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Title:   Author  item
                                            KAMVAR, Z - Oregon State Universityitem
                                            LOPEZ-URIBE, M - North Carolina State Universityitem
                                            COUGHLAN, S - National University Of Irelanditem
                                            Grunwald, Niklaus

                                                         - *Nik*item
                                            LAPP, H - Duke Universityitem
                                            MANEL, S - Institut National De La Recherche Agronomique (INRA)
  Submitted to: Molecular Ecology Resources
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 5/2/2016
                                    Publication  Date: 7/12/2016
                                    Citation: Kamvar, Z.N., Lopez-Uribe, M.M., Coughlan, S., Grunwald, N.J., Lapp, H., Manel, S. 2016. Developing educational resources for population genetics in R: An open and collaborative approach. Molecular Ecology Resources. 17(1):120-128. doi: 10.1111/1755-0998.12558.Interpretive Summary: The R computing and statistical language community has developed a myriad of resources for conducting populations genetic analyses. However, resources for learning how to carry out population genetic analyses in R are scattered and often incomplete, which can make acquiring this skill unnecessarily difficult and time-consuming. We developed an online community resource with guidance and working demonstrations for conducting population genetic analyses in R. We invite the community of population geneticists working in R to contribute to this resource, by contributing their own primers.
                                Technical Abstract: The R computing and statistical language community has developed a myriad of resources for conducting populations genetic analyses. However, resources for learning how to carry out population genetic analyses in R are scattered and often incomplete, which can make acquiring this skill unnecessarily difficult and time-consuming. To address this gap, we developed an online community resource with guidance and working demonstrations for conducting population genetic analyses in R. The resource is freely available at http://popgen.nescent.org, and includes material for both novices and advanced users of R for population genetics. To facilitate continued maintenance and growth of this resource, we developed a toolchain, process, and conventions designed to (1) minimize financial and labor costs of upkeep; (2) to provide a low barrier to contribution; and (3) to ensure strong quality assurance. The toolchain includes automatic integration testing of every change and rebuilding of the website when new vignettes or edits are accepted. The process and conventions largely follow a common, distributed version control-based contribution workflow, which is used to provide and manage open peer review by designated website editors. The online resources include detailed documentation of this process, including video tutorials. We invite the community of population geneticists working in R to contribute to this resource, whether for a new use-case of their own, or as one of the vignettes from the “wish list” we maintain, or by improving existing vignettes.


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