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Content from Welcome to Pre-seeds (Research 101)!

Last updated on 2025-06-28 | Edit this page

Overview

Questions

What is this course about?
Who is it for?
How can I get the most out of it?

Objectives

Get oriented with the course’s tone and approach.
Understand who this course is designed for.
Feel excited and supported as you begin your learning journey.

Introduction

Welcome! 🎉

We’re so glad you’re here. This course—Pre-seeds (Research 101)—is not your typical introduction to research. It’s built for you: the curious, the hopeful, the hands-on learners who may not always see themselves in traditional research spaces but know they have something to contribute.

Whether you’re stepping into research for the first time or circling back with fresh eyes, you’re in the right place.

This isn’t about throwing jargon at you or expecting you to “catch up.” We’ll take things step by step, building confidence and skills in a way that’s practical, inclusive, and deeply human. Expect check-ins, relatable examples, and thoughtful pauses—not just facts.

We believe research is for everyone, and that includes you!

Challenge

Challenge 1:

Who is this course designed for?

Only people with a science degree
Researchers at elite institutions
Anyone curious about research, no matter their background
People who already know everything

Show me the solution

Answer: C

This course was built for anyone who’s curious about research, especially folks who may not come from traditional academic paths.

Challenge

Challenge 2:

What kind of experience can you expect from this course?

Lots of memorisation and final exams
Strict grading and formal lectures
A practical, inclusive, step-by-step journey
Pure chaos, honestly

Show me the solution

Answer: C

We’re keeping things practical and human—this is a supportive space to explore and grow.

Challenge

Challenge 3:

Which of the following might already make you a budding researcher?

Asking good questions
Looking for patterns
Being curious about the world
All of the above

Show me the solution

Answer: D

Yep—if you’ve done any of these, you’ve already started thinking like a researcher!

Figures

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Callout

You don’t need to be an expert

This course is designed assuming you have little to no formal research training. If you’ve ever asked good questions, looked for patterns, or been curious about the world—congrats, you already have the beginnings of a researcher’s mind.

Just a heads-up

Throughout this course, you’ll find short challenges, real-world scenarios, and opportunities to apply what you’ve learned. There’s no grading—just growth.

Each module builds on the one before it, but you can always circle back or skip ahead if something calls to you.

So take a breath, settle in, and get ready to stretch your brain gently.

You belong here. 💛

Key Points

This course is beginner-friendly and community-rooted.
You don’t need a research background to get started.
Learning is nonlinear, and that’s okay.

Content from Episode 1.1: Introduction to research: What is research?

Last updated on 2025-06-17 | Edit this page

Overview

Questions

What makes research different from everyday opinions?
What does it mean for research to be “replicable”?
Why is critical thinking important in research?

Objectives

Learners will be able to:

Define research in their own words, and how it stands apart from other ways of knowing (e.g. opinion, belief, or anecdote)
Identify the defining features (characteristics) of credible research.
List reasons for conducting research, and the importance of research in various contexts.
Identify real-world examples of research in action.

Think Like a Researcher

Imagine you’re sitting in a university lecture hall. Every time you glance around, more students seem to be glued to their phones. Some are scrolling through social media, others texting, and a few seem genuinely disengaged from the class.

You start to wonder: Why is this happening? Is it boredom, habit, or maybe something deeper about how students are taught today?

Now pause—what if you wanted to understand this behavior, not just guess at it? How would you systematically investigate this problem in a way that produces useful insights?

Hold on to that question. By the end of this lesson, you’ll know how a researcher would approach it.

A Wise Researcher Once Said…

“Research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.” — Earl Robert Babbie, American Sociologist

Let’s break that down. “Systematic inquiry” means we don’t just ask questions and hope for the best. We follow a method, apply logic, and rely on evidence.

What Is Research, Really?

Research is the engine behind most of the advancements we see in medicine, technology, social policy, and even the arts.

At its core, research is a structured way of asking and answering questions about the world. It’s how we move from guessing to knowing.

Unlike casual observations or personal beliefs, research depends on: - Gathering data - Organising and analysing it - Interpreting it logically - Drawing conclusions that others can test or build upon

Key Characteristics of Research

Let’s look at what separates research from, say, a viral tweet or a hunch you have about something:

Systematic Approach Research follows a clear plan or methodology. You don’t jump from question to conclusion—you walk through the steps carefully.
Objective and Unbiased Good research minimises personal opinions or preferences. It focuses on what the data says, not what we want it to say.
Empirical Evidence It uses real-world observations—things we can see, measure, or document—not just ideas or feelings.
Replicability Someone else, following the same steps, should be able to reproduce your results (or at least understand how you got them).
Critical Thinking Researchers must ask tough questions of their own work and be open to alternative interpretations.

Why Do We Do Research?

Not all research is done for the same reason. Depending on your goal, you might approach the same topic very differently.

Purpose	Goal
Exploratory	To investigate new or poorly understood phenomena.
Descriptive	To paint a detailed picture of a population or situation.
Explanatory	To figure out why something happens—cause and effect.
Applied	To solve a practical, real-world problem.

Think of these like different lenses you can look through—each one helps you focus on a particular aspect of your research question.

Why Does Research Matter?

Research isn’t just for scientists or academics. It affects all of us.

In healthcare: It helps us understand disease and develop treatments.
In education: It helps improve how we teach and learn.
In policy-making: It ensures decisions are backed by facts, not just opinions.
In everyday life: It sharpens our critical thinking and helps us avoid misinformation.

Simply put: without research, we’re just guessing.

Illustrative Example: When Clean Water Becomes a Crisis

Let’s say a rural community starts experiencing a rise in cases of waterborne diseases. Some people think the cause is the local river, others blame poor hygiene, and some say it’s just a coincidence.

What would a researcher do?

Start by clearly defining the problem: When and where are cases happening?
Collect data: Water samples, health records, sanitation practices.
Analyse patterns: Are certain water sources contaminated? Are specific villages more affected?
Draw conclusions and make recommendations: Maybe the source is an open well near a farm using chemical fertilizers.

This kind of systematic, evidence-based process transforms a community crisis into an opportunity for real, impactful change.

Wrap-Up: Research as a Way of Seeing the World

To do research is to say: “I want to understand, not assume.”

Whether you’re investigating disease outbreaks, classroom dynamics, or the impact of climate change, the tools of research help you navigate uncertainty with clarity.

Test Your Knowledge!

Challenge

Challenge 1:

A key characteristic of research is that it follows a systematic and structured process. (True/False)

Show me the solution

True.

Challenge

Challenge 2:

All research must include an experiment in order to be valid. (True/False)

Show me the solution

False.

Challenge

Challenge 3:

Which of the following is NOT a reason for conducting research?

To satisfy personal curiosity.
To improve decision-making.
To confirm pre-existing biases.
To solve real-world problems.

Show me the solution

Answer: C.

Figures

You belong in The Carpentries!

Callout

💡 Not all knowledge is created equal.

What sets research apart from everyday opinions or anecdotes is its structured, objective, and evidence-based approach. If you can’t explain how you arrived at a conclusion, it probably isn’t research.

Key Points

Research is a systematic, logical, and evidence-based process for asking and answering questions about the world.
It differs from opinion or belief because it relies on data, critical thinking, and clear methodology.
Good research is replicable, objective, and empirical—others should be able to follow your steps and understand your conclusions.
Research serves various purposes: it can explore new topics, describe conditions, explain relationships, or solve real-world problems.

Content from Episode 1.2: The research process: Steps involved in conducting research

Last updated on 2025-06-20 | Edit this page

Overview

Questions

What is the research process, and why is it important?
What are the key phases of the research process?
How do the phases of the research process connect to each other?
Why is the research process iterative rather than strictly linear?

