ACNN Workshop Shareable Resources Form (Responses)

	A	B	C	D	E	F	G	H	I	J	K	L
1	Timestamp	Your Name	Faculty	Resource Name	Resource Type	Resource URL	Resource License (include URL)	Description	Institution	Developers (list of names)	Email Contact	Version

2	6/22/2016 10:43:26	Ivo Dinov	Faculty	Brain 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 Dinov	2
3	6/22/2016 11:44:25	Moira Dowling	Staff	Data 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.edu	1.2
4	7/20/2017 9:59:58	Scheler, Gabriele	Scholar/ 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.com	1
5	8/4/2017 8:49:42	Dinov, I	Faculty	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.edu	Summer 2017
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100