Extracting insights from the shape of complex data using topology
- Journal
- Scientific Reports
In The Last Decade
doi.org/10.1038/srep01236 →Countries where authors are citing Extracting insights from the shape of complex data using topology
This map shows the geographic impact of Extracting insights from the shape of complex data using topology. 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 Extracting insights from the shape of complex data using topology with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Extracting insights from the shape of complex data using topology more than expected).
Fields of papers citing Extracting insights from the shape of complex data using topology
This network shows the impact of Extracting insights from the shape of complex data using topology. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Extracting insights from the shape of complex data using topology.
About Extracting insights from the shape of complex data using topology
This paper, published in 2013, received 312 indexed citations . Written by Pek Yee Lum, Satwinder Singh, Amy Lehman, Mikael Vejdemo‐Johansson, Muthuraman Alagappan, John Gunnar Carlsson and Gunnar Carlsson covering the research area of Biophysics, Computational Theory and Mathematics and Geometry and Topology. It is primarily cited by scholars working on Computational Theory and Mathematics (157 citations), Molecular Biology (85 citations) and Computer Vision and Pattern Recognition (55 citations). Published in Scientific Reports.
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/srep01236.