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This study was supported by a Carnegie Trust for the Universities of Scotland Carnegie PhD Scholarship (to F.D.W.C.), hosted in the Edinburgh E3 U.K. Natural Environment Research Council (NERC) Doctoral Training Partnership (NE/L002558/1) and the Scottish Alliance for Geoscience, Environment and Society (SAGES) Graduate School. The study was also produced with the financial assistance of the Prince Albert II of Monaco Foundation (to F.D.W.C.), the NERC / U.S National Science Foundation (NSF) International Thwaites Glacier Collaboration grants NE/S006613 (ITGC-GHOST; to R.G.B.) and NE/S006796 (ITGC-PROPHET; to N.G.) (ITGC contribution no. ITGC-088), NERC grant NE/T001607/1 (QuORUM project to N.G. and S.F.B.T.), the ESA 4D Antarctica and Digital Twin Antarctica projects 4000128611/19/I‐DT (to N.G.), and NSF grant 2045075 (to E.J.S.). This dataset contains the grounding-line and ice-velocity change observations presented in Christie et al. (Nature Communications, 2023). Data are provided in ESRI .shp (grounding line location and change records) and .TIF (ice velocity and change records) formats, and detailed information about the data collection methods, sources and other technical information can be found within the accompanying README files inside the .ZIP folder.

This research was undertaken while KB was in receipt of a United Kingdom Natural Environment Research Council PhD studentship awarded through the University of Cambridge C-CLEAR Doctoral Training Partnership (grant number: NE/S007164/1). This work was also produced with financial assistance (to FDWC) of the Prince Albert II of Monaco Foundation, and (to ICW) from the United Kingdom Natural Environment Research Council awarded to the University of Cambridge (grant number: NE/T006234/1). TN and JW acknowledge support from the European Space Agency through the Antarctic Ice Sheet Climate Change Initiative (CCI) program. This dataset contains the location of the grounding line along George VI Ice Shelf (West Antarctic Peninsula), Wilkins Ice Shelf and Bach Ice Shelf (Alexander Island), as determined from a compilation of datasets, including double-differential interferometric synthetic aperture radar (DInSAR) from Sentinel-1 imagery acquired May-October 2020, Sentinel-1 grounding-line information from 2018 (Mohajerani et al., 2021) and the MEaSUREs 1994-1996 grounding line dataset (Rignot et al., 2016). The dataset is provided in ESRI Shapefile (.shp) format.

This research was undertaken while KB was in receipt of a United Kingdom Natural Environment Research Council PhD studentship awarded through the University of Cambridge C-CLEAR Doctoral Training Partnership (grant number: NE/S007164/1). This work was also produced with financial assistance (to FDWC) of the Prince Albert II of Monaco Foundation, and (to ICW) from the United Kingdom Natural Environment Research Council awarded to the University of Cambridge (grant number: NE/T006234/1). TN and JW acknowledge support from the European Space Agency through the Antarctic Ice Sheet Climate Change Initiative (CCI) program. This dataset contains the seasonal ice velocity products calculated from SAR- and optically derived velocity fields of land ice draining to George VI Ice Shelf. The dataset is provided in TIFF image (.tiff) format.

TBoxes for OntoGasGrid and OntoClimateObservations. Please see README file for details

Recorded sensor readings and the respective environment (indoor/outdoor) label for the paper "Deep Learning-based Multivariate Time Series Classification for Indoor Outdoor Detection". Each column contains the recorded values of a specific feature (name accordingly), while the last column is the target variable (1 refers to indoors and 0 to outdoors). The column "New_seq" indicates when a new recording starts (different date and place), it is not used as a training feature, and it allows to locate the different time series. This work was supported by European Commission through the Horizon 2020 Framework Programme, H2020-MSCA-ITN-2019, Grant No. 860239, BANYAN.

