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Executive Summary

Academic journals and institutional review boards are struggling with a reproducibility crisis that damages scientific credibility and wastes research funding. When peer reviewers receive papers with computational components, they rarely have time to verify that the code runs or produces the claimed results, and journals lack infrastructure to enforce reproducibility checks at scale. This creates risk for everyone: journals publish flawed research, institutions face reputational damage from retractions, and funders cannot trust that their dollars produced valid science. The problem is most acute in computational biology, machine learning research, econometrics, and quantitative social sciences where a single code error can invalidate months of work. The MVP would be a submission portal that accepts research code repositories, automatically spins up appropriate execution environments, runs the analysis pipeline, and generates a verification report comparing outputs to paper claims. Start with Python and R codebases, support the three most common data formats, and target a 30-minute automated verification turnaround. The initial product would serve journal editors who need a pass/fail reproducibility check before sending papers to peer review, priced at $200-300 per submission. This creates a $600 average annual spend per researcher publishing 2-3 papers, reaching the $1.8B market through 3 million researchers in code-intensive fields who face growing institutional pressure to demonstrate reproducibility. The timing is compelling because regulatory mandates now force behavior change rather than relying on researcher goodwill, and the technical infrastructure for safe code execution finally exists at reasonable cost. New NIH and European Commission policies require computational reproducibility documentation, which gives purchasing departments explicit budget justification for verification tools. Meanwhile, platforms like E2B and Modal have solved the hard sandboxing problem, letting you focus on the research-specific workflow rather than building secure infrastructure from scratch. Incumbent competitors like Code Ocean focus on long-term preservation rather than pre-publication verification, while manual reproducibility services charge $2,000+ per paper and take weeks, leaving a clear gap for fast automated checks. The first validation move is securing pilot agreements with two mid-tier journals in computational biology and economics that already have reproducibility policies but struggle with enforcement. The GTM path runs through journal editor networks and society conferences where editorial boards gather, since journals control the submission gates and have budget authority for process improvements. The biggest risk to test immediately is whether research codebases are too heterogeneous and messy for automated verification, which you can assess by manually attempting to reproduce 50 papers from target journals and measuring what percentage run with under two hours of debugging effort.

Executive Summary

Why Now

Reproducibility crisis in science has intensified pressure from funders (NIH, NSF mandates for data/code sharing as of 2023) and publishers (Nature portfolio, Science now require code availability statements). AI code-execution sandboxes (like those powering GitHub Copilot Workspace, Modal, E2B) have matured enough to safely run arbitrary research code across languages. Remote compute costs have dropped 10x in five years, making per-submission cloud execution economically viable. Universities are hiring reproducibility officers and journals are adopting stricter policies, creating budget and workflow entry points.

Pain Points

Peer reviewers must manually run code from research papers to verify claims, which is time-consuming and delays publication cycles

Checking that code produces the exact values reported in papers is a major bottleneck in the review process

Current peer review lacks standardized reproducibility checks, creating inconsistent quality control across journals

MVP Preview

GitHub/Zenodo repository ingestion -- Automatically clone or download code repositories linked in paper submissions, parse directory structure, identify entry points (main scripts, notebooks), and flag missing dependencies or broken paths. Output: structured report of code accessibility status. Benefit: Saves reviewers 15-30 minutes per paper just verifying code is runnable.

Automated code and reproducibility verification for peer-reviewed research papers

Executive Summary

Automated code and reproducibility verification for peer-reviewed research papers

Executive Summary

Executive Summary

Why Now

Pain Points

MVP Preview

Market Validation

Market Opportunity

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Validation Questions

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