Experiment Protocol & Pre-registration Template

Purpose and how to use this template

This template helps you create a clear, auditable experiment protocol you can preregister and follow. Complete every section before beginning data collection. Be specific: ambiguous endpoints or missing methods are the largest causes of irreproducibility. Keep a version history and sign-offs so later reviewers can see what changed and why.

Instructions

1. Fill each field with actionable detail (who, what, when, where, how, and why).
2. Attach or link to SOPs, data collection forms, code, and calibration records.
3. Preregister the protocol (institutional registry, OSF, preprint server, or internal registry) before unblinded analysis.
4. Record version changes and obtain required sign-offs prior to starting experiments.

Protocol template

1. Administrative information

• Title:
• Protocol ID / Short code:
• Version:
• Date:
• Authors / Responsible investigator(s): (name, role, contact)
• Affiliations / Lab / Team:
• Related project or registry link (URL / DOI):

2. Objective, hypothesis, and rationale

• Primary objective: (concise statement of the goal)
• Primary hypothesis: (explicit, testable, directional if applicable)
• Secondary hypotheses / exploratory questions:
• Rationale & brief background (key references): (why this matters and prior evidence)

3. Outcomes, endpoints, and success criteria

• Primary outcome(s): (operational definition, unit, measurement method, time point)
• Secondary outcome(s): (as above)
• Derived variables / composite outcomes: (clearly define formulas)
• Success / decision criteria: (pre-specified thresholds, equivalence margins, minimum detectable effect, or decision rules)
• Stopping rules / interim analysis rules: (criteria for early stopping, safety triggers, alpha spending plan if relevant)

4. Study design

• Design type: (e.g., randomized controlled experiment, factorial, within-subject, crossover, observational)
• Arms / groups and interventions: (detailed description of each arm)
• Allocation ratio: (e.g., 1:1, 2:1)
• Unit of analysis: (e.g., sample, subject, batch)
• Timeline / schedule of measurements: (timepoints, durations, key milestones)

5. Materials, equipment and supply details

• Critical materials & suppliers (catalog / lot numbers):
• Instruments & calibrated equipment: (make/model, calibration dates)
• Reagent preparation and storage conditions:

6. Stepwise methods (detailed protocol)

Provide a numbered, time-stamped procedure detailed enough for another trained person to reproduce the experiment:

1. Sample sourcing/preparation (including inclusion/exclusion criteria).
2. Pre-analytical handling (temperature, timing, shipping).
3. Exact procedural steps (instrument settings, volumes, incubation times).
4. Data collection procedures (who records what, when).
5. Post-procedure handling (storage, disposal).

7. Randomization and blinding

• Randomization method: (algorithm, software, seed, block/stratification details)
• Allocation concealment: (how assignment is concealed from performers)
• Blinding: (who is blinded — performers, assessors, analysts — and how blinding is maintained)
• Unblinding procedure: (who can unblind and under what conditions)

8. Sample size and power justification

• Sample size calculation: (formula or software used, inputs: expected effect, SD, alpha, power)
• Assumptions and sources for inputs: (pilot data, literature, expert judgment)
• Planned number per group and total N:
• Planned interim analyses and adjustments: (if any)

9. Data capture, variables and file formats

• Data collection forms (links or attached templates):
• Variable list / data dictionary: (variable name, type, units, permissible values)
• File formats and naming conventions: (CSV, .xlsx, raw instrument formats)
• Provenance and metadata capture: (timestamps, operator, instrument ID)
• Data storage, backup and access controls:

10. Quality assurance and controls

• Calibration & QA checks: (frequency, acceptance ranges)
• Negative/positive controls and replicates: (number and placement)
• Criteria for repeating an assay or discarding data:
• Audit & monitoring plan: (who inspects, schedule)

11. Analysis plan (pre-specified)

• Primary analysis method: (statistical test/model, assumptions)
• Software and versions: (e.g., R 4.2.1, Python 3.9, SPSS v27)
• Data preprocessing steps: (transformations, normalization)
• Handling missing data and outliers: (rules, imputation methods)
• Planned subgroup or sensitivity analyses: (pre-specified only)
• Multiplicity correction strategy: (if multiple outcomes/tests)

12. Reproducibility, transparency and sharing

• Code availability: (repository link, expected release timing, license)
• Data sharing plan: (public deposit, embargo, restricted access)
• Materials and reagent sharing: (how others can obtain critical materials)
• Preregistration link and timestamp: (include DOI or registry ID)

13. Ethical, safety and regulatory approvals

• Approvals required and status: (IRB, IACUC, biosafety, other)
• Consent procedures (if human subjects):
• Hazardous materials handling:

14. Risks, contingencies and limitations

• Known risks and mitigation plans:
• Contingency plans for supply or equipment failure:
• Limitations that may affect interpretation:

15. Version history, review and sign-offs

• Version log: (version, date, author, summary of change)
• Approvals / sign-offs: (name, role, electronic signature or initials, date)

16. Appendices (attach as needed)

• Data dictionary (CSV or table)
• Raw instrument output examples
• Randomization seed and code snippet
• Power calculation worksheet
• Standard operating procedures referenced above

Pre-registration checklist (quick)

• All primary and secondary outcomes fully specified and operationalized.
• Sample size calculation documented with assumptions and software.
• Primary analysis method and handling of missing data specified.
• Randomization and blinding plan documented (including seed where applicable).
• Materials, instruments, and QA plans described.
• Version saved and sign-offs completed prior to unblinded analysis.
• Preregistration entry created and link recorded in protocol.

Quick tips to reduce common errors

• Prefer precise numeric definitions for outcomes (avoid vague language like “improve”).
• Record instrument settings and exact software versions—these matter for reproducibility.
• Save randomization seeds and code; store them with the protocol.
• Don't change primary outcomes after seeing data; if you must, clearly document and version the change.
• When in doubt, add clarity: it's better to over-document than to leave gaps for future reviewers.

Example (brief): Primary outcome = mean enzyme activity (U/mL) at 24 hours measured by Assay X (Kit #123, lot 456) using plate reader Y (Model, calibrated on 2026-01-10). Analysis: two-sample t-test on log-transformed activity; alpha=0.05; planned N per group=12 based on effect size=0.8, SD=0.7 (see power worksheet in Appendix).

End of template. Use this template as the canonical protocol to be stored with experimental data and analysis code. Attach linked resources and record the preregistration URL on the cover page.