David Soergel

3.0k total citations
7 papers, 607 citations indexed

About

David Soergel is a scholar working on Molecular Biology, Ecology and Artificial Intelligence. According to data from OpenAlex, David Soergel has authored 7 papers receiving a total of 607 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Molecular Biology, 2 papers in Ecology and 2 papers in Artificial Intelligence. Recurrent topics in David Soergel's work include Machine Learning and Data Classification (2 papers), Animal Ecology and Behavior Studies (1 paper) and RNA modifications and cancer (1 paper). David Soergel is often cited by papers focused on Machine Learning and Data Classification (2 papers), Animal Ecology and Behavior Studies (1 paper) and RNA modifications and cancer (1 paper). David Soergel collaborates with scholars based in United States. David Soergel's co-authors include Steven E. Brenner, Neelendu Dey, Rob Knight, Liana F. Lareau, Angela N. Brooks, Qi Meng, Susanna Repo, Andrew McCallum, Adam M. Saunders and D. Sculley and has published in prestigious journals such as The ISME Journal, Animal Behaviour and Advances in experimental medicine and biology.

In The Last Decade

David Soergel

7 papers receiving 596 citations

Peers

David Soergel
Comparison fields: 5 of 112
  • Molecular Biology 433
  • Ecology 113
  • Food Science 72
  • Genetics 60
  • Epidemiology 48
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Upasana Mangrolia India
Brett Bowman United States
Piotr Gawron Luxembourg
Tae-Hyuk Ahn United States
Lea Vaas Germany
Yukihiro Kobayashi Japan
Kranti Konganti United States
Xue Zhou China
Matthew G. Durrant United States
Anh Dinh United States
Upasana Mangrolia India View profile →
Citations per field, relative to David Soergel
David Soergel · 1×
Citations per year, relative to David Soergel
David Soergel · 1×

Countries citing papers authored by David Soergel

Since Specialization
Citations

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

Fields of papers citing papers by David Soergel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Soergel

This figure shows the co-authorship network connecting the top 25 collaborators of David Soergel. A scholar is included among the top collaborators of David Soergel 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 David Soergel. David Soergel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

7 of 7 papers shown
# Work Indexed citations
1 1
2
TensorFlow.js: Machine Learning for the Web and Beyond
5
3 7
4 97
5
Open Scholarship and Peer Review: a Time for Experimentation
14
6 290
7 193

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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