Data for Figure 3.21 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.21 shows the seasonal evolution of observed and simulated Arctic and Antarctic sea ice area (SIA) over 1979-2017. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has several subplots, but they are unidentified, so the data is stored in the parent directory. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains Sea Ice Area anomalies over 1979-2017 relative to the 1979-2000 means from: - Observations (OSISAF, NASA Team, and Bootstrap) - Historical simulations from CMIP5 and CMIP6 multi-model means - Natural only simulations from CMIP5 and CMIP6 multi-model means --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - arctic files are used for the plots on the left side of the figure - antarctic files are used for the plots on the right side of the figure - _OBS_NASATeam files are used for the first row of the plot - _OBS_Bootstrap are used for the second row of the plot - _OBS_OSISAF are used for the third row of the plot - _ALL_CMIP5 are used in the fourth row of the plot - _ALL_CMIP6 are used in the fifth row of the plot - _NAT_CMIP5 are used in the sixth row of the plot - _NAT_CMIP6 are used in the seventh row of the plot --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The significance are for the grey dots, it's nan or 1 values. The data has to be overplotted to colored squares. Grey dots indicate multi-model mean anomalies stronger than inter-model spread (beyond ± 1 standard deviation). The coordinates of the data are indices, but in global attributes 'comments' of each file there are relations of indices to months, since months are the y coordinate. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

Introduction: The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees.Abstract: The existing vegetation height (EVH) data layer is an important input to LANDFIRE modeling efforts. Canopy height is generated separately for tree, shrub and herbaceous cover life forms using training data and a series of geospatial data layers. Plots from the Forest Inventory and Analysis (FIA) program of USDA Forest Service (http://fia.fs.usda.gov/) were used as the training data for tree canopy height mapping. EVH is determined by the average height weighted by species cover and based on existing vegetation type (EVT) life-form assignments. Dominant life-form height of each plot is then binned as follows: (A) Tree classes; 0-5 m, 5-10 m, 10-25 m, 25-50 m, and greater than 50 m, (B) Shrub classes; 0-0.5 m, 0.5-1.0 m, 1.0-3.0 m, greater than 3.0 m, (C) Herbaceous vegetation classes; 0-0.5 m; 0.5-1.0 m, greater than 1 m. Go to https://www.landfire.gov/participate_refdata_sub.php for more information regarding contributors of field plot data. Decision tree models using field reference data and Landsat imagery, digital elevation model data, and biophysical gradient data, are then developed separately for each of the three life forms using C5 software. Life-form specific cross-validation error matrices are generated during this process to assess levels of accuracy of the models. Decision tree relationships are then used to generate life-form specific height class spatial data layers, which are later merged into a single composite height data layer. The final EVH layer is evaluated and rectified through a series of QA/QC measures to ensure that the life-form of the cover code matched the life-form of the existing vegetation type. EVH is used in many subsequent LANDFIRE data layers.LF 2012 (lf_1.3.0) used modified LF 2010 (lf_1.2.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape 2013 and 2014. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. The final disturbance data used in LANDFIRE is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE Events data call. Vegetation growth was modeled where both disturbance and non-disturbance occurs.Urban, agriculture, and wetlands were refined to reflect a 2012 landscape using the National Conservation Easement Database, National Wetlands Inventory (NWI), and Common Land Unit database (CLU) data. Metadata and Downloads

