BigData IV: Visualizing, Sharing and Annotating Large Image Data in the Cloud -NEUBIAS Academy @Home

BIG DATA IV: Visualizing, Sharing and Annotating Large Image Data in the Cloud
3 February, 2021
Catmaid and MoBIE
Presenters:
Tom Kazimiers, Open Source Research Software Engineer at kazmos GmbH (DE);
Chris Barnes, MRC LMB, Cambridge (UK);
Albert Cardona, Programme Leader at MRC LMB, Cambridge (UK);
Christian Tischer, Bioimage Analyst at EMBL, Heidelberg (DE);
Kimberly Meechan, PhD candidate at EMBL (DE);
Constantin Pape, PhD candidate at EMBL (DE).
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Moderators:
Anna Klemm, Rocco D'Antuono, Julien Colombelli
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Contact: [e-mail]
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Q&A posted on image.sc Forum:
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Content:
Two software projects are presented: CATMAID and MoBIE.
Both work with large remote data sets of various kinds.
1. Remote data and collaborative neuron tracing in CATMAID (40 + 5 min)
Learn the basic usage of CATMAID, how to create own projects on public servers and how to work with basic neuron tracing and analysis workflows in a collaborative environment. We will also talk about preparing data for publication, sharing data and linking into a dataset. Briefly we will also explore how to connect to public CATMAID servers from Python.
2. Multi-modal big image data exploration in MoBIE (40 + 5 min) MoBIE is a BigDataViewer based Fiji plugin for multi-modal big image data exploration. MoBIE enables browsing of large image datasets, including inspection of segmentation results and exploration of measured object features.Thanks to lazy-loading even TB-sized datasets can be smoothly explored on a standard computer. MoBIE datasets can be stored both locally or “in the cloud”.
github.com/mobie