| Literature DB >> 32766455 |
Johnny Hay1,2, Eilidh Troup1,2, Ivan Clark2, Julian Pietsch2, Tomasz Zieliński2, Andrew Millar2.
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. Copyright:Entities:
Keywords: Data sharing; Docker; OMERO; metadata; microscopy; research data management
Year: 2020 PMID： 32766455 PMCID： PMC7388197 DOI： 10.12688/wellcomeopenres.15853.2
Source DB: PubMed Journal: Wellcome Open Res ISSN： 2398-502X