28 datasets found
  1. f

    PhD Origin and Current Affiliation of Global GIS Faculty

    • datasetcatalog.nlm.nih.gov
    • figshare.com
    Updated Sep 16, 2024
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    Chen, Yanbing (2024). PhD Origin and Current Affiliation of Global GIS Faculty [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001366803
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    Dataset updated
    Sep 16, 2024
    Authors
    Chen, Yanbing
    Description

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

  2. Global GIS Faculty CSV file

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    csv
    Updated Mar 3, 2025
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    Yanbing Chen (2025). Global GIS Faculty CSV file [Dataset]. http://doi.org/10.6084/m9.figshare.28468529.v1
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    csvAvailable download formats
    Dataset updated
    Mar 3, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Yanbing Chen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

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

  3. a

    Demographic Statistics - Cities

    • hub.arcgis.com
    • data-sccphd.opendata.arcgis.com
    • +1more
    Updated Feb 9, 2018
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    Santa Clara County Public Health (2018). Demographic Statistics - Cities [Dataset]. https://hub.arcgis.com/datasets/sccphd::demographic-statistics-cities
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    Dataset updated
    Feb 9, 2018
    Dataset authored and provided by
    Santa Clara County Public Health
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    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

  4. Careers With GIS - Patrick Rickles

    • teachwithgis.co.uk
    Updated Mar 31, 2022
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    Esri UK Education (2022). Careers With GIS - Patrick Rickles [Dataset]. https://teachwithgis.co.uk/items/7d9de42f8d7f40688c3116da30c451e0
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    Dataset updated
    Mar 31, 2022
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri UK Education
    Description

    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.

  5. f

    Technology Cluster Data extracted from USPTO Patent Grants (2000-2011)

    • figshare.com
    • data.4tu.nl
    txt
    Updated Jun 12, 2023
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    Pieter Stek (2023). Technology Cluster Data extracted from USPTO Patent Grants (2000-2011) [Dataset]. http://doi.org/10.4121/18858683.v1
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    txtAvailable download formats
    Dataset updated
    Jun 12, 2023
    Dataset provided by
    4TU.ResearchData
    Authors
    Pieter Stek
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    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.

  6. d

    Data from: Preliminary spatial parameters for PRMS based on the Geospatial...

    • search.dataone.org
    Updated Oct 29, 2016
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    Roland J. Viger, PhD., US Geological Survey, Research GeographerSteven L. Markstrom, US Geological Survey, Research HydrologistLauren E. Hay, PhD., US Geological Survey, Research Hydrologist (2016). Preliminary spatial parameters for PRMS based on the Geospatial Fabric, NLCD2001 and SSURGO [Dataset]. https://search.dataone.org/view/10e755d0-43b5-4504-861e-bbd2107af408
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    Dataset updated
    Oct 29, 2016
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Roland J. Viger, PhD., US Geological Survey, Research GeographerSteven L. Markstrom, US Geological Survey, Research HydrologistLauren E. Hay, PhD., US Geological Survey, Research Hydrologist
    Area covered
    Variables measured
    felev, fflux, flulc, hru_x, hru_y, fsoils, hru_id, hru_lat, cov_type, hru_area, and 28 more
    Description

    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

  7. a

    Demographic Statistics - Zip Code

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • data-sccphd.opendata.arcgis.com
    • +1more
    Updated Feb 21, 2018
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    Santa Clara County Public Health (2018). Demographic Statistics - Zip Code [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/sccphd::demographic-statistics-zip-code
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    Dataset updated
    Feb 21, 2018
    Dataset authored and provided by
    Santa Clara County Public Health
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    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

  8. d

    Data from: GIS Features of the Geospatial Fabric for National Hydrologic...

    • search.dataone.org
    • data.usgs.gov
    • +2more
    Updated Apr 13, 2017
    + more versions
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    Roland J. Viger, PhD., US Geological Survey, Research Geographer; Andrew Bock, US Geological Survey, Hydrologist (2017). GIS Features of the Geospatial Fabric for National Hydrologic Modeling [Dataset]. https://search.dataone.org/view/1e9e2db9-5ec7-47e0-82ef-aa3c52d629db
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    Dataset updated
    Apr 13, 2017
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Roland J. Viger, PhD., US Geological Survey, Research Geographer; Andrew Bock, US Geological Survey, Hydrologist
    Area covered
    Variables measured
    FTYPE, Shape, hru_x, hru_y, INC_DA, POI_ID, hru_id, region, seg_id, FLOWDIR, and 35 more
    Description

    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

  9. r

    GIS-based paleo-shoreline extraction toolbox - Uplifted Quartnernary...

    • radar-service.eu
    tar
    Updated Nov 28, 2019
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    Bastian Schneider (2019). GIS-based paleo-shoreline extraction toolbox - Uplifted Quartnernary terraces in Oman. Shoreline Extraction, dating and uplift calculation [Dataset]. http://doi.org/10.22000/263
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    tar(99102720 bytes)Available download formats
    Dataset updated
    Nov 28, 2019
    Dataset authored and provided by
    Bastian Schneider
    Area covered
    Oman
    Description

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

  10. n

    Survey of the Casey quarry, September 1998

    • access.earthdata.nasa.gov
    • researchdata.edu.au
    • +1more
    Updated Oct 2, 2017
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    (2017). Survey of the Casey quarry, September 1998 [Dataset]. http://doi.org/10.4225/15/59d1c7d3cee88
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    Dataset updated
    Oct 2, 2017
    Time period covered
    Sep 1, 1998 - Sep 30, 1998
    Area covered
    Description

    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.

