Data Integrity
2 research articles on Data Integrity — drawn from 53 integrated data sources with computational validation.
NULL Should Never Mean Favorable: How Default-Optimistic Screening Corrupts Results
A data-stitching audit revealed that missing phonon data defaulted to 'stable,' missing patent counts defaulted to 'novel,' and missing PFAS flags defaulted to 'clean' across our screening pipeline. Every screen that treats NULL as favorable silently promotes unvalidated candidates. Here's what we found and how we fixed it.
When All 13 Compute Results Were Error Payloads
Every single Modal QE DFPT and HSE06 result from our April 2026 GPU compute campaign was an error payload, not usable data. 13 files landed on the volume with status ERROR, PSEUDO_ERROR, SCF_FAILED, DFPT_FAILED, PARSE_ERROR, or NOT_CONVERGED. The honest-null pattern in our ledger is what saved rankings from corruption.