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SDR Deposit of the Week: Picture this!

September 17, 2014
Amy E. Hodge
San Francisco street scene

When you think about scientific data, you might think primarily about numbers and graphs and charts. But some data sets consist of rich image collections, including these data sets that have been preserved in the Stanford Digital Repository!


Touch reception in C. elegans

Micrograph of a C. elegans worm cross sectionResearchers Juan Cueva and Miriam Goodman have performed studies using C. elegans to examine how certain touch receptor neurons are activated. They generated nearly 3300 electron micrographs of worm cross sections that have been preserved in the Stanford Digital Repository (SDR) and are now available for download and reuse by other researchers around the world.

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Neural connections in the mouse brain 

Image from Brady, et al, DOI: 10.1016/j.neuron.2014.06.024Brady Weissbourd, a graduate student in Liqun Luo's lab in the biology department, was looking for a way to make a large set of valuable mouse brain images easier for other researchers to access. A former post-doctoral researcher in his lab (another "he told a friend…" example!) alerted him to the existence of the Stanford Digital Repository -- and it turned out to be exactly what he was looking for!

The research performed by Brady and his colleagues mapped the connectivity of serotonin and GABA neurons in the dorsal raphe nucleus of mice. They showed that these two types of neurons receive direct inputs from a wide range of specific regions of the brain.

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Image and video searching

San Francisco street sceneOther valuable image datasets come from the Image, Video, and Multimedia Systems group, a research team in the Department of Electrical Engineering, led by Professor Bernd Girod and an impressive group of graduate students with a respectable publishing track record. These researchers are exploring technologies to realize the promise of image and video search, with a particular focus on mobile devices. They create these datasets to develop and demonstrate their methods for automatic recognition of landmarks or a person’s gender or age, and then share the datasets with peers in their field in order that they may do the same.

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