  11. Network Analysis

    • figshare.com
    csv
    Updated Sep 27, 2024
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    Yanbing Chen (2024). Network Analysis [Dataset]. http://doi.org/10.6084/m9.figshare.27122478.v1
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    csvAvailable download formats
    Dataset updated
    Sep 27, 2024
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Yanbing Chen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  12. Ordinary collective housing buildings in Porto (Portugal)

    • zenodo.org
    xls
    Updated May 28, 2020
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    David Pereira-Martínez; David Pereira-Martínez; Gisela Lameira; Gisela Lameira (2020). Ordinary collective housing buildings in Porto (Portugal) [Dataset]. http://doi.org/10.5281/zenodo.3723568
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    xlsAvailable download formats
    Dataset updated
    May 28, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    David Pereira-Martínez; David Pereira-Martínez; Gisela Lameira; Gisela Lameira
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Porto, Portugal
    Description

    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.

  13. O

    Otago Geology Theses Metadata Polygons

    • otagogeology.koordinates.com
    csv, dwg, geodatabase +6
    Updated Nov 10, 2019
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    University of Otago - Department of Geology (2019). Otago Geology Theses Metadata Polygons [Dataset]. https://otagogeology.koordinates.com/layer/104207-otago-geology-theses-metadata-polygons/
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    geodatabase, csv, pdf, mapinfo mif, dwg, geopackage / sqlite, kml, shapefile, mapinfo tabAvailable download formats
    Dataset updated
    Nov 10, 2019
    Dataset authored and provided by
    University of Otago - Department of Geology
    License

    https://otagogeology.koordinates.com/license/attribution-sharealike-4-0-international/https://otagogeology.koordinates.com/license/attribution-sharealike-4-0-international/

    Area covered
    Otago Region,
    Description

    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.

    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).

    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)

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

    Updated July 2021

  14. o

    Supplementary materials for PhD thesis “Constraints and opportunities for...

    • ordo.open.ac.uk
    Updated Sep 12, 2025
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    Robert Benjamin Francis McMorran (2025). Supplementary materials for PhD thesis “Constraints and opportunities for integrated multifunctional forest management in the Cairngorms region of Scotland” [Dataset]. http://doi.org/10.21954/ou.rd.28436945.v1
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    Dataset updated
    Sep 12, 2025
    Dataset provided by
    The Open University
    Authors
    Robert Benjamin Francis McMorran
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Scotland, Cairngorms
    Description

    This dataset comprises the files contained on a CD-ROM which was attached to the thesis when it was submitted in 2008. It was uploaded to ORDO in 2025 for preservation purposes. For more information, please refer to the thesis  “Constraints and opportunities for integrated multifunctional forest management in the Cairngorms region of Scotland” via ORO.Scottish forestry policy promotes integrated multifunctional forestry and continued forest expansion, and the private forestry sector is key to achieving these goals. However, a variety of factors can constrain multifunctional forestry, particularly in upland areas. Management objectives for private forests are also not always widely known and the potential impacts of government policy, and particularly conservation designations, on the delivery of multifunctional forestry have not been explored. Furthermore, the effects of landownership on forest management are unclear. The key aim of this research was to evaluate how forests and woodlands in the Cairngorms are being managed, and to determine the main constraints and opportunities to the further application of a multifunctional approach to forest management in the region at various scales.The research incorporated GIS analysis, a postal questionnaire survey and semi-structured interviews. A typology was developed which identified three key management themes: sustainable multifunctional management; restricted functionality forestry; and dual function management. Forest owner preferences, economic concerns, government policy and the wider land use context all influence forest management. No consistent relationship was evident between specific types of landownership and forest management functionality. A link was apparent between species and structural diversity and forest multifunctionality. Overall, 'social' objectives were relatively weak, particularly for private forests, while 'environmental' objectives were consistently strong and 'productive/ economic' objectives varied in importance.The themes of constraint and opportunity were: spatial fragmentation and integration; forestry markets; bureaucracy and policy integration; public pressure and public support; and unpredictability and forest resource resilience. Many constraints result from temporal, spatial and organizational scale mis-matches between social and natural systems. The matching up of scales at the management, organizational and market levels and the treatment of forests as complex social-ecological systems is recommended. Further recommendations include: further land management integration; increased policy regionalization; timber market localization; and enhanced long-term planning.

  15. d

    Time-series maps of shrub expansion on the North Slope of Alaska, USA

    • search.dataone.org
    • arcticdata.io
    Updated Oct 22, 2016
    + more versions
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    Adam T. Naito; David M. Cairns (2016). Time-series maps of shrub expansion on the North Slope of Alaska, USA [Dataset]. http://doi.org/10.18739/A2HK93
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    Dataset updated
    Oct 22, 2016
    Dataset provided by
    Arctic Data Center
    Authors
    Adam T. Naito; David M. Cairns
    Time period covered
    Jun 1, 1955 - Aug 14, 2010
    Area covered
    Description

    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.

