Formation transfrontalière UniGR: AMASE - Erasmus Mundus Master in Advanced Material Science and Engineering (M.Sc.) - Source: UniGR

UniGR cross-border study DFHI-ISFATES: Computer Science (M.Sc.) Source: DFHI-ISFATES Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2273&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/0214a3be-688b-4bac-b174-724c62857ff8 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Cross_border_programmes_science_mathematics_computing_2023_WMS/guest with layer name(s): -DFHI_ISFATES_Computer_Science_MSc

UniGR cross-border study programme: AMASE - Erasmus Mundus Master in Advanced Material Science and Engineering (M.Sc.) Source: UniGR Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2249&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/7542c173-d6fb-4ffd-82d3-923a3bdf6552 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Cross_border_programmes_engineering_manufacturing_constructing_2023_WMS/guest with layer name(s): -UniGR_AMASE_MSc

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Masters Of Science In Gis Technology. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Masters Of Science In Gis Technology. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Geographic Information Science (Gis). It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Geographic Information Science (Gis). This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This map shows locations that provide ADN (associate degree nursing), AE-MSN (alternate entry master of science in nursing), Diploma, BSN (bachelor of science in nursing), DE-MSN (direct entry master of science in nursing), and LVN (licensed vocation nursing) certifications. The data includes information on pass rates from 2020 through 2024.This map was created with data from Texas Center for Nursing Workforce Studies and last updated in May 2025.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Geospatial Information Science (Gis) And Technology. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Geospatial Information Science (Gis) And Technology. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Geogrphic Information Science (Gis). It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Geogrphic Information Science (Gis). This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

An orthoimage is remotely-sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The Landsat Mosaic orthoimagery database contains Landsat Thematic Mapper imagery for the conterminous United States. The more than 700 Landsat scenes have been resampled to a 1-arc-second (approximately 30-meter) sample interval in a geographic coordinate system using the North American Horizontal Datum of 1983. Three bands have been selected from the eight spectral bands available for each frame. These are bands 4 (near-infrared), 3 (red), and 2 (green), typically displayed as red, green, and blue, respectively. The image is a full-resolution (spectral and spatial), 24-bit color-infrared composite that simulates color infrared film as a "false color composite". NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

This Innovative Technology Experiences for Students and Teachers (ITEST) project has developed, implemented, and evaluated a series of innovative Socio-Environmental Science Investigations (SESI) using a geospatial curriculum approach. It is targeted for economically disadvantaged 9th grade high school students in Allentown, PA, and involves hands-on geospatial technology to help develop STEM-related skills. SESI focuses on societal issues related to environmental science. These issues are multi-disciplinary, involve decision-making that is based on the analysis of merged scientific and sociological data, and have direct implications for the social agency and equity milieu faced by these and other school students. This project employed a design partnership between Lehigh University natural science, social science, and education professors, high school science and social studies teachers, and STEM professionals in the local community to develop geospatial investigations with Web-based Geographic Information Systems (GIS). These were designed to provide students with geospatial skills, career awareness, and motivation to pursue appropriate education pathways for STEM-related occupations, in addition to building a more geographically and scientifically literate citizenry. The learning activities provide opportunities for students to collaborate, seek evidence, problem-solve, master technology, develop geospatial thinking and reasoning skills, and practice communication skills that are essential for the STEM workplace and beyond. Despite the accelerating growth in geospatial industries and congruence across STEM, few school-based programs integrate geospatial technology within their curricula, and even fewer are designed to promote interest and aspiration in the STEM-related occupations that will maintain American prominence in science and technology. The SESI project is based on a transformative curriculum approach for geospatial learning using Web GIS to develop STEM-related skills and promote STEM-related career interest in students who are traditionally underrepresented in STEM-related fields. This project attends to a significant challenge in STEM education: the recognized deficiency in quality locally-based and relevant high school curriculum for under-represented students that focuses on local social issues related to the environment. Environmental issues have great societal relevance, and because many environmental problems have a disproportionate impact on underrepresented and disadvantaged groups, they provide a compelling subject of study for students from these groups in developing STEM-related skills. Once piloted in the relatively challenging environment of an urban school with many unengaged learners, the results will be readily transferable to any school district to enhance geospatial reasoning skills nationally. The student sample is a convenience sample obtained via an urban school district local to the researchers, specifically three local high schools. Two traditional high schools provided responses forming a control group and a third, newly established high school served as the experimental test group. Datasets: DS0: Study-Level Files DS1: Y1 Data DS2: Y1 Feedback DS3: Y2 Data DS4: Y2 Scores DS5: Y3 Data DS6: Y3 Feedback Economically disadvantaged high school freshman in an urban school district. mixed mode; on-site questionnaire; web-based survey

Summary: Week 2: Quiz Answer KeyStorymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) K: Standard K-ESS3-2 - Earth and Human Activity - Ask questions to obtain information about the purpose of weather forecasting to prepare for, and respond to, severe weatherGrade level(s) 1: Standard 1-LS1-1 - From Molecules to Organisms: Structures and Processes - Use materials to design a solution to a human problem by mimicking how plants and/or animals use their external parts to help them survive, grow, and meet their needsGrade level(s) K-2: Standard K-2-ETS1-1 - Engineering Design - Ask questions, make observations, and gather information about a situation people want to change to define a simple problem that can be solved through the development of a new or improved object or tool.Grade level(s) 3: Standard 3-PS2-3 - Motion and Stability: Forces and Interactions - Ask questions to determine cause and effect relationships of electric or magnetic interactions between two objects not in contact with each otherGrade level(s) 4: Standard 4-PS3-3 - Energy - Ask questions and predict outcomes about the changes in energy that occur when objects collideGrade level(s) 6-8: Standard MS-PS2-3 - Motion and Stability: Forces and Interactions - Ask questions about data to determine the factors that affect the strength of electric and magnetic forcesGrade level(s) 6-8: Standard MS-ESS1-3 - Earth’s Place in the Universe - Analyze and interpret data to determine scale properties of objects in the solar systemGrade level(s) 6-8: Standard MS-ESS1-4 - Earth’s Place in the Universe - Construct a scientific explanation based on evidence from rock strata for how the geologic time scale is used to organize Earth’s 4.6-billion-year-old historyGrade level(s) 6-8: Standard MS-ESS2-2 - Earth’s Systems - Construct an explanation based on evidence for how geoscience processes have changed Earth’s surface at varying time and spatial scalesGrade level(s) 6-8: Standard MS-ESS3-5 - Earth and Human Activity - Ask questions to clarify evidence of the factors that have caused the rise in global temperatures over the past centuryGrade level(s) 9-12: Standard HS-PS1-3 - Matter and Its Interactions - Plan and conduct an investigation to gather evidence to compare the structure of substances at the bulk scale to infer the strength of electrical forces between particlesGrade level(s) 9-12: Standard HS-PS1-7 - Matter and Its Interactions - Use mathematical representations to support the claim that atoms, and therefore mass, are conserved during a chemical reactionGrade level(s) 9-12: Standard HS-PS1-8 - Matter and Its Interactions - Develop models to illustrate the changes in the composition of the nucleus of the atom and the energy released during the processes of fission, fusion, and radioactive decay.Grade level(s) 9-12: Standard HS-PS3-2 - Energy - Develop and use models to illustrate that energy at the macroscopic scale can be accounted for as a combination of energy associated with the motion of particles (objects) and energy associated with the relative position of particles (objects).Grade level(s) 9-12: Standard HS-PS4-2 - Waves and Their Applications in Technologies for Information Transfer - Evaluate questions about the advantages of using digital transmission and storage of informationGrade level(s) 9-12: Standard HS-LS2-1 - Ecosystems: Interactions, Energy, and Dynamics - Use mathematical and/or computational representations to support explanations of factors that affect carrying capacity of ecosystems at different scalesGrade level(s) 9-12: Standard HS-LS2-2 - Ecosystems: Interactions, Energy, and Dynamics - Use mathematical representations to support and revise explanations based on evidence about factors affecting biodiversity and populations in ecosystems of different scalesGrade level(s) 9-12: Standard HS-LS3-1 - Heredity: Inheritance and Variation of Traits - Ask questions to clarify relationships about the role of DNA and chromosomes in coding the instructions for characteristic traits passed from parents to offspringGrade level(s) 9-12: Standard HS-ESS2-1 - Earth’s Systems - Develop a model to illustrate how Earth’s internal and surface processes operate at different spatial and temporal scales to form continental and ocean-floor features.Most frequently used words:questionscaleApproximate Flesch-Kincaid reading grade level: 9.8. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

Summary:

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

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

Terms of Use:

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

Associated Files:

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

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

Column Information for the datasets:

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

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

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

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

Acknowledgements:

