AndreaBozzo/ceres-open-data-index huggingface dataset:
Ontario Indian reserves

Land identifying Indian Reserve boundaries for the Province of Ontario defined by the Legal Surveys Division of Natural Resources Canada. Does not include settlements or communities that have not been officially registered as reserves. Additional Time Period Information: The updates are based on data received from the Federal government. Updates are required whenever a Indian Reserve is either surrendered or created. The package product formerly available has been removed from circulation. There are various data sources. The original source was through the digital Ontario Base Mapping Program. Other sources include Crown Land surveys and plans in the survey records area, as well as access to the NRCan survey web site and digital information as per MOU. The majority of the boundaries were captured through the Ontario Base Mapping Program. Updates or revisions to the boundaries are captured by the use of surveys, plans, and descriptions available through Crown Survey records and the NRCan website, as well as access to NRCan's digital administrative boundary data as per signed MOU. The Indian Reserves were realigned with the Improved Geographic Township fabric and Improved Lot Fabric, as well as aligned to the land tenure fabric where applicable. Discrepancies between this data set and the NRCAN Indian Reserve data were researched and documented. In house surveys as well as NRCAN's on-line survey plans were also used to improve the reserve boundaries
Great Lakes statistical district lines

Individual boundary polylines were created by first making a point shapefile of the line endpoints or a series of points, then converting the points to a polyline. The point/polyline conversion was done using XTools 'Make One Polyline from Points' tool. Point locations were based on latitude/longitude coordinates given in the technical report or geographic landmark (i.e. islands, points, state/international boundary lines, etc.). Points requiring an azimuth bearing were created in a projected view (UTM Zone 17 NAD27) using the Distance and Azimuth Tools v. 1.6 extension developed by Jenness Enterprises. The polyline shapefiles created in step 1 and an existing polyline shapefile of the international boundary were merged together using the ArcView GeoProcessing Wizard. The shapefile generated in step 2 was converted to a line coverage using the ArcToolbox Conversion Tools - Feature Class to Coverage. The line coverage topology was cleaned and updated using the ArcInfo Workstation CLEAN (dangle length and fuzzy tolerance both set to 0.001) and BUILD commands. The boundary line coverage and an existing Lake Erie shoreline shapefile (derived from ESRI 100k data) were merged together using the ArcView GeoProcessing Wizard. The shapefile generated in step 5 was converted to a line coverage using the ArcToolbox Conversion Tools - Feature Class to Coverage. Topology of the boundary/shoreline coverage was cleaned and updated using the ArcInfo Workstation CLEAN (dangle length and fuzzy tolerance both set to 0.00001) and BUILD commands. BUILD was done for both line and polygon topology. The polygon feature from the coverage generate in step 7 was converted to a shapefile using Theme\Convert to Shapefile in ArcView.
Great Lakes statistical district polygons

Individual boundary polylines were created by first making a point shapefile of the line endpoints or a series of points, then converting the points to a polyline. The point/polyline conversion was done using XTools 'Make One Polyline from Points' tool. Point locations were based on latitude/longitude coordinates given in the technical report or geographic landmark (i.e. islands, points, state/international boundary lines, etc.). Points requiring an azimuth bearing were created in a projected view (UTM Zone 17 NAD27) using the Distance and Azimuth Tools v. 1.6 extension developed by Jenness Enterprises. The polyline shapefiles created in step 1 and an existing polyline shapefile of the international boundary were merged together using the ArcView GeoProcessing Wizard. The shapefile generated in step 2 was converted to a line coverage using the ArcToolbox Conversion Tools - Feature Class to Coverage. The line coverage topology was cleaned and updated using the ArcInfo Workstation CLEAN (dangle length and fuzzy tolerance both set to 0.001) and BUILD commands. The boundary line coverage and an existing Lake Erie shoreline shapefile (derived from ESRI 100k data) were merged together using the ArcView GeoProcessing Wizard. The shapefile generated in step 5 was converted to a line coverage using the ArcToolbox Conversion Tools - Feature Class to Coverage. Topology of the boundary/shoreline coverage was cleaned and updated using the ArcInfo Workstation CLEAN (dangle length and fuzzy tolerance both set to 0.00001) and BUILD commands. BUILD was done for both line and polygon topology. The polygon feature from the coverage generate in step 7 was converted to a shapefile using Theme\Convert to Shapefile in ArcView.
New York State fish and wildlife coastal habitat designation

The data set consists of boundaries of Significant Coastal Fish and Wildlife Habitats as identified by NYS Department of State, Division of Coastal Resources. The areas involved are all limited to the perimeter waters of NY State. These include Lake Erie, Niagara River, and Lake Ontario. Original data was hand-drawn over DOT topographic quadrangle sections and filed with local governments. Each identified habitat has an accompanying text narrative describing the living resources values leading to designation as a significant coastal habitat: these narratives are not included as part of this data set. For information on the significant coastal fish and wildlife habitats documentation contact Gregory L. Capobianco at Department of State at the address noted below or at . Digital data set (version 1.1) was previewed by NYS DEC Habitat Inventory Unit and subsequently corrected as per their recommendations. This is version 1.2 of the significant Coastal Fish and Wildlife Habitats. Polygons on Long Island (Nassau and Suffolk Counties) do not have the same density of vertices as found in the remainder of the coverage based on the original draft digitizing done for these polygons - these are identified in the habitat name field with capital letters. Polygons in the eastern (Peconics) and northern (Long Island Sound) sections of Long Island are expected to be updated in summer of 1999. Scale: 1:24000 Most polygons were created by "heads-up" digitizing using scanned registered planimetric quads as a base. Some polygons were hand digitized using USGS and DOT Topo Quads and NOAA Navigational charts as base maps and checked against scanned registered DOT Planimetric Quads. Hand digitized polygons had a Root Mean Square error values ranging from 0.003 - 0.000. Map is consistent with 1:24000 scale. Plus or minus approximately 13 meters. Comparison against 1:24000 planimetric. maps.
Michigan Native American Treaty boundaries

Boundaries for Native American Treaties with the State of Michigan and the US were created from Treaty lines based on old maps and treaty descriptions to illustrate spatial relationships of statewide Treaty Boundaries. Use with caution. Authenticity is questionable. Designed for Cartographic purposes to illustrate spatial relationships of statewide Treaty Boundaries. Treaty lines are based on old maps and treaty descriptions used by Jim Ekdahl, Treaty Master. The boundaries fornative American Treaties with the State of Michigan and the US wasreprojected from Michigan georef to Decimal Degrees using the MI DNR Projection Extension.
Michigan DNR land and mineral ownership

DNR Land and Mineral Ownership compiled and mapped at quarter-quarter section level, based on parcel data in Real Estate Information System (REIS). Includes type of ownership (mineral rights, surface rights, and fee) and acreage for each type, by quarter-quarter section. State ownership files are in an ArcView shapefile format, with one shapefile for each county. Associated attribute table includes: number of parcels, type of ownership, and acreage of ownership for each quarter-quarter section.
Lake St Clair submerged aquatic vegetation raster mean plant height 2003 to 2007

Michigan DNR Fisheries Division hydroacoustic survey data (2003-2011) were collected annually at survey stations in Lake St. Claire to document the abundance and distribution of submerged aquatic vegetation. The hydrouacoustic surveys at each station consisted of 11 parallel east-west boat transects spaced 10 m apart within a square hectare plot. Not all survey stations were sampled every year; to allow for sufficient data for mapping, sonar records across multiple years were compiled and averaged on a per station basis. Plant height, percent of lake bottom covered by aquatic plants and percent of water column containing plant biomass interpolated layers were generated from this data using ordinary kriging and the ArcGIS Geostatistical Analyst. The MDNR Fisheries Research Report 2099, documenting hydroacoustic survey methods and results from the years 2003-2007. Kriging parameters are as follows: SAV_mean_height_2003_2007; no transformations, no trend removal; Gaussian semivariogram model, nugget = 0.0166, major range = 8253, partial sill = 0.0359, number of lags = 12, lag size = 1032; smooth search neighborhood, smoothing factor = 0.1
Lake St Clair submerged aquatic vegetation raster mean plant height 2008 to 2011

