| 1. |
- Conroy, Melanie, et al.
(author)
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Uncertainty in humanities network visualization
- 2024
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In: Frontiers in Communication. - : Frontiers Media S.A.. - 2297-900X. ; 8
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Journal article (peer-reviewed)abstract
- Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated into the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations.
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| 2. |
- Jain, Yashvardhan, et al.
(author)
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Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms
- 2023
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 14:1
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Journal article (peer-reviewed)abstract
- The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We create a dataset containing 880 histology images with 12,901 segmented structures, engaging 1175 teams from 78 countries in community-driven, open-science development of machine learning models. Tissue variations in the dataset pose a major challenge to the teams which they overcome by using color normalization techniques and combining vision transformers with convolutional models. The best model will be productized in the HuBMAP portal to process tissue image datasets at scale in support of Human Reference Atlas construction.
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| 3. |
- Schreiber, Falk, et al.
(author)
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Heterogeneous Networks on Multiple Levels
- 2014
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In: MULTIVARIATE NETWORK VISUALIZATION. - Cham : Springer. - 9783319067926 - 9783319067926 ; , s. 175-206
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Conference paper (peer-reviewed)abstract
- Heterogeneous networks and multi-level networks occur in several application fields where their integration, combination, comparison, analysis, and visualization poses major challenges. In this chapter, we analyze the general characteristics of this type of data and identify examples in three application domains: biology, social sciences, and software engineering. Conceptually, we focus on sets of multivariate networks at two or more levels. Each level may describe a specific scale, and within each level several related heterogeneous networks are represented. We allow n:m mappings within the same level, but only 1:n mappings across levels that must be consecutive. This leads to a structured data set that is the basis for further visual analysis. Our chapter ends with ideas to visualize those networks together with the relationships between them and highlights research challenges.
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| 4. |
- Singhal, Dhruv, et al.
(author)
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Mapping the lymphatic system across body scales and expertise domains : A report from the 2021 National Heart, Lung, and Blood Institute workshop at the Boston Lymphatic Symposium
- 2023
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In: Frontiers in Physiology. - : Frontiers Media S.A.. - 1664-042X. ; 14
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Research review (peer-reviewed)abstract
- Enhancing our understanding of lymphatic anatomy from the microscopic to the anatomical scale is essential to discern how the structure and function of the lymphatic system interacts with different tissues and organs within the body and contributes to health and disease. The knowledge of molecular aspects of the lymphatic network is fundamental to understand the mechanisms of disease progression and prevention. Recent advances in mapping components of the lymphatic system using state of the art single cell technologies, the identification of novel biomarkers, new clinical imaging efforts, and computational tools which attempt to identify connections between these diverse technologies hold the potential to catalyze new strategies to address lymphatic diseases such as lymphedema and lipedema. This manuscript summarizes current knowledge of the lymphatic system and identifies prevailing challenges and opportunities to advance the field of lymphatic research as discussed by the experts in the workshop.
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