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Documents authored by Scheuermann, Gerik

Document
DOI: 10.4230/DagRep.8.11.139
Network Visualization in the Humanities (Dagstuhl Seminar 18482)

Authors: Katy Börner, Oyvind Eide, Tamara Mchedlidze, Malte Rehbein, and Gerik Scheuermann

Published in: Dagstuhl Reports, Volume 8, Issue 11 (2019)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 18482 "Network Visualization in the Humanities", which took place November 26-30, 2019. The seminar brought together 27 researchers on Network Visualization and Digital Humanities communities. During the seminar the participants shared knowledge on the existing methods of network visualization and on network visualization challenges present in the Humanities through the introductory talks, the abstracts of which are included in this report. Multiple innovative research challenges for Network Visualisation in the Humanities have been identified and according to those four working groups have been set up that discussed the topics in detail. The summary of the discussions of the working groups is given in this report.
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Katy Börner, Oyvind Eide, Tamara Mchedlidze, Malte Rehbein, and Gerik Scheuermann. Network Visualization in the Humanities (Dagstuhl Seminar 18482). In Dagstuhl Reports, Volume 8, Issue 11, pp. 139-153, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@Article{borner_et_al:DagRep.8.11.139,
  author =	{B\"{o}rner, Katy and Eide, Oyvind and Mchedlidze, Tamara and Rehbein, Malte and Scheuermann, Gerik},
  title =	{{Network Visualization in the Humanities (Dagstuhl Seminar 18482)}},
  pages =	{139--153},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{8},
  number =	{11},
  editor =	{B\"{o}rner, Katy and Eide, Oyvind and Mchedlidze, Tamara and Rehbein, Malte and Scheuermann, Gerik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.11.139},
  URN =		{urn:nbn:de:0030-drops-103593},
  doi =		{10.4230/DagRep.8.11.139},
  annote =	{Keywords: digital humanities, network visualization, graph drawing, human computer interaction, topic modelling, cyberinfrastructures, distant reading}
}
Document
DOI: 10.4230/DagRep.8.1.100
Foundations of Data Visualization (Dagstuhl Seminar 18041)

Authors: Helwig Hauser, Penny Rheingans, and Gerik Scheuermann

Published in: Dagstuhl Reports, Volume 8, Issue 1 (2018)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 18041 "Foundations of Data Visualization". It includes a discussion of the motivation and overall organization, an abstract from each of the participants, and a report about each of the working groups.
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Helwig Hauser, Penny Rheingans, and Gerik Scheuermann. Foundations of Data Visualization (Dagstuhl Seminar 18041). In Dagstuhl Reports, Volume 8, Issue 1, pp. 100-123, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{hauser_et_al:DagRep.8.1.100,
  author =	{Hauser, Helwig and Rheingans, Penny and Scheuermann, Gerik},
  title =	{{Foundations of Data Visualization (Dagstuhl Seminar 18041)}},
  pages =	{100--123},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{8},
  number =	{1},
  editor =	{Hauser, Helwig and Rheingans, Penny and Scheuermann, Gerik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.1.100},
  URN =		{urn:nbn:de:0030-drops-92853},
  doi =		{10.4230/DagRep.8.1.100},
  annote =	{Keywords: Foundations, Interdisciplinary Cooperation, Theory, Visualization}
}
Document
DOI: 10.4230/DagRep.4.6.1
Scientific Visualization (Dagstuhl Seminar 14231)

Authors: Min Chen, Charles D. Hansen, Penny Rheingans, and Gerik Scheuermann

Published in: Dagstuhl Reports, Volume 4, Issue 6 (2015)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 14231 "Scientific Visualization". It includes a discussion of the motivation and overall organization, an abstract from each of the participants, and a report from each of the working groups.
Cite as

Min Chen, Charles D. Hansen, Penny Rheingans, and Gerik Scheuermann. Scientific Visualization (Dagstuhl Seminar 14231). In Dagstuhl Reports, Volume 4, Issue 6, pp. 1-28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@Article{chen_et_al:DagRep.4.6.1,
  author =	{Chen, Min and Hansen, Charles D. and Rheingans, Penny and Scheuermann, Gerik},
  title =	{{Scientific Visualization (Dagstuhl Seminar 14231)}},
  pages =	{1--28},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{4},
  number =	{6},
  editor =	{Chen, Min and Hansen, Charles D. and Rheingans, Penny and Scheuermann, Gerik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.4.6.1},
  URN =		{urn:nbn:de:0030-drops-46821},
  doi =		{10.4230/DagRep.4.6.1},
  annote =	{Keywords: data visualization, multi-fields, uncertainty, environmental visualization}
}
Document
DOI: 10.4230/DFU.SciViz.2010.62
Towards Automatic Feature-based Visualization

