Benjamin Risse

54 papers receiving 1.1k citations

Benjamin Risse's Hit Papers

Perspectives in machine learning for wildlife conservation 2022 · 417 citations
4170+1+2Years since publication100200300400

Peers

Benjamin Risse
Comparison fields: 5 of 142
  • Ecological Modeling 148
  • Aging 46
  • Developmental Biology 51
  • Cellular and Molecular Neuroscience 276
  • Health Informatics 14
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Alfonso Pérez‐Escudero Spain
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Robert C. Hinz Portugal
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Countries citing papers authored by Benjamin Risse

Since Specialization
Citations

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

Fields of papers citing papers by Benjamin Risse

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Perspectives in machine learning for wildlife conservation
Hit paper breakdown →
2022417
2 201384
3 201765
4 201242
5 201936
6 201836
7 201735
8 201433
9 201929
10 202326
11 201325
12 201723
13 202320
14 201619
15 201418
16 202316
17 202314
18 201914
19 202113
20 202413

About Benjamin Risse

Benjamin Risse is a scholar working on Cellular and Molecular Neuroscience, Computer Vision and Pattern Recognition, Artificial Intelligence, Ecology, Evolution, Behavior and Systematics and Genetics, having authored 59 papers that have together received 1.1k indexed citations. Recurring topics across this work include Neurobiology and Insect Physiology Research (16 papers), Insect and Arachnid Ecology and Behavior (6 papers), Species Distribution and Climate Change (6 papers), Cell Image Analysis Techniques (5 papers), Advanced Vision and Imaging (5 papers), Plant and animal studies (4 papers), Neural Networks and Reservoir Computing (4 papers) and Genetics, Aging, and Longevity in Model Organisms (4 papers). The work is most often cited by research in Ecological Modeling (148 citations), Aging (46 citations), Developmental Biology (51 citations), Cellular and Molecular Neuroscience (276 citations) and Health Informatics (14 citations). Benjamin Risse has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Christian Klämbt, Xiaoyi Jiang, Nils Otto, Dimitri Berh, Barbara Webb, Michael Mangan, Devis Tuia, Tanya Berger‐Wolf, Martin Wikelski and Grant Van Horn. Their work appears in journals such as Computers in Biology and Medicine, PLoS ONE, Nature Communications, Journal of the Optical Society of America B and IEEE Transactions on Biomedical Engineering.

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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