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.
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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.