When to Trigger
Activate this skill when the user mentions:
- Reproducibility, replicability, replication crisis
- Pre-registration, registered reports, AsPredicted
- Open data, data sharing, FAIR principles
- Open code, computational reproducibility, Docker, containers
- Open access publishing, preprints, green/gold OA
- Research transparency, open science framework (OSF)
- P-hacking, HARKing, questionable research practices
- Power analysis for replication, replication study design
Step-by-Step Methodology
- Assess current reproducibility state - Evaluate the research against reproducibility dimensions: methodological (sufficient detail to replicate), computational (code + data + environment = same results), and results reproducibility (independent replication yields consistent findings). Identify specific gaps.
- Pre-registration - Guide pre-registration of hypotheses, methods, and analysis plan BEFORE data collection. Use appropriate platform: OSF Registries, AsPredicted, ClinicalTrials.gov (clinical), or PROSPERO (systematic reviews). Distinguish confirmatory from exploratory analyses.
- Data management - Apply FAIR principles: Findable (persistent identifier, metadata), Accessible (open or controlled access with clear process), Interoperable (standard formats, vocabularies), Reusable (license, provenance). Create data dictionary documenting every variable. Use tidy data formats.
- Code and computational environment - Share analysis code in a public repository (GitHub, GitLab, Zenodo for DOI). Document dependencies with requirements.txt, renv.lock, or conda environment.yml. For full reproducibility: containerize with Docker or use Binder. Include README with execution instructions.
- Replication study design - For direct replication: match original methods as closely as possible. For conceptual replication: test same hypothesis with different methods. Conduct power analysis based on original effect size (use safeguard power: assume smaller effect). Determine sample size for meaningful replication test (use equivalence testing or Bayesian replication factors).
- Reporting transparency - Follow reporting guidelines (CONSORT, STROBE, ARRIVE, PRISMA). Report all pre-specified analyses regardless of results. Clearly label exploratory analyses. Share full materials (stimuli, protocols, instruments) as supplementary files.
- Open science practices - Adopt open science badges (data, materials, pre-registration). Consider registered reports format (peer review before results). Use preprint servers (bioRxiv, medRxiv, arXiv, SSRN). Choose open access publication route.
Key Platforms and Tools
- OSF (Open Science Framework) - Project management and pre-registration
- AsPredicted - Streamlined pre-registration
- Zenodo - Data and code archival with DOI
- GitHub / GitLab - Code version control and sharing
- Docker / Binder - Computational environment reproducibility
- FAIR self-assessment tool - Data FAIRness evaluation
- COS (Center for Open Science) - Reproducibility guidelines
Output Format
- Reproducibility assessment: checklist of current state vs. best practices.
- Pre-registration template: hypotheses, design, sample, variables, analysis plan.
- Data sharing package: dataset + data dictionary + codebook + license + README.
- Computational reproducibility: repository structure, Dockerfile, execution instructions.
- Replication study protocol: power analysis, design, success criteria (equivalence test bounds or replication Bayes factor thresholds).
Quality Checklist
- Pre-registration completed before data collection/analysis
- Confirmatory and exploratory analyses clearly distinguished
- Data deposited in trusted repository with persistent identifier (DOI)
- FAIR principles self-assessment completed
- Analysis code shared and tested on a clean environment
- Computational environment documented or containerized
- All materials sufficient for independent replication
- Reporting guideline checklist completed
- License specified for data (CC-BY, CC0) and code (MIT, Apache)
- Deviations from pre-registration documented and justified