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Author Correction : Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (AI) in the wider Pacific Rim using big data sets (Scientific Reports, (2020), 10, 1, (16817), 10.1038/s41598-020-73664-2). / Gulyaeva, Marina; Huettmann, Falk; Shestopalov, Alexander и др.

в: Scientific Reports, Том 11, № 1, 3758, 12.2021.

Результаты исследований: Научные публикации в периодических изданиях › комментарий, выступление › Рецензирование

BibTeX

@article{8c27cbccb4d545d2abaf92b77d80d858,

title = "Author Correction: Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (AI) in the wider Pacific Rim using big data sets (Scientific Reports, (2020), 10, 1, (16817), 10.1038/s41598-020-73664-2)",

abstract = "An amendment to this paper has been published and can be accessed via a link at the top of the paper.",

author = "Marina Gulyaeva and Falk Huettmann and Alexander Shestopalov and Masatoshi Okamatsu and Keita Matsuno and Chu, {Duc Huy} and Yoshihiro Sakoda and Alexandra Glushchenko and Elaina Milton and Eric Bortz",

note = "Funding Information: “We are grateful to our eASIA funders; the kind collaboration and efforts are widely acknowledged. Further we acknowledge the contributions of all data providers in IRD, as well as USDA for sharing their coarse non-geo-referenced data. FH acknowledges the kind Salford Predictive Modeler (SPM) -Minitab-software license support, the efficient UAF Writing Center, as well as the great Cup and Porcupine and their support and full recovery during this study. The study was funded by RFBR according to the research project № 18-54-70006. This is EWHALE lab publication # 251.” Funding Information: “We are grateful to our eASIA funders; the kind collaboration and efforts are widely acknowledged. Further we acknowledge the contributions of all data providers in IRD, as well as USDA for sharing their coarse non-geo-referenced data. FH acknowledges the kind Salford Predictive Modeler (SPM) -Minitab-software license support, the efficient UAF Writing Center, as well as the great Cup and Porcupine and their support and full recovery during this study. The study was funded by RFBR according to the research project № 18-54-70006. This is EWHALE lab publication # 251. This work was in part supported by a NIAID CEIRS award (HHSN272201400008C).” This has now been corrected in the PDF and HTML versions of the Article, and in the accompanying Supplementary information 1 file. Publisher Copyright: {\textcopyright} 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = dec,

doi = "10.1038/s41598-021-83100-8",

language = "English",

volume = "11",

journal = "Scientific Reports",

issn = "2045-2322",

publisher = "Nature Publishing Group",

number = "1",

}

RIS

TY - JOUR

T1 - Author Correction

T2 - Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (AI) in the wider Pacific Rim using big data sets (Scientific Reports, (2020), 10, 1, (16817), 10.1038/s41598-020-73664-2)

AU - Gulyaeva, Marina

AU - Huettmann, Falk

AU - Shestopalov, Alexander

AU - Okamatsu, Masatoshi

AU - Matsuno, Keita

AU - Chu, Duc Huy

AU - Sakoda, Yoshihiro

AU - Glushchenko, Alexandra

AU - Milton, Elaina

AU - Bortz, Eric

N1 - Funding Information: “We are grateful to our eASIA funders; the kind collaboration and efforts are widely acknowledged. Further we acknowledge the contributions of all data providers in IRD, as well as USDA for sharing their coarse non-geo-referenced data. FH acknowledges the kind Salford Predictive Modeler (SPM) -Minitab-software license support, the efficient UAF Writing Center, as well as the great Cup and Porcupine and their support and full recovery during this study. The study was funded by RFBR according to the research project № 18-54-70006. This is EWHALE lab publication # 251.” Funding Information: “We are grateful to our eASIA funders; the kind collaboration and efforts are widely acknowledged. Further we acknowledge the contributions of all data providers in IRD, as well as USDA for sharing their coarse non-geo-referenced data. FH acknowledges the kind Salford Predictive Modeler (SPM) -Minitab-software license support, the efficient UAF Writing Center, as well as the great Cup and Porcupine and their support and full recovery during this study. The study was funded by RFBR according to the research project № 18-54-70006. This is EWHALE lab publication # 251. This work was in part supported by a NIAID CEIRS award (HHSN272201400008C).” This has now been corrected in the PDF and HTML versions of the Article, and in the accompanying Supplementary information 1 file. Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/12

Y1 - 2021/12

N2 - An amendment to this paper has been published and can be accessed via a link at the top of the paper.

AB - An amendment to this paper has been published and can be accessed via a link at the top of the paper.

UR - http://www.scopus.com/inward/record.url?scp=85100675774&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/16edbfb6-db8d-3427-8d0f-9a276bf9e8aa/

U2 - 10.1038/s41598-021-83100-8

DO - 10.1038/s41598-021-83100-8

M3 - Comment/debate

C2 - 33558644

AN - SCOPUS:85100675774

VL - 11

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 3758

ER -

ID: 27766762

Author Correction: Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (AI) in the wider Pacific Rim using big data sets (Scientific Reports, (2020), 10, 1, (16817), 10.1038/s41598-020-73664-2)

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