Biology subjects

Linkert, M.

Publications and source records attributed to Linkert, M..

3 recordsLinked to original sources

bioRxiv2023

OME-Zarr: a cloud-optimized bioimaging file format with international community support

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself - OME-Zarr - along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

bioinformatics↗

bioRxiv2021

OME-NGFF: scalable format strategies for interoperable bioimaging data

Biological imaging is one of the most innovative fields in the modern biological sciences. New imaging modalities, probes, and analysis tools appear every few months and often prove decisive for enabling new directions in scientific discovery. One feature of this dynamic field is the need to capture new types of data and data structures. While there is a strong drive to make scientific data Findable, Accessible, Interoperable and Reproducible (FAIR 1), the rapid rate of innovation in imaging impedes the unification and adoption of standardized data formats. Despite this, the opportunities for sharing and integrating bioimaging data and, in particular, linking these data to other "omics" datasets have never been greater. Therefore, to every extent possible, increasing "FAIRness" of bioimaging data is critical for maximizing scientific value, as well as for promoting openness and integrity. In the absence of a common, FAIR format, two approaches have emerged to provide access to bioimaging data: translation and conversion. On-the-fly translation produces a transient representation of bioimage metadata and binary data but must be repeated on each use. In contrast, conversion produces a permanent copy of the data, ideally in an open format that makes the data more accessible and improves performance and parallelization in reads and writes. Both approaches have been implemented successfully in the bioimaging community but both have limitations. At cloud-scale, those shortcomings limit scientific analysis and the sharing of results. We introduce here next-generation file formats (NGFF) as a solution to these challenges.

bioinformatics↗

bioRxiv2016

OME Files - An open source reference library for the OME-XML metadata model and the OME-TIFF file format

Digital imaging is now used throughout biological and biomedical research to measure the architecture, composition and dynamics of cells, tissues and organisms. The many different imaging technologies create data in many different formats. This diversity of data formats arises because the creation of common, cross-domain data standards is difficult and the pace of innovation challenges the durability of any defined standard and the implementation of data stewardship principles such as FAIR1. The Open Microscopy Environment (OME; http://openmicroscopy.org) has therefore developed the OME Data Model, an open, extensible specification for imaging metadata, that supports metadata related to an imaging experiment, data acquisition and any derived analytic results2-4. The OME Data Model has been implemented in OME-XML so that metadata can be stored and accessed by any sof ...

bioinformatics↗