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skills/peer-review/references/common_issues.md

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+# Common Methodological and Statistical Issues in Scientific Manuscripts
+
+This document catalogs frequent issues encountered during peer review, organized by category. Use this as a reference to identify potential problems and provide constructive feedback.
+
+## Statistical Issues
+
+### 1. P-Value Misuse and Misinterpretation
+
+**Common Problems:**
+- P-hacking (selective reporting of significant results)
+- Multiple testing without correction (familywise error rate inflation)
+- Interpreting non-significance as proof of no effect
+- Focusing exclusively on p-values without effect sizes
+- Dichotomizing continuous p-values at arbitrary thresholds (p=0.049 vs p=0.051)
+- Confusing statistical significance with biological/clinical significance
+
+**How to Identify:**
+- Suspiciously high proportion of p-values just below 0.05
+- Many tests performed but no correction mentioned
+- Statements like "no difference was found" from non-significant results
+- No effect sizes or confidence intervals reported
+- Language suggesting p-values indicate strength of effect
+
+**What to Recommend:**
+- Report effect sizes with confidence intervals
+- Apply appropriate multiple testing corrections (Bonferroni, FDR, Holm-Bonferroni)
+- Interpret non-significance cautiously (lack of evidence ≠ evidence of lack)
+- Pre-register analyses to avoid p-hacking
+- Consider equivalence testing for "no difference" claims
+
+### 2. Inappropriate Statistical Tests
+
+**Common Problems:**
+- Using parametric tests when assumptions are violated (non-normal data, unequal variances)
+- Analyzing paired data with unpaired tests
+- Using t-tests for multiple groups instead of ANOVA with post-hoc tests
+- Treating ordinal data as continuous
+- Ignoring repeated measures structure
+- Using correlation when regression is more appropriate
+
+**How to Identify:**
+- No mention of assumption checking
+- Small sample sizes with parametric tests
+- Multiple pairwise t-tests instead of ANOVA
+- Likert scales analyzed with t-tests
+- Time-series data analyzed without accounting for repeated measures
+
+**What to Recommend:**
+- Check assumptions explicitly (normality tests, Q-Q plots)
+- Use non-parametric alternatives when appropriate
+- Apply proper corrections for multiple comparisons after ANOVA
+- Use mixed-effects models for repeated measures
+- Consider ordinal regression for ordinal outcomes
+
+### 3. Sample Size and Power Issues
+
+**Common Problems:**
+- No sample size justification or power calculation
+- Underpowered studies claiming "no effect"
+- Post-hoc power calculations (which are uninformative)
+- Stopping rules not pre-specified
+- Unequal group sizes without justification
+
+**How to Identify:**
+- Small sample sizes (n<30 per group for typical designs)
+- No mention of power analysis in methods
+- Statements about post-hoc power
+- Wide confidence intervals suggesting imprecision
+- Claims of "no effect" with large p-values and small n
+
+**What to Recommend:**
+- Conduct a priori power analysis based on expected effect size
+- Report achieved power or precision (confidence interval width)
+- Acknowledge when studies are underpowered
+- Consider effect sizes and confidence intervals for interpretation
+- Pre-register sample size and stopping rules
+
+### 4. Missing Data Problems
+
+**Common Problems:**
+- Complete case analysis without justification (listwise deletion)
+- Not reporting extent or pattern of missingness
+- Assuming data are missing completely at random (MCAR) without testing
+- Inappropriate imputation methods
+- Not performing sensitivity analyses
+
+**How to Identify:**
+- Different n values across analyses without explanation
+- No discussion of missing data
+- Participants "excluded from analysis"
+- Simple mean imputation used
+- No sensitivity analyses comparing complete vs. imputed data
+
+**What to Recommend:**
+- Report extent and patterns of missingness
+- Test MCAR assumption (Little's test)
+- Use appropriate methods (multiple imputation, maximum likelihood)
+- Perform sensitivity analyses
+- Consider intention-to-treat analysis for trials
+
+### 5. Circular Analysis and Double-Dipping
+
+**Common Problems:**
+- Using the same data for selection and inference
+- Defining ROIs based on contrast then testing that contrast in same ROI
+- Selecting outliers then testing for differences
+- Post-hoc subgroup analyses presented as planned
+- HARKing (Hypothesizing After Results are Known)
+
+**How to Identify:**
