Data from: Towards drift-free high-throughput nanoscopy through adaptive intersection maximization

Single-molecule localization microscopy (SMLM) often suffers from
suboptimal resolution due to imperfect drift correction. Existing
marker-free drift-correction algorithms often struggle to reliably track
high-frequency drift and lack the computational efficiency to manage
large, high-throughput localization datasets. We present an adaptive
intersection maximization-based method (AIM) that leverages the entire
dataset's information content to minimize drift correction errors,
particularly addressing high-frequency drift, thereby enhancing the
resolution of existing SMLM systems. We demonstrate that AIM can robustly
and efficiently achieve an angstrom-level tracking precision for
high-throughput SMLM datasets under various imaging conditions, resulting
in an optimal resolution in simulated and biological experimental
datasets. We offer AIM as simple and model-free software for instant
resolution enhancement with standard CPU devices.