Organizer: University of Cambridge

Host institution: University of Cambridge Bioinformatics Training

Start: Friday, 08 December 2017 @ 09:30

End: Friday, 08 December 2017 @ 17:00

Venue: Craik-Marshall Building

City: Cambridge

Country: United Kingdom

Postcode: CB2 3AR

Scientific topic: Data management, Biological imaging, Bioinformatics

Target audience:
  • Life scientists with programming skills
  • bioinformaticians and image analysts.
  • Anybody interested in using Jupyter and OMERO
  • Graduate students
  • Postdocs and Staff members from the University of Cambridge
  • Institutions and other external Institutions or individuals
Description:

The Open Microscopy Environment (OME) is an open-source software project that develops tools that enable access, analysis, visualization, sharing and publication of biological image data.

OME has three components:

  • OME-TIFF, standardised file format and data model;
  • Bio-Formats, a software library for reading proprietary image file formats; and
  • OMERO, a software platform for image data management and analysis.

In this one day course, we will present the OMERO platform, and show how to transition from manual data processing to automated processing workflows. We will introduce how to write applications against the OMERO API, how to integrate a variety of processing tools with OMERO and how to automatically generate output ready for publication.

This course is organized alongside a one day course on Biological Imaging Data Management for Life Scientists. More information on this event are available here.

This course will be delivered by members of the OMERO team. The OME project is supported by BBSRC and Wellcome Trust.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.''

Event type:
  • Workshops and courses

Keywords: HDRUK

Biological Imaging Data Processing for Data Scientists https://tess.elixir-europe.org/events/biological-imaging-data-processing-for-data-scientists [The Open Microscopy Environment](https://www.openmicroscopy.org/) (OME) is an open-source software project that develops tools that enable access, analysis, visualization, sharing and publication of biological image data. OME has three components: * OME-TIFF, standardised file format and data model; * Bio-Formats, a software library for reading proprietary image file formats; and * OMERO, a software platform for image data management and analysis. In this one day course, we will present the OMERO platform, and show how to transition from manual data processing to automated processing workflows. We will introduce how to write applications against the OMERO API, how to integrate a variety of processing tools with OMERO and how to automatically generate output ready for publication. This course is organized alongside a one day course on Biological Imaging Data Management for Life Scientists. More information on this event are available [here](https://training.csx.cam.ac.uk/bioinformatics/event/2239247). This course will be delivered by members of the OMERO team. The OME project is supported by BBSRC and Wellcome Trust. Please note that if you are not eligible for a University of Cambridge [Raven](http://www.ucs.cam.ac.uk/docs/faq/raven/n5) account you will need to book or register your interest by linking [here](http://bioinfotraining.bio.cam.ac.uk/booking-form/?event-id=2280783&course-title=Biological%20imaging%20data%20management%20for%20data%20scientists).'' 2017-12-08 09:30:00 UTC 2017-12-08 17:00:00 UTC University of Cambridge Craik-Marshall Building, Cambridge, United Kingdom Craik-Marshall Building Cambridge United Kingdom CB2 3AR Data management Biological imaging Bioinformatics University of Cambridge Bioinformatics Training [] Life scientists with programming skillsbioinformaticians and image analysts.Anybody interested in using Jupyter and OMEROGraduate studentsPostdocs and Staff members from the University of CambridgeInstitutions and other external Institutions or individuals workshops_and_courses [] HDRUK