ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate. Monitoring Stations - shapefile with approximate locations of monitoring stations. DSM2 v8.2.0, calibrated version: dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map. dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map. dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map. dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages: dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes dsm2_8_2_0_calibrated_nodes - DSM2 nodes dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2 dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2 dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2 DSM2 v8.2.1, historical version: DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022 DSM2 v8.2.1, historical version grid map, single zoom level (PDF) DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper. DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology. DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map. Change Log 7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

Tracking an animal's location from video has many applications, from providing information on health and welfare to validating sensor-based technologies. Typically, accurate location estimation from video is achieved using cameras with overhead (top-down) views, but structural and financial limitations may require mounting cameras at other angles. We describe a user-friendly solution to manually extract an animal's location from non-overhead video. Our method uses QGIS, an open-source geographic information system, to: (1) assign facility-based coordinates to pixel coordinates in non-overhead frames; 2) use the referenced coordinates to transform the non-overhead frames to an overhead view; and 3) determine facility-based x, y coordinates of animals from the transformed frames. Using this method, we could determine an object's facility-based x, y coordinates with an accuracy of 0.13 ± 0.09 m (mean ± SD; range: 0.01–0.47 m) when compared to the ground truth (coordinates manually recorded with a laser tape measurer). We demonstrate how this method can be used to answer research questions about space-use behaviors in captive animals, using 6 ewe-lamb pairs housed in a group pen. As predicted, we found that lambs maintained closer proximity to their dam compared to other ewes in the group and lamb-dam range sizes were strongly correlated. However, the distance traveled by lambs and their dams did not correlate, suggesting that activity levels differed within the pair. This method demonstrates how user-friendly, open-source GIS tools can be used to accurately estimate animal location and derive space-use behaviors from non-overhead video frames. This method will expand capacity to obtain spatial data from animals in facilities where it is not possible to mount cameras overhead.

Questions we asked in the Georeferencing for Research Follow Up Survey done 3 months after the workshop.

Georeferencing the "Atlas du plan général de la ville de Paris par Edme Verniquet" Géoréférencement de l'Atlas du plan général de la ville de Paris par Edme Verniquet This dataset contains the necessary data control points to georeference the "Atlas du plan général de la ville de Paris par Edme Verniquet" based on 2 different versions of the atlas: one digitized by the Bibliothèque nationale de France (BnF) and the other by The David Rumsey Historical Map Collection. The dataset contains the control points in QGIS format (.points files) and as Allmaps georeference annotations. It also contains the georeferenced map sheets as geotiff.

This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Dataset includes individually classified scene masks for Colville (2014), Kolyma (2014), Lena (2016), Mackenzie (2014), Yenisei (2013), and Yukon (2014). We also provide .mat files for each delta that include a 2D array of the mosaicked images that is cropped to include only the area used in our analyses (see Piliouras and Rowland, 2020, Journal of Geophysical Research - Earth Surface), as well as the X (easting) and Y (northing) arrays for georeferencing, with coordinates in UTMs.

This dataset was generated by the TU Wien Department of Geodesy and Geoinformation. European Sentinel-1 forest type and tree cover density maps represent first continental-scale forest layers based on Sentinel-1 C-Band Synthetic Aperture Radar (SAR) backscatter data. For the year 2017 they cover the majority of European continent with 10 m and 100 m sampling for forest type and tree cover density, respectively. The maps were derived using the method described in https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1479788. The forest type map shows the dominant forest type class (coniferous, broadleaf). Tree cover density map shows the percentage of forest canopy cover within the 100 m pixel. Please be referred to our peer-reviewed article at https://doi.org/10.3390/rs13030337 for details and accuracy assessment accross Europe. Dataset Record The forest type and tree cover density maps are sampled at 10 m and 100 m pixel spacing respectively, georeferenced to the Equi7Grid and divided into square tiles of 100km extent ("T1"-tiles). With this setup, the forest maps consist of 728 tiles over the European continent, with data volumes of 3.12 GB and 378.3 MB. The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given. In this repository, we provide each forest map as tiles, whereas two zipped dataset-collections are available for download below. Code Availability For the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629. Acknowledgements The computational results presented have been achieved using the Vienna Scientific Cluster (VSC).

The Digital Geologic-GIS Map of Yosemite Valley Glacial and Postglacial Deposits, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (yova_glacial_and_surficial_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (yova_glacial_and_surficial_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (yova_glacial_and_surficial_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (yose_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (yova_glacial_and_surficial_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (yova_glacial_and_surficial_geology_metadata_faq.pdf). Please read the yose_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (yova_glacial_and_surficial_geology_metadata.txt or yova_glacial_and_surficial_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The data sets include 10 scanned historical maps (Plan 1-9 and 11) from 1920 of the region of Grosses Moos in Switzerland. The maps have been drawn by different surveyors with the plane table principle in 1920 represented with a map including topographical information such as contour lines, water bodies and houses and other urban infrastructures. In addition, the original measurement points were marked with a grid of 20-30m spacing and additional points if needed. These individual points were digitized by first georeferencing the individual maps with QGIS and then digitising each single measurement point along with their respective recorded heights. 44319 points were digites in LV03 (Swiss coordinate system) with the corresponding height (stored in the file "HoehendatenPunktwolke_Ins_1920.geojson").