+- ROIs or features selected based on results
+- Unexpected subgroup analyses
+- Post-hoc analyses not clearly labeled as exploratory
+- No data-independent validation
+- Introduction that perfectly predicts findings
+
+**What to Recommend:**
+- Use independent datasets for selection and testing
+- Pre-register analyses and hypotheses
+- Clearly distinguish confirmatory vs. exploratory analyses
+- Use cross-validation or hold-out datasets
+- Correct for selection bias
+
+### 6. Pseudoreplication
+
+**Common Problems:**
+- Technical replicates treated as biological replicates
+- Multiple measurements from same subject treated as independent
+- Clustered data analyzed without accounting for clustering
+- Non-independence in spatial or temporal data
+
+**How to Identify:**
+- n defined as number of measurements rather than biological units
+- Multiple cells from same animal counted as independent
+- Repeated measures not acknowledged
+- No mention of random effects or clustering
+
+**What to Recommend:**
+- Define n as biological replicates (animals, patients, independent samples)
+- Use mixed-effects models for nested or clustered data
+- Account for repeated measures explicitly
+- Average technical replicates before analysis
+- Report both technical and biological replication
+
+## Experimental Design Issues
+
+### 7. Lack of Appropriate Controls
+
+**Common Problems:**
+- Missing negative controls
+- Missing positive controls for validation
+- No vehicle controls for drug studies
+- No time-matched controls for longitudinal studies
+- No batch controls
+
+**How to Identify:**
+- Methods section lists only experimental groups
+- No mention of controls in figures
+- Unclear baseline or reference condition
+- Cross-batch comparisons without controls
+
+**What to Recommend:**
+- Include negative controls to assess specificity
+- Include positive controls to validate methods
+- Use vehicle controls matched to experimental treatment
+- Include sham surgery controls for surgical interventions
+- Include batch controls for cross-batch comparisons
+
+### 8. Confounding Variables
+
+**Common Problems:**
+- Systematic differences between groups besides intervention
+- Batch effects not controlled or corrected
+- Order effects in sequential experiments
+- Time-of-day effects not controlled
+- Experimenter effects not blinded
+
+**How to Identify:**
+- Groups differ in multiple characteristics
+- Samples processed in different batches by group
+- No randomization of sample order
+- No mention of blinding
+- Baseline characteristics differ between groups
+
+**What to Recommend:**
+- Randomize experimental units to conditions
+- Block on known confounders
+- Randomize sample processing order
+- Use blinding to minimize bias
+- Perform batch correction if needed
+- Report and adjust for baseline differences
+
+### 9. Insufficient Replication
+
+**Common Problems:**
+- Single experiment without replication
+- Technical replicates mistaken for biological replication
+- Small n justified by "typical for the field"
+- No independent validation of key findings
+- Cherry-picking representative examples
+
+**How to Identify:**
+- Methods state "experiment performed once"
+- n=3 with no justification
+- "Representative image shown"
+- Key claims based on single experiment
+- No validation in independent dataset
+
+**What to Recommend:**
+- Perform independent biological replicates (typically ≥3)
+- Validate key findings in independent cohorts
+- Report all replicates, not just representative examples
+- Conduct power analysis to justify sample size
+- Show individual data points, not just summary statistics
+
+## Reproducibility Issues
+
+### 10. Insufficient Methodological Detail
+
+**Common Problems:**
+- Methods not described in sufficient detail for replication
+- Key reagents not specified (vendor, catalog number)
+- Software versions and parameters not reported
+- Antibodies not validated
+- Cell line authentication not verified
+
+**How to Identify:**
+- Vague descriptions ("standard protocols were used")
+- No information on reagent sources
+- Generic software mentioned without versions
+- No antibody validation information
+- Cell lines not authenticated
+
+**What to Recommend:**
+- Provide detailed protocols or cite specific protocols
+- Include reagent vendors, catalog numbers, lot numbers
+- Report software versions and all parameters
+- Include antibody validation (Western blot, specificity tests)
+- Report cell line authentication method (STR profiling)
+- Make protocols available (protocols.io, supplementary materials)
+
+### 11. Data and Code Availability
+
+**Common Problems:**
+- No data availability statement
+- "Data available upon request" (often unfulfilled)
+- No code provided for computational analyses
+- Custom software not made available
+- No clear documentation
+
+**How to Identify:**
+- Missing data availability statement
+- No repository accession numbers
+- Computational methods with no code
+- Custom pipelines without access
+- No README or documentation
+
+**What to Recommend:**
+- Deposit raw data in appropriate repositories (GEO, SRA, Dryad, Zenodo)
+- Share analysis code on GitHub or similar
+- Provide clear documentation and README files
+- Include requirements.txt or environment files
+- Make custom software available with installation instructions
+- Use DOIs for permanent data citation
+
+### 12. Lack of Method Validation
+
+**Common Problems:**
+- New methods not compared to gold standard
+- Assays not validated for specificity, sensitivity, linearity
+- No spike-in controls
+- Cross-reactivity not tested
+- Detection limits not established
+
+**How to Identify:**
+- Novel assays presented without validation
+- No comparison to existing methods
+- No positive/negative controls shown
+- Claims of specificity without evidence
+- No standard curves or controls
+
+**What to Recommend:**
+- Validate new methods against established approaches
+- Show specificity (knockdown/knockout controls)
+- Demonstrate linearity and dynamic range
+- Include positive and negative controls
+- Report limits of detection and quantification
+- Show reproducibility across replicates and operators
+
+## Interpretation Issues
+
+### 13. Overstatement of Results
+
+**Common Problems:**
+- Causal language for correlational data
+- Mechanistic claims without mechanistic evidence
+- Extrapolating beyond data (species, conditions, populations)
+- Claiming "first to show" without thorough literature review
+- Overgeneralizing from limited samples
+
+**How to Identify:**
+- "X causes Y" from observational data
+- Mechanism proposed without direct testing
+- Mouse data presented as relevant to humans without caveats
+- Claims of novelty with missing citations
+- Broad claims from narrow samples
+
+**What to Recommend:**
+- Use appropriate language ("associated with" vs. "caused by")
+- Distinguish correlation from causation
+- Acknowledge limitations of model systems
+- Provide thorough literature context
+- Be specific about generalizability
+- Propose mechanisms as hypotheses, not conclusions
+
+### 14. Cherry-Picking and Selective Reporting
+
+**Common Problems:**
+- Reporting only significant results
+- Showing "representative" images that may not be typical
+- Excluding outliers without justification
+- Not reporting negative or contradictory findings
+- Switching between different statistical approaches
+
+**How to Identify:**
+- All reported results are significant
+- "Representative of 3 experiments" with no quantification
+- Data exclusions mentioned in results but not methods
+- Supplementary data contradicts main findings
+- Multiple analysis approaches with only one reported
+
+**What to Recommend:**
+- Report all planned analyses regardless of outcome
+- Quantify and show variability across replicates
+- Pre-specify outlier exclusion criteria
+- Include negative results
+- Pre-register analysis plan
+- Report effect sizes and confidence intervals for all comparisons
+
+### 15. Ignoring Alternative Explanations
+
+**Common Problems:**
+- Preferred explanation presented without considering alternatives
+- Contradictory evidence dismissed without discussion
+- Off-target effects not considered
+- Confounding variables not acknowledged
+- Limitations section minimal or absent
+
+**How to Identify:**
+- Single interpretation presented as fact
+- Prior contradictory findings not cited or discussed
+- No consideration of alternative mechanisms
+- No discussion of limitations
+- Specificity assumed without controls
+
+**What to Recommend:**
+- Discuss alternative explanations
+- Address contradictory findings from literature
+- Include appropriate specificity controls
+- Acknowledge and discuss limitations thoroughly
+- Consider and test alternative hypotheses
+
+## Figure and Data Presentation Issues
+
+### 16. Inappropriate Data Visualization
+
+**Common Problems:**
+- Bar graphs for continuous data (hiding distributions)
+- No error bars or error bars not defined
+- Truncated y-axes exaggerating differences
+- Dual y-axes creating misleading comparisons
+- Too many significant figures
+- Colors not colorblind-friendly
+
+**How to Identify:**
+- Bar graphs with few data points
+- Unclear what error bars represent (SD, SEM, CI?)
