[ELMI 2025] Workshops: "Organizing data in OMERO" and "Hands-on: REMBI compliant annotations in OMERO"

Description

Abstract

Bioimaging data management with OMERO

Tom Boissonnet, Heinrich Heine University Düsseldorf, Germany

Christian Schmidt, German Cancer Research Center, Heidelberg, Germany

OMERO (Open Microscopy Environment Remote Objects) is a well-established open-source software platform designed to manage and visualize microscopy data in a collaborative online environment. In OMERO, data is organized utilizing “Projects” and “Datasets” tied to the underlying database. However, these organizational levels are frequently confused with a hierarchical folder structure that researchers are familiar with from classical file systems. As a consequence, users find it challenging to organize a growing body of uploaded data within a seemingly limited two-folder hierarchy. In this workshop, we demonstrate strategies to properly leverage OMERO’s object-oriented storage utilizing tags for data organization. We explain how matching datasets to biological replicates significantly reduces the effort required to organize data through an easy-to-adopt practical data management workflow. Thus, researchers can obtain an even more versatile “folder-like” structure that benefits data findability and data utilization in downstream processing and analysis. In addition, we will showcase the latest features of OMERO.figure, the OMERO plugin enabling to create publication-ready figures with OMERO-hosted data that comply with QUAREP checklists for microscopy data publication with minimal effort (Schmied et al., 2024).

Schmied, C., Nelson, M.S., Avilov, S. et al. Community-developed checklists for publishing images and image analyses. Nat Methods 21, 170–181 (2024).

Abstract

Hands-on training: REMBI-compliant annotations in OMERO

Christian Schmidt, German Cancer Research Center, Heidelberg, Germany

Tom Boissonnet, Heinrich Heine University Düsseldorf, Germany

The FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for data management and data stewardship (Wilkinson et al., 2016) provide a framework to increase the value of scientific data. However, these guidelines are not "a standard", nor do they dictate discrete technical solutions. Research communities have to establish consensus on what FAIR means in their field, and how FAIR data can be achieved. A key focus of the FAIR principles is on metadata, the important accessory information around the measurement data required to understand the data from a bioimaging experiment (Kunis & Dohle, 2022). To this end, achieving machine-readability is an important aspect for FAIR data. Therefore, metadata should adhere to consented standards with respect to which information is stored, how it is implemented in a data model, and how the description becomes well-structured and unambiguous by means of controlled vocabularies and ontologies. The Recommended Metadata for Biological Images (REMBI) are a set of community-established metadata items, structured into modules that help data producers annotate their data with a minimum set of metadata (Sarkans et al., 2021). In this workshop, we show how to get started with metadata enrichment from the ground up and how to use REMBI-compliant annotation within the data management system OMERO. We demonstrate the integration of REMBI items within OMERO’s “Project” and “Dataset” structure using Key-Value Pair annotations with ISA-aligned linkage to ontology-terms.

Kunis S & Dohle J (2022). Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data. Zenodo. https://doi.org/10.5281/zenodo.7018750

Sarkans, U., Chiu, W., Collinson, L. et al. REMBI: Recommended Metadata for Biological Images —enabling reuse of microscopy data in biology. Nat Methods 18, 1418–1422 (2021). https://doi.org/10.1038/s41592-021-01166-8

Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18

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