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PyOmeroUpload: A Python toolkit for uploading images and metadata to OMERO.
Hay, Johnny; Troup, Eilidh; Clark, Ivan; Pietsch, Julian; Zielinski, Tomasz; Millar, Andrew.
Afiliação
  • Hay J; EPCC, University of Edinburgh, Edinburgh, EH9 3FD, UK.
  • Troup E; SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, UK.
  • Clark I; EPCC, University of Edinburgh, Edinburgh, EH9 3FD, UK.
  • Pietsch J; SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, UK.
  • Zielinski T; SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, UK.
  • Millar A; SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FD, UK.
Wellcome Open Res ; 5: 96, 2020.
Article em En | MEDLINE | ID: mdl-32766455
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article