GIFplots Files containing GIF images of spectral plots: - GIFplots_splib07a.zip contains plots of measured spectra, including * plots showing the full wavelength range of the measured spectra, organized in chapter sub-folders as described previously for the ASCII data. * plots showing specific portions of the electromagnetic spectrum are organized folders within the “plots_by_wavelength_region” folder, including: - range1_uv_to_visible (0.2 - 1.0 microns) - range2_visible_to_swir (0.2 - 2.5 microns) - range3_swir (1.5 - 5.5 microns) - range4_swir_to_mir (2.5 - 25 microns) - range5_swir_to_fir_wavenumber (4,000 - 50 cm-1 which spans 2.5 - 200 microns) - plots of spectra interpolated to a higher number of more finely-spaced channels showing the full wavelength range , organized in chapter sub-folders (GIFplots_splib07b.zip) - plots of spectra convolved to other spectrometers showing the full wavelength range of the spectrometer, organized in chapter sub-folders, for example * Analytical Spectral Devices (GIFplots_splib07b_cvASD.zip) * AVIRIS-Classic 2014 characteristics (GIFplots_splib07b_cvAVIRISc2014.zip) * Hyperspectral Mapper 2014 characteristics (GIFplots_splib07b_cvHYMAP2014.zip) * and others - plots of spectra resampled to multispectral sensors showing the full wavelength range of the sensor, organized in chapter sub-folders, for example: * Advanced Spaceborne Thermal Emission and Reflection Radiometer (GIFplots_splib07b_rsASTER.zip) * and others GENERAL LIBRARY DESCRIPTION This data release provides the U.S. Geological Survey (USGS) Spectral Library Version 7 and all related documents. The library contains spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns). Laboratory samples of specific minerals, plants, chemical compounds, and man-made materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically-constructed as well as mathematically-computed mixtures. Measurements of rocks, soils, and natural mixtures of minerals have also been made with laboratory and field spectrometers. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. The related U.S. Geological Survey Data Series publication, "USGS Spectral Library Version 7", describes the instruments used, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements (Kokaly and others, 2017). Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of ASD field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Two fundamental spectrometer characteristics significant for interpreting and utilizing spectral measurements are sampling position (the wavelength position of each spectrometer channel) and bandpass (a parameter describing the wavelength interval over which each channel in a spectrometer is sensitive). Bandpass is typically reported as the Full Width at Half Maximum (FWHM) response at each channel (in wavelength units, for example nm or micron). The linked publication (Kokaly and others, 2017), includes a comparison plot of the various spectrometers used to measure the data in this release. Data for the sampling positions and the bandpass values (for each channel in the spectrometers) are included in this data release. These data are in the SPECPR files, as separate data records, and in the American Standard Code for Information Interchange (ASCII) text files, as separate files for wavelength and bandpass. Spectra are provided in files of ASCII text format (files with a .txt file extension). In the ASCII files, deleted channels (bad bands) are indicated by a value of -1.23e34. Metadata descriptions of samples, field areas, spectral measurements, and results from supporting material analyses – such as XRD – are provided in HyperText Markup Language HTML formatted ASCII text files (files with .html file extension). In addition, Graphics Interchange Format (GIF) images of plots of spectra are provided. For each spectrum a plot with wavelength in microns on the x-axis is provided. For spectra measured on the Nicolet spectrometer, an additional GIF image with wavenumber on the x-axis is provided. Data are also provided in SPECtrum Processing Routines (SPECPR) format (Clark, 1993) which packages spectra and associated metadata descriptions into a single file (see the linked publication, Kokaly and others, 2017, for additional details on the SPECPR format and freely-available software than can be used to read files in SPECPR format). The data measured on the source spectrometers are denoted by the “splib07a” tag in filenames. In addition to providing the original measurements, the spectra have been convolved and resampled to different spectrometer and multispectral sensor characteristics. The following list specifies the identifying tag for the measured and convolved libraries and gives brief descriptions of the sensors. splib07a – this is the name of the SPECPR file containing the spectra measured on the Beckman, ASD, Nicolet and AVIRIS spectrometers. The data are provided with their original sampling positions (wavelengths) and bandpass values. The prefix “splib07a_” is at the beginning of the ASCII and GIF files pertaining to the measured spectra. splib07b – this is the name of the SPECPR file containing a modified version of the original measurements. The results from using spectral convolution to convert measurements to other spectrometer characteristics can be improved by oversampling (increasing sample density). Thus, splib07b is an oversampled version of the library, computed using simple cubic-spline interpolation to produce spectra with fine sampling interval (therefore a higher number of channels) for Beckman and AVIRIS measurements. The spectra in this version of the library are the data used to create the convolved and resampled versions of the library. The prefix “splib07b_” is at the beginning of the ASCII and GIF files pertaining to the oversampled spectra. s07_ASD – this is the name of the SPECPR file containing the spectral library measurements convolved to standard resolution ASD full range spectrometer characteristics. The standard reported wavelengths of the ASD spectrometers used by the USGS were used (2151 channels with wavelength positions starting at 350 nm and increasing in 1 nm increments). The bandpass values of each channel were determined by comparing measurements of reference materials made on ASD spectrometers in comparison to measurements made of the same materials on higher resolution spectrometers (the procedure is described in Kokaly, 2011, and discussed in Kokaly and Skidmore, 2015, and Kokaly and others, 2017). The prefix “s07ASD_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV95 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1995 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV95_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV96 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1996 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV96_” is at the beginning of the ASCII, and GIF files. s07_AV97 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1997 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV97_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV98 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1998 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV98_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV99 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1999 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV99_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV00 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2000 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV00_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer.

This data set contains photomosaic images of coral reef benthic communities, created as a product for the NOAA Coral Reef Conservation Program’s (CRCP) National Coral Reef Monitoring Program (NCRMP). Photomosaics are a composite of many underwater images, digitally stitched together into a single cohesive photo. These mosaics have approximately the same resolution and clarity of the component pictures but collectively produce a “landscape view†of the coral reef community within each plot. To produce a photomosaic, a scuba diver holds the mosaic rig, containing two separate cameras, above the reef plot while swimming back and forth in a crosshatch pattern. Images are taken from roughly one to two meters above the benthos, at a rate of one image per second per camera. This swimming technique allows the mosaic rig to gather 1500-3000 images which are then compiled into a single photomosaic using Photoscan (Agisoft). Six reef plots (10 m x 10 m each) were captured at Cheeca Rocks Coral Reef, Islamorada, Florida as part of this dataset. A total of six TIFF files are included in this data submission, each corresponding to one reef plot.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

The Great Isaiah Scroll (1QIsaa) data set for writer identification This data set is collected for the ERC project: The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls PI: Mladen Popović Grant agreement ID: 640497 Project website: https://cordis.europa.eu/project/id/640497 Copyright (c) University of Groningen, 2021. All rights reserved. Disclaimer and copyright notice for all data contained on this .tar.gz file: 1) permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes. 2) provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed. 3) provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data. 4) the user should refer to the first public article on this data set: Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). arXiv preprint arXiv:2010.14476. BibTeX: @article{popovic2020artificial, title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)}, author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert}, journal={arXiv preprint arXiv:2010.14476}, year={2020} } 5) the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy. Organisation of the data: The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions. The 'images' directory contains NetPBM images of the columns of 1QIsaa. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (arxiv.org/abs/1911.07930) system, and the other one is the manually cleaned version of the binarized output. The file names for the direct binarized output are of the format '1QIsaa_col

Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening hundreds and thousands of varieties, and policymakers in decisions contributing to the overall improvement of agriculture and food production systems. Effective, generalizable approaches to track plant growth and predict yield at the individual plot level require large matched datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional and field scales. Despite their lower spatial resolution, satellite platforms exhibit multiple logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments integrated with the ground truth measurements are not yet publicly available. Here we generated, described, and evaluated a set of more than 20,000 plot-level images of over 80 hybrid maize (Zea mays) varieties grown in six locations across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth measurements of crop yield. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing data collected from drones for both within-environment and cross-environment yield prediction. Large, multimodal, multi-environment, genetically diverse training datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important new addition to the tool of farmers, plant geneticists, crop breeders, and policymakers. Methods UAV Image Acquisition and Processing UAV visible spectral (RGB) imagery was collected at three time points per location. The goal was to acquire images of maize during the vegetative, reproductive, and post-flowering growth stages from the fields at each location, capturing images at three different time points (Supplemental Data Set S1). In Scottsbluff, NE, images were acquired with DJI Matrice 600 Pro with DJI Zenmuse X3 and a 12 Mega Pixel (MP) RGB (red, green, blue) camera as an image acquisition sensor. Images were acquired at an altitude of 100 ft (30.48 m) with a front overlap of 90% and a side overlap of 65%. In North Platte, images were acquired using DJI Inspire 2 with a Sentra Double 4K AG+ RGB camera as an image sensor at an altitude of 50 ft (15.24 m) with front and side overlap of 70%. In Lincoln, images were acquired with a DJI Phantom 4 RTK with a DJI Zenmuse P1 camera, with a 45 MP RGB camera as an image acquisition sensor. Images were acquired at an altitude of 115 ft (35 m) with front and side overlap of 80%. In Missouri Valley, Ames, and Crawfordsville, IA, DJI Phantom 4 Pro V2.0 with DJI 20 MP RGB cameras was used as an image acquisition sensor, and images were acquired at an altitude of 100 ft (30.48 m) with front and side overlap of 80%. The UAV images were processed and stitched using Pix4D Mapper 4.8.4 (Pix4D 2024) and AgiSoft Metashape 1.8.4 (Agisoft Metashape 2024), photogrammetric software to create RGB orthomosaic images using default parameters during image processing. Satellite Image Acquisition Pléiades Neo was used to capture images at all locations at six different time points (approximately two weeks apart), with the first three time points close to the dates of the three UAV image acquisitions at each location. Table 1 shows the specifications of this satellite constellation. The average widths of the six bands in satellite multispectral images are as follows: Red (620 – 690 nm), Green (530 – 590 nm), Blue (450 – 520 nm), Near-infrared (NIR, 770 – 880 nm), Red Edge (700 – 750 nm), and Deep Blue (400 – 450 nm). Along with multispectral images, a single-band panchromatic raster file with a wide width band of approximately 450-800 nm was generated. Each image captured a total area of 100 km × 100 km per location, covering the entire experimental field at each location simultaneously. Final 16-bit GeoTIFF satellite images with 30-cm resolution were generated and provided to us after panchromatic sharpening or pan-sharpening using panchromatic band image files, manual ortho-rectification, and atmospheric correction by Pleiades Neo. Assigning Plot-level Labels to Images For precise segmentation of small plots from satellite images, a single UAV image captured at a single time point at each location was used as a reference for plot segmentation due to the low resolution of the satellite image. With available UAV images, before the plot-level segmentation, satellite images were registered to the UAV image captured at the nearest date to the date of satellite image capture using ArcGIS Pro 10.8.2 through georeferencing. Registration was performed using the ground control points installed at either side of each field when available. able field features in satellite and UAV images were used for accurate registration as much as possible. After registration, the first-time point registered UAV and satellite image pair were used for plot segmentation for each location. All computation was performed using the ArcPy Jupyter Notebook environment implemented in ArcGIS Pro V3.2.0. Plot grids generated as described above were used to crop field images into plots. For each plot grid, a minimum bounding rectangle with four corners of rectangles surrounding/enclosing the plot grids was generated. When pixels inside the rectangle lacked values due to the geographical orientation of the fields, those regions were masked with zero pixel values for further data acquisition. Plot grids generated from the images at the first time point were used to segment the corresponding satellite images within the same location from all other time points.

The maize root system is crucial for plant establishment as well as water and nutrient uptake. There is substantial genetic and phenotypic variation for root architecture, which gives opportunity for selection. Root traits, however, have not been used as selection criterion mainly due to the difficulty in measuring them, as well as their quantitative mode of inheritance. Seedling root traits offer an opportunity to study multiple individuals and to enable repeated measurements per year as compared to adult root phenotyping. We developed a new software framework to capture various traits from a single image of seedling roots. This framework is based on the mathematical notion of converting images of roots into an equivalent graph. This allows automated querying of multiple traits simply as graph operations. This framework is furthermore extendable to 3D tomography image data. In order to evaluate this tool, a subset of the 384 inbred lines from the Ames panel, for which extensive genotype by sequencing data are available, was investigated. A genome wide association study was applied to this panel for two traits, Total Root Length and Total Surface Area, captured from seedling root images from WinRhizo Pro 9.0 and the current framework (called ARIA) for comparison using 135,311 single nucleotide polymorphism markers. The trait Total Root Length was found to have significant SNPs in similar regions of the genome when analyzed by both programs. This high-throughput trait capture software system allows for large phenotyping experiments and can help to establish relationships between developmental stages between seedling and adult traits in the future.

