Land cover and land cover change maps were created within the European Commission's Copernicus Global Land Monitoring Service's Hot-Spot Monitoring framework program. During the program's first phase, a total of 560442km2 area in Sub-Saharan Africa was mapped, from which 153665km2 was mapped with 8 land cover classes while 406776km2 was mapped with up to 32 classes based on FAO's Land Cover Classification System (LCCS). High-resolution optical satellite imagery were used to generate dense time-series data from which thematic land cover and change maps were derived. Each map was fully verified and validated by an independent team to achieve Copernicus' strict data quality requirements.Independent validation datasets for each KLCs were also collected and they are presented here. The validation datasets contain 35671 verified points for two dates (LC and LCC). Furthermore, a predefined symbology (QGIS legend file) for the land cover/change and validation datasets based on FAO's Land Cover Classification System is also shared here to ease the visualization of them. Further details regarding the sites selection, mapping and validation procedures are described in the corresponding publication: Szantoi, Z., Brink, A., Lupi, A., Mannone, C., and Jaffrain, G.: Key Landscapes for Conservation Land Cover and Change Monitoring, Thematic and Validation Datasets for Sub-Saharan Africa, Earth Syst. Sci. Data, 12-3001-2020, https://doi.org/10.5194/essd-12-3001-2020. There are twelve datasets shared as a supplement to the “Key Landscapes for Conservation Land Cover and Change Monitoring, Thematic and Validation Datasets for Sub-Saharan Africa” publication here. Data format: vector (shapefile, polygon (LC/LCC dataset), point (validation dataset),Geographic Coordinate System (LC/LCC dataset): World Geodetic System 1984 (EPSG:4326) and its datum (EPSG:6326),Projected coordinate system (validation data): Africa Albers Equal Area Conic (EPSG:102022),Minimum mapping unit: 0.5-5ha.Land cover and land cover change dataset attributes:[mapcode_A] - dichotomous class,[mapcode_B] - modular class,[name_A] - corresponding dichotomous class names (KLCs classified only at the dichotomous level),[name_B] - corresponding modular class name.Validation dataset attributes:[plaus201X] - land cover,[plaus2000X] - land cover change.The naming of all attributes follow the same structure in all shapefiles – see Table 2 Dichotomous and Modular thematic land cover/use classes in the corresponding publication.

The data are derived from interpretation of seismic reflection profiles within the offshore Corinth Rift, Greece (the Gulf of Corinth) integrated with IODP scientific ocean drilling borehole data from IODP Expedition 381 (McNeill et al., 2019a, 2019b). The data include rift fault coordinate (location, geometry) information and slip rate and extension rate information for the major faults. Seismic reflection data were published in Taylor et al. (2011) and in Nixon et al. (2016). Preliminary fault interpretations and rate data, prior to IODP drilling, were published in Nixon et al. (2016). Details of datasets: The data can be viewed in GIS software (ArcGIS, QGIS) or the Excel and .dbf files can be used for viewing of rate data and import of fault coordinates into other software. The 4 folders are for different time periods with shape files for the N-Dipping and S-Dipping Faults in the offshore Corinth Rift and respective slip and extension (horizontal) rates. The shapefiles are digitised fault traces for the basement offsetting faults, picked from the Multichannel Seismic Data collected by the R/V Maurice Ewing. Fault traces are segmented and each segment has an average throw (vertical) rate (Tavg) in mm/yr. The rates for the segments are averages based on measurements at the ends of each segment. The major fault trace segments also have slip-rates (slip_rate) and extension-rates (ext_rate or extension_) in mm/yr. All rates as well as the names for major faults can be located in the attribute table of the shape files along with X- and Y-coordinates. The coordinate system is WGS84 UTM Zone 34N. The shape files can be loaded into a GIS (ArcGIS, QGIS etc.) allowing mapping and visualization of the fault traces and their activity rates. In addition, the attribute tables are .dbf files found within each folder. These have also been provided as .xlsx (Excel) files which include the fault coordinate information, and slip rates and extension rates along the major faults. References McNeill, L.C., Shillington, D.J., Carter, G.D.O., and the Expedition 381 Participants, 2019a. Corinth Active Rift Development. Proceedings of the International Ocean Discovery Program, 381: College Station, TX (International Ocean Discovery Program). McNeill, L.C., Shillington, D.J., et al., 2019b, High-resolution record reveals climate-driven environmental and sedimentary changes in an active rift, Scientific Reports, 9, 3116. Nixon, C.W., McNeill, L.C., Bull, J.M., Bell, R.E., Gawthorpe, R.L., Henstock, T.J., Christodoulou, D., Ford, M., Taylor, B., Sakellariou, S. et al., 2016. Rapid spatiotemporal variations in rift structure during development of the Corinth Rift, central Greece. Tectonics, 35, 1225–1248. Taylor, B., J. R. Weiss, A. M. Goodliffe, M. Sachpazi, M. Laigle, and A. Hirn (2011), The structures, stratigraphy and evolution of the Gulf of Corinth Rift, Greece, Geophys. J. Int., 185(3), 1189–1219.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The Yangambi land cover change dataset covers an area of 7276km2 and mapped with both 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 change map was derived (2019). The map was 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 2421 verified land cover points based on the [up to] 14 aggregated land cover change 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|https://www.earth-system-science-data.net/]). 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 changeLand 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 classesThe 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 GIS database contains the data of aufeis (naled) in the Yana River basin (Russia) from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects). Historical data collection is created based on the Cadastre of aufeis (naled) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 381 aufeis with a total area 731.6 km² within the studied basin. Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2017. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season (e.g. between May, 15 and June, 18), to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 571, with their total area about 432 km². The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

