NeuroData's Graph DataBase

Lowering the barrier to entry for scalable neuroscience

Graphs may also be built programmatically

The base url for building graphs is

http://openconnecto.me/graph-services/buildgraph/

The example buildsubj.py can be used as follows from a terminal session:

python buildsubj.py \
http://openconnecto.me/graph-services/buildgraph/PROJECT/SITE/SUBJECT/SESSION/SCAN_ID/s \
FIBER.dat myemail@domain.com -a ATLAS.nii -i cc tri ss1 mad deg eig

The parameter directly after SCAN_ID can either be s or b. The -a atlas is optional. If not selected the Desikan atlas is used by default. Using s or small builds a small graph 70 vertices while b or big builds a big graph on the order of 100 milion vertices.
The particular invocation above would build a small graph and compute all invariants currently available. A list of invariant contractions is available below. The result of buildsubj.py is either an error or success message. Simply wait for an email response with the download url. We recommend starting a tmux or screen session in case your upload time is lengthy.

The optional arguments given as -i are simply appended to the final url in buildsubj.py. The options available must be in a comma separated list and are as follows:

• ss1 for the Scan Statistic 1
• tri for the Triangle Count
• cc for the Clustering Coefficient
• mad for the Maximum Average Degree
• deg for The Local Degree Count
• eig for the Top-k (100 or maximum available/computable) eigenvectors and eigenvalues

Specifications for graphs built

• A graphML file formated graph. The format is readable by any xml-parser.
• It is also natively supported by the igraph graph analysis framework in C, R and Python.
• NOTE: Iterable data items are represented as JSON encoded strings as an extension to the graphML file format.

What's the difference between big and small graphs?

The terms big/small graph are particularly relevant to fiber streamline graphs.
Small graphs have 70 vertices and up to thousands of edges, whereas
Big graphs can be in the millions of vertices and 100s of millions of edges.