2 research outputs found
University of Chicago CRESCAT Project
The CRESCAT project
is an interdisciplinary collaboration between computer scientists,
paleobiologists, archaeologists, economic historians, and other
social scientists. The goal is to demonstrate the value of an integrative
software ecosystem that spans the social and natural sciences and can
facilitate any research characterized by overlapping models of temporal and
spatial relations or by conflicting terminologies and taxonomies. CRESCAT’s
representation of scientific knowledge eschews forced standardization, which is
impractical in many cases due to lack of an enforcement mechanism and is also
questionable in principle since divergent ontologies often legitimately reflect
different theoretical assumptions and research agendas. Central to the CRESCAT
suite of tools is an innovative data-integration system that represents
explicitly both research data and the ontologies inherent in the data. CRESCAT’s data-integration system operates at a level of abstraction
sufficient to provide a predictable and efficiently queryable database
structure based on an abstract global schema, which in turn is based on an
“upper ontology” specified in terms of fundamental concepts and relationships
applicable to all scientific and scholarly disciplines. The data-integration
system is implemented in an enterprise-class XQuery DBMS that serves as a
data warehouse (using a non-relational graph data model) to store diverse data from a wide range of research projects representing many
disciplines. The terminology and conceptual distinctions of each research
project are fully preserved. The approach to research data taken in the CRESCAT
project is (1) coherent, tightly integrating software tools and data formats
within a single analytical framework; (2) open-ended, interconnecting existing
tools while allowing the addition of new tools in the future; (3)
non-exclusive, in no way preventing its component tools from participating in
other software ecosystems; (4) scalable, designed to handle large-scale data
management, analysis, and visualization; and (5) sustainable, maintaining
shared resources to meet common needs for software and technical support and
thus enabling substantial economies of scale