David Gotz
-
- Data Visualization and Analytics 50
- Video Analysis and Summarization 10
- Image and Video Quality Assessment 7
- Signal Processing top 2%
- Time Series Analysis and Forecasting 11
- Artificial Intelligence top 1%
- Advanced Text Analysis Techniques 13
- Data Analysis with R 9
-
- Multimedia Communication and Technology 7
-
- Biomedical Text Mining and Ontologies 6
- Co-authors
- Nan CaoMichelle X. ZhouAdam PererJimeng SunKrist WongsuphasawatZhen WenHarry StavropoulosDavid Borland
- Journals
- IEEE Transactions on Visualization and Computer Graphics (18 papers)Information Visualization (5 papers)Journal of the American Medical Informatics Association (3 papers)
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
David Gotz
84 papers receiving 2.4k citations
Peers
Comparison fields: 5 of 134
- Computer Vision and Pattern Recognition 1.6k
- Signal Processing 432
- Health Information Management 140
- Artificial Intelligence 909
- Information Systems and Management 170
Countries citing papers authored by David Gotz
This map shows the geographic impact of David Gotz'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 David Gotz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Gotz more than expected).
Fields of papers citing papers by David Gotz
This network shows the impact of papers produced by David Gotz. 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 David Gotz. The network helps show where David Gotz may publish in the future.
Co-authorship network
The 25 scholars most cited alongside David Gotz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 6 | |
| 4 | 2024 | 2 | |
| 5 | 2023 | 5 | |
| 6 | 2023 | 2 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 0 | |
| 9 | 2019 | 13 | |
| 10 | 2018 | 36 | |
| 11 | 2018 | 38 | |
| 12 | 2018 | 8 | |
| 13 | 2017 | 43 | |
| 14 | 2017 | 31 | |
| 15 | INTEGRAL detection of a SGR-like burst likely from Sgr J1745-29. | 2013 | 1 |
| 16 | Visual Analytics in Healthcare. | 2012 | 3 |
| 17 | 2012 | 150 | |
| 18 | 2011 | 100 | |
| 19 | 1999 | 20 | |
| 20 | 1991 | 15 |
About David Gotz
David Gotz is a scholar working on Computer Vision and Pattern Recognition, Computational Mathematics and Signal Processing, having authored 92 papers that have together received 2.5k indexed citations. Recurring topics across this work include Data Visualization and Analytics (50 papers), Advanced Text Analysis Techniques (13 papers), Time Series Analysis and Forecasting (11 papers), Video Analysis and Summarization (10 papers), Data Analysis with R (9 papers), Image and Video Quality Assessment (7 papers), Multimedia Communication and Technology (7 papers) and Biomedical Text Mining and Ontologies (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.6k citations), Signal Processing (432 citations) and Health Information Management (140 citations). David Gotz has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Nan Cao, Michelle X. Zhou, Adam Perer, Jimeng Sun, Krist Wongsuphasawat, Zhen Wen, Harry Stavropoulos, David Borland, Charles D. Stolper and Shunan Guo. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Information Visualization, Journal of the American Medical Informatics Association, IEEE Computer Graphics and Applications and Computer Graphics Forum.
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.