| Popis: |
Ongoing technological advances have led to a rapid increase in the number, type, and scope of animal tracking studies. In response, many software tools have been developed to analyze animal movement data. These tools generally focus on movement modelling, but the steps required to clean raw data files from different tracking devices have been largely ignored. Such pre-processing steps are often time-consuming and involve a steep learning curve. Moreover, decisions made at this early stage can substantially influence subsequent analyses.Here we present an open-access, reproducible toolkit written in the programming language R for processing raw data files into a single cleaned data set for analyses and upload to online tracking databases. Additionally, we provide a Shiny app to enable appropriate parameter determination during data cleaning.The toolkit is generalizable to different data formats and device types, uses modern ‘tidy coding’ practices, and has minimal dependencies. We provide a set of key principles and transparent, flexible code to flatten the learning curve associated with animal movement data processing, and produce robust, reproducible datasets.Overall, we envision our resource as a time-saving approach that provides a reproducible pipeline from data collection to archiving, useful for anyone conducting animal movement analyses. |
