Semantic Data Dictionaries (SDD)

The aim of the Semantic Data Dictionary (SDD) approach is to annotate datasets such that it is machine readable, uses best practice ontologies, and follows FAIR Guiding Principles. It is a project that was developed to address machines’ difficulty in understanding data dictionaries, a standard method used to describe datasets through the use of tables that identify information about data variables’ content, description, and format. With SDD, there is an extension and integration of data from multiple domains using a common metadata standard. Using a structure based on the Semanticscience Integrated Ontology’s high-level, domain-agnostic conceptualization of scientific data, the SDD format will make the specification, curation, and search of data much easier than the direct search of data dictionaries through terminology alignment and the use of "compositional" classes for column descriptions. A number of TWC projects use SDD, including including HHEAR, MaterialsMine, and HEALS. Learn more at This project leverages the work completed in the  HADatAc (Human-Aware Data Acquisition framework), an open-source infrastructure that enables combined acquisitions of data and metadata in a way that metadata is properly and logically connected to data.  For more information, visit

TWC Faculty

Selected Publications