Derivation of midbrain dopamine neurons from human embryonic stem cells
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Countries where authors are citing Derivation of midbrain dopamine neurons from human embryonic stem cells
This map shows the geographic impact of Derivation of midbrain dopamine neurons from human embryonic stem cells. 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 Derivation of midbrain dopamine neurons from human embryonic stem cells with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Derivation of midbrain dopamine neurons from human embryonic stem cells more than expected).
Fields of papers citing Derivation of midbrain dopamine neurons from human embryonic stem cells
This network shows the impact of Derivation of midbrain dopamine neurons from human embryonic stem cells. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Derivation of midbrain dopamine neurons from human embryonic stem cells.
About Derivation of midbrain dopamine neurons from human embryonic stem cells
This paper, published in 2004, received 800 indexed citations . Written by Anselme L. Perrier, Viviane Tabar, Tiziano Barberi, María E. Rubio, Juan L. Brusés, Norbert Topf, Neil L. Harrison and Lorenz Studer covering the research area of Molecular Biology and Cellular and Molecular Neuroscience. It is primarily cited by scholars working on Molecular Biology (702 citations), Cellular and Molecular Neuroscience (345 citations), Developmental Neuroscience (236 citations), Biomedical Engineering (103 citations) and Surgery (91 citations). Published in Proceedings of the National Academy of Sciences.
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/10.1073/pnas.0404700101.