It includes data that support the findings of the study (GIScience Faculty Mobility)

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.

The two files is the csv version of PhD Origin and Current Affiliation of Global GIS Faculty

Hi, I'm Patrick,I initially pursued an undergraduate degree in Computer Science because I wanted to make video games; however, after taking an Environmental Science course, I wanted to see if there was a way I could study both. This led me to GIS and I made that my specialism, doing a Masters and later PhD on the subject.

This record serves as a specification of a pre-configured subset of the tables in the Geospatial Fabric Attribute Tables for PRMS (Preliminary)data set(https://www.sciencebase.gov/catalog/folder/537a3ec1e4b0efa8af08150a). This subset provides an initial set of values corresponding to many of the spatial parameters needed for application of the USGS PRMS watershed model. This subset should be considered provisional. Users should carry out quality assurance and calibration according to their needs. soils parameters: http://dx.doi.org/doi:10.5066/F7RX9937 land cover parameters: http://dx.doi.org/doi:10.5066/F7N58JD8 topographic parameters: http://dx.doi.org/doi:10.5066/F70C4ST6 geography parameters: http://dx.doi.org/doi:10.5066/F7HD7SPJ subsurface flux parameters: http://dx.doi.org/doi:10.5066/F7CN71XR surface depression parameters: http://dx.doi.org/doi:10.5066/F7445JHG

The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature class and its derivation is provided within the

Count of high school graduates for each public school in Alaska. Data covers the School Year 2013 to the present. Each year's count includes students graduating at any point during the school year (July 1 to June 30).Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center.

This dataset consists of point data from a GPS survey of the quarry at Casey in September 1998 by Jonny Stark, a PhD student with the Australian Antarctic Division. Jonny was assisted by Ian Snape. The data includes locations of the extent of the quarry and its working face and the nearest bird nests. The data is shown on map number 12221 in the SCAR Map Catalogue which can be searched via the provided URL.

NSF grant #ARC-0806506: "Modeling patch-scale expansion of shrubs." Dr. David M. Cairns is the Principal Investigator and is a Professor of Geography in the Department of Geography at Texas A&M University Adam T. Naito is a PhD candidate in the Department of Geography at Texas A&M University, working under the supervision of Dr. Cairns. The expansion of shrubs in the northern Brooks Range and North Slope uplands is a phenomenon that has occurred over much of the 20th century. Its historic spatial characteristics and environmental influences, however, remain understudied. Naito's dissertation research addresses this issue in two ways by developing: 1) time-series maps of historic shrub expansion based upon vertical aerial photographs (acquired from the United States Geological Survey (USGS) Earth Resources and Observing System (EROS)) and high-resolution satellite imagery, and 2) a spatially-explicit, stochastic simulation model that simulates shrub expansion based upon parameters relating to topographically-derived hydrological characteristics, shrub growth rates, and reproductive characteristics. Analysis of the model output results will foster hypothesis generation regarding environmental influences on shrub expansion. This archive contains compressed GeoTIFFs of the time-series maps of shrub expansion created for the grant award noted above. These are suitable for display in any GIS that will recognize GeoTIFF format files.These maps were generated using an semi-automatic unsupervised ISODATA classification of historic black-and-white, color-infrared, and high-resolution satellite imagery (IKONOS/GeoEye/QuickBird). Classification was manually corrected to remove shadows, non-shrub features, etc. that may have been classified as shrub.

Arc-GIS 10.X toolbox for a semi-automated extraction of paleo-shorelines from high-resolution DEMs

These CSV files contain data extracted from the “PhD Origin and Current Affiliation of Global GIS Faculty” dataset, filtered by continent. They are prepared for creating network graphs in Gephi, including one global network graph and four continental network graphs. The focus is on faculty relationships between their PhD origin and current affiliations across different continents.

This delivery represents the final products from a two year post-doctoral research project that was carried out by a consortium led by Durham University.

This repository includes a graphical and analytical information of ordinary collective buildings (mainly layouts) of the city of Porto, Portugal.

The source of the information is the PhD thesis of the author Gisela Lameira (https://hdl.handle.net/10216/109984), in which the original plans found in the archives of the services of Porto municipality were redrawn.

This is very important for studying in PhD

My name is Kerim, I am a PhD candidate for Civil and Environmental Engineering, and I am both American and British! My research focuses on water resources, specifically the transfer of water between basins in the United States, and leans on GIS resources heavily. My email address is kedickso@andrew.cmu.edu. I enjoy quite a few activities outside of academics, ranging from computer gaming and old-school pen and paper roleplaying games to archery and soccer!

The Beyoglu Preservation Area Building Features Database. A large and quite comprehensive GIS database was constructed in order to implement the data mining analysis, based mainly on the traditional thematic maps of the Master Plan for the Beyoğlu Preservation Area. This database consists of 45 spatial and non-spatial features attributed to the 11,984 buildings located in the Beyoğlu Preservation Area and it is one of the original outputs of the PhD Thesis entitled "A Knowledge Discovery Approach to Urban Analysis: The Beyoglu Preservation Area as a data mine".

