisheries” clusters. Another multitopic cluster found at the bottom map periphery gathers similar public policy research from different topics, like mitigating climate change impacts on fisheries and earth system science in Canada.

Detailed (36 topic) map of research documents (2009–2019)

The detailed map is made up of relatively even distributions of topic clusters. One exception is the “fecal” cluster on the right edge of the map, which is small and separated; its nearest neighbor is the “lakes” cluster below it. A larger “nanoparticles” cluster at the top of the map is associated with ERI's productive Center for Environmental Implications of Nanotechnology.

Central clusters tend to be less uniform than those at the edges. The “water,” “conservation,” and “methane” topic clusters are interspersed with documents addressing marine isotopes, stream mapping at a battlefield conservation site, and stream nitrate concentrations in mountainous watersheds. This is contrasted with the homogeneous clusters found at the edges, such as the “ice” cluster on the left edge dominated by documents addressing glaciers.

In the detailed map, we see that there are distinct, yet adjacent, areas of research involving similar researchers and shared ideas, such as integrating wildfire risk with the study of agricultural encroachment. The “conservation” and “fire” clusters are adjacent in the detailed map; in the coarse map, these documents fall under the “climate change” topic. In the detailed map, most “fire” research documents border the “sediment” and “fisheries” clusters, suggesting that documents about wildfire recovery and river restoration share similarities.

We have presented maps at two selected levels of detail: coarse (9 topics) and detailed (36 topics). The maps are systematically produced with the goal of improving upon the ad hoc definition and interpretation of research thrusts in the institutional review process. “Reading” these data-driven maps generates qualitative insights, as they represent topics extracted from the text of research documents. The maps also possess emergent qualities, revealing more than the sum of their parts; 41 they show patterns in ERI's research that were previously difficult or impossible to see when inspecting single documents, publication and project lists, or the work of individual researchers.

Deploying a map dashboard

To distribute and evaluate our maps, we deploy a public-facing dashboard ( https://eri-research-dashboard.herokuapp.com/ ) using Plotly, Dash for Python, and Heroku. The dashboard's “About” panel describes the map and allows users to select a level of detail, topics to map, and a year range. Figure 7 shows the “Search” panel, which allows users to filter data by ERI researcher or by academic department and return metadata for a selected document, including its DOI when available. We make time explicit by showing a map snapshot for each year, which can be filtered by a range of years. This provides a backdrop for the interpretation of events, such as the acquisition of major grants or the hiring of new faculty in growing research areas. We provide evidence supporting these interpretations in the next section, Evaluation .

Search panel of the interactive research map dashboard

Do the maps we developed support “distant reading of research documents in the context of an institutional review”? To answer this question, we evaluate the maps in two main ways. First, we use the maps to interpret and answer standard questions asked in the institutional review process. Second, we evaluate the maps in action, considering how they are used by the researchers whose work is being assessed. 53 We surveyed leading ERI researchers who determined if and how they think the maps support “reading at a distance.”

Institutional review questions

How do maps of research topics support questions commonly posed to reviewers? Here, we consider the six institutional review questions about research accomplishment that UCSB's ORUs must regularly address ( https://www.research.ucsb.edu/organized-research-unit-oru-administration ). They are currently answered using quantitative evidence, for example, numbers of publications by field of research and amounts of funding per researcher. Although these benchmark questions are particular to UCSB, the concerns they address are representative of similar contexts elsewhere:

• • Research quality and significance: describe the quality and significance of the research accomplished and in progress.
• • Trends and research specialties: comment on significant trends within the disciplines represented in the unit and relate these to current research specialties in your ORU.
• • Benefits to campus and departments: comment on how the ORU benefits the campus in general and academic departments in particular.
• • Participant productivity, influence, and prominence: comment on the continuing productivity and influence of unit participants, locally as well as nationally. Comment on evidence of prominence in the fields represented in the ORU.
• • Collaborations and interdisciplinarity: comment on the unit's collaborative/interdisciplinary work, its quality, and its impact on ORU research efforts and the campus.
• • Extramural funding: describe the possible sources and availability of extramural funds to support the unit's research. Are your participants sufficiently active in the pursuit of extramural funds in light of funding possibilities? How does the extent of annual extramural research funding compare with similar units nationwide?

We have claimed that maps of research topics can complement current evaluation metrics by supporting qualitative narratives. Here, we show how each of these questions can be addressed with maps of research topics:

Research quality and significance

JIFs are a typical quantitative metric. Our maps complement this by generating a broader picture of cross-disciplinary topics from research publications. They highlight researchers' and departments' main topics and topical reach (diffuse or tightly clustered). Researchers in the Bren School of Environmental Science and Management are represented across all major topics, while those affiliated with biology concentrate mainly in the “species” and “oceans” topic clusters.

