Reanalysis becomes a first-class research activity

When data persists across decades with cryptographically verifiable integrity, applying new analytical methods to older data stops being archaeological and becomes routine. Methods developed in 2035 can be applied to data collected in 2015 without negotiating access to an emeritus PI's personal drive, reconstructing experimental context from fragmentary documentation, or accepting that the comparison cannot be run.

The scientific record compounds into a queryable substrate rather than an accumulating list of unverifiable claims. Each new analytical method can be backtested against decades of preserved data with the same confidence as it can be applied to fresh data, because the integrity attestation guarantees that the data has not drifted.

This Claim is generative. It identifies a research practice that becomes routinely possible for the first time at architectural scale, rather than an existing practice that is improved. The implication for research methodology is substantial: longitudinal meta-research, cross-cohort comparison, and re-application of new statistical or machine-learning methods to pre-existing data become standard activities, not heroic ones.

The case is also implicit in C-0032's open-data multiplier (the Landsat reanalysis multiplier, the Mauna Loa cross-decade comparison, the cancer drug pipeline drawing on 10+ year-old PDB structures). What was occasional under the current regime becomes routine under Tier 3.