Daniel Braun

884 citations
52 papers · 434 · h-index 11

Impact in

Papers in

Daniel Braun

46 papers receiving 414 citations

Peers

Daniel Braun
Comparison fields: 5 of 112
  • Artificial Intelligence 213
  • Computer Science Applications 22
  • Health Informatics 5
  • Human-Computer Interaction 16
  • Computer Vision and Pattern Recognition 43
Replace Zijie J. Wang with:
Zijie J. Wang United States
Caslon Chua Australia
Nuha Alruwais Saudi Arabia
Shazia Afzal United Kingdom
Miguel Torres-Ruiz Mexico
Al Amin Biswas Bangladesh
Rajnish Ratna Bhutan
Nathan Bartley United States
Ananya Ganesh United States
Rohayanti Hassan Malaysia
Daniel Braun relative to Zijie J. Wang United States Zijie J. Wang's profile →
Citations per field
00.5×3.2×
Zijie J. Wang · 1×
Citations per year

Countries citing papers authored by Daniel Braun

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Braun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Braun, 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 Daniel Braun Line = papers co-authored together Daniel Braun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017107
2 202145
3 202132
4 201731
5 200723
6 201921
7 200215
8 201814
9 202014
10 202211
11 202210
12 20159
13 20237
14 20176
15 20226
16 20235
17
Customer-centered LegalTech: Automated Analysis of Standard Form Contracts
20185
18 20195
19 20235
20 20215

About Daniel Braun

Daniel Braun is a scholar working on Artificial Intelligence, Political Science and International Relations, Computer Vision and Pattern Recognition, Social Psychology and Information Systems, having authored 52 papers that have together received 434 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Topic Modeling (8 papers), Artificial Intelligence in Law (6 papers), AI in Service Interactions (4 papers), Human-Automation Interaction and Safety (3 papers), Law, AI, and Intellectual Property (3 papers), Advanced Text Analysis Techniques (3 papers) and Autonomous Vehicle Technology and Safety (2 papers). The work is most often cited by research in Artificial Intelligence (213 citations), Computer Science Applications (22 citations), Health Informatics (5 citations), Human-Computer Interaction (16 citations) and Computer Vision and Pattern Recognition (43 citations). Daniel Braun has collaborated with scholars based in Germany, Netherlands and United States. Frequent co-authors include Florian Matthes, Friedhelm Schwenker, David S. Fischer, Ehud Reiter, Advaith Siddharthan, Johannes Huwer, Cameron N. Riviere, Brian C. Becker, Hans A. Kestler and Joseph N. Martel. Their work appears in journals such as Theory and Decision, Water Research, Neural Computation, Frontiers in Physiology and Ecological Informatics.

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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