In this blog I’ll share the workflow and tools used in the GIS part of this analysis. To understand where crashes are occurring, first the dataset had to be mapped. The software of choice in this instance was ArcGIS, though most of the analysis could have been done using QGIS. Heat maps are all the rage, and if you want to make simple heat maps for free and you appreciate good documentation, I recommend the QGIS Heatmap plugin. There are also some great tools in the free open-source program GeoDa for spatial statistics.

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network. The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

This dataset shows the results of mapping the connectivity of key values (natural heritage, indigenous heritage, social and historic and economic) of the Great Barrier Reef with its neighbouring regions (Torres Strait, Coral Sea and Great Sandy Strait). The purpose of this mapping process was to identify values that need joint management across multiple regions. It contains a spreadsheet containing the connection information obtained from expert elicitation, all maps derived from this information and all GIS files needed to recreate these maps. This dataset contains the connection strength for 59 attributes of the values between 7 regions (GBR Far Northern, GBR Cairns-Cooktown, GBR Whitsunday-Townsville, GBR Mackay-Capricorn, Torres Strait, Coral Sea and Great Sandy Strait) based on expert opinion. Each connection is assessed based on its strength, mechanism and confidence. Where a connection was known to not exist between two regions then this was also explicitly recorded. A video tutorial on this dataset and its maps is available from https://vimeo.com/335053846.

Methods:

The information for the connectivity maps was gathered from experts (~30) during a 3-day workshop in August 2017. Experts were provided with a template containing a map of Queensland and the neighbouring seas, with an overlay of the regions of interest to assess the connectivity. These were Torres Strait, GBR:Far North Queensland, GBR:Cairns to Cooktown, GBC: Townsville to Whitsundays, GBR: Mackay to Capricorn Bunkers and Great Sandy Strait (which includes Hervey bay). A range of reference maps showing locations of the values were provided, where this information could be obtained. As well as the map the template provided 7x7 table for filling in the connectivity strength and connection type between all combinations of these regions. The experts self-organised into groups to discuss and complete the template for each attribute to be mapped. Each expert was asked to estimate the strength of connection between each region as well as the connection mechanism and their confidence in the information. Due to the limited workshop time the experts were asked to focus on initially recording the connections between the GBR and its neighbouring regions and not to worry about the internal connections in the GBR, or long-distance connections along the Queensland coast. In the second half of the workshop the experts were asked to review the maps created and expand on the connections to include those internal to the GBR. After the workshop an initial set of maps were produced and reviewed by the project team and a range of issues were identified and resolved. Additional connectivity maps for some attributes were prepared after the workshop by the subject experts within the project team. The data gathered from these templates was translated into a spreadsheet, then processing into the graphic maps using QGIS to present the connectivity information. The following are the value attributes where their connectivity was mapped: Seagrass meadows: pan-regional species (e.g. Halophila spp. and Halodule spp.) Seagrass meadows: tropical/sub-tropical (Cymodocea serrulata, Syringodium isoetifolium) Seagrass meadows: tropical (Thalassia, Cymodocea, Thalassodendron, Enhalus, Rotundata) Seagrass meadows: Zostera muelleri Mangroves & saltmarsh Hard corals Crustose coralline algae Macroalgae Crown of thorns starfish larval flow Acropora larval flow Casuarina equisetifolia & Pandanus tectorius Argusia argentia Pisonia grandis: cay vegetation Inter-reef gardens (sponges + gorgonians) (Incomplete) Halimeda Upwellings Pelagic foraging seabirds Inshore and offshore foraging seabirds Migratory shorebirds Ornate rock lobster Yellowfin tuna Black marlin Spanish mackerel Tiger shark Grey nurse shark Humpback whales Dugongs Green turtles Hawksbill turtles Loggerhead turtles Flatback turtles Longfin & Shortfin Eels Red-spot king prawn Brown tiger prawn Eastern king prawns Great White Shark Sandfish (H. scabra) Black teatfish (H. whitmaei) Location of sea country Tangible cultural resources Location of place attachment Location of historic shipwrecks Location of places of social significance Location of commercial fishing activity Location of recreational use Location of tourism destinations Australian blacktip shark (C. tilstoni) Barramundi Common black tip shark (C. limbatus) Dogtooth tuna Grey mackerel Mud crab Coral trout (Plectropomus laevis) Coral trout (Plectropomus leopardus) Red throat emperor Reef manta Saucer scallop (Ylistrum balloti) Bull shark Grey reef shark

