Ontologies for scientific data transformation

A common task for scientists is to collect data and apply algorithms to process the data. Scientists who aim to use data collected or processed by other scientists must be able to determine the applicability of the data. Supporting a scientist\u27s capability to identify and reason about data can be achieved by associating the data with metadata about the conditions under which the data was collected and how it was processed, i.e., its provenance. This dissertation presents the Workflow-Driven Ontology (WDO) framework for representing formal knowledge about data with respect to how it is collected and transformed. Formally represented knowledge supports computing tasks that require reasoning capabilities, allowing scientists to use computers to support decisions about the appropriateness of data for use. The approach uses abstraction to capture the scientist\u27s perspective of relevant components associated with collecting and transforming data, postponing technical nuances to a later stage. The contribution of the WDO framework is that it supports the creation of scientist-driven data process documentation by the combination of the following features: the capability to create ontological representations of end-to-end processes; the capability to create knowledge bases about data provenance, and; the capability to link documentation to actual data process resources