Michigan DNR Fisheries Division hydroacoustic survey data (2003-2011) were collected annually at survey stations in Lake St. Claire to document the abundance and distribution of submerged aquatic vegetation. The hydrouacoustic surveys at each station consisted of 11 parallel east-west boat transects spaced 10 m apart within a square hectare plot. Not all survey stations were sampled every year; to allow for sufficient data for mapping, sonar records across multiple years were compiled and averaged on a per station basis. Plant height, percent of lake bottom covered by aquatic plants and percent of water column containing plant biomass interpolated layers were generated from this data using ordinary kriging and the ArcGIS Geostatistical Analyst.The MDNR Fisheries Research Report 2099, documenting hydroacoustic survey methods and results from the years 2003-2007. Kriging parameters are as follows: SAV_mean_height_2008_2011; no transformations, no trend removal; Gaussian semivariogram model, nugget = 0.0219, major range = 8051, partial sill = 0.0421, number of lags = 12, lag size = 1006; smooth search neighborhood, smoothing factor = 0.1
Lake St Clair submerged aquatic vegetation raster mean percent bottom covered 2003 to 2007

Michigan DNR Fisheries Division hydroacoustic survey data (2003-2011) were collected annually at survey stations in Lake St. Claire to document the abundance and distribution of submerged aquatic vegetation. The hydrouacoustic surveys at each station consisted of 11 parallel east-west boat transects spaced 10 m apart within a square hectare plot. Not all survey stations were sampled every year; to allow for sufficient data for mapping, sonar records across multiple years were compiled and averaged on a per station basis. Plant height, percent of lake bottom covered by aquatic plants and percent of water column containing plant biomass interpolated layers were generated from this data using ordinary kriging and the ArcGIS Geostatistical Analyst. The MDNR Fisheries Research Report 2099, documenting hydroacoustic survey methods and results from the years 2003-2007. Kriging parameters are as follows: SAV_mean_per_cover_2003_2007; no transformations, no trend removal; Gaussian semivariogram model, nugget = 745, major range = 9902, partial sill = 579, number of lags = 12, lag size = 1089; smooth search neighborhood, smoothing factor = 0.1
Lake St Clair submerged aquatic vegetation raster mean percent bottom covered 2008 to 2011

Michigan DNR Fisheries Division hydroacoustic survey data (2003-2011) were collected annually at survey stations in Lake St. Claire to document the abundance and distribution of submerged aquatic vegetation. The hydrouacoustic surveys at each station consisted of 11 parallel east-west boat transects spaced 10 m apart within a square hectare plot. Not all survey stations were sampled every year; to allow for sufficient data for mapping, sonar records across multiple years were compiled and averaged on a per station basis. Plant height, percent of lake bottom covered by aquatic plants and percent of water column containing plant biomass interpolated layers were generated from this data using ordinary kriging and the ArcGIS Geostatistical Analyst. The MDNR Fisheries Research Report 2099, documenting hydroacoustic survey methods and results from the years 2003-2007. Kriging parameters are as follows: SAV_mean_per_cover_2008_2011; no transformations, no trend removal; Gaussian semivariogram model, nugget = 999, major range = 9063, partial sill = 759, number of lags = 12, lag size = 1133; smooth search neighborhood, smoothing factor = 0.1
Lake St Clair submerged aquatic vegetation contours mean percent of water column 2003 2007

Michigan DNR Fisheries Division hydroacoustic survey data (2003-2011) were collected annually at survey stations in Lake St. Claire to document the abundance and distribution of submerged aquatic vegetation. The hydrouacoustic surveys at each station consisted of 11 parallel east-west boat transects spaced 10 m apart within a square hectare plot. Not all survey stations were sampled every year; to allow for sufficient data for mapping, sonar records across multiple years were compiled and averaged on a per station basis. Plant height, percent of lake bottom covered by aquatic plants and percent of water column containing plant biomass interpolated layers were generated from this data using ordinary kriging and the ArcGIS Geostatistical Analyst.The MDNR Fisheries Research Report 2099, documenting hydroacoustic survey methods and results from the years 2003-2007. Kriging parameters are as follows: SAV_mean_bio_vol_2003_2007; no transformations, no trend removal; Gaussian semivariogram model, nugget = 41.8, major range = 8556, partial sill = 102, number of lags = 12, lag size = 1070; smooth search neighborhood, smoothing factor = 0.1
Lake ST Clair submerged aquatic vegetation contours mean percent of water column 2008 to 2011

