A guide to pre-processing high-throughput animal tracking data.

ABSTRACT: Modern, high-throughput animal tracking increasingly yields 'big data' at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis-estimation of behaviours inferred from movement. 'Cleaning' the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce. A pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre-processing pipeline is efficiently segmenting and clustering lo

SUBMITTER: Gupte PR 

PROVIDER: S-EPMC9299236 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature