Robust single-particle tracking in live-cell time-lapse sequences
Impact in
- Biophysics 458
Classified as
- Journal
- Nature Methods
In The Last Decade
doi.org/10.1038/nmeth.1237 →Countries where authors are citing Robust single-particle tracking in live-cell time-lapse sequences
This map shows the geographic impact of Robust single-particle tracking in live-cell time-lapse sequences. 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 Robust single-particle tracking in live-cell time-lapse sequences with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robust single-particle tracking in live-cell time-lapse sequences more than expected).
Fields of papers citing Robust single-particle tracking in live-cell time-lapse sequences
This network shows the impact of Robust single-particle tracking in live-cell time-lapse sequences. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Robust single-particle tracking in live-cell time-lapse sequences.
About Robust single-particle tracking in live-cell time-lapse sequences
This paper, published in 2008, received 1.4k indexed citations . Written by Khuloud Jaqaman, Dinah Loerke, Marcel Mettlen, Hirotaka Kuwata, Sergio Grinstein, Sandra L. Schmid and Gaudenz Danuser covering the research area of Molecular Biology and Biophysics. It is primarily cited by scholars working on Molecular Biology (802 citations), Biophysics (458 citations), Cell Biology (334 citations), Biomedical Engineering (186 citations) and Genetics (124 citations). Published in Nature Methods.
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.
This paper is also available at doi.org/10.1038/nmeth.1237.