103,051 research outputs found
Exploring the Use of Virtual Worlds as a Scientific Research Platform: The Meta-Institute for Computational Astrophysics (MICA)
We describe the Meta-Institute for Computational Astrophysics (MICA), the
first professional scientific organization based exclusively in virtual worlds
(VWs). The goals of MICA are to explore the utility of the emerging VR and VWs
technologies for scientific and scholarly work in general, and to facilitate
and accelerate their adoption by the scientific research community. MICA itself
is an experiment in academic and scientific practices enabled by the immersive
VR technologies. We describe the current and planned activities and research
directions of MICA, and offer some thoughts as to what the future developments
in this arena may be.Comment: 15 pages, to appear in the refereed proceedings of "Facets of Virtual
Environments" (FaVE 2009), eds. F. Lehmann-Grube, J. Sablating, et al., ICST
Lecture Notes Ser., Berlin: Springer Verlag (2009); version with full
resolution color figures is available at
http://www.mica-vw.org/wiki/index.php/Publication
Educational Technology as Seen Through the Eyes of the Readers
In this paper, I present the evaluation of a novel knowledge domain
visualization of educational technology. The interactive visualization is based
on readership patterns in the online reference management system Mendeley. It
comprises of 13 topic areas, spanning psychological, pedagogical, and
methodological foundations, learning methods and technologies, and social and
technological developments. The visualization was evaluated with (1) a
qualitative comparison to knowledge domain visualizations based on citations,
and (2) expert interviews. The results show that the co-readership
visualization is a recent representation of pedagogical and psychological
research in educational technology. Furthermore, the co-readership analysis
covers more areas than comparable visualizations based on co-citation patterns.
Areas related to computer science, however, are missing from the co-readership
visualization and more research is needed to explore the interpretations of
size and placement of research areas on the map.Comment: Forthcoming article in the International Journal of Technology
Enhanced Learnin
Data Driven Discovery in Astrophysics
We review some aspects of the current state of data-intensive astronomy, its
methods, and some outstanding data analysis challenges. Astronomy is at the
forefront of "big data" science, with exponentially growing data volumes and
data rates, and an ever-increasing complexity, now entering the Petascale
regime. Telescopes and observatories from both ground and space, covering a
full range of wavelengths, feed the data via processing pipelines into
dedicated archives, where they can be accessed for scientific analysis. Most of
the large archives are connected through the Virtual Observatory framework,
that provides interoperability standards and services, and effectively
constitutes a global data grid of astronomy. Making discoveries in this
overabundance of data requires applications of novel, machine learning tools.
We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data
from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure
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
The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
Recent and forthcoming advances in instrumentation, and giant new surveys,
are creating astronomical data sets that are not amenable to the methods of
analysis familiar to astronomers. Traditional methods are often inadequate not
merely because of the size in bytes of the data sets, but also because of the
complexity of modern data sets. Mathematical limitations of familiar algorithms
and techniques in dealing with such data sets create a critical need for new
paradigms for the representation, analysis and scientific visualization (as
opposed to illustrative visualization) of heterogeneous, multiresolution data
across application domains. Some of the problems presented by the new data sets
have been addressed by other disciplines such as applied mathematics,
statistics and machine learning and have been utilized by other sciences such
as space-based geosciences. Unfortunately, valuable results pertaining to these
problems are mostly to be found only in publications outside of astronomy. Here
we offer brief overviews of a number of concepts, techniques and developments,
some "old" and some new. These are generally unknown to most of the
astronomical community, but are vital to the analysis and visualization of
complex datasets and images. In order for astronomers to take advantage of the
richness and complexity of the new era of data, and to be able to identify,
adopt, and apply new solutions, the astronomical community needs a certain
degree of awareness and understanding of the new concepts. One of the goals of
this paper is to help bridge the gap between applied mathematics, artificial
intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in
Astronomy, special issue "Robotic Astronomy
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Experiences in involving analysts in visualisation design
Involving analysts in visualisation design has obvious benefits, but the knowledge-gap between domain experts ("analysts") and visualisation designers ("designers") often makes the degree of their involvement fall short of that aspired. By promoting a culture of mutual learning, understanding and contribution between both analysts and designers from the outset, participants can be raised to a level at which all can usefully contribute to both requirement definition and design. We describe the process we use to do this for tightly-scoped and short design exercises -- with meetings/workshops, iterative bursts of design/prototyping over relatively short periods of time, and workplace-based evaluation -- illustrating this with examples of our own experience from recent work with bird ecologists
Hypothesis exploration with visualization of variance.
BackgroundThe Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes-to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. It represented an application of phenomics-wide-scale, systematic study of phenotypes-to neuropsychiatry research.ResultsThis paper reports on a system for exploration of hypotheses in data obtained from the LA2K, LA3C, and LA5C studies in CNP. ViVA is a system for exploratory data analysis using novel mathematical models and methods for visualization of variance. An example of these methods is called VISOVA, a combination of visualization and analysis of variance, with the flavor of exploration associated with ANOVA in biomedical hypothesis generation. It permits visual identification of phenotype profiles-patterns of values across phenotypes-that characterize groups. Visualization enables screening and refinement of hypotheses about variance structure of sets of phenotypes.ConclusionsThe ViVA system was designed for exploration of neuropsychiatric hypotheses by interdisciplinary teams. Automated visualization in ViVA supports 'natural selection' on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data. Large-scale perspective of this kind could lead to better neuropsychiatric diagnostics
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