Michigan DNR Fisheries Division hydroacoustic survey data (2003-2011) were collected annually at survey stations in Lake St. Claire to document the abundance and distribution of submerged aquatic vegetation. The hydrouacoustic surveys at each station consisted of 11 parallel east-west boat transects spaced 10 m apart within a square hectare plot. Not all survey stations were sampled every year; to allow for sufficient data for mapping, sonar records across multiple years were compiled and averaged on a per station basis. Plant height, percent of lake bottom covered by aquatic plants and percent of water column containing plant biomass interpolated layers were generated from this data using ordinary kriging and the ArcGIS Geostatistical Analyst.The MDNR Fisheries Research Report 2099, documenting hydroacoustic survey methods and results from the years 2003-2007. Kriging parameters are as follows: SAV_mean_bio_vol_2008_2011; no transformations, no trend removal; Gaussian semivariogram model, nugget = 39.7, major range = 7756, partial sill = 103, number of lags = 12, lag size = 970; smooth search neighborhood, smoothing factor = 0.1
Lake St Clair submerged aquatic vegetation contours mean plant height 2003 to 2007

Michigan DNR Fisheries Division hydroacoustic survey data (2003-2011) were collected annually at survey stations in Lake St. Claire to document the abundance and distribution of submerged aquatic vegetation. The hydrouacoustic surveys at each station consisted of 11 parallel east-west boat transects spaced 10 m apart within a square hectare plot. Not all survey stations were sampled every year; to allow for sufficient data for mapping, sonar records across multiple years were compiled and averaged on a per station basis. Plant height, percent of lake bottom covered by aquatic plants and percent of water column containing plant biomass interpolated layers were generated from this data using ordinary kriging and the ArcGIS Geostatistical Analyst. The MDNR Fisheries Research Report 2099, documenting hydroacoustic survey methods and results from the years 2003-2007. Kriging parameters are as follows: SAV_mean_height_2003_2007; no transformations, no trend removal; Gaussian semivariogram model, nugget = 0.0166, major range = 8253, partial sill = 0.0359, number of lags = 12, lag size = 1032; smooth search neighborhood, smoothing factor = 0.1
Lake St Clair submerged aquatic vegetation contours mean plant height 2008 to 2011

Michigan DNR Fisheries Division hydroacoustic survey data (2003-2011) were collected annually at survey stations in Lake St. Claire to document the abundance and distribution of submerged aquatic vegetation. The hydrouacoustic surveys at each station consisted of 11 parallel east-west boat transects spaced 10 m apart within a square hectare plot. Not all survey stations were sampled every year; to allow for sufficient data for mapping, sonar records across multiple years were compiled and averaged on a per station basis. Plant height, percent of lake bottom covered by aquatic plants and percent of water column containing plant biomass interpolated layers were generated from this data using ordinary kriging and the ArcGIS Geostatistical Analyst. The MDNR Fisheries Research Report 2099, documenting hydroacoustic survey methods and results from the years 2003-2007. Kriging parameters are as follows: SAV_mean_height_2003_2007; no transformations, no trend removal; Gaussian semivariogram model, nugget = 0.0166, major range = 8253, partial sill = 0.0359, number of lags = 12, lag size = 1032; smooth search neighborhood, smoothing factor = 0.1
Great Lakes Aquatic Invasive Species Locations Great Lakes basin only

Data are collected from various sources including: research reports, government reports, datasets, among others. This layer therefore is not a comprehensive representation of all aquatic invasives in the country; rather it represents observed occurances through time that have been incorporated into the database. Observances are broken into categories by inveterbrates and vertebrates, and subdivided into groups such as reptiles, viruses, plants, bryozoans, and mollusks. This dataset contains all reported observances in the NAS/GLANSIS databases as of July 2014, which was limited to coincide with the extent of the Great Lakes Basin as defined by the GLAHF Hydrology Data v1.0 watershed layer. See "Fields" section of this metadata record for information on individual fields.
Great Lakes Goodyear Spawning Atlas

