The CyVerse discovery environment handles the technology of collaborative research so researchers can focus on the science.

Nirav Merchant, the principal investigator for the CyVerse project at the University of Arizona, talks about the CyVerse Discovery Environment at the iRODS User Group meeting, held June 8 and 9 in Chapel Hill, NC.

When experts involved in large collaborative research projects combine their expertise, their tools and technologies, and their knowledge about scientific research communities, the results can benefit all of science.

A case in point: The DataNet Federation Consortium (DFC), a National Science Foundation-funded effort to develop data management infrastructure to support collaborative multidisciplinary research, brought together nine scientific and technological research teams across the U.S. to prototype cyberinfrastructure that serves data-driven collaborative science.   Read more

DataStart provides real-world experiences for students while helping entrepreneurial companies

Graduate students from six universities in the southern U.S. will spend the summer working on data challenges important to the success of new and growing technology companies thanks to a program called the Southern Startup Internship Program in Data Science (DataStart).

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In genomics and other medical fields, researchers are now able to capture and store large amounts of data from electronic medical records (EMRs). But while capturing massive amounts of medical data is becoming common, figuring out how to analyze, interpret and use it in ways that benefit the health of patients remains challenging.

To meet the needs of researchers and clinicians eager to translate data into better health outcomes, researchers at RENCI and the University of North Carolina School of Medicine developed a prototype Genomic Clinical Decision Support System (CDSS). The framework so far has been used in genomic research projects, such as NCGENES, a project led by the UNC genetics department to evaluate the use of whole exome sequencing as a diagnostic tool in clinical settings. However, the developers of the CDSS have conceptualized it to be used in other data-intensive medical fields, such as medical imaging. It is designed to provide clinicians with a dynamic visual snapshot of the genomic data most relevant to an individual patient and to capture, store, and curate more comprehensive genomic data so it can be easily accessed and used in future research and clinical practices.

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