+- Y-axis doesn't start at zero for ratio/percentage data
+- Left and right y-axes with different scales
+- Values reported to excessive precision (p=0.04562)
+- Red-green color schemes
+
+**What to Recommend:**
+- Show individual data points with scatter/box/violin plots
+- Always define error bars (SD, SEM, 95% CI)
+- Start y-axis at zero or indicate breaks clearly
+- Avoid dual y-axes; use separate panels instead
+- Report appropriate significant figures
+- Use colorblind-friendly palettes (viridis, colorbrewer)
+- Include sample sizes in figure legends
+
+### 17. Image Manipulation Concerns
+
+**Common Problems:**
+- Excessive contrast/brightness adjustment
+- Spliced gels or images without indication
+- Duplicated images or panels
+- Uneven background in Western blots
+- Selective cropping
+- Over-processed microscopy images
+
+**How to Identify:**
+- Suspicious patterns or discontinuities
+- Very high contrast with no background
+- Similar features in different panels
+- Straight lines suggesting splicing
+- Inconsistent backgrounds
+- Loss of detail suggesting over-processing
+
+**What to Recommend:**
+- Apply adjustments uniformly across images
+- Indicate spliced gels with dividing lines
+- Show full, uncropped images in supplementary materials
+- Provide original images if requested
+- Follow journal image integrity policies
+- Use appropriate image analysis tools
+
+## Study Design Issues
+
+### 18. Poorly Defined Hypotheses and Outcomes
+
+**Common Problems:**
+- No clear hypothesis stated
+- Primary outcome not specified
+- Multiple outcomes without correction
+- Outcomes changed after data collection
+- Fishing expeditions presented as hypothesis-driven
+
+**How to Identify:**
+- Introduction doesn't state clear testable hypothesis
+- Multiple outcomes with unclear hierarchy
+- Outcomes in results don't match those in methods
+- Exploratory study presented as confirmatory
+- Many tests with no multiple testing correction
+
+**What to Recommend:**
+- State clear, testable hypotheses
+- Designate primary and secondary outcomes a priori
+- Pre-register studies when possible
+- Apply appropriate corrections for multiple outcomes
+- Clearly distinguish exploratory from confirmatory analyses
+- Report all pre-specified outcomes
+
+### 19. Baseline Imbalance and Selection Bias
+
+**Common Problems:**
+- Groups differ at baseline
+- Selection criteria applied differentially
+- Healthy volunteer bias
+- Survivorship bias
+- Indication bias in observational studies
+
+**How to Identify:**
+- Table 1 shows significant baseline differences
+- Inclusion criteria different between groups
+- Response rate <50% with no analysis
+- Analysis only includes completers
+- Groups self-selected rather than randomized
+
+**What to Recommend:**
+- Report baseline characteristics in Table 1
+- Use randomization to ensure balance
+- Adjust for baseline differences in analysis
+- Report response rates and compare responders vs. non-responders
+- Consider propensity score matching for observational data
+- Use intention-to-treat analysis
+
+### 20. Temporal and Batch Effects
+
+**Common Problems:**
+- Samples processed in batches by condition
+- Temporal trends not accounted for
+- Instrument drift over time
+- Different operators for different groups
+- Reagent lot changes between groups
+
+**How to Identify:**
+- All treatment samples processed on same day
+- Controls from different time period
+- No mention of batch or time effects
+- Different technicians for groups
+- Long study duration with no temporal analysis
+
+**What to Recommend:**
+- Randomize samples across batches/time
+- Include batch as covariate in analysis
+- Perform batch correction (ComBat, limma)
+- Include quality control samples across batches
+- Report and test for temporal trends
+- Balance operators across conditions
+
+## Reporting Issues
+
+### 21. Incomplete Statistical Reporting
+
+**Common Problems:**
+- Test statistics not reported
+- Degrees of freedom missing
+- Exact p-values replaced with inequalities (p<0.05)
+- No confidence intervals
+- No effect sizes
+- Sample sizes not reported per group
+
+**How to Identify:**
+- Only p-values given with no test statistics
+- p-values reported as p<0.05 rather than exact values
+- No measures of uncertainty
+- Effect magnitude unclear
+- n reported for total but not per group
+
+**What to Recommend:**
+- Report complete test statistics (t, F, χ², etc. with df)
+- Report exact p-values (except p<0.001)
+- Include 95% confidence intervals
+- Report effect sizes (Cohen's d, odds ratios, correlation coefficients)
+- Report n for each group in every analysis
+- Consider CONSORT-style flow diagram
+
+### 22. Methods-Results Mismatch
+
+**Common Problems:**
+- Methods describe analyses not performed
+- Results include analyses not described in methods
+- Different sample sizes in methods vs. results
+- Methods mention controls not shown
+- Statistical methods don't match what was done
+
+**How to Identify:**
+- Analyses in results without methodological description
+- Methods describe experiments not in results
+- Numbers don't match between sections
+- Controls mentioned but not shown
+- Different software mentioned than used
+
+**What to Recommend:**
+- Ensure complete concordance between methods and results
+- Describe all analyses performed in methods
+- Remove methodological descriptions of experiments not performed
+- Verify all numbers are consistent
+- Update methods to match actual analyses conducted
+
+## How to Use This Reference
+
+When reviewing manuscripts:
+1. Read through methods and results systematically
+2. Check for common issues in each category
+3. Note specific problems with evidence
+4. Provide constructive suggestions for improvement
+5. Distinguish major issues (affect validity) from minor issues (affect clarity)
+6. Prioritize reproducibility and transparency
+
+This is not an exhaustive list but covers the most frequently encountered issues. Always consider the specific context and discipline when evaluating potential problems.

skills/peer-review/references/reporting_standards.md

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+# Scientific Reporting Standards and Guidelines
+
+This document catalogs major reporting standards and guidelines across scientific disciplines. When reviewing manuscripts, verify that authors have followed the appropriate guidelines for their study type and discipline.
+
+## Clinical Trials and Medical Research
+
+### CONSORT (Consolidated Standards of Reporting Trials)
+**Purpose:** Randomized controlled trials (RCTs)
+**Key Requirements:**
+- Trial design, participants, and interventions clearly described
+- Primary and secondary outcomes specified
+- Sample size calculation and statistical methods
+- Participant flow through trial (enrollment, allocation, follow-up, analysis)
+- Baseline characteristics of participants
+- Numbers analyzed in each group
+- Outcomes and estimation with confidence intervals
+- Adverse events
+- Trial registration number and protocol access
+
+**Reference:** http://www.consort-statement.org/
+
+### STROBE (Strengthening the Reporting of Observational Studies in Epidemiology)
+**Purpose:** Observational studies (cohort, case-control, cross-sectional)
+**Key Requirements:**
+- Study design clearly stated
+- Setting, eligibility criteria, and participant sources
+- Variables clearly defined
+- Data sources and measurement methods
+- Bias assessment
+- Sample size justification
+- Statistical methods including handling of missing data
+- Participant flow and characteristics
+- Main results with confidence intervals
+- Limitations discussed
+
+**Reference:** https://www.strobe-statement.org/
+
+### PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)
+**Purpose:** Systematic reviews and meta-analyses
+**Key Requirements:**
+- Protocol registration
+- Systematic search strategy across multiple databases
+- Inclusion/exclusion criteria
+- Study selection process
+- Data extraction methods
+- Quality assessment of included studies
+- Statistical methods for meta-analysis
+- Assessment of publication bias
+- Heterogeneity assessment
+- PRISMA flow diagram showing study selection
+- Summary of findings tables
+
+**Reference:** http://www.prisma-statement.org/
