One example: the Majorana signals
A new signal, ordinary lookalikes, and a selection of data that favored the exciting reading. It took years to sort out.
In 2018 a Nature paper reported quantized Majorana conductance, a signature that topological quantum computing was meant to build on. In 2021 the authors retracted it, after Sergey Frolov and Vincent Mourik pointed out inconsistencies between the raw data shared with them and the published figures.1
The lab behind it then shared the data of its retracted papers in public repositories and adopted a policy of publishing all the raw data behind its figures, with the processing scripts.2
In January 2026 Frolov and eleven co-authors looked at four more 'smoking gun' signatures of topological effects in Science. Limited data matched each one. With more of the data, the most likely explanations were trivial, meaning not topological. They argue that releasing complete datasets, discussing other explanations and saying how much was measured in total would make such claims more reliable.3
Part of this is structural. Samples vary from device to device, and showing the clearest device is often legitimate: disorder, bad contacts and gate leakage hide the physics in many devices. But it can also work like testing many hypotheses and reporting the one that fit, and from the paper alone a reader can't tell which: how many devices were measured, how many showed the effect, or why the others were set aside.
The assistant asks for that count and lists the lookalikes to rule out. With a run log tagged by device, the count comes from the record.