Daniel Haas

43 papers receiving 988 citations

Peers

Daniel Haas
Comparison fields: 5 of 145
  • Structural Biology 63
  • Radiation 227
  • Computer Science Applications 79
  • Management Science and Operations Research 86
  • Media Technology 55
Replace Ulf Lorenz with:
Ulf Lorenz Germany
Kristof T. Schütt Germany
Richard Archibald United States
Hideitsu Hino Japan
Katsuhiko Murakami Japan
Jie Gao United States
Florian Häse Canada
Yanjun Ma United States
Min Xu United States
Stefan Chmiela Germany
Daniel Haas relative to Ulf Lorenz Germany Ulf Lorenz's profile →
Citations per field
00.5×10×13.2×
Ulf Lorenz · 1×
Citations per year

Countries citing papers authored by Daniel Haas

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Haas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011227
2 2015110
3 200992
4 196688
5 196580
6 197069
7 201551
8 201641
9 201537
10 201533
11 197923
12 201523
13 199723
14 196421
15 196418
16 199817
17 196817
18 200811
19 196910
20 196610

About Daniel Haas

Daniel Haas is a scholar working on Artificial Intelligence, Media Technology, Aerospace Engineering, Materials Chemistry and Radiation, having authored 49 papers that have together received 1.1k indexed citations. Recurring topics across this work include Remote-Sensing Image Classification (7 papers), Infrared Target Detection Methodologies (5 papers), Enzyme Structure and Function (5 papers), Advanced X-ray Imaging Techniques (5 papers), Mobile Crowdsensing and Crowdsourcing (5 papers), Privacy-Preserving Technologies in Data (4 papers), Synthesis and Characterization of Heterocyclic Compounds (3 papers) and Protein Structure and Dynamics (3 papers). The work is most often cited by research in Structural Biology (63 citations), Radiation (227 citations), Computer Science Applications (79 citations), Management Science and Operations Research (86 citations) and Media Technology (55 citations). Daniel Haas has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Alexander Rack, Timm Weitkamp, D. Wȩgrzynek, Michael J. Franklin, M.G. Rossmann, Jake Bello, Helene R. Bello, David R. Harris, Eugene Wu and Michael I. Jordan. Their work appears in journals such as Proceedings of the VLDB Endowment, Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms, Journal of Synchrotron Radiation, Journal of the American Chemical Society and Biochemistry.

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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