EVE is an open modular data analysis software for event-based localization microscopy
Bibliographic record
Abstract
Abstract Event-based sensors (EBS), or neuromorphic vision sensors, offer a novel approach to imaging by recording light intensity changes asynchronously, unlike conventional cameras that capture light over fixed exposure times. This capability results in high temporal resolution, reduced data redundancy, and a wide dynamic range. This makes EBS ideal for Single-Molecule Localization Microscopy (SMLM) as SMLM relies on the sequential imaging of sparse, blinking fluorescent emitters to achieve super-resolution. Recent studies have shown that EBS can effectively capture these emitters, achieving spatial resolution comparable to traditional cameras. However, existing analyses of event-based SMLM (eveSMLM) data have relied on converting event lists into image frames for conventional analysis, limiting the full potential of the technology. To overcome this limitation, we developed EVE, a specialized software for analyzing eveSMLM data. EVE offers an integrated platform for detection, localization, and post-processing, with various algorithmic options tailored for the unique structure of eveSMLM data. EVE is user-friendly and features an open, modular infrastructure that supports ongoing development and optimization. EVE is the first dedicated tool for event-based SMLM, transforming the analysis process to fully utilize the spatiotemporal data generated by EBS. This allows researchers to explore the full potential of eveSMLM and encourages the development of new analytical methods and experimental improvements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".