Jonas Ries

12.1k total citations · 5 hit papers
111 papers, 7.6k citations indexed

About

Jonas Ries is a scholar working on Biophysics, Molecular Biology and Structural Biology. According to data from OpenAlex, Jonas Ries has authored 111 papers receiving a total of 7.6k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Biophysics, 50 papers in Molecular Biology and 39 papers in Structural Biology. Recurrent topics in Jonas Ries's work include Advanced Fluorescence Microscopy Techniques (73 papers), Advanced Electron Microscopy Techniques and Applications (39 papers) and Near-Field Optical Microscopy (16 papers). Jonas Ries is often cited by papers focused on Advanced Fluorescence Microscopy Techniques (73 papers), Advanced Electron Microscopy Techniques and Applications (39 papers) and Near-Field Optical Microscopy (16 papers). Jonas Ries collaborates with scholars based in Germany, Switzerland and United Kingdom. Jonas Ries's co-authors include Petra Schwille, Philipp Hoess, Salvatore Chiantia, Markus Mund, Helge Ewers, Jan Ellenberg, J.R. Deschamps, Kai Simons, Charlotte Kaplan and Nicoletta Kahya and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Jonas Ries

109 papers receiving 7.5k citations

Hit Papers

Spatial Regulators for Bacterial Cell Division Self-Organ... 2008 2026 2014 2020 2008 2020 2022 2022 2023 100 200 300 400

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jonas Ries Germany 46 4.3k 3.3k 1.4k 1.2k 1.2k 111 7.6k
Suliana Manley Switzerland 45 3.8k 0.9× 4.1k 1.3× 2.0k 1.5× 1.5k 1.2× 1.0k 0.9× 106 9.2k
Joerg Bewersdorf United States 48 3.2k 0.7× 4.0k 1.2× 2.0k 1.4× 1.7k 1.3× 1.0k 0.9× 114 7.8k
Samuel T. Hess United States 29 4.6k 1.1× 4.7k 1.4× 2.6k 1.9× 1.6k 1.3× 784 0.7× 77 8.9k
Hazen P. Babcock United States 33 3.7k 0.8× 2.7k 0.8× 1.9k 1.4× 935 0.7× 516 0.4× 38 7.7k
Benjamin Schuler Switzerland 53 8.1k 1.9× 2.4k 0.7× 890 0.6× 471 0.4× 834 0.7× 145 10.6k
Paul R. Selvin United States 45 5.5k 1.3× 3.0k 0.9× 1.7k 1.2× 776 0.6× 1.8k 1.5× 91 10.6k
Maxime Dahan France 55 5.5k 1.3× 2.5k 0.8× 1.9k 1.4× 524 0.4× 873 0.7× 114 11.1k
Gerhard J. Schütz Austria 45 3.8k 0.9× 1.6k 0.5× 1.4k 1.0× 341 0.3× 833 0.7× 175 6.5k
Mike Heilemann Germany 59 5.4k 1.2× 7.0k 2.1× 2.7k 1.9× 2.9k 2.3× 1.1k 0.9× 212 12.4k
Takashi Funatsu Japan 41 3.8k 0.9× 1.4k 0.4× 2.1k 1.5× 334 0.3× 1.1k 1.0× 190 8.6k

