Deep Mapping of the Visual Literature

Howe, Bill; Lee, Po-Shen; Grechkin, Maxim; Yang, Sean T.; West, Jevin D.

doi:10.1145/3041021.3053065

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…25,26 The growing popularity of different guidelines for effective scientific visualizations has led to a recognition of the importance of examining the structure and grammar of visualizations in scientific journals. 27–30 However, no previous study has empirically examined visualization displays in the field of ecology.…”

Section: Visualizations In Academic Journalsmentioning

confidence: 99%

“…She also reported on pitfalls researchers encounter encoding visualizations in preparing manuscripts, especially visualizations in which straightforward design approach is often not sufficient. A different approach was taken by Howe et al, 28 who built a web-based search engine that allowed users to search for visualizations in scholarly work based on type. This platform extracted visual information from the scientific literature.…”

Section: Visualizations In Academic Journalsmentioning

confidence: 99%

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Data and visual displays in the Journal of Ecology 1996–2016

Friedman

2020

Information Visualization

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Scholars in scientific disciplines face unique challenges in the creation of visualizations, especially in publications that require insights derived from analyses to be visually displayed. The literature on visualizations describes different techniques and best practices for the creation of graphs. However, these techniques have not been used to evaluate the impact of visualizations in academic publications. In the field of ecology, as in other scientific fields, graphs are an essential part of journal articles. Little is known about the connections between the kind of data presented and domain in which the researchers conducted their study that together produces the visual graphics. This study focused on articles published in the Journal of Ecology between 1996 and 2016 to explore possible connections between data type, domain, and visualization type. Specifically, this study asked three questions: How many of the graphics published between 1996 and 2016 follow a particular set of recommendations for best practices? What can Pearson correlations reveal about the relationships between type of data, domain of study, and visual displays? Can the findings be examined through an inter-reliability test lens? Out of the 20,080 visualizations assessed, 54% included unnecessary graphical elements in the early part of the study (1996–2010). The most common type of data was univariate (35%) and it was often displayed using line graphs. Twenty-one percent of the articles in the period studied could be categorized under the domain type “single species.” Pearson correlation analysis showed that data type and domain type was positively correlated ( r = 0.08; p ≤ 0.05). Cohen’s kappa for the reliability test was 0.86, suggesting good agreement between the two categories. This study provides evidence that data type and domain types are equally important in determining the type of visualizations found in scientific journals.

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Section: Visualizations In Academic Journalsmentioning

confidence: 99%

Section: Visualizations In Academic Journalsmentioning

confidence: 99%

Data and visual displays in the Journal of Ecology 1996–2016

Friedman

2020

Information Visualization

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EZLearn: Exploiting Organic Supervision in Automated Data Annotation

Grechkin

Poon

Howe

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Many real-world applications require automated data annotation, such as identifying tissue origins based on gene expressions and classifying images into semantic categories. Annotation classes are often numerous and subject to changes over time, and annotating examples has become the major bottleneck for supervised learning methods. In science and other high-value domains, large repositories of data samples are often available, together with two sources of organic supervision: a lexicon for the annotation classes, and text descriptions that accompany some data samples. Distant supervision has emerged as a promising paradigm for exploiting such indirect supervision by automatically annotating examples where the text description contains a class mention in the lexicon. However, due to linguistic variations and ambiguities, such training data is inherently noisy, which limits the accuracy of this approach. In this paper, we introduce an auxiliary natural language processing system for the text modality, and incorporate co-training to reduce noise and augment signal in distant supervision. Without using any manually labeled data, our EZLearn system learned to accurately annotate data samples in functional genomics and scientific figure comprehension, substantially outperforming state-of-the-art supervised methods trained on tens of thousands of annotated examples.

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Deep Mapping of the Visual Literature

Cited by 2 publications

References 15 publications

Data and visual displays in the Journal of Ecology 1996–2016

Data and visual displays in the Journal of Ecology 1996–2016

EZLearn: Exploiting Organic Supervision in Automated Data Annotation

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