In the context of science, abstract workflows can bridge the gap between scientists and technologists towards using computer systems to carry out scientific processes. Provenance traces provide evidence required to validate scientific products and support their use by others. With abstract workflows and provenance traces based on formal semantics, a knowledge-based framework that merges both technologies are devised, allowing scientists to formally document their processes of data collection and transformation and allowing others to use semantic-based technologies to discover and assess data, processes, and derived data products. This paper presents an approach for evaluating the level of scientist support in frameworks that integrate abstract workflows and provenance traces. In order to support discovery of scientific results, it is essential to provide tools for scientists to document the processes they use to obtain the results. The claim is that the complementary technologies of abstract workflows and provenance traces need to be flexible enough to support a scientist’s perspective and minimize imposition of technically-oriented abstractions that may be extraneous to them. The evaluation approach uses criteria that are derived from tasks performed by scientists using both technologies, i.e., process authoring, process analysis, process interoperability, provenance capturing, provenance analysis, and provenance interoperability.