the research project on

Illustration by James Round

How to plan a research project

Whether for a paper or a thesis, define your question, review the work of others – and leave yourself open to discovery.

by Brooke Harrington   + BIO

is professor of sociology at Dartmouth College in New Hampshire. Her research has won international awards both for scholarly quality and impact on public life. She has published dozens of articles and three books, most recently the bestseller Capital without Borders (2016), now translated into five languages.

Edited by Sam Haselby

Need to know

‘When curiosity turns to serious matters, it’s called research.’ – From Aphorisms (1880-1905) by Marie von Ebner-Eschenbach

Planning research projects is a time-honoured intellectual exercise: one that requires both creativity and sharp analytical skills. The purpose of this Guide is to make the process systematic and easy to understand. While there is a great deal of freedom and discovery involved – from the topics you choose, to the data and methods you apply – there are also some norms and constraints that obtain, no matter what your academic level or field of study. For those in high school through to doctoral students, and from art history to archaeology, research planning involves broadly similar steps, including: formulating a question, developing an argument or predictions based on previous research, then selecting the information needed to answer your question.

Some of this might sound self-evident but, as you’ll find, research requires a different way of approaching and using information than most of us are accustomed to in everyday life. That is why I include orienting yourself to knowledge-creation as an initial step in the process. This is a crucial and underappreciated phase in education, akin to making the transition from salaried employment to entrepreneurship: suddenly, you’re on your own, and that requires a new way of thinking about your work.

What follows is a distillation of what I’ve learned about this process over 27 years as a professional social scientist. It reflects the skills that my own professors imparted in the sociology doctoral programme at Harvard, as well as what I learned later on as a research supervisor for Ivy League PhD and MA students, and then as the author of award-winning scholarly books and articles. It can be adapted to the demands of both short projects (such as course term papers) and long ones, such as a thesis.

At its simplest, research planning involves the four distinct steps outlined below: orienting yourself to knowledge-creation; defining your research question; reviewing previous research on your question; and then choosing relevant data to formulate your own answers. Because the focus of this Guide is on planning a research project, as opposed to conducting a research project, this section won’t delve into the details of data-collection or analysis; those steps happen after you plan the project. In addition, the topic is vast: year-long doctoral courses are devoted to data and analysis. Instead, the fourth part of this section will outline some basic strategies you could use in planning a data-selection and analysis process appropriate to your research question.

Step 1: Orient yourself

Planning and conducting research requires you to make a transition, from thinking like a consumer of information to thinking like a producer of information. That sounds simple, but it’s actually a complex task. As a practical matter, this means putting aside the mindset of a student, which treats knowledge as something created by other people. As students, we are often passive receivers of knowledge: asked to do a specified set of readings, then graded on how well we reproduce what we’ve read.

Researchers, however, must take on an active role as knowledge producers . Doing research requires more of you than reading and absorbing what other people have written: you have to engage in a dialogue with it. That includes arguing with previous knowledge and perhaps trying to show that ideas we have accepted as given are actually wrong or incomplete. For example, rather than simply taking in the claims of an author you read, you’ll need to draw out the implications of those claims: if what the author is saying is true, what else does that suggest must be true? What predictions could you make based on the author’s claims?

In other words, rather than treating a reading as a source of truth – even if it comes from a revered source, such as Plato or Marie Curie – this orientation step asks you to treat the claims you read as provisional and subject to interrogation. That is one of the great pieces of wisdom that science and philosophy can teach us: that the biggest advances in human understanding have been made not by being correct about trivial things, but by being wrong in an interesting way . For example, Albert Einstein was wrong about quantum mechanics, but his arguments about it with his fellow physicist Niels Bohr have led to some of the biggest breakthroughs in science, even a century later.

Step 2: Define your research question

Students often give this step cursory attention, but experienced researchers know that formulating a good question is sometimes the most difficult part of the research planning process. That is because the precise language of the question frames the rest of the project. It’s therefore important to pose the question carefully, in a way that’s both possible to answer and likely to yield interesting results. Of course, you must choose a question that interests you, but that’s only the beginning of what’s likely to be an iterative process: most researchers come back to this step repeatedly, modifying their questions in light of previous research, resource limitations and other considerations.

Researchers face limits in terms of time and money. They, like everyone else, have to pose research questions that they can plausibly answer given the constraints they face. For example, it would be inadvisable to frame a project around the question ‘What are the roots of the Arab-Israeli conflict?’ if you have only a week to develop an answer and no background on that topic. That’s not to limit your imagination: you can come up with any question you’d like. But it typically does require some creativity to frame a question that you can answer well – that is, by investigating thoroughly and providing new insights – within the limits you face.

In addition to being interesting to you, and feasible within your resource constraints, the third and most important characteristic of a ‘good’ research topic is whether it allows you to create new knowledge. It might turn out that your question has already been asked and answered to your satisfaction: if so, you’ll find out in the next step of this process. On the other hand, you might come up with a research question that hasn’t been addressed previously. Before you get too excited about breaking uncharted ground, consider this: a lot of potentially researchable questions haven’t been studied for good reason ; they might have answers that are trivial or of very limited interest. This could include questions such as ‘Why does the area of a circle equal π r²?’ or ‘Did winter conditions affect Napoleon’s plans to invade Russia?’ Of course, you might be able to make the argument that a seemingly trivial question is actually vitally important, but you must be prepared to back that up with convincing evidence. The exercise in the ‘Learn More’ section below will help you think through some of these issues.

Finally, scholarly research questions must in some way lead to new and distinctive insights. For example, lots of people have studied gender roles in sports teams; what can you ask that hasn’t been asked before? Reinventing the wheel is the number-one no-no in this endeavour. That’s why the next step is so important: reviewing previous research on your topic. Depending on what you find in that step, you might need to revise your research question; iterating between your question and the existing literature is a normal process. But don’t worry: it doesn’t go on forever. In fact, the iterations taper off – and your research question stabilises – as you develop a firm grasp of the current state of knowledge on your topic.

Step 3: Review previous research

In academic research, from articles to books, it’s common to find a section called a ‘literature review’. The purpose of that section is to describe the state of the art in knowledge on the research question that a project has posed. It demonstrates that researchers have thoroughly and systematically reviewed the relevant findings of previous studies on their topic, and that they have something novel to contribute.

Your own research project should include something like this, even if it’s a high-school term paper. In the research planning process, you’ll want to list at least half a dozen bullet points stating the major findings on your topic by other people. In relation to those findings, you should be able to specify where your project could provide new and necessary insights. There are two basic rhetorical positions one can take in framing the novelty-plus-importance argument required of academic research:

• Position 1 requires you to build on or extend a set of existing ideas; that means saying something like: ‘Person A has argued that X is true about gender; this implies Y, which has not yet been tested. My project will test Y, and if I find evidence to support it, that will change the way we understand gender.’
• Position 2 is to argue that there is a gap in existing knowledge, either because previous research has reached conflicting conclusions or has failed to consider something important. For example, one could say that research on middle schoolers and gender has been limited by being conducted primarily in coeducational environments, and that findings might differ dramatically if research were conducted in more schools where the student body was all-male or all-female.

Your overall goal in this step of the process is to show that your research will be part of a larger conversation: that is, how your project flows from what’s already known, and how it advances, extends or challenges that existing body of knowledge. That will be the contribution of your project, and it constitutes the motivation for your research.

Two things are worth mentioning about your search for sources of relevant previous research. First, you needn’t look only at studies on your precise topic. For example, if you want to study gender-identity formation in schools, you shouldn’t restrict yourself to studies of schools; the empirical setting (schools) is secondary to the larger social process that interests you (how people form gender identity). That process occurs in many different settings, so cast a wide net. Second, be sure to use legitimate sources – meaning publications that have been through some sort of vetting process, whether that involves peer review (as with academic journal articles you might find via Google Scholar) or editorial review (as you’d find in well-known mass media publications, such as The Economist or The Washington Post ). What you’ll want to avoid is using unvetted sources such as personal blogs or Wikipedia. Why? Because anybody can write anything in those forums, and there is no way to know – unless you’re already an expert – if the claims you find there are accurate. Often, they’re not.

Step 4: Choose your data and methods

Whatever your research question is, eventually you’ll need to consider which data source and analytical strategy are most likely to provide the answers you’re seeking. One starting point is to consider whether your question would be best addressed by qualitative data (such as interviews, observations or historical records), quantitative data (such as surveys or census records) or some combination of both. Your ideas about data sources will, in turn, suggest options for analytical methods.

You might need to collect your own data, or you might find everything you need readily available in an existing dataset someone else has created. A great place to start is with a research librarian: university libraries always have them and, at public universities, those librarians can work with the public, including people who aren’t affiliated with the university. If you don’t happen to have a public university and its library close at hand, an ordinary public library can still be a good place to start: the librarians are often well versed in accessing data sources that might be relevant to your study, such as the census, or historical archives, or the Survey of Consumer Finances.

Because your task at this point is to plan research, rather than conduct it, the purpose of this step is not to commit you irrevocably to a course of action. Instead, your goal here is to think through a feasible approach to answering your research question. You’ll need to find out, for example, whether the data you want exist; if not, do you have a realistic chance of gathering the data yourself, or would it be better to modify your research question? In terms of analysis, would your strategy require you to apply statistical methods? If so, do you have those skills? If not, do you have time to learn them, or money to hire a research assistant to run the analysis for you?

Please be aware that qualitative methods in particular are not the casual undertaking they might appear to be. Many people make the mistake of thinking that only quantitative data and methods are scientific and systematic, while qualitative methods are just a fancy way of saying: ‘I talked to some people, read some old newspapers, and drew my own conclusions.’ Nothing could be further from the truth. In the final section of this guide, you’ll find some links to resources that will provide more insight on standards and procedures governing qualitative research, but suffice it to say: there are rules about what constitutes legitimate evidence and valid analytical procedure for qualitative data, just as there are for quantitative data.

Circle back and consider revising your initial plans

As you work through these four steps in planning your project, it’s perfectly normal to circle back and revise. Research planning is rarely a linear process. It’s also common for new and unexpected avenues to suggest themselves. As the sociologist Thorstein Veblen wrote in 1908 : ‘The outcome of any serious research can only be to make two questions grow where only one grew before.’ That’s as true of research planning as it is of a completed project. Try to enjoy the horizons that open up for you in this process, rather than becoming overwhelmed; the four steps, along with the two exercises that follow, will help you focus your plan and make it manageable.

Key points – How to plan a research project

• Planning a research project is essential no matter your academic level or field of study. There is no one ‘best’ way to design research, but there are certain guidelines that can be helpfully applied across disciplines.
• Orient yourself to knowledge-creation. Make the shift from being a consumer of information to being a producer of information.
• Define your research question. Your question frames the rest of your project, sets the scope, and determines the kinds of answers you can find.
• Review previous research on your question. Survey the existing body of relevant knowledge to ensure that your research will be part of a larger conversation.
• Choose your data and methods. For instance, will you be collecting qualitative data, via interviews, or numerical data, via surveys?
• Circle back and consider revising your initial plans. Expect your research question in particular to undergo multiple rounds of refinement as you learn more about your topic.

Good research questions tend to beget more questions. This can be frustrating for those who want to get down to business right away. Try to make room for the unexpected: this is usually how knowledge advances. Many of the most significant discoveries in human history have been made by people who were looking for something else entirely. There are ways to structure your research planning process without over-constraining yourself; the two exercises below are a start, and you can find further methods in the Links and Books section.

The following exercise provides a structured process for advancing your research project planning. After completing it, you’ll be able to do the following:

• describe clearly and concisely the question you’ve chosen to study
• summarise the state of the art in knowledge about the question, and where your project could contribute new insight
• identify the best strategy for gathering and analysing relevant data

In other words, the following provides a systematic means to establish the building blocks of your research project.

Exercise 1: Definition of research question and sources

This exercise prompts you to select and clarify your general interest area, develop a research question, and investigate sources of information. The annotated bibliography will also help you refine your research question so that you can begin the second assignment, a description of the phenomenon you wish to study.

Jot down a few bullet points in response to these two questions, with the understanding that you’ll probably go back and modify your answers as you begin reading other studies relevant to your topic:

• What will be the general topic of your paper?
• What will be the specific topic of your paper?

b) Research question(s)

Use the following guidelines to frame a research question – or questions – that will drive your analysis. As with Part 1 above, you’ll probably find it necessary to change or refine your research question(s) as you complete future assignments.

• Your question should be phrased so that it can’t be answered with a simple ‘yes’ or ‘no’.
• Your question should have more than one plausible answer.
• Your question should draw relationships between two or more concepts; framing the question in terms of How? or What? often works better than asking Why ?

c) Annotated bibliography

Most or all of your background information should come from two sources: scholarly books and journals, or reputable mass media sources. You might be able to access journal articles electronically through your library, using search engines such as JSTOR and Google Scholar. This can save you a great deal of time compared with going to the library in person to search periodicals. General news sources, such as those accessible through LexisNexis, are acceptable, but should be cited sparingly, since they don’t carry the same level of credibility as scholarly sources. As discussed above, unvetted sources such as blogs and Wikipedia should be avoided, because the quality of the information they provide is unreliable and often misleading.

To create an annotated bibliography, provide the following information for at least 10 sources relevant to your specific topic, using the format suggested below.

Name of author(s):

Publication date:

Title of book, chapter, or article:

If a chapter or article, title of journal or book where they appear:

Brief description of this work, including main findings and methods ( c 75 words):

Summary of how this work contributes to your project ( c 75 words):

Brief description of the implications of this work ( c 25 words):

Identify any gap or controversy in knowledge this work points up, and how your project could address those problems ( c 50 words):

Exercise 2: Towards an analysis

Develop a short statement ( c 250 words) about the kind of data that would be useful to address your research question, and how you’d analyse it. Some questions to consider in writing this statement include:

• What are the central concepts or variables in your project? Offer a brief definition of each.
• Do any data sources exist on those concepts or variables, or would you need to collect data?
• Of the analytical strategies you could apply to that data, which would be the most appropriate to answer your question? Which would be the most feasible for you? Consider at least two methods, noting their advantages or disadvantages for your project.

Links & books

One of the best texts ever written about planning and executing research comes from a source that might be unexpected: a 60-year-old work on urban planning by a self-trained scholar. The classic book The Death and Life of Great American Cities (1961) by Jane Jacobs (available complete and free of charge via this link ) is worth reading in its entirety just for the pleasure of it. But the final 20 pages – a concluding chapter titled ‘The Kind of Problem a City Is’ – are really about the process of thinking through and investigating a problem. Highly recommended as a window into the craft of research.

Jacobs’s text references an essay on advancing human knowledge by the mathematician Warren Weaver. At the time, Weaver was director of the Rockefeller Foundation, in charge of funding basic research in the natural and medical sciences. Although the essay is titled ‘A Quarter Century in the Natural Sciences’ (1960) and appears at first blush to be merely a summation of one man’s career, it turns out to be something much bigger and more interesting: a meditation on the history of human beings seeking answers to big questions about the world. Weaver goes back to the 17th century to trace the origins of systematic research thinking, with enthusiasm and vivid anecdotes that make the process come alive. The essay is worth reading in its entirety, and is available free of charge via this link .

