Abstract
Tools and software that automate repetitive tasks, such as metadata extraction and deposition to data repositories, are essential for researchers to share Open Data, routinely. For research that generates microscopy image data, OMERO is an ideal platform for storage, annotation and publication according to open research principles. We present PyOmeroUpload, a Python toolkit for automatically extracting metadata from experiment logs and text files, processing images and uploading these payloads to OMERO servers to create fully annotated, multidimensional datasets. The toolkit comes packaged in portable, platform-independent Docker images that enable users to deploy and run the utilities easily, regardless of Operating System constraints. A selection of use cases is provided, illustrating the primary capabilities and flexibility offered with the toolkit, along with a discussion of limitations and potential future extensions. PyOmeroUpload is available from: https://github.com/SynthSys/pyOmeroUpload.
Highlights
Creating Open Data through sharing, discovery and re-use of research data are integral activities for promoting Open Science[1]
The more streamlined the process of depositing data, and enriching data with metadata that has been captured at the point of generation, the greater the quantity and quality of data that can be shared
The data in each dataset was structured into one directory per microscope position, containing individual files that adhered to a file naming convention specifying the channel, z-section and timepoint of each image, with metadata residing at the top directory level in two semi-structured text files
Summary
Background Creating Open Data through sharing, discovery and re-use of research data are integral activities for promoting Open Science[1]. The platform is frequently updated and supports importing over 150 image formats, full multi-dimensional image viewing, analysis with scripts and plugins, data conversion and publishing through URLs. OMERO provides excellent cataloguing capabilities, where data can be annotated with tags, comments, key-value pairs, tables and supplementary files; images can be browsed or searched through and shared with collaborators. At the time of initiating the experiment, the biological context is known (including strains, medium, and conditions) and it is the optimum moment to capture this information, for example in a text file These types of experiments are perfect candidates for automation of data deposition, wherein large quantities of images are generated (typically 90,000 raster images or more, constituting 30 GB per experiment) and detailed descriptions can be constructed by combining technical metadata obtained from the experimental setup (such as time resolution, exposure time, z-positions) and user input. At the same time, using Python software on Windows platforms – which dominate laboratories’ IT infrastructure and microscopy management software – can still be cumbersome
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