This is Image Data for Exp 1 to 30 described from "Quantum Computing Dataset of Maximum Independent Set Problem on King’s Lattice of over Hundred Rydberg Atoms". Raw image files are in TIF format, constituting a list of image files structured as a three-dimensional array. The first two dimensions denote the image, while the third dimension represents the stack of images across experimental repetitions. Due to the substantial file size, raw data is segmented into multiple files for the same experiments, named as ‘Exp{Exp#}_X{#}.tif’, where Exp# corresponds to the experiment index, and # is assigned in sequential order. The raw data can be utilized to generate fluorescence data (‘flouAreshape’) and digitized data (‘floudigreshape’), provided in https://doi.org/10.6084/m9.figshare.23828004, using the code (‘Digitze_Data.m’) available on https://doi.org/10.6084/m9.figshare.23911368.Finding the maximum independent set (MIS) of a large-size graph is a nondeterministic polynomial-time (NP)-complete problem not efficiently solvable with classical computations but may be suitable for quantum computation. In recent years, there are growing interests in using Rydberg-atom arrays to solve the MIS problem. Here, we report a set of quantum adiabatic computing data of Rydberg-atom experiments performed with up to 141 atoms randomly arranged on the King’s lattice. A total of 582,916 events of Rydberg-atom measurements are collected for experimental MIS solutions of 733,853 different graphs. We provide the raw image data along with the entire binary determinations of the measured many-body ground states and the classified graph data, to offer bench-mark testing and advanced data-driven analyses for validation of the performance of the Rydberg-atom approach as well as system improvements.

Two ENC display services provide simple HTTP interfaces for user applications to request geo-registered nautical chart images from NOAA databases for display in online and offline applications for which a basemap of nautical chart data is desired, including GIS, web-based, and mobile mapping applications.

The chart images are rendered from the latest NOAA electronic navigational chart (NOAA ENC®) data. The ENC data and the chart images derived from it are updated weekly. Each display service portrays the ENC data with a different symbology set.

The ECDIS Display Service uses symbology developed by the International Hydrographic Organization (IHO) for the display of ENC data on Electronic Chart Display and Information Systems (ECDIS) that large ocean- going vessels and many smaller commercial ships use for navigation. This symbol set is commonly referred to by its IHO specification number, "S-52," or as "ECDIS symbology."

The ENC Viewer portrays ENC data using this ECDIS symbology.

ECDIS Display Service ECDIS Display Service rendering of ENC along the Columbia River with symbology specified by the IHO. https://www.nauticalcharts.noaa.gov/data/gis-data-and-services.html#enc-display-services

1999-2000 forward. The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data can be plotted as trends and stratified by age group, sex, and race/ethnicity.

This dataset is the dataset used to train and test the object condensation particle flow approach described in arxiv:2002.03605.

The data can be read with DeepJetCore 3.1 (https://github.com/DL4Jets/DeepJetCore) The entries in the truth array are of dimension (batch, 200, N_truth). The truth inputs are:

isElectron, isGamma, isPositron, true_energy, true_x, true_y

The entries in the feature array are of dimension (batch, 200, N_features), with the features being:

rechit_energy, rechit_x, rechit_y, rechit_z, rechit_layer, rechit_detid

The "train.zip" file contains the training sample The "test.zip" file the test sample

The main test sample is identical to the training sample in composition, but statistically independent. Other samples can be found in subfolders:

test/flatNpart: sample with flat distribution of additional particles in the event w.r.t. each individual particle Test/hiNPart: sample with up to 15 particles per event

We demonstrate the outcomes of our graph-based diffusion method that employs random walk with restart on a multi-layered graph using the publicly available Pl@ntleaves (H. Go¨eau, P. Bonnet, A. Joly, N. Boujemaa, D. Barth´el´emy, J.-F. Molino, P. Birnbaum, E. Mouysset, M. Picard, The clef 2011 plant images classification task, Vol. 1177, 2011.) dataset. This dataset consists of 233 high-resolution leaf images captured in their natural surroundings. The images present various challenges for segmentation, including shadows, varying lighting conditions, and overlapping leaves. Our algorithm focuses on leaf portions by spreading intensity scores from foreground templates to image boundaries. By applying a threshold to the saliency maps generated through the diffusion process, we obtain binary masks that separate the leaves from the backgrounds. Ground truth images are provided to visually assess the effectiveness of our algorithm's performance. Folders description: * JPEGimages: Leaf color images. * masks: The ground truth binary masks that accurately delineat the leaf regions. * foreground_template: contains the bounding boxes that localize the leaves in blue and the foreground templates in red drawn on dataset images. * saliency_maps: contains saliency maps obtained by diffusing foreground queries within a multi-layer graph. * segmentation_results : contains the segmentation results obtained after thresholding the saliency maps. *MLG_Segmentation_results: a compressed folder containing the above folders.

