Protocol preregistration templates

Predefine your methods and analysis plans so results are clearer, more credible, and easier to reproduce.

Why preregister?

Preregistration means writing down your study plan before you collect or analyze data. Doing so makes assumptions and decisions visible, reduces the chance of selective reporting or post-hoc changes, and speeds evaluation by reviewers, collaborators, and stakeholders. Whether you run lab experiments, observational studies, clinical trials, machine-learning benchmarks, or qualitative interviews, a clear preregistration helps your team make deliberate choices and defend them transparently.

What this resource helps you do

Use these templates to:

• Specify hypotheses, outcomes, inclusion/exclusion criteria, and primary endpoints;
• Document sampling plans, power/sample-size reasoning, stopping rules, and randomization;
• Predefine data cleaning, transformation, and statistical or analytic methods (including model selection and cross-validation strategies);
• Declare planned subgroup analyses, interim looks, and secondary outcomes;
• Record data sharing, privacy, consent, and ethical considerations so disclosure is responsible;
• Provide an accessible audit trail for collaborators, funders, journals, and registries.

Who benefits

Researchers and teams across disciplines — individual investigators, small labs, multi-site collaborations, clinical teams, data scientists, and nonprofit or industry R&D groups — can use these templates. Examples: a bench scientist documenting experimental methods and blinding, an epidemiologist fixing an analysis plan for a cohort study, a data scientist predefining model evaluation for a benchmark, or a social scientist specifying coding rules for qualitative analysis.

How to use the templates in practice

Start by choosing the template closest to your study type (experimental, observational, simulation, qualitative, or computational). Adapt fields for your discipline, record rationale where choices are exploratory, and keep clear separation between confirmatory and exploratory analyses. When possible, copy a template into your research domain to preserve a dated record. Use interactive forms to complete and save preregistrations so your team can track versions and share them with reviewers or registries.

Boundaries and good practice

Templates reduce ambiguity but do not replace ethics review, data protection requirements, or domain-specific methodological oversight. Do not disclose sensitive participant data or proprietary details without appropriate consent, IRB approval, or legal clearance. If your study must remain confidential for safety, legal, or IP reasons, use the templates to document decisions internally and consult relevant governance before public sharing.