Daniel B. Rowe

1.8k citations
57 papers · 1.2k indexed · h-index 16

Impact in

Papers in

Daniel B. Rowe

52 papers receiving 1.2k citations

Peers

Daniel B. Rowe
Comparison fields: 5 of 108
  • Computational Mathematics 23
  • Radiology, Nuclear Medicine and Imaging 835
  • Cognitive Neuroscience 449
  • Statistics and Probability 88
  • Signal Processing 73
Replace Cheng Guan Koay with:
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Maria Filomena Santarelli Italy
Klaus Fritzsche Germany
Antonio Tristán‐Vega Spain
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Citations per field
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Citations per year

Countries citing papers authored by Daniel B. Rowe

Since Specialization
Citations

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

Fields of papers citing papers by Daniel B. Rowe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 57 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2003373
2 2004100
3 200586
4 200482
5 201254
6 200242
7 200740
8 200934
9 200733
10 200432
11 200829
12 200728
13 201823
14 201819
15 201019
16 200116
17 200615
18 200912
19 201111
20 200711

About Daniel B. Rowe

Daniel B. Rowe is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Statistics and Probability, Signal Processing and General Decision Sciences, having authored 57 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced MRI Techniques and Applications (34 papers), Functional Brain Connectivity Studies (30 papers), Advanced Neuroimaging Techniques and Applications (23 papers), Blind Source Separation Techniques (7 papers), Medical Imaging Techniques and Applications (5 papers), Statistical Methods and Inference (5 papers), MRI in cancer diagnosis (5 papers) and Bayesian Methods and Mixture Models (5 papers). The work is most often cited by research in Computational Mathematics (23 citations), Radiology, Nuclear Medicine and Imaging (835 citations), Cognitive Neuroscience (449 citations), Statistics and Probability (88 citations) and Signal Processing (73 citations). Daniel B. Rowe has collaborated with scholars based in United States, Spain and Switzerland. Frequent co-authors include Brent R. Logan, James S. Hyde, Kathleen M. Schmainda, Hanbing Lu, Kevin M. Bennett, Andrew S. Nencka, Andrew D. Hahn, Luis Hernández-García, Hongtu Zhu and Joseph G. Ibrahim. Their work appears in journals such as Magnetic Resonance Imaging, NeuroImage, Magnetic Resonance in Medicine, Journal of the American Statistical Association and IEEE Transactions on Medical Imaging.

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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