27 April 2022 in Methods in Ecology and Evolution
18 November 2016 in Biodiversity and Conservation
11 May 2014 in Conservation Genetics
20 April 2025 in International Journal of Science Education, Part B
2 July 2019 in Biodiversity Information Science and Standards
The bdverse is a collection of packages that form a general framework for facilitating biodiversity science in R. We build it to serve as a sustainable and agile infrastructure that enhances the value of biodiversity data by allowing users to conveniently employ R, for data exploration, quality assessment, data cleaning, and standardization. The bdverse supports users with and without programming capabilities. It includes six unique packages in a hierarchal structure — representing different functionality levels (Fig. 1). Major features of three core packages will be highlighted and demonstrated: (i) bdDwC provides an interactive Shiny app and a set of functions for standardizing field names in compliance with Darwin Core (DwC) format; (ii) bdchecks is an infrastructure for performing, filtering and managing various biodiversity data checks; (iii) bdclean is a user-friendly data cleaning Shiny app for the inexperienced R user. It provides features to manage complete workflow for biodiversity data cleaning, including data upload; user input - in order to adjust cleaning procedures; data cleaning; and finally, generation of various reports and versions of the data. We are now working on submitting the bdverse packages to rOpenSci software review, and as soon as the packages meet core requirements, we will officially release the bdverse. The bdverse project won the 2nd prize in the 2018 Ebbe Nielsen Challenge.
22 May 2018 in Biodiversity Information Science and Standards
A new R package for biodiversity data cleaning, 'bdclean', was initiated in the Google Summer of Code (GSoC) 2017 and is available on github. Several R packages have great data validation and cleaning functions, but 'bdclean' provides features to manage a complete pipeline for biodiversity data cleaning; from data quality explorations, to cleaning procedures and reporting. Users are able go through the quality control process in a very structured, intuitive, and effective way. A modular approach to data cleaning functionality should make this package extensible for many biodiversity data cleaning needs. Under GSoC 2018, 'bdclean' will go through a comprehensive upgrade. New features will be highlighted in the demonstration.