Nature Computational Science

527 papers and 8.7k indexed citations

About

The 527 papers published in Nature Computational Science in the last decades have received a total of 8.7k indexed citations. Papers published in Nature Computational Science usually cover Molecular Biology (165 papers), Materials Chemistry (96 papers) and Artificial Intelligence (90 papers) specifically the topics of Machine Learning in Materials Science (80 papers), Computational Drug Discovery Methods (55 papers) and Single-cell and spatial transcriptomics (43 papers). The most active scholars publishing in Nature Computational Science are Shyue Ping Ong, Chi Chen, Steven L. Brunton, Ricardo Vinuesa, Robert Vaser, Mile Šikić, J. Parker Mitchell, Bill Kay, Catherine D. Schuman and Maryam Parsa.

In The Last Decade

Nature Computational Science

412 papers receiving 8.4k citations

Fields of papers published in Nature Computational Science

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Nature Computational Science. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Nature Computational Science.

Countries where authors publish in Nature Computational Science

Since Specialization
Citations

This map shows the geographic impact of research published in Nature Computational Science. 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 papers published in Nature Computational Science with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nature Computational Science more than expected).

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.

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