Software or Web Services to Automate Metadata Enrichment and Standardization for Data on Infectious and Immune – Mediated Diseases

Fast Track Proposals will be accepted. Direct-to-Phase II will be accepted. Number of anticipated awards: 1-3 Budget (total costs): Phase I: $ 300,000 for up to 1 year. Phase II: $ 1.5 million for up to 3 years. Page 120 Background The ability of innovative data science approaches to accelerate research on infectious and immune-mediated diseases highly depends on the availability of high-quality, machine-actionable data compliant with the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles. A central tenet of the FAIR principles is rich, standardized, and interoperable metadata in machine-actionable format. FAIR compliant metadata can accelerate discovery of new knowledge through automated, machine-assisted methods, such as automated reasoning, machine-learning, and artificial intelligence. Different types of metadata are used by the community, including descriptive, structural, administrative, reference, and other metadata. The term metadata is also used by some to denote patient phenotypic information related to clinical specimens. In some cases, the distinction between metadata and data can be unclear as both data and metadata represent knowledge and information about entities and relationships. This contract topic focuses on descriptive and administrative metadata that enable the discovery of data for secondary use, including information about the creators, data provenance, access and use permissions, data content and methods used to collect the data, etc. Creating FAIR-compliant metadata is time-consuming and requires specialized skills. As a result, metadata is often incomplete, of limited quality, and rarely machine actionable. There is an urgent need for (semi-)automated approaches and technologies that help researchers and data curators with creating new and augmenting existing metadata. Automated approaches should create, and augment metadata based on widely used and well documented ontologies and standard vocabularies, to enable computer algorithms to interpret the metadata. More efficient approaches for creating and augmenting metadata will also help researchers to comply with the growing demand for FAIR data sharing as recommended by new data sharing policies by publishers and funding agencies, such as the new NIH Data Management and Sharing Policy.