Julian Uszkoreit
- Spectroscopy top 2%
- Advanced Proteomics Techniques and Applications 25
- Mass Spectrometry Techniques and Applications 12
- Molecular Biology top 10%
- Metabolomics and Mass Spectrometry Studies 11
- Muscle Physiology and Disorders 6
- Genomics and Phylogenetic Studies 5
- Machine Learning in Bioinformatics 4
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- Genetic Neurodegenerative Diseases 4
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- Cardiomyopathy and Myosin Studies 4
- Co-authors
- Martin EisenacherKatrin MarcusYasset Pérez‐RiverolKatalin BarkovitsJuan Antonio VizcaínoSimone SteinbachSandra PacharraJohannes Griss
- Journals
- Journal of Proteome Research (7 papers)Molecular & Cellular Proteomics (6 papers)Journal of Proteomics (4 papers)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Julian Uszkoreit
36 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 107
- Spectroscopy 470
- Information Systems and Management 86
- Molecular Biology 833
- Cell Biology 106
- Cellular and Molecular Neuroscience 92
Countries citing papers authored by Julian Uszkoreit
This map shows the geographic impact of Julian Uszkoreit'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 Julian Uszkoreit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julian Uszkoreit more than expected).
Fields of papers citing papers by Julian Uszkoreit
This network shows the impact of papers produced by Julian Uszkoreit. 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 Julian Uszkoreit. The network helps show where Julian Uszkoreit may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Julian Uszkoreit, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2023 | 0 | |
| 3 | 2023 | 9 | |
| 4 | 2023 | 35 | |
| 5 | 2022 | 5 | |
| 6 | 2021 | 3 | |
| 7 | 2020 | 19 | |
| 8 | 2020 | 27 | |
| 9 | 2019 | 1 | |
| 10 | 2018 | 3 | |
| 11 | 2017 | 6 | |
| 12 | 2017 | 42 | |
| 13 | 2017 | 19 | |
| 14 | 2016 | 45 | |
| 15 | 2016 | 47 | |
| 16 | 2015 | 135 | |
| 17 | 2014 | 16 | |
| 18 | 2013 | 30 | |
| 19 | 2012 | 66 | |
| 20 | 2012 | 6 |
About Julian Uszkoreit
Julian Uszkoreit is a scholar working on Spectroscopy, Molecular Biology, Information Systems and Management, Biophysics and Cellular and Molecular Neuroscience, having authored 38 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Proteomics Techniques and Applications (25 papers), Mass Spectrometry Techniques and Applications (12 papers), Metabolomics and Mass Spectrometry Studies (11 papers), Muscle Physiology and Disorders (6 papers), Genomics and Phylogenetic Studies (5 papers), Machine Learning in Bioinformatics (4 papers), Genetic Neurodegenerative Diseases (4 papers) and Cardiomyopathy and Myosin Studies (4 papers). The work is most often cited by research in Spectroscopy (470 citations), Information Systems and Management (86 citations), Molecular Biology (833 citations), Cell Biology (106 citations) and Cellular and Molecular Neuroscience (92 citations). Julian Uszkoreit has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Martin Eisenacher, Katrin Marcus, Yasset Pérez‐Riverol, Katalin Barkovits, Juan Antonio Vizcaíno, Simone Steinbach, Sandra Pacharra, Johannes Griss, Kathy Pfeiffer and Helmut E. Meyer. Their work appears in journals such as Journal of Proteome Research, Molecular & Cellular Proteomics, Journal of Proteomics, Acta Neuropathologica Communications and Bioinformatics.
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.