A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei

314 indexed citations
published 2018

Countries where authors are citing A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei

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This map shows the geographic impact of A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. 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 A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei more than expected).

Fields of papers citing A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei.

About A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei

This paper, published in 2018, received 314 indexed citations . Written by Wolfgang M. Pauli and J. Michael Tyszka covering the research area of Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Biophysics. It is primarily cited by scholars working on Cognitive Neuroscience (168 citations), Radiology, Nuclear Medicine and Imaging (108 citations) and Neurology (95 citations). Published in Scientific Data.

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/sdata.2018.63.

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