For those seeking a more in-depth, professional-level discussion of the logic of research design, the political scientist Harvey Starr provides insight in a compact format in the article ‘Cumulation from Proper Specification: Theory, Logic, Research Design, and “Nice” Laws’ (2005). Starr reviews the ‘research triad’, consisting of the interlinked considerations of formulating a question, selecting relevant theories and applying appropriate methods. The full text of the article, published in the scholarly journal Conflict Management and Peace Science , is available, free of charge, via this link .

Finally, the book Getting What You Came For (1992) by Robert Peters is not only an outstanding guide for anyone contemplating graduate school – from the application process onward – but it also includes several excellent chapters on planning and executing research, applicable across a wide variety of subject areas. It was an invaluable resource for me 25 years ago, and it remains in print with good reason; I recommend it to all my students, particularly Chapter 16 (‘The Thesis Topic: Finding It’), Chapter 17 (‘The Thesis Proposal’) and Chapter 18 (‘The Thesis: Writing It’).

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Overview of research process.

The Research Process

Anything you write involves organization and a logical flow of ideas, so understanding the logic of the research process before beginning to write is essential. Simply put, you need to put your writing in the larger context—see the forest before you even attempt to see the trees.

In this brief introductory module, we’ll review the major steps in the research process, conceptualized here as a series of steps within a circle, with each step dependent on the previous one. The circle best depicts the recursive nature of the process; that is, once the process has been completed, the researcher may begin again by refining or expanding on the initial approach, or even pioneering a completely new approach to solving the problem.

Identify a Research Problem

You identify a research problem by first selecting a general topic that’s interesting to you and to the interests and specialties of your research advisor. Once identified, you’ll need to narrow it. For example, if teenage pregnancy is your general topic area, your specific topic could be a comparison of how teenage pregnancy affects young fathers and mothers differently.

Review the Literature

Find out what’s being asked or what’s already been done in the area by doing some exploratory reading. Discuss the topic with your advisor to gain additional insights, explore novel approaches, and begin to develop your research question, purpose statement, and hypothesis(es), if applicable.

Determine Research Question

A good research question is a question worth asking; one that poses a problem worth solving. A good question should:

• Be clear . It must be understandable to you and to others.
• Be researchable . It should be capable of developing into a manageable research design, so data may be collected in relation to it. Extremely abstract terms are unlikely to be suitable.
• Connect with established theory and research . There should be a literature on which you can draw to illuminate how your research question(s) should be approached.
• Be neither too broad nor too narrow. See Appendix A for a brief explanation of the narrowing process and how your research question, purpose statement, and hypothesis(es) are interconnected.

Appendix A Research Questions, Purpose Statement, Hypothesis(es)

Develop Research Methods

Once you’ve finalized your research question, purpose statement, and hypothesis(es), you’ll need to write your research proposal—a detailed management plan for your research project. The proposal is as essential to successful research as an architect’s plans are to the construction of a building.

See Appendix B to view the basic components of a research proposal.

Appendix B Components of a Research Proposal

Collect & Analyze Data

In Practical Research–Planning and Design (2005, 8th Edition), Leedy and Ormrod provide excellent advice for what the researcher does at this stage in the research process. The researcher now

• collects data that potentially relate to the problem,
• arranges the data into a logical organizational structure,
• analyzes and interprets the data to determine their meaning, 
• determines if the data resolve the research problem or not, and
• determines if the data support the hypothesis or not.

Document the Work

Because research reports differ by discipline, the most effective way for you to understand formatting and citations is to examine reports from others in your department or field. The library’s electronic databases provide a wealth of examples illustrating how others in your field document their research.

Communicate Your Research

Talk with your advisor about potential local, regional, or national venues to present your findings. And don’t sell yourself short: Consider publishing your research in related books or journals.

Refine/Expand, Pioneer

Earlier, we emphasized the fact that the research process, rather than being linear, is recursive—the reason we conceptualized the process as a series of steps within a circle. At this stage, you may need to revisit your research problem in the context of your findings. You might also investigate the implications of your work and identify new problems or refine your previous approach.

The process then begins anew . . . and you’ll once again move through the series of steps in the circle.

Continue to Module Two

Appendix C - Key Research Terms

Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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38 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

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• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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

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

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• Indian J Anaesth
• v.60(9); 2016 Sep

How to write a research proposal?

Department of Anaesthesiology, Bangalore Medical College and Research Institute, Bengaluru, Karnataka, India

Devika Rani Duggappa

Writing the proposal of a research work in the present era is a challenging task due to the constantly evolving trends in the qualitative research design and the need to incorporate medical advances into the methodology. The proposal is a detailed plan or ‘blueprint’ for the intended study, and once it is completed, the research project should flow smoothly. Even today, many of the proposals at post-graduate evaluation committees and application proposals for funding are substandard. A search was conducted with keywords such as research proposal, writing proposal and qualitative using search engines, namely, PubMed and Google Scholar, and an attempt has been made to provide broad guidelines for writing a scientifically appropriate research proposal.

INTRODUCTION

A clean, well-thought-out proposal forms the backbone for the research itself and hence becomes the most important step in the process of conduct of research.[ 1 ] The objective of preparing a research proposal would be to obtain approvals from various committees including ethics committee [details under ‘Research methodology II’ section [ Table 1 ] in this issue of IJA) and to request for grants. However, there are very few universally accepted guidelines for preparation of a good quality research proposal. A search was performed with keywords such as research proposal, funding, qualitative and writing proposals using search engines, namely, PubMed, Google Scholar and Scopus.

Five ‘C’s while writing a literature review

BASIC REQUIREMENTS OF A RESEARCH PROPOSAL

A proposal needs to show how your work fits into what is already known about the topic and what new paradigm will it add to the literature, while specifying the question that the research will answer, establishing its significance, and the implications of the answer.[ 2 ] The proposal must be capable of convincing the evaluation committee about the credibility, achievability, practicality and reproducibility (repeatability) of the research design.[ 3 ] Four categories of audience with different expectations may be present in the evaluation committees, namely academic colleagues, policy-makers, practitioners and lay audiences who evaluate the research proposal. Tips for preparation of a good research proposal include; ‘be practical, be persuasive, make broader links, aim for crystal clarity and plan before you write’. A researcher must be balanced, with a realistic understanding of what can be achieved. Being persuasive implies that researcher must be able to convince other researchers, research funding agencies, educational institutions and supervisors that the research is worth getting approval. The aim of the researcher should be clearly stated in simple language that describes the research in a way that non-specialists can comprehend, without use of jargons. The proposal must not only demonstrate that it is based on an intelligent understanding of the existing literature but also show that the writer has thought about the time needed to conduct each stage of the research.[ 4 , 5 ]

CONTENTS OF A RESEARCH PROPOSAL

The contents or formats of a research proposal vary depending on the requirements of evaluation committee and are generally provided by the evaluation committee or the institution.

In general, a cover page should contain the (i) title of the proposal, (ii) name and affiliation of the researcher (principal investigator) and co-investigators, (iii) institutional affiliation (degree of the investigator and the name of institution where the study will be performed), details of contact such as phone numbers, E-mail id's and lines for signatures of investigators.

The main contents of the proposal may be presented under the following headings: (i) introduction, (ii) review of literature, (iii) aims and objectives, (iv) research design and methods, (v) ethical considerations, (vi) budget, (vii) appendices and (viii) citations.[ 4 ]

Introduction

It is also sometimes termed as ‘need for study’ or ‘abstract’. Introduction is an initial pitch of an idea; it sets the scene and puts the research in context.[ 6 ] The introduction should be designed to create interest in the reader about the topic and proposal. It should convey to the reader, what you want to do, what necessitates the study and your passion for the topic.[ 7 ] Some questions that can be used to assess the significance of the study are: (i) Who has an interest in the domain of inquiry? (ii) What do we already know about the topic? (iii) What has not been answered adequately in previous research and practice? (iv) How will this research add to knowledge, practice and policy in this area? Some of the evaluation committees, expect the last two questions, elaborated under a separate heading of ‘background and significance’.[ 8 ] Introduction should also contain the hypothesis behind the research design. If hypothesis cannot be constructed, the line of inquiry to be used in the research must be indicated.

Review of literature

It refers to all sources of scientific evidence pertaining to the topic in interest. In the present era of digitalisation and easy accessibility, there is an enormous amount of relevant data available, making it a challenge for the researcher to include all of it in his/her review.[ 9 ] It is crucial to structure this section intelligently so that the reader can grasp the argument related to your study in relation to that of other researchers, while still demonstrating to your readers that your work is original and innovative. It is preferable to summarise each article in a paragraph, highlighting the details pertinent to the topic of interest. The progression of review can move from the more general to the more focused studies, or a historical progression can be used to develop the story, without making it exhaustive.[ 1 ] Literature should include supporting data, disagreements and controversies. Five ‘C's may be kept in mind while writing a literature review[ 10 ] [ Table 1 ].

Aims and objectives

The research purpose (or goal or aim) gives a broad indication of what the researcher wishes to achieve in the research. The hypothesis to be tested can be the aim of the study. The objectives related to parameters or tools used to achieve the aim are generally categorised as primary and secondary objectives.

Research design and method

The objective here is to convince the reader that the overall research design and methods of analysis will correctly address the research problem and to impress upon the reader that the methodology/sources chosen are appropriate for the specific topic. It should be unmistakably tied to the specific aims of your study.

In this section, the methods and sources used to conduct the research must be discussed, including specific references to sites, databases, key texts or authors that will be indispensable to the project. There should be specific mention about the methodological approaches to be undertaken to gather information, about the techniques to be used to analyse it and about the tests of external validity to which researcher is committed.[ 10 , 11 ]

The components of this section include the following:[ 4 ]

Population and sample

Population refers to all the elements (individuals, objects or substances) that meet certain criteria for inclusion in a given universe,[ 12 ] and sample refers to subset of population which meets the inclusion criteria for enrolment into the study. The inclusion and exclusion criteria should be clearly defined. The details pertaining to sample size are discussed in the article “Sample size calculation: Basic priniciples” published in this issue of IJA.

Data collection

The researcher is expected to give a detailed account of the methodology adopted for collection of data, which include the time frame required for the research. The methodology should be tested for its validity and ensure that, in pursuit of achieving the results, the participant's life is not jeopardised. The author should anticipate and acknowledge any potential barrier and pitfall in carrying out the research design and explain plans to address them, thereby avoiding lacunae due to incomplete data collection. If the researcher is planning to acquire data through interviews or questionnaires, copy of the questions used for the same should be attached as an annexure with the proposal.

Rigor (soundness of the research)

This addresses the strength of the research with respect to its neutrality, consistency and applicability. Rigor must be reflected throughout the proposal.

It refers to the robustness of a research method against bias. The author should convey the measures taken to avoid bias, viz. blinding and randomisation, in an elaborate way, thus ensuring that the result obtained from the adopted method is purely as chance and not influenced by other confounding variables.

Consistency

Consistency considers whether the findings will be consistent if the inquiry was replicated with the same participants and in a similar context. This can be achieved by adopting standard and universally accepted methods and scales.

Applicability

Applicability refers to the degree to which the findings can be applied to different contexts and groups.[ 13 ]

Data analysis

This section deals with the reduction and reconstruction of data and its analysis including sample size calculation. The researcher is expected to explain the steps adopted for coding and sorting the data obtained. Various tests to be used to analyse the data for its robustness, significance should be clearly stated. Author should also mention the names of statistician and suitable software which will be used in due course of data analysis and their contribution to data analysis and sample calculation.[ 9 ]

Ethical considerations

Medical research introduces special moral and ethical problems that are not usually encountered by other researchers during data collection, and hence, the researcher should take special care in ensuring that ethical standards are met. Ethical considerations refer to the protection of the participants' rights (right to self-determination, right to privacy, right to autonomy and confidentiality, right to fair treatment and right to protection from discomfort and harm), obtaining informed consent and the institutional review process (ethical approval). The researcher needs to provide adequate information on each of these aspects.

Informed consent needs to be obtained from the participants (details discussed in further chapters), as well as the research site and the relevant authorities.

When the researcher prepares a research budget, he/she should predict and cost all aspects of the research and then add an additional allowance for unpredictable disasters, delays and rising costs. All items in the budget should be justified.

Appendices are documents that support the proposal and application. The appendices will be specific for each proposal but documents that are usually required include informed consent form, supporting documents, questionnaires, measurement tools and patient information of the study in layman's language.

As with any scholarly research paper, you must cite the sources you used in composing your proposal. Although the words ‘references and bibliography’ are different, they are used interchangeably. It refers to all references cited in the research proposal.

Successful, qualitative research proposals should communicate the researcher's knowledge of the field and method and convey the emergent nature of the qualitative design. The proposal should follow a discernible logic from the introduction to presentation of the appendices.

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Research Process – Steps, Examples and Tips

Table of Contents

Research Process

Definition:

Research Process is a systematic and structured approach that involves the collection, analysis, and interpretation of data or information to answer a specific research question or solve a particular problem.

Research Process Steps

Research Process Steps are as follows:

Identify the Research Question or Problem

This is the first step in the research process. It involves identifying a problem or question that needs to be addressed. The research question should be specific, relevant, and focused on a particular area of interest.

Conduct a Literature Review

Once the research question has been identified, the next step is to conduct a literature review. This involves reviewing existing research and literature on the topic to identify any gaps in knowledge or areas where further research is needed. A literature review helps to provide a theoretical framework for the research and also ensures that the research is not duplicating previous work.

Formulate a Hypothesis or Research Objectives

Based on the research question and literature review, the researcher can formulate a hypothesis or research objectives. A hypothesis is a statement that can be tested to determine its validity, while research objectives are specific goals that the researcher aims to achieve through the research.

Design a Research Plan and Methodology

This step involves designing a research plan and methodology that will enable the researcher to collect and analyze data to test the hypothesis or achieve the research objectives. The research plan should include details on the sample size, data collection methods, and data analysis techniques that will be used.

Collect and Analyze Data

This step involves collecting and analyzing data according to the research plan and methodology. Data can be collected through various methods, including surveys, interviews, observations, or experiments. The data analysis process involves cleaning and organizing the data, applying statistical and analytical techniques to the data, and interpreting the results.

Interpret the Findings and Draw Conclusions

After analyzing the data, the researcher must interpret the findings and draw conclusions. This involves assessing the validity and reliability of the results and determining whether the hypothesis was supported or not. The researcher must also consider any limitations of the research and discuss the implications of the findings.

Communicate the Results

Finally, the researcher must communicate the results of the research through a research report, presentation, or publication. The research report should provide a detailed account of the research process, including the research question, literature review, research methodology, data analysis, findings, and conclusions. The report should also include recommendations for further research in the area.

Review and Revise

The research process is an iterative one, and it is important to review and revise the research plan and methodology as necessary. Researchers should assess the quality of their data and methods, reflect on their findings, and consider areas for improvement.

Ethical Considerations

Throughout the research process, ethical considerations must be taken into account. This includes ensuring that the research design protects the welfare of research participants, obtaining informed consent, maintaining confidentiality and privacy, and avoiding any potential harm to participants or their communities.

Dissemination and Application

The final step in the research process is to disseminate the findings and apply the research to real-world settings. Researchers can share their findings through academic publications, presentations at conferences, or media coverage. The research can be used to inform policy decisions, develop interventions, or improve practice in the relevant field.

Research Process Example

Following is a Research Process Example:

Research Question : What are the effects of a plant-based diet on athletic performance in high school athletes?

Step 1: Background Research Conduct a literature review to gain a better understanding of the existing research on the topic. Read academic articles and research studies related to plant-based diets, athletic performance, and high school athletes.