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 dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

The Digital Geologic-GIS Map of Black Canyon of the Gunnison River and Vicinity, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (blcg_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (blcg_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (blcg_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (blca-cure_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (blca-cure_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (blcg_geology_metadata_faq.pdf). Please read the blca-cure_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (blcg_geology_metadata.txt or blcg_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:31,680 and United States National Map Accuracy Standards features are within (horizontally) 16.1 meters or 52.8 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.

Updated quarterly, the Landgate Basemap is comprised of simplified cadastre, topographic and road centreline information, and is the perfect backdrop to provide context for projects that require commonly used underlying WA centric location information. The Landgate Basemap provides a stylized (familiar ‘StreetSmart’ style ) layout, current, geo-referenced and view only map base. This is a view only service (i.e no data download capability) and can be viewed in combination with Landgate’s other subscription datasets, SLIP public datasets and other geo-referenced data. Designed for use within GIS and online mapping applications, the tile cached Basemap service introduces faster panning and redrawing of location information commonly used across many sectors. Key information • WA centric basemap comprising commonly used Landgate location information • cached map tiles • ESRI cache map service and WMTS (web map tile service) - publishes in WGS84 only • Update cycle: quarterly • Coverage: whole of state (includes Christmas and Cocos Keeling Islands) • QGIS 2.18 minimum required for WMTS usage. © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions. For more information and access to Subscription Services contact Landgate's Business Sales and Service team. Email: BusinessSolutions@landgate.wa.gov.au Services Note, the following services require 3rd party software that supports OGC Standards and Esri services.

The Digital Geologic-GIS Map of Big Cypress National Preserve and Vicinity, Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (bicy_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (bicy_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (bicy_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (bicy_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (bicy_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (bicy_geology_metadata_faq.pdf). Please read the bicy_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Florida Geological Survey, U.S. Geological Survey and Earthfx Incorporated/BEM Systems Inc.. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (bicy_geology_metadata.txt or bicy_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:675,000 and United States National Map Accuracy Standards features are within (horizontally) 342.9 meters or 1125 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This layer shows soils in south Karamoja in 1959. The dataset is from the European Digital Archives of Soil Maps(EuDASM) 2005 at http://eusoils.jrc.ec.europa.eu/esdb_archive/eudasm/africa/images/maps/download/afr_ug3004_1so.jpg. The map was georeferenced using QGIS and reprojected to WGS84.

Example RGB and Multispectral data for processing with Agisoft Metashape and QGIS to create georeferenced orthomosaics and classifications.

Polygon dataset showing the 6 counties of Northern Ireland e.g. County Armagh, County Tyrone etc which were the primary local government geography of Northern Ireland before the introduction of unitary authorities in 1972. A PNG map showing the Northern Ireland county boundaries was downloaded from wikipedia: http://en.wikipedia.org/wiki/File:Northern_Ireland_-_Counties.png The PNG was georeferenced in QGIS using control points with reference to an OGL dataset downloaded from the UK Data Service showing the Northern Ireland coastline. Internal county boundaries were digitised from the georeferenced PNG as a set of polylines. These polylines were then snapped to the coastline features and polygons were generated. A county name was then assigned to each polygon in the attribute table. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-02-24 and migrated to Edinburgh DataShare on 2017-02-22.

Abstract

The dataset is a geodatabase focusing on the distribution of freshwater fish species in Northern Greece. The study area encompasses various lakes and rivers within the regions of Thrace, Eastern, Central, and Western Macedonia, and Epirus. It classifies fish species into three categories based on their conservation status according to the IUCN Red List: Critically Endangered, Endangered, and Vulnerable. The data analysis reveals that the study area is characterized by high fish diversity, particularly in certain ecosystems such as the Evros River, Strymonas River, Aliakmonas River, Axios River, Volvi Lake, Nestos River, and Prespa Lake. These ecosystems serve as important habitats for various fish species. Mapping of the dataset shows the geographic distribution of threatened fish species, indicating that Northern Greece is a hotspot for species facing extinction risks. Overall, the dataset provides valuable insights for researchers, policymakers, and conservationists in understanding the status of fish fauna in Northern Greece and developing strategies for the protection and preservation of these important ecosystems.