Limitations of the data:

The connectivity information in this dataset is only rough in nature, capturing the interconnections between 7 regions. The connectivity data is based on expert elicitation and so is limited by the knowledge of the experts that were available for the workshop. In most cases the experts had sufficient knowledge to create robust maps. There were however some cases where the knowledge of the participants was limited, or the available scientific knowledge on the topic was limited (particularly for the ‘inter-reefal gardens’ attribute) or the exact meaning of the value attribute was poorly understood or could not be agreed up on (particularly for the social and indigenous heritage maps). This information was noted with the maps. These connectivity maps should be considered as an initial assessment of the connections between each of the regions and should not be used as authoritative maps without consulting with additional sources of information. Each of the connectivity links between regions was recorded with a level of confidence, however these were self-reported, and each assessment was performed relatively quickly, with little time for reflection or review of all the available evidence. It is likely that in many cases the experts tended to have a bias to mark links with strong confidence. During subsequent revisions of some maps there were substantial corrections and adjustments even for connections with a strong confidence, indicating that there could be significant errors in the maps where the experts were not available for subsequent revisions. Each of the maps were reviewed by several project team members with broad general knowledge. Not all connection combinations were captured in this process due to the limited expert time available. A focus was made on capturing the connections between the GBR and its neighbouring regions. Where additional time was available the connections within 4 regions in the GBR was also captured. The connectivity maps only show connections between immediately neighbouring regions, not far connections such as between Torres Strait and Great Sandy Strait. In some cases the connection information for longer distances was recorded from the experts but not used in the mapping process. The coastline polygon and the region boundaries in the maps are not spatially accurate. They were simplified to make the maps more diagrammatic. This was done to reduce the chance of misinterpreting the connection arrows on the map as being spatially explicit.

Format:

This dataset is made up of a spreadsheet that contains all the connectivity information recorded from the expert elicitation and all the GIS files needed to recreate the generated maps.

original/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_Master_v2018-09-05.xlsx: ‘Values connectivity’: This sheet contains the raw connectivity codes transcribed from the templates produced prepared by the subject experts. This is the master copy of the connection information. Subsequent sheets in the spreadsheet are derived using formulas from this table. 1-Vertical-data: This is a transformation of the ‘Values connectivity’ sheet so that each source and destination connection is represented as a single row. This also has the connection mechanism codes split into individual columns to allow easier processing in the map generation. This sheet pulls in the spatial information for the arrows on the maps (‘LinkGeom’ attribute) or crosses that represent no connections (‘NoLinkGeom’) using lookup tables from the ‘Arrow-Geom-LUT’ and ‘NoConnection-Geom-LUT’ sheets. 2.Point-extract: This contains all the ‘no connection’ points from the ‘Values connectivity’ dataset. This was saved as working/ GBR_NESP-TWQ-3-3-3_Seascape-connectivity_no-con-pt.csv and used by the QGIS maps to draw all the crosses on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘NoLinkGeom’ attribute is used to filter out all line features, by unchecking blank rows in the ‘NoLinkGeom’ filter. 2.Line-extract: This contains all the ‘connections’ between regions from the ‘Values connectivity’ dataset. This was saved as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_arrows.csv and used by the QGIS maps to draw all the arrows on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘LinkGeom’ attribute is used to filter out all point features, by unchecking blank rows in the ‘LinkGeom’ filter. Map-Atlas-Settings: This contains the metadata for each of the maps generated by QGIS. This sheet was exported as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_map-atlas-settings.csv and used by QGIS to drive its Atlas feature to generate one map per row of this table. The AttribID is used to enable and disable the appropriate connections on the map being generated. The WKT attribute (Well Known Text) determines the bounding box of the map to be generated and the other attributes are used to display text on the map. map-image-metadata: This table contains metadata descriptions for each of the value attribute maps. This metadata was exported as a CSV and saved into the final generated JPEG maps using the eAtlas Image Metadata Editor Application