Countries citing papers authored by Jonas Ries

Since Specialization
Citations

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

Fields of papers citing papers by Jonas Ries

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonas Ries

This figure shows the co-authorship network connecting the top 25 collaborators of Jonas Ries. A scholar is included among the top collaborators of Jonas Ries based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jonas Ries. Jonas Ries is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
2.
Deguchi, Takahiro, et al.. (2024). Tracking Single Kinesin in Live Cells Using MINFLUX. Methods in molecular biology. 2881. 119–131. 1 indexed citations
3.
Shi, Wei, Lulu Zhou, Jie Yang, et al.. (2023). Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging. Nature Methods. 20(3). 459–468. 59 indexed citations
4.
Cieśliński, Konstanty, et al.. (2022). Nanoscale structural organization and stoichiometry of the budding yeast kinetochore. The Journal of Cell Biology. 222(4). 17 indexed citations
5.
Mund, Markus, Aline Tschanz, Yule Wu, et al.. (2022). Clathrin coats partially preassemble and subsequently bend during endocytosis. The Journal of Cell Biology. 222(3). 15 indexed citations
6.
Wu, Yule, Philipp Hoess, Aline Tschanz, et al.. (2022). Maximum-likelihood model fitting for quantitative analysis of SMLM data. Nature Methods. 20(1). 139–148. 27 indexed citations
7.
Wang, Jingyu, Edward S. Allgeyer, George Sirinakis, et al.. (2020). Implementation of a 4Pi-SMS super-resolution microscope. Nature Protocols. 16(2). 677–727. 36 indexed citations
8.
Mund, Markus & Jonas Ries. (2020). How good are my data? Reference standards in superresolution microscopy. Molecular Biology of the Cell. 31(19). 2093–2096. 9 indexed citations
9.
Pike, Jeremy A., Abdullah O. Khan, Steven G. Thomas, et al.. (2019). Topological data analysis quantifies biological nano-structure from single molecule localization microscopy. Bioinformatics. 36(5). 1614–1621. 36 indexed citations
10.
Frei, Michelle S., Philipp Hoess, Marko Lampe, et al.. (2019). Photoactivation of silicon rhodamines via a light-induced protonation. Nature Communications. 10(1). 4580–4580. 64 indexed citations
11.
Thevathasan, Jervis Vermal, Maurice Kahnwald, Konstanty Cieśliński, et al.. (2019). Nuclear pores as versatile reference standards for quantitative superresolution microscopy. Nature Methods. 16(10). 1045–1053. 231 indexed citations
12.
Schlichthaerle, Thomas, Maximilian T. Strauss, Florian Schueder, et al.. (2019). Direct Visualization of Single Nuclear Pore Complex Proteins Using Genetically‐Encoded Probes for DNA‐PAINT. Angewandte Chemie. 131(37). 13138–13142. 14 indexed citations
13.
Schlichthaerle, Thomas, Maximilian T. Strauss, Florian Schueder, et al.. (2019). Direct Visualization of Single Nuclear Pore Complex Proteins Using Genetically‐Encoded Probes for DNA‐PAINT. Angewandte Chemie International Edition. 58(37). 13004–13008. 75 indexed citations
14.
Li, Yiming, Markus Mund, Philipp Hoess, et al.. (2018). Real-time 3D single-molecule localization using experimental point spread functions. Nature Methods. 15(5). 367–369. 189 indexed citations
15.
Mund, Markus, Katia Cosentino, Jale Schneider, et al.. (2016). Bax assembly into rings and arcs in apoptotic mitochondria is linked to membrane pores. The EMBO Journal. 35(4). 389–401. 232 indexed citations
16.
Picco, Andrea, Markus Mund, Jonas Ries, François Nédélec, & Marko Kaksonen. (2015). Visualizing the functional architecture of the endocytic machinery. eLife. 4. 95 indexed citations
17.
Deschamps, J.R., Markus Mund, & Jonas Ries. (2015). 3D Superresolution Microscopy by Supercritical Angle Detection. Biophysical Journal. 108(2). 36a–36a. 2 indexed citations
18.
Ries, Jonas, Gábor Csúcs, Ronald Dirkx, et al.. (2010). Automated suppression of sample-related artifacts in Fluorescence Correlation Spectroscopy. Optics Express. 18(11). 11073–11073. 25 indexed citations
19.
Loose, Martin, Elisabeth Fischer‐Friedrich, Jonas Ries, Karsten Kruse, & Petra Schwille. (2008). Spatial Regulators for Bacterial Cell Division Self-Organize into Surface Waves in Vitro. Science. 320(5877). 789–792. 403 indexed citations breakdown →
20.
Ries, Jonas & Petra Schwille. (2006). Studying Slow Membrane Dynamics with Continuous Wave Scanning Fluorescence Correlation Spectroscopy. Biophysical Journal. 91(5). 1915–1924. 136 indexed citations

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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