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ZIP Archive - 994.8 MB - MD5: b148c744c048abb425470b7a170023a3
ZIP Archive - 52.3 GB - MD5: df9f9cd746ec58e8d2476df9822e06be
ZIP Archive - 2.4 GB - MD5: b94bea222afaba4c4a364f659ffdd2de
Nov 12, 2025 - University of Inland Norway
Kirchner, Theresa M; Devineau, Olivier; Thompson, Daniel P; Thiel, Alexandra; Chimienti, Marianna; Evans, Alina L; Crouse, John; Eriksen, Ane, 2025, "Programming code for article: Predicting energy expenditure from dynamic body acceleration in a large boreal ungulate species", https://doi.org/10.18710/3EB2DM, DataverseNO, V1
These files contain code to prepare data for and fit a dynamic generalized additive model to predict energy heart rate and energy expenditure from dynamic body acceleration in moose (Alces alces). In this study, we collected heart rate data using implanted loggers and accelerometer data using tri-axial accelerometer collars. To quantify heart rate...
R Syntax - 8.2 KB - MD5: 3da9a28b237125dc23df834a54db97e1
Time drift correction of HR logger measurements
R Syntax - 13.7 KB - MD5: 8d216d940d8f850882ceef151c705c1a
Manual validation of HR logger measurements
R Syntax - 7.5 KB - MD5: c65364fe42d51ee3e733a75217ce6170
Prepare accelerometer data to predict behavior using random forest model (Kirchner et al. 2023)
R Syntax - 3.3 KB - MD5: 952f91918bb5729986bc4258d7baab7c
Join heart rate and ODBA and behavioral data by timestamps
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