Data Model for Scientific Models and Hypotheses

New instruments and techniques used in capturing scientific data are exponentially increasing the volume of data consumed by

in-silico

research, which has been usually referred to as data deluge. Once captured, scientific data goes through a cleaning workflow before getting ready for analysis that will eventually confirm the scientist’s hypothesis. The whole process is, nevertheless, complex and takes the focus of the scientist’s attention away from his/her research and towards solving the complexity associated with managing computing products. Moreover, as the research evolves, references to previous results and workflows are needed as source of provenance data. Based on these observations, we claim that

in-silico

experiments must be supported by a hypotheses data model that describes the elements involved in a scientific exploration and supports hypotheses assessment. Adopting a data perspective to represent hypotheses allow high-level references to experiments and provides support for hypotheses evolution. The data model drives the proposal of a data management system that would support scientists in describing, running simulations and interpreting their results.