Trends and research specialties

Funding agency priorities (e.g., NSF's “10 big ideas”) and publisher classification schemes (e.g., fields of research) are typical sources of evidence. Our maps define research topics emerging from publications and projects that are not constrained by external classification schemes or historic disciplinary boundaries. The detailed map captures the topical diversity of research across affiliations, while the coarse map emphasizes earth and environmental science topics unifying ERI's researchers.

Benefits to campus and departments

Evidence includes faculty recruitment, research computing infrastructure, and educational outreach programs. Temporal sequencing in our maps can be used to assess the impact of events, like the inception of educational programs (e.g., the Kids in Nature Program in 2012) or influential funding (e.g., a 2017 NSF award to upgrade campus computing resources). Although causality cannot be determined, it is interesting to note growth in certain topic areas following these events (e.g., a rise in ecological restoration projects following the start of educational programming and community outreach). These insights provide concrete and solid support over anecdotal discussions in institutional reviews.

Participant productivity, influence, and prominence

The professional accolades of individual participants, such as awards, are often reported as evidence. Our maps provide a more objective picture of the topics that each researcher addresses by showing the topical distribution of each researcher's documents. For example, geographer David Siegel's work is concentrated mainly in the “ocean” and “species” topic clusters, while geographer Dar Roberts's work is more broadly dispersed across “species,” “climate,” “ocean,” “snow,” “sediment,” and “soil moisture.” Although both accomplished researchers work extensively with remotely sensed imagery, differentiating their areas of expertise supports institutional management and reporting.

Collaborations and interdisciplinarity

The affiliations of collaborators on funded projects are typically offered as evidence of interdisciplinarity. Our maps currently annotate each project by a single researcher and do not emphasize projects that have collaborators from multiple departments. This functionality could be added if ERI's leadership were interested to see who drives collaborations, not just what common topics they address.

Extramural funding

This is currently based on award amounts. Our maps do not incorporate this kind of information because existing indicators are effective. The projects currently shown in the map have all been funded, but it could be valuable to also show the topics of unfunded projects, for example, to reveal changes to topics prioritized over time by funding agencies.

Researcher survey

How do ERI's leading researchers interpret their own role in ERI's evolving research? We seek to understand researchers' interpretations of topics and relationships shown in the maps. To gather feedback, we administered an online survey to researchers on ERI's advisory board. This survey also served as a rehearsal and internal review for the imminent 5-year review in which the primary map users will be external reviewers in leadership positions at similar institutes. The survey was kept intentionally short and contained the following items:

• • ERI topics: take a minute to explore the first map, at both the coarse and the fine levels of detail. How well do you think these topics represent ERI's research overall?
• • Principal investigator (PI) topics: next, find yourself in the Search panel. Your publications or projects will be highlighted. How well do you think this map represents your research?
• • Topic evolution: finally, try filtering the research documents using the time slider. Do you notice any trends, and do these coincide with any events in ERI's history that you can recall?
• • Other: do you have any other comments or ideas for improving this tool?

We received responses from 5/13 members of the ERI advisory board. The main ideas that emerged from the responses can be separated into observations made from the maps and comments about map design. These responses provide suggestive evidence, which is summarized as follows:

A majority (3/5) of respondents felt that the coarse map adequately described ERI's research, while the remainder had some objections. One noted that the coarse map “lacks several important categories (e.g., biogeochemistry, inland waters, carbon cycle)” but that “the detailed map represents the range of research.” Another felt that the topics reduced all of ERI's research to “physical entities” that made it seem like a geology department. These concerns may relate to the design decision to label and color the documents by main topics; the labels include the first term from the topic descriptor with the second and third included in parentheses. Because topic modeling does not produce labels for the resulting topics, any succinct labeling in support of readability and verbalization skews the presentation. This feedback suggests that alternative approaches to labeling the topics could help because the objections raised were related to category names rather than the clustering of documents.

Researcher topics

Respondents (3/5) felt that they understood the positions of their documents relative to ERI's research landscape. Several mentioned that their “assignments” aligned with their identities as researchers; one noted “I was largely in the species topic group and I do identify as a species-based researcher.” Another felt that their work was categorized “imperfectly at best” as they work mainly on carbon cycling but had been associated with soils. These observations raise interesting challenges for visualizing perceived differences between researchers' self-assigned specialties and positions assigned to their work based on a relatively short period of time.

