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Data-intensive science has the potential to transform scientific
research and quickly translate scientific progress into complete
solutions, policies, and economic success. But this collaborative
science is still lacking the effective access and exchange of
knowledge among scientists, researchers, and policy makers across a
range of disciplines. Bringing together leaders from multiple
scientific disciplines, Data-Intensive Science shows how a
comprehensive integration of various techniques and technological
advances can effectively harness the vast amount of data being
generated and significantly accelerate scientific progress to
address some of the world's most challenging problems. In the book,
a diverse cross-section of application, computer, and data
scientists explores the impact of data-intensive science on current
research and describes emerging technologies that will enable
future scientific breakthroughs. The book identifies best practices
used to tackle challenges facing data-intensive science as well as
gaps in these approaches. It also focuses on the integration of
data-intensive science into standard research practice, explaining
how components in the data-intensive science environment need to
work together to provide the necessary infrastructure for
community-scale scientific collaborations. Organizing the material
based on a high-level, data-intensive science workflow, this book
provides an understanding of the scientific problems that would
benefit from collaborative research, the current capabilities of
data-intensive science, and the solutions to enable the next round
of scientific advancements.
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