The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research (SCAR) project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. The RAATD project team consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets constitute the compiled tracking data from a large number of research groups that have worked in the Antarctic since the 1990s. This metadata record pertains to the "filtered" version of the data files. These files contain position estimates that have been processed using a state-space model in order to estimate locations at regular time intervals. For technical details of the filtering process, consult the data paper. The filtering code can be found in the https://github.com/SCAR/RAATD repository. This data set comprises one metadata csv file that describes all deployments, along with data files (3 files for each of 17 species). For each species there is: - an RDS file that contains the fitted filter model object and model predictions (this file is RDS format that can be read by the R statistical software package) - a PDF file that shows the quality control results for each individual model - a CSV file containing the interpolated position estimates For details of the file contents and formats, consult the data paper. The data are also available in a standardized version (see https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Standardised) that contain position estimates as provided by the original data collectors (generally, raw Argos or GPS locations, or estimated GLS locations) without state-space filtering.

The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research (SCAR) project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. The RAATD project team consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets constitute the compiled tracking data from a large number of research groups that have worked in the Antarctic since the 1990s. This metadata record pertains to the "standardized" version of the data files. These files contain position estimates as provided by the original data collectors (generally, raw Argos or GPS locations, or estimated GLS locations). Original data files have been converted to a common format and quality-checking applied, but have not been further filtered or interpolated. Periods at the start or end of deployments were identified and discarded if there was evidence that location data during these periods did not represent the animals' at-sea movement. For example, tags may have been turned on early (thereby recording locations prior to their deployment on animals) or animals may have remained at the deployment site, e.g. the breeding colony, for an extended period at the start or end of the tag deployment. Some tracks also showed a marked deterioration in the frequency and quality (for PTTs) of location estimates near the end of a track. Such locations were visually identified based on maps of each track in conjunction with plots of location distance from deployment site against time. This information is captured in the location_to_keep column appended to each species’ data file (1 = keep, 0 = discard). The code used to trim the tracks can be found in the https://github.com/SCAR/RAATD repository. This data set comprises one metadata csv file that describes all deployments, along with data csv files (17 files, one per species) containing the position data. For details of the file formats, consult the data paper. The data are also available in a filtered version (see https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018) that have been processed using a state-space model in order to estimate locations at regular time intervals.