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title: Web App for Benchmarking Scholarly Performance
date: 2018-07-10
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css: style.css
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A paper I co-authored titled "Benchmarking Scholarly Performance by Faculty in Forestry and Forest Products" was recently [published](https://academic.oup.com/jof/article/116/4/320/4930773) in *Journal of Forestry*.
Here's the abstract:
> Measures of scholarly performance have proliferated, without corresponding efforts to standardize comparisons among faculty.
> An exception was a recent use of regression to model sources of variation in scholarly performance by fisheries and wildlife faculty.
> We applied this model-based method to data for 404 forestry and forest products faculty from 33 doctoral-degree-granting institutional members of the National Association of University Forest Resources Programs.
> Regression models were developed for h-index, the number of publications with at least h citations, and m quotient, the annual rate of change in h-index since conferral of the Ph.D. Years since Ph.D. and percent of appointment allocated to research were important predictors for h-index and m quotient.
> We also noted positive subdisciplinary effects for research foci in conservation, ecology, disease, and quantitative methods, and negative effects for management and social science.
> Standardized residuals enabled relative performance to be compared among faculty who differ in academic age, research appointment, and subdisciplinary focus.
> Model-based benchmarking provides much-needed context for interpretation of quantitative performance metrics and can supplement comprehensive peer evaluation.
> An interactive web application is provided to facilitate such benchmarking.
My main contribution to the paper was development of the [Shiny](https://shiny.rstudio.com/) web application.
We hope it's a useful tool for faculty and administrators to assess one metric of the impact of a scientist's scholarly work (i.e., citations).
You can try out the app below.
The R source code for the app is [here](https://github.com/kenkellner/naufrp-benchmark).