support AI-readiness data checks in data quality engine

[mbjones]

For the FAIR Data Quality check engine (issue #328), consult the ESIP AI-Readiness Checklist for a list of data quality checks that the community has felt are important for assessment for prep for ML tooling readiness. See:

• AI-Readiness Checklist
  • ESIP Data Readiness Cluster (2022). Checklist to Examine AI-readiness for Open Environmental Datasets. ESIP. Online resource. https://doi.org/10.6084/m9.figshare.19983722.v1
• ESIP announcement: https://www.esipfed.org/merge/collaboration-updates/checklist-ai-ready-data
• ESIP Data Readiness cluster: https://wiki.esipfed.org/Data_Readiness

I propose that these would be a good candidate set of checks that have already been vetted by ESIP and would be useful way to vet the data quality engine. Maybe it would be it's own suite?

[mbjones]

Also see the Analysis Ready Data (ARD) standards from CEOS:

• CEOS-ARD: https://ceos.org/ard/
• For example, here's the CEOS ARD standard for surface reflectance data:
  • https://ceos.org/ard/files/PFS/SR/v5.0/CARD4L_Product_Family_Specification_Surface_Reflectance-v5.0.pdf

There is a new OGC working group focused on ARD data standards: https://www.ogc.org/press-release/ogc-forms-new-analysis-ready-data-standards-working-group/