Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

Wildfires are a common and dangerous threat for California, with most notable past wildfires like the Medocino Complex fire in 2018, and the August Complex fire in 2020. With the immediate threat of the fire, the need to evacuate civilians and get the fire under control is of utmost importance during the event. What about the risks associated after the fire like air quality? Air quality effects everyone in the US, but most notably at-risk populations with dispositions like asthma, cardiac diseases and respiratory diseases, with children and the elderly at the highest risk. These at-risk populations are at greater risk of severe health effects during and following wildfire events due to the impacts of air quality in the region. The objective of my research was the communication of wildfire vs. air quality risk on Twitter during and after two of California's largest recent fire events: Mendocino Complex and August Complex fires through CALFire's twitter, but also air quality risk alerts during those fires and even weeks afterwards to find how much communication is given for air quality to the public.

The Digital Bedrock Geologic-GIS Map of Minuteman National Historical Site and Vicinity, Massachusetts 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 (mima_bedrock_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 and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (mima_bedrock_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.) this file (mima_geology.gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (mima_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 (mima_bedrock_geology_metadata_faq.pdf). Please read the mima_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: http://www.google.com/earth/index.html. 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: Boston College and 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 (mima_bedrock_geology_metadata.txt or mima_bedrock_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) 25.4 meters or 83.3 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 dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

The Digital Geologic-GIS Map of Yellowstone National Park and Vicinity, Wyoming, Montana, and Idaho is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (yell_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 3.X map file (.mapx) file (yell_geology.mapx) and individual Pro 3.X layer (.lyrx) 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 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 (yell_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (yell_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 (yell_geology_metadata_faq.pdf). Also included is a zip containing a Montana State University Master's thesis and supporting documents and data. The thesis focuses on addressing map boundary inconsistencies and remapping portions of the park. Data and documents supporting the thesis are 1.) a geodatabase containing field data points, 2.) a collection of documents describing field sites, 3.) spreadsheets containing geochemical analysis results, and 4.) photographs taken during field work. Please read the yell_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: 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, Montana Bureau of Mines and Geology and Montana State University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (yell_geology_metadata.txt or yell_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 1:125,000 and United States National Map Accuracy Standards features are within (horizontally) 63.5 meters or 208.3 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 Pro, 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 dataset contains a land cover map of Poitiers in 1993 over an area of 225km².

The land cover map was achieved using aerial images of the French National Geographic Institute (IGN) and Landsat-5 TM images combined with remote sensing methods. Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classifications produced a reliable land cover map at a 1m of spatial resolution.

Orthophotos produced as well as training and validating polygons to achieve the classifications were added into this dataset.

As land cover changes is crucial to land management, this map will help to understand changes from 1993 to now for urban, agricultural issues but also their impact on ecological processes. Data will be easily used in GIS applications for any users.

This work is part of the thesis of Elie Morin which was funded by la région Nouvelle-Aquitaine and Grand Poitiers Communauté urbaine, among others.

The Digital Geologic-GIS Map of Knife River Indian Villages National Historic Site and Vicinity, North Dakota is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (knri_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 3.X map file (.mapx) file (knri_geology.mapx) and individual Pro 3.X layer (.lyrx) 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 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 (knri_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (knri_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 (knri_geology_metadata_faq.pdf). Please read the knri_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: 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: University of North Dakota, Department of Anthropology and Archeology. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (knri_geology_metadata.txt or knri_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 Pro, 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 dataset is a supplement for P.E. Stek's PhD Thesis project titled "The Development of Technology Cluster InnovationPerformance: Health and Sustainable Energy" (January 2022). The dataset covers approximately 20 high technology sectors and is useful for comparative technology sector analysis. The patent distance data used to measure the effectiveness of the cluster identification method is also included.

This data contains the dates and locations of various instances of resistance and opposition to the Ku Klux Klan in the 1920s. It also includes a description of each event, as well as who was behind it. Links to the newspaper source are included too, along with the name of the newspaper archive from which each event is taken

Metadata for PhD, MSc, BSc(Hons) and PGDipSci theses completed at the Geology Department, University of Otago. Polygon or multipolygon for mapped or studied area.

Most, but not all, mapped / studied areas are shown in this database.

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Access to these theses is available via the university library ( www.otago.ac.nz/library/ ) or the geology department thesis library, Room 1n16, geology building (contact: geology@otago.ac.nz, ph + 64 3 479 7519).