Objectives

Learners will be able to:

Outline the major phases of the research process
Match research activities to their corresponding phases.
Describe the iterative nature of research
Develop a conceptual foundation for the rest of the course

A Wise Researcher Once Said…

“Experienced researchers loop back and forth, move forward a step or two before going back in order to move ahead again, change directions, all the while anticipating stages not yet begun. And no matter how carefully you plan, research follows a crooked path, taking unexpected turns, sometimes up blind alleys, even looping back on itself.” — Wayne C. Booth (The Craft of Research)

What is the Research Process?

The research process is a systematic journey of asking questions, gathering evidence, analysing information, and sharing insights. Whether you’re investigating a public health challenge or evaluating the impact of a new product, or even assessing how frequently you drink water, the research process provides a structured path to ensure that your conclusions are credible, relevant, and reproducible. But don’t be fooled by neat diagrams that suggest a rigid step-by-step path. In practice, the research process is more like a loop than a ladder. Ideas evolve, questions sharpen, methods shift, and results can take us back to the drawing board. And that’s not a failure—that’s research done right!.

Why Learn the Research Process?

Imagine starting a long journey without a map or GPS. You might wander around and eventually find your way—but it’ll take longer, cost more, and you might end up somewhere you didn’t intend.

The research process is your map. It helps you:

Stay organised
Ask sharper questions
Design stronger studies
Avoid common pitfalls
Work ethically and transparently

It also builds your credibility as a researcher, whether you’re publishing in a journal, advising decision-makers, or giving yourself a pat on the back for staying hydrated.

An Overview of the Key Stages

We’ll cover each of these in detail in later modules of the course, but for now, here’s the big picture:

An infographic titled "The Research Process" showing 10 stages in a flowchart format. The stages are: 1) Problem Identification – What do I want to know?, 2) Literature Review – What’s already known?, 3) Objectives/Hypothesis – What do I predict?, 4) Research Design – How will I find out?, 5) Data Collection – Go get the facts!, 6) Data Analysis – What do the numbers say?, 7) Result Interpretation – What does it mean?, 8) Conclusion – So what?, 9) Share – Share the story, 10) Evaluation – What worked? What next? Each stage is accompanied by an icon and arranged left to right as a horizontal arrow.

Aisha, a market woman and community volunteer in a rural town, begins to notice that many children in her area frequently miss school due to malaria. Concerned about the possible link between environmental factors and malaria cases, she decides to investigate whether improper waste disposal and stagnant water around homes contribute to the high incidence of malaria among school-aged children.

Identifying a Problem or Question

Define and articulate the research question or problem that you want to investigate. What issue do you want to explore? This step often emerges from curiosity, observations, literature reviews, or real-world challenges.

Aisha defines her research problem: “Does poor environmental sanitation contribute to the frequency of malaria infections among school-aged children in her community?” Her goal is to uncover patterns that could inform local health actions.

Reviewing the Literature

Conduct a thorough review of existing literature to understand what has already been studied and published on your topic. What have others already discovered? What gaps remain? Reviewing existing research ensures you’re building on a solid foundation and not reinventing the wheel.

She asks a local teacher to help her access some online articles and health brochures. From these, she learns that malaria is linked to stagnant water, uncovered containers, and poor drainage. She also speaks with a health worker to understand how similar studies have been done elsewhere.

Formulating Objectives or Hypotheses

Develop a clear and testable hypothesis or hypotheses based on your research question and literature review. These are your study’s compass. Objectives guide the focus; hypotheses offer testable predictions.

Aisha’s hypothesises: “Children living in households with poor environmental sanitation are more likely to suffer repeated episodes of malaria than those in cleaner environments.” This simple, clear hypothesis helps her structure her inquiry.

Choosing a Research Design

Determine the research design and methodology, including selecting participants (sampling), data collection methods (e.g., surveys, experiments), and procedures. Will you conduct experiments, surveys, case studies, or secondary data analysis? This step aligns your tools with your goals.

She chooses a simple observational survey. She plans to assess environmental conditions around households and collect information on malaria history from parents of school-aged children. She creates a basic checklist with help from a local nurse, including signs of poor sanitation like stagnant water, open drains, and exposed refuse.

Data Collection

Time to gather information! Collect empirical data based on your chosen methodology. This could be through interviews, questionnaires, sensors, or even scraping online data. How you collect data must be ethical, accurate, and purposeful.

Over two weeks, Aisha visits 50 homes. She observes the environment and asks parents how often their children have had malaria in the past 6 months. She records her findings using her notebook and a checklist, with permission from participants.

Data Analysis

Use appropriate statistical or qualitative analysis techniques to analyse the collected data and test your hypotheses. This is where your raw data becomes meaningful. You’ll look for patterns, test hypotheses, and answer your research questions.

With help from her nephew, who is good with Excel, Aisha organises the data. They create simple charts comparing the number of malaria episodes with the sanitation scores. The results suggest that children in homes with poor sanitation had significantly more malaria episodes.

Interpreting Results

Interpret the results of your data analysis in the context of your research question and hypotheses. Consider implications, limitations, and future research directions. What do your findings actually mean? Are they consistent with previous research? Do they raise new questions?

Aisha interprets the findings: in her community, poor sanitation practices appear strongly linked to repeated malaria infections. She notes that many families lack access to covered bins, drainage systems, or insecticide-treated nets.

Draw Conclusions

Draw conclusions based on your findings and discuss how they contribute to the field of study or address the research problem.

She concludes that community-wide sanitation improvements could reduce malaria infections. She emphasises the need for proper waste disposal, draining of stagnant water, and health education on malaria prevention.

Your research isn’t complete until it’s communicated. This could be through papers, presentations, infographics, or conversations with stakeholders.

Aisha presents her findings at the monthly community meeting. She uses simple language and posters to explain the link between the environment and health. The town chief and local health workers are impressed and agree to help with a community clean-up drive.

Evaluate and Reflect

Reflect on the entire research process, evaluate its strengths and weaknesses, and consider areas for improvement or further exploration.

Aisha reflects that although she is not a professional researcher, her local knowledge and passion made the study meaningful. She notes that involving others from the beginning would have improved data accuracy and plans to train some youth to help with future community surveys.

The Process is Connected, Not Compartmentalised

Each stage flows into the next, and each decision you make affects those that follow. For example:

Poorly defined objectives can lead to unclear analysis.
Weak data collection methods can ruin great research designs.

That’s why this course doesn’t just teach techniques. We’ll emphasise how everything connects—because research isn’t just about what you do, but why and how you do it.

Research is Iterative (And That’s a Good Thing!)

You might design a perfect study on paper… only to find that your participants misunderstood your survey, or your data has gaps, or your findings raise a brand-new (and even more exciting) question.

That’s not a problem—it’s progress.

Research often involves:

Revisiting your question after early data collection
Refining your analysis plan mid-study
Updating your literature review when new studies emerge

In short: you don’t have to get it all right on the first try. But you do need a process that helps you notice when something needs to change—and gives you the tools to adjust.

Reflection

Think on a real-world problem that interests you. Which of the 10 research stages do you think would be the most challenging for you, and why?

What’s Next?

In the rest of the course, we’ll take a closer look at each of the stages you’ve just seen. And for the rest of this introductory module, you’ll learn:

The various types of Research and their applications
The strengths and limitations of each type

Show me the solution

False

Callout

Add callout from lesson.

Content from Episode 1.3: How is Research Classified?

Last updated on 2025-06-20 | Edit this page

Overview

Questions

Why do researchers use different classification systems to describe their studies?
Can a single study belong to more than one research category?

Objectives

Learners will be able to:

List at least four common criteria used to classify research (e.g. purpose, methodology, design, timeframe).
Explain how each criterion influences study design and interpretation.
Match a brief study description to two appropriate classification labels.

Why Classify Research at All?

Imagine you and your classmates are each investigating students’ phone use during lectures.
One of you runs an experiment, another conducts interviews, and a third mines university log-data.
Even though you share a topic, you are not doing the same kind of research.

Giving studies the right labels helps us:

choose methods that fit our goals,
communicate findings precisely, and
compare work across disciplines.

In this episode, we step back to see the whole classification map before zooming in on specific types in later lessons.

The Big Picture: Common Ways to Classify Research

Classification Criterion	Typical Labels (examples)	Key Question Answered
Purpose	Basic / Applied	Why is the study being done?
Methodology	Quantitative / Qualitative / Mixed	What kind of data will be collected?
Research Design	Descriptive / Correlational / Experimental	How will the data be gathered and analysed?
Goal (Depth of Study)	Exploratory / Descriptive / Explanatory / Evaluative	To what end will the findings be used?
Focus	Theoretical / Empirical	Does the work build concepts or test them in the real world?
Timeframe	Cross-sectional / Longitudinal	When and how long will observations occur?
Data Source	Primary / Secondary	Are you collecting new data or analysing existing material?

Note: A single project can legitimately wear several labels.
For instance, a longitudinal applied mixed-methods evaluative study is perfectly possible.

A Closer Look at Our Categories

1. Purpose

Basic research asks fundamental “how or why” questions (e.g. How does attention work?).
Applied research seeks direct solutions (e.g. Will locking phone pouches improve grades?).

2. Methodology

Quantitative: counts phone glances per lecture.
Qualitative: interviews students about distraction.
Mixed: does both to get numbers and narratives.

3. Design

Descriptive: records what happens.
Correlational: checks if phone use relates to low marks.
Experimental: randomly assigns half the class to “no-phone” rules.

(We will unpack each design in Episodes 1.4 and 1.5.)

4. Goal

Exploratory work might map new distraction patterns.
Explanatory work tests whether boredom causes phone use.

5. Focus

A theoretical paper could model digital distraction behaviour;
while an empirical study would test that model in real lectures.

6. Timeframe

Cross-sectional: a one-off survey this semester.
Longitudinal: tracking the same cohort for four years.

7. Data Source

Primary: your own classroom observations.
Secondary: institutional attendance records from past years.

Test Your Knowledge!

Challenge

Challenge 1

A research team videotapes every lecture of a single course for one term and counts phone-checking events each week.
Which two classification labels (from different criteria) fit best?

Show me the solution

Possible answer: Applied (purpose) and Longitudinal (timeframe).
Another reasonable combination is Quantitative and Descriptive.

Challenge

Challenge 2

True | False: A study can never be both basic and applied.

Show me the solution

False. A project can develop basic theory in its early phase and apply that knowledge in a later phase—or run both threads in parallel.

Key Points

Research can be classified by purpose, methodology, design, goal, focus, timeframe, and data source.
These labels guide methodological choices and clarify how findings should be interpreted.
Most real studies blend several categories; classifications are tools, not rigid boxes.
Recognising the map of research types prepares you to plan and communicate your own projects.

Figures

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Callout

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Content from Episode 1.4: Types of Research I: Basic, Applied; Quantitative, Qualitative

Last updated on 2025-06-27 | Edit this page

Overview

Questions

What is the difference between basic and applied research?
When would you use qualitative instead of quantitative research?
Can a study be both qualitative and quantitative?
What are descriptive and experimental research, and when are they used?
How do different research types affect the kind of data you collect?

Objectives

Learners will be able to:

Distinguish between basic and applied research.
Compare and contrast quantitative, qualitative, descriptive, and experimental research.
Identify when and why each type is used.
Connect each research type to real-world examples and questions.

Think Like a Researcher

Let’s go back to our earlier curiosity:
Why are so many students distracted by their phones during lectures?

Now imagine five different researchers trying to answer this question, each with their own method and mindset:

One carefully observes students and documents their behavior.
Another hands out a questionnaire to hundreds of students.
A third digs into journal articles to find trends across universities.
Another conducts interviews to understand students’ perspectives.
And yet another runs an experiment to see if a new teaching method reduces phone use.

Are all of these research? Yes.
Are they all the same type of research? Not quite.

This episode explores several popular pathways of research, each tailored to a particular kind of question, context, or goal. While not an exhaustive list, these are among the most commonly used types. You’ll learn how different kinds of research give us different kinds of answers.

What are the types of Research?

There’s more than one way to slice the research pie. But most research falls into one or more of the broad categories below:

1. Basic vs. Applied Research

Basic (or Pure) Research

This is aimed at expanding our general knowledge, without necessarily needing immediate application. That is, we don’t intend to solve a problem today.

Instead, basic research asks: How does the world work?

So, it is common in theoretical disciplines or foundational sciences.

Example: Studying how memory works in the brain, even if no product or intervention is being developed.

Applied Research

Unlike basic research, this is focused on solving a specific, real-world problem.

Applied research asks: How can we use knowledge to improve something?

It is common in public health, education, engineering, and business.

Example: Investigating how mobile phone use during lectures affects exam performance and then designing strategies to reduce it.

These two types, they often work together. Basic research builds the foundation, applied research builds the bridge to real-life solutions.

2. Quantitative vs. Qualitative Research

Quantitative Research

This involves numbers, statistics, and measurable variables.

Good for answering: How much? How many? How often? Is there a correlation?

(Task: Define correlation in a call out)

This type of research uses tools like surveys, experiments, statistical analysis.

Example: Measuring how many students use phones during lectures, how long they spend on them, and whether this correlates with their grades.

Qualitative Research

This focuses on experiences, meanings, stories, and context.

Good for answering: Why? How? What was the experience like?

Qualitative research tools include interviews, focus groups, observations, and content analysis.

Example: Interviewing students to understand why they check their phones, what they feel during lectures, and what might help them focus more.

3. Descriptive vs. Experimental Research

Descriptive Research

This is about observing, recording, and describing a phenomenon without manipulating any variables. It answers: What is happening? Who is involved? How widespread is it?

Descriptive research helps to build a picture of a situation as it naturally occurs. - Example: Surveying how many students report being distracted by phones and tracking differences across age groups or courses.

Experimental Research

This involves actively manipulating one variable to observe its effect on another. It answers: Does this cause that? What happens if we intervene? Experimental research is key when you want to establish cause and effect.

Example: Introducing a no-phone policy in some classes and comparing exam scores with classes that kept phones.

Illustrative example

Imagine you want to study vaccine hesitancy in your community:

Quantitative: How many people are hesitant? Which demographics?
Qualitative: Why are they hesitant? What fears or beliefs do they have?
Descriptive: What are the most common concerns expressed in public forums or media?
Experimental: What happens when people are shown targeted educational videos — does their willingness to vaccinate increase?

All offer important insights. Some give you patterns, others give you meaning or causal relationships.

Test Your Knowledge!

Challenge

Challenge 1:

Which type of research is most likely to involve large data sets and statistical analysis?

Applied
Basic
Quantitative
Qualitative

Show me the solution

Answer: c) Quantitative

Challenge

Challenge 2:

True or False:
Applied research has no value unless it’s immediately applied to a problem, policy or practice.

Show me the solution

False. Applied research still builds knowledge, even if implementation is delayed.

Key Points

Basic research builds theory; applied research solves problems.
Quantitative research answers “how much” with numbers.
Qualitative research answers “why” with stories and context.
Mixed methods combine the strengths of both.
Descriptive research tells you what’s happening without changing anything.
Experimental research tests cause and effect by manipulating variables.