Weekly snapshot of Cleveland City Planning Commission datasets that are featured on the City Planning Zoning Viewer. For the official, most current record of zoning info, use the CPC Zoning Viewer.This file is an open-source geospatial (GIS) format called GeoPackage, which can contain multiple layers. It is similar to Esri's file geodatabase format. Free and open-source GIS software like QGIS, or software like ArcGIS, can read the information to view the tables and map the information.It includes the following mapping layers officially maintained by Cleveland City Planning Commission:Planner Assignment AreasPlanned Unit Development OverlayResidential FacilitiesResidential Facilities 1000 ft. BufferPolice DistrictsLandmarks / Historic LayersLocal Landmark PointsLocal Landmark ParcelsLocal Landmark DistrictsNational Historic DistrictsCentral Business DistrictDesign Review RegionsDesign Review DistrictsOverlay Frontage LinesForm & PRO Overlay DistrictsLive-Work Overlay DistrictsSpecific SetbacksStreet CenterlinesZoningUpdate FrequencyWeekly on Mondays at 4:30 AMContactCity Planning Commission, Zoning & Technology

This RINGS Quantarctica-friendly dataset is a compilation of new datasets generated by the RINGS Action Group and presented in a group review paper (Matsuoka et al., in review, DOI: XXXX), along with other previously published datasets cited in the same paper. Note that the latter is not comprehensive, as some datasets are already widely available elsewhere.

Open the included QGIS project file to view these datasets, all formatted to be compatible with Quantarctica. You can use the QGIS “Copy Layer” function (right-click on a layer or group and select it from the dropdown menu) to paste layers into your own Quantarctica package or a fresh installation, which includes base maps and other scientific data available at www.npolar.no/quantarctica. The current version of Quantarctica includes scientific data as new as 2017 when its version 3 was released. The Quantarctica project is working to release its version 4 with updated datasets with an anticipated release in sometime in 2025.

We kindly ask all users to cite the original datasets directly rather than citing this compilation alone. This aligns with our commitment to FAIR data use principles.

Please note that the new datasets introduced in Matsuoka et al. (in review) may be revised during the peer-review process. Any updates will be documented in the metadata of the affected datasets at the NPI Polar Data Centre (data.npolar.no).

If you have any questions, please contact Jenny Arthur (jennifer.arthur@npolar.no) or Kenny Matsuoka (kenichi.matsuoka@npolar.no) at the Norwegian Polar Institute.

This data package includes the following datasets: - RINGS/Bedmap3 grounding line of the Antarctic Ice Sheet. https://doi.org/10.21334/NPOLAR.2025.D1062D2A - Simplified Antarctic grounding line for primary ring surveys. https://doi.org/10.21334/NPOLAR.2025.8CEC2D20 - Comparison of recent ice-discharge estimates for 27 drainage basins of the Antarctic Ice Sheet. https://doi.org/10.21334/NPOLAR.2025.742D0E9B - Modelled Antarctic annual surface mass balance averaged between 1987 and 2015. https://doi.org/10.21334/NPOLAR.2025.75A66608 - Antarctic Automatic Weather Station locations. https://doi.org/10.5194/essd-15-411-2023 - Antarctic Ice Sheet surface mass balance measurements. https://doi.org/10.5194/essd-13-3057-2021 - Grounding line transects for ensemble surface mass balance extraction. - Antarctic mean annual melt days. https://doi.org/10.1017/jog.2021.112. - Radar survey and bed elevation data availability under the Antarctic Ice Sheet in the coastal zone, within 100 km landward from the RINGS/Bedmap3 grounding line. https://doi.org/10.21334/NPOLAR.2025.ECC09169 Projected Antarctic grounding line retreat from the ISMIP6 model ensembles by the end of 2100. https://doi.org/10.21334/NPOLAR.2025.8218FFFA - Seabed topography data availability under Antarctic ice shelves. https://doi.org/10.21334/NPOLAR.2025.D6285F9C

The data set shows the location of parking spaces on public roads in Frederiksberg Municipality.

The data set can be accessed in GeoJSON format (4326 - WGS 84) and can be loaded directly into QGIS.

NOTE! The data set is currently undergoing quality assurance by manual review on aerial photo and then physically, why parts of the data set have not been updated and quality assured.

