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BirdFlow: Learning Seasonal Bird Movements from eBird Data

View ORCID ProfileMiguel Fuentes, View ORCID ProfileBenjamin M. Van Doren, Daniel Fink, Daniel Sheldon
doi: https://doi.org/10.1101/2022.04.12.488057
Miguel Fuentes
1Manning College of Information and Computer Sciences, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA 01003, USA
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  • ORCID record for Miguel Fuentes
  • For correspondence: mmfuentes@cs.umass.edu
Benjamin M. Van Doren
2Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
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  • ORCID record for Benjamin M. Van Doren
Daniel Fink
2Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
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Daniel Sheldon
1Manning College of Information and Computer Sciences, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA 01003, USA
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Article Information

doi 
https://doi.org/10.1101/2022.04.12.488057
History 
  • November 8, 2022.

Article Versions

  • Version 1 (April 13, 2022 - 09:40).
  • You are viewing Version 2, the most recent version of this article.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.

Author Information

  1. Miguel Fuentes*,1,
  2. Benjamin M. Van Doren2,
  3. Daniel Fink2 and
  4. Daniel Sheldon1
  1. 1Manning College of Information and Computer Sciences, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA 01003, USA
  2. 2Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
  1. ↵*Corresponding Author
    : Email: mmfuentes{at}cs.umass.edu
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Posted November 08, 2022.
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BirdFlow: Learning Seasonal Bird Movements from eBird Data
Miguel Fuentes, Benjamin M. Van Doren, Daniel Fink, Daniel Sheldon
bioRxiv 2022.04.12.488057; doi: https://doi.org/10.1101/2022.04.12.488057
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BirdFlow: Learning Seasonal Bird Movements from eBird Data
Miguel Fuentes, Benjamin M. Van Doren, Daniel Fink, Daniel Sheldon
bioRxiv 2022.04.12.488057; doi: https://doi.org/10.1101/2022.04.12.488057

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