Figures

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You belong in The Carpentries!

Callout

💡 Note: Some research combines both approaches. This is called Mixed Methods Research.

Content from Episode 1.5: Strengths, Limitations, and Applications of Research Types

Last updated on 2025-06-17 | Edit this page

Overview

Questions

What are the strengths and limitations of the different types of research?
How can understanding these differences guide the design of better studies?
In what ways are these research types applied in real-world contexts?

Objectives

Learners will be able to:

Identify at least two strengths and limitations for each of the types of research.
Match each type of research to a practical example or disciplinary use case.
Decide which research type(s) may be most appropriate for a given research question or real-world scenario.

Think Like a Researcher

Imagine your university is considering launching a mental health app for students. You’re part of the team evaluating its impact. What’s the best way to approach the task?

Would you: - Measure students’ stress levels before and after using the app? - Interview students to understand how they feel about using it? - Compare the app to others in use at different schools? - Or do a little bit of everything?

The way you choose to investigate the problem depends on the kind of research you conduct—and each type brings its own strengths and tradeoffs. In this lesson, we’ll explore those strengths, limitations, and the contexts where each approach thrives.

A Quick Recap

In the previous episode, we introduced four common types of research, often grouped by purpose:

Type	Goal
Baic	Expand fundamental knowledge without immediate use
Applied	Address real-world problems directly
Descriptive	Document or quantify what is happening
Experimental	Test cause-and-effect relationships
Qualitative	Understand experiences, meanings, context
Quantitative	Measure variables using numerical data

These types can work independently or in combination, depending on the research question.

Let’s now take a deeper look at each research type and how it plays out in practice.

Basic Research

Used when: You want to understand how things work at a fundamental level.

Strengths

Builds foundational knowledge and theories.
Often leads to future innovation and discovery.

Limitations

May not have immediate application.
Hard to justify in applied or results-driven environments.
Results tend to be tentative and may not lead to actionable conclusions on their own.

Real-World Applications

Studying how memory is encoded in the brain.
Investigating the basic principles of quantum computing.

Common Methods: Literature reviews, theoretical modeling, laboratory experiments.

Applied Research

Used when: The goal is to solve a specific, practical problem.

Strengths

Results are actionable and directly relevant to practice or policy.
Often interdisciplinary, integrating knowledge from different fields.
Supports innovation and impact.

Limitations

May be constrained by political, commercial, or time pressures.
Can prioritize short-term fixes over long-term understanding.
Results may not always be published or widely disseminated.

Real-World Applications

A hospital testing a new nurse scheduling algorithm to reduce staff burnout.
A city evaluating traffic sensors to improve road safety.

Common Methods: Case studies, evaluations, needs assessments, feasibility studies.

Descriptive Research

Used when: You want to document or quantify what is currently happening.

Strengths

Helps build a foundational understanding of populations or phenomena.
Supports policy-making and planning with concrete data.
Often large-scale and generalizable.

Limitations

Does not explore causes or explanations.
Can be misleading if poorly designed or biased in data collection.

Real-World Applications

A national census on employment trends across industries.
A school district tracking student attendance and engagement.

Common Methods: Observational studies, cross-sectional surveys, routine data audits.

Experimental (Causal) Research

Used when: You want to test cause-and-effect relationships by manipulating variables.

Strengths

Provides strong evidence for causality
Often highly systematic and replicable

Limitations

Can be complex and time-intensive.
May require ethical safeguards, especially in experiments.
Difficult to fully control variables in real-life settings.

Real-World Applications

A clinical trial testing whether a new vaccine reduces infection rates.
A randomized controlled study on whether gamified lessons improve student retention.

Common Methods: Experiments, longitudinal studies, regression modeling.

Quantitative Research

Used when: You want to measure variables and test hypotheses using numbers.

Strengths

Enables statistical analysis and generalization.
Suits large-scale studies and trend analysis

Limitations

May overlook context or nuance.
Can miss “why” behind the numbers

Real-World Applications

Measuring the number of app logins and correlating with mood scores.
Calculating percentage change in academic performance

Common Methods: Surveys, experiments, correlational studies

Qualitative Research

Used when: You want to understand how people make sense of their experiences.

Strengths

Offers rich, contextual, in-depth insights.
Flexible and adaptive to new findings.

Limitations

Findings are harder to generalize.
Can be time-intensive to collect and analyze

Real-World Applications

Interviewing students about mental health stigma.
Analyzing social media posts related to stress

Common Methods: Interviews, focus groups, ethnography, content analysis

When One Type Isn’t Enough

In the real world, many studies span multiple research types. Consider the case of the university’s mental health app:

Basic: Understanding psychological mechanisms behind stress
Applied: Designing and implementing the app
Descriptive: Tracking usage rates and stress reports
Experimental: Testing impact on mental health through a controlled study
Quantitative: Measuring shifts in mood using survey scales
Qualitative: Interviewing users about their experiences

This is where mixed methods come in—combining qualitative depth with quantitative breadth for a fuller picture.

Cross-Disciplinary Lens

Different academic and professional fields tend to favor different types of research based on their goals:

Discipline	Typical Research Type	Sample Topic
Public Health	Applied, Qualitative, Descriptive	Are health interventions reaching target populations?
Education	Descriptive, Qualitative	What do students report as barriers to learning?
Engineering	Applied, Experimental	How efficient is a new solar energy prototype?
Psychology	Basic, Experimental, Quantitative	What are the cognitive effects of screen time?
Sociology	Qualitative	How do young people define identity in online spaces?

Understanding the types of research favored in a field can help you collaborate more effectively, apply for grants, and interpret findings with nuance.

Test Your Knowledge!

Challenge

Challenge: Match the Type

Scenario: A local government wants to understand whether its free school meal program improves student performance.

Which research types could apply?

Show me the solution

Experimental: Randomly assign some schools to receive the meal program and others not to, then compare performance outcomes between the two groups.
Applied: Evaluate the effectiveness of the current program and offer policy recommendations on whether it should be expanded or revised.
Descriptive: Collect data on how many students are receiving meals.
Quantitative: Conduct interviews or focus groups with students, parents, and teachers to explore how the meal program affects learning, focus, and well-being.
Qualitative: Evaluate whether the program should be expanded based on findings.

Key Points

Each type of research—basic, applied, descriptive, experimental, qualitative, and quantitative—has unique strengths and limitations.
Complex problems benefit from mixed methods that draw on multiple types.
Being intentional about research type improves clarity, coherence, and usefulness of findings.
Different disciplines apply research types in different ways, tailored to their questions and practices.

Figures

Add infographic:

You belong in The Carpentries!

Callout

Add callout from lesson.

Content from Episode 12.1: Introduction to Open Science: What Is It, and Why Does It Matter?

Last updated on 2026-09-08 | Edit this page

Overview

Questions

What is open science, and how is it different from just “publishing research”?
What are the core ideas holding the open science movement together?
Why should every researcher — not only experienced scientists — care about it?

Objectives

Learners will be able to:

Define open science (also called open research or open scholarship) in their own words.
Identify the three core ideas behind open science: transparent processes, collaboration and reuse, and accessible knowledge.
Explain why openness matters for the trustworthiness of research, using a real research example.
Recognise that open science does not mean sharing everything, regardless of ethics.

Think Like a Researcher

A few months after Aisha’s malaria study wraps up, an email arrives from a researcher at another university:

“Hello Aisha — I read about your malaria project, and we’d like to run something similar in another community. Would it be possible to use your questionnaire, and learn how you analysed your results?”

Aisha is delighted. Then she starts looking.

