Adler, Peter B.

English

DigitalCommons@USU

Utah State University DigitalCommons@USU Funded Research Records 2-12-2020 Collaborative Research: A general approach to partitioning contributions from multiple drivers affecting individuals, populations, and communities Peter B. Adler peter.adler@usu.edu Follow this and additional works at: https://digitalcommons.usu.edu/funded_research_data  Part of the Natural Resources Management and Policy Commons Recommended Citation Adler, Peter B., "Collaborative Research: A general approach to partitioning contributions from multiple drivers affecting individuals, populations, and communities" (2020). Funded Research Records. Paper 130. https://digitalcommons.usu.edu/funded_research_data/130 This Grant Record is brought to you for free and open access by DigitalCommons@USU. It has been accepted for inclusion in Funded Research Records by an authorized administrator of DigitalCommons@USU. For more information, please contact digitalcommons@usu.edu. 1 Data Management Plan  The activities in this grant will generate no new empirical data that will need to be hosted, but will rather involve the analysis of existing data sets and models, many of which are already freely available for download by the public. The data (in the broad sense) to be generated by this project will primarily be of two types:  • Software written in the R programing language. • Simulation results and analysis of previously collected empirical data.  Public access to these products will be provided in two ways: 1. Software will be provided in the form of re-usable software or packages in the R programming language. Th R language is publicly available at no charge from http://www.r-project.com, and packages are hosted at http://cran.r-project.com.  2. Authors will submit code to reproduce all simulations, figures and data analyzes, as supplementary materials to papers published, which will be hosted on journal websites. Our policy is that each figure or table caption in a publication includes the name of the script or scripts that produced those results, and online journal readers will have access to those scripts. Any empirical data that are not freely available for downloading will be provided in the same way, in connection with any article where it is used. Where original data owners object to the provision of data in this manner, or where they are too large to be stored on journal websites, links to data sources will be provided. 1933497

Collaborative Research: A general approach to partitioning contributions from multiple drivers affecting individuals, populations, and communities

https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1133&context=funded_research_data

Collaborative Research: A general approach to partitioning contributions from multiple drivers affecting individuals, populations, and communities

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