The topographic frame map set TK25 is a map sheet section defined by geographical network lines with a size of 10’ geographical longitude and 6’ geographical latitude. The sheet numbering of the TK25 follows a tabular system The first two digits indicate the row (numbered from north to south), the last two digits the column (numbered from west to east).

Data for manuscript "Age-related deficits in reference frame switching of map-based navigation ability" - UNDER REVIEW

This dataset represents the cadastral maps created by the Geomatics branch in support of real property acquisitions within the Department of Water Resources. The geographic extent of each map frame was created after using all the spatial attributes available in each map to appropriately georeference it and create the extents from the outer frame of the map. The maps were digitally scanned from the original paper format that were archived after moving to the new resources building. As new maps are created by the branch for real property acquisition services, they will be georeference, attributed and updated into this dataset. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.6, dated September 27, 2023. DWR makes no warranties or guarantees either expressed or implied as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Original internal source projection for this dataset was Teale Albers/NAD83. For copies of data in the original projection, please contact DWR. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov as available and appropriate.

The map describes the voting patterns in California about the labeling of genetically modified organisms in food, ilustrated as percentages of yes votes. To design the map, first I add the layers that the UC Davis give for the assignment, then i create a spatoal join on with one to many optionr to have the information about yes votes and total votes in each county. Also is important to add the basemap to have more information and I change the symbology creating a normalization between yes votes and total number of votes to create the representation of percentages of yes in the counties and create the layout map, inserting the elements like title, north arrow, scale , date and name. I create a new data frame, to visualize a context map, adding a base map on the corner of my layot and finally add the labels of the counties names. The total ammount of votes is 13921985

The topographic frame map work TK50 is a map sheet section defined according to geographical network lines. Starting from the map sheet sections of the TK25 with a size of 10’ geographical longitude and 6’ geographical latitude, the sheet sections of the TK50 result from the aggregation of 4 TK25 sheet sections each, i.e. they have an extension of 20’ geographical longitude and 12’ geographical latitude. The sheet numbering of the TK25 follows a tabular system The first two digits indicate the row (numbered from north to south), the last two digits the column (numbered from west to east). The sheet numbering of the TK50 results from the lower left TK25 sheet number with the prefix "L" (Roman for 50).

In optical DNA mapping technologies sequence-specific intensity variations (DNA barcodes) along stretched and stained DNA molecules are produced. These “fingerprints” of the underlying DNA sequence have a resolution of the order one kilobasepairs and the stretching of the DNA molecules are performed by surface adsorption or nano-channel setups. A post-processing challenge for nano-channel based methods, due to local and global random movement of the DNA molecule during imaging, is how to align different time frames in order to produce reproducible time-averaged DNA barcodes. The current solutions to this challenge are computationally rather slow. With high-throughput applications in mind, we here introduce a parameter-free method for filtering a single time frame noisy barcode (snap-shot optical map), measured in a fraction of a second. By using only a single time frame barcode we circumvent the need for post-processing alignment. We demonstrate that our method is successful at providing filtered barcodes which are less noisy and more similar to time averaged barcodes. The method is based on the application of a low-pass filter on a single noisy barcode using the width of the Point Spread Function of the system as a unique, and known, filtering parameter. We find that after applying our method, the Pearson correlation coefficient (a real number in the range from -1 to 1) between the single time-frame barcode and the time average of the aligned kymograph increases significantly, roughly by 0.2 on average. By comparing to a database of more than 3000 theoretical plasmid barcodes we show that the capabilities to identify plasmids is improved by filtering single time-frame barcodes compared to the unfiltered analogues. Since snap-shot experiments and computational time using our method both are less than a second, this study opens up for high throughput optical DNA mapping with improved reproducibility.

