The Early Development Instrument (EDI) is a community-based measure of young children’s developmental health and early learning intended to identify areas, both developmentally and geographically, in which children have the greatest need and require the most supports. It is a population measure that is based on developmental rather than curriculum benchmarks, and it assesses five general areas, or domains, of child development: physical health and well-being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge). EDI data are collected through a questionnaire that senior kindergarten teachers complete for all children in their classrooms every three years. Three measures of children’s developmental health are included in the analysis of the York Region EDI results: “scoring low” (or EDI vulnerability), meeting of “few/no” developmental expectations, and domain averages. All measures are assessed separately for each of the five developmental domains. “Scoring low” is the most widely used measure. Data are reported at the regional and neighborhood level.

This dataset contains the White Mountain National Forest Boundary. The boundary was extracted from the National Forest boundaries coverage for the lower 48 states, including Puerto Rico developed by the USDA Forest Service - Geospatial Service and Technology Center. The coverage was projected from decimal degrees to UTM zone 19. This dataset includes administrative unit boundaries, derived primarily from the GSTC SOC data system, comprised of Cartographic Feature Files (CFFs), using ESRI Spatial Data Engine (SDE) and an Oracle database. The data that was available in SOC was extracted on November 10, 1999. Some of the data that had been entered into SOC was outdated, and some national forest boundaries had never been entered for a variety of reasons. The USDA Forest Service, Geospatial Service and Technology Center has edited this data in places where it was questionable or missing, to match the National Forest Inventoried Roadless Area data submitted for the President's Roadless Area Initiative. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N.

The United States Public Land Survey (PLS) divided land into one square mile units, termed sections. Surveyors used trees to locate section corners and other locations of interest (witness trees). As a result, a systematic ecological dataset was produced with regular sampling over a large region of the United States, beginning in Ohio in 1786 and continuing westward.
We digitized and georeferenced archival hand drawn maps of these witness trees for 27 counties in Ohio. This dataset consists of a GIS point shapefile with 11,925 points located at section corners, recording 26,028 trees (up to four trees could be recorded at each corner). We retain species names given on each archival map key, resulting in 70 unique species common names. PLS records were obtained from hand-drawn archival maps of original witness trees produced by researchers at The Ohio State University in the 1960’s. Scans of these maps are archived as “The Edgar Nelson Transeau Ohio Vegetation Survey” at The Ohio State University: http://hdl.handle.net/1811/64106.
The 27 counties are: Adams, Allen, Auglaize, Belmont, Brown, Darke, Defiance, Gallia, Guernsey, Hancock, Lawrence, Lucas, Mercer, Miami, Monroe, Montgomery, Morgan, Noble, Ottawa, Paulding, Pike, Putnam, Scioto, Seneca, Shelby, Williams, Wyandot. Coordinate Reference System: North American Datum 1983 (NAD83). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.

This dataset was created as a part of the Ecological Homogenization of Urban America project. It represents shapefiles and land cover summary data for specific parcels throughout the greater Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, and Phoenix, AZ areas.

64 EDI neighbourhoods have been delineated in York Region. Neighbourhoods are identified by socio-demographic similarities and distinctive characteristics throughout a geographic area, and they reflect the communities in which children live. Several criteria were assessed when delineating the EDI Neighbourhood boundaries: an extensive community consultation process was conducted that identified geographic areas which reflected residents’ perceptions of the communities in which they lived; natural boundaries such as highways, railways and watercourses were taken into consideration; it was required that the EDI sample size be large enough in each neighbourhood to support statistically sound data analysis and to ensure that no child could be identified; it was necessary to limit the EDI neighbourhoods to a reasonable number for data analysis purposes; EDI neighbourhood boundaries were based on dissemination areas (DAs) to facilitate the use of Census/NHS data which are available at the DA level. Each EDI neighbourhood consists of a complete set of DAs; EDI results for each neighbourhood include all children that live within the neighbourhood regardless of which school they attend.

This data package, LAGOS-NE-GIS v1.0, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO v1.087.1: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE GIS v1.0 module includes GIS datasets for: lake polygons and their hydrologic classification; wetland polygons and their classification; streams as a line coverage and their classification by stream order; the zones used for this study (state and county; hydrologic units [at the 4, 8 and 12 scales]); and, lake watersheds (IWS). We also include boundaries of U.S. states and Canadian provinces for mapping.

      Citation for the full documentation of this database:


      Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M.
      Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K.
      Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan,
      J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A.
      Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a
      multi-scaled geospatial temporal ecology database from disparate data
      sources: Fostering open science and data reuse. GigaScience 4:28
      doi:10.1186/s13742-015-0067-4


      Citation for the data paper for this database:


      Soranno, P.A., L.C. Bacon, M. Beauchene, K.E. Bednar, E.G. Bissell,
      C.K. Boudreau, M.G. Boyer, M.T. Bremigan, S.R. Carpenter, J.W. Carr,
      K.S. Cheruvelil, S.T. Christel, M. Claucherty, S.M.Collins, J.D.
      Conroy, J.A. Downing, J. Dukett, C.E. Fergus, C.T. Filstrup, C. Funk,
      M.J. Gonzalez, L.T. Green, C. Gries, J.D. Halfman, S.K. Hamilton, P.C.
      Hanson, E.N. Henry, E.M. Herron, C. Hockings, J.R. Jackson, K.
      Jacobson-Hedin, L.L. Janus, W.W. Jones, J.R. Jones, C.M. Keson, K.B.S.
      King, S.A. Kishbaugh, J.F. Lapierre, B. Lathrop, J.A. Latimore, Y.
      Lee, N.R. Lottig, J.A. Lynch, L.J. Matthews, W.H. McDowell, K.E.B.
      Moore, B.P. Neff, S.J. Nelson, S.K. Oliver, M.L. Pace, D.C. Pierson,
      A.C. Poisson, A.I. Pollard, D.M. Post, P.O. Reyes, D.O. Rosenberry,
      K.M. Roy, L.G. Rudstam, O. Sarnelle, N.J. Schuldt, C.E. Scott, N.K.
      Skaff, N.J. Smith, N.R. Spinelli, J.J. Stachelek, E.H. Stanley, J.L.
      Stoddard, S.B. Stopyak, C.A. Stow, J.M. Tallant, P.-N. Tan, A.P.
      Thorpe, M.J. Vanni, T. Wagner, G. Watkins, K.C. Weathers, K.E.
      Webster, J.D. White, M.K. Wilmes, S. Yuan. In Review. LAGOS-NE: A
      multi-scaled geospatial and temporal database of lake ecological
      context and water quality for thousands of U.S. lakes. In Review at
      GigaScience. Submitted April 2017.

Tags

   social system, socio-economic resources, justice, BES, Environmental disamentities, Environmental Justice, Zoning Board of Appeals




   Summary


   For use in the environmental injustices study of Baltimore relating to patterns of environmental disamenties in relation to low income/minority communities.


   Description


   This feature class layer is a point dataset of authorizing ordinances from the Baltimore City Council and Mayor from 1930 until 1999 concerning identified environmental disamentities. The data was gathered from records from the City Council since 1930 relating to decisions concerning land-uses considered to be environmental disamentities and is to be used to examine environmental injustices involving low income/minority communities in Baltimore. To examine if environmental injustices exist in Baltimore, this point layer will be overlayed with race/income data to determine if patterns of inequity exist. Points were placed manually using the associated addresses from the Ordinance_master dataset and using ISTAR 2004 data in conjunction with Baltimore parcel data. The Ordinance_ID number associated with each point relates to its appeal number from the City Council. Multiple points on the data layer have the same Ordinance_ID. This point layer can be joined with the Ordinance_master data layer based on the field "Ordinance_ID" and using the relationship "Ordinance_point_relationship".


   Credits


   UVM Spatial Analysis Lab


   Use limitations


   None. There are no restrictions on the use of this dataset. The authors of this dataset make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.



   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

This file contains one of many raster grids of the Elevation Derivatives for National Applications (EDNA), a multi-layered database that provides systematic and consistent topographically-derived hydrologic derivatives. The filled DEM grid was created from the original elevation data by filling all of the depressions, or sinks, in the original DEM. To create this grid, an algorithm was used to loacted and fill all depressions or sinks where there was no flow from pixel to pixel. During this process, efforts were made to maintain natural sink features. Originator: U.S. Geological Survey. Publication_Date: 2006. Title: cpcrw_dem.tif. Edition: Stage I Data. Geospatial_Data_Presentation_Form: Remote-sensing image. Series_Information: Series_Name: Elevation Derivatives for National Applications (EDNA). Publication_Information: Publication_Place: USGS EROS, Sioux Falls, South Dakota. Publisher: U.S. Geological Survey.

This data package contains the area boundary line for the Bonanza Creek Experimental forest in ArcGIS format. Geospatial_Data_Presentation_Form: vector digital data.

Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.

Spot 5 pan-sharpened satellite image in the Standard Creek area of the Tanana Valley, Alaska. This image was acquired and processed as part of the "Vegetation and Community Mapping of the Tanana Valley" project, conducted cooperatively by the State of Alaska Department of Natural Resources, Division of Forestry and Tanana Chiefs Conference. Originator: State of Alaska, Dept. of Nat. Resources, Division of Forestry. Publication_Date: 3/21/05. Geospatial_Data_Presentation_Form: remote-sensing image.

