Chemically defined generation of human cardiomyocytes
Impact in
- Surgery 398
Classified as
- Journal
- Nature Methods
In The Last Decade
doi.org/10.1038/nmeth.2999 →Countries where authors are citing Chemically defined generation of human cardiomyocytes
This map shows the geographic impact of Chemically defined generation of human cardiomyocytes. 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 Chemically defined generation of human cardiomyocytes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chemically defined generation of human cardiomyocytes more than expected).
Fields of papers citing Chemically defined generation of human cardiomyocytes
This network shows the impact of Chemically defined generation of human cardiomyocytes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Chemically defined generation of human cardiomyocytes.
About Chemically defined generation of human cardiomyocytes
This paper, published in 2014, received 1.1k indexed citations . Written by Paul W. Burridge, Elena Matsa, Praveen Shukla, Ziliang Lin, Jared M. Churko, Antje Ebert, Feng Lan, Sebastian Diecke, Bruno Hüber and Nicholas M. Mordwinkin covering the research area of Molecular Biology and Cellular and Molecular Neuroscience. It is primarily cited by scholars working on Molecular Biology (817 citations), Surgery (398 citations), Biomedical Engineering (295 citations), Cardiology and Cardiovascular Medicine (224 citations) and Cellular and Molecular Neuroscience (209 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.2999.