Fields: "pid" - Unique polygon id, "vehicle_type" - Vehicle type for that space, "p_retning" - The direction of the parking space ie. parrallel, perpendicular or oblique parking and "quality assured" - Is the space updated and quality assured

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

Credits: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston, Trust for Public Lands, and City of Cambridge.

This dataset contains supplemental data required to reproduce the results of the paper Interplay of river and tidal forcings promotes loops in coastal channel networks (in review at Geophysical Research Letters). We provide raw and extracted channel network data for 19 river deltas/coastal marsh sites. For each site, the following files are provided:

XXX_base.tif : the raw binary mask of the river channel network XXX_clipper.shp (and associated .dbf, .prj, .qpj, and .shx files) : polygon(s) used to clip the raw mask XXX_clipped.tif : the binary mask of the river channel network after being clipped by XXX_clipper.shp XXX_filled.tif : the binary mask after filling islands via the method specified in the paper XXX_inlet_nodes.shp (and associated .dbf, .prj, .qpj, and .shx files) : locations of the inlet nodes; used by RivGraph XXX_shoreline.shp (and associated .dbf, .prj, .qpj, and .shx files) : location of the shoreline; used by RivGraph XXX_links.json : GeoJSON file containing the geometries, connectivities, and widths of each link in the network XXX_nodes.json : GeoJSON file containing the locations of each node of the network process_XXX.py : the python script used to generate the above files

All files listed below (except .py files) are georeferenced (i.e. can be opened with QGIS, ArcGIS or another GIS). Exceptions to the provided files include:

Barnstable: no "base.tif" is provided. Use "filled.tif". GBM: some hand-cleaning was performed on "filled.tif". Mackenize: "clipper.shp" is not provided, but "clipped.tif" is. Mississippi: "clipper.shp" is not provided as the mask was made from a shapefile.

In order to run process_XXX.py, the RivGraph package will need to be installed. Instructions can be found at https://github.com/jonschwenk/RivGraph.

This dataset provides a 10 m resolution map of cropland in Togo (togo_cropland_2019.zip). Each pixel represents a posterior probability (ranging 0 to 1) that the pixel contains crops, predicted using an LSTM classifier and multi-spectral time series of Sentinel-2 satellite observations. For more details on the method, please see Kerner and Tseng, et al. (full reference below).

This dataset also provides the hand-labeled polygons used for training (crop_merged_v2.zi, noncrop_merged_v2.zip) and testing (togo_test_majority.zip) the model, which were created by experts based on photointerpretation of high-resolution imagery (primarily SkySat and PlanetScope) in QGIS and Google Earth Pro.

If you use any part of this dataset, please cite the following paper: Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe, Brian Barker, Blake Munshell, Madhava Paliyam, and Mehdi Hosseini. 2020. Rapid Response Crop Maps in Data Sparse Regions. In review for KDD ’20: ACMSIGKDD Conference on Knowledge Discovery and Data Mining Workshops, August 22–27, 2020, San Diego, CA.

The Niassa Selous land cover and change dataset covers an area of 139 163km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3943 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data).

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset), Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326), Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change Land cover/change dataset attributes: [map_codeA] - dichotomous level, [map_code} - modular level, [class_name] - corresponding modular class name. Validation dataset attributes (not all are present): [plaus200X] - corresponding class for the change map (i.e. 2000), modular level [plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes [plaus20XX] - corresponding class for the land cover map (i.e. 2017), modular level [plaus20XXr] - corresponding class for the land cover map (i.e. 2017), aggregated classes The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

Glasgow City Council is required to review and assess air quality within its area. These reviews are the basis of local air quality management and are intended to compare current and future concentrations of key air pollutants with the objectives set in the National Air Quality Strategy. The National Air Quality Strategy has set and updated target concentrations for eight key air pollutants - benzene, 1,3-butadiene, carbon monoxide, lead, nitrogen dioxide, ozone, particles and sulphur dioxide. This strategy is currently under review. As of the 1st March 2012, the Executive Committee of Glasgow City Council approved amendments to two of the three existing Air Quality Management Areas and the creation of a further AQMA covering the whole of the city. Glasgow now has AQMAs located at the City Centre, Byres Rd / Dumbarton Rd and Parkhead Cross. All of these have been declared for the pollutant nitrogen dioxide (NO2). The AQMA covering the whole of the city has been declared for the pollutant particles PM10. Data presented is a Shape file showing the location of these areas on a map. To view or use these files, a compression software and GIS software like ESRI ArcGIS or QGIS is needed. Projected coordinate system is OSGB36. Contains Ordnance Survey data (c) Crown Copyright 2013. Licence: None

This dataset is presented in Figure 25 of Matsuoka et al. (in review, DOI XXXX).

