279,396 research outputs found
Immersive and Collaborative Data Visualization Using Virtual Reality Platforms
Effective data visualization is a key part of the discovery process in the
era of big data. It is the bridge between the quantitative content of the data
and human intuition, and thus an essential component of the scientific path
from data into knowledge and understanding. Visualization is also essential in
the data mining process, directing the choice of the applicable algorithms, and
in helping to identify and remove bad data from the analysis. However, a high
complexity or a high dimensionality of modern data sets represents a critical
obstacle. How do we visualize interesting structures and patterns that may
exist in hyper-dimensional data spaces? A better understanding of how we can
perceive and interact with multi dimensional information poses some deep
questions in the field of cognition technology and human computer interaction.
To this effect, we are exploring the use of immersive virtual reality platforms
for scientific data visualization, both as software and inexpensive commodity
hardware. These potentially powerful and innovative tools for multi dimensional
data visualization can also provide an easy and natural path to a collaborative
data visualization and exploration, where scientists can interact with their
data and their colleagues in the same visual space. Immersion provides benefits
beyond the traditional desktop visualization tools: it leads to a demonstrably
better perception of a datascape geometry, more intuitive data understanding,
and a better retention of the perceived relationships in the data.Comment: 6 pages, refereed proceedings of 2014 IEEE International Conference
on Big Data, page 609, ISBN 978-1-4799-5665-
3D-Stereoscopic Immersive Analytics Projects at Monash University and University of Konstanz
Immersive Analytics investigates how novel interaction and display technologies may support analytical reasoning and decision making. The Immersive Analytics initiative of Monash University started early 2014. Over the last few years, a number of projects have been developed or extended in this context to meet the requirements of semi- or full-immersive stereoscopic environments. Different technologies are used for this purpose: CAVE2â„¢ (a 330 degree large-scale visualization environment which can be used for educative and scientific group presentations, analyses and discussions), stereoscopic Powerwalls (miniCAVEs, representing a segment of the CAVE2 and used for development and communication), Fishtanks, and/or HMDs (such as Oculus, VIVE, and mobile HMD approaches). Apart from CAVE2â„¢ all systems are or will be employed on both the Monash University and the University of Konstanz side, especially to investigate collaborative Immersive Analytics. In addition, sensiLab extends most of the previous approaches by involving all senses, 3D visualization is combined with multi-sensory feedback, 3D printing, robotics in a scientific-artistic-creative environment
A Bibliometric Analysis and Visualization of the Scientific Publications of Universities: A Study of Hamadan University of Medical Sciences during 1992-2018
The evaluation of universities from different perspectives is important for their scientific development. Analyzing the scientific papers of a university under the bibliometric approach is one main evaluative approach. The aim of this study was to conduct a bibliometric analysis and visualization of papers published by Hamadan University of Medical Science (HUMS), Iran, during 1992-2018. This study used bibliometric and visualization techniques. Scopus database was used for data collection. 3753 papers were retrieved by applying Affiliation Search in Scopus advanced search section. Excel and VOSviewer software packages were used for data analysis and bibliometric indicator extraction. An increasing trend was seen in the numbers of HUMS's published papers and received citations. The highest rate of collaboration in national level was with Tehran University of Medical Sciences. Internationally, HUMS's researchers had the highest collaboration with the authors from the United States, the United Kingdom and Switzerland, respectively. All highly-cited papers were published in high level Q1 journals. Term clustering demonstrated four main clusters: epidemiological studies, laboratory studies, pharmacological studies, and microbiological studies. The results of this study can be beneficial to the policy-makers of this university. In addition, researchers and bibliometricians can use this study as a pattern for studying and visualizing the bibliometric indicators of other universities and research institutions
The SSDC contribution to the improvement of knowledge by means of 3D data projections of minor bodies
The latest developments of planetary exploration missions devoted to minor
bodies required new solutions to correctly visualize and analyse data acquired
over irregularly shaped bodies. ASI Space Science Data Center (SSDC-ASI,
formerly ASDC-ASI Science Data Center) worked on this task since early 2013,
when started developing the web tool MATISSE (Multi-purpose Advanced Tool for
the Instruments of the Solar System Exploration) mainly focused on the
Rosetta/ESA space mission data. In order to visualize very high-resolution
shape models, MATISSE uses a Python module (vtpMaker), which can also be
launched as a stand-alone command-line software. MATISSE and vtpMaker are part
of the SSDC contribution to the new challenges imposed by the "orbital
exploration" of minor bodies: 1) MATISSE allows to search for specific
observations inside datasets and then analyse them in parallel, providing
high-level outputs; 2) the 3D capabilities of both tools are critical in
inferring information otherwise difficult to retrieve for non-spherical targets
and, as in the case for the GIADA instrument onboard Rosetta, to visualize data
related to the coma. New tasks and features adding valuable capabilities to the
minor bodies SSDC tools are planned for the near future thanks to new
collaborations
The Topology ToolKit
This system paper presents the Topology ToolKit (TTK), a software platform
designed for topological data analysis in scientific visualization. TTK
provides a unified, generic, efficient, and robust implementation of key
algorithms for the topological analysis of scalar data, including: critical
points, integral lines, persistence diagrams, persistence curves, merge trees,
contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots,
Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due
to a tight integration with ParaView. It is also easily accessible to
developers through a variety of bindings (Python, VTK/C++) for fast prototyping
or through direct, dependence-free, C++, to ease integration into pre-existing
complex systems. While developing TTK, we faced several algorithmic and
software engineering challenges, which we document in this paper. In
particular, we present an algorithm for the construction of a discrete gradient
that complies to the critical points extracted in the piecewise-linear setting.
This algorithm guarantees a combinatorial consistency across the topological
abstractions supported by TTK, and importantly, a unified implementation of
topological data simplification for multi-scale exploration and analysis. We
also present a cached triangulation data structure, that supports time
efficient and generic traversals, which self-adjusts its memory usage on demand
for input simplicial meshes and which implicitly emulates a triangulation for
regular grids with no memory overhead. Finally, we describe an original
software architecture, which guarantees memory efficient and direct accesses to
TTK features, while still allowing for researchers powerful and easy bindings
and extensions. TTK is open source (BSD license) and its code, online
documentation and video tutorials are available on TTK's website
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