This dataset consists of point intercept data, sampled with a point frame, from three 1 ha sites along an elevation and precipitation gradient within Reynolds Creek Experimental Watershed collected between late May and mid July, 2019. The lowest elevation site ('wbs1', 1,425 m) was vegetated by shrub steppe dominated Wyoming big sage (Artemisia tridentata ssp. wyomingensis). Vegetation at the middle elevation site ('los1', 1,680 m) was shrub steppe dominated by low sage (Artemisia arbuscula). Shrub steppe at the highest elevation site ('mbs1', 2,110 m) was dominated by mountain big sage (Artemisia tridentata ssp. vaseyana) and Utah snowberry (Symphoricarpos oreophilus utahensis). At each site 30 randomly located square 1 m^2 plots were sampled. The plots were oriented with one axis randomly chosen from 45, 90, 135, 180, 225, 270, 315 and 360 degrees north azimuth. A point frame of 20 pins was orientated perpendicular to the azimuth and each pin was lowered through the canopy and each contact was recorded to species or other plant material category. Whether the contacted material was photosynthetic (coded as a '+') or non-photosynthetic (coded as '-') was also recorded. Last seasons senesced plant material that is alive but not photosynthetic is coded as '.'. There may be 0, 1, 2 or more canopy hits for each pin (numbered 1 through n with 1 being the top-most canopy hit). A final basal hit is recorded for each pin and coded as hit 0. The point frame was moved so that a total of 5 rows were recorded for a total of 100 pins for each plot. The plant species codes used follow the USDA Plants Database. Resources in this dataset:Resource Title: Data from: UAS imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient . File Name: point_frame_2019_reynoldscreek.xlsxResource Description: This dataset consists of point frame data from three 1 ha sites along an elevation and precipitation gradient within Reynolds Creek Experimental Watershed collected between late May and mid July, 2019. The lowest site's ('wbs1', 1,425 m) characteristic dominant shrub is Wyoming big sage (Artemisia tridentata ssp. wyomingensis). The middle elevation site's ('los1', 1,680 m) dominant shrub is low sage (Artemisia arbuscula). The highest elevation site's ('mbs1', 2,110 m) dominant shrubs are mountain big sage (Artemisia tridentata ssp. vaseyana) and Utah snowberry (Symphoricarpos oreophilus utahensis). At each site 30 randomly located square 1 m^2 plots were sampled. The plots were oriented with one axis randomly chosen from 45, 90, 135, 180, 225, 270, 315 and 360 degrees north azimuth. A point frame of 20 pins was orientated perpendicular to the azimuth and each pin was lowered through the canopy and each contact was recorded to species or other plant material category. Whether the contacted material was photosynthetic (coded as a '+') or non-photosynthetic (coded as '-') was also recorded. Last seasons senesced plant material that is alive but not photosynthetic is coded as '.'. There may be 0, 1, 2 or more canopy hits for each pin (numbered 1 through n with 1 being the top-most canopy hit). A final basal hit is recorded for each pin and coded as hit 0. The point frame was moved so that a total of rows rows were recorded for a total of 100 pins for each plot. The plant species codes used follow the USDA Plants Database.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GeoJSON. File Name: ReynoldsCrkExpWtrshdGeoJSON.json

The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed. The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed. The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed. The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed.

The topographic frame map system TK100 is a map sheet section defined according to geographical network lines. Starting from the map sheet sections of the TK25 with a size of 10’ geographical longitude and 6’ geographical latitude, the sheet sections of the TK100 result from the aggregation of 16 TK25 sheet sections each, i.e. they have an extension of 40’ geographical longitude and 24’ geographical latitude. The sheet numbering of the TK25 follows a tabular system The first two digits indicate the row (numbered from north to south), the last two digits the column (numbered from west to east). The sheet numbering of the TK100 results from the lower left TK25 sheet number with the prefix "C" (Roman for 100).

Polygon of habitat (sf object) is saved as elgar.habLocations of all birds (sf object) is in data.birds, Razimuth data.frame that was input in the model (raw data) is in razimuth.dataLocations of the finches in data.frame format is model.matrix List of the MCMC models out stored in model.output.list

Data frame layers that incude the data and interpretations for the database plate of Lac Qui Parle County and went into the publication of the Lac Qui Parle County Geologic Atlas, Part A.The types, locations, and density of information used to prepare the Lac qui Parle County atlas are shown on this map. The data are described on the map to aid the user in assessing what types may be useful for a particular information need. The Database Map serves as a guide to the precision of the other maps in the atlas. It shows where data are sparse or lacking and interpretation and extrapolation were required to prepare the maps. All data were collected by Minnesota Geological Survey staff unless otherwise specified.

The data set contains the frame grid of the map set of the German Basic Map 1 consisting of 4191 map sheets : 5 000. In addition to the geometry of the frame grid, it also contains the number and name of each card sheet.