The Upemba land cover change dataset covers an area of 47 318km2 and mapped with both 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 change map was derived (year 2019). The map was 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 3228 verified land cover points based on the [up to] 14 aggregated land cover change 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.920847

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.

Primary biodiversity records were queried from the Global Biodiversity Information Facility on January 30 and May 10, 2021 for plants (Plantae; https://doi.org/10.15468/dl.th5tn8; https://doi.org/10.15468/dl.76jc24), June 3, 2022 for birds (Aves; https://doi.org/10.15468/dl.jh3u2u), and August 23, 2021 for insects (Insecta; https://doi.org/10.15468/dl.4q2972), and mammals (Mammalia; https://doi.org/10.15468/dl.cujmgz). We then assessed the frequency of the geographic coordinates and identified the most frequently recurring sets of coordinates across each taxonomic group. Coordinates were assessed as provided in the “decimalLatitude” and “decimalLongitude” columns of the downloaded data without any rounding to be conservative. Rounding coordinates before assessing their frequency would increase the overall number of records associated with each set of coordinates and increase the risk of associating true points with georeferenced ones. Only exact matches were counted to calculate the frequency of each unique set of coordinates. We determined which of the highly-recurrent coordinates are likely artificial by examining metadata and images from datasets comprising over 40 million records to date; assessing spatial distributions of associated datasets; contacting data managers; and reviewing literature (Fig. 2). We used QGIS software to validate grid centroid coordinates by plotting the grid systems over the reported occurrence coordinates to confirm the grid centroid, grid size and the coordinate reference system. Countries represented in our dataset that utilized such grids were identified through occurrence record metadata, visual inspection of associated datasets, literature review, and data managers, and included France, the United Kingdom, Germany, the Netherlands, Belgium, Switzerland, and Spain. For each group, we started by evaluating the most recurrent set of coordinates and proceeded in order of decreasing frequency. We initially examined the top 100 recurring coordinates for plants and the top 50 recurring coordinates for each animal group. These coordinates were manually curated into the following categories when possible: grid centroid, geopolitical centroid, georeferenced location, and true observation or collection site. Some coordinates could be associated with multiple categories. It is possible that the determinations we made for highly-recurrent coordinates could also be extended to additional, less recurrent, coordinates that were assigned to other records in the datasets they belonged to (but not included in our initial survey). These data were compiled into AHOI, an inventory of highly-recurrent GBIF coordinates, with their descriptions and determinations. To validate our approach and assess whether artificial biodiversity hotspots are the result of systemic practices or errors, we additionally evaluated data from the Field Museum of Natural History, as some of the top 100 most recurring coordinates were associated with the institution. We downloaded all plant records from this dataset and evaluated all coordinates that were assigned to at least 1000 records. We found that the coordinates from this dataset represented artificial aggregates of specimens around geopolitical centroids. These verifications were also included in AHOI. Further, we listed the rationale for each individual coordinate determination and provides examples of relevant information from occurrence record metadata in the “example_description” and “reasoning” fields respectively. Aim: Species occurrence records are essential to understanding Earth’s biodiversity and addressing global environmental issues, but do not always reflect actual locations of occurrence. Certain geographic coordinates are assigned repeatedly to thousands of observation/collection records. This may result from imperfect data management and georeferencing practices, and can greatly bias the inferred distribution of biodiversity and associated environmental conditions. Nonetheless, these ‘biodiverse’ coordinates are often overlooked in taxon-centric studies, as they are identifiable only in aggregate across taxa and datasets, and it is difficult to determine their true circumstance without in-depth, focused investigation. Here we assess highly recurring coordinates in biodiversity data to determine artificial hotspots of occurrences. Location: Global Taxon: Land plants, birds, mammals, insects Methods: We identified highly recurring coordinates across plant, bird, insect, and mammal records in the Global Biodiversity Information Facility, the largest aggregator of biodiversity data. We determined which are likely artificial hotspots by examining metadata from over 40 million records; assessing spatial distributions of associated datasets; contacting data managers; and reviewing literature. These results were compiled into the Artificial Hotspot Occurrence Inventory (AHOI). Results...

