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The realization of an international cyberinfrastructure of shared resources to overcome time and space limitations is challenging scientists to rethink how to document their processes. Many known scientific process requirements that would normally be considered impossible to implement a few years ago are close to becoming a reality for scientists, such as large scale integration and data reuse, data sharing across distinct scientific domains, comprehensive support for explaining process results, and full search capability for scientific products across domains. This article introduces the CI-Miner approach that can be used to aggregate knowledge about scientific processes and their products through the use of semantic annotations. The article shows how this aggregated knowledge is used to benefit scientists during the development of their research activities. The discussion is grounded on lessons learned through the use of CI-Miner to semantically annotate scientific processes in the areas of geo-sciences, environmental sciences and solar physics: A use case in the field of geo-science illustrates the CI-Miner approach in action.

Two challenges related to capturing provenance about scientific data are: 1) determining an adequate level of granularity to encode provenance, and 2) encoding provenance in a way that facilitates end-user interpretation and analysis. A solution to address these challenges consists in integrating two technologies: Semantic Abstract Workflows (SAWs), which are used to capture a domain expert’s understanding of a scientific process, and PML, an extensible language used to encode provenance. This paper describes relevant features of these technologies for addressing the granularity and interpretation challenges of provenance encoding and presents a discussion about their integration.

The US Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) is adopting the use of formalized provenance to support observational data products produced by ARM operations and relied upon by researchers. Because of the diversity of needs in the climate community provenance will need to be conveyed in a domain-oriented context. This paper explores a use case where semantic abstract workflows (SAW) are employed as a means to filter, aggregate, and contextually describe the historical events responsible for the ARM data product the scientist is relying upon.

An approach that results in the development of Workflow-Driven Ontologies (WDO) (called the WDO approach) allows domain scientists to capture process knowledge in the form of concepts as well as relations between concepts. Program synthesis techniques can be employed to generate algorithmic solutions by transforming the process knowledge expressed in the WDO as concepts and relations to variables and functions and computing unknown variables from known ones based on the process knowledge documented by the domain scientist. Furthermore, the algorithmic solutions that are generated by program synthesis potentially can support the composition of services, which result in the creation of executable scientific workflows. The ultimate goal of this work is to provide an end-to-end solution for scientists beginning with modeling the processes for creating work products in terminology from the scientist's own domains and ending with component-based applications that can be used to automate processes that can advance their scientific endeavors. These applications can exploit distributed components that are supported by current cyber-infrastructure efforts. This paper discusses extensions to the WDO approach that support program synthesis. To elucidate this scenario, an example related to earth sciences is presented.

Ontologies can be tailored in ways that can facilitate the description of workflows by specifying how concepts representing services are used to access and create concepts representing data and products. Early work on the development of such ontologies, and reported in this paper, has resulted in the construction of a gravity data ontology. The relationships that are defined in the ontology capture inputs and outputs of methods, e.g., derived data and products, as well as other associations that are related to workflow computation. This paper presents the basis for a computation-driven ontology that evolved into the workflow-driven ontology approach. In addition, the paper describes the process used to construct an ontology for gravity data using the computation-driven approach, and it presents a gravity ontology that documents the processes and methods associated with gravity data and related products.