This dataset presents the grounding line retreat by 2100 projected by a selected set of ISMIP6 model ensembles (Seroussi et al., https://doi.org/10.5194/tc-14-3033-2020) downloaded from Ghub https://theghub.org/resources/4748.

In total, 51 ISMIP6 experiments were used to calculate retreat probability here, including 12 experiments based on RCP8.5 scenarios from 6 CMIP5 models; 4 experiments based on RCP 2.6 scenarios from 2 CMIP5 models; 2 experiments testing ice-shelf collapse; 2 experiments testing the uncertainty in the basal-melt parameterization, and 2 experiments testing the uncertainty in the melt calibration. The retreat probability is represented by the count of the number of experiments projecting grounding line retreat (ranging 0 to 51) in each cell of 8 km by 8 km. The results are based on model output computed from the ISMIP6 native grids that vary between models (ranging from 8 to 32 km).

_ISMIP6_GL_retreat.shp:_ Grounding line retreat probability, representing the number of modelled retreat cells in IMSIP6 experiments.

For full details of the participating models and experiments, see Seroussi et al. (2020; https://doi.org/10.5194/tc-14-3033-2020).

Shapefile fields:

count: accumulated count of modelled grounding line retreat cells from 51 ISMIP6 model experiments. These values represent the total number of grid cells in which the ISMIP6 model experiments have simulated retreat, and were calculated using the 'Count Overlap' tool in QGIS.

sector: Antarctic sectors used in ISMIP6 analysis (Seroussi et al., 2020) on an 8 km grid, numbered from 1 to 18.

area: total area (in square kilometres) of all modelled grounding line retreat cells across 51 ISMIP6 experiments. Note: this is not a strict measure of total retreat area per sector. Instead, it reflects the combined spatial footprint of all grounding line retreat instances in each of these ISMIP6 experiments, derived from stacking all grounding line retreat polygons from these experiments. This approach highlights the maximum potential extent of grounding line retreat by 2100, as projected by the ensemble.

Workflow to create ISMIP6_GL_Retreat.shp:

For each Model/Experiment, original netCDF files of grounded ice area fraction were converted to polygon shapefiles. The grounding line was extracted where grounded ice area fraction =1.

For calculating the number of modelled retreat cells between 2016 and 2100 for each experiment, a polygon shapefile was created by differencing the 2016 and 2100 fully grounded model grid cells (i.e. where grounded ice area fraction = 1).

'_groundinglines_' contains individual grounding line positions as GIS polygon shapefiles for ISMIP6 experiments, file naming format: 'Group_ModelName_experiment_grounded_Year'. These were created from the original netCDF files of grounded ice area fraction, by selecting all cells where grounded ice area fraction =1.

UngroundedIceFrac.xlsx contains a summary of the area of ungrounded cells for each of the 18 sectors used in ISMIP6 (Seroussi et al., 2016). This includes the percentage of the grounding line that is projected to retreat at least 50 km from the present-day grounding line in different regions of Antarctica, based on the ISMIP6 experiments analysed here.

The Timor-Leste land cover and change dataset covers an area of 14 931km² and mapped with dichotomous (8 land cover classes)and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2016, LCC: 2000, 2005, 2010). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 4413 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data).

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset),
Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326),
Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change
Land cover/change dataset attributes:
[map_codeA] - dichotomous level,
[map_code} - modular level,
[class_name] - corresponding modular class name.
Validation dataset attributes (not all are present):
[plaus200X] - corresponding class for the change map (i.e. 2000), modular level
[plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes
[plaus20XX] - corresponding class for the land cover map (i.e. 2016), modular level
[plaus20XXr] - corresponding class for the land cover map (i.e. 2016), aggregated classes
The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

The raw data as well as the R code from the study 'A major spatial reorganization of the North Atlantic Oscillation around 4000 BP' by J. Schirrmacher and M. Weinelt in review at Nature Communications Earth & Environment is archived. The final plots presented in the paper have beenmade with Grapher16 and QGIS 3.10.