This map shows the number of yes votes in favour of proposition 37 in California by county. Proposition 37 looked to mandate GMO labelling on all products.The data for counties and yes_votes_proposition_37 were used and analyzed to compose this map. The two data sets were joined together. Then a spatial join was used to combine the information for the number of votes in favour of the proposition to the county in order to show the ratio of yes votes to total votes by county. A gradient symbology was used to best show the voting trends by county. The county layer was formatted to show the county names, with smaller counties only showing their labels at a close scale to prevent label cluttering when viewing the map at full scale. A second data frame was added in order to add a basemap which gives reference to where the map is located on a global scale.

The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed. The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed. The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed. The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed.

Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89,000 ha), Poland (Milicz forest district - 21,000 ha) and Slovenia (Snežnik forest - 4,700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100,000 ha including about 64,000 ha of forest and consist of more than 42 million trees of 51 different species. For each virtual landscape we provide a table (in .csv format) with the following columns:- cellID25: the unique ID of each 25x25 m² cell- sp: species latin names- n: number of trees. n is an integer >= 1, meaning that a specific set of species "sp", diameter "dbh" and height "h" can be present multiple times in a cell.- dbh: tree diameter at breast height (cm)- h: tree height (m) We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. The coordinate reference systems are EPSG: 2154 for the Bauges, EPSG: 2180 for Milicz, and EPSG: 3912 for Sneznik. The v2.0.0 presents the algorithm in its final state. Finally, we provide a proof of how our algorithm makes it possible to reach the total BA and the BA proportion of broadleaf trees provided by the ALS mapping using the alpha correction coefficient and how it maintains the Dg ratios observed on the field plots between the different species (see algorithm presented in the associated Open Research Europe article). Below is an example of R code that opens the datasets and creates a tree density map. ------------------------------------------------------------# load package library(terra) library(dplyr)

set work directory

setwd() # define path to the I-MAESTRO_data folder

load tree data

tree <- read.csv2('./sneznik/sneznik_trees.csv', sep = ',')

load spatial data

cellID <- rast('./sneznik/sneznik_cellID25.asc')

set coordinate reference system

Bauges:

crs(cellID) <- "epsg:2154"

Milicz:

crs(cellID) <- "epsg:2180"

Sneznik:

crs(cellID) <- "epsg:3912"

convert raster into dataframe

cellIDdf <- as.data.frame(cellID) colnames(cellIDdf) <- 'cellID25'

calculate tree density from tree dataframe

dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n))

merge the two dataframes

dens <- left_join(cellIDdf, dens, join_by(cellID25))

add density to raster

cellID$dens <- dens$n

plot density map

plot(cellID$dens)

Aim: Many countries lack informative and high‐resolution, wall‐to‐wall vegetation or land‐cover maps. Such maps are useful for land‐use and nature management, and for input to regional climate and hydrological models. Land‐cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location: mainland Norway, covering ca. 324 000 km2. Methods: We used presence‐absence data for 31 different VTs, mapped wall‐to‐wall in an area‐frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 117 explanatory variables, recorded in 100×100‐m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to independent evaluation dataset. Results: Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that, rare VTs are better predicted than common ones, and coastal VTs are better predicted than inland ones. Conclusions: Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.

Aim Species distribution models (SDMs) are powerful tools for assessing suitable habitats across large areas and at fine spatial resolution. Yet, the usefulness of SDMs for mapping species' realised distributions is often limited since data biases or missing information on dispersal barriers or biotic interactions hinder them from accurately delineating species' range limits. One way to overcome this limitation is to integrate SDMs with expert range maps, which provide coarse-scale information on the extent of species' ranges and thereby range limits that are complementary to information offered by SDMs.

Innovation Here, we propose a new approach for integrating expert range maps in SDMs based on an ensemble method called stacked generalisation. Specifically, our approach relies on training a meta-learner regression model using predictions from one or more SDM algorithms alongside the distance of training points to expert-defined ranges as predictor variables. We demonstrate our app..., , , # The best of two worlds: using stacked generalization for integrating expert range maps in species distribution models

https://doi.org/10.5061/dryad.6q573n65m

This repository contains Supporting Information for the article "The best of two worlds: using stacked generalization for integrating expert range maps in species distribution models" (https://doi.org/10.1111/geb.13911).

