Fiji: an open-source platform for biological-image analysis
Impact in
- Molecular Biology 18.0k
- Cell Biology 5.8k
Classified as
- Journal
- Nature Methods
In The Last Decade
doi.org/10.1038/nmeth.2019 →Countries where authors are citing Fiji: an open-source platform for biological-image analysis
This map shows the geographic impact of Fiji: an open-source platform for biological-image 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 Fiji: an open-source platform for biological-image analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fiji: an open-source platform for biological-image analysis more than expected).
Fields of papers citing Fiji: an open-source platform for biological-image analysis
This network shows the impact of Fiji: an open-source platform for biological-image analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Fiji: an open-source platform for biological-image analysis.
About Fiji: an open-source platform for biological-image analysis
This paper, published in 2012, received 44.9k indexed citations . Written by Johannes Schindelin, Ignacio Arganda‐Carreras, Erwin Frise, Verena Kaynig, Mark Longair, Tobias Pietzsch, Stephan Preibisch, Curtis Rueden, Stephan Saalfeld and Benjamin Schmid covering the research area of Molecular Biology and Biophysics. It is primarily cited by scholars working on Molecular Biology (18.0k citations), Cell Biology (5.8k citations), Cellular and Molecular Neuroscience (4.7k citations), Biomedical Engineering (4.6k citations) and Plant Science (3.8k 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.2019.