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

UniGR cross-border study programme: Master en sciences et gestion de l'environnement / Master en développement durable Source: UniGR Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2256&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/064548fb-1ab0-4736-abd5-10d992dfea16 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Cross_border_programmes_science_mathematics_computing_2023_WMS/guest with layer name(s): -UniGR_Master_gestion_environnement_dev_durable

These data, which comprise part of the Smithsonian Institution Master Sediment data file, were abstracted by the staff of the Smithsonian Institution from materials submitted for archival by various groups and individuals. Most of the data in this set were collected by the National Ocean Service (NOS, formerly the U.S. Coast and Geodetic Survey) for the purpose of charting the coastal waters and navigable waterways of the United States. Prior to 1985, the NOS data were released as part of the National Ocean Surveys Hydrographic Database. After 1985, sediment samples collected by NOS during surveys were transferred to the Smithsonian for archival and textural analysis. All of the data in this set were collected post 1985 and have been processed by the Smithsonian. These data were supplied by the National Geophysical Data Center (NGDC), but this data set contains fields that are only a subset of those fields available in the full Smithsonian data set. For example, the data have been clipped to eliminate those stations that were not from the Gulf of Maine, Georges Bank, or the shelf and slope off southeastern New England. Last update of this file was July, 2001.

This data set comprises the Environmental Sensitivity Index (ESI) data for the Virgin Islands. ESI data characterize estuarine environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. This atlas was developed to be utilized within desktop GIS systems and contains GIS files and related D-base files. Associated files include MOSS (Multiple Overlay Statistical System) export files, .PDF maps, and detailed user guides and metadata.

Summary: NEW VERSION is at https://esriurl.com/agoschoolcompStorymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) 6-8: Standard MS-LS4-4 - Biological Evolution: Unity and Diversity - Construct an explanation based on evidence that describes how genetic variations of traits in a population increase some individuals’ probability of surviving and reproducing in a specific environmenGrade level(s) 6-8: Standard MS-LS4-6 - Biological Evolution: Unity and Diversity - Use mathematical representations to support explanations of how natural selection may lead to increases and decreases of specific traits in populations over timeGrade level(s) 6-8: Standard MS-ESS1-2 - Earth’s Place in the Universe - Develop and use a model to describe the role of gravity in the motions within galaxies and the solar systemGrade level(s) 6-8: Standard MS-ESS2-4 - Earth’s Systems - Develop a model to describe the cycling of water through Earth’s systems driven by energy from the sun and the force of gravityGrade level(s) 9-12: Standard HS-PS1-2 - Matter and Its Interactions - Construct and revise an explanation for the outcome of a simple chemical reaction based on the outermost electron states of atoms, trends in the periodic table, and knowledge of the patterns of chemical propertiesGrade level(s) 9-12: Standard HS-LS2-1 - Ecosystems: Interactions, Energy, and Dynamics - Use mathematical and/or computational representations to support explanations of factors that affect carrying capacity of ecosystems at different scalesGrade level(s) 9-12: Standard HS-LS4-2 - Biological Evolution: Unity and Diversity - Construct an explanation based on evidence that the process of evolution primarily results from four factors: (1) the potential for a species to increase in number, (2) the heritable genetic variation of individuals in a species due to mutation and sexual reproduction, (3) competition for limited resources, and (4) the proliferation of those organisms that are better able to survive and reproduce in the environment.Most frequently used words:competitionesrihsstateApproximate Flesch-Kincaid reading grade level: 10.3. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

ResourcesMapTeacher guide Student worksheetVocabulary and puzzlesSelf-check questionsGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Science standardsNGSS: MS-ESS2-4 – Global movements of water and its changes in form are propelled by sunlight and gravity. NGSS: MS-ESS2.C – Water continually cycles among land, ocean, and atmosphere via transpiration, evaporation, condensation and crystallization, and precipitation, as well as downhill flows on land. Learning outcomesStudents will explore local streams to determine from where their home use water originates.Students will follow local streams to see how water returns back to the nearest sea.

ResourcesMapTeacher guide Student worksheetVocabulary and puzzlesSelf-check questionsGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Science standardsNGSS: MS-ESS2-3 – Analyze and interpret data to provide evidence for phenomena. NGSS: MS-ESS2-4 – Develop a model to describe unobservable mechanisms. Learning outcomesStudents will describe how the energy from breaking rocks at an earthquake epicenter travels away in waves.Students will determine where earthquakes occur using the difference in speed of waves from the seismograph.