pre-registration
Create pre-registration documents for research transparency. Use when: (1) Before data collection, (2) Grant submissions, (3) Ensuring reproducibility, (4) Meeting open science standards, (5) Preventing HARKing.
When & Why to Use This Skill
This Claude skill facilitates the creation of comprehensive pre-registration documents to enhance research transparency and academic integrity. By guiding researchers through the formalization of hypotheses, study designs, and analysis plans before data collection, it helps prevent questionable research practices such as HARKing (Hypothesizing After the Results are Known) and ensures compliance with global open science standards and journal requirements.
Use Cases
- Grant Submissions: Drafting rigorous study protocols and methodological frameworks required by funding agencies to demonstrate project feasibility and transparency.
- Open Science Compliance: Generating standardized documentation for registration on platforms like OSF (Open Science Framework) or AsPredicted to ensure study reproducibility.
- Methodological Planning: Defining precise sample sizes, power analyses, and inclusion/exclusion criteria to minimize bias before the data collection phase begins.
- Statistical Rigor: Pre-specifying primary statistical tests and data cleaning procedures to eliminate post-hoc analysis bias and improve the credibility of findings.
| name | pre-registration |
|---|---|
| description | "Create pre-registration documents for research transparency. Use when: (1) Before data collection, (2) Grant submissions, (3) Ensuring reproducibility, (4) Meeting open science standards, (5) Preventing HARKing." |
| allowed-tools | Read, Write |
| version | 1.0.0 |
Pre-Registration Skill
Purpose
Document study plans before data collection to ensure transparency and prevent questionable research practices.
Pre-Registration Components
1. Study Information
- Title, authors, institutions
- Funding sources
2. Hypotheses
- Primary hypothesis (pre-specified)
- Secondary hypotheses
- Exploratory questions
3. Design
- Study type (observational, experimental)
- Blinding (who, how)
- Randomization details
4. Sampling
- Sample size (with power analysis)
- Recruitment strategy
- Inclusion/exclusion criteria
- Stopping rules
5. Variables
- Independent variables (manipulation)
- Dependent variables (outcomes)
- Control variables
- Measurement instruments
6. Analysis Plan
- Primary statistical test
- Handling missing data
- Outlier treatment
- Multiple comparison corrections
7. Timeline
- Start date
- Data collection period
- Analysis completion
Platforms
- OSF (Open Science Framework): osf.io
- AsPredicted: aspredicted.org
- ClinicalTrials.gov: For clinical trials
Version: 1.0.0