ArticleIEEE transactions on visualization and computer graphics2018
Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization.
Article in IEEE transactions on visualization and computer graphics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
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- L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator.... IEEE Workshop on Visual Analytics in Healthcare. IEEE Workshop on Visual Analytics in Healthcare · 2025Article
- FlavorCharter: Development of a Smartphone App for Quantifying Food Flavor.Proceedings ... ICDM workshops. IEEE International Conference on Data Mining · 2025Article
- mEnergy: Development of a Novel mHealth Multi-device Platform for Near Real-time Remote Assessment of Energy Disorders.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer.IEEE transactions on visualization and computer graphics · 2025Article
- Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining.IEEE transactions on visualization and computer graphics · 2024Article
- DASS Good: Explainable Data Mining of Spatial Cohort Data.Computer graphics forum : journal of the European Association for Computer Graphics · 2023Article
- Visual Analysis and Detection of Contrails in Aircraft Engine Simulations.IEEE transactions on visualization and computer graphics · 2023Article
- Alveolus analysis: a web browser-based tool to analyze lung intravital microscopy.BMC pulmonary medicine · 2022Article
- A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care.IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium · 2022Article
- THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.IEEE transactions on visualization and computer graphics · 2022Article
- Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology.IEEE Visualization Conference : VIS. IEEE Conference on Visualization · 2020Article
- Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration.IEEE transactions on visualization and computer graphics · 2020Article
- Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots.IEEE transactions on visualization and computer graphics · 2019Article
- DarkSky Halos: Use-Based Exploration of Dark Matter Formation Data in a Hybrid Immersive Virtual Environment.Frontiers in robotics and AI · 2019Article
- Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations.IEEE transactions on visualization and computer graphics · 2018Article
- Empowering Communities: Tailored Pandemic Data Visualization for Varied Tasks and Users.IEEE computer graphics and applicationsArticle
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Although visualization design models exist in the literature in the form of higher-level methodological frameworks, these models do not present a clear methodological prescription for the domain characterization step. This work presents a framework and end-to-end model for requirements engineering in problem-driven visualization application design. The framework and model are based on the activity-centered design paradigm, which is an enhancement of human-centered design. The proposed activity-centered approach focuses on user tasks and activities, and allows an explicit link between the requirements engineering process with the abstraction stage-and its evaluation-of existing, higher-level visualization design models. In a departure from existing visualization design models, the resulting model: assigns value to a visualization based on user activities; ranks user tasks before the user data; partitions requirements in activity-related capabilities and nonfunctional characteristics and constraints; and explicitly incorporates the user workflows into the requirements process. A further merit of this model is its explicit integration of functional specifications, a concept this work adapts from the software engineering literature, into the visualization design nested model. A quantitative evaluation using two sets of interdisciplinary projects supports the merits of the activity-centered model. The result is a practical roadmap to the domain characterization step of visualization design for problem-driven data visualization. Following this domain characterization model can help remove a number of pitfalls that have been identified multiple times in the visualization design literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.