The Salonga land cover and change dataset covers an area of 66 625km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2016, LCC:2019). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3069 verified land cover points based on the 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data).

Related dataset: https://doi.pangaea.de/10.1594/PANGAEA.920845

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset), Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326), Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change Land cover/change dataset attributes: [map_codeA] - dichotomous level, [map_code} - modular level, [class_name] - corresponding modular class name. Validation dataset attributes (not all are present): [plaus200X] - corresponding class for the change map (i.e. 2000), modular level [plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes [plaus20XX] - corresponding class for the land cover map (i.e. 2015), modular level [plaus20XXr] - corresponding class for the land cover map (i.e. 2015), aggregated classes The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

The Madagascar land cover and change dataset covers an area of 124 012km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3995 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data/).

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset), Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326), Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change Land cover/change dataset attributes: [map_codeA] - dichotomous level, [map_code} - modular level, [class_name] - corresponding modular class name. Validation dataset attributes (not all are present): [plaus200X] - corresponding class for the change map (i.e. 2000), modular level [plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes [plaus20XX] - corresponding class for the land cover map (i.e. 2017), modular level [plaus20XXr] - corresponding class for the land cover map (i.e. 2017), aggregated classes The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

The Caribbean land cover and change dataset covers an area of 89 883km2 and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 4029 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data).

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset), Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326), Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change Land cover/change dataset attributes: [map_codeA] - dichotomous level, [map_code} - modular level, [class_name] - corresponding modular class name. Validation dataset attributes (not all are present): [plaus200X] - corresponding class for the change map (i.e. 2000), modular level [plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes [plaus20XX] - corresponding class for the land cover map (i.e. 2017), modular level [plaus20XXr] - corresponding class for the land cover map (i.e. 2017), aggregated classes The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

The Wapok land cover and change dataset covers an area of 57 776km² and mapped with dichotomous (8 land cover classes) and modular (up to 32 land cover classes) levels based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which the thematic land cover and change maps were derived (LC: 2017, LCC: 2000). The maps were fully verified and validated by an independent team to achieve the Copernicus Global Land Monitoring Programme's strict data quality requirements. An independent validation dataset was also collected and it is shared here. The validation dataset contains 3522 verified land cover points based on the [up to] 32 modular level land cover classes. Furthermore, two predefined symbology (QGIS legend files) for the land cover and validation datasets based on FAO's LCCS is also shared here to ease the visualization of them (Dichotomous and Modular levels). Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Zoltan; Brink, Andreas; Lupi, Andrea (2021): An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific region (in review, Earth System Science Data).

Data format: vector (shapefile, polygon - LC/LCC dataset), vector (shapefile, point - validation dataset),
Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326),
Minimum mapping unit: 3ha for land cover and 0.5ha for land cover change
Land cover/change dataset attributes:
[map_codeA] - dichotomous level,
[map_code} - modular level,
[class_name] - corresponding modular class name.
Validation dataset attributes (not all are present):
[plaus200X] - corresponding class for the change map (i.e. 2000), modular level
[plaus200Xr] - corresponding class for the change map (i.e. 2000), aggregated classes
[plaus20XX] - corresponding class for the land cover map (i.e. 2017), modular level
[plaus20XXr] - corresponding class for the land cover map (i.e. 2017), aggregated classes
The naming of all attributes follow the same structure in all shapefiles - see Table 2 Dichotomous and Modular thematic land cover/use classes and in the "3.5 Validation dataset production" section in the corresponding publication.

This is the published version (v1.04) of the GIS package for Antarcitca's Sedimentary Basins Distribution and Classification. Supplement to Aitken, A. R. et al .,2023. Antarctica's sedimentary basins and their influence on ice sheet dynamics. Review of Geophysics. With the release of the published version of the GIS package, future updates to the sedimentary basin mapping can be found at https://github.com/LL-Geo/AntarcticBasins, and at https://doi.org/10.5281/zenodo.7955525. You can download individual GeoTIFF, Shapefile, and GeoJSON files. The complete DistroPackage contains all files, including styles in QGIS and ArcGIS projects.