The Image Data Resource: Publishing, Integrating and Mining Biological Imaging Data @ Scale
Recorded On: 10/10/2017
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About the Presenter
Jason Swedlow, PhD
Professor of Quantitative Cell Biology
School of Life Sciences, University of Dundee
Jason Swedlow earned a BA in chemistry from Brandeis University in 1982 and PhD in biophysics from UCSF in 1994. After a postdoctoral fellowship with Dr. TJ Mitchison at UCSF and then Harvard Medical School, Dr. Swedlow established his own laboratory in 1998 at the Wellcome Trust Biocentre at the University of Dundee as a Wellcome Trust Career Development Fellow. He was awarded a Wellcome Trust Senior Research Fellowship in 2002 and named Professor of Quantitative Cell Biology in 2007.
His lab focuses on studies of mitotic chromosome structure and dynamics, and he has published numerous leading papers in the field. He is co-founder of the Open Microscopy Environment (OME), a community-led open source software project that develops specifications and tools for biological imaging. In 2005, he founded Glencoe Software Inc., a commercial start-up that provides commercial licenses and customization for OME software. In 2011, Dr. Swedlow and the OME Consortium were named BBSRC's Social Innovator of the Year and Overall Innovator of the Year. In 2012, he was named Fellow of the Royal Society of Edinburgh.
Dr. Swedlow has organized or directed several courses in quantitative microscopy at the Marine Biological Laboratory, Woods Hole (USA), Cold Spring Harbor Laboratory (USA), and the National Centre for Biological Science (Bangalore, India).
Much of the published research in the life sciences is based on image datasets that sample 3D space, time, and the spectral characteristics of detected signal to provide quantitative measures of cell, tissue, and organismal processes and structures.
To address this challenge, we have built a next-generation imaging resource, the Image Data Resource (IDR), an added value resource that combines data from multiple independent imaging experiments and from many different imaging modalities, integrates them into a single resource, and makes the data available for re-analysis in a convenient, scalable form.
Who Should Attend this Webinar
Biologists, imaging scientists, computational scientists, and policymakers interested in using, sharing, analyzing, and publishing rich, multi-dimensional image datasets.
CMLE Credit: 1.0