In this paper, we describe the approach of the Earth, Life and Semantic Web (ELSEWeb) project that facilitates the discovery and transformation of Earth observation data sources for the creation of species distribution models (data-to-model) transformations. ELSEWeb automates the discovery and processing of voluminous, heterogeneous satellite imagery and other geospatial data available at the Earth Data Analysis Center to be included in Lifemapper Species Distribution models by using AI knowledge representation and reasoning techniques developed by the Semantic Web community. The realization of the ELSEWeb semantic infrastructure provides the possibility of combinatoric explosions of scientific results, automatically generated by orchestrations of data mash-ups and service composition. We report on the key elements that contributed to the ELSEWeb project and the role of automated reasoning in streamlining the Species Distribution Model generation and execution.

When constructing visualization pipelines using toolkits, developers must understand what sequencing of operators will transform their data from its raw state to some requested visual representation. In some cases, the requested visual representation must be generated from hybrid pipelines, composed of both toolkit-based and custom operators. Traditionally, developers learn about how to construct these visualization pipelines by word of mouth, by reading documentation and by inspecting code examples, all of which can be costly in terms of time and effort expended. The Visualization Knowledge Project (VisKo) is built on a knowledge base of visualization toolkit operators including rules for how operators are chained together to form pipelines. VisKo helps scientists by automatically generating and suggesting fully functional visualization pipelines, alleviating scientists from having to write any pipeline code. This paper reports on the kinds of knowledge required to support automatic pipeline generation as well our successes when applying VisKo to a number of visualizations scenarios spanning geophysics, environmental and materials science.