She never saved a final, clean version of the questionnaire — just several edited drafts. Her notebook has her observations, but no explanation of how she scored each household’s sanitation level. Her nephew analysed the numbers in Excel, but the formulas were never labelled, and he cleared the original file months ago while freeing up space on his laptop.

Aisha remembers roughly what she did. She just can’t fully show it. The second team will have to start almost from scratch.

If good research is supposed to help others learn from and build on it, shouldn’t researchers make that as easy as possible? That question sits at the centre of open science.

A Wise Scholar Once Said…

““Nullius in verba” which means take nobody’s word for it. It has been the motto of the Royal Society, one of the world’s oldest scientific academies, since the 1660s. Long before anyone used the phrase “open science,” this was already the underlying idea: a claim only counts as knowledge once it can be checked.”

Isn’t Research Already Open?

It might seem like it should be. Papers are published every day; universities produce thousands of them a year. So what’s missing? Picture a chef who wins a cooking competition. You see a photo of the winning dish (and you’re told it’s delicious!), but the chef won’t share the recipe, the exact ingredients, or the method. Could you recreate it? Almost certainly not. Now picture a chef who shares everything: the recipe, the exact quantities, even the mistakes made along the way. Thousands of people can now recreate the dish — and some will improve it. Publishing a paper’s conclusions is the first chef. Sharing the plan, the data, and the reasoning behind the conclusions is the second. Most published research still looks a lot more like the first.

What Is Open Science, Really?

Open science (sometimes called open scholarship or open research) is an umbrella term. Different fields and institutions define it slightly differently, but they all circle back to the same idea: Open science is the practice of making the process of research — not just the finished paper — visible and available, so that other people can check it, use it, and build on it.

Traditionally, the public only ever sees the last 10% of a research project: a polished paper, sitting behind a journal’s paywall, describing results without showing the raw data, the false starts, or the exact steps that produced them. Open science tries to open up the other 90% — the plans, the materials, the data, the code, the review process, where ethical — so that research can be verified rather than just taken on faith. And it applies across the whole research lifecycle: planning, doing the work, publishing and not just the moment a paper comes out.

Key Pillars of Open Science

Transparency: Explaining how a result was reached. Including how participants were selected, how something was measured, why a particular test was used. This is what lets someone else spot an honest mistake before it spreads.

Collaboration and Reuse: No one researcher has every skill a question needs. Sharing methods, data, and tools lets a statistician, a lab specialist, and a community expert build on the same foundation instead of working in isolation. If teams in several countries investigate a question like Aisha’s using comparable, shared methods, each study adds to a much bigger picture than any one of them could produce alone.

Accessible Knowledge: A finding only helps the people who can actually reach it. A rural clinic without a journal subscription, a policymaker, or a student writing their first proposal. Accessibility is what decides whether they ever see it at all. This covers more than papers; it includes the teaching materials and tools that let people participate in research to begin with (like Pre-seeds!).

This is where people often get open science wrong. It does not mean uploading every file, spreadsheet, or participant record. Ethics comes first, always — identifiable medical records, confidential therapy notes, and culturally sensitive information from a community all stay protected, regardless of how “open” the rest of a project is. Open science means sharing as much as is genuinely useful and appropriate, not everything that exists.

Why Does Open Science Matter?

Open science isn’t just a courtesy extended to other researchers. It affects how much weight research can carry in the real world. In health and medicine: It lets other scientists verify that a treatment or intervention really works before it’s scaled up to entire populations. In policy-making: It gives decision-makers a way to check the evidence behind a recommendation, instead of taking a summary on faith. In education and training: It means students and early-career researchers can learn from real data and real materials, not just a tidy final narrative. In everyday trust in science: When findings can be checked and often don’t hold up, it damages public confidence in research generally as openness is part of how that trust gets rebuilt.

Reflection

Think of a project you finished a while ago. If someone asked you today to explain exactly how you reached your conclusion, could you? Would your notes, files, and reasoning still make sense to you, let alone to someone else?

Wrap-Up: Open Science as a Way of Doing Research

To practise open science is to say: “Show your work, so it can be trusted and built on.” Not necessarily because anyone assumes researchers are dishonest, but because a result nobody can check is a result nobody can fully rely on. In the next episode, we’ll look at exactly why this became such an urgent conversation across so many fields at once.

Test Your Knowledge!

Challenge

Challenge 1:

Open science is mainly about making the final paper free to read.

Show me the solution

False. Free access is one piece of it (you’ll meet it properly in Episode 2.6), but open science is the broader practice of opening up the whole research process — plans, data, materials, and reasoning — not just the finished write-up.

Challenge

Challenge 2:

Which of these best captures what open science means?

A. Research conducted without any oversight
B. Making research more transparent, collaborative, and accessible
C. Publishing only the findings that turned out well
D. Sharing participant records publicly, regardless of consent

Show me the solution

B. The other three options actually work against good, ethical research rather than supporting it.

Challenge

Challenge 3:

Which of the following would NOT count as an open science practice?

A. Sharing a documented protocol so someone else could repeat a study
B. Clearly labelling and organising a dataset before archiving it
C. Withholding a method deliberately so others can’t reuse it
D. Making teaching materials available under an open licence

Show me the solution

C. Deliberately withholding a method runs directly against openness. Everything else on the list supports it.

Figures

You belong in The Carpentries!

To-do: Add infographic - “The Pillars of Open Science”.

Callout

💡 Open science is not all-or-nothing. A study can be more or less open depending on which practices it adopts. You’ll see the full range of practices in the next few episodes.

Key Points

Open science is the practice of making the process of research — plans, data, materials, and code — visible and available.
Its three core pillars are transparent processes, collaboration and reuse, and accessible knowledge.
Openness applies throughout the research lifecycle, not only at the point of publication.
Open science is not “share everything” , ethical and legal protections around sensitive information always come first.

Content from Episode 12.2: The Reproducibility Crisis: Why Researchers Needed to Change

Last updated on 2026-09-08 | Edit this page

Overview

Questions

What’s the real difference between reproducibility and replication?
What was the “reproducibility crisis,” and what actually caused it?
Why did it push so many fields toward open science?

Objectives

Learners will be able to:

Define reproducibility and replication precisely, and explain what each one verifies.
Describe what the reproducibility crisis was, and roughly how widespread it turned out to be.
Identify the main contributing factors: misaligned incentives, under-resourced researchers, and questionable research practices (QRPs).
Explain why the crisis is generally seen as a sign of science working, not failing.

Think Like a Researcher

The researcher who emailed Aisha follows up a few months later:

“We used your questionnaire, but we’re stuck reproducing your analysis. How exactly did you classify households with partially covered drainage? It wasn’t in your report.”

Aisha checks her notebook. She recorded the observation, but never wrote down how she’d handled the borderline cases: a handful of households that didn’t fit neatly into her scoring system. She made a call in the moment. It felt obvious at the time. Months later, she can’t fully reconstruct her own reasoning.

Nobody is accusing Aisha of dishonesty. The second team just needs to see exactly how she got from data to conclusion, and that trail went cold.

A Wise Scholar Once Said…

“Science is the belief in the ignorance of experts.” — Richard Feynman

Feynman’s point wasn’t that experts are useless. It’s that a claim earns trust by surviving scrutiny, not by whoever said it. That’s exactly what reproducibility checks for.

Reproduction vs. Replication

These two get used interchangeably in casual conversation, and in a lot of older news coverage, but they check different things.

Reproduction takes the original researcher’s data and code, and re-runs the same analysis. Same inputs, same steps: do you land on the same numbers? Reproduction verifies the analysis.

Replication is a new, independent study of the same question: new data, ideally the same or a similar method. Do you land on a similar answer? Replication tests whether the finding generalises beyond the original sample.