Step 2: Develop a Hypothesis Based on the literature review, develop a hypothesis that a plant-based diet positively affects athletic performance in high school athletes.

Step 3: Design the Study Design a study to test the hypothesis. Decide on the study population, sample size, and research methods. For this study, you could use a survey to collect data on dietary habits and athletic performance from a sample of high school athletes who follow a plant-based diet and a sample of high school athletes who do not follow a plant-based diet.

Step 4: Collect Data Distribute the survey to the selected sample and collect data on dietary habits and athletic performance.

Step 5: Analyze Data Use statistical analysis to compare the data from the two samples and determine if there is a significant difference in athletic performance between those who follow a plant-based diet and those who do not.

Step 6 : Interpret Results Interpret the results of the analysis in the context of the research question and hypothesis. Discuss any limitations or potential biases in the study design.

Step 7: Draw Conclusions Based on the results, draw conclusions about whether a plant-based diet has a significant effect on athletic performance in high school athletes. If the hypothesis is supported by the data, discuss potential implications and future research directions.

Step 8: Communicate Findings Communicate the findings of the study in a clear and concise manner. Use appropriate language, visuals, and formats to ensure that the findings are understood and valued.

Applications of Research Process

The research process has numerous applications across a wide range of fields and industries. Some examples of applications of the research process include:

• Scientific research: The research process is widely used in scientific research to investigate phenomena in the natural world and develop new theories or technologies. This includes fields such as biology, chemistry, physics, and environmental science.
• Social sciences : The research process is commonly used in social sciences to study human behavior, social structures, and institutions. This includes fields such as sociology, psychology, anthropology, and economics.
• Education: The research process is used in education to study learning processes, curriculum design, and teaching methodologies. This includes research on student achievement, teacher effectiveness, and educational policy.
• Healthcare: The research process is used in healthcare to investigate medical conditions, develop new treatments, and evaluate healthcare interventions. This includes fields such as medicine, nursing, and public health.
• Business and industry : The research process is used in business and industry to study consumer behavior, market trends, and develop new products or services. This includes market research, product development, and customer satisfaction research.
• Government and policy : The research process is used in government and policy to evaluate the effectiveness of policies and programs, and to inform policy decisions. This includes research on social welfare, crime prevention, and environmental policy.

Purpose of Research Process

The purpose of the research process is to systematically and scientifically investigate a problem or question in order to generate new knowledge or solve a problem. The research process enables researchers to:

• Identify gaps in existing knowledge: By conducting a thorough literature review, researchers can identify gaps in existing knowledge and develop research questions that address these gaps.
• Collect and analyze data : The research process provides a structured approach to collecting and analyzing data. Researchers can use a variety of research methods, including surveys, experiments, and interviews, to collect data that is valid and reliable.
• Test hypotheses : The research process allows researchers to test hypotheses and make evidence-based conclusions. Through the systematic analysis of data, researchers can draw conclusions about the relationships between variables and develop new theories or models.
• Solve problems: The research process can be used to solve practical problems and improve real-world outcomes. For example, researchers can develop interventions to address health or social problems, evaluate the effectiveness of policies or programs, and improve organizational processes.
• Generate new knowledge : The research process is a key way to generate new knowledge and advance understanding in a given field. By conducting rigorous and well-designed research, researchers can make significant contributions to their field and help to shape future research.

Tips for Research Process

Here are some tips for the research process:

• Start with a clear research question : A well-defined research question is the foundation of a successful research project. It should be specific, relevant, and achievable within the given time frame and resources.
• Conduct a thorough literature review: A comprehensive literature review will help you to identify gaps in existing knowledge, build on previous research, and avoid duplication. It will also provide a theoretical framework for your research.
• Choose appropriate research methods: Select research methods that are appropriate for your research question, objectives, and sample size. Ensure that your methods are valid, reliable, and ethical.
• Be organized and systematic: Keep detailed notes throughout the research process, including your research plan, methodology, data collection, and analysis. This will help you to stay organized and ensure that you don’t miss any important details.
• Analyze data rigorously: Use appropriate statistical and analytical techniques to analyze your data. Ensure that your analysis is valid, reliable, and transparent.
• I nterpret results carefully : Interpret your results in the context of your research question and objectives. Consider any limitations or potential biases in your research design, and be cautious in drawing conclusions.
• Communicate effectively: Communicate your research findings clearly and effectively to your target audience. Use appropriate language, visuals, and formats to ensure that your findings are understood and valued.
• Collaborate and seek feedback : Collaborate with other researchers, experts, or stakeholders in your field. Seek feedback on your research design, methods, and findings to ensure that they are relevant, meaningful, and impactful.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Aims and Objectives – A Guide for Academic Writing
• Doing a PhD

One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and your reader clarity, with your aims indicating what is to be achieved, and your objectives indicating how it will be achieved.

Introduction

There is no getting away from the importance of the aims and objectives in determining the success of your research project. Unfortunately, however, it is an aspect that many students struggle with, and ultimately end up doing poorly. Given their importance, if you suspect that there is even the smallest possibility that you belong to this group of students, we strongly recommend you read this page in full.

This page describes what research aims and objectives are, how they differ from each other, how to write them correctly, and the common mistakes students make and how to avoid them. An example of a good aim and objectives from a past thesis has also been deconstructed to help your understanding.

What Are Aims and Objectives?

Research aims.

A research aim describes the main goal or the overarching purpose of your research project.

In doing so, it acts as a focal point for your research and provides your readers with clarity as to what your study is all about. Because of this, research aims are almost always located within its own subsection under the introduction section of a research document, regardless of whether it’s a thesis , a dissertation, or a research paper .

A research aim is usually formulated as a broad statement of the main goal of the research and can range in length from a single sentence to a short paragraph. Although the exact format may vary according to preference, they should all describe why your research is needed (i.e. the context), what it sets out to accomplish (the actual aim) and, briefly, how it intends to accomplish it (overview of your objectives).

To give an example, we have extracted the following research aim from a real PhD thesis:

Example of a Research Aim

The role of diametrical cup deformation as a factor to unsatisfactory implant performance has not been widely reported. The aim of this thesis was to gain an understanding of the diametrical deformation behaviour of acetabular cups and shells following impaction into the reamed acetabulum. The influence of a range of factors on deformation was investigated to ascertain if cup and shell deformation may be high enough to potentially contribute to early failure and high wear rates in metal-on-metal implants.

Note: Extracted with permission from thesis titled “T he Impact And Deformation Of Press-Fit Metal Acetabular Components ” produced by Dr H Hothi of previously Queen Mary University of London.

Research Objectives

Where a research aim specifies what your study will answer, research objectives specify how your study will answer it.

They divide your research aim into several smaller parts, each of which represents a key section of your research project. As a result, almost all research objectives take the form of a numbered list, with each item usually receiving its own chapter in a dissertation or thesis.

Following the example of the research aim shared above, here are it’s real research objectives as an example:

Example of a Research Objective

• Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
• Investigate the number, velocity and position of impacts needed to insert a cup.
• Determine the relationship between the size of interference between the cup and cavity and deformation for different cup types.
• Investigate the influence of non-uniform cup support and varying the orientation of the component in the cavity on deformation.
• Examine the influence of errors during reaming of the acetabulum which introduce ovality to the cavity.
• Determine the relationship between changes in the geometry of the component and deformation for different cup designs.
• Develop three dimensional pelvis models with non-uniform bone material properties from a range of patients with varying bone quality.
• Use the key parameters that influence deformation, as identified in the foam models to determine the range of deformations that may occur clinically using the anatomic models and if these deformations are clinically significant.

It’s worth noting that researchers sometimes use research questions instead of research objectives, or in other cases both. From a high-level perspective, research questions and research objectives make the same statements, but just in different formats.

Taking the first three research objectives as an example, they can be restructured into research questions as follows:

Restructuring Research Objectives as Research Questions

• Can finite element models using simplified experimentally validated foam models to represent the acetabulum together with explicit dynamics be used to mimic mallet blows during cup/shell insertion?
• What is the number, velocity and position of impacts needed to insert a cup?
• What is the relationship between the size of interference between the cup and cavity and deformation for different cup types?

Difference Between Aims and Objectives

Hopefully the above explanations make clear the differences between aims and objectives, but to clarify:

• The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved.
• Research aims are relatively broad; research objectives are specific.
• Research aims focus on a project’s long-term outcomes; research objectives focus on its immediate, short-term outcomes.
• A research aim can be written in a single sentence or short paragraph; research objectives should be written as a numbered list.

How to Write Aims and Objectives

Before we discuss how to write a clear set of research aims and objectives, we should make it clear that there is no single way they must be written. Each researcher will approach their aims and objectives slightly differently, and often your supervisor will influence the formulation of yours on the basis of their own preferences.

Regardless, there are some basic principles that you should observe for good practice; these principles are described below.

Your aim should be made up of three parts that answer the below questions:

• Why is this research required?
• What is this research about?
• How are you going to do it?

The easiest way to achieve this would be to address each question in its own sentence, although it does not matter whether you combine them or write multiple sentences for each, the key is to address each one.

The first question, why , provides context to your research project, the second question, what , describes the aim of your research, and the last question, how , acts as an introduction to your objectives which will immediately follow.

Scroll through the image set below to see the ‘why, what and how’ associated with our research aim example.

Note: Your research aims need not be limited to one. Some individuals per to define one broad ‘overarching aim’ of a project and then adopt two or three specific research aims for their thesis or dissertation. Remember, however, that in order for your assessors to consider your research project complete, you will need to prove you have fulfilled all of the aims you set out to achieve. Therefore, while having more than one research aim is not necessarily disadvantageous, consider whether a single overarching one will do.

Research Objectives

Each of your research objectives should be SMART :

• Specific – is there any ambiguity in the action you are going to undertake, or is it focused and well-defined?
• Measurable – how will you measure progress and determine when you have achieved the action?
• Achievable – do you have the support, resources and facilities required to carry out the action?
• Relevant – is the action essential to the achievement of your research aim?
• Timebound – can you realistically complete the action in the available time alongside your other research tasks?

In addition to being SMART, your research objectives should start with a verb that helps communicate your intent. Common research verbs include:

Table of Research Verbs to Use in Aims and Objectives

Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.

To bring all this together, let’s compare the first research objective in the previous example with the above guidance:

Checking Research Objective Example Against Recommended Approach

Research Objective:

1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.

Checking Against Recommended Approach:

Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).

Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.

Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.

Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.

Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.

Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.

Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.

Mistakes in Writing Research Aims and Objectives

1. making your research aim too broad.

Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .

Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.

2. Making Your Research Objectives Too Ambitious

Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.

3. Formulating Repetitive Research Objectives

Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.

Fortunately, this oversight can be easily avoided by using SMART objectives.

Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.

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How to Do a Research Project?

• Updated on  
• Mar 21, 2023

To begin your thesis, the first step is to find a suitable topic per your interests and selecting a good topic is only the beginning of it all. Carrying out a research project, scholars aim to provide answers to the research questions through an in-depth study of the topic. Many universities require their students to submit a research project as part of their course, especially at the master’s level of study and doctoral degrees . So if you are wondering how to do a research project, this blog is for you! Read on to know the different steps to follow to write an excellent research project.

This Blog Includes:

Select a topic of your interest, find a supervisor, invest time in secondary research, locate and analyse primary sources, start drafting your paper, put proper citations, proofread the research project, tips & tricks for doing a research project.

The first step towards doing a research project is choosing a relevant topic. Many students find it difficult to select a topic for their research project since they want to research a popular but less researched topic, something which is difficult to find. Browse through the recent studies and works in your chosen field and then find the best one that you are inclined towards. It is essential to have an innate interest and passion for the topic you have chosen since it will help you work out through all the challenges and hurdles during your research.

An expert supervisor plays an essential role in mentoring the student throughout the journey of the research project. Supervisors are generally professors and scholars in a university who guide students throughout their research addressing their queries regarding the topic along with familiarising them with different research methodologies and processes. So choosing a research knowledgeable supervisor in your field of study is very important in the development of your research project.

You need to have a good grasp of the contemporary work and studies done related to your topic to find out the gaps that you can address in your research project. For this, you need to invest your time in reading the published papers related to your topic diligently. Reading a huge volume of secondary sources will add to your knowledge about the topic and also present you with different primary sources which you can look for.

Primary sources are the backbone of your research. You can look for primary sources in different archives or libraries depending on your discipline, and you can also find out about primary sources and their location from papers already published on your chosen and related topics. Make sure to find and analyse the primary sources in detail and note down your findings.

Use your notes while drafting your paper. Remember that you need to make any changes through multiple drafts before you come up with a paper worthy of commendation. Make sure to send your drafts to your supervisor for feedback and corrections and doubts, and work on the suggestions they provide on your paper.

Citations are an important part of your research project. Not putting proper citations can mark your project as plagiarized. Since universities take plagiarism quite seriously, it is better to know the proper way of citation as specified by the research project format provided by your university. You can also get in touch with your research mentor or supervisor and ask for their advice on citing the sources in your project. 

You should not skip proofreading your paper after completing it. Spelling or grammar mistakes are inevitable while working on a lengthy research project and therefore it is necessary to check it multiple times for such errors. Re-reading your draft can not only help you in making it better by fixing the errors, but also you may identify any gaps or issues in the paper that you can rectify. 

Now that you are familiar with the process of doing a research project, here are some more tips and tricks that you might find useful:

• Create a schedule mapping down every step of your research and adhere to the same.
• Research your topic online and offline to know about the different sources you can explore.
• List down all the sources, both primary and secondary that you consulted for your project as this will help you in adding the citations. 
• Always keep notes to write new ideas and findings of your research as you can easily use them later to add to your project.
• Stay in touch with your supervisor throughout the course of your research project as they can help you efficiently tackle all the challenges and problems and make your thesis as comprehensive as possible.

Ans. These types of questions you can ask in a research project What exactly do you want to study? What is your research question or questions? Why is it worth studying? What is the purpose or significance of your study? Does the proposed study have practical significance?

Ans. There should be no uncertainty in the research topic. Clarity also requires that the research topic be goal-oriented and that it establish the entire research methodology. Half of the formula for good research is a clearly defined and well-phrased research topic.

Ans. Research is the process of looking for solutions to a certain issue. It can be carried out to comprehend a phenomenon, observe behaviour, or test a theory, among other things. Systematic research is carried out, adding to the corpus of knowledge and bolstering numerous theories.

Thus, carrying out a research project is not everyone’s cup of tea as it will need meticulous studying and preparation to finally accomplish it as you have hypothesized. Planning to pursue a research degree? Our Study Abroad experts are here to help you find the best course and university along with sorting out the admission process to ensure that you send a winning application. Sign up for a free session with us today!

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Home » Education » What is the Difference Between Research and Project

What is the Difference Between Research and Project

The main difference between research and project is that research is the systematic investigation and study of materials and sources to establish facts and reach new conclusions, while a project is a specific and finite activity that gives a measurable and observable result under preset requirements.

Both research and projects use a systematic approach. We also sometimes use the term research project to refer to research studies.

Key Areas Covered

1.  What is Research       – Definition, Features 2. What is a Project      – Definition, Features 3.  Difference Between Research and Project      – Comparison of Key Differences

Research, Project

What is Research

Research is a careful study a researcher conducts using a systematic approach and scientific methods. A research study typically involves several components: abstract, introduction ,  literature review ,  research design, and method , results and analysis, conclusion, bibliography. Researchers usually begin a formal research study with a hypothesis; then, they test this hypothesis rigorously. They also explore and analyze the literature already available on their research subject. This allows them to study the research subject from multiple perspectives, acknowledging different problems that need to be solved.

