Search (2 results, page 1 of 1)

  • × theme_ss:"Visualisierung"
  • × year_i:[2020 TO 2030}
  1. Petrovich, E.: Science mapping and science maps (2021) 0.02 
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    Abstract
    Science maps are visual representations of the structure and dynamics of scholarly knowl­edge. They aim to show how fields, disciplines, journals, scientists, publications, and scientific terms relate to each other. Science mapping is the body of methods and techniques that have been developed for generating science maps. This entry is an introduction to science maps and science mapping. It focuses on the conceptual, theoretical, and methodological issues of science mapping, rather than on the mathematical formulation of science mapping techniques. After a brief history of science mapping, we describe the general procedure for building a science map, presenting the data sources and the methods to select, clean, and pre-process the data. Next, we examine in detail how the most common types of science maps, namely the citation-based and the term-based, are generated. Both are based on networks: the former on the network of publications connected by citations, the latter on the network of terms co-occurring in publications. We review the rationale behind these mapping approaches, as well as the techniques and methods to build the maps (from the extraction of the network to the visualization and enrichment of the map). We also present less-common types of science maps, including co-authorship networks, interlocking editorship networks, maps based on patents' data, and geographic maps of science. Moreover, we consider how time can be represented in science maps to investigate the dynamics of science. We also discuss some epistemological and sociological topics that can help in the interpretation, contextualization, and assessment of science maps. Then, we present some possible applications of science maps in science policy. In the conclusion, we point out why science mapping may be interesting for all the branches of meta-science, from knowl­edge organization to epistemology.
    Date
    27. 5.2022 18:19:29
  2. Zou, J.; Thoma, G.; Antani, S.: Unified deep neural network for segmentation and labeling of multipanel biomedical figures (2020) 0.01 
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    Abstract
    Recent efforts in biomedical visual question answering (VQA) research rely on combined information gathered from the image content and surrounding text supporting the figure. Biomedical journals are a rich source of information for such multimodal content indexing. For multipanel figures in these journals, it is critical to develop automatic figure panel splitting and label recognition algorithms to associate individual panels with text metadata in the figure caption and the body of the article. Challenges in this task include large variations in figure panel layout, label location, size, contrast to background, and so on. In this work, we propose a deep convolutional neural network, which splits the panels and recognizes the panel labels in a single step. Visual features are extracted from several layers at various depths of the backbone neural network and organized to form a feature pyramid. These features are fed into classification and regression networks to generate candidates of panels and their labels. These candidates are merged to create the final panel segmentation result through a beam search algorithm. We evaluated the proposed algorithm on the ImageCLEF data set and achieved better performance than the results reported in the literature. In order to thoroughly investigate the proposed algorithm, we also collected and annotated our own data set of 10,642 figures. The experiments, trained on 9,642 figures and evaluated on the remaining 1,000 figures, show that combining panel splitting and panel label recognition mutually benefit each other.

Authors