paleopal: a highly interactive Shiny app for building reproducible data science workflows in paleontology

The field of computational paleontology is rapidly advancing with many recently developed open-source R packages leading the charge for more standardized, reproducible, and open research. This push is a relief for many data-science-minded paleontologists who have previously toiled over writing their own scripts to download, clean, analyze, and visualize their data. Many of these steps are now covered by functions in these new packages (and those of other packages in the R universe). However, this push for more script-based research may introduce a wrench in the existing scientific workflows of less technical researchers who lack a background in coding or cause a greater learning curve for new researchers introduced to the field. Therefore, bridging the gap between visual, hands-on workflows and digital, code-based workflows is imperative to the collaborative future of computational paleontology. Here I present a new open-source Shiny app, paleopal (https://github.com/willgearty/paleopal), that provides a user-friendly interface to build paleontological data science workflows without any programming knowledge. The app connects existing paleontological R packages such as palaeoverse1 and deeptime2 with the tidyverse3 suite of R package to encourage standardized scientific pipelines. Specifically, paleopal presents users with a curated set of workflow “steps” (e.g., data upload, data cleaning, and data visualization) that they can then choose from, customize, and reorder to develop their pipeline. The app is a built on top of the shinypal4 package which uses the shinymeta5 R package to provide a live code and results panel and a downloadable RMarkdown script as the user develops their pipeline. To increase accessibility, I have hosted the shiny app as a serverless application on GitHub Pages (http://williamgearty.com/paleopal/) using the shinylive6 R package and the webR framework. To my knowledge, this is the first use of shinymeta within a webR project which has presented many technological hurdles to overcome, including dealing with browser filesystems, restricted access to operating system software, and cross-browser support. Nonetheless, paleopal aims to spearhead the next generation of training of computational paleontologists, regardless of age, background, or technical expertise. Additionally, the extensible nature of paleopal makes it easy to add further curated workflow “steps”, and the underlying shinypal package could also be used to create similar shiny apps for other scientific fields.