There are different types of research, the main two categories being quantitative research and qualitative research. Depending on their research method and design, we can also categorize research as descriptive research, exploratory research, longitudinal research, cross-sectional research, etc.

Furthermore, research should always be objective or unbiased. Moreover, if the research involves participants, for example, in surveys or interviews, the researcher should always make sure to obtain their written consent first.

What is a Project

A project is a collaborative or individual enterprise that is carefully planned to achieve a particular aim. We can also describe it as a specific and finite activity that gives a measurable and observable result under preset requirements. This result can be tangible or intangible; for example, product, service, competitive advantage, etc. A project generally involves a series of connected tasks planned for execution over a fixed period of time and within certain limitations like quality and cost. The Project Management Body of Knowledge (PMBOK) defines a project as a “temporary endeavor with a beginning and an end, and it must be used to create a unique product, service or result.”

Difference Between Research and Project

Research is a careful study conducted using a systematic approach and scientific methods, whereas a project is a collaborative or individual enterprise that is carefully planned to achieve a particular aim.

Research studies are mainly carried out in academia, while projects can be seen in a variety of contexts, including businesses.

The main aim of the research is to seek or revise facts, theories, or principles, while the main aim of a project is to achieve a tangible or intangible result; for example, product, service, competitive advantage, etc.

The main difference between research and project is that research is the systematic investigation and study of materials and sources to establish facts and reach new conclusions, while the project is a specific and finite activity that gives a measurable and observable result under preset requirements.

1. “ What Is a Project? – Definition, Lifecycle and Key Characteristics .” Your Guide to Project Management Best Practices .

Image Courtesy:

1. “ Research ” by Nick Youngson (CC BY-SA 3.0) via The Blue Diamond Gallery 2. “ Project-group-team-feedback ” (CC0) via Pixabay

About the Author: Hasa

Hasanthi is a seasoned content writer and editor with over 8 years of experience. Armed with a BA degree in English and a knack for digital marketing, she explores her passions for literature, history, culture, and food through her engaging and informative writing.

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A New Chapter for Irish Historians’ ‘Saddest Book’

A globe-spanning research project has turned the catalog of a public archive destroyed in Ireland’s civil war into a model for reconstruction.

By Ed O’Loughlin

Reporting from Dublin

In the first pitched battle of the civil war that shaped a newly independent Ireland, seven centuries of history burned.

On June 30, 1922, forces for and against an accommodation with Britain, Ireland’s former colonial ruler, had been fighting for three days around Dublin’s main court complex. The national Public Record Office was part of the complex, and that day it was caught in a colossal explosion . The blast and the resulting fire destroyed state secrets, church records, property deeds, tax receipts, legal documents, financial data, census returns and much more, dating back to the Middle Ages.

“It was a catastrophe,” said Peter Crooks, a medieval historian at Trinity College Dublin. “This happened just after the First World War, when all over Europe new states like Ireland were emerging from old empires. They were all trying to recover and celebrate their own histories and cultures, and now Ireland had just lost the heart of its own.”

But perhaps it was not lost forever. Over the past seven years, a team of historians, librarians and computer experts based at Trinity has located duplicates for a quarter of a million pages of these lost records in forgotten volumes housed at far-flung libraries and archives, including several in the United States. The team then creates digital copies of any documents that it finds for inclusion in the Virtual Record Treasury of Ireland , an online reconstruction of the archive. Still a work in progress, the project says its website has had more than two million visits in less than two years.

Funded by the Irish government as part of its commemorations of a century of independence, the Virtual Treasury relies in part on modern technologies — virtual imaging, online networks, artificial intelligence language models and the growing digital indexes of archives around the world — but also on dusty printed catalogs and old-school human contacts. Key to the enterprise has been a book, “A Guide to the Records Deposited in the Public Record Office of Ireland,” published three years before the fire by the office’s head archivist, Herbert Wood.

“For a long time, Wood’s catalog was known to Irish historians as the saddest book in the world, because it only showed what was lost in the fire,” Dr. Crooks said. “But now it has become the basis for our model to recreate the national archive. There were 4,500 series of records listed in Wood’s book, and we went out to look for as many of them as we could find.”

A major partner in this hunt was the National Archives in Britain, to which centuries of Irish government records — notably tax receipts — had been sent in duplicate. The Public Record Office of Northern Ireland, which remains part of the United Kingdom, has also been a major partner, contributing records from the centuries before Ireland was partitioned in 1921.

A considerable haul of documents has also been uncovered in the United States. The Library of Congress, for example, dug up dozens of volumes of lost debates from Ireland’s 18th-century Parliament. According to David Brown, who leads the Virtual Treasury’s trawl through domestic and overseas archives, before this trove of political history came into Congress’s possession, one previous owner had tried to sell it as fuel. Serendipity has often played a role in such U.S. discoveries, he said.

“You would have old family records stored away in some gentleman’s library, and he’d move to the colonies, and take the books with him,” Dr. Brown said. “Or else heirs would eventually sell the old library off to collectors, and eventually an American university or library might buy the collection, maybe because they wanted something important in it, and they took everything else that came with it. Archivists may not always know what they have, but they never throw anything out.”

The Huntington Library in California, and libraries of the universities of Kansas, Chicago, Notre Dame, Yale and Harvard are among around a dozen U.S. organizations to respond positively to the hopeful request from the Irish: “Do you have anything there that might be of interest to us?” And in the process of hunting down material that is already on its radar, the Virtual Treasury team is also uncovering, and incorporating, unexpected treasures.

One is a previously unnoticed 1595 letter shown to Dr. Brown late last year while he was visiting Yale’s Lewis Walpole Library to view some other material. In it, Sir Ralph Lane — a founder and survivor of the infamous lost colony of Roanoke, off North Carolina, which had vanished in the decade before this letter was written — petitions Queen Elizabeth I to order the conquest of Ulster, then a Gaelic stronghold in the north of English-ruled Ireland.

Dr. Brown, a specialist in early modern Atlantic history, said the letter — long overlooked because it was bound in a volume with much later documents — showed the close connection between England’s colonial conquests in North America and Ireland, both in the personalities involved and their motivation. The letter suggests conquering Ulster primarily so that the English could seize the inhabitants’ land, and it proposes paying for the war by looting the Ulster chiefs’ cattle. The area was ultimately conquered and colonized in 1609, six years after Lane’s death.

“For the Elizabethan adventurers, colonialism was a branch of piracy. All they wanted was land,” Dr. Brown said. “Roanoke hadn’t worked out for Lane, and Elizabeth had just granted Sir Walter Raleigh 10,000 acres of land in Munster,” in the south of Ireland. “So Lane thought, if Raleigh got 10,000 acres in Munster, why can’t I have 10,000 acres in Ulster?”

Another contribution to the project could be seen in contemporary Northern Ireland, at the Public Record Office in Belfast. The head of conservation, Sarah Graham, was restoring and preserving a collection of records and letters kept by Archbishop John Swayne, who led the church in Ireland in the 15th century. Watching her at work was Lynn Kilgallon, research fellow in medieval history for the Virtual Treasury. Once preserved, its pages will be digitized and added to Dublin’s online archive.

“If you don’t understand the words in a book, it becomes just an object,” Ms. Graham said. “You need someone to read it — medievalists like Lynn here, to bring it to life.”

You do not necessarily need to be a specialist to read the documents in the Virtual Treasury, however. New artificial intelligence models developed for the project allow archivists to turn ancient handwriting into searchable digital text, with modern translations.

The site went online in June 2022, the 100th anniversary of the records office fire, and is aiming for 100 million searchable words by 2025, a target it says it is three-quarters of the way to reaching. Eventually, it hopes to recover 50 to 90 percent of records from some priority areas, such as censuses from before and after Ireland’s Great Famine in the mid-19th century, which are of particular value to historians, and to people of Irish descent tracing their roots. More than half of the details of the first nationwide census of Ireland, a religious head count in 1766, have been retrieved and published.

“Cultural loss is sadly a very prominent theme in the world right now, and I don’t think there is an example like this, where there’s been so much international cooperation in the reconstruction of a lost archive,” Dr. Crooks said. “It shows that the collective culture of many countries can be brought together to achieve a goal. Borders are fluid.”

• NIH Grants & Funding
• Blog Policies

NIH Extramural Nexus

Preparing for Funding Opportunities Using the Simplified Review Framework 

Last October, we announced that NIH was implementing a simplified review framework for most research project grants (RPGs). As a reminder, in the simplified review framework NIH aims to better facilitate the mission of scientific peer review – identification of the strongest, highest-impact research. The changes are intended to:

• Enable peer reviewers to better focus on answering the key questions necessary to assess the scientific and technical merit of proposed research projects: Can and should the proposed research project be conducted?
• Mitigate the effect of reputational bias by refocusing the evaluation of investigator/environment to within the context of the proposed research.
• Reduce reviewer burden by shifting policy compliance activities to NIH staff.

Today, NIH released a Guide Notice ( NOT-OD-24-085) to provide an update on our implementation plans for the simplified review framework. The Notice provides guidance to applicants on navigating new and updated funding opportunities expected to be published between now and January 2025.

Prior to January, the community will notice that many funding opportunities for applicable activity codes are being reissued to include the simplified review framework language in Section V. Application Review Information. Active funding opportunities with due dates on or after January 25, 2025, will be expired early and most will be reissued to reflect the simplified review framework. Funding opportunities with pending expiration dates prior to January 25, 2025, including impacted parent announcements, may be extended with additional due dates until they can be reissued with the simplified review framework.

Applicants should pay close attention to the Related Notices and Key Dates sections of funding opportunities to ensure they are applying to the right opportunity for their target due date. To stay informed of notices and funding opportunity reissuances, we encourage you to monitor the NIH Guide for Grants and Contracts, where any changes will be published (you can also subscribe to a weekly digest of new NIH Guide posts).

Please note that although there are no application changes associated with the simplified review framework, NIH is transitioning to updated application forms (FORMS-I) to support other initiatives. FORMS-I will be required for due dates on or after January 25, 2025 (See these Notices for FORMS-I and Changes Coming in 2025 , as well as this blog on 2025 changes ). New and reissued funding opportunities using the simplified review framework may initially be posted without an application forms package. In these cases, application forms will be added 30 – 60 days prior to the first application due date. Applicants can begin drafting their application attachments before FORMS-I becomes available using the current FORMS-H instructions and adjust if needed once FORMS-I instructions are available.

We encourage applicants to review today’s Guide Notice for more details on NIH’s implementation plans, as well as register for a public webinar on April 17 from 1 – 2pm EDT to learn more about navigating these changes and have an opportunity to get their questions answered.

We have provided various resources on our Simplified Review Framework web page to help the community understand the changes associated with the simplified review framework, including a summary statement mockup, critique template , drop-in slides , and a recording of the November online briefing. Applicants and reviewers can expect to receive plenty of guidance and support as we near January 2025.

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NASA is celebrating the Sun during the Heliophysics Big Year , which extends through the end of 2024. You can get involved to help us learn more about our star and its influence on our planet. With exciting experiments happening during the total solar eclipse that will cross North America on April 8, to widespread investigations going on throughout the year, keep reading to find a project that's right for you.

What Is Citizen Science (Also Called Participatory Science)?

NASA defines citizen science as “a form of open collaboration in which individuals or organizations participate in the scientific process in various ways” from collecting and analyzing data to making discoveries and solving problems. ”Citizen” here refers to citizens of planet Earth, and these projects are open to everyone, regardless of country of birth or legal citizenship status.

NASA sponsors citizen science projects across all five areas of research that it pursues: Earth science, planetary science, astrophysics, biological and physical sciences, and heliophysics. And yes, there are a few projects that are focused on the April 8 solar eclipse!

What You Can Do

Depending which project you join, you might:

• Observe and record in pictures or words natural phenomena like clouds, animal noises, or a solar eclipse.
• Learn how to recognize or classify patterns in data or pictures of a comet or solar jet.
• Learn how to build and use scientific equipment like radio telescopes or ham radios.

Your contribution may be a large or small piece of the picture, but what you do as part of a NASA citizen science project is essential to answering the research question or need that the project addresses. And while you’re contributing to science, you might also develop new skills and make friends. You can read about some project participants – and what motivates them – in these profiles .

The Projects

NASA citizen science projects related to the April 8, 2024, eclipse and solar science are presented in four groups below. You can see all NASA citizen science projects on this website .

Use the tables below to find the project for you! A few notes:

• “ Minimum time required ” refers to how much time it would take you to get up to speed from the start.
• “ Where ” refers to where you need to be in order to participate.

Are you an educator looking for ways to involve your formal or informal students in eclipse-related science? Check out this companion blog post for some tips for educators.

Eclipse Projects That Need You on April 8!

Quick-start projects that require no special equipment, more demanding projects that require special equipment, heliophysics projects that you can do anytime, quick-start projects, no special equipment required, advanced participation.

Many NASA citizen science projects start out with a straightforward, structured task, but that doesn’t have to be where your contributions end. Some projects offer webinars or host regular video conference calls where enthusiastic volunteers can learn about and participate in the work that comes after data collection or classification. Hundreds of volunteers have become involved in deep ways. Over 450 volunteers have even been recognized for their contributions by being named as co-authors of scientific papers, which are the formal way in which scientists announce new discoveries and ideas.

By Sarah Kirn Citizen Science Strategist, NASA, at the Gulf of Maine Research Institute

Related Terms

• 2024 Solar Eclipse
• Citizen Science
• Skywatching
• Solar Eclipses

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• Published: 26 March 2024

Australian human-induced native forest regeneration carbon offset projects have limited impact on changes in woody vegetation cover and carbon removals

• Andrew Macintosh   ORCID: orcid.org/0000-0001-5700-7105 1 ,
• Don Butler   ORCID: orcid.org/0000-0002-6019-1078 1 ,
• Pablo Larraondo 2 ,
• Megan C. Evans 3 ,
• Dean Ansell 1 ,
• Marie Waschka   ORCID: orcid.org/0009-0001-4574-4834 1 ,
• Rod Fensham   ORCID: orcid.org/0000-0003-3658-5867 4 ,
• David Eldridge   ORCID: orcid.org/0000-0002-2191-486X 5 ,
• David Lindenmayer   ORCID: orcid.org/0000-0002-4766-4088 1 ,
• Philip Gibbons 1 &
• Paul Summerfield 1  

Communications Earth & Environment volume  5 , Article number:  149 ( 2024 ) Cite this article

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• Climate-change mitigation
• Environmental studies

Carbon offsets are a widely used climate policy instrument that can reduce mitigation costs and generate important environmental and social co-benefits. However, they can increase emissions if they lack integrity. We analysed the performance of one of the world’s largest nature-based offset types: human-induced regeneration projects under Australia’s carbon offset scheme. The projects are supposed to involve the human-induced regeneration of permanent even-aged native forests through changes in land management. We analysed 182 projects and found limited evidence of regeneration in credited areas. Changes in woody vegetation cover within the areas that have been credited also largely mirror changes in adjacent comparison areas, outside the projects, suggesting the observable changes are predominantly attributable to factors other than the project activities. The results add to the growing literature highlighting the practical limitations of offsets and the potential for offset schemes to credit abatement that is non-existent, non-additional and potentially impermanent.

