Daniel Abler

694 citations
35 papers · 531 · h-index 10

Impact in

  • Radiation top 5%
    • Radiation Detection and Scintillator Technologies
    • Nuclear Physics and Applications
    • Mathematical Biology Tumor Growth

Papers in

    • Radiomics and Machine Learning in Medical Imaging 7
    • Medical Imaging Techniques and Applications 5
    • MRI in cancer diagnosis 3
    • Radiation Detection and Scintillator Technologies 6
    • Nuclear Physics and Applications 3

Daniel Abler

34 papers receiving 521 citations

Peers

Daniel Abler
Comparison fields: 5 of 81
  • Radiation 198
  • Modeling and Simulation 42
  • Radiology, Nuclear Medicine and Imaging 93
  • Oncology 107
  • Atomic and Molecular Physics, and Optics 126
Replace Rui Pu with:
Rui Pu China
K Hendrickson United States
C. Damiani Italy
Juliane Daartz United States
Changran Geng China
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Abler

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Abler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010115
2 202074
3 201071
4 201857
5 202028
6 201826
7 202415
8 201915
9 202114
10 201014
11 20199
12 20089
13 20238
14 20218
15 20238
16 20118
17 20226
18 20096
19 20185
20 20135

About Daniel Abler

Daniel Abler is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation, Pulmonary and Respiratory Medicine, Oncology and Electrical and Electronic Engineering, having authored 35 papers that have together received 531 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (7 papers), Radiation Detection and Scintillator Technologies (6 papers), Medical Imaging Techniques and Applications (5 papers), MRI in cancer diagnosis (3 papers), Mathematical Biology Tumor Growth (3 papers), Nuclear Physics and Applications (3 papers), Particle Detector Development and Performance (3 papers) and Scientific Computing and Data Management (2 papers). The work is most often cited by research in Radiation (198 citations), Modeling and Simulation (42 citations), Radiology, Nuclear Medicine and Imaging (93 citations), Oncology (107 citations) and Atomic and Molecular Physics, and Optics (126 citations). Daniel Abler has collaborated with scholars based in Switzerland, United States and France. Frequent co-authors include P. Lecoq, E. Auffray, Russell C. Rockne, A. G. Petrosyan, Christophe Dujardin, K. L. Ovanesyan, Philippe Büchler, Didier Perrodin, David Amans and C. Mancini. Their work appears in journals such as Scientific Reports, Journal of Radiation Research, APL Bioengineering, JCO Clinical Cancer Informatics and Journal of Crystal Growth.

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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