Key Points

Welcome to Pre-seeds (Research 101)!

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.

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.

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.

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.

Reproduction re-runs the original data and analysis to verify the result; replication collects new data to test whether the finding holds up more generally.
The reproducibility crisis refers to a well-documented pattern, especially visible from 2015 onward, of published findings failing to hold up when other researchers tried to reproduce or replicate them.
Most of the problem traces to misaligned incentives, under-resourcing, and questionable research practices like p-hacking, HARKing, and selective reporting, not outright fraud.
Open science responds by making the research process visible enough to check at every stage, which is why the crisis is widely seen as evidence that science self-corrects, not that it’s broken.

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.

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.

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.