Image Files Repository for the manuscript "Best Practices in Plant Fluorescence Imaging and Reporting"
Bibliographic record
Abstract
Best Practices in Plant Fluorescence Imaging and Reporting Abstract Microscopy is a fundamental approach for plant cell and developmental biology as well as an essential tool for mechanistic studies in plant research. However, setting up a new microscopy-based experiment can be challenging, especially for beginner users, when implementing new imaging workflows or when working in an imaging facility where staff may not have extensive experience with plant samples. The basic principles of optics, chemistry, imaging and data handling are shared amongst all cell types. However, unique challenges are faced when imaging plant specimens due to their waxy cuticles, strong/broad spectrum autofluorescence, recalcitrant cell walls and air spaces that impede fixation or live imaging, impacting sample preparation and image quality. As expert plant microscopists, we share our collective experience on best practices to improve the quality of published microscopy results and promote transparency, reproducibility and data reuse for meta-analyses. We offer plant-specific advice and examples for microscope users at all stages of fluorescence microscopy workflows, from experimental design through sample preparation, image acquisition, processing, and analyses, to image display and methods reporting in manuscripts. We also present standards for methods reporting that will be valuable to all users and offer tools to improve reproducibility and data sharing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.758 | 0.565 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".