690 citations
- University of North Carolina at Chapel HillUS24 papers
- Lawrence Berkeley National LaboratoryUS7 papers
- Johns Hopkins UniversityUS4 papers
- University of Colorado AnschutzUS4 papers
- University of FloridaUS4 papers
- European Bioinformatics InstituteGB3 papers
- European Molecular Biology LaboratoryDE3 papers
- Harvard UniversityUS3 papers
- Indiana UniversityUS3 papers
- Oregon State UniversityUS3 papers
- University of ChicagoUS3 papers
- University of WashingtonUS3 papers
30 papers
Contextualization or Rationalization? The Effect of Causal Priors on Data Visualization Interpretation
Arran Zeyu Wang, David Borland, Estella Calcaterra +1
Understanding how individuals interpret charts is a crucial concern for visual data communication. This imperative has motivated a number of studies, including past work demonstrat…
LinkML: An Open Data Modeling Framework
Sierra A. T. Moxon, Harold Solbrig, Nomi L. Harris +33
Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, non-standardized spreadsheets, or data r…
Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis
Arran Zeyu Wang, David Borland, David Gotz
Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualiza…
Causal Priors and Their Influence on Judgements of Causality in Visualized Data
Arran Zeyu Wang, David Borland, Tabitha C. Peck +2
"Correlation does not imply causation" is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlati…
Redefining DDoS Attack Detection Using A Dual-Space Prototypical Network-Based Approach
Fernando Martinez, Mariyam Mapkar, Ali Alfatemi +4
Distributed Denial of Service (DDoS) attacks pose an increasingly substantial cybersecurity threat to organizations across the globe. In this paper, we introduce a new deep learnin…
The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization
Kathleen R. Mullen, Imke Tammen, Nicolas A. Matentzoglu +13
Background: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the app…