2,241 to 2,250 of 2,441 Results
R Syntax - 19.6 KB -
MD5: ff6cb759f9d3f99c1437dbc8ea2489db
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Tabular Data - 1.7 KB - 6 Variables, 38 Observations - UNF:6:8B4lKYUAzL5+urufWWM3Eg==
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Tabular Data - 2.1 KB - 7 Variables, 38 Observations - UNF:6:7xuOFRjpOjusid3dWP7s1Q==
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Plain Text - 2.3 KB -
MD5: 3f8c2b0fc5bef4c94d5f84a9618af9f4
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Jan 5, 2023
Coddington, Charles, 2023, "Forest structure predicts species richness and functional diversity in Amazonian mixed-species bird flocks", https://doi.org/10.13021/ORC2020/JUM6RX, George Mason University Dataverse, V1, UNF:6:WM13QqYvZOf4Y9YQHDYImg== [fileUNF]
These data were used for the study "Forest structure predicts species richness and functional diversity in Amazonian mixed-species bird flocks". All necessary .csv files used to reproduce our statistical analyses and figures are included, as well as the R-script we used to genera... |
Jan 5, 2023 -
Forest structure predicts species richness and functional diversity in Amazonian mixed-species bird flocks
Tabular Data - 5.4 MB - 30 Variables, 23740 Observations - UNF:6:5EkYaBFVg30XHK2wl9YIfw==
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Jan 5, 2023 -
Forest structure predicts species richness and functional diversity in Amazonian mixed-species bird flocks
R Syntax - 53.8 KB -
MD5: feded2a2527752a76ce98b5c8af08e39
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Jan 5, 2023 -
Forest structure predicts species richness and functional diversity in Amazonian mixed-species bird flocks
Tabular Data - 15.0 KB - 48 Variables, 101 Observations - UNF:6:eTDo0FPwth1neR1QSDP4Zg==
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Jan 5, 2023 -
Forest structure predicts species richness and functional diversity in Amazonian mixed-species bird flocks
Plain Text - 11.2 KB -
MD5: 31f749b631726d1a283357beb5fac63e
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Aug 22, 2022
Weinstein, Ali A, 2022, "Second victim experiences and moral injury as predictors of hospitalist burnout before and during the COVID-19 pandemic", https://doi.org/10.13021/ORC2020/GFT7FQ, George Mason University Dataverse, V1, UNF:6:h6WNUjYjYF9796rphKhg0g== [fileUNF]
Open data for publication: Second victim experiences and moral injury as predictors of hospitalist burnout before and during the COVID-19 pandemic |