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Introduction

Carbon offsets are a widely used climate policy instrument that are considered integral to government and corporate decarbonisation plans 1 , 2 , 3 . Under offset schemes, projects that reduce emissions relative to counterfactual baselines receive credits, which can be used by others to offset their emissions. The benefits of offsets include that they can reduce mitigation costs, generate important environmental and social co-benefits, and reduce political resistance to carbon pricing by lowering compliance costs for facilities with carbon liabilities 4 , 5 , 6 .

Whether the environmental and economic benefits of offsets materialise depends on the environmental integrity of the credits. If the credits lack integrity, offsets can facilitate increases in emissions and thereby work against greenhouse gas mitigation objectives. Carbon offsets are considered to have environmental integrity when there is high confidence they represent real, additional and permanent abatement 1 , 7 , 8 , 9 . In this context, ‘realness’ refers to the extent to which credits reflect carbon removals or emission reductions that are directly attributable to the project activities 1 , 9 , 10 . ‘Additionality’ requires that the credited removals or emission reductions would not have occurred without the incentive provided by the offset scheme 11 . Permanence relates exclusively to sequestration projects and requires credited removals to persist in relevant carbon stocks like vegetation and soils 9 , 11 , 12 .

Research on the integrity of carbon offsets has found material issues with the realness, additionality and permanence of credited abatement, raising questions about their effectiveness in assisting decarbonisation 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 . Similar issues have arisen with biodiversity offsets 26 , 27 .

Carbon offsets have been a central feature of climate policy in Australia for two decades. Under a provincial mandatory carbon trading scheme (the world’s first) that operated in New South Wales and the Australian Capital Territory between 2003 and 2012 covered facilities were allowed to use offsets from designated project types to meet their emission reduction obligations 28 . In late 2011, a national carbon offset scheme was introduced, which was relied upon as the main Australian Government mitigation policy between 2014 and 2022 29 . The object of the national offset scheme is to incentivise offset projects that help Australia meet its international greenhouse gas mitigation obligations 29 . Each credit issued under the scheme is supposed to represent abatement equivalent to one tonne of CO 2 .

The national offset scheme is now linked to a national mandatory carbon pricing instrument; the Safeguard Mechanism. As with the previous provincial carbon trading scheme, facilities covered by the Safeguard Mechanism can use credits issued under the offset scheme to meet their emission reduction obligations. There are no restrictions on the extent to which covered facilities can rely on offset credits to meet their obligations. The only relevant restriction is that the credits must come from projects registered under the national scheme.

The most popular project type under the national offset scheme is human-induced regeneration of permanent even-aged native forests (HIR) 30 . HIR projects received 37 million credits to June 2023, almost 30% of the issuances under the scheme 31 . The projects cover almost 42 million hectares, an area larger than Japan 31 . As of 30 June 2023, they were the world’s fifth largest nature-based offset type by credit issuances, and the largest when projects involving avoided emissions are excluded (Supplementary Fig. S 1 ).

Under the applicable rules (found in the ‘HIR method’), HIR projects should involve the human-induced regeneration of permanent even-aged native forests across the entirety of the areas that are credited (‘credited areas’) (Fig.  1 ) 30 . The projects do not involve planting or direct seeding. Regeneration must be induced by the project activities from ‘the germination of in situ seed, or the growth of in situ seedlings, rootstock or lignotuber’ 30 . The project activities can include reducing grazing pressure from livestock and feral animals, management of non-native plants, and cessation of clearing of native plant regrowth 30 , 32 , 33 .

In the baseline scenario (yellow ribbon), clearing, grazing and/or weeds suppress regeneration of woody plants, ensuring the credited area has predominantly non-woody cover throughout the projection period. In the project scenario (black-red ribbon), the credited area initially has predominantly non-woody cover due to the effects of clearing, grazing and/or weeds. The project involves the removal or mitigation of these suppressors, which leads to even-aged forest regeneration. The credited area should transition from predominantly non-woody cover to predominantly sparse woody cover, and then to forest cover, and retain forest cover throughout the permanence period. In the regions where HIR projects are located, credited areas should have forest cover when tree and debris biomass reaches ~7.2–11 dry metric tonnes (dmt) per hectare.

Sequestration in HIR projects is not directly measured, it is estimated as the product of the size of the credited areas and sequestration per unit area, which is modelled using the Australian Government’s Full Carbon Accounting Model 34 . The model uses a simple tree yield formula to estimate above-ground biomass per hectare in regenerating forests 35 , 36 , 37 . It assumes credited areas start with little woody biomass and grow towards their maximum woody biomass potential under native vegetation. Maximum above-ground woody biomass potential ( M ) is modelled spatially using a range of biophysical parameters calibrated against measurements of intact native vegetation 38 . The most recent calibration of the tree yield formula estimates above-ground biomass in regeneration under average climate conditions after a years to be M.e (−23.81/a) (Supplementary Fig. S 2 ) 37 .

The above-ground biomass estimates from the model’s tree yield formula are partitioned into biomass and debris pools via standardised allocation ratios (e.g. root-shoot), and turnover and decomposition rates, to calculate carbon accumulation in live above- and below-ground biomass and debris 39 . The model includes a soil carbon module but it is not used for HIR projects; the projects are credited for increases only in live biomass and dead organic matter.

Most HIR projects are claiming to regenerate native forests by reducing grazing pressure from livestock and/or feral herbivores in arid and semi-arid ‘rangeland’ areas (<350 mm average annual rainfall) that have never been comprehensively cleared of native vegetation (Fig.  2 , Supplementary Fig. S 3 ). The location of the projects in uncleared rangelands (where there is often limited and highly variable rainfall) raises questions about the capacity of the credited areas to permanently support material additional woody biomass, and the realness, additionality and permanence of credited abatement 40 , 41 , 42 , 43 .

The 182 projects analysed in this paper in light green. Source: Australian Government. Area-based Emissions Reduction Fund (ERF) projects. Commonwealth of Australia, Canberra (2023); Department of Climate Change, Energy, the Environment and Water. National Vegetation Information System (NVIS) data products, version 6. Commonwealth of Australia, Canberra (2023); New South Wales Government. NSW State Vegetation Type Map. NSW Government, Sydney (2023); Geoscience Australia. GEODATA COAST 100K 2004. Commonwealth of Australia, Canberra (2023).

Plant growth is constrained by the availability of resources (water, nutrients, light etc.), which limit woody biomass potential under native vegetation 44 , 45 , 46 . In most of Australia’s uncleared rangelands, the key limiting resource is water and its availability depends on variable rainfall, which fluctuates over time-scales from months to decades 47 . Variations in rainfall and water availability drive changes in plant growth, including woody biomass 47 , 48 .

The primary way grazing could affect forest cover in uncleared areas is by impeding woody plant recruitment during recovery after periods of drought or fires, where cover has been lost through tree death 49 , 50 , 51 . For grazing to prevent the regeneration of forests in these circumstances, grazing intensity after a mortality event would need to be sufficiently intense to prevent recruitment and then be maintained over multiple decades to suppress subsequent recruitment. Grazing in Australia’s uncleared rangelands has been shown to have local, short-term effects on regeneration, but assessments over larger spatial and temporal scales show that grazing has not generally reduced tree cover 52 , and that the influence of grazing alone on woody plants is minimal compared to the effect of variable rainfall 53 , 54 , 55 .

This is illustrated through the well-documented increase in tree cover that occurred across substantial parts of Australia’s grazed eastern rangelands through the twentieth century, particularly following a series of La Niña events from the 1950s that brought above-average rainfall 56 , 57 , 58 , 59 . Similarly, more than 300,000 ha of secondary native forest is re-cleared annually in Australia in areas previously cleared for grazing 39 , 60 , 61 , typically on cycles of around 8–30 years 53 , 60 . This re-clearing would not be necessary if grazing was suppressing regeneration of native forests.

Because grazing does not have a material negative influence on tree cover in Australia’s rangelands, HIR projects are unlikely to regenerate permanent native forest through grazing control in uncleared areas. In some cases, reduced grazing could increase tree cover but, generally, any management-induced increases are likely to be relatively small and often short-lived (since droughts can remove excess biomass accumulated during wet times) 62 .

The modelling approach used to calculate abatement for HIR projects compounds the resulting integrity risks. Projects could be credited for forest regeneration that has not occurred or that does not persist. The HIR method also does not control for the over-riding impacts of rainfall on regeneration in the rangelands, creating a risk that projects will be credited for increases in tree cover that are mainly attributable to natural variations in rainfall rather than the project activities (i.e. non-additional) 42 , 43 .

Here we present the results of an analysis of HIR projects conducted using the Australian Government’s National Forest and Sparse Woody (NFSW) dataset (Version 7.0) 63 . The dataset provides Landsat-derived estimates of the spatial extent of three classes of woody vegetation cover across Australia over the period 1988 to 2022. The data are a near-annual time series in which 25 m grid cells are classified as either non-woody, sparse woody (sub-forest woody cover where crown cover is between 5–19%) or forest (woody vegetation ≥2 m tall with crown cover >20% over at least 0.2 ha).

The object of our analysis was to assess the performance of HIR projects using two metrics:

the extent of the increase in forest cover and ‘woody cover’ (areas with either forest or sparse woody cover) in the credited areas of HIR projects; and

the extent to which changes in forest and woody cover in the credited areas of HIR projects have mirrored trends in paired controls for each project, comprising 3 km wide buffer areas outside the project boundaries that exclude areas in other HIR projects (‘comparison areas’).

Metric (1) provides a proxy measure of the likely increases in woody biomass in the credited areas of HIR projects. When combined with data on credit issuances, it serves as an indicator of over-crediting risk (i.e. whether sequestered CO 2 is likely to be materially less than credited sequestration). Metric (2) provides a measure of the extent to which changes in forest and woody cover in the credited areas of HIR projects are additional to what would otherwise have occurred (i.e. attributable to the project activities or other factors such as rainfall variability). Together, metrics (1) and (2) provide a basis on which to draw conclusions about the extent to which HIR projects have helped Australia meet its international mitigation obligations, consistent with the scheme’s objectives 29 .

Published estimates of the accuracy of the classifications of pixels to forest, sparse or non-woody in the NFSW dataset suggest accuracy of 95% or more for forest and non-woody classes where no change is indicated, with lower confidence for classification of sparse woody pixels (~66%) 39 , 64 . Error rates are likely to be somewhat higher for classification of changes between years, but there is also no reason to expect biases in error between credited areas and comparison areas used in our analysis. Notably, the Australian Government relies on the NFSW dataset to estimate land sector emissions and removals in its greenhouse accounts 39 . Greenhouse gas outcomes from changes in tree cover in Australia’s rangelands are not accounted for in Australia’s greenhouse accounts if they are not detected in the NFSW dataset. The fact that the Australian Government relies on the NFSW dataset to track reforestation and revegetation for greenhouse accounting purposes justifies its use to assess outcomes from HIR projects.

All HIR projects whose credited area location data were published as of 22 June 2023 and that were registered in or before 2018 (providing at least four data points in the NFSW time series post registration) were included in the analysis, except where they were completely surrounded by other projects or the published spatial files were corrupt 65 , 66 . The projects ( n  = 182) included in the analysis covered a combined area of 9.5 M ha, with their credited areas covering 3.4 M ha (Fig.  2 , Table  1 ). The projects in the sample were registered over the period 11 December 2013 to 30 November 2018, with most (75%) registered in 2015, 2016 and 2017 (Supplementary Fig. S 4 ).

Change in forest and sparse woody cover

The analysed projects received 27.4 million credits over the period from 11 December 2013 (when the first HIR project was registered) to 30 June 2022, suggesting a substantial proportion of the credited areas should have transitioned from non-woody cover to either sparse woody or forest cover because of the human-induced forest regeneration 31 . This has not occurred.

Almost 50% of the credited areas had sparse woody or forest cover when the projects were registered (median woody cover 46.5% (sd 22.5%), median forest cover 12.7% (sd 12.9%)). This is problematic as it indicates that most projects are seeking to regenerate permanent even-aged native forests on land that contained material amounts of pre-existing woody vegetation. Competition from the pre-existing woody vegetation is likely to limit additional forest regeneration.

Consistent with this, there was relatively little change in woody cover in the credited areas over the study period. Almost 80% of projects ( n  = 143) experienced negative or negligible change in woody cover in the credited areas over the period from project registration to 2022 (Table  1 , see methods for definitions of negative, negligible and positive woody cover change). Despite the absence of positive woody cover change, these 143 projects received 22.9 million credits over the period 31 .

At an aggregate level, woody cover increased by a mere 0.8% (28,155 ha) across the 3.4 M ha credited area: forest cover increased by 3.6% (124,852 ha); and sparse woody cover decreased by −2.8% (96,697 ha) (Supplementary Fig.  5 ). By comparison, gains and losses in sparse woody cover alone across Australia averaged 2.2 M ha year −1 and −2.1 M ha year −1 respectively over the period 2013–14 to 2020–21 39 .

The modest gain in woody cover in the credited areas after project registration continued a trend that started in the late 2000s, before the HIR method was developed (Fig.  3 ). The increase in woody cover in the credited areas that pre-dates the method is difficult to reconcile with the premise of the projects: that grazing was previously suppressing regeneration and that, without the projects, it would not occur (Fig.  1 ).

Source: Department of Climate Change, Energy, the Environment and Water. National Forest and Sparse Woody Vegetation Data (Version 7.0 - 2022 Release) (2023); Clean Energy Regulator. Emissions Reduction Fund project register. Commonwealth of Australia, Canberra (2023). The green bar shows when most (75%) of the HIR projects in the sample were registered (2015–2017).

There is a relationship between biomass in forest regeneration (above- and below-ground live biomass, litter, and dead wood) and crown cover in the forest systems where HIR projects are located 67 . This relationship suggests that forest cover (>20% crown cover) should be achieved when tree and debris biomass reaches 7.2 to 11 tonnes of dry matter per hectare, equivalent to 13.2–20.2 tCO 2 ha −1   67 . To 30 June 2022, estimated average credited sequestration in the 182 projects in the sample was 12.9 tCO 2 ha −1 (median 11.5 tCO 2 ha −1 , sd 8.9 tCO 2 ha −1 ) 31 . The estimated credited sequestration in 75 of these projects (41%) was ≥13.2 tCO 2 ha −1 (mean 21.6 tCO 2 ha −1 , median 20.8 tCO 2 ha −1 , sd 6.5 tCO 2 ha −1 ) 31 . This suggests that, based on the credits that have been issued, a substantial proportion of the total credited area should have already attained forest cover. However, for the 75 projects with credited sequestration ≥13.2 tCO 2 ha −1 , only 21% (188,880 ha) of the 898,680 ha total credited area had forest cover in 2022, and this was only a 1.8% (16,530 ha) increase relative to forest cover when the projects were registered (Fig.  4 , Supplementary Fig. S 6 ). There is a large apparent disparity between the credited and observed sequestration in the projects.

Note that under the HIR method, forest cover at project registration should be at or near 0% and reach 100% within ~10–15 years of when regeneration is modelled to have commenced. Source: Department of Climate Change, Energy, the Environment and Water. National Forest and Sparse Woody Vegetation Data (Version 7.0 − 2022 Release) (2023); Clean Energy Regulator. Emissions Reduction Fund project register. Commonwealth of Australia, Canberra (2023).

