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| Created: | Apr 23, 2024 at 4:53 p.m. (UTC) | |
| Last updated: | Apr 23, 2024 at 6:39 p.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Private (Accessible via direct link sharing) |
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Abstract
Anthropogenic drivers are transforming global ecosystems at unprecedented rates. Addressing impacts with effective management and conservation actions requires information on the links between established human pressures and localized documented effects on ecological attributes, structure, and function. This dataset corresponds with a study lead by the University of Florida and Food and Agriculture Organization of the United Nations that synthesized evidence of direct anthropogenic threats to major inland fisheries using coupled manual and automated classification methods. Researchers screened 9,336 abstracts from 45 major river basins using manual and computational methods; 1,152 abstracts contained evidence of direct threats to fish. Provided here are 1) the abstract dataset generated from a standardized search in Web of Science in 2020, the dataset classified by manual review, and the dataset classified by natural language processing; 2) the script used to run natural language processing algorithms; and 3) the script used to generate the formatted dataset and figures used in the paper titled, "Computational approaches improve evidence synthesis and inform fisheries trends." Results can inform the relative influence of threats across global inland fisheries and provide a coupled approach for improved efficiency of synthesis to fill data gaps in driver-impact-response relationships.
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Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| U.S. National Science Foundation | Graduate Research Fellowship | Grant No. DGE-1842473 |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
|---|---|---|---|---|
| Samuel J. Smidt | University of Florida | Florida, US | ||
| John V. Flores | Florida State University | |||
| Chelsie Romulo | University of Northern Colorado | |||
| Jesse P. Wong | Kent State University | |||
| Connor A. Morang | ETH Zurich | |||
| Simon Funge-Smith | FAO | |||
| John Valbo-Jorgensen | FAO |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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