Multi-Omics Metadata Standards Integration (MOMSI) Research Data Alliance Working Group wanted to build a machine-actionable, query-based, interactive dashboard. The dashboard will render information from their existing Landscape Review, currently contained in a Google Sheet format.
Omnipy and whyqd (/wɪkɪd/) are independently-developed Python libraries offering general functionality for auditable and executable metadata mappings. In this project, we will integrate Omnipy and Whyqd to develop executable mappings that transform existing metadata from biodiversity projects, such as ERGA, to conform to the FGA-WG metadata model, kickstarting the process of FAIRifying genome annotation GFF3 files.
whyqd (/wɪkɪd/) is a curatorial toolkit intended to produce well-structured and predictable data for research analysis. It provides an intuitive method for schema-to-schema data transforms for research data reuse, and for restructuring ugly data to conform to a standardised metadata schema. It supports data managers and researchers looking to rapidly, and continuously, ensure schema interoperability for tabular data using a simple series of steps. Once complete, you can import wrangled data into more complex analytical systems or full-feature wrangling tools.