Change in woody cover relative to trends in external comparison areas

Changes in forest and sparse woody cover in credited areas were far more strongly correlated with changes in cover in comparison areas than to the timing of project registration. Table  2 presents standardised coefficients from hierarchical regression models predicting annual cover changes in credited areas as a function of cover changes in comparison areas and a variable indicating whether the year of observation was before or after project registration. The coefficients for comparison areas are many times larger than those for project registration. Project registration did have a statistically significant effect for forest cover change, but not for woody cover change. While statistically significant, the identified effect of project registration on forest cover was small, being equivalent to ~0.5% per year following project registration.

The extent to which changes in forest and sparse woody cover within credited areas have mirrored changes in comparison areas suggests the limited changes observed within the credited areas are largely non-additional. As shown in Figs.  3 , 5 (Supplementary Table  S1 , Fig. S 7 ), there was a strong correlation between forest and sparse woody cover changes in the credited areas and comparison areas over the period before projects were first registered. Post registration, the correlation was maintained, suggesting factors other than the project activities (most likely rainfall variability) have been the dominant influence on woody cover changes.

Dashed lines indicate 1:1. Source: Department of Climate Change, Energy, the Environment and Water. National Forest and Sparse Woody Vegetation Data (Version 7.0 − 2022 Release) (2023); Clean Energy Regulator. Emissions Reduction Fund project register. Commonwealth of Australia, Canberra (2023).

It is important to note that, while changes in forest and sparse woody cover relative to external comparison areas provide a useful indicator of the impact of project activities, they should not be construed as the only indicator of project effectiveness. HIR projects are credited on the basis that even-aged native forest is regenerating across the entirety of the credited area and that, within ~10–15 years of when regeneration is modelled to have commenced, all of the credited area will have forest cover. The modest gain in woody cover observed within credited areas, and small effect of project registration on forest cover change, suggest this is unlikely to occur.

Reforestation, avoided forest conversion and improved forest management have the potential to generate substantial amounts of low-cost abatement, while providing important biodiversity and other co-benefits 68 , 69 . Carbon offset schemes can incentivise these activities and reduce the economic cost of decarbonisation. However, the benefits of these nature-based offsets are contingent on offset projects being credited only for real, additional and permanent increases in relevant carbon stocks. Our findings suggest that HIR projects in Australia’s uncleared rangelands do not meet this requirement.

There was only a small positive overall increase in forest cover (3.6%), and negligible increase in combined sparse woody and forest cover (0.8%), across the combined 3.4 Mha credited area, where the 182 assessed projects are supposedly regenerating permanent even-aged native forests. Despite the absence of material increases in woody cover, the projects received 27.4 million credits over the study period 31 ; 22.9 million credits were issued to projects whose woody cover declined or was largely stagnant.

Given the levels of credited sequestration, the changes in woody cover should be readily apparent, beyond the levels of classification error in the underlying data, which is likely to be in the order of 5–10% 39 , 64 . A substantial proportion of the credited areas should have already attained forest cover and, at the very least, there should have been large increases in sparse woody cover that go well beyond changes observed in the external comparison areas. Neither has occurred.

Trends in forest and woody cover in the credited areas largely mirrored fluctuations in comparison areas, both before and after project registration. Regression models of changes in forest and woody cover in the credited areas identified far smaller effects for project registration than for cover changes in comparison areas. The results suggest the changes in forest and woody cover in the credited areas were largely non-additional, presumably because they reflect rainfall variability rather than responses to project activities 47 , 48 , 52 , 53 , 54 , 55 .

The small increases in forest and woody cover, and the small effect of project registration relative to variation in cover in the comparison areas, suggest HIR projects have done little to help Australia meet its international mitigation obligations, both in absolute terms and relative to credit issuances 39 . The underperformance is accentuated by the fact that, to date, the Australian Government alone has spent ~AU$300 million in purchasing credits from HIR projects and is contractually committed to purchase a further ~AU$1.2 billion 70 , 71 .

The results add to the growing literature highlighting the practical limitations of offsets and the potential for offset schemes to credit abatement that is non-existent, non-additional, and potentially impermanent 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 . They also serve as a reminder of why offsets are considered a high-risk policy instrument 10 , 72 , 73 .

Offsets are high-risk because of two factors: likelihood of error and the consequences when they occur. There is a high probability of error in the design and administration of the rules and processes that are intended to ensure credits are issued only for real, additional and permanent abatement. This is due to multiple factors, including the uncertainties associated with determining counterfactual baselines (what would net emissions have been within the project boundaries in the absence of the incentive provided by the scheme?) and the errors inherent in the measurement of emissions and removals from often dispersed sources and sinks 72 , 74 , 75 . Other pertinent factors that contribute to the likelihood of errors include the difficulty in overcoming adverse selection when seeking to exclude non-additional projects 74 , 76 , and the persistent tension within offset schemes to lower the stringency of measurement protocols to reduce transaction costs and thereby promote participation 77 .

Regardless of the cause, where errors occur and result in the issuance of low integrity credits, their use can lead to worse climate outcomes. This is because offsets are a permission to pollute, issued on the premise that the offset project has abated one tonne of emissions. Hence, when the credited abatement is not real, additional and permanent, offsets can enable an increase in emissions from a polluter with no offsetting emission reduction elsewhere.

The high-risk nature of offsets is why they are deprioritised in the ‘mitigation hierarchy’ that is often used in biodiversity-related regulatory approval processes 78 ; they are supposed to be a last resort reserved for when all other viable avoidance and mitigation options have been exhausted. The risk also provides the basis for the principle that offsets credits should only be used where there is high confidence the credits are likely to represent real, additional and permanent abatement 11 , 29 , 72 , 73 .

The root cause of the integrity issues with HIR projects is that credited areas have been allowed to be located in areas where native vegetation has not previously been comprehensively cleared, where the capacity to permanently increase forest carbon stocks is generally likely to be small, and in semi-arid and arid rangeland areas where there is substantial natural variability, which makes it difficult to separate the impacts of project activities from rainfall-induced changes 11 . The integrity problems with HIR projects have been compounded by the use of a modelled approach to the estimation of sequestration and allowing the model to be used in circumstances it was not calibrated for (i.e. to estimate regeneration on sites that contain material amounts of pre-existing woody vegetation) 30 , 37 .

Despite the risks, and the evidence of their limitations, carbon offsets are seen as indispensable by many policymakers; as evidenced through the Paris Agreement’s Article 6.4 Mechanism, the Carbon Offsetting and Reduction Scheme for International Aviation and other similar initiatives 8 , 79 . With the commitment to their continued use, the challenge for policymakers is to demonstrate that offset schemes can have integrity.

The experience with HIR projects provides two generalisable lessons. First, that sequestration-related offsets are inappropriate for use in situations where the relevant carbon stocks are likely to be at or near their maximum sustainable potentials and where natural variability in the stocks is materially larger than the likely effects of management change. Second, care needs to be taken where models are used to estimate carbon stocks to ensure they are applied appropriately and with due regard to the need for conservativism.

Australia’s experience with HIR projects also highlights the importance of transparency 11 , 80 . From January 2013 until June 2023, no data on the location of credited areas were published under the scheme, which shielded projects from scrutiny. At the time of writing, proponents were still not required to publish offset reports or audit reports, or information on how they have modelled sequestration. Proponents are also not required to undertake any direct measurements of biomass in HIR projects and, where biomass measurements are voluntarily undertaken, they are not required to be published. Effective offset schemes require constant scrutiny and critical assessment, including from third parties. This cannot be provided without the public release of all information that is necessary for the proper evaluation of the performance and integrity of offset projects 11 .

HIR projects included in sample

As at 10 December 2023, there were 469 registered HIR projects. Up until April 2023, it was unlawful for the Clean Energy Regulator to publish data on the location of credited areas. Following changes to the law in April 2023, the Regulator first published credited area location data for 223 HIR projects on 6 June 2023.

To be included in the sample for the analysis, projects had to have published credited area location data and at least 4 years of data in the NFSW time series post the year they were registered ( n  = 191). This was to ensure there was a valid basis for determining the response of woody vegetation to the project activities. In addition, projects were excluded from the sample if they were completely surrounded by other projects ( n  = 4). These projects were excluded because it was not possible to designate a valid comparison area in accordance with the method described below. A further five projects were removed because the spatial data on their credited areas were not useable.

Credited area location data

The Clean Energy Regulator publishes credited area data in vector format. To facilitate our analysis, the dataset was converted into raster format. Due to differences between the map projections of each dataset, credited area data were rasterised using 10-m resolution vs the 25-m of the NFSW dataset. This increase in resolution allowed for improving the accuracy of the masking operation around partially intercepted pixels. To perform this conversion we used the standard gdal_rasterize command from the GDAL library to generate a raster preserving the original projection of the vector dataset 81 .

Carbon credit issuances

Carbon credit issuance data were obtained from the ERF Project Register published by the Clean Energy Regulator 31 . The Register contains data on total issuances and total issuances by Australian financial year (1 July–30 June). Credit issuances were included in a calendar year only where they were issued prior to 30 June of the same year. This ensured conservative estimates of credit issuances for the purposes of making comparisons of project performance.

Relative size of HIR projects

The relative size of HIR projects was analysed using data from the registries of seven offset schemes for the period 2013–2023: the ACCU scheme; Clean Development Mechanism; Verified Carbon Standard (VCS, or Verra); Gold Standard; American Carbon Registry; Climate Action Reserve; and Plan Vivo. Data on credit issuances by project type are provided in Supplementary Fig. S 1 .

External comparison areas

HIR projects have an outer project boundary, which is typically the boundary of the property on which it is located. The credited areas lie within the project boundary. The comparison areas, which are used as paired controls for each project, comprised 3 km wide buffer areas around the outside of project boundaries, excluding areas that intersected with other HIR projects.

The use of these 3 km wide comparison areas is likely to overstate the relative effects of the projects on woody cover. This is due to the way the credited areas are delineated. Under the HIR method, credited areas must contain only areas that have the potential to achieve forest cover (woody vegetation ≥2 m tall with crown cover >20% at 0.2 ha scale). They are also not allowed to have forest cover at commencement. This results in credited areas having exclusions inside and around them, even when the areas are subject to the same project activities (i.e. grazing control) and lie within the same fenced areas. In contrast, the comparison areas are comprised of all land within the 3 km wide buffers, excluding other HIR projects.

The characteristics of the credited areas and comparison areas means that, where the same changes in tree cover occur, there is likely to be a greater proportionate increase (or decrease) in cover in the credited areas relative to the comparison areas. This approach was adopted to ensure conservative outputs and because of the practical difficulty associated with delineating areas that share the same characteristics as the areas included in the credited areas.

National forest and sparse woody dataset analysis

Changes in woody cover (forest and sparse woody cover) in the credited areas and comparison areas were analysed using the Australian Government’s NFSW dataset (Version 7.0) 63 . The dataset provides Landsat-derived estimates of the extents of three classes of woody vegetation cover across Australia over the period 1988–2022. The data are a near-annual time series in which 25 m grid cells are classified as either non-woody, sparse woody (sub-forest woody cover where crown cover is between 5–19%) or forest (woody vegetation ≥2 m tall with crown cover >20% over at least 0.2 ha).

The analysis was undertaken using Terrak.io, a geospatial analytics platform developed by Haizea Analytics. This platform builds upon Cloud infrastructure and can provide on-demand analytics on large satellite and climate datasets through an API. Users can rely on Terrak.io to generate maps or zonal statistics showing temporal trends for large numbers or areas, defined using custom vector polygons. This infrastructure was used to calculate zonal statistics on the frequency of forest, sparse woody, and non-woody cover pixels within each project’s credited areas and comparison areas.

Classifying project changes in forest and sparse woody cover since registration

To assess whether woody cover in the credited areas of each project (i.e. the proportion of pixels with forest or sparse woody cover) experienced negative, negligible and positive change since the projects were registered (i.e. the results presented in Table  1 ), simple linear regression models were fit for each project to the time-series of the forest and sparse woody percentages in their credited areas from the year of registration forward, with the percentage of each cover class as the response variable and year as the only independent variable.

Projects were classified as having increased woody cover if the slope of either of the fits for forest or sparse cover was greater than 0.25% per year, provided the slope for the fit to the other woody cover class (i.e. sparse if the forest fit has a positive slope >0.25%) was greater than −0.05% per year.

Tree cover was deemed to be negligible if the sum of the slopes from the linear models fit to the sparse and forest cover were greater than −0.25% per year, and they did not meet the ‘increased’ requirements.

Projects whose tree cover did not meet either the ‘increased’ or ‘negligible’ requirements were deemed to have decreasing cover.

Comparing cover trends in carbon estimation areas and adjacent comparison areas

Changes in woody cover were calculated from the time-series of NFSW data from 1988 to 2022 63 . The percentage of pixels within the credited areas and comparison area classified as forest in each year was subtracted from the percentage of forest pixels in the preceding time point in the time series. Most time steps were annual, including all from 2004 on, but some spanned two or more years (’89–’91, ’92–’95, ’95–’98, ’98–2000, 2000–’02 and ’02–’04).

Hierarchical linear regression models, built using the lme4 package 82 in R (4.3.0, R Core Team 2022) 83 , were used to model cover changes within credited areas as a function of cover changes in comparison areas (indicating responses to broader environmental drivers) and a binary variable indicating whether the interval over which cover changes occurred was before or after the year of project registration. Models were built to include random effects accommodating the numerous observations made for each project by fitting separate intercepts for projects, as well as coefficients for the two fixed-effects (cover change in comparison areas and project registration) and a higher level intercept (Eq. ( 1 ), in the syntax of lmer: project_cover_change ~ comparison_area_change + registration + (1|project_ID)). Statistical significance of predictors was assessed via the anova function, by comparing the full model to models with each predictor removed in turn.

The cover change variables for credited areas (response) and comparison areas (fixed effect 1) were standardised (centred and scaled) by subtracting the variable mean from each observation, and dividing by its standard deviation (Supplementary Table  S2 ). The binary variable for project registration was not standardised (pre-registration = 0, post-registration = 1). This means that the coefficient for the comparison area predictor in each model is a measure of effect size, indicating the expected magnitude of change in the response credited area cover variable (in standard deviations) for a one standard deviation change in comparison area cover. The coefficient for project registration ( β 2 ) indicates the effect of project registration on year-to-year cover change in credited areas, again in units of standard deviation for the response variable, i.e. cover change in credited areas.

The strength of portfolio-scale correlations between the extent of each cover class (forest, sparse or woody) in the combined credited areas and in comparison areas, across the 182 projects (i.e. variables plotted in Fig.  3 ), was assessed using Pearson’s correlation coefficient from the cor.test function in R 83 . Correlation coefficients were also calculated for annual cover changes inside credited areas and in adjacent comparison areas, for both forest and woody cover classes (Supplementary Table  S1 ).

Estimating credited sequestration

Credited sequestration was estimated using data from the ERF Project Register 31 . Total credit issuances to each project to 30 June 2022 were adjusted to account for relevant discounts (5% risk of reversal buffer and a 20% permanence period discount for projects with 25 year permanence periods). A uniform and conservative 0.5% deduction was made to account for fossil fuel use, based on Australian Government analysis of a sample of HIR projects that found average fuel use emissions were less than 0.02% of total project abatement 84 . The resulting estimates were converted from CO 2 to C using the atomic mass ratio, 44/12.

