Abstract
CIMantic Graphs (aka CIM-Graph) is a new python library developed by PNNL to reduce the burden of working with the
Common Information Model. CIMantic Graphs takes a novel approach of building in-memory labeled property graphs for
creating, parsing, and editing CIM power system models.
- Developers:
-
Anderson, Alexander [1] ; Allwardt, Craig [1] ; Stephan, Eric [1]
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Release Date:
- 2024-05-07
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC05-76RL01830
- Code ID:
- 127055
- Site Accession Number:
- Battelle IPID 32838-E
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Country of Origin:
- United States
Citation Formats
Anderson, Alexander, Allwardt, Craig, and Stephan, Eric.
CIMantic Graphs.
Computer Software.
https://github.com/PNNL-CIM-Tools/CIM-Graph.
USDOE.
07 May. 2024.
Web.
doi:10.11578/dc.20240507.3.
Anderson, Alexander, Allwardt, Craig, & Stephan, Eric.
(2024, May 07).
CIMantic Graphs.
[Computer software].
https://github.com/PNNL-CIM-Tools/CIM-Graph.
https://doi.org/10.11578/dc.20240507.3.
Anderson, Alexander, Allwardt, Craig, and Stephan, Eric.
"CIMantic Graphs." Computer software.
May 07, 2024.
https://github.com/PNNL-CIM-Tools/CIM-Graph.
https://doi.org/10.11578/dc.20240507.3.
@misc{
doecode_127055,
title = {CIMantic Graphs},
author = {Anderson, Alexander and Allwardt, Craig and Stephan, Eric},
abstractNote = {CIMantic Graphs (aka CIM-Graph) is a new python library developed by PNNL to reduce the burden of working with the
Common Information Model. CIMantic Graphs takes a novel approach of building in-memory labeled property graphs for
creating, parsing, and editing CIM power system models.},
doi = {10.11578/dc.20240507.3},
url = {https://doi.org/10.11578/dc.20240507.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240507.3}},
year = {2024},
month = {may}
}