+
+### SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials)
+**Purpose:** Clinical trial protocols
+**Key Requirements:**
+- Administrative information (title, registration, funding)
+- Introduction (rationale, objectives)
+- Methods (design, participants, interventions, outcomes, sample size)
+- Ethics and dissemination
+- Trial schedule and assessments
+
+**Reference:** https://www.spirit-statement.org/
+
+### CARE (CAse REport guidelines)
+**Purpose:** Case reports
+**Key Requirements:**
+- Patient information and demographics
+- Clinical findings
+- Timeline of events
+- Diagnostic assessment
+- Therapeutic interventions
+- Follow-up and outcomes
+- Patient perspective
+- Informed consent
+
+**Reference:** https://www.care-statement.org/
+
+## Animal Research
+
+### ARRIVE (Animal Research: Reporting of In Vivo Experiments)
+**Purpose:** Studies involving animal research
+**Key Requirements:**
+- Title indicates study involves animals
+- Abstract provides accurate summary
+- Background and objectives clearly stated
+- Ethical statement and approval
+- Housing and husbandry details
+- Animal details (species, strain, sex, age, weight)
+- Experimental procedures in detail
+- Experimental animals (number, allocation, welfare assessment)
+- Statistical methods appropriate
+- Exclusion criteria stated
+- Sample size determination
+- Randomization and blinding described
+- Outcome measures defined
+- Adverse events reported
+
+**Reference:** https://arriveguidelines.org/
+
+## Genomics and Molecular Biology
+
+### MIAME (Minimum Information About a Microarray Experiment)
+**Purpose:** Microarray experiments
+**Key Requirements:**
+- Experimental design clearly described
+- Array design information
+- Samples (origin, preparation, labeling)
+- Hybridization procedures and parameters
+- Image acquisition and quantification
+- Normalization and data transformation
+- Raw and processed data availability
+- Database accession numbers
+
+**Reference:** http://fged.org/projects/miame/
+
+### MINSEQE (Minimum Information about a high-throughput Nucleotide Sequencing Experiment)
+**Purpose:** High-throughput sequencing (RNA-seq, ChIP-seq, etc.)
+**Key Requirements:**
+- Experimental design and biological context
+- Sample information (source, preparation, QC)
+- Library preparation (protocol, adapters, size selection)
+- Sequencing platform and parameters
+- Data processing pipeline (alignment, quantification, normalization)
+- Quality control metrics
+- Raw data deposition (SRA, GEO, ENA)
+- Processed data and analysis code availability
+
+### MIGS/MIMS (Minimum Information about a Genome/Metagenome Sequence)
+**Purpose:** Genome and metagenome sequencing
+**Key Requirements:**
+- Sample origin and environmental context
+- Sequencing methods and coverage
+- Assembly methods and quality metrics
+- Annotation approach
+- Quality control and contamination screening
+- Data deposition in INSDC databases
+
+**Reference:** https://gensc.org/
+
+## Structural Biology
+
+### PDB (Protein Data Bank) Deposition Requirements
+**Purpose:** Macromolecular structure determination
+**Key Requirements:**
+- Atomic coordinates deposited
+- Structure factors for X-ray structures
+- Restraints and experimental data for NMR
+- EM maps and metadata for cryo-EM
+- Model quality validation metrics
+- Experimental conditions (crystallization, sample preparation)
+- Data collection parameters
+- Refinement statistics
+
+**Reference:** https://www.wwpdb.org/
+
+## Proteomics and Mass Spectrometry
+
+### MIAPE (Minimum Information About a Proteomics Experiment)
+**Purpose:** Proteomics experiments
+**Key Requirements:**
+- Sample processing and fractionation
+- Separation methods (2D gel, LC)
+- Mass spectrometry parameters (instrument, acquisition)
+- Database search and validation parameters
+- Peptide and protein identification criteria
+- Quantification methods
+- Statistical analysis
+- Data deposition (PRIDE, PeptideAtlas)
+
+**Reference:** http://www.psidev.info/
+
+## Neuroscience
+
+### COBIDAS (Committee on Best Practices in Data Analysis and Sharing)
+**Purpose:** MRI and fMRI studies
