CRIS — Computational research infrastructure for science

The challenges facing the scientific community are common and real: conduct relevant and verifiable research in a rapidly changing collaborative landscape with an ever increasing scale of data. It has come to a point where research activities cannot scale at the rate required without improved cyberinfrastructure (CI). In this paper we describe CRIS (The Computational Research Infrastructure for Science), with its primary tenets to provide an easy to use, scalable, and collaborative scientific data management and workflow cyberinfrastructure for scientists lacking extensive computational expertise. Some of the key features of CRIS are: 1) semantic definition of scientific data using domain vocabularies; 2) embedded provenance for all levels of research activity (data, workflows, tools etc.); 3) easy integration of existing heterogeneous data and computational tools on local or remote computers; 4) automatic data quality monitoring for syntactic and domain standards; and 5) shareable yet secure access to research data, computational tools and equipment. CRIS currently has a community of users in Agronomy, Biochemistry, Bioinformatics and Healthcare Engineering at Purdue University (cris.cyber.purdue.edu).