HTMLmetadata Text files in HTML-format containing metadata about samples and spectra. Also included in the zip file are folders containing information linked to from the HTML files, including: - README: contains a HTML version of the USGS Data Series publication, linked to this data release, that describes this spectral library (Kokaly and others, 2017). The folder also contains an HTML version of the release notes. - photo_images: contains full resolution images of photos of samples and field sites. - photo_thumbs: contains low-resolution thumbnail versions of photos of samples and field sites. GENERAL LIBRARY DESCRIPTION This data release provides the U.S. Geological Survey (USGS) Spectral Library Version 7 and all related documents. The library contains spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns). Laboratory samples of specific minerals, plants, chemical compounds, and man-made materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically-constructed as well as mathematically-computed mixtures. Measurements of rocks, soils, and natural mixtures of minerals have also been made with laboratory and field spectrometers. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. The related U.S. Geological Survey Data Series publication, "USGS Spectral Library Version 7", describes the instruments used, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements (Kokaly and others, 2017). Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of ASD field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Two fundamental spectrometer characteristics significant for interpreting and utilizing spectral measurements are sampling position (the wavelength position of each spectrometer channel) and bandpass (a parameter describing the wavelength interval over which each channel in a spectrometer is sensitive). Bandpass is typically reported as the Full Width at Half Maximum (FWHM) response at each channel (in wavelength units, for example nm or micron). The linked publication (Kokaly and others, 2017), includes a comparison plot of the various spectrometers used to measure the data in this release. Data for the sampling positions and the bandpass values (for each channel in the spectrometers) are included in this data release. These data are in the SPECPR files, as separate data records, and in the American Standard Code for Information Interchange (ASCII) text files, as separate files for wavelength and bandpass. Spectra are provided in files of ASCII text format (files with a .txt file extension). In the ASCII files, deleted channels (bad bands) are indicated by a value of -1.23e34. Metadata descriptions of samples, field areas, spectral measurements, and results from supporting material analyses – such as XRD – are provided in HyperText Markup Language HTML formatted ASCII text files (files with .html file extension). In addition, Graphics Interchange Format (GIF) images of plots of spectra are provided. For each spectrum a plot with wavelength in microns on the x-axis is provided. For spectra measured on the Nicolet spectrometer, an additional GIF image with wavenumber on the x-axis is provided. Data are also provided in SPECtrum Processing Routines (SPECPR) format (Clark, 1993) which packages spectra and associated metadata descriptions into a single file (see the linked publication, Kokaly and others, 2017, for additional details on the SPECPR format and freely-available software than can be used to read files in SPECPR format). The data measured on the source spectrometers are denoted by the “splib07a” tag in filenames. In addition to providing the original measurements, the spectra have been convolved and resampled to different spectrometer and multispectral sensor characteristics. The following list specifies the identifying tag for the measured and convolved libraries and gives brief descriptions of the sensors. splib07a – this is the name of the SPECPR file containing the spectra measured on the Beckman, ASD, Nicolet and AVIRIS spectrometers. The data are provided with their original sampling positions (wavelengths) and bandpass values. The prefix “splib07a_” is at the beginning of the ASCII and GIF files pertaining to the measured spectra. splib07b – this is the name of the SPECPR file containing a modified version of the original measurements. The results from using spectral convolution to convert measurements to other spectrometer characteristics can be improved by oversampling (increasing sample density). Thus, splib07b is an oversampled version of the library, computed using simple cubic-spline interpolation to produce spectra with fine sampling interval (therefore a higher number of channels) for Beckman and AVIRIS measurements. The spectra in this version of the library are the data used to create the convolved and resampled versions of the library. The prefix “splib07b_” is at the beginning of the ASCII and GIF files pertaining to the oversampled spectra. s07_ASD – this is the name of the SPECPR file containing the spectral library measurements convolved to standard resolution ASD full range spectrometer characteristics. The standard reported wavelengths of the ASD spectrometers used by the USGS were used (2151 channels with wavelength positions starting at 350 nm and increasing in 1 nm increments). The bandpass values of each channel were determined by comparing measurements of reference materials made on ASD spectrometers in comparison to measurements made of the same materials on higher resolution spectrometers (the procedure is described in Kokaly, 2011, and discussed in Kokaly and Skidmore, 2015, and Kokaly and others, 2017). The prefix “s07ASD_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV95 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1995 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV95_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV96 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1996 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV96_” is at the beginning of the ASCII, and GIF files. s07_AV97 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1997 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV97_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV98 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1998 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV98_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV99 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1999 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV99_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV00 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2000 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV00_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV01 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2001 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV01_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV05 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2005 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV05_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV06 – this is the name of the SPECPR file containing the spectral library measurements convolved to