This Python script (Shape2DJI_Pilot_KML.py) will scan a directory, find all the ESRI shapefiles (.shp), reproject to EPSG 4326 (geographic coordinate system WGS84 ellipsoid), create an output directory and make a new Keyhole Markup Language (.kml) file for every line or polygon found in the files. These new *.kml files are compatible with DJI Pilot 2 on the Smart Controller (e.g., for M300 RTK). The *.kml files created directly by ArcGIS or QGIS are not currently compatible with DJI Pilot.

This dataset demarcates the school district boundaries within Allegheny County If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Civic Vitality and Governance Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: current Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: none Other: none Related Document(s): Data Dictionary (none) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

This dataset contains weekly trajectory information of Gulf Stream Warm Core Rings from 2011-2020. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 282 WCRs present between January 1, 2011 and December 31, 2020. Each Warm Core Ring and is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2017 having a trailing alphabet of 'E' indicates that four rings were carried over from 2016 which were still observed on January 1, 2017. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km², and “Date” in days - representing the week since Jan 01, 0000.

The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2011-2020. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986).

Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.

Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.

QGIS Development Team. QGIS Geographic Information System (2016).

Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).

The GIS database contains the data of aufeis (naleds) in the Indigirka River basin (Russia) from historical and nowadays sources, and complete ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. All data and projects have WGS 1984 coordinate system (without projection). ArcGIS and Qgis projects contain two layers, such as Aufeis_kadastr (historical aufeis data collection, point objects) and Aufeis_Landsat (satellite-derived aufeis data collection, polygon objects).

Historical data collection is created based on the Cadastre of aufeis (naleds) of the North-East of the USSR (1958). Each aufeis was digitized as point feature by the inventory map (scale 1:2 000 000), or by topographic maps. Attributive data was obtained from the Cadastre of aufeis. According to the historical data, there were 896 aufeis with a total area 2063.6 km² within the studied basin.

Present-day aufeis dataset was created by Landsat-8 OLI images for the period 2013-2017. Each aufeis was delineated by satellite images as polygon. Cloud-free Landsat images are obtained immediately after snowmelt season (e.g. between May, 15 and June, 18), to detect the highest possible number of aufeis. Critical values of Normalized Difference Snow Index (NDSI) were used for semi-automated aufeis detection. However, a detailed expert-based verification was performed after automated procedure, to distinguish snow-covered areas from aufeis and cross-reference historical and satellite-based data collections. According to Landsat data, the number of aufeis reaches 1213, with their total area about 1287 km².

The difference between the Cadastre (1958) and the satellite-derived data may indicate significant changes of aufeis formation environments.

Detailed information about the methods can be found in the publication to which this dataset is a supplement.

This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings from 2000-2010. This work builds upon Silver et al. (2022a) ( https://doi.org/10.5281/zenodo.6436380) which contained Warm Core Ring trajectory information from 2011 to 2020. Combining the two datasets a total of 21 years of weekly Warm Core Ring trajectories can be obtained. An example of how to use such a dataset can be found in Silver et al. (2022b).

The format of the dataset is similar to that of Silver et al. (2022a), and the following description is adapted from their dataset. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 374 WCRs present between January 1, 2000 and December 31, 2010. Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2002 having a trailing alphabet of 'F' indicates that five rings were carried over from 2001 which were still observed on January 1, 2002. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km2, and “Date” in days - representing the days since Jan 01, 0000.

The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2000-2010. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986).

Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022a). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380

Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. Journal of Geophysical Research: Oceans, 127(8), e2022JC018737. https://doi.org/10.1029/2022JC018737

Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.

Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.

QGIS Development Team. QGIS Geographic Information System (2016).

Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).

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

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 dataset is a 10 m-resolution DEM in grid format covering the whole Italian territory. The DEM is encoded as “ESRI ASCII Raster” obtained by interpolating the original DEM in Triangular Irregular Network (TIN) format. The TIN version benefited from the systematic application of the DEST algorithm. The projection is UTM, the World Geodetic System 1984 (WGS 84). To provide the dataset as a single seamless DEM, the sole zone 32 N was selected, although about half of Italy belongs to zone 33 N. The database is arranged in 193 square tiles having 50 km side. Data e Risorse Questo dataset non ha dati ambiente terremoti vulcani

Study area

The Zwin is a nature reserve situated along the Belgian North Sea coast, northeast of Knokke, in the province of West-Flanders, Flanders, Belgium. The area is managed by the Flemish Agency for Nature and Forest and consists of a tidal marsh, coastal dunes with Ammophila arenaria, dune grasslands and/or shrub (Hippophae rhamnoides, Salix repens), and a transitional grassland zone that stretches from the inner edge of the coastal dunes into the polders.

Data collection

Data were collected by the Research Institute for Nature and Forest (INBO) with a fixed wing drone Gatewing X100 in 2014 and 2015 (15 flights). RGB data were acquired using an off-the-shelf Ricoh GR Digital IV camera, with the following image bands: 1: red, 2: green, 3: blue, 4: alpha channel. CIR (color-infrared) data were acquired using a NIR-enabled Ricoh GR Digital IV camera, with the following info bands: 1: NIR, 2: red, 3: green, 4: alpha channel.

Data processing

The raw data were processed to Digital Surface Models and orthophotos by the Flemish Institute for Technological Research (VITO) in 2017. Images with coarse GPS coordinates were imported and processed in Agisoft PhotoScan Pro 1.4.x, a structure-from-motion (SfM) based photogrammetry software program. After extraction and matching of tie points, a bundle adjustment leads to a sparse point cloud and a refined set of camera position and orientation values. Ground control points (either artificially installed markers on the terrain, or other photo-identifiable points, measured on the ground with RTK GNSS) were used to further refine the camera calibration and obtain a pixel-level georeferencing accuracy. From there, a point cloud densification and classification into ground and non-ground points was performed, leading to a rasterized digital surface model (DSM) and digital terrain model (DTM). Finally, a true orthomosaic was projected onto the DTM.

Coordinate reference system

All geospatial data have the coordinate reference system EPSG:31370 - Belgian Lambert 72.

Files

• Raw flight data: images and logs collected by the drone during flight. These files are zipped per flight, with the date (yyyymmdd) and flight number (x) indicated in the file name (flight_yyyymmdd_Zwin_x.zip).
• Processed data: Digital Surface Models (filename_DSM.tif) and orthophotos (filename_Ortho.tif) stitched together from the raw data. The included flights are indicated in the file name (e.g. 6 flights for 20150709_Zwin_1-3_20150710_Zwin_1-3_DSM.tif).
• Ground control points: fixed ground control points (GCP) were placed on 2014-04-07, coordinates of which are available in GCP_20140407_Zwin_fixed.tsv. These GCPs are visible (but fading over time) in all orthophotos except 20151012_Zwin_1-4_Ortho.tif which covers a different area. Additional temporary GCPs were placed on 2014-04-07, 2014-04-10 and 2015-07-09 (visible in orthophotos of those dates), coordinates of which are available in the respective GCP_yyyymmdd_Zwin.tsv file.

Cloud Optimized GeoTIFF

The most efficient way to explore the processed data is by loading the Cloud Optimized GeoTIFFs we created for each processed file. Copy one of the file URLs below and follow e.g. the QGIS tutorial to load this type of file.

• http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140407_Zwin_1-2_DSM.tif
• http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140407_Zwin_1-2_Ortho.tif CIR
• http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140410_Zwin_1-3_DSM.tif
• http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20140410_Zwin_1-3_Ortho.tif CIR
• http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150709_Zwin_1-3_20150710_Zwin_1-3_DSM.tif
• http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20150709_Zwin_1-3_20150710_Zwin_1-3_Ortho.tif RGB
• http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151012_Zwin_1-4_DSM.tif
• http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20151012_Zwin_1-4_Ortho.tif RGB

See this page for an overview of public INBO RPAS data.