DIRISP 2012 - Symposium on Discovery Informatics: The Role of AI Research in Innovating Scientific Processes

Addressing the ambitious research agendas put forward by many scientific disciplines requires meeting a multitude of challenges in intelligent systems, information sciences, and human-computer interaction. Many aspects of the scientific discovery process are often largely manual and could be automated, improved, or made more efficient. Better interfaces for collaboration, visualization, and understanding would significantly improve scientific practice. Scientific data, publications, and tools could be published in open formats with appropriate semantic descriptions and metadata annotations to improve sharing and dissemination. Opportunities for broader participation in well-defined scientific tasks enable human contributors to provide large amounts of data, annotations, or complex processing results that could not otherwise be obtained. Improvements and innovations across the spectrum of scientific processes and activities will have a profound impact on the rate of scientific discoveries.
This symposium will provide a forum for researchers interested in understanding the role of AI techniques in improving or innovating scientific processes. We seek submissions that: (1) report on success stories that illustrate the potential of future research in this field; (2) discuss lessons learned in the process of addressing challenging aspects of the scientific process; (3) analyze the impact of a particular technique in an area of science and reflect on its potential for broader applicability in other sciences; and (4) propose future concepts grounded in lessons learned and an understanding of the challenges in the scientific discovery process.
Topics
Topics of interest include but are not limited to the following:
Ontologies and knowledge bases that model particular areas of scientific knowledge
Semantic representations of metadata for all aspects of scientific processes
Techniques for organizing scientific literature
Workflow systems to manage complex data analysis processes
Knowledge discovery techniques that are embedded in the context of scientific investigations
Integrative approaches of machine learning and scientific model induction
Automated systems for experiment design, data analysis, and hypothesis generation and refinement
User-centered design of intelligent systems that partner with scientists to perform complex tasks
Integrated approaches to visualizing data, models, and the connections between them to foster new insights
Cognitive-centered design of scientist aids
Social computing systems that let novice participants contribute to scientific tasks