Copy-paste errors plague scientific datasets — AI-driven dataset QA to catch them

Copy-paste errors plague scientific datasets — AI-driven dataset QA to catch them targets a $8.4B = 140,000 research organizations x $60K ACV (universities, pharma, biotech, CROs, gov labs) total addressable market with low saturation and a year-over-year growth rate of 18% CAGR for data-quality and scientific informatics spend driven by AI adoption.

Key trends driving demand: Reproducibility crisis -- funders and journals increasing focus on data provenance raises demand for dataset QA; AI pattern detection -- LLMs and embeddings can surface subtle errors (misaligned rows, copy-paste artifacts) at scale; Cloud/ELN adoption -- growing use of ELNs/LIMS and centralized data stores makes automated QA integration practical; Regulatory scrutiny in pharma -- data integrity requirements force investment in tooling for auditability.