It contains three files:

1. "model_df.csv": CSV table containing modeling data frame with occurrence information (presence/background) for 49 bat species alongside values of predictors used for building SDMs
2. "predictor_stack_agg.tif": GeoTIFF containing raster stack of predictor variables used in SDMs, re-sampled to a reduced spatial resolution of 10km for demonstration purposes
3. "iucn_dists.tif": GeoTIFF containing raster stack of distance layers describing the distance of raster cells to the boundary of IUCN ranges for 49 ...

As of this writing (11/5/2010), this product had been issued by the IBEX team in two releases. Release 1 IBEX-Hi map data are superseded by Release 2 data which are more time-extensive (12 months vs. 6 months) and are richer in content. While Release 1 IBEX-Hi map data and their documentation remain accessible at ftp://nssdcftp.gsfc.nasa.gov/spacecraft_data/ibex/hi/maps/digital_data/release_1/ and ftp://nssdcftp.gsfc.nasa.gov/spacecraft_data/ibex/hi/maps/images/release_1/, this description supports the retrieval and use of Release 2 data only.

The Release_2 IBEX-Hi map data contain all-sky 6 deg x 6 deg resolution maps in J2000 ecliptic coordinates of energetic neutral fluxes reaching the near-Earth IBEX spacecraft from the distant heliosphere and heliosheath. Maps are in both a spacecraft frame and, via Compton-Getting corrections, a heliospheric frame. Basic maps are each accumulated over 6-months duration, so far for epochs 1 (12/2008-06/2009) and 2 (06/2009-12/2009). Maps are available for five of the energy bands of IBEX-Hi (0.52-0.95, 0.84-1.55, 1.36-2.50, 1.99-3.75, 3.13-6.00 keV) and, for the heliospheric frame only, also for six monoenergetic points (0.71, 1.11, 1.74, 2.73, 3.0, 4.29 keV). In addition, monoenergetic maps are available as summed over the data collected during epochs 1 and 2 combined. All maps are available in digital text format and in PNG image format (considered as separate data sets within VEPO).

Given that VHO/VEPO typically displays clickable granule (file) names for any given specification of data product and time span, and given that IBEX-Hi digital maps and PNG-formatted display maps are each considered single data products, a request for data from either product will yield .GE.20 names of files containing data spanning or within the requested time span. For IBEX, the names shown by VHO/VEPO actually have the form subdirectory/filename. The subdirectories are named map1, map1cg, map2, map2cg, and map-combined. Maps involving "1" ("2") are from the first (second) 6-month epoch, while "combined" means composed of data taken during epochs 1 and 2. Maps involving (not involving) "cg" means heliospheric frame (spacecraft frame). In the first four subdirectories, file names are of the forms ha60.hide-trp-flux100-hi-N-flux.ext and ha60.hide-trp-mono_80-P.PP-flux.ext, where "ext" is "png" or "txt" according to which product is requested, N is the energy step number for the IBEX-Hi instrument (2-6), P.PP is the energy level, in keV, of a monoenergetic flux. Files in the "combined" subdirectories are named "combined-P.PP-flux.ext, where P.PP has the same meaning as above.

In addition to the flux maps to which VEPO provides access, several map files whose data support the determination of the fluxes are also available in the underlying nssdcftp directories and are described by aareadme files there (and by IBEX web pages at SWRI: http://ibex.swri.edu/ibexpublicdata/Data_Release_2/)

The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed. The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed. The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate register information system ALKIS and visualised according to the national ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (single layers, gray combination and color combination). The data are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data are divided into different levels according to cartographic content elements. They are delivered as single-coloured single layers (layers) and as colored combination outputs in a uniform resolution. In addition, the data in the standard sheet section (with map frame and legend) are offered as PDF and as a plotted card. They are available as web services, raster data and analog card prints (plots). When using the data, the license conditions must be observed.

This data set summarized biological and environmental sampling data from Reef Visual Census (RVC) surveys in southern Florida in conjunction with remote-sensed, high-resolution mapping data to take significant strides in moving from qualitative to quantitative habitat characterization of the RVC coral reef sampling frame. The data set contains two GIS shape files, one for the Dry Tortugas regio...