If a bookshelf comes with a full set of assembly instructions (every screw, every panel, every step), anyone following them should end up with the same bookshelf. That’s reproduction: same materials, same steps, same result. Replication is more like a different carpenter building the same design from scratch, in a different workshop, and checking whether it still holds together.

Both matter. A finding that can’t even be reproduced from its own data is on very shaky ground. One that reproduces but hasn’t been replicated independently is a step further along, but still unconfirmed outside its original sample.

Two more terms worth knowing: Credibility - How much trust a finding deserves, based on how rigorously it was produced and checked. Robustness - Getting a similar answer even when small, reasonable choices in the analysis are changed.

What Was the “Reproducibility Crisis”?

Starting around 2015, a wave of reporting (in outlets like the Washington Post and The Atlantic) highlighted a problem researchers had started documenting seriously: a surprising share of published findings, especially in psychology, weren’t holding up when other teams tried to reproduce or replicate them. Two large-scale efforts, one in psychology and one in cancer biology research, each tried to replicate a batch of high-profile published studies. In both, a large proportion of the original findings failed to replicate, and even the ones that did tended to show a much smaller effect the second time around.

A widely cited 2016 survey went further, finding that a majority of researchers across fields had failed to reproduce another scientist’s results at least once, and more than half had failed to reproduce their own. A failed reproduction doesn’t automatically mean a study was wrong. But at that scale, it’s a strong signal that something about how research was being done and documented needed a second look.

What Actually Caused It?

It’s tempting to assume fraud. In reality, deliberate misconduct explains only a small share of it. Most of the problem traces back to three ordinary, often well-intentioned pressures:

Misaligned incentives: Careers are often built on the number of publications, not their long-term reliability. Novel, surprising results get published and cited far more readily than careful replications or null results, which quietly pushes researchers toward whichever analysis produces an exciting result, rather than whichever one is accurate.

Under-resourcing: Researchers are often expected to publish more, manage larger projects, and share data properly, without necessarily being given the time, training, or staff to do all of that well. Good documentation takes time that grant timelines don’t always allow for.

Questionable Research Practices (QRPs): These sit in a grey area short of outright fraud, often unintentional, but still capable of distorting results.

p-hacking (also called data dredging): running many different analyses until one produces a “significant” result, then reporting only that one. HARKing (Hypothesizing After the Results are Known): noticing a pattern in the data first, then writing it up as though it had been predicted all along.

Selective reporting: publishing only the outcomes that worked out, and quietly leaving out the ones that didn’t.

Silent post-hoc data collection: gathering extra data after an initial analysis fell short, in hopes of pushing it over the line, without disclosing that this happened.

None of these require intent to deceive. Most researchers are taught not to do them, and end up doing some version of them anyway, especially under pressure. But across an entire field, practices like these add up, and produce exactly the pattern the 2015 headlines were describing.

How Open Science Responds

Every factor above shares a common fix: make the process visible enough that it can be checked.

Problem	Open Science Response
Methods aren’t fully described	Share a detailed protocol and materials
Analysis can’t be verified	Share the data and analysis code
Hypotheses shift quietly after seeing results	Preregister the hypothesis and analysis plan
Findings can’t be checked at all	Make outputs available, not just conclusions

None of this guarantees a study is correct; researchers will always make mistakes. What it changes is how quickly those mistakes get noticed and fixed, instead of quietly shaping the next ten studies that build on them.

What Would Have Helped Aisha

A short note in her protocol, “households with partially covered drainage were scored as moderate risk,” written the day she made that call, not reconstructed from memory months later. That’s the whole fix: not more work, just documentation captured while the reasoning is still fresh.

Reflection

Think about a decision you made partway through a recent project: a judgment call that felt obvious in the moment. Did you write down why you made it? If someone asked you to justify it six months from now, could you?

Wrap-Up: A Sign Science Is Working

“Crisis” sounds like failure. It isn’t, quite. A field that discovers its own findings don’t hold up as often as assumed, and responds by changing how it documents and shares work, is doing exactly what science is supposed to do: testing its own claims and correcting course. The reproducibility crisis didn’t break trust in research. It’s a large part of why open science practices exist at all, which is where the rest of this module picks up.

Test Your Knowledge!

Challenge

Challenge 1:

A team re-runs another lab’s published analysis using the exact same dataset, and gets the exact same numbers. What have they demonstrated?

A. Replication
B. Reproduction
C. A questionable research practice
D. Nothing meaningful

Show me the solution

B. Reproduction: same data, same steps, same result. Replication would mean collecting new data on the same question.

Challenge

Challenge 2:

Which of these is a Questionable Research Practice?

A. Preregistering a hypothesis before collecting data
B. Reporting all outcomes measured, even the ones that didn’t work out
C. Running several analyses and reporting only the one that came out significant
D. Sharing analysis code alongside a paper

Callout

💡 A study that fails to replicate isn’t automatically wrong, and a researcher whose earlier work doesn’t hold up isn’t automatically careless. The point of reproducibility isn’t to catch people out; it’s to make sure confidence in a finding is actually earned.

Content from Episode 12.3: Open Science Across the Research Lifecycle

Last updated on 2026-09-08 | Edit this page

Overview

Questions

Where in the research process does “openness” actually happen?
Does open science mean doing something extra at the end, or something different throughout?
How do open practices at one stage of research make later stages easier?

Objectives

Learners will be able to:

Map specific open science practices onto the ten stages of the research process introduced in Episode 1.2.
Explain why open practices are easiest to adopt early, and hardest to bolt on after the fact.
Describe how an open choice at one stage changes what’s possible at a later stage.

A Wise Scholar Once Said…

“Science is a way of thinking much more than it is a body of knowledge.”
— Carl Sagan

Revisiting Aisha’s Study

In Episode 1.2, we followed Aisha, a final-year public health student and community volunteer, as she investigated whether poor sanitation was contributing to malaria among school-aged children in her town. She moved through the ten stages of the research process: identifying her problem, reviewing what was already known, forming a hypothesis, choosing a design, collecting data from 50 homes, analysing it with her nephew, interpreting the results, drawing conclusions, presenting her findings, and reflecting on the whole process.

Aisha’s study worked. But nothing about it was built to be checked or reused by anyone outside her town. Let’s walk back through those same ten stages and ask: what would it look like if Aisha (or a research team doing something similar at a larger scale) built openness in at every step, instead of only thinking about it once the paper was ready?

Openness at Each Stage

Problem Identification: State the research question clearly and put a timestamp on it (in a lab notebook, a project registry, or a public repository) before any data exists. This matters later: it’s the only way anyone can tell whether a hypothesis was predicted in advance or shaped to fit results that had already come in.

Literature Review: Record which sources were searched, which databases, and which search terms, not just which papers ended up cited. A documented search can be checked and repeated; a vague “we reviewed the literature” cannot.

Objectives and Hypotheses: This is where preregistration happens: publicly and permanently recording the hypothesis and the planned analysis, before collecting data. It’s the single practice with the biggest effect on credibility, because it closes off the option to quietly change the question after seeing the answer.

Research Design: Share the full protocol (sampling plan, instruments, checklists) in enough detail that someone else could run the same study in a different location, the way Aisha’s sanitation checklist could, in principle, be reused in a neighbouring town.

Data Collection: Collect data using standard formats and clear documentation from day one (a “codebook” describing every variable), rather than reorganising messy field notes only once someone asks for the raw data.

Data Analysis: Share the analysis code or syntax alongside the results (the actual steps Aisha’s nephew used in Excel), not just the resulting chart. This is what makes a result reproducible in the strict sense you’ll see defined precisely in Episode 2.2.

Result Interpretation: Distinguish, in writing, between what was predicted in advance (confirmatory) and what was noticed only after looking at the data (exploratory). Both are valuable, but conflating them overstates how strong the evidence really is.