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Most theses from recent years are available https://ourarchive.otago.ac.nz/handle/10523/93 (University of Otago database) and http://theses.otagogeology.org.nz/ (Department of Geology database)

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A more up to date database of Otago Geology theses is available at https://otago.maps.arcgis.com/apps/dashboards/1400eecc1cf84378b2b14d3c040f2482

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Updated July 2021

This dataset contains raw GIS data sourced from the BAG (Basisregistratie Adressen en Gebouwen; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: https://github.com/simonvanlierde/msc-thesis-ie The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High) Background research data The background_research_data.xlsx spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:

Cooling Technologies: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis. LCA Results of Ventilation Systems: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment. Material Scarcity: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment. Heat Plans per Neighbourhood: Forecasts of future heating solutions for each neighbourhood in The Hague. Building Stock: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022. Climate Change: Computations of climate-related parameters based on KNMI climate scenarios. Electricity Mix Analysis: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid. Input data Geographic divisions

The outline of The Hague municipality through the Municipal boundaries (Gemeenten) layer, sourced from the Administrative boundaries (Bestuurlijke Gemeenten) dataset on the PDOK WFS service. District (Wijken) and Neighbourhood (Buurten) layers were downloaded from the PDOK WFS service (from the CBS Wijken en Buurten 2022 data package) and clipped to the outline of The Hague. The 4-digit postcodes layer was downloaded from PDOK WFS service (CBS Postcode4 statistieken 2020) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file. The census block layer was downloaded from the PDOK WFS service (from the CBS Vierkantstatistieken 100m 2021 data package) and also clipped to the outline of The Hague. These layers have been combined in the GeographicDivisions_TheHague GeoPackage. BAG data

BAG data was acquired through the download of a BAG GeoPackage from the BAG ATOM download page. In the resulting GeoPackage, the Residences (Verblijfsobject) and Building (Pand) layers were clipped to match The Hague's outline. The resulting residence data can be found in the BAG_buildings_TheHague GeoPackage. 3D BAG

Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the 3D BAG website. These GeoPackages were merged using the ogr2ogr append function from the GDAL library in bash. Roof elevation data was extracted from the LoD 1.2 2D layer from the resulting GeoPackage. Ground elevation data was obtained from the Pand layer. Both of these layers were clipped to match The Hague's outline. Roof and ground elevation data from the LoD 1.2 2D and Pand layers were joined to the Pand layer in the BAG dataset using the BAG ID of each building. The resulting data can be found in the BAG_buildings_TheHague GeoPackage. Energy labels

Energy labels were downloaded from the Energy label registry (EP-online) and stored in energy_labels_TheNetherlands.csv. UHI effect data

A bitmap with the UHI effect intensity in The Hague was retrieved from the from the Dutch Natural Capital Atlas (Atlas Natuurlijk Kapitaal) and stored in UHI_effect_TheHague.tiff. Output data

The residence-level data joined to the building layer is contained in the BAG_buildings_with_residence_data_full GeoPackage. The results for each building, according to different scenarios, are compiled in the buildings_with_CDM_results_[scenario]_full GeoPackages. The scenarios are abbreviated as follows:

SQ: Status Quo, covering the 2018-2022 reference period. 2030: An average scenario projected for the year 2030. 2050_L: A low-impact, best-case scenario for 2050. 2050_M: A medium-impact, moderate scenario for 2050. 2050_H: A high-impact, worst-case scenario for 2050.

Raw data used in MSc Thesis. Available for reproducing methodology

The longevity of the USGS/NASA Landsat Program (1972-present) allows researchers to track long-term phenological trends (Melaas et al 2018; Zhu et al. 2016; Park et al. 2021).Satellite imagery is a useful tool in monitoring vegetation health over large areas with the use of near infrared (NIR) light (Forkel et al. 2013; Eastman et al. 2013).

Diploma thesis at Johannes Kepler University Linz Rottenmann In order to realise a continuous digital data flow for the GIS-based alpine assessment model already introduced in the Gesäuse National Park, it seemed to the responsible head of the Nature Conservation and Nature Area Mag. MSc. Daniel Kreiner is useful to replace the usual procedure for analog data acquisition with pencil and paper with the use of modern adequate technology. As part of a thesis, a mobile database-based recording system was to be implemented for the Nationalpark Gesäuse GmbH. The resulting data must be compared with an Access database on the PC.

A commercial PDA should be used as a mobile detection device. The client’s main focus was on the use of modern and future-oriented technologies. Furthermore, it had to be built on the already introduced and correspondingly extensive Microsoft Access database. No structural changes could be made to this, as the company’s entire applications access this central database. As a central point in development, the best possible adaptation of the application to the previous recording processes was seen in order to promote acceptance by the user and on the other hand to obtain a product that can be used as intuitively as possible. The user interface should be very clearly structured. A thematic grouping of the recording parameters as in the analogue recording sheets was required. Furthermore, a mandatory sequential recording of the individual parameters should be avoided in order to give the user the greatest possible degree of flexibility.

As a relief for the user, a possibility of positioning was required. In order to be able to orient yourself locally, the current position should be displayed on orthophotos available in the company.

Urban sprawl and associated road infrastructure are leading causes of increased habitat loss and fragmentation in cities across the United States (Elmqvist, Zipperer and Güneralp 2016). Habitat fragmentation disrupts animal migration patterns, territories, and mating behaviors and increases human-wildlife conflict, such as wildlife-vehicle collisions and attacks from large mammals (Acharya et al. 2017, Elmqvist, Zipperer and Güneralp 2016, Vartan 2019).