The approach used to compare credited sequestration to forest cover is conservative. Ideally, the comparison of forest cover to sequestration would be undertaken using the modelled sequestration for each project. This would ensure the estimates account for the fact that projects have been allowed to commence modelling regeneration before the projects were registered. Due to this, the amount of modelled sequestration across the projects is greater than the credited sequestration, accentuating the extent of relative underperformance. It was not possible to analyse the modelled sequestration because of transparency issues. Estimates of modelled sequestration are not published and no verified data are published on the modelling parameters used in HIR projects. At the time of writing, information about the choice of model calibration and modelling commencement dates had been published by the proponents of 63 HIR projects. However, the published data were incomplete (e.g. modelling points are not published) and unverified, rendering them unusable for these purposes.

Australian Government expenditure on carbon credits from HIR projects

We estimate that, to 4 December 2023, the Australian Government had spent ~AU$300 million in purchasing credits from HIR projects and was contractually committed to purchase a further ~AU$1.2 billion. The Australian Government does not publish data on carbon credit purchases or contracted credit prices by project. Due to this, our estimate of Australian Government expenditure on credits from HIR projects was based on the number of credits sold by each project to the Australian Government under Emissions Reduction Fund contracts, up until 4 December 2023 70 . Where contracts had multiple projects, the recorded credit sales were assumed to be sourced evenly from the contracted projects. Sale prices were assigned to each project based on the published weighted average carbon credit purchase price from the Emissions Reduction Fund auction at which the relevant project was contracted (range AU$10.23-AU$17.35) 71 . The estimate of the value of the remaining HIR credits contracted by the Australian Government was based on the number of credits originally contracted, less those delivered and the number of credits released or lapsed from delivery obligations 70 . The contracted prices assigned to each project were again based on the weighted average carbon credit purchase price from the Emissions Reduction Fund auction at which the relevant project was contracted 70 , 71 .

Data availability

The datasets generated during and/or analysed during the current study, including a summary of individual project data, are available on Figshare at: https://figshare.com/ [DOI: 10.6084/m9.figshare.25199786 and 10.6084/m9.figshare.25199789].

Code availability

Details of the hierarchical linear regression models developed in the study are provided above.

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A.M., D.B., P.L. and M.E. initiated and designed the research, with input from D.A. and M.W. A.M. led the drafting, with input from all authors, except P.S. D.B. designed and performed the statistical analysis. P.L. processed and analysed the woody cover data. A.M. compiled and analysed data on projects and crediting. R.F., D.E., D.L. and P.G. provided input on drafting and the literature review. P.S. designed and illustrated Fig.  1 .

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The authors declare the following competing interests. A.M. is a non-executive director of Paraway Pastoral Company Ltd. Paraway Pastoral Company Ltd has offset projects under Australia’s offset scheme. Paraway Pastoral Company Ltd does not have any human-induced regeneration projects. A.M., D.B., D.A. and M.W. advise public and private entities on environmental markets and Australia’s carbon offset scheme, including on the design of carbon offset methods. The remaining authors have no competing interests.

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Macintosh, A., Butler, D., Larraondo, P. et al. Australian human-induced native forest regeneration carbon offset projects have limited impact on changes in woody vegetation cover and carbon removals. Commun Earth Environ 5 , 149 (2024). https://doi.org/10.1038/s43247-024-01313-x

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“I hope that phytoremediation can become a feasible option for removing PFAS from soil,” said project researcher Sara Nason, Ph.D., of the Connecticut Agricultural Experiment Station (CAES). “Current options are very limited overall, and phytoremediation on its own is not effective enough to produce the results many industries are seeking.”

The start of a collaborative project

In 2019, members of the Mi’kmaq Nation, an Indigenous tribe of about 1,500 people, and Upland Grassroots contacted CAES, a state government research organization. The goal was to kick-start a project using fiber hemp plants to remove PFAS from contaminated water and soil on land belonging to the Aroostook Band of the Mi’kmaq Nation. The land was formerly the site of Loring Air Force Base, which had for decades been a firefighting testing area. Firefighting foams usually contain PFAS because of the chemicals’ ability to suppress fire.

The collaborators chose fiber hemp because it grows quickly, takes up large amounts of water, and is usually not grazed by livestock. In addition, parts of the plant that are less suitable for PFAS storage, such as stems, may be used by tribal members to make products such as bricks and rope.

However, hemp plants are not able to remove all PFAS from soil and water because some of the molecules stay stuck in the soil. To address this challenge, CAES teamed up with researchers at Yale University and the University of Minnesota to study how nanomaterials can improve the ability of plants to absorb PFAS. SRP began to fund this research in 2021.

Nurturing community-based research

“I am proud that what started as a small pilot project working with Mi’kmaq community members and local organizations has grown into a large, multi-institution effort that still has ties to the community where the work started,” Nason said. “Having CAES staff funded by our SRP project has enabled us to be a stronger participant in remediation work with the tribe, while also pursuing new technologies to improve our current remediation options.”

While the nanomaterials are not yet ready for deployment in the field, work at the former Loring site has continued. CAES scientists work closely with Upland Grassroots and tribal leaders to develop plans for soil sample collection and hemp planting that allows the community to conduct most of the field research, while producing scientifically useful data.

“Listening to the needs of community researchers and designing projects around their interests and capabilities has been key to our success,” Nason said. “I hope that our work with Upland Grassroots and the Mi’kmaq Nation can be an example to other scientists doing community-based research.”

Citations : Nason SL, Thomas S, Stanley C, Silliboy S, Blumenthal M, Zhang W, Liang Y, Jones JP, Zuverza-Mena N, White JC, Haynes CL, Vasiliou V, Timko MP, Berger BW. 2024. A comprehensive trial on PFAS remediation: hemp phytoextraction and PFAS degradation in harvested plants . Env Sci Adv 3(2):304-313.

Lewis RE, Huang CH, White JC, Haynes CL. 2023. Using 19F NMR to investigate cationic carbon dot association with per- and polyfluoroalkyl substances (PFAS) . ACS Nanosci Au 3(5):408-417.

(Mali Velasco is a research and communication specialist for MDB Inc., a contractor for the NIEHS Division of Extramural Research and Training.)

Modified Iron Particles Could Improve Bioremediation of PFAS

This September 2023 NIEHS video describes how SRP-funded researchers at Princeton University are using nanoparticles to improve the ability of a type of bacteria to break down PFAS in the environment. (1:18)

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ARPA-H Project Awardees

The ARPA-H Mission Office Innovative Solutions Openings (ISOs) and Open Broad Agency Announcement (BAA) provide funding for research that aims to improve health outcomes across a wide range of patient populations, communities, diseases, and conditions. These projects focus on transformative ideas for health research breakthroughs or technological advancements.

Awards made from the ISOs and Open BAA are generally in the form of contracts. Exact award amounts are dependent upon meeting milestones, typical of the ARPA-H process .

ARPA-H is pleased to announce the following awardees:

Open BAA awardees

Ct-neuro: cell therapies for neuroinflammation and neurodegeneration.

The overarching goal of the CT-NEURO program is to develop strategies for targeting therapeutic immune cells to the central nervous system using biological logic gates. Three specific objectives include 1) development of an immune cell-based, disease-agnostic platform that targets therapeutic payload to the brain, 2) demonstrating that this platform can be expanded to generate engineered cells to selectively target other organs and tissue types, and 3) employing this platform to deliver therapeutic payloads to treat diverse neurological conditions such as brain tumors, neuroinflammatory diseases, demyelination, and neurodegeneration. 

• Date Awarded April 1, 2024
• Amount Awarded Up to $35M
• Prime Awardee Institution University of California San Francisco
• Principal Investigator Wendell A. Lim, Ph.D.
• Location San Francisco, CA

MATRIX: ML/AI-Aided Therapeutic Repurposing In eXtended uses

Millions of individuals worldwide suffer from diseases for which there are no available treatments. While the Food & Drug Administration (FDA) has approved roughly 3,000 drugs to address a corresponding number of diseases, there remain an additional 9,000 diseases without a single approved therapy. Given that numerous diseases share common underlying mechanisms of action, and individual drugs can target multiple mechanisms, the existing pool of 3,000 FDA-approved drugs holds the promise of addressing the 9,000 diseases that currently lack therapeutic options. 

MATRIX (Machine Learning/Artificial Intelligence-Enabled Therapeutic Repurposing in eXtended uses) aims to develop computational methodologies for identifying the FDA-approved drugs most likely to treat diseases with inadequate treatment options, and to identify and validate top candidates for drug repurposing using these methodologies. 

This program aims to develop the first comprehensive scoring system that queries the world’s biomedical knowledge of “all drugs vs all diseases” to predict the efficacy for every drug to treat every human disease. The resulting information on the pharmaco-phenome will be made available open-source, allowing researchers to view the probability of efficacy across the entire landscape of FDA-approved drugs and human diseases.  

• Date Awarded February 23, 2024
• Amount Awarded up to $48 million
• Prime Awardee Institution Every Cure 
• Principal Investigator David Fajgenbaum, M.D., MBA
• Location Philadelphia, PA

PIC-OCT: Enabling Technologies for Photonic Chips-based Optical Coherence Tomography

As the US population ages, and with the increasing prevalence of obesity and related chronic health problems that affect the eye, debilitating eye disease poses a substantial medical and cost concern. With early diagnosis and appropriate management, > 90% of severe vision loss may be prevented. Imaging modalities that excel at screening, early diagnosis, staging, and tracking treatment response, and can do so safely, quickly, and inexpensively are highly valued by ophthalmologists. Optical Coherence Tomography (OCT)-based technologies have revolutionized eye disease diagnosis, along with demonstrating clinical potential across a myriad of other areas – including cardiology, urology, dentistry, and more. However, high cost and complex assembly of current systems limit their widespread adoption and hamper their broad implementation. This project will develop next-generation OCT systems based on photonic integrated circuits (PICs) and custom-designed electronic integrated circuits (ICs). By leveraging the latest advances in the nanofabrication of photonic and electronic ICs, acquisition speeds 50 times faster than the current standard will be achieved alongside immense decreases in the OCT system footprint (i.e., readily employable at walk-in clinics) and unprecedented reductions in manufacturing cost that will facilitate community-wide accessibility. Pediatric patients will especially benefit from the shorter scan times. Altogether, by enhancing patient treatment and adherence to repetitive testing, PIC-OCT will substantially reduce vision loss and its medical and societal costs. 

• Date Awarded February 7, 2024
• Amount Awarded Up to $20M
• Prime Awardee Institution Washington University in St. Louis
• Principal Investigator Chao Zhou, Ph.D.
• Location St. Louis, MO

REO: REvolutionizing the Oral route: delivery of electroceuticals and mRNA therapeutics for transforming health

Metabolic diseases are on the rise, with roughly 40% of Americans being obese and 10% diabetic. Treating these chronic diseases currently requires daily injections, surgery, or expensive drugs. Recent innovations, such as continuous glucose monitoring and insulin pumps, have greatly lowered the burden on patients but can still be painful to use and can limit activity.  

The MIT team aims to revolutionize these treatments by developing two orally delivered pill-sized devices. The first device will sense its location in the gastrointestinal tract and then inject mRNA into the tract lining that provides long term treatment for diabetes or obesity. The second device will temporarily reside in the GI tract, electrically stimulating it to release hormones associated with hunger and satiety. The devices will be remotely controlled and wirelessly powered for enhanced efficacy and safety.  

Although the proof-of-concept effort focuses on metabolic diseases, the designs could be applied to deliver therapies for many clinical conditions. Critically, this innovative delivery of therapies could provide treatment access to socioeconomically disadvantaged classes, who are most affected by metabolic diseases. The self-administration of capsule-sized devices could also reduce healthcare worker involvement, the need for hospitalizations, and healthcare costs associated with the need to store, stabilize, and medications. 

• Date Awarded January 31, 2024
• Amount Awarded Up to $65.6M
• Prime Awardee Institution Massachusetts Institute of Technology
• Principal Investigator C. Giovanni Traverso, M.D., Ph.D., MBBCH. 
• Location Cambridge, MA

DARTS: Defeating Antibiotic Resistance through Transformative Solutions

Bacterial infections remain a leading cause of death worldwide and will likely become an even greater health care challenge. The number of antibiotic-resistant pathogens grows daily while the discovery of new antibiotics lags dangerously. When a patient arrives at a hospital with a bloodstream infection, every minute matters but choosing the correct antibiotic is also crucial to success. Current methods of bacterial identification and antibiotic susceptibility are not up to the challenge. Testing can take hours, if not days, resulting in longer hospital stays, major complications, and higher mortality rates. Defeating Antibiotic Resistance through Transformative Solutions (DARTS) aims to address these challenges by advancing an ultra-high throughput imaging and culturing platform that can continuously track and test billions of bacteria one by one. If successful, the system will serve as a rapid platform for the discovery and development of new antibiotics. It will also be adapted for patient use as a microbial diagnostic that can rapidly identify the pathogen and the appropriate antibiotic to prescribe, enhancing the stewardship of antibiotics that remain effective. Such a rapid microbial diagnostic would enhance health outcomes, not just for the tested patient, but for everyone, as the diagnostic would greatly reduce the misuse of the antibiotics that remain effective.

• Date Awarded September 26, 2023
• Amount Awarded Up to $104M
• Prime Awardee Institution Harvard Medical School
• Principal Investigator Johan Paulsson, Ph.D.
• Location Boston, MA

CDTR: Stem Cell-Derived Thymus Rejuvenation

Thymmune Therapeutics’ Stem Cell-Derived Thymus Rejuvenation (CDTR) project aims to restore immune and endocrine function in patients lacking a functional thymus by using engineered stem cell-derived treatment. The thymus is an organ responsible for supporting normal immune cell development. The project is divided into two phases. The goal of the first phase is to use a combination of chemical and genetic factors to make best-in-class human induced pluripotent stem thymic epithelial cells (iPS-TECs) with capacity for supporting T lymphocytes (white blood cells) development in vitro. In the second phase, Thymmune plans to develop protocols for transplantation and long-term engraftment of iPS-TEC in animal models to achieve effective immune function, demonstrating a path towards using iPS-TEC to ultimately treat patients lacking functional thymus. Overall, Thymmune’s disease-agnostic approach to combat thymus dysfunction by bolstering immune responses against pathogens, cancer, and vaccines presents a potentially revolutionary means to reboot immunity. Thymmune has the potential to both rescue patients lacking a functional thymus from morbidity and mortality and addresses a crucial unmet need to rejuvenate immunity in the aging population.

• Date Awarded September 25, 2023
• Amount Awarded Up to $37M
• Prime Awardee Institution Thymmune Therapeutics
• Principal Investigator Bing Lim, M.D., Ph.D. and Stan Wang, M.D., Ph.D.

CODA: Mapping the Cancer and Organ Degradome Atlas to Unlock Synthetic Biomarkers for Multi-Cancer Early Detection

For most tumor types, there are currently no effective diagnostic tests for detecting most cancers at the earliest stages, when tumors are still localized and most responsive to treatment. Ongoing efforts that focus on native tumor-shed biomarkers face significant challenges, as these markers are often found in vanishingly small quantities in blood or other fluids. The CODA (Cancer and Organ Degradome Atlas) platform uses cutting-edge synthetic biology and cell engineering technologies to catalog cellular profiles unique to diseased cancer cells and leverages them to build bioengineered sensors that can be deployed inside the body to hunt for malignant cells. These biosensors use unique metabolic changes in tumor cells to drive the release of synthetic biomarkers that can reach high enough levels in biofluids to enable earlier cancer detection. This technology has the potential to produce a highly precise, accurate, and cost-effective test for multi-cancer early detection (MCED) that can identify common cancers earlier, when treatment can be most effective, and streamline clinical intervention when tumors are still small.