This dataset contains ESRI shapefiles of Larsen C Ice Shelf’s grounding line location (GLL), 2019, as constrained from double-difference interferometric synthetic aperture radar (SAR) techniques applied to ESA Sentinel-1a/b SAR imagery. Multiple GLLs are provided and correspond to the position of the grounding line as imaged from consecutive Sentinel-1a/b 6-day repeat-pass interferometric image pairs acquired during austral winter and early springtime (June, July, August, and September) of 2019. Further methods are outlined in the associated manuscript, and detailed information on the metadata associated with each shapefile is provided in the README.txt file. How to Cite If using these products, please cite the following: Christie, F.D.W, Benham T.J., Batchelor, C.L., Rack, W., Montelli, W. and Dowdeswell, J.A. (2022a). Antarctic ice-shelf advance driven by anomalous atmospheric and sea-ice circulation, Nature Geoscience, doi:10.1038/s41561-022-00938-x Christie, F.D.W, Benham T.J., Batchelor, C.L., Rack, W., Montelli, W. and Dowdeswell, J.A. (2022c). Antarctic Grounding Line Location from Sentinel-1A/B double-difference interferometry, 2019, supporting ‘Antarctic ice-shelf advance driven by anomalous atmospheric and sea-ice circulation’. Cambridge Apollo (2022); https://doi.org/10.17863/CAM.54489 This work was funded by the Flotilla Foundation, Marine Archaeology Consultants Switzerland and with the financial assistance of the Prince Albert II of Monaco Foundation.

This dataset contains ESRI shapefiles of the eastern Antarctic Peninsula’s ice front, 1979-2021, as constrained from a suite of spaceborne optical (Landsat 1, Landsat 3, Landsat 7, Landsat 8, Sentinel-2a/b) and radar (ERS-1/2, Sentinel-1a/b) image acquisitions. Methods are outlined in the associated manuscript, and detailed information on the metadata associated with each shapefile is provided in the README.txt file. Separate shapefiles are provided for each year and in concatenated format. For reference, the concatenated product also contains the previously published historical ice front positions referred to in the manuscript (see also Cook et al., 2005, 2010; Ferrigno et al., 2005; Haran et al., 2005; Wuite et al., 2015). How to Cite: If using these products, please cite the following: Christie, F.D.W, Benham T.J., Batchelor, C.L., Rack, W., Montelli, W. and Dowdeswell, J.A. (2022a). Antarctic ice-shelf advance driven by anomalous atmospheric and sea-ice circulation, Nature Geoscience, doi:10.1038/s41561-022-00938-x Christie, F.D.W, Benham T.J., Batchelor, C.L., Rack, W., Montelli, W. and Dowdeswell, J.A. (2022b). Antarctic ice front positions, 1979–2021, supporting ‘Antarctic ice-shelf advance driven by anomalous atmospheric and sea-ice circulation’ (Cambridge Apollo); https://doi.org/10.17863/CAM.54490 This work was funded by the Flotilla Foundation and Marine Archaeology Consultants Switzerland and with the financial assistance of the Prince Albert II of Monaco Foundation.

This dataset contains habitat and bird diversity data collected in Thetford Forest. The data were derived from Breeding Bird Surveys, LiDAR structural information and management databases. Further details of the method and statistics are available in Tew et al “Recommendations to enhance breeding bird diversity in managed plantation forests determined using LiDAR” (Ecological Applications). Each row contains data for a management/age class group. Columns ‘Management type’, ‘Age class’ and ‘(Log) total area’ were derived from forest management databases. Columns ‘Horizontal heterogeneity, ‘Gap fraction’, ‘Top canopy height’, ‘Vertical heterogeneity’, ‘Shrub density’ and ‘Canopy density’ were derived from high-resolution remote sensing LiDAR data. ‘Horizontal heterogeneity’, ‘Gap fraction’ and ‘Top canopy height’ are summarised at four different resolutions: 0.5 m, 2 m, 5 m, 10 m. Bird diversity data were derived from bird surveys. NERC

Code in support of "Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery" by R.L. Dell and others. The scripts provided facilitate the pre-processing of Landsat 8 images for the training, validation, and application of of a Random Forest Classifier. Scripts to train, validate, and apply a Random Forest Classifier are also provided. All scipts are written in Google Earth Engine. The methodological information relating to these scripts can be found in the companion paper: Dell RL, Banwell AF, Willis IC, Arnold NS, Halberstadt ARW, Chudley TR, Pritchard HD (2021). Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery. Journal of Glaciology 1-14. https://doi.org/10.1017/jog.2021.114.