Daniel Weiskopf
About
In The Last Decade
Daniel Weiskopf
368 papers receiving 7.6k citations
Peers
Comparison fields: 5 of 173
- Computer Vision and Pattern Recognition 5.4k
- Computer Graphics and Computer-Aided Design 2.1k
- Artificial Intelligence 1.3k
- Computational Mechanics 1.1k
- Human-Computer Interaction 1.1k
Countries citing papers authored by Daniel Weiskopf
This map shows the geographic impact of Daniel Weiskopf's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Weiskopf with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Weiskopf more than expected).
Fields of papers citing papers by Daniel Weiskopf
This network shows the impact of papers produced by Daniel Weiskopf. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Weiskopf. The network helps show where Daniel Weiskopf may publish in the future.
Co-authorship network of co-authors of Daniel Weiskopf
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Weiskopf. A scholar is included among the top collaborators of Daniel Weiskopf based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Weiskopf. Daniel Weiskopf is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 18 | |
| 3 | 8 | |
| 4 | VISUALIZING DYNAMIC QUANTITATIVE DATA IN HIERARCHIES - TimeEdgeTrees: Attaching Dynamic Weights to Tree Edges | 0 |
| 5 | Human-centered machine learning through interactive visualization | 13 |
| 6 | Human-centered machine learning through interactive visualization: review and open challenges. | 28 |
| 7 | Flip-Book Visualization of Dynamic Graphs. | 1 |
| 8 | Interactive Volume Rendering on Mobile Devices | 17 |
| 9 | Interactive Direct Volume Rendering on Mobile Devices. | 1 |
| 10 | 21 | |
| 11 | 19 | |
| 12 | Interactive Visualization of Divergence in Unsteady Flow by Level-Set Dye Advection. | 8 |
| 13 | A hybrid physical/device-space approach for spatio-temporally coherent interactive texture advection on curved surfaces | 40 |
| 14 | 12 | |
| 15 | The G 2 -Buffer Framework. | 4 |
| 16 | GPU-Based 3D Texture Advection for the Visualization of Unsteady Flow Fields | 16 |
| 17 | Automatic generation and non-photorealistic rendering of 2+1D Minkowski diagrams | 4 |
| 18 | 40 | |
| 19 | Hardware-Accelerated Visualization of Time-Varying 2D and 3D Vector Fields by Texture Advection via Programmable Per-Pixel Operations | 44 |
| 20 | An Immersive Virtual Environment for Special Relativity. | 8 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.