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Published March 18, 2022 | Version 2022-03-18
Dataset Open

SPARC Connectivity Knowledge base of the Autonomic Nervous System

  • 1. University of California, San Diego
  • 2. University of Auckland, Auckland, New Zealand
  • 3. Peoples' Friendship University of Russia: Moscow, RU

Contributors

Data curator:

  • 1. University of California, San Diego

Description

The SPARC Knowledge base of the Automatic Nervous System (SCKAN) is an integrated graph database composed of three parts: the SPARC dataset metadata graph, ApiNATOMY and NPO models of connectivity, and the larger ontology used  by SPARC which is a combination of the NIF-Ontology and community ontologies.

The fastest way to get querying is to follow the instructions in the SCKAN readme file.

For background information please see https://scicrunch.org/sawg/about/SCKAN and the SPARC portal resource page about SCKAN.

This release contains the raw and compiled data for SCKAN. The release-*.zip contains raw data inputs along with the Blazegraph journal file, the sparc-sckan-graph-*.zip contains the SciGraph database, and sckan-data-*.tar.gz is a Docker image that contains the Blazegraph journal file and the SciGraph database along with the configuration files for running each of the servers. The image is intended  to be used as a data volume with another Docker container that runs the SciGraph and Blazegraph server software.

The Docker image containing this data is available live and is likely easier to use than the archived image included in this release. See the SCKAN readme file for the most up-to-date instructions.

We would like to thank the members of the SAWG (SPARC Anatomy Working Group, RRID:SCR_018709) for their work on the various connectivity models included in this release.

This work was funded by the NIH Common Fund under 3OT2OD030541-01S1.

Files

release-2022-03-18T233638Z-sckan.zip

Files (1.1 GB)

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md5:2dc1a42278391202bd80f4ef12a24e68
525.9 MB Download
md5:0391284433e8ebd5fd8ab80e640efb9f
86.7 MB Preview Download
md5:b3138e669f56d2e3c4ef8c71de51d891
451.4 MB Preview Download

Additional details