Draw Conclusions: State the limitations plainly, including anything that didn’t go according to plan: a missed household, an unusually hot week, a checklist item that turned out to be ambiguous.

Sharing Findings: Publish somewhere accessible to the people who most need it: in Aisha’s case, that’s the town chief and local health workers, not a journal none of them could read even if they wanted to. Where possible, share the underlying data and materials alongside the write-up, not just the narrative.

Evaluate and Reflect: Document what would be worth doing differently, including openness itself. Aisha reflected that involving others earlier would have improved her data; an open project keeps a written record of exactly that kind of lesson, so the next person doing similar work doesn’t have to rediscover it.

The Pattern: Open Early, Not Just at the End

Notice a theme running through all ten stages above: every open practice is far easier to build in from the start than to reconstruct afterwards. A hypothesis can only be preregistered before you’ve seen your data, never after. A codebook is quick to write while you’re still collecting data, and painfully slow to reconstruct from memory a year later. Openness that’s treated as a final step, tacked on right before submitting a paper, usually ends up thin: a data file with unlabelled columns, a note that says “code available on request” that nobody ever actually requests.

This is also why open science and the research process from Episode 1.2 fit together so naturally. Both are iterative, both connect stage to stage, and a weak link early on (an unregistered hypothesis, an undocumented dataset) limits what’s possible at every stage that follows.

Test Your Knowledge!

Challenge

Challenge 1:

At which stage of the research process does preregistration happen?

A. Data analysis B. Objectives/hypothesis formulation C. Sharing findings D. Evaluate and reflect

Show me the solution

B. Preregistration happens when hypotheses and the analysis plan are formed, and it only counts if it’s timestamped before data collection begins.

Challenge

Challenge 2:

A researcher shares their dataset for the first time only after a journal asks for it during peer review, several months after data collection ended. What’s the main risk with leaving documentation this late?

A. The journal will reject the paper automatically. B. The researcher may not accurately remember or reconstruct exactly what each variable and decision meant. C. Late sharing is against copyright law. D. There is no real risk; timing doesn’t matter.

Show me the solution

B. Documentation degrades with time and memory. Codebooks and protocols are far easier to write accurately while the work is fresh, which is why open practices work best when built in from the start.

Challenge

Challenge 3:

True or False: Once a study is designed, it’s generally too late to make it meaningfully more open.

Show me the solution

False, though it gets harder. Data collection and analysis can still be documented as they happen, and findings can still be shared accessibly. But some practices (like preregistering a hypothesis) genuinely can only happen before data collection starts, which is why “open early” beats “open eventually.”

Key Points

Open science isn’t a separate step tacked onto the end of a study; it’s a set of choices available at every stage of the research process.
Preregistration, protocol-sharing, documented data collection, shared analysis code, and accessible publishing each map onto a specific stage from Episode 1.2.
Open practices are far easier to build in as you go than to reconstruct after the fact, especially anything that depends on a timestamp, like preregistration.
A weak link early in the process (an undocumented decision, an unregistered hypothesis) limits how open and how credible everything downstream can be.

To-do: Add infographic. Can use the one in episode 1.2.

Callout

💡 You don’t need to open every single stage to benefit from open science. Even one or two practices (say, preregistering your hypothesis and sharing your dataset) meaningfully raise how much confidence others can place in your work.

Content from Episode 12.4: How Are Open Practices Classified?

Last updated on 2026-09-08 | Edit this page

Overview

Questions

Open science covers a lot of ground: how do we make sense of it as a whole?
Do all open practices solve the same problem, or different ones?
Can a single study use more than one open practice at once?

Objectives

Learners will be able to:

List the major categories of open practice and what part of research each one opens up.
Match a research scenario to the open practice(s) it’s using.
Explain why most well-designed open studies combine several practices rather than relying on just one.

Why Classify Open Practices at All?

Imagine three researchers, each proud to say their work is “open.” One preregistered a hypothesis before collecting any data. Another uploaded their dataset to a public repository after publishing. A third made their paper free to read on a preprint server. All three are doing something real, but they’re opening up three completely different parts of the research process, and none of them, alone, gives you the full picture.

Sorting open practices into categories helps us:

see exactly what part of a study each practice makes visible, avoid assuming one open habit (like posting a preprint) covers everything, and combine practices deliberately, instead of by accident.

The Big Picture: A Map of Open Practices

Practice	What It Opens Up	Key Question It Answers	Common Tools / Examples
Preregistration & Registered Reports	The hypothesis and analysis plan	Was this predicted in advance, or found after the fact?
OSF Registries, AsPredicted
Open Data	The raw (usually de-identified) dataset	Can someone else verify the numbers, or reuse them for a new question?	Data repositories, institutional data archives
Open Materials & Code	Instruments, protocols, and analysis scripts	Can someone else rerun the exact same analysis?	Code repositories, shared protocol documents
Open Access & Preprints	The write-up itself	Who is actually able to read this?	Preprint servers, open-access journals
Open Educational Resources (OER)	Teaching and training materials	Who gets to learn from this, and can they adapt it?	Open licences, shared course materials
Open Evaluation	The review and quality-control process	How was this judged, and by whom?	Open or signed peer review, public reviewer reports
Team Science & Open Collaboration	Who participates, and how credit is shared	Who gets to contribute, and who gets recognised for it?	Multi-site consortia, open contribution guidelines
Open Source Software	The tools used to produce or manage the research	Can the tools themselves be inspected, trusted, and improved?	Publicly maintained code, open licences

Note: A single project can, and usually should, use several of these at once. A preregistered study with open data, open code, and a preprint isn’t unusual; it’s what a fully open project typically looks like.

A Closer Look at Each Category

Preregistration & Registered: Reports Preregistration locks in a hypothesis and analysis plan before data collection. Registered Reports take it a step further: the study design itself is peer-reviewed and provisionally accepted for publication before the results exist, so the decision to publish never depends on how the results turned out.
Open Data: Sharing the underlying dataset (properly documented and, where needed, de-identified) so others can check an analysis or reuse the data for a new question entirely.
Open Materials & Code: Sharing the instruments (surveys, checklists, stimuli) and the exact analysis code, so the process of turning data into results is visible, not just the data itself.
Open Access & Preprints: Making the written output freely available. A preprint is a version shared publicly before (or alongside) formal peer review; open access refers more broadly to removing paywalls, whether at a preprint stage or after formal publication.
Open Educational Resources: Teaching materials (course notes, slide decks, textbooks, problem sets) released under licences that let others use, adapt, and redistribute them, rather than locking them behind a single classroom or institution.
Open Evaluation: Making the peer review process itself visible: publishing reviewer reports, naming reviewers, or opening review up to public comment, instead of it happening entirely behind closed doors.
Team Science & Open Collaboration: Structuring a project so that contribution and authorship are transparent, and so people who aren’t already part of an established network (students, researchers at smaller institutions, researchers in other countries) have a real way in.
Open Source Software: Applying the same transparency principle to the tools researchers use: statistical packages, data-collection apps, lab equipment firmware. If the tool itself can be inspected, errors in it can be found and fixed by anyone, not just its original authors.

Test Your Knowledge!

Challenge

Challenge 1:

A team preregisters their hypothesis, then shares their anonymised dataset and analysis code on a public repository after publication. They still submit their paper to a journal that sits behind a paywall. Which practice from the table above have they not adopted?

Show me the solution

Open access. They’ve adopted preregistration, open data, and open materials/code, but the paper itself is still not freely readable.

Challenge

Challenge 2:

True or False: Posting a paper on a preprint server automatically means the data behind it is also open.

