What is the downstream signature of research data loss in the published record?

A subsidiary question under Q-0003 (cost to science and institutions). §4.1 frames the inquiry: when data is lost, what does the loss look like at the publication level?

The answer is C-0022: the downstream signature is reproducibility failure. The empirical signature is consistent across every domain measured — >70% of researchers report failed reproduction of others' experiments and >50% of their own (Nature 2016 survey of 1,576), 11% replication success in landmark cancer biology (Amgen), 97% of Molecular Brain manuscript authors unable to produce raw data on request, 74% of published R analysis files failing to execute without error.

The architectural reading: when underlying data survives in a form a third party can verify, reproducibility failures become diagnosable — the independent investigator can examine the source, trace the divergence, identify whether the issue lies in the protocol, the analysis, or the measurement. When the data does not survive, reproducibility failure becomes the terminal state of the investigation because the evidence required to diagnose anything else is already gone.

The reproducibility crisis is the accumulated consequence of single-copy architecture operating across the research enterprise for decades. The fix is upstream of the proximate causes.