Empirical Study on the Representation of 3D Scatterplots as 2D Figures
June 10, 2024 Β· Declared Dead Β· + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Philippos Papaphilippou, Lucy Hederman
arXiv ID
2406.06146
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.GR
Citations
0
Last Checked
5 months ago
Abstract
3D scatterplots are a well-established plotting technique that can be used to represent data with three or more dimensions. On paper and computer monitors they are essentially two-dimensional projections of the three-dimensional Cartesian coordinate system. This transition from the 3D space to two dimensions is not done consistently among scientific software, as there is currently limited quantifiable evidence on the effectiveness of each approach. Notably, the frequent lack of visual cues such as with regard to depth perception is equivalent to a reduction of dimensionality by one. Hence, their use in manuscripts is less common or straightforward. In this empirical study, an online survey is conducted within an academic institution to identify and quantify the effectiveness of feature or feature combinations on 3D scatterplots in terms of reading time and accuracy.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted