Unify and clean-up heterogeneous data

Indexed knowledge graphs help solve some of the key challenges of big data. Ontologies bring logic in a variety of data, using formal naming conventions, unique identifiers (or URIs) and metadata to identify different entities. As a result, information is categorized, standardized and organized from the outset. Next, linked data establishes clear relationships across heterogeneous data. These two steps result in five-star linked data, ready to be explored and reused by a variety of applications.

Embrace the concept of FAIR data

To fully leverage the power of your data, it must be readable by both humans and machines. DISQOVER operates according to the principles of FAIR data, which state data should be Findable, Accessible, Interoperable and Reusable (FAIR). These guidelines are used by  researchers, publishers and other organizations worldwide, and help solve real-world data challenges

Open architecture with a focus on interoperability

If you want to get the most out of your data, your data analysis tools cannot operate in isolation. With DISQOVER’s open architecture for both data and functionality, you can easily connect the platform to third-party applications and services. Examples of interoperability include the addition of non-text-based searching or the inclusion of artificial intelligence tools within the data ingestion process.