Development of Semantic Web-based Imaging Database for Biological Morphome
This work addresses the problem of handling gigabyte-level imaging data for biologists and researchers in microstructural imaging, though it is incremental as it applies existing semantic web technologies to a new domain.
The researchers tackled the challenge of managing and visualizing large-scale microstructural imaging data by developing a semantic web-based database using RDF and Linked Open Data, resulting in successful management of vast numbers of images and metadata, including interpretation of morphological phenotypes in sub-cellular components and biosamples.
We introduce the RIKEN Microstructural Imaging Metadatabase, a semantic web-based imaging database in which image metadata are described using the Resource Description Framework (RDF) and detailed biological properties observed in the images can be represented as Linked Open Data. The metadata are used to develop a large-scale imaging viewer that provides a straightforward graphical user interface to visualise a large microstructural tiling image at the gigabyte level. We applied the database to accumulate comprehensive microstructural imaging data produced by automated scanning electron microscopy. As a result, we have successfully managed vast numbers of images and their metadata, including the interpretation of morphological phenotypes occurring in sub-cellular components and biosamples captured in the images. We also discuss advanced utilisation of morphological imaging data that can be promoted by this database.