Species occurrence data were obtained from the Atlas of Spawning and Nursery Areas of Great Lakes Fishes (Goodyear et al. 1982). The atlas contains information on all of the commercially and recreationally important species that use the tributaries, littoral and open-water areas of the Great Lakes as spawning and nursery habitats. Close to 9500 geo-referenced data records (occurrences of fish species) were imported into ArcView GIS. The 139 fish taxa reported in the Atlas had to be grouped into fewer broad categories to produce meaningful distribution maps. We chose three functional classification schemes. Jude and Pappas (1992) used Correspondence Analysis to partition fish species associated with the open water of each of the five Great Lakes and nine coastal wetlands. Three species complexes were suggested: a Great Lakes taxocene; a transitional taxocene, which utilized open water, near-shore, and wetlands; and a wetland taxocene. We chose this as one of the classification schemes because we are particularly interested in identifying the distribution pattern of fish with coastal wetlands; for clarity sake, we have renamed these taxocenes coastal, intermediate and open-water, respectively. For comparison, we also used Coker et al.’s (2001) classification based on temperature preferenda (5 classes) and Balon’s (1975) reproductive guild classification (32 guilds). Density maps produced by a GIS are useful for looking at distribution patterns of fish along the shoreline of the Great Lakes. These maps provide spatial information that can be easily interpreted by a wide variety of experts, as well as by the general public and help integrate interdisciplinary information and identify information gaps that are important in fish habitat and species conservation. All density maps were created in ArcView with Spatial Analyst extension. The following parameters were used to produce the density maps: Search Radius = 10 km (lake by lake); 20 km (Great Lakes) Density type = Kernel Area Units = Square Kilometres
Great Lakes Substrate Texture Raster

Input datasets were compiled by many sources over years ranging from 1968-present. The majority of sources are published journal articles with maps that were digitized. Lake Erie Habitat Task Group data was collected using side scan sonar and proofed with grab samples and underwater video. The shoreline material was extended from the shoreline to the nearshore areas (0 - 30 m of depth) areas of all the Great Lakes except Erie. The shoreline material information is from the the U.S. Army Corps of Engineers (2012) and the shoreline classification from Environment Canada Environmental Sensitivity Atlas (1990s). The shoreline material classification was extended to areas deeper than 30 m for missing data locations in northern Lake Superior. The Euclidean Allocation Tool, part of the Spatial Analyst Extension, was used to extend the shoreline material line feature class values into the nearshore and missing data areas.
Great Lakes Substrate Class Raster

Input datasets were compiled by many sources over years ranging from 1968-present. The majority of sources are published journal articles with maps that were digitized. Lake Erie Habitat Task Group data was collected using side scan sonar and proofed with grab samples and underwater video. The shoreline material was extended from the shoreline to the nearshore areas (0 - 30 m of depth) areas of all the Great Lakes except Erie. The shoreline material information is from the the U.S. Army Corps of Engineers (2012) and the shoreline classification from Environment Canada Environmental Sensitivity Atlas (1990s). The shoreline material classification was extended to areas deeper than 30 m for missing data locations in northern Lake Superior. The Euclidean Allocation Tool, part of the Spatial Analyst Extension, was used to extend the shoreline material line feature class values into the nearshore and missing data areas.
Lake Superior Upwelling 1996 to 2013

The annual upwelling index (# of days per year) was derived from the NOAA CoastWatch Great Lakes Surface Environmental Analysis (GLSEA) Sea Surface Temperature (SST) product. Using methods described in Plattner et al. (2006) and Wegscheider (2006) the upwelling was derived on a daily basis, between ordinal days 140 and 339, and creating a binary layer depict upwelling (1) or no upwelling (0). The daily upwelling layers were manually reviewed and any days at the beginning of the time frame that showed mid-lake spring warming were removed, as well as any ice cover near the end of the time frame. The last step was to add together all the days for a given lake and year to create an annual upwelling index.