Show me the solution

False. A preprint opens up the write-up. It says nothing about whether the underlying data or code has been shared; those are separate practices.

Challenge

Challenge 3:

A university course releases its full syllabus, lecture slides, and assignments under a licence that allows other instructors to reuse and adapt them for their own classes. Which category does this best fit?

A. Open Data B. Open Educational Resources C. Open Evaluation D. Team Science

Show me the solution

B. Open Educational Resources: this is specifically about teaching and training materials, distinct from research data or the review process.

Key Points

Open science is not one practice; it’s a set of distinct practices, each opening up a different part of the research process.
The major categories are: preregistration and Registered Reports, open data, open materials and code, open access and preprints, open educational resources, open evaluation, team science, and open source software.
A single study can combine several of these, and fully open projects usually do. Knowing which category a practice belongs to helps you spot what’s still missing, even when a study already looks “open” on the surface.

To-do: Add infographic.

Callout

💡 If someone tells you a study is “open,” a good follow-up question is: open in what way? The practices in this episode give you the vocabulary to ask that precisely.

Content from Episode 12.4: Types of Open Practices I: Preregistration & Registered Reports; Open Data (FAIR & CARE)

Last updated on 2026-09-08 | Edit this page

Overview

Questions

What’s the difference between preregistration and a Registered Report?
What does it actually take for data to be “open” in a useful way?
What are the FAIR and CARE principles, and why do we need both?

Objectives

Learners will be able to:

Distinguish preregistration from Registered Reports, and explain what problem each solves.
Apply the FAIR principles to evaluate whether a dataset is genuinely reusable.
Explain what the CARE principles add that FAIR alone does not cover.
Connect each practice to a real research example.

Think Like a Researcher

Picture five different teams, each convinced their vaccine hesitancy study is rigorous:

One team collects their data first, looks for whatever patterns turn up, then writes the hypothesis to match what they found. A second team locks in their hypothesis and analysis plan on a public registry weeks before touching any data. A third team goes further still: a journal reviews and provisionally accepts their entire study design before a single data point exists. A fourth team finishes their study and, when asked, says the data is “available upon request.” A fifth team uploads their dataset to a public repository, with every variable clearly labelled, in a format anyone can open.

All five believe they’ve done solid research. Only some of them have actually protected their work from a specific kind of bias, and only one has made their data something a stranger could genuinely reuse. This episode looks at exactly how and why.

Preregistration and Registered Reports

Preregistration: This means publicly and permanently recording a hypothesis and analysis plan before collecting data.

It exists to solve a specific, well-documented problem: it’s very easy, without intending to, to notice a pattern in your data and then write it up as though you predicted it all along. Preregistration draws a hard line between what you predicted and what you noticed afterwards, because once it’s timestamped, it can’t quietly change.

Example: Before surveying a single household, a research team registers their prediction (that fear of side effects is the strongest driver of vaccine hesitancy in their study area) along with exactly how they’ll measure and test it.

Registered Reports: This takes preregistration a step further. The entire study (the question, the design, the planned analysis) is submitted to a journal and peer-reviewed before any data exists. If reviewers approve the design, the journal commits, in principle, to publishing the results regardless of whether they turn out to be exciting, boring, or a flat contradiction of the hypothesis.

Registered Reports exist to fix a different problem: journals have historically been far more likely to publish “positive,” surprising results than clear null findings, which quietly pushes researchers toward chasing whichever result is publishable, rather than whichever result is true.

Example: A team wanting to test whether a short video reduces vaccine hesitancy gets their entire study design reviewed and accepted first. Whether the video works, doesn’t work, or makes no measurable difference, the result gets published either way.

Open Data: FAIR and CARE

Publishing a data file isn’t automatically the same as making data genuinely usable. Two sets of principles describe what “usable” actually requires.

FAIR: Findable, Accessible, Interoperable, Reusable

Findable: the dataset can actually be located, usually through a stable identifier and clear metadata (not buried in a personal folder no one else can find). Accessible: it can be retrieved through a known process, even if some access conditions apply for sensitive data. Interoperable: it uses standard, well-documented formats, so it can be combined with other datasets without extensive reformatting. Reusable: it’s documented clearly enough (units, variable meanings, collection method) that someone else can understand and reuse it correctly.

Example: The vaccine hesitancy team’s dataset has a permanent identifier and a full codebook explaining every column. A researcher on the other side of the country can locate it, download it, understand exactly what “hesitancy score” means, and reanalyse it without ever contacting the original team.

CARE: Collective Benefit, Authority to Control, Responsibility, Ethics

FAIR describes how usable data is. CARE, developed particularly around Indigenous and community data, addresses something FAIR doesn’t: who the data is about, and who gets a say in how it’s used.

Collective Benefit: the data should generate real benefit for the people or communities it describes, not just for outside researchers. Authority to Control: the people or communities the data comes from have a legitimate say in how it’s governed and shared. Responsibility: those handling the data are accountable for how they use it and what it’s used for. Ethics: the community’s rights and wellbeing come first, ahead of convenience for the research team.

Example: If the vaccine hesitancy study collected data from a specific community, CARE would ask the team to involve that community in decisions about how the data is shared, not just make it technically FAIR-compliant and call it done.

FAIR and CARE Together

FAIR without CARE can produce data that’s technically easy to reuse, but taken from communities who had no say in it. CARE without FAIR can produce data that’s ethically governed, but practically impossible for anyone to find or use. Well-designed open data practice treats them as complementary, not as a choice between one or the other.

Test Your Knowledge!

Challenge

Challenge 1:

A researcher analyses their data first, notices an unexpected pattern, and then writes up the paper describing that pattern as their original hypothesis. What open practice would have prevented this, and how?

Show me the solution

Preregistration. Recording the hypothesis and analysis plan before collecting data creates a timestamped record, so it’s clear afterward what was predicted in advance versus noticed only once the data was in.

Challenge

Challenge 2:

What is the key difference between preregistration and a Registered Report?
A. Registered Reports don’t require a hypothesis. B. In a Registered Report, the study design is peer-reviewed and provisionally accepted before data collection; preregistration alone does not involve journal review. C. Preregistration is only used in qualitative research. D. There is no real difference.

Show me the solution

B. Both lock in a plan in advance, but a Registered Report adds formal peer review and provisional publication acceptance before any data exists.

Challenge

Challenge 3:

A dataset has a permanent link and a detailed codebook, but the only way to request it is by emailing the original author, who left the institution two years ago and never replies. Which FAIR principle is most clearly failing here?

A. Findable B. Accessible C. Interoperable D. Reusable

Show me the solution

B. Accessible. The data may be findable (it has a link) and well-documented, but there’s no working way to actually retrieve it.

Challenge

Challenge 4:

True or False: FAIR data principles and CARE data principles are trying to solve the same problem.

Show me the solution

False. FAIR is about making data technically usable and discoverable. CARE is about who has a say in how data (especially data about specific communities) is governed and used. They’re complementary, not interchangeable.

Key Points

Preregistration timestamps a hypothesis and analysis plan before data collection, separating genuine predictions from patterns noticed after the fact.
Registered Reports go further, having the study design peer-reviewed and provisionally accepted before results exist, which removes the incentive to chase “exciting” results over accurate ones.
FAIR (Findable, Accessible, Interoperable, Reusable) describes what makes a dataset genuinely usable by others.
CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) addresses who has a say in how data about people and communities is governed: a question FAIR alone doesn’t answer.
Strong open data practice applies both FAIR and CARE together.

To-do: Add infographic.

Callout

💡 Preregistering a hypothesis doesn’t mean a study can’t explore anything unplanned. It just means the write-up has to be honest about which parts were predicted in advance and which were exploratory.