MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis
- Journal
- Nature Microbiology
In The Last Decade
doi.org/10.1038/nmicrobiol.2016.77 →Countries where authors are citing MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis
This map shows the geographic impact of MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis. 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 MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis more than expected).
Fields of papers citing MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis
This network shows the impact of MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis.
About MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis
This paper, published in 2016, received 638 indexed citations . Written by Adrien Ducret, Ellen M. Quardokus and Yves V. Brun covering the research area of Molecular Biology, Media Technology and Biophysics. It is primarily cited by scholars working on Molecular Biology (420 citations), Genetics (322 citations) and Ecology (145 citations). Published in Nature Microbiology.
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/nmicrobiol.2016.77.