Promotes research reproducibility through guidance on pre-registration, open data/code sharing, replication study design, computational reproducibility, and open science best practices; trigger when users discuss replication, open science, pre-registration, data sharing, or research transparency.
--- name: reproducibility description: Promotes research reproducibility through guidance on pre-registration, open data/code sharing, replication study design, computational reproducibility, and open science best practices; trigger when users discuss replication, open science, pre-registration, data sharing, or research transparency. --- ## 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 1. **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. 2. **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. 3. **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. 4. **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. 5. **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). 6. **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. 7. **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--- name: reproducibility description: Promotes research reproducibility through guidance on pre-registration, open data/code sharing, replication study design, computational reproducibility, and open science best practices; trigger when users discuss replication, open science, pre-registration, data sharing, or research transparency. --- ## 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 1. **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. 2. **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. 3. **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. 4. **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. 5. **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). 6. **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. 7. **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
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.