Topic evolution

One researcher stated that trends in the map pointed to the founding of the UC Center for Environmental Implications of Nanotechnology at UCSB in 2013. Another noted that the map “appears to start out along the edges then fills in the middle … maybe it is selective hiring of people to bridge gaps?” These interpretations speak to the utility of the spatialization approach; researchers are able to associate patterns in the map with probable events in which interdisciplinary research topics emerge, bridging traditional clusters. Changes in topical “coverage” following a faculty hire or large funding awards were observable to the respondents when they used the maps in combination with the time slider. Their observations demonstrate the kinds of insights that we envisioned the temporally sequenced maps might offer.

Most of the comments about map functionality address click interactions, background color, alphabetization of lists, and other details that are easily changed. Suggestions for additional functionality included ways to browse lists of related documents based on shared topics, to “visualize closely linked topics,” and to search based on grants and papers. We expect to incorporate respondents' suggestions in preparation for the upcoming institutional review. We take the leading researchers' responses as a qualified endorsement of the generalization and visual presentation of work done at their institute.

We applied science mapping, dimensionality reduction, and visualization techniques to uncover research relationships and temporal trends in a corpus of research documents. To confirm the utility of this approach, we surveyed researchers represented within the maps. Our research has immediate benefits for ERI as they prepare for their external review. It facilitates ERI's efforts to identify research trends and areas of expertise, determine the impact of various investments on ERI's productivity, and differentiate scholars' unique areas of contribution. Similar systems would be useful for other research enterprises and funders interested in understanding their own trends and productivity.

One limitation of our approach is that it primarily takes advantage of the thematic dimension of data and treats the spatial and temporal components of the data as secondary. Although temporal views are incorporated in our maps, allowing for document subsetting by time span and event detection, making time a primary dimension could prove valuable. Previous work on semantic signatures has shown that time and space offer two complementary ways to order knowledge. 35 Views ordered primarily by time could be thought of as temporalizations, rather than the spatializations we develop, tracking the evolution of topics in the form of graphs from distant reading. 41

Another limitation is that our approach does not take advantage of all of the core concepts of spatial information presented in Table 1 . This interpretation suggests technical ways in which our work can be extended. Currently, we embed research documents ( objects ) in a continuous topic space ( field ), which forms regions of research topics. The number of research topics selected ( granularity ) influences the configurations of the topic regions; in our maps, these configurations (detailed and coarse) are independent and are not linked. Time is also handled as a series of annual snapshots over a decade, where change is depicted as the reconfiguration of topic regions between these intervals ( event ).

First, adopting additional topic modeling approaches, such as hierarchical 54 and dynamic topic models, 55 would account for multiple levels of thematic and temporal detail within a single model rather than producing separate models at different levels of detail. Second, adopting other visualization methods to depict network information from the documents 17 would convey additional relationships holding among the documents, such as co-authorship or funding patterns. Future modeling and visualization choices should be guided by the priorities of the institute in order to ensure they support the review process.

In terms of evaluation, we are also interested in expanding the survey we conducted to coincide with ERI's external review. This would give us further insights into how external reviewers who do not have a personal connection to ERI's research interpret and evaluate the research topics. To determine the applicability and maturity of our approach for adoption in a broader context, we would also be interested in surveying researchers or leaders affiliated with similar ORUs. This would allow us to build consensus around strategies for adopting maps of research as robust decision support tools.

At the outset of this article, we proposed that maps of the research “territory” could provide actionable decision support. The maps we have produced give an impression of the underlying thematic structure of the research in the form of research regions that are meaningful within, and possibly across, institutions. Just as land use maps are used to manage resources and forecast growth in a regional planning context, maps of research can be used to do the same in an institutional setting. We envision maps of research topics being used internally as part of the ORU's self-assessment and externally as a communication tool describing research trends and developments, which are likely of interest to external reviewers, other research units, and the public.

Experimental procedures

Resource availability, lead contact.

Sara Lafia is the lead contact of this study and can be reached at [email protected] .

Materials availability

The code developed for the topic models and data visualizations reported in this article are available in our public Github repository: https://github.com/saralafia/ERI-maps . The code developed for the reporting dashboard is available in our public Github repository: https://github.com/saralafia/ERI-dashboard .

Data and code availability

Acknowledgments.

We thank the members of ERI's advisory board, along with Daniel R. Montello and James Frew at UCSB, for supporting and guiding this study. We also acknowledge support from an anonymous private grant ( http://spatial.ucsb.edu/research/spatial-discovery ) awarded to the UCSB Center for Spatial Studies and UCSB Library to study challenges and strategies that libraries and researchers face when trying to discover research data on diverse platforms. This material is based upon work supported by the National Science Foundation under grant 1930645.