  16. O

    California School Campus Database

    • data.smcgov.org
    csv, xlsx, xml
    Updated Dec 12, 2016
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    The first release of CSCD was developed by the Stanford Prevention Research Center and GreenInfo Network, with funding from the Tobacco-Related Disease Research Program grant #22RT-0142, PI: Lisa Henriksen, PhD. (2016). California School Campus Database [Dataset]. https://data.smcgov.org/Education/California-School-Campus-Database/sa7d-zpha/about
    Explore at:
    csv, xml, xlsxAvailable download formats
    Dataset updated
    Dec 12, 2016
    Dataset authored and provided by
    The first release of CSCD was developed by the Stanford Prevention Research Center and GreenInfo Network, with funding from the Tobacco-Related Disease Research Program grant #22RT-0142, PI: Lisa Henriksen, PhD.
    Area covered
    California
    Description

    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.

  17. Land Use and Cover Changes in Dongsithouane National Production Forest in...

    • figshare.com
    bin
    Updated Apr 18, 2023
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    Souvanthone DOUANGPHACHANH; Phouvong Phami; Puvadol Doydee; Watit Khokthong; Yuanchun Yu (2023). Land Use and Cover Changes in Dongsithouane National Production Forest in Lao PDR: A Case Study of Savannakhet Province [Dataset]. http://doi.org/10.6084/m9.figshare.22653742.v1
    Explore at:
    binAvailable download formats
    Dataset updated
    Apr 18, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Souvanthone DOUANGPHACHANH; Phouvong Phami; Puvadol Doydee; Watit Khokthong; Yuanchun Yu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Laos, Savannakhet Province
    Description

    This is very important for studying in PhD

  18. R

    Characterization of the physical environment at the municipal level in...

    • entrepot.recherche.data.gouv.fr
    bin, pdf, tsv
    Updated Jul 21, 2025
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    Martin Paumelle; Martin Paumelle; Florent Occelli; Lara-Maria Wakim; Delphine Brousmiche; Damien Cuny; Caroline Lanier; Annabelle Deram; Florent Occelli; Lara-Maria Wakim; Delphine Brousmiche; Damien Cuny; Caroline Lanier; Annabelle Deram (2025). Characterization of the physical environment at the municipal level in Northern France [Dataset]. http://doi.org/10.57745/XH5HDW
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    tsv(3340396), bin(6729728), pdf(3124181)Available download formats
    Dataset updated
    Jul 21, 2025
    Dataset provided by
    Recherche Data Gouv
    Authors
    Martin Paumelle; Martin Paumelle; Florent Occelli; Lara-Maria Wakim; Delphine Brousmiche; Damien Cuny; Caroline Lanier; Annabelle Deram; Florent Occelli; Lara-Maria Wakim; Delphine Brousmiche; Damien Cuny; Caroline Lanier; Annabelle Deram
    License

    https://spdx.org/licenses/etalab-2.0.htmlhttps://spdx.org/licenses/etalab-2.0.html

    Area covered
    France
    Dataset funded by
    Fondation I-SITE ULNE
    Agence nationale de la recherche
    Description

    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....

  19. H

    kDickson_homewatershed

    • beta.hydroshare.org
    • hydroshare.org
    • +2more
    zip
    Updated Apr 12, 2017
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    Kerim Dickson (2017). kDickson_homewatershed [Dataset]. https://beta.hydroshare.org/resource/99f26dba81f343a89e5b335658e16beb/
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    zip(2.3 KB)Available download formats
    Dataset updated
    Apr 12, 2017
    Dataset provided by
    HydroShare
    Authors
    Kerim Dickson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    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!

  20. u

    GADM database of Global Administrative Areas

    • ai.di.uoa.gr
    • data.wu.ac.at
    7z
    Updated Sep 12, 2023
    + more versions
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    University of Athens (2023). GADM database of Global Administrative Areas [Dataset]. https://ai.di.uoa.gr/
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    7zAvailable download formats
    Dataset updated
    Sep 12, 2023
    Dataset provided by
    Robert Hijmans, in collaboration with colleagues at the University of California, Berkeley Museum of Vertebrate Zoology (Julian Kapoor and John Wieczorek), the International Rice Research Institute (Nel Garcia, Aileen Maunahan, Arnel Rala) and the University of California, Davis (Alex Mandel), and with contributions of many others.
    Authors
    University of Athens
    Time period covered
    2015
    Area covered
    World
    Description

    GADM is a spatial database of the location of the world's administrative areas (or adminstrative boundaries) for use in GIS and similar software.

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Chen, Yanbing (2024). PhD Origin and Current Affiliation of Global GIS Faculty [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001366803

PhD Origin and Current Affiliation of Global GIS Faculty

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Dataset updated
Sep 16, 2024
Authors
Chen, Yanbing
Description

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

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