Method / tool

REMBI

Recommended Metadata for Biological Images — a community-consensus framework that defines the metadata fields required to make microscopy datasets FAIR: findable, accessible, interoperable, and reusable across labs and archives.

The problem — A microscopy dataset deposited without structured metadata is a folder of pixels with a story no one can verify. Without an agreed schema, two datasets ostensibly from the same assay may record acquisition settings in incompatible fields, omit sample context entirely, or collapse provenance metadata and quality metadata into a single free-text comment. The result is data that cannot be searched, cannot be compared, and cannot be reused — defeating the purpose of a public archive.

What it is / how it works — REMBI is a community-authored metadata schema for biological images, published by Sarkans and colleagues across 39 international institutions (Nature Methods, 2021). It organises required and recommended fields into modules that cover the full study chain: a Study module (title, organism, experimental purpose), a Biosample module (sample composition and preparation), an Image acquisition module (instrument, objective, illumination, channel identities, pixel size), an Image data module (format, dimensionality, resolution levels), and an Analysed data module for image-derived measurements. Each module maps to existing community standards — Dublin Core, DataCite, OME data model — so a REMBI-compliant record is already partially interoperable with genomics repositories and general data catalogues. The BioImage Archive at EMBL-EBI adopts REMBI as its deposition standard; tools like Micro-Meta App operationalise it at the bench, guiding instrument operators through the required fields at acquisition time rather than at submission time.

Where it breaks — REMBI is a schema, not enforcement. The most common failure is capturing the schema headline (Study title, organism) but omitting the instrument-level fields (objective NA, pixel size, channel identities) that make image data computable. Downstream, that omission means a pipeline cannot validate pixel-size assumptions, a feature extraction step produces measurements in "pixels" with no micron conversion, and run manifest traceability breaks the moment anyone asks "at which magnification?" The discipline is to treat REMBI fields as a checklist at ingest, not a deposition form filled in retrospectively.

A REMBI record that covers the study level but omits the acquisition module is metadata decoration — the fields that make data computable (pixel size, channel identities, objective NA) live in the acquisition module, not the title.

References

Appears in these notes

  • How to Make Imaging Data Ready for ReanalysisReanalysis-ready means a third party with no contact with the original lab can re-run, re-segment, or re-interpret the data correctly — which requires FAIR principles, complete metadata, and deposition in a public archive. The bar is reuse by a stranger, not retrieval by the author.

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