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.
In its Autumn Budget, the UK Government made a commitment to transform the business rates over the parliament into a fairer system that supports investment and is fit for the 21st century. Businesses have raised concerns that the business rates system disincentivises investment and is slow to respond to changing economic conditions. They have called for response.
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.
The Millennium Challenge Corporation developed a compact to support The Gambia's education development with a focus on ensuring the digital readiness of secondary schools. Ensuring appropriate support requires knowledge of what already exists, and what the limits are to digital readiness.
The 1,000 Nurse-led Research Global Challenge is intended to reach nurses, midwives, and community health workers in low-resource settings, managing diseases of poverty to enable them to design and complete a pragmatic and achievable study within their care setting: from setting the research question, running the study, right through to taking the findings up into practice and sharing their recommendations.
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.
