INDIVIDUAL RESEARCH PROJECT 13
Integration of spatiotemporal image analysis results into OMERO, a scalable and FAIR data management framework
Abstract
The ingestion of analysis results in scalable and efficient open formats, and the development of tools to efficiently query these analysis results, are critical challenges to the democratisation of image analysis. To address these challenges, we will focus on the storage of analytical results allowing queries without loading in memory all the results associated with an analysis, by contributing to the development of analytical pipelines (ChromTrack3D workflows) and the storage and access requirements (S1).
We will develop the retrieval of possible associated labels (stored as OME-Zarr) including the integration of OME-Zarr file format, integration into the Biom3D Deep learning tool and carry out benchmarking between several large NoSQL data storage solutions to determine the most efficient and scalable options for storing and retrieving analytical results. These objectives will address both the storage and retrieval aspects of image analysis data, contributing to more efficient and accessible analytical workflows in quantitative microscopy.
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Training benefits
- Build advanced bioimage expertise
- Develop novel open and FAIR tools
- Evolve at the forefront of next-generation file formats for microscopy data
- Work in an interdisciplinary project involving computational and experimental researchers
Requirements
Background knowledge and experience with microscopy image data including file formats. Basic notions of programming are desirable.
Environment
The University of Dundee school of Life sciences offers a vibrant environment for researchers. We will be part of a world-leading team working developing novel software solutions to manage, access and interact with imaging data. The James Hutton institute has also a campus on the outskirt of Dundee (https://www.hutton.ac.uk/invergowrie-campus/)
