The Hub serves as the reporting and management layer for data quality . It imports results from Data Quality Assessments to track metrics like: : Percentage of correct values. Completeness : Absence of null or missing data. Conformity : Adherence to required formats. 3. Governance and Compliance
# submit_metadata.py from aim_hub import extract_vasp, submit ab initio metadata hub
: Centrally manages valid value lists and domain definitions used for validation and consistency across applications. Key Capabilities and Features 1. End-to-End Data Lineage The Hub serves as the reporting and management
: Users can view lineage at high levels (systems and applications) or drill down to individual fields and columns. Conformity : Adherence to required formats
The primary motivation for the Hub is adherence to the FAIR principles:
Computational materials science relies heavily on calculations to predict material properties without experimental input. As computing power has increased, the volume of generated data has shifted from "data poor" to "data rich." However, this data has traditionally been stored in the "short-tail"—individual researchers' hard drives in proprietary formats (e.g., VASP's OUTCAR , Quantum ESPRESSO's .pwo , FHI-aims output).