CRIS is a data management workflow cyberinfrastructure for scientific research..

CRIS Source Code











The CRIS project is partially funded by NSF under the project BD Spokes: PLANNING: MIDWEST: Cyberinfrastructure to Enhance Data Quality and Support Reproducible Results in Sensor Originated Big Data (Award Number:1636891).

CRIS Architecture

Team

About Us

Our team at Purdue University has been working on the research and development of CRIS since 2013.

CRIS Team

Elisa Bertino

Professor of Computer Science Website

Amani Abu Jabal

Ph.D. Student

Past Team Key Members

Peter Baker

Technical Coordinator

Jia Xu

Senior Software Engineer

About

CRIS, the Computational Research Infrastructure for the Sciences, provides an easy to use, trustworthy, cost-effective, and scalable cyber infrastructure. CRIS is a broadly accessible cloud-based graphical community platform which provides scientists with a robust framework to manage, analyze and share their research data and computational tools without custom software development efforts.


The CRIS philosophy is not to re-invent data networks, but to allow existing distributed data and computational tools to be “wrapped” into the system, thus providing broader and more uniform access. In this manner, CRIS brings together the pieces that exist today into an infrastructure to allow scientists to focus on understanding and extending their research efforts in new, verifiable and innovative ways. This is accomplished by providing:

  1. A dedicated workspace to securely store and access your research data;

  2. An extensive suite of tools to automatically capture, transform, and analyze data;

  3. Embedded provenance for all levels of research activity (data, workflows, revisions, etc.);

  4. Integrated vocabularies for data definition;

  5. Automatic data quality monitoring;

  6. Interactive research workflows;

  7. Easy integration of existing data and computational tools;

  8. Long-term storage and access to organized and managed data; and

  9. Search and resource recommendation engines for cross-disciplinary research in CRIS.


Through the use of the CRIS, scientists are improving the quality of what they can already produce, accomplish more through improved efficiencies, enhance their scientific rigor, avoid duplicative efforts, and advance understanding through more complete access to all research components.


Example applications which have been created using CRIS include:

  • Data collection and quality validation from field based sensors

  • Laboratory Information Management Systems (LIMS)

  • Data collection automation and analysis tools

  • Educational surveys, analysis and reporting systems

  • Demonstration website for complex analytical tools

Screenshots

Current CRIS screenshots

Publications