Andreas Ipsen
Impact in
- Statistics and Probability top 2%
- Markov Chains and Monte Carlo Methods
- Modeling and Simulation top 2%
- COVID-19 epidemiological studies
Papers in ⓘ
-
- Mass Spectrometry Techniques and Applications 6
- Analytical Chemistry and Chromatography 5
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- Metabolomics and Mass Spectrometry Studies 5
- Co-authors
- Natalja Strelkowa (1 shared paper)Michael P. H. Stumpf (1 shared paper)David Welch (1 shared paper)Tina Toni (1 shared paper)Timothy M. D. Ebbels (4 shared papers)Elizabeth J. Want (2 shared papers)John C. Lindon (1 shared paper)Sandilya Garimella (1 shared paper)
- Journals
- Analytical Chemistry (5 papers)Journal of the American Society for Mass Spectrometry (2 papers)Journal of The Royal Society Interface (1 paper)The Analyst (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
Andreas Ipsen
9 papers receiving 1000 citations
Hit Papers
Peers
Comparison fields: 5 of 132
- Statistics and Probability 243
- Modeling and Simulation 124
- Statistics, Probability and Uncertainty 84
- Artificial Intelligence 243
- Spectroscopy 111
Countries citing papers authored by Andreas Ipsen
This map shows the geographic impact of Andreas Ipsen'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 Andreas Ipsen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Ipsen more than expected).
Fields of papers citing papers by Andreas Ipsen
This network shows the impact of papers produced by Andreas Ipsen. 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 Andreas Ipsen. The network helps show where Andreas Ipsen may publish in the future.
Co-authors
The 14 scholars most cited alongside Andreas Ipsen, 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 | Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems Hit paper breakdown → | 2008 | 900 |
| 2 | 2015 | 44 | |
| 3 | 2010 | 23 | |
| 4 | 2014 | 21 | |
| 5 | 2010 | 10 | |
| 6 | 2015 | 9 | |
| 7 | 2017 | 8 | |
| 8 | 2012 | 6 | |
| 9 | 2014 | 5 |
About Andreas Ipsen
Andreas Ipsen is a scholar working on Spectroscopy, Molecular Biology, Computer Networks and Communications, Statistical and Nonlinear Physics and Ecology, having authored 9 papers that have together received 1.0k indexed citations. Recurring topics across this work include Mass Spectrometry Techniques and Applications (6 papers), Metabolomics and Mass Spectrometry Studies (5 papers), Analytical Chemistry and Chromatography (5 papers), Advanced Chemical Sensor Technologies (1 paper), Ion-surface interactions and analysis (1 paper), Pesticide Residue Analysis and Safety (1 paper), Atomic and Molecular Physics (1 paper) and Spectroscopy and Chemometric Analyses (1 paper). The work is most often cited by research in Statistics and Probability (243 citations), Modeling and Simulation (124 citations), Statistics, Probability and Uncertainty (84 citations), Artificial Intelligence (243 citations) and Spectroscopy (111 citations). Andreas Ipsen has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Natalja Strelkowa, Michael P. H. Stumpf, David Welch, Tina Toni, Timothy M. D. Ebbels, Elizabeth J. Want, John C. Lindon, Sandilya Garimella, Gordon Anderson and Yehia Ibrahim. Their work appears in journals such as Analytical Chemistry, Journal of the American Society for Mass Spectrometry, Journal of The Royal Society Interface and The Analyst.
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.