Methods

Data Collection: The dataset was collected through a combination of field surveys, literature reviews, and the compilation of existing data from various reliable sources. Here's an overview of how the dataset was collected and processed:

Freshwater Fishes and Lampreys of Greece: An Annotated Checklist

The Red Book of Endangered Animals of Greece

The "Red List of Threatened Species"

The study "Monitoring and Evaluation of the Conservation Status of Fish Fauna Species of Community Interest in Greece"

The international online fish database FishBase

Data Digitization and Georeferencing: To create a comprehensive database, we digitized and georeferenced the collected data from various sources. This involved converting information from papers, reports, and surveys into digital formats and associating them with specific geographic coordinates. Georeferencing allowed us to map the distribution of fish species within the study area accurately.

Data Integration: The digitized and georeferenced data were then integrated into a unified geodatabase. The geodatabase is a central repository that contains both spatial and descriptive data, facilitating further analysis and interpretation of the dataset.

Data Analysis: We analyzed the collected data to assess the distribution of fish species in Northern Greece, evaluate their conservation status according to the IUCN Red List categories, and identify the threats they face in their respective ecosystems. The analysis involved spatial mapping to visualize the distribution patterns of threatened fish species.

Data Validation: To ensure the accuracy and reliability of the dataset, we cross-referenced the information from different sources and validated it against known facts about the species and their habitats. This process helped to eliminate any discrepancies or errors in the dataset.

Interpretation and Findings: Finally, we interpreted the analyzed data and derived key findings about the diversity and conservation status of freshwater fish species in Northern Greece. The results were presented in the research paper, along with maps and visualizations to communicate the spatial patterns effectively.

Overall, the dataset represents a comprehensive and well-processed collection of information about fish fauna in the study area. It combines both spatial and descriptive data, providing valuable insights for understanding the distribution and conservation needs of freshwater fish populations in Northern Greece.

Usage notes

The data included with the submission is stored in a geodatabase format, specifically an ESRI Geodatabase (.gdb). A geodatabase is a container that can hold various types of geospatial data, including feature classes, attribute tables, and raster datasets. It provides a structured and organized way to store and manage geographic information.

To open and work with the geodatabase, you will need GIS software that supports ESRI Geodatabase formats. The primary software for accessing and manipulating ESRI Geodatabases is ESRI ArcGIS, which is a proprietary GIS software suite. However, there are open-source alternatives available that can also work with Geodatabase files.

Open-source software such as QGIS has support for reading and interacting with Geodatabase files. By using QGIS, you can access the data stored in the geodatabase and perform various geospatial analyses and visualizations. QGIS is a powerful and widely used open-source Geographic Information System that provides similar functionality to ESRI ArcGIS.

For tabular data within the geodatabase, you can export the tables as CSV files and open them with software like Microsoft Excel or the open-source alternative, LibreOffice Calc, for further analysis and manipulation.

Overall, the data provided in the submission is in a geodatabase format, and you can use ESRI ArcGIS or open-source alternatives like QGIS to access and work with the geospatial data it contains.

The folder contains 3 shapefiles usable in GIS (geographic information system). These data result from the processing of the french map of devastated regions ("carte des régions dévastées"). The map was edited in 1920 by the geographic service of French army. The objective was to classify lands depending on the intensity of destruction, and to locate areas where substantial restoration work was necessary. The 47 map sheets of the collection at scale 1:50,000 have been scanned and can be obtained from the National Geographic Institute (IGN) in .jpg format. The map shows large red-colored zones representing heavily damaged front-line area by trenches and bombing according to the map legend. There are also red-hatched features locating destroyed cities, roads and destroyed or cut forests. The blue-colored symbols show new constructions, such as memorials and cemeteries. For the methodology of georeferencing, classification and vectorization, see Nelly Paradelle, Marianne Laslier, Guillaume DeCocq, "Automatic extraction of former WWI battlefields from ancient maps," Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 127270H (17 October 2023); https://doi.org/10.1117/12.2684009

-MANUAL_ENVELOPE.shp : This dataset contains the envelope bordering the local destructions from the dataset "RED POLYGONS", and drawn manually within QGIS.

-RED_POLYGONS.shp : This dataset contains only polygons of local destructions (cities, roads, buildings, destroyed or cut forests etc.) extracted from the map of devastated regions

-RED_ZONE.shp : This dataset contains only polygons of the large red-colored areas representing heavily damaged front-line area by trenches and bombing extracted from the map of devastated regions

Files with extension .qmd provide metadata.

A robust and comprehensive assessment of intrinsic aquifer vulnerability at continental scale map may represent an essential initial step towards a more sustainable land-use and water management.

This repository contains the outcomes of an intrinsic aquifer vulnerability assessment of South America, performed by the DRASTIC method. The assets included in this repository are mainly raster maps (.tif, .geotif), created and georeferenced in QGIS (v3.16). Coordinate reference system (CRS) of the dataset is WGS84.

Technical specifications of all graphical outcomes are stored in a dedicated file (README.txt).