Education and data-intensive science in the beginning of the 21st century.

Data-intensive science will open up new avenues to explore, new questions to ask, and new ways to answer. Yet, this potential cannot be unlocked without new emphasis on education of the researchers gathering data, the analysts analyzing data and the cross-disciplinary participants working together to make it happen. This article is a summary of the education issues and challenges of data-intensive sciences and cloud computing as discussed in the Data-Intensive Science (DIS) workshop in Seattle, September 19-20, 2010.

Design and initial characterization of the SC-200 proteomics standard mixture.

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards.