The pyQuARC tool reads and evaluates metadata records with a focus on the consistency and robustness of the metadata. pyQuARC flags opportunities to improve or add to contextual metadata information in order to help the user connect to relevant data products. pyQuARC also ensures that information common to both the data product and the file-level metadata are consistent and compatible. pyQuARC frees up human evaluators to make more sophisticated assessments such as whether an abstract accurately describes the data and provides the correct contextual information. The base pyQuARC package assesses descriptive metadata used to catalog Earth observation data products and files. As open source software, pyQuARC can be adapted and customized by data providers to allow for quality checks that evolve with their needs, including checking metadata not covered in base package.
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Added validate_granule_data_format_against_collection_check and granule_data_format_presence_check #193
The validate_granule_data_format_against_collection_check is intended to check the data format provided in the granule (both echo10 and umm-g) matches the data format provided in the collection. The granule_data_format_presence_check is intended to check that a data format is provided in the granule metadata (both echo10 and umm-g).
The validate_granule_data_format_against_collection_check is intended to check the data format provided in the granule (both echo10 and umm-g) matches the data format provided in the collection. The granule_data_format_presence_check is intended to check that a data format is provided in the granule metadata (both echo10 and umm-g).