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  "Title": "Classify Aquatic Animal Behaviours from Vertical Movement Data",
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  "Description": "Quantitatively analyse depth time-series data from pop-up\nsatellite archival tags (PSATs) through the application of\ncontinuous wavelet transformation (CWT) combined with Principal\nComponent Analysis (PCA), and k-means clustering. Import, crop,\nand plot depth time-depth records (TDRs). Using CWT to detect\nimportant signals within the non-stationary data, we create\ndaily wavelet statistics to summarise vertical movements on\ndifferent wavelet periods and combine with daily and diel depth\nstatistics. Classify depth time-series with unsupervised\nk-means clustering into 24-hour periods of vertical movement\nbehaviour with distinct patterns of vertical movement. Plot\nexample days from each behaviour cluster, and plot the TDR\ncoloured by cluster. Based on principles of combining CWT with\nk-means first developed by Sakamoto et al. (2009)\n<doi:10.1371/journal.pone.0005379> and redeveloped by Beale et\nal. (2026) <doi:10.1186/s40462-025-00622-w>.",
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      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/calvinsbeale/actions/runs/41806823678"
    }
  ]
}