Poor Reproducibility - Only 25% Score

[murr2k]

Problem Description

The quality control audit revealed a reproducibility score of only 25%, far below acceptable standards for scientific research. This severely limits the ability of others to verify or build upon these results.

Reproducibility Assessment Results

Current Status (1/4 criteria met):

• ✅ Source code available (Rust implementation)
• ⚠️ Raw data partially available (FASTA files missing)
• ❌ Parameters not documented
• ❌ Environment/dependencies not specified

Critical Reproducibility Gaps

1. Missing Raw Data

• 3 of 5 expected experimental result files not found
• Original FASTA files for analysis not included
• No data repository or accession numbers provided

2. Incomplete Parameter Documentation

• Window size for sliding window analysis not specified
• Confidence score calculation method undocumented
• NeuroDNA v0.0.2 configuration parameters missing
• Threshold values for trait detection unclear

3. No Environment Specification

• Rust version not specified
• Python package versions missing
• CUDA/GPU configuration undocumented
• Operating system dependencies unclear

4. Lack of Analysis Pipeline

• No end-to-end workflow documentation
• Missing step-by-step reproduction instructions
• No automated pipeline scripts
• Manual steps not documented

5. Missing Computational Details

• Random seeds not set/documented
• Hardware specifications not provided
• Runtime parameters not logged
• Memory requirements unknown

Required Improvements

1. Complete Data Package

Create a data repository containing:

• All input FASTA files
• Intermediate analysis files
• Final results in standardized format
• Checksums for data integrity

2. Comprehensive Documentation

• with step-by-step instructions
• Parameter configuration files
• Example commands with expected outputs
• Troubleshooting guide

3. Containerized Environment

• Docker/Singularity container with all dependencies
• Version-locked requirements files
• Environment YAML for conda
• CI/CD pipeline for testing

4. Analysis Notebooks

• Jupyter notebooks showing complete workflow
• Inline documentation and visualizations
• Parameter sensitivity analysis
• Results interpretation guide

5. Reproducibility Checklist

Following established standards:

• FAIR data principles
• MINSEQE guidelines
• Nature reproducibility checklist
• CodeOcean or similar platform

Example Reproducibility Package Structure

Impact

Current 25% reproducibility means:

• Results cannot be independently verified
• Other researchers cannot build on this work
• Findings do not meet publication standards
• Grant/funding requirements not satisfied

Acceptance Criteria

• 100% of raw data files available with checksums
• Docker container successfully runs full pipeline
• Independent tester can reproduce results within 5% tolerance
• All parameters documented in configuration files
• Automated tests verify reproducibility
• Data deposited in public repository (e.g., Zenodo)
• Methods section sufficient for replication
• Reproducibility score ≥ 90%

Priority: CRITICAL
Type: Reproducibility Issue
Standards: Does not meet FAIR, MINSEQE, or journal requirements