• Amount Awarded $49.5M
• Prime Awardee Institution Georgia Institute of Technology
• Principal Investigator Gabe A. Kwong, Ph.D.
• Location Atlanta, GA

HEART: Health Enabling Advancements through Regenerative Tissue Printing

Over 3 million patients in the United States need tissue transplants, with more than 100,000 patients on the national transplant waiting list. Unfortunately, many of these people die while waiting for a donated organ. The Health Enabling Advancements through Regenerative Tissue Printing (HEART) project proposes to advance multiple technologies, including the optimization of purity and scalability of human cells, improved 3D printing technology and speed, advances in computational modeling, and novel approaches to organ maturation and implantation. The end result is the 3D printing of a human heart in one hour. This ambitious project has the potential to revolutionize the fields of human tissue and organ printing through large advances across multiple technologies. HEART could create a world where a doctor could 3D print an organ for their patient instead of waiting for a donor, effectively ending waitlists for transplants. This advance would improve the lifespan and quality of life for many Americans and provide broader patient access across all communities.

• Amount Awarded Up to $26M
• Prime Awardee Institution Stanford University
• Principal Investigator Mark Skylar-Scott, Ph.D.
• Location Palo Alto, CA

SPIKEs: Programmable Scalable Therapeutics for Immune-directed Cancer-killing

Cancer immunotherapy, which harnesses the body’s own immune system to attack tumor cells, holds great potential. However, this therapy is currently hampered by very high costs, long and involved preparation processes, and frequent inefficacy against solid tumors. The University of Missouri’s Synthetic Programmable bacteria for Immune-directed Killing in tumor Environments (SPIKEs) project aims to develop a new class of living cancer immunotherapy that that can effectively address these limitations. The SPIKEs platform utilizes genetically programmable bacteria designed to sense tumor-associated metabolites as an exquisitely precise homing mechanism and then deliver therapeutic payloads that activate immune-directed killing of solid tumor cells without the need for the long and costly processes currently used. Bacterial therapeutics carrying programmable genetic circuitry that allows safe tissue targeting, on-demand activation and clearance, and multiple therapeutic functions – including immune cell recruitment, activation, and targeting – represent an innovative, scalable, cost-effective, and accessible treatment modality for cancer.

• Amount Awarded Up to $19.9M
• Prime Awardee Institution University of Missouri
• Principal Investigator Paul de Figueiredo, Ph.D.
• Location Columbia, MO

THOR: Targeted Hybrid Oncotherapeutic Regulation

Peritoneal cancers, such as ovarian and primary colorectal cancers, cause more than one third of all cancer deaths in the United States. An effective treatment could save 187,000 American lives each year, yet unfortunately many solid tumors, such as those found in peritoneal cancers, often do not respond effectively to current immunotherapies. A revolutionary approach in the fight against peritoneal cancers has emerged, thanks to the convergence of several technological and medical innovations. This new effort, called Targeted Hybrid Oncotherapeutic Regulation (THOR), will create a compact device designed to trigger the immune system against tumors. The device will be implanted in proximity of the tumor and will house specialized cells responsible for producing and delivering therapeutic molecules. These molecules will activate the immune system both locally around the tumor site and throughout the body. Additionally, the device will incorporate advanced sensors to detect and monitor biomarkers of cancer. The integration of these two components in a single device will enable precise delivery of therapeutic doses tailored to each patient’s needs.

• Amount Awarded Up to $45M
• Prime Awardee Institution Rice University
• Principal Investigator Omid Veiseh, Ph.D.
• Location Houston, TX

CUREIT: Curing the Uncurable via RNA-Encoded Immunogene Tuning

More than 25 million Americans currently live with autoimmune disease, and almost two million are projected to be diagnosed with cancer in 2023. Immune dysregulation is an underlying component of not only cancer and autoimmune diseases, but also infectious diseases, transplant rejection, and other common medical conditions. Current methods of immune modulation used to treat and mitigate these conditions are often expensive or not completely effective. Curing the Uncurable via RNA-Encoded Immunogene Tuning (CUREIT) aims to address immune dysregulation by directly programming immune cell function. Advances in gene-encoded technology will be leveraged to develop a platform capability able to both enhance protective immune responses as well as modulate insufficient or ineffective immune profiles. CUREIT seeks to develop a disease-agnostic toolbox of methods and technologies, including the in vivo delivery of mRNA-based drugs, cell targeting lipid nanoparticles, and ex vivo modulation of immune cells. This technology has the potential to make significant advancements towards managing or eliminating many diseases and conditions affecting all ages and demographics, including diseases that are currently untreatable.

• Date Awarded August 23, 2023
• Amount Awarded Up to $24M
• Prime Awardee Institution Emory University
• Principal Investigator Philip J. Santangelo, Ph.D.

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New funding for research infrastructure at a B.C. university supports an innovative project that will use the most realistic and robust methods available to identify resilient features of our body’s metabolism that could be targeted to fight common and debilitating diseases such as obesity, diabetes and obesity-driven cancers.

“Understanding how our bodies manage energy requires better tools and B.C.’s post-secondary institutions are responsible for life-changing research that has significantly improved the quality of life for people living in our province,” said Brenda Bailey, Minister of Jobs, Economic Development and Innovation. “Through our support of the B.C. Knowledge Development Fund, we continue to invest in research to ensure our province leads in innovative solutions and cutting-edge research to improve the lives of all British Columbians.”

Through another round of B.C. Knowledge Development Fund (BCKDF) funding, the Government of B.C. is providing approximately $52.3 million to support infrastructure for 25 research projects at five post-secondary institutions, including the University of British Columbia, University of Victoria (UVic), Simon Fraser University (SFU), University of the Fraser Valley (UFV) and British Columbia Institute of Technology (BCIT).

This includes $6.84 million for the project Improved Pre-clinical Modeling of Metabolic Resilience for Novel Obesity and Diabetes Therapeutics. BCKDF will help fund UBC research infrastructure to better understand how bodies manage obesity and diabetes. Researchers will utilize approaches that include animal, cell culture and human organoid models and a hormone discovery platform to study metabolic resilience and develop new treatments.

The goal of the project is to identify and leverage the body’s natural strengths to design therapies that correct the metabolic defects that cause obesity, diabetes and related health issues that are on the rise in B.C.

“We welcome this B.C. Knowledge Development Fund support, which gives a vital boost to our team’s ongoing research into diabetes and obesity,” says Dr. Jim Johnson, professor in UBC’s faculty of medicine and lead researcher on the   project. “This funding enhances our team’s ability to explore how the body regulates energy, opening up new treatment avenues for individuals affected by these serious chronic conditions.”

Other projects supported by the BCKDF include:

• UVic - Environmental Simulation Facility – Climate Change Impact on Plant Health - The new setup will help researchers study how climate change affects farm crops and forests. Using climate-controlled chambers, scientists can study how different weather conditions and stresses impact plants. The goal is to find the best disease-resistant trees for breeding and reforestoration, create better tests for plant sickness, identify new plant pathogens and predict how climate change will affect forests and berry farms in B.C.
• SFU - The Pacific Ocean Neutrino Experiment Demonstrator - The Pacific Ocean Neutrino Experiment is a new neutrino telescope to be deployed at Ocean Networks Canada off the coast of Vancouver Island. The telescope will aim to reveal insights about the inner workings of the brightest and most enigmatic places in the universe and discover previously unknown astronomical phenomena.
• BCIT - Advanced Mobility Devices to Expand Wheelchair Capabilities for Increased Community Participation – Researchers will investigate and improve new wheelchair technologies to help people move better and faster. These include power wheelchairs with robotic skills such as stair-climbing, and hybrid-manual wheelchairs with ebike-like power assist units. The research may lead to improved health and daily life for wheelchair users, and guide clinicians and consumers in how they select and use these new products safely.
• UFV - Berry Environmental Resilience Research and Innovation Lab – A 980-square-foot (91 square metres) facility outfitted with state-of-the-art technology at UFV’s Chilliwack campus will help researchers understand how berry systems adapt to climate challenges to help address food security issues and strengthen B.C.’s position as a global leader in sustainable berry production.

“These amazing researchers at B.C.’s post-secondary institutions are finding solutions to help British Columbians live healthier and happier lives,” said Lisa Beare, Minister of Post-Secondary Education and Future Skills. “This important research investment through the B.C. Knowledge Development Fund continues to support the expansion of critical research capabilities and discoveries at B.C. post-secondary institutions and showcases B.C.’s research excellence.”

The B.C. Knowledge Development Fund helps grow the economy by improving B.C.’s productivity and competitiveness, which is a key objective of the StrongerBC Economic Plan. Other benefits include potential commercialization, spinoffs, patents, improved environmental management and discoveries that directly affect the health and well-being of people in B.C.

By investing in research infrastructure projects, the B.C. government is continuing to support post-secondary institutions to build toward a more innovative, sustainable and inclusive future.

Benoit-Antoine Bacon, president and vice-chancellor, UBC –

“The B.C. Knowledge Development Fund investment in research infrastructure empowers UBC faculty, post-docs and students to drive research and to find solutions to some of our most urgent problems. Supporting critical work in renewable bioproducts, Indigenous archeology and biomedical innovations in areas such as diabetes treatment, RNA vaccines and genetic medicines, today’s investments reflect a breadth of research strengths across UBC. We are thankful for the B.C. government’s vision in supporting research that is improving the lives of British Columbians.”

Lesley Cormack, principal and deputy vice-chancellor, UBC Okanagan –

“I am delighted to see this investment in research infrastructure, which will support UBC Okanagan’s newest Canada Research Chairs and their colleagues. I’m grateful to the Province of B.C. for helping enable our researchers’ cutting-edge and important work in DNA nanotechnology and developing Indigenous approaches to overcoming intergenerational trauma and loss.”

Lisa Kalynchuk, vice-president, research and innovation, UVic –

“The support of the B.C. Knowledge Development Fund is critical to help UVic researchers mitigate the impacts of climate change. This important research will increase the resilience of our forests and food crops in B.C.”

Dugan O’Neil, vice-president research and international, SFU –

“Simon Fraser University is delighted with BCKDF funding support for the Pacific Ocean Neutrino Experiment. This project will leverage research excellence at SFU to expand the foundations of knowledge and advance our understanding of the universe.”

Joanne MacLean, president and vice-chancellor, UFV –

“The University of the Fraser Valley is proud to contribute to our future food security through this opportunity to study the climate resilience of berry horticultural systems and ecosystems. We appreciate that this important work is valued and supported by the B.C. Knowledge Development Fund.”

Jeff Zabudsky, president, BCIT –

“From advances in wheelchair technology to advancing our understanding of the universe, BCIT researchers are putting Canada at the forefront of global innovation. Investment from the governments of Canada and British Columbia enable BCIT students, researchers and faculty to continue to push boundaries and advance applied research in their respective industries. Jaimie Borisoff, Barry Pointon and their colleagues embody the BCIT vision of empowering people, shaping B.C. and inspiring global progress.”

Quick Facts:

• The BCKDF, established in 1998, is the B.C. government’s primary investment in research infrastructure in the province.
• Since 2017, the BCKDF has awarded more than $288 million for 541 projects.
• Funding is available for research at public post-secondary institutions and their affiliated research hospitals and non-profit organizations.
• BCKDF supplies funding for qualified projects, covering up to 40% of the total cost.

Learn More:

To learn more about the B.C. Knowledge Development Fund, visit:  https://www2.gov.bc.ca/gov/content/governments/technology-innovation/bckdf

To learn more about research at the University of British Columbia, visit: https://research.ubc.ca

To learn more about research at the University of Victoria, visit: https://www.uvic.ca/researchinnovation/research-at-uvic

To learn more about research at Simon Fraser University, visit: https://www.sfu.ca/research

To learn more about the University of the Fraser Valley, visit: https://www.ufv.ca/research

To learn more about research at the British Columbia Institute of Technology, visit: https://www.bcit.ca/applied-research  

A backgrounder follows.

Backgrounders

Universities and projects receiving research infrastructure funding.

University of British Columbia – Total for both campuses $47,057,256

University of British Columbia-Vancouver campus (UBC) BCKDF funding amounts and research project descriptions – Total: $46,617,035

Canadian Technology Accelerator for Digital Transformation of Manufacturing (CAN-DX) $1,017,507 (researcher: Yusuf Altintas)

Indigenous archaeology Lab for Indigenous Futures (IaLIF) $170,084 (researcher: Kristen Barnett)

Infrastructure for Accelerated In Vivo Molecular Imaging and Theranostic Research $6,227,953 (researcher: François Bénard)

Materials for Net-Zero: Enabling the Clean Energy Transition $7,466,233 (researcher: Ben Britton)

SMART Labs $2,146,587 (researcher: Janice Eng)

Transformative and Disruptive Systems Immunology $2,491,655 (researcher: Leonard Foster)

Functional genomics research for sustainable food and agriculture production $125,000 (researcher: Leluo Guan)

Unlocking the Next Generation of Quantum Materials $2,909,739 (researcher: Alannah Hallas)

A New Correlator for CHIME $2,041,380 (researcher: Mark Halpern)

Improved Pre-clinical Modeling of Metabolic Resilience for Novel Obesity and Diabetes Therapeutics $6,838,943 (researcher: James Johnson)

Mid-Infrared Quantum Sensing and Spectroscopy (MIR-QUEST) $763,339 (researcher: David Jones)

CGEn: Canada's National Platform for Genome Sequencing and Analysis $2,376,856 (researcher: Steven Jones)

Multi-scale Seeing is Believing Platform: Imaging and Advancing RNA Vaccines and Genetics Medicines $1,661,144 (researcher: Sabrina Leslie)

High Performance Scientific Computing of 3D Cell Migration using Geometric-and Bulk-Surface PDEs (3DGeoCell Lab) $125,000 (researcher: Anotida Madzvamuse)

A Canadian Free Electron Laser $3,606,500 (researcher: Takamasa Momose)

Field Camera and Shim System for Precision MRI $448,250 (researcher: Alexander Rauscher)

BioSEED-Biopolymer Synthesis, Engineering, Extraction, and Design $3,799,436 (researcher: Anne Lacey Samuels)

TRaC: Therapies for Rare Cancers $1,681,429 (multi-B.C. institution project; lead B.C. researcher: Natalie Strynadka -UBC)

Ecocosms and Mesocosms jointly Predict Ocean Weather and Ecological Response (EMPOWER) $720,000 (multi-B.C. institution project; lead B.C. researcher: Mary O’Connor - UBC)

University of British Columbia-Okanagan campus (UBC-O) BCKDF funding amounts and research project descriptions – Total: $440,221

Indigenous Community Based Participatory Research Communication Centre for Health and Cultural Revitalization $80,221 (researcher: Alanaise Ferguson)

DNA Nanotechnology Laboratory $360,000 (researcher: William Hughes)

University of Victoria (UVic) BCKDF funding amounts and research project descriptions – Total: $2,100,000

Environmental Simulation Facility – Climate Change Impact on Plant Health (ESF-PH) $1,250,000 (researchers: Juergen Ehlting & Peter Constabel)

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

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

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

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

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

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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

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

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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

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

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