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Best practices include identifying standard and archetype-specific data management processes, self-monitoring methods for data quality, and recommendations for implementing maturity models. It also emphasizes data management planning for engineering, prioritization of software development goals, and clarifying institutional needs for access to RDMO.
Provides best practices for data stewardship and digital infrastructure, including workflows for version control, issue tracking, continuous integration, and test-driven development. It advocates for automatic quantification, visualization, and evaluation on HPC systems.
Aims to leverage FAIR Digital Objects and Linked Data for data storage and publication, utilizing established standards and ontologies. The focus is on interconnecting data with registries and showcasing the power of linking research data and knowledge.
Focuses on the FAIR principles for metadata, ensuring data is assigned unique identifiers, described with rich metadata, and registered in searchable resources. It emphasizes using formal, accessible knowledge representation languages and vocabularies aligned with FAIR principles.