Nonparametric analysis of longitudinal data in factorial experiments
Impact in
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In The Last Decade
doi.org/w6481077 →Countries where authors are citing Nonparametric analysis of longitudinal data in factorial experiments
This map shows the geographic impact of Nonparametric analysis of longitudinal data in factorial experiments. 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 Nonparametric analysis of longitudinal data in factorial experiments with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nonparametric analysis of longitudinal data in factorial experiments more than expected).
Fields of papers citing Nonparametric analysis of longitudinal data in factorial experiments
This network shows the impact of Nonparametric analysis of longitudinal data in factorial experiments. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Nonparametric analysis of longitudinal data in factorial experiments.
About Nonparametric analysis of longitudinal data in factorial experiments
This paper, published in 2002, received 559 indexed citations . Written by Edgar Brunner, S. Domhof and Frank Langer. It is primarily cited by scholars working on Plant Science (85 citations), Statistics and Probability (63 citations), Oral Surgery (52 citations), Cellular and Molecular Neuroscience (49 citations) and Molecular Biology (47 citations). Published in John Wiley eBooks.
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/w6481077.