ACNN Workshop Shareable Resources Form (Responses)
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TimestampName (Last, First)RoleResource NameResource TypeResource URL
Resource License (include URL)
DescriptionInstitution
Developers (list of names)
Email ContactVersion
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6/22/2016 10:43:26Ivo DinovFacultyBrain Viewer
Web/Browser Stereotactic Viewer
http://socr.umich.edu/HTML5/BrainViewer/
LGPL
A dynamic 3D viewer of neuroimaging and brain mapping data including volumes (.nii / .nii.gz / .img&.hdr / .mgh / .mgz / .nrrd), manifold shapes (.dx / .vtk / .stl / FreeSurfer), and tractography fibers (.trk).
University of Michigan
XTK, MGH, LONI/INI, SOCR
Ivo Dinov2
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6/22/2016 11:44:25Moira DowlingStaffData Dashboard
Highly Scalable APIs, Cloud-services
http://socr.umich.edu/HTML5/Dashboard/
LGPL
The Data Dashboard webapp provides a mechanism to integrate dispersed multi-source data and service the mashed information via human and machine interfaces in a secure, scalable manner. The Dashboard enables the exploration of subtle associations between variables, population strata, or clusters of data elements, which may be opaque to standard independent inspection of the individual sources. This a new platform includes a device agnostic tool for graphical querying, navigating and exploring the multivariate associations in complex heterogeneous datasets.
Statistics Online Computaitonal Resource, University of Michigan
SOCR Group (http://www.socr.ucla.edu/htmls/SOCR_Team.html)
statistics@umich.edu1.2
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7/20/2017 9:59:58Scheler, GabrieleScholar/ Fellow
GNN- Generic Neural Network Simulator
Computational Resources
https://github.com/gscheler/GNN
Simulation software

Generates X blocks of N neurons with optional recurrence
(excitatory and inhibitory blocks)
employs defined distributions (Gaussian, uniform, lognormal etc.) for initialization of weights and gains
uses homeostatic and Hebbian adaptation rules
Carl Correns Foundation
Gabriele Scheler, Johann Schumann
gscheler@gmail.com1
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8/4/2017 8:49:42Dinov, IFaculty
Data Science and Precictive Analytics (DSPA) Course Materials
Cloud-services, Computational Resources, Education and Training Opportunities
http://dspa.predictive.space
LGPL and CC-BY
The Data Science and Predictive Analytics (DSPA) course (offered as a massive open online course, MOOC, as well as a traditional University of Michigan class) builds computational abilities, inferential thinking, and practical skills for tackling core data scientific challenges. It explores foundational concepts in data management, processing, statistical computing, and dynamic visualization using modern programming tools and agile web-services. Concepts, ideas, and protocols are illustrated through examples of real observational, simulated and research-derived datasets. Some prior quantitative experience in programming, calculus, statistics, mathematical models, or linear algebra will be necessary.
University of Michigan
The SOCR Resource (lead by Ivo Dinov)
dinov@umich.eduSummer 2017
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