Daniela Oelke

1.0k citations
30 papers · 608 indexed · h-index 14

Impact in

Papers in

Daniela Oelke

28 papers receiving 550 citations

Peers

Daniela Oelke
Comparison fields: 5 of 77
  • Computer Vision and Pattern Recognition 385
  • Human-Computer Interaction 53
  • Artificial Intelligence 295
  • Computer Graphics and Computer-Aided Design 22
  • Statistical and Nonlinear Physics 71
Replace Matt McKeon with:
Matt McKeon United States
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Citations per field
00.5×4.2×
Matt McKeon · 1×
Citations per year

Countries citing papers authored by Daniela Oelke

Since Specialization
Citations

This map shows the geographic impact of Daniela Oelke'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 Daniela Oelke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela Oelke more than expected).

Fields of papers citing papers by Daniela Oelke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniela Oelke. 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 Daniela Oelke. The network helps show where Daniela Oelke may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Daniela Oelke, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniela Oelke Line = papers co-authored together Daniela Oelke links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202127
2 20200
3 201956
4 201426
5 201322
6
Advanced Visual Analytics Methods for Literature Analysis
201214
7
Visual Analytics and the Language of Web Query Logs – A Terminology Perspective
20122
8 201125
9 20116
10 20116
11
Real-Time Visualization of Streaming Text Data: Tasks and Challenges
201111
12 201010
13
Large-scale Comparative Sentiment Analysis of News Articles
20094
14 200968
15
Visual Sentiment Analysis of RSS News Feeds Featuring the US Presidential Election in 2008
200934
16 200986
17 200818
18 200815
19 200781
20 20062

About Daniela Oelke

Daniela Oelke is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Statistical and Nonlinear Physics and General Social Sciences, having authored 30 papers that have together received 608 indexed citations. Recurring topics across this work include Data Visualization and Analytics (18 papers), Advanced Text Analysis Techniques (11 papers), Video Analysis and Summarization (7 papers), Complex Network Analysis Techniques (4 papers), Natural Language Processing Techniques (4 papers), Topic Modeling (3 papers), Semantic Web and Ontologies (3 papers) and Explainable Artificial Intelligence (XAI) (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (385 citations), Human-Computer Interaction (53 citations), Artificial Intelligence (295 citations), Computer Graphics and Computer-Aided Design (22 citations) and Statistical and Nonlinear Physics (71 citations). Daniela Oelke has collaborated with scholars based in Germany, United States and Austria. Frequent co-authors include Daniel A. Keim, Christian Rohrdantz, Hendrik Strobelt, Andreas Stoffel, Oliver Deußen, Halldór Janetzko, Umeshwar Dayal, Ming Hao, Dimitrios Kokkinakis and Johannes Fuchs. Their work appears in journals such as Computer Graphics Forum, IEEE Transactions on Visualization and Computer Graphics, IEEE Computer Graphics and Applications, DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) and KOPS (University of Konstanz).

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.

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