48,291 research outputs found
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Spin communication over 30 m long channels of chemical vapor deposited graphene on SiO
We demonstrate a high-yield fabrication of non-local spin valve devices with
room-temperature spin lifetimes of up to 3 ns and spin relaxation lengths as
long as 9 m in platinum-based chemical vapor deposition (Pt-CVD)
synthesized single-layer graphene on SiO/Si substrates. The spin-lifetime
systematically presents a marked minimum at the charge neutrality point, as
typically observed in pristine exfoliated graphene. However, by studying the
carrier density dependence beyond n ~ 5 x 10 cm, via
electrostatic gating, it is found that the spin lifetime reaches a maximum and
then starts decreasing, a behavior that is reminiscent of that predicted when
the spin-relaxation is driven by spin-orbit interaction. The spin lifetimes and
relaxation lengths compare well with state-of-the-art results using exfoliated
graphene on SiO/Si, being a factor two-to-three larger than the best values
reported at room temperature using the same substrate. As a result, the spin
signal can be readily measured across 30 m long graphene channels. These
observations indicate that Pt-CVD graphene is a promising material for
large-scale spin-based logic-in-memory applications
Sciunits: Reusable Research Objects
Science is conducted collaboratively, often requiring knowledge sharing about
computational experiments. When experiments include only datasets, they can be
shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers
(DOIs). An experiment, however, seldom includes only datasets, but more often
includes software, its past execution, provenance, and associated
documentation. The Research Object has recently emerged as a comprehensive and
systematic method for aggregation and identification of diverse elements of
computational experiments. While a necessary method, mere aggregation is not
sufficient for the sharing of computational experiments. Other users must be
able to easily recompute on these shared research objects. In this paper, we
present the sciunit, a reusable research object in which aggregated content is
recomputable. We describe a Git-like client that efficiently creates, stores,
and repeats sciunits. We show through analysis that sciunits repeat
computational experiments with minimal storage and processing overhead.
Finally, we provide an overview of sharing and reproducible cyberinfrastructure
based on sciunits gaining adoption in the domain of geosciences
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced
with a choice between several computational methods for performing data
analyses. Benchmarking studies aim to rigorously compare the performance of
different methods using well-characterized benchmark datasets, to determine the
strengths of each method or to provide recommendations regarding suitable
choices of methods for an analysis. However, benchmarking studies must be
carefully designed and implemented to provide accurate, unbiased, and
informative results. Here, we summarize key practical guidelines and
recommendations for performing high-quality benchmarking analyses, based on our
experiences in computational biology.Comment: Minor update
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology
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