Authors: Heike Jänicke and Gerik Scheuermann

Published in: Dagstuhl Follow-Ups, Volume 1, Scientific Visualization: Advanced Concepts (2010)

Abstract
Visualizations are well suited to communicate large amounts of complex data. With increasing resolution in the spatial and temporal domain simple imaging techniques meet their limits, as it is quite difficult to display multiple variables in 3D or analyze long video sequences. Feature detection techniques reduce the data-set to the essential structures and allow for a highly abstracted representation of the data. However, current feature detection algorithms commonly rely on a detailed description of each individual feature. In this paper, we present a feature-based visualization technique that is solely based on the data. Using concepts from computational mechanics and information theory, a measure, local statistical complexity, is defined that extracts distinctive structures in the data-set. Local statistical complexity assigns each position in the (multivariate) data-set a scalar value indicating regions with extraordinary behavior. Local structures with high local statistical complexity form the features of the data-set. Volume-rendering and iso-surfacing are used to visualize the automatically extracted features of the data-set. To illustrate the ability of the technique, we use examples from diffusion, and flow simulations in two and three dimensions.
Cite as

Heike Jänicke and Gerik Scheuermann. Towards Automatic Feature-based Visualization. In Scientific Visualization: Advanced Concepts. Dagstuhl Follow-Ups, Volume 1, pp. 62-77, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InCollection{janicke_et_al:DFU.SciViz.2010.62,
  author =	{J\"{a}nicke, Heike and Scheuermann, Gerik},
  title =	{{Towards Automatic Feature-based Visualization}},
  booktitle =	{Scientific Visualization: Advanced Concepts},
  pages =	{62--77},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-19-4},
  ISSN =	{1868-8977},
  year =	{2010},
  volume =	{1},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.SciViz.2010.62},
  URN =		{urn:nbn:de:0030-drops-26976},
  doi =		{10.4230/DFU.SciViz.2010.62},
  annote =	{Keywords: Feature Detection Techniques, Feature-based Visualization, Local Statistical Complexity}
}
Document
DOI: 10.4230/DFU.SciViz.2010.124
Tracking Lines in Higher Order Tensor Fields

Authors: Mario Hlawitschka and Gerik Scheuermann

Published in: Dagstuhl Follow-Ups, Volume 1, Scientific Visualization: Advanced Concepts (2010)

Abstract
While tensors occur in many areas of science and engineering, little has been done to visualize tensors with order higher than two. Tensors of higher orders can be used for example to describe complex diffusion patterns in magnetic resonance imaging (MRI). Recently, we presented a method for tracking lines in higher order tensor fields that is a generalization of methods known from first order tensor fields (vector fields) and symmetric second order tensor fields. Here, this method is applied to magnetic resonance imaging where tensor fields are used to describe diffusion patterns for example of hydrogen in the human brain. These patterns align to the internal structure and can be used to analyze interconnections between different areas of the brain, the so called tractography problem. The advantage of using higher order tensor lines is the ability to detect crossings locally, which is not possible in second order tensor fields. In this paper, the theoretical details will be extended and tangible results will be given on MRI data sets.
Cite as

Mario Hlawitschka and Gerik Scheuermann. Tracking Lines in Higher Order Tensor Fields. In Scientific Visualization: Advanced Concepts. Dagstuhl Follow-Ups, Volume 1, pp. 124-135, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InCollection{hlawitschka_et_al:DFU.SciViz.2010.124,
  author =	{Hlawitschka, Mario and Scheuermann, Gerik},
  title =	{{Tracking Lines in Higher Order Tensor Fields}},
  booktitle =	{Scientific Visualization: Advanced Concepts},
  pages =	{124--135},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-19-4},
  ISSN =	{1868-8977},
  year =	{2010},
  volume =	{1},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.SciViz.2010.124},
  URN =		{urn:nbn:de:0030-drops-27013},
  doi =		{10.4230/DFU.SciViz.2010.124},
  annote =	{Keywords: Tensor Field, Line Tracking}
}
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