Author contributions

Conceptualization, S.L., W.K., and K.C.; methodology, S.L.; data curation, S.L. and K.C.; writing – original draft, S.L., W.K., and K.C.; writing – review & editing, S.L. and L.H.; visualization, S.L.; supervision, W.K. and L.H.; funding acquisition, K.C. and L.H.

Declaration of interests

The authors declare no competing interests.

Published: February 15, 2021

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First-Year Writing

• Let's Get Started!
• The Research Process

What do you do if you can't cite Wikipedia?

Developing ideas and keywords, additional search tips, developing a research question, mapping your research ideas, keyword search rules.

• Use JumboSearch
• Use Article Databases
• Evaluating Information
• Cite Your Sources

Wikipedia is a background source, which includes lots of helpful information. But you can't cite it for lots of reasons (anyone can edit Wikipedia entries and, even worse, we don't know who they are). So what do you do? Wikipedia is a source of background information, which comes from places like dictionaries and subject encyclopedias. Background sources provide contextual information and answer straightforward questions. They include definitions, statistics, and other details. You can use this type of source to:

• Help narrow your research topic
• Find data to support your thesis
• Identify keywords and main ideas to use as search terms.

Most importantly, the library has academic sources of background information , which you can find on this page!

• Credo Reference Online reference resources from numerous publishers. This reference resource can be searched by individual title, broad subject headings, cross-references, audio and images. Use its research mapper to search for terms and topics that are interconnected and displayed in (a) visual form. Examples of titles are: Bloomsbury Guide to Art, Bridgeman Art Library Archive, Columbia Encyclopedia, Taber's Cyclopedic Medical Dictionary, Harvard Dictionary of Music, and the Concise Corsini Encyclopedia of Psychology and Behavioral Science. The complete list of titles is available on the CREDO Reference site.
• Britannica Academic With contributions from Nobel laureates, historians, curators, professors, and other notable experts, Britannica Academic provides trusted information with balanced, global perspectives and insights.
• Oxford Reference Includes Oxford University Press dictionaries, encyclopedias, and other reference works in the humanities, social sciences, foreign languages, science, technology and medicine, the performing arts, and religion. Individual reference works can be searched separately or across the entire databases.

One of the hardest tasks when starting research in a new area is identifying what the major issues and concepts that can provide you with an important frame of reference. Before you start looking for in-depth information we suggest that you do the following:

• Think about what you already know about the issue
• Think about what you want to know more about
• Learn more about what issues are being discussed related to your area of inquiry

Once you have identified important concepts translate them into keywords or short phrases so that you can start to search. As you conduct your research you will be able to identify additional and more specific search terms.

As you start searching select one term or phrase from Column A and one term or phrase from Column B . Run additional searches with alternate terms from each column.

Sample Question : I want to learn more about the environmental impacts associated with global warming.

Phrase Search : Use "quotation marks" to search for a particular phrase.  Example:  "greenhouse gas emissions"

Truncation : Use an asterisk to find variations of a word. Put an asterisk following the root of the word to find all variations of that word, including singular and plural.  Example : environment* (finds environments, environmental, environmentalist, etc.)

Grouping or Nesting Keywords : Use parentheses ( ) as a way to group your search terms together.  Example : (climate change OR global warming) AND population growth

What makes a research question "researchable"? ( Courtesy of Northern Kentucky University Steely Library )

Using AND/OR/NOT (Boolean Search Operators)

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• Last Updated: Jul 29, 2024 4:22 PM
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Video: How to Brainstorm a Topic

Video: mapping your research ideas, video: developing a research question, video: how to use keywords to form a research strategy, video: how to develop a search phrase, video: how library stuff works: information has value, developing a research question.

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Source: "brainstormPrezi" by Holman Library is licensed under CC BY-NC-ND 4.0

Source: "Mapping Your Research Ideas" by UCLA Library , is licensed under a Standard YouTube License.

Source: "Developing a Research Question" by Steely Library NKU , is licensed under a Standard YouTube License.

Source: "From topic to search results in two minutes! " by Holman Library is licensed under CC BY-NC-ND 4.0

Source: "DevelopSearchPhrasePrezi" by Holman Library is licensed under CC BY-NC-ND 4.0

Source: "How Library Stuff Works: Information Has Value" by McMaster Libraries , is licensed under a Standard YouTube License.

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• Last Updated: Jun 16, 2024 9:05 PM
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* Psychology

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Develop a Research Question That Works

Mapping your research ideas, from topic to question - refining as you research.

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• Last Updated: Jul 11, 2024 9:37 AM
• URL: https://libguides.lib.rochester.edu/PSYC