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Insights Reproducibility Command

The insights reproducibility command analyzes papers ranked by reproducibility score, helping identify well-documented, implementable research.

Basic Usage

scoutml insights reproducibility [OPTIONS]

Examples

General Analysis

# Top reproducible papers
scoutml insights reproducibility

# Domain-specific
scoutml insights reproducibility --domain "computer vision"

Filtered Analysis

# Recent reproducible papers
scoutml insights reproducibility \
  --year-min 2022 \
  --limit 30

Options

Option Type Default Description
--domain TEXT None Filter by research domain
--year-min INTEGER None Minimum publication year
--year-max INTEGER None Maximum publication year
--limit INTEGER 20 Number of results
--output CHOICE rich Output format: rich/json/csv
--export PATH None Export results to file

Reproducibility Factors

The analysis considers:

Code Availability

  • Official implementation
  • Multiple implementations
  • Framework diversity
  • Documentation quality

Data Accessibility

  • Public datasets
  • Data preprocessing steps
  • Download instructions
  • Synthetic data options

Documentation Quality

  • Implementation details
  • Hyperparameter specifications
  • Training procedures
  • Evaluation protocols

Community Validation

  • Reproduction attempts
  • Independent verification
  • Blog posts/tutorials
  • Course materials

Computational Requirements

  • Hardware specifications
  • Training time estimates
  • Memory requirements
  • Cost estimates

Reproducibility Scores

Score Interpretation

  • 90-100: Exceptional reproducibility
  • 80-89: Highly reproducible
  • 70-79: Good reproducibility
  • 60-69: Moderate challenges
  • Below 60: Significant challenges

Score Components

# See detailed scoring
scoutml insights reproducibility --output json | \
  jq '.[] | {paper: .title, score: .reproducibility_score, components: .score_breakdown}'

Use Cases

Implementation Planning

# Find implementable papers in domain
scoutml insights reproducibility \
  --domain "nlp" \
  --year-min 2021 \
  --limit 20 \
  --export implementable_nlp.json

Research Selection

# Papers for course projects
scoutml insights reproducibility \
  --domain "computer vision" \
  --limit 30 \
  --export course_papers.csv

Benchmark Studies

# Well-documented benchmarks
scoutml insights reproducibility \
  --domain "reinforcement learning" \
  --year-min 2020

Industry Adoption

# Production-ready research
scoutml insights reproducibility \
  --limit 50 | \
  grep -i "efficient\|fast\|lightweight\|optimized"

Advanced Usage

Trend Analysis

# Reproducibility over time
for year in 2019 2020 2021 2022 2023; do
    echo "=== Year $year ==="
    scoutml insights reproducibility \
        --year-min $year \
        --year-max $year \
        --limit 10 \
        --output json | \
        jq -r '.[] | .reproducibility_score' | \
        awk '{sum+=$1} END {print "Average:", sum/NR}'
done

Domain Comparison

# Compare domains
domains=("computer vision" "nlp" "reinforcement learning")

for domain in "${domains[@]}"; do
    echo "=== $domain ==="
    scoutml insights reproducibility \
        --domain "$domain" \
        --limit 20 \
        --output json | \
        jq -r '.[] | .reproducibility_score' | \
        awk '{sum+=$1} END {print "Average:", sum/NR}'
done

Finding Exemplars

# Best practices examples
scoutml insights reproducibility \
  --limit 10 \
  --output json | \
  jq '.[] | select(.reproducibility_score > 90) | {
    title: .title,
    arxiv_id: .arxiv_id,
    factors: .positive_factors
  }'

Output Examples

Rich Output (Default)

Displays: - Ranked table of papers - Color-coded scores - Key reproducibility factors - Implementation links

JSON Output

{
  "arxiv_id": "2010.11929",
  "title": "An Image is Worth 16x16 Words...",
  "reproducibility_score": 92,
  "score_breakdown": {
    "code_availability": 95,
    "documentation": 90,
    "data_accessibility": 95,
    "community_validation": 88,
    "computational_feasibility": 92
  },
  "positive_factors": [
    "Official implementation available",
    "Multiple framework versions",
    "Detailed training recipes",
    "Pre-trained models provided"
  ],
  "challenges": [
    "Large model requires significant GPU memory"
  ]
}

CSV Output

arxiv_id,title,score,code,documentation,data,validation,compute
2010.11929,"An Image is Worth...",92,95,90,95,88,92
1810.04805,"BERT: Pre-training...",89,90,85,92,90,87

Interpretation Guide

High Scores Indicate

  1. Available code - Can start immediately
  2. Clear instructions - Less debugging
  3. Known requirements - Can plan resources
  4. Community support - Help available

Low Scores Suggest

  1. Missing details - Implementation gaps
  2. Proprietary data - Can't fully reproduce
  3. Unclear methods - Ambiguous descriptions
  4. High complexity - Difficult to implement

Best Practices

Paper Selection

  1. Score > 80 for critical projects
  2. Score > 70 for exploration
  3. Check specific factors you care about
  4. Read associated critiques

Implementation Success

  1. Start with high scores when learning
  2. Check community repos for help
  3. Look for tutorials and blog posts
  4. Join paper discussions

Common Workflows

Course Material Selection

# Find teachable papers
scoutml insights reproducibility \
  --domain "computer vision" \
  --year-min 2020 \
  --limit 50 \
  --export course_papers.json

# Filter for specific topics
cat course_papers.json | \
  jq '.[] | select(.title | contains("transformer")) | 
      select(.reproducibility_score > 80)'

Research Baseline Selection

# Find reliable baselines
scoutml insights reproducibility \
  --domain "nlp" \
  --limit 30 | \
  grep -E "(BERT|GPT|T5|RoBERTa)"

Industry Evaluation

# Production-viable research
scoutml insights reproducibility \
  --year-min 2022 \
  --output json | \
  jq '.[] | select(.computational_feasibility > 85) | 
      {title, score: .reproducibility_score, compute: .computational_feasibility}'

Tips and Tricks

Quick Filters

# Has official code
scoutml insights reproducibility --output json | \
  jq '.[] | select(.code_availability > 90)'

# Low compute requirements  
scoutml insights reproducibility --output json | \
  jq '.[] | select(.computational_feasibility > 85)'

# Recent and reproducible
scoutml insights reproducibility \
  --year-min 2023 \
  --output json | \
  jq '.[] | select(.reproducibility_score > 85)'

Validation Strategy

  1. Check multiple sources - GitHub, Papers with Code
  2. Read issues/discussions - Common problems
  3. Look for reimplementations - Alternative versions
  4. Check citations - Who successfully used it