This dataset was developed as part of a PhD research work titled 'Multi-dimensional description of the environment at the territorial scale : contribution to researching environmental determinants in the etiology of chronic diseases' (Paumelle, 2023). This work also aligns with two research projects: (i) ‘Ecologic historic and environnemental influence in Crohn Disease' funded by the French National Research Agency (CROPS/ANR-20-CE34-0015); and (ii) ‘Health and Environment: from territorial to individual risk' funded by the I-SITE ULNE Foundation (R-PILOTE-01 SANTE et ENVIRONNEMENT project). Finally, this dataset was notably used to develop a territorial typology and define the environmental profiles of municipalities in Northern France. The associated publication, is as follows: Paumelle et al., 2023. The dataset provides an integrated description of the physical and outdoor environment at the municipal level in four French departments: Nord (59), Pas-de-Calais (62), Somme (80), and Seine-Maritime (76). In total, the dataset incorporates 109 spatial indicators characterizing the environmental dimensions of the 3,041 municipalities within the study area. The administrative boundaries used are based on the 2016 edition of the French National Institute of Geographic and Forest Information (IGN). The source databases used are exclusively open-access. A total of 23 different databases were used. Regardless of their original format, these datasets were processed to generate spatial indicators at the municipal scale. In the final dataset, three types of indicators can be distinguished: (i) indicators integrated as-is without significant manipulation of raw data; (ii) indicators requiring simple normalization steps (e.g., scaling by municipal surface area or population); and (iii) indicators calculated through more complex processing. These operations were performed using QGIS (QGIS Development Team, 2021) or the R software (R Core Team, 2021), depending on the complexity. This dataset provides an integrated description of the physical environment through 109 spatial indicators divided into seven sub-dimensions: (i) contamination levels of environmental media (air, water, soil); (ii) pollutant emission levels; (iii) proximity to emission sources; (iv) land use; (v) agricultural practices; (vi) territorial naturalness; (vii) climate. These indicators help characterize human activities such as industry, agriculture, and transport. They also describe the pressures these activities exert on the environment, including pollution (air, water, soil), emissions, land artificialization, and climate change. Additionally, environmental amenities are captured through indicators of territorial naturalness. The objective of this work is to provide insights for reusing open environmental data. The data management steps and protocols developed to generate all spatial indicators are detailed in the second chapter of the thesis manuscript (Paumelle, 2023). This chapter also includes a critical perspective on the dataset (e.g., quality of source data, interpretative limitations of indicators). For appropriate use of the dataset, consulting this information beforehand is strongly recommended. Initially, the dataset was developed to explore the relationship between environment and health, particularly for studying Inflammatory Bowel Diseases (IBD). This explains why the spatial coverage of the dataset coincides with that of the EPIMAD registry, the only French health registry dedicated to IBD (Gower-Rousseau et al., 1994). However, this work could be extended geographically since all selected open-access databases are theoretically available nationwide. To facilitate the adoption and reuse of this dataset, two file formats are provided: (i) a GIS-compatible format titled 'Data - GIS Format.gpkg'; and (ii) a spreadsheet format titled 'Data - Spreadsheet Format.tab'. Additionally, a list of all indicators produced is provided, along with a brief description of each: 'List and Description of Indicators.pdf'. This document is concise, and it is recommended to refer to Chapter 2 of the thesis manuscript for more detailed information. References : Gower-Rousseau, C., Salomez, J.L., Dupas, J.L., Marti, R., Nuttens, M.C., Votte, A., Lemahieu, M., Lemaire, B., Colombel, J.F., Cortot, A., 1994. Incidence of inflammatory bowel disease in northern France (1988-1990). Gut 35, 1433–1438. https://doi.org/10.1136/gut.35.10.1433 Paumelle, M., 2023. Description multi-dimensionnelle de l’environnement à l’échelle des territoires : contribution pour la recherche de déterminants environnementaux dans l’étiologie des maladies chroniques (Thèse de doctorat). Université de Lille. Paumelle, M., Occelli, F., Wakim, L.M., Brousmiche, D., Bouhadj, L., Ternynck, C., Lanier, C., Cuny, D., Deram, A., 2023. Description of the multi-dimensional environment at the territorial scale: A holistic framework using cluster analysis and open data in France. Ecol. Indic....

CCSD is a GIS data set that contains detailed outlines of the lands used by public schools for educational purposes. The campus boundaries of schools with kindergarten through 12th grade instruction are each accurately mapped at the assessor parcel level. CCSD is the first statewide database of this information and is available for use without restriction.

City, Population Size, African American, Asian/Pacific Islander, Latino, White, Foreign-born, Speaks a language other than English at home, Single parent households, Households with children, Average household size, 0-5 years, 6-11 years, 12-17 years, 18-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years,Ages 65 and older, Ages 17 and younger. Percentages unless otherwise noted. Source information provided at: https://www.sccgov.org/sites/phd/hi/hd/Documents/City%20Profiles/Methodology/Neighborhood%20profile%20methodology_082914%20final%20for%20web.pdf

Zip Code; Population Size; African American; Asian/Pacific Islander; Latino; White; Foreign-born; Speaks a language other than English at home; Single parent households; Households with children; Average household size; 0-5 years; 6-11 years; 12-17 years; 18-24 years; 25-34 years; 35-44 years; 45-54 years; 55-64 years; Ages 65 and older; Ages 17 and younger. Percentages unless otherwise noted. Source information provided at: https://www.sccgov.org/sites/phd/hi/hd/Documents/City%20Profiles/Methodology/Neighborhood%20profile%20methodology_082914%20final%20for%20web.pdf