Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average within
it), or reaches the top citation threshold in at least one of its specific research topics.
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
20182.5k citationsAlexander Mathis, Pranav Mamidanna et al.Nature Neuroscienceprofile →
This map shows the geographic impact of Kevin M. Cury'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 Kevin M. Cury with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin M. Cury more than expected).
This network shows the impact of papers produced by Kevin M. Cury. 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 Kevin M. Cury. The network helps show where Kevin M. Cury may publish in the future.
Co-authorship network of co-authors of Kevin M. Cury
This figure shows the co-authorship network connecting the top 25 collaborators of Kevin M. Cury.
A scholar is included among the top collaborators of Kevin M. Cury 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 Kevin M. Cury. Kevin M. Cury is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
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.