Note
This project is under active development.
Welcome to LabGraph’s documentation!¶
LabGraph is a Python library for storing and retrieving materials science data stored in MongoDB. This library enforces a data model tailored for experimental materials data.
At a high-level, data is stored as a directed graph of four node types: Material, Action, Measurement, and Analysis. The content of these nodes is up to you – we just make sure that any data you enter results in a valid graph.
This is a graph for a single Sample. Learn more in the Schema Overview section.¶
Node Types¶
Here is a brief overview of the four node types and their roles in the data model. Further details on allowed node relationships can be found in the Schema Overview section.
Materialnodes are the fundamental building blocks of the data model. These represent a material in a given state.Actionnodes are operations that generateMaterialnodes have incoming edges from any inputMaterialcan generateMaterialfrom a vendor or receiving aMaterialfrom a collaborator.Measurementnodes act upon aMaterialnode to yield some form of raw data.Analysisnodes act uponMeasurementand/orAnalysisnodes to yield some form of processed data.
Samples = Graphs¶
As materials scientists, we execute sets of Action to synthesize and study Material. A Sample is simply a graph of nodes that captures the steps performed in an experiment. In typical usage, we will enter nodes into the database as part of a Sample. This achieves a few things:
We can ensure that the graph we are entering is valid (i.e. it is a directed acyclic graph (DAG) with no isolated nodes).
Given a node, we can easily retrieve the most related nodes that belong to the same
Sample.We can record any
Sample-level metadata (e.g. sample name, sample description, sample author, etc.).
Database Backend¶
LabGraph uses MongoDB as our backend database. We communicate with the database using the pymongo package.