+**Key Requirements:**
+- Scanner and sequence parameters
+- Preprocessing pipeline details
+- Software versions and parameters
+- Statistical analysis approach
+- Multiple comparison correction
+- ROI definitions
+- Data sharing (raw data, analysis scripts)
+
+**Reference:** https://www.humanbrainmapping.org/cobidas
+
+## Flow Cytometry
+
+### MIFlowCyt (Minimum Information about a Flow Cytometry Experiment)
+**Purpose:** Flow cytometry experiments
+**Key Requirements:**
+- Experimental overview and purpose
+- Sample characteristics and preparation
+- Instrument information and settings
+- Reagents (antibodies, fluorophores, concentrations)
+- Compensation and controls
+- Gating strategy
+- Data analysis approach
+- Data availability
+
+**Reference:** http://flowcyt.org/
+
+## Ecology and Environmental Science
+
+### MIAPPE (Minimum Information About a Plant Phenotyping Experiment)
+**Purpose:** Plant phenotyping studies
+**Key Requirements:**
+- Investigation and study metadata
+- Biological material information
+- Environmental parameters
+- Experimental design and factors
+- Phenotypic measurements and methods
+- Data file descriptions
+
+**Reference:** https://www.miappe.org/
+
+## Chemistry and Chemical Biology
+
+### MIRIBEL (Minimum Information Reporting in Bio-Nano Experimental Literature)
+**Purpose:** Nanomaterial characterization
+**Key Requirements:**
+- Nanomaterial composition and structure
+- Size, shape, and morphology characterization
+- Surface chemistry and functionalization
+- Purity and stability
+- Experimental conditions
+- Characterization methods
+
+## Quality Assessment and Bias
+
+### CAMARADES (Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies)
+**Purpose:** Quality assessment for animal studies in systematic reviews
+**Key Items:**
+- Publication in peer-reviewed journal
+- Statement of temperature control
+- Randomization to treatment
+- Blinded assessment of outcome
+- Avoidance of anesthetic with marked intrinsic properties
+- Use of appropriate animal model
+- Sample size calculation
+- Compliance with regulatory requirements
+- Statement of conflict of interest
+- Study pre-registration
+
+### SYRCLE's Risk of Bias Tool
+**Purpose:** Assessing risk of bias in animal intervention studies
+**Domains:**
+- Selection bias (sequence generation, baseline characteristics, allocation concealment)
+- Performance bias (random housing, blinding of personnel)
+- Detection bias (random outcome assessment, blinding of assessors)
+- Attrition bias (incomplete outcome data)
+- Reporting bias (selective outcome reporting)
+- Other sources of bias
+
+## General Principles Across Guidelines
+
+### Common Requirements
+1. **Transparency:** All methods, materials, and analyses fully described
+2. **Reproducibility:** Sufficient detail for independent replication
+3. **Data Availability:** Raw data and analysis code shared or deposited
+4. **Registration:** Studies pre-registered where applicable
+5. **Ethics:** Appropriate approvals and consent documented
+6. **Conflicts of Interest:** Disclosed for all authors
+7. **Statistical Rigor:** Methods appropriate and fully described
+8. **Completeness:** All outcomes reported, including negative results
+
+### Red Flags for Non-Compliance
+- Methods section lacks critical details
+- No mention of following reporting guidelines
+- Data availability statement missing or vague
+- No database accession numbers for omics data
+- No trial registration for clinical studies
+- Sample size not justified
+- Statistical methods inadequately described
+- Missing flow diagrams (CONSORT, PRISMA)
+- Selective reporting of outcomes
+
+## How to Use This Reference
+
+When reviewing a manuscript:
+1. Identify the study type and discipline
+2. Find the relevant reporting guideline(s)
+3. Check if authors mention following the guideline
+4. Verify that key requirements are addressed
+5. Note any missing elements in your review
+6. Suggest the appropriate guideline if not mentioned
+
+Many journals require authors to complete reporting checklists at submission. Reviewers should verify compliance even if a checklist was submitted.