.png){kind=icon}
Return
The materials experiment knowledge graph
.png){kind=icon}
OA
AI
DOI：10.1039/d3dd00067b.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Materials knowledge is inherently hierarchical. While high-level descriptors such as composition and structure are valuable for contextualizing materials data, the data must ultimately be considered in the context of its low-level acquisition details. Graph databases offer an opportunity to represent hierarchical relationships among data, organizing semantic relationships into a knowledge graph. Herein, we establish a knowledge graph of materials experiments whose construction encodes the complete provenance of each material sample and its associated experimental data and metadata. Additional relationships among materials and experiments further encode knowledge and facilitate data exploration. We illustrate the Materials Experiment Knowledge Graph (MekG) using several use cases, demonstrating the value of modern graph databases for the enterprise of data-driven materials science. Graph representations of hierarchical knowledge, including experiment provenances, will help usher in a new era of data-driven materials science.
Journal
IF:
5.6
Papers:
997
Citations:
1.7K
Organization
View more organizations
Cited Papers
ShareSave
ShareSaveShareSaveTowards more reproducible and FAIRer research data: documenting provenance during data acquisition using the Infofile format.png)
DIGITAL DISCOVERYOAAI
DIGITAL DISCOVERYIF5.6
OAAIShareSave View more