PFIA - Pipeline for Image Analysis of Cell Density in Mouse Brain Sections - Test Image Files and Associated Scripts
Bibliographic record
Abstract
This dataset contains microscopy image files (".lif"), ImageJ Macro scripts (".ijm"), and R-programming language scripts (".R") associated with the methods article "A framework for systematic, open-science, and user-friendly fluorescence microscopy for reproducible quantification of cells in mouse brain sections" by Sanchez-Arias, Carrier et al., 2021 (under review; this description will be updated if the work is accepted for publication or uploaded to a preprint server). In this work, we review and discussed experimental design and image acquisition and analysis standards for reproducible and open science research. We also provide a FIJI-ImageJ -based systematic workflow to analyze mouse coronal brain section images that includes extracting and storing metadata, exporting and organizing files, and semi-automated registration and cell density analysis. A GitHub repository with the script files is available at https://github.com/SwayneLab/PFIA/ for pull requests, forks, and most up-to-date versions of the scripts. License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.128 |
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".