REMBI Compatible Excel Templates for Collecting Experimental and Sample Preparation Metadata for Microscopy Image Data
Bibliographic record
Abstract
These are REMBI- and NBO-Q Microscopy Metadata specifications compliant Automated Metadata Annotation Excel Spreadsheet Templates for Pre-Publication Image Data Management. They were developed in collaboration between the BioImage DMS Core at UMass Chan, MIA CellaVie Inc., the Advanced BioImaging Facility (ABIF; RRID:SCR_017697) at McGill University, and the Canada BioImaging Open Science Project. The templates were developed in collaboration with members of laboratories that needed help with data curation. Example Experiment type Use-Case Compatible OMERO Structure 1 Fixed cells Immuno Fluorescence Immuno Fluorescence experiment to compare different conditions Project/Dataset/Image (PDI) 2 High-Trhoughput Screen with Fixed Immuno Fluorescence Assessment of antibody quality Screen/Plate/Well (SPW) The Templates are focused on Experimental and Sample Preparation metadata collection and are designed to be used as one of the components of the OMERO Automated Metadata Annotation and Import (OMERO importer) pipeline developed at UMass Chan. Specifically, The Templates are set up to colllect metadata in a machine-readable manner as Key/Value pairs To reduce data entry and metadata duplication, metadata collection is subdivied in the following sections (Tabs) in the spreadsheet: Project-, Dataset- and Image-level in case of low-throughtput image acquisitions Screen-, Plate-, Well-level in case of high-throughtput screens Templates contain Excel Macros to facilitate transfer of filled in metadata Key/Value pairs to supplemental CSV files. The Templates contain an "Allowable Values", tab that contains lists of controlled vocabulary terms for specific metadarta fields. The Allowable Values lists highlighted in Green should be customizd by adding values that are specific for your lab (e.g., in the "Data_Publisher_Principal_Investigator" list in Column C, you can add the name(s) of specific PIs that work in your Department). The OMERO importer pipeline also imports existing Micro-Meta App generated Microscope-Hardware and Image Acquisition-Settings JSON files and associates with the relevant images as OMERO attachments. For more details on the OMERO importer pipeline and how to use the Templates in this context can be found on ReadTheDocs. In case an OMERO repository is not available, the Templates can be used as stand-alone tools to collect Experimental and Sample Preparation metadata in accordance to the REMBI recommendations. In this case, metadata can be stored as supplementary files to be saved alongside the image data files either as XLS or CSV formats. To facilitate this task, the Templates contain Excel Macros to transfer of metadata to CSV files. In case you are performing a different type of experiment the Template can be used as a starting point for customization.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.157 |
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".