In 2000, the Maryland Geological Survey (MGS) was awarded a Coastal ZoneManagement grant to complete the acquisition of a recent (ca. 1990) digital shoreline for the coastal regions of Maryland - the Chesapeake Bay, its tributaries, the coastal bays, and the Atlantic coast.MGS contracted the services of EarthData International, Inc. (EDI) of Gaithersburg, Md., to extract shorelines from an existing wetlands delineation, which was based on photo interpretation of 3.75-minute digital orthophoto quarter quads (DOQQs). In areas where a wetlands coverage was not yet available, EDI interpreted shorelines directly from the orthophotography. DOQQ registration (Maryland State Plane Coordinate System, NAD 83, meters) was transferred automatically to the shoreline vectors. Following shoreline extraction or interpretation, EDI assigned attributes to the vectors based on shoreline type: beach, vegetated, structure, or water's edge. This data set has been merged into a state wide file for ease of processing and decision making.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_MarineBoundaries/FeatureServer/0**Please note, due to the size of this dataset, you may receive an error message when trying to download the dataset. You can download this dataset directly from MD iMAP Services at: https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_MarineBoundaries/MapServer/exts/MDiMAPDataDownload/customLayers/0**

This dataset represents field observations of reproductive development (flowering phenology) in 135 species of flowering plants collected at 12 field sites in the vicinity of Crested Butte, Colorado starting in 2019. Sites were visited approximately weekly from early May until early August, and all species in flower were recorded in 25 segments along a 50m transect at each site, and species were recorded if they were within 1m of either side of the transect. Datasets included in this package are 1) cleaned field observations of flowering phenology, 2) taxonomic identity of all recorded species, 3) spatial data representing the location of the center of each transect segment, and 4) spatial data representing the segment polygons.

Boundary around the Caribou - Poker Creek Research Watershed. Geospatial_Data_Presentation_Form: vector digital data.

The EDI is a teacher-completed instrument that measures children's school readiness for grade one on five domains:Physical health and well-being;Social knowledge and competence;Emotional maturity;Language and cognitive development; andGeneral knowledge and communication skillsChildren who score below the 10th percentile in one or more EDI domains are considered to be vulnerable to not be able to meet the demands of school, which could impact their achievement of school success.Early Development Instrument (EDI) Scores for 2017-2018 are grouped into Service Delivery Areas (2017). If you would like to compare the data to the 2015 EDI scores, please use the 2015 EDI table that is provided at the 2017 SDA Level.Notes:Records are aggregated to achieve anonymity.Vulnerability was calculated using the Ontario cycle 4 cut-points.Records are aggregated to achieve anonymity and Service Delivery Areas that have 10 or less responses are suppressed.

Streams lines generated for 63,360 Digital Line Grafics (DLG) in and near our research areas. Geospatial_Data_Presentation_Form: vector digital data.

Mayor's Order 2017-115 establishes a comprehensive data policy for the District government. The data created and managed by the District government are valuable assets and are independent of the information systems in which the data reside. As such, the District government shall: maintain an inventory of its enterprise datasets; classify enterprise datasets by level of sensitivity; regularly publish the inventory, including the classifications, as an open dataset; and strategically plan and manage its investment in data.The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. For more information, please visit https://opendata.dc.gov/pages/edi-overview. Previous years of EDI can be found on Open Data.

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429).
The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection.

  No Abstract Available

Community ecology has sought to understand the mechanisms by which plant communities are assembled through time and space. One prominent way to address how communities are assembled is by quantifying functional traits. While there is a tremendous body of literature on functional traits, debate persists about how to account for variation in measured traits. For example, intraspecific trait variation (ITV) can be equal to or greater than interspecific trait variation and ITV has also been found to vary greatly across years. Therefore, there is a need to account for variability in functional trait measures among and within species and through time to improve our understanding of community assembly. Chronosequences are a powerful tool to address temporal changes in community dynamics, however, the inclusion of understory plants in forest chronosequence studies is still relatively uncommon. Previous chronosequence studies have been primarily performed in grasslands or in a limited subset of forest types, so further work is needed in understory plant traits across other ecosystems and climates to improve trait-based understanding of understory plant communities through time. Additionally, because plant traits change as ecosystems age, community interactions are likely to change with ecosystem age. Interactions of particular interest are herbivory, arthropod predation, and the influence of plant traits on arthropod diversity.

This data set documents the expansion of the distribution of opossums in Michigan. Each row in the data file consists of a specimen record or observation and includes the year and location (latitude/longitude) of the record.