This story map is centered around a spatial comparison of the 1968 and 2020 uprisings that occurred in Louisville, Kentucky.Created by Noah Hayes | Mentored by Dr. Carrie MottA senior thesis project submitted to the Department of Geography and Geosciences University of Louisville | Louisville, KY | April 2022

This data is from the master's thesis titled- Repeatable methods for classification of alien and native vegetation in the Montane grasslands. Figure 2.6 in the thesis is the result of this dataset. The dataset includes a shapefile of the South African boundary; a GeoTIFF generated from Google Earth Engine containing the classified map of the study area; as well as a QGIS file containing the final map produced for Figure 2.6.Date of data collection: February 2020.Location of data collection: Blyde River Conservancy and its surrounds, in Mpumalanga/Limpopo Provinces, South Africa.

The shared archived combined in Supplementary Datasets represent the actual databases used in the investigation considered in two papers:

Meteorological conditions affecting black vulture (Coragyps atratus) soaring behavior in the southeast of Brazil: Implications for bird strike abatement (in submission)

Remote sensing applications for abating the aircraft-bird strike risks in the southeast of Brazil (Human-Wildlife Interactions Journal, in print)

The papers were based on my Master’s thesis defended in 2016 in the Institute of Biology of the University of Campinas (UNICAMP) in partial fulfilment of the requirements for the degree of Master in Ecology. Our investigation was devoted to reducing the risk of aircraft collision with Black vultures. It had two parts considered in these two papers. In the first one we studied the relationship between soaring activity of Black vultures and meteorological characteristics. In the second one we explored the dependence of soaring activity of vultures on superficial and anthropogenic characteristics. The study was implemented within surroundings of two airports in the southeast of Brazil taken as case studies. We developed the methodological approaches combining application of GIS and remote sensing technologies for data processing, which were used as the main research instrument. By dint of them we joined in the georeferenced databases (shapefiles) the data of bird's observation and three types of environmental factors: (i) meteorological characteristics collected together with the bird’s observation, (ii) superficial parameters (relief and surface temperature) obtained from the products of ASTER imagery; (iii) parameters of surface covering and anthropogenic pressure obtained from the satellite images of high resolution. Based on the analyses of the georeferenced databases, the relationship between soaring activity of vultures and environmental factors was studied; the behavioral patterns of vultures in soaring flight were revealed; the landscape types highly attractive for this species and forming the increased concentration of birds over them were detected; the maps giving a numerical estimation of hazard of bird strike events over the airport vicinities were constructed; the practical recommendations devoted to decrease the risk of collisions with vultures and other bird species were formulated.

This archive contains all materials elaborated and used for the study, including the GIS database for two papers, remote sensing data, and Microsoft Excel datasets. You can find the description of supplementary files in the Description of Supplementary Dataset.docx. The links on supplementary files and their attribution to the text of papers are considered in the Attribution to the text of papers.docx. The supplementary files are in the folders Datasets, GIS_others, GIS_Raster, GIS_Shape.

For any question please write me on this email: natalieenov@gmail.com

Natalia Novoselova

Datasets generated during and after Jabari Jones' Master's thesis at Utah State University, focused on channel change of Sixth Water Creek and Diamond Fork River, Utah, USA (Jones, J.C., 2018. Historical channel change caused by a century of flow alteration on Sixth Water Creek and Diamond Fork River, UT. Master's thesis, Utah State University). This resource includes data collected in the field as well as data generated in GIS. Field data include cross-section surveys, RTK GPS surveys, sediment transport measurements, bed grain size analysis, and unmanned aerial vehicle (drone) photography. GIS data include shapefiles generated from aerial imagery, digital elevation models, and data generated to evaluate incision of the Sixth Water valley. Data were collected and generated between July 2016 and November 2021 All data, metadata and related materials meet the quality standards relative to the purpose for which they were collected and generated.

Data added to this updated resource include channel width measurements from 2018 aerial photographs, regional width analysis from 2018 aerial photographs, and an analysis of incision in the Sixth Water and Upper Diamond Fork valleys.

These files consist of GIS and vegetation plot data examined in our paper "Ongoing fen-bog transition in a boreal aapa mire inferred from repeated field sampling, aerial images, and Landsat data" (manuscript, Ecosystems).

Mahlaneva mire GIS data includes eight data sets. Flark_pools_Tolonen_1959 is the flark pool delineation of 1959 in the special study area, based on Professor Kimmo Tolonen’s (1959) thesis. Flarks_1947, Flarks_1988, Flarks_1997, and Flarks_2019 are the flark boundaries in those years according to aerial image classification. Mire_catchment is the topographic catchment of the study mire. Mire_parts_and_reference_sites includes delineations of the vegetation types (flark fen, flark fen special area, and Sphagnum mire) in the study area, as well as the reference sites (raised bog and coniferous forest). These delineations follow the Landsat pixel boundaries. The file Study_area_Landsat_pixels includes the Landsat pixel delineations in the study area.

File "Vegetation_plots_1959_2018" consist of vegetation plot data from the transect A surveyed in 1959 and 2018. In 2018, plant cover was estimated along two contiguous transects (A0 and A1) to consider the possible inaccuracy in the relocation.