The data set has been downloaded via the OAI-PMH endpoint of the Berlin State Library/Staatsbibliothek zu Berlin’s Digitized Collections (https://digital.staatsbibliothek-berlin.de/oai) on March 1^st 2019 and converted into common tabular formats on the basis of the provided Dublin Core metadata. It contains 146,000 records.

In addition to the bibliographic metadata, representative images of the works have been downloaded, resized to a 512 pixel maximum thumbnail image and saved in JPEG format. The image data is split into title pages and first pages. Title pages have been derived from structural metadata created by scan operators and librarians. If this information was not available, first pages of the media have been downloaded. In case of multi-volume media, title pages are not available.

In total, 141,206 images title/first pages are available.

Furthermore, the tabular data has been cleaned and extended with geo-spatial coordinates provided by the OpenStreetMap project (https://www.openstreetmap.org). The actual data processing steps are summarized in the next section. For the sake of transparency and reproducibility, the original data taken from the OAI-PMH endpoint is still present in the table.

To conclude with, various graphs in GML file format are available that can be loaded directly into graph analysis tools such as Gephi (https://gephi.org/).

The implementation of the data processing steps (incl. graph creation) are available as a Jupyter notebook provided at https://github.com/elektrobohemian/SBBrowse2018/blob/master/DataProcessing.ipynb.

Tabular Metadata

The metadata is available in Excel (cleanedData.xlsx) and CSV (cleanedData.csv) file formats with equal content.

The table contains the following columns. Italique columns have not been processed.

· title The title of the medium

· creator Its creator (family name, first name)

· subject A collection’s name as provided by the library

· type The type of medium

· format A MIME type for full metadata download

· identifier An additional identifier (most often the PPN)

· language A 3-letter language code of the medium

· date The date of creation/publication or a time span

· relation A relation to a project or collection a medium has been digitized for.

· coverage The location of publication or origin (ranging from cities to continents)

· publisher The publisher of the medium.

· rights Copyright information.

· PPN The unique identifier that can be used to find more information about the current medium in all information systems of Berlin State Library/Staatsbibliothek zu Berlin.

· spatialClean In case of multiple entries in coverage, only the first place of origin has been extracted. Additionally, characters such as question marks, brackets, or the like have been removed. The entries have been normalized regarding whitespaces and writing variants with the help of regular expressions.

· dateClean As the original date may contain various format variants to indicate unclear creation dates (e.g., time spans or question marks), this field contains a mapping to a certain point in time.

· spatialCluster The cluster ID determined with the help of the Jaro-Winkler distance on the spatialClean string. This step is needed because the spatialClean fields still contain a huge amount of orthographic variants and latinizations of geographic names.

· spatialClusterName A verbal cluster name (controlled manually).

· latitude The latitude provided by OpenStreetMap of the spatialClusterName if the location could be found.

· longitude The longitude provided by OpenStreetMap of the spatialClusterName if the location could be found.

· century A century derived from the date.

· textCluster A text cluster ID on the basis of a k-means clustering relying on the title field with a vocabulary size of 125,000 using the tf*idf model and k=5,000.

· creatorCluster A text cluster ID based on the creator field with k=20,000.

· titleImage The path to the first/title page relative to the img/ subdirectory or None in case of a multi-volume work.

Other Data

graphs.zip

Various pre-computed graphs.

img.zip

First and title pages in JPEG format.

json.zip

JSON files for each record in the following format:

ppn "PPN57346250X"

dateClean "1625"

title "M. Georgii Gutkii, Gymnasii Berlinensis Rectoris Habitus Primorum Principiorum, Seu Intelligentia; Annexae Sunt Appendicis loco Disputationes super eodem habitu tum in Academia Wittebergensi, tum in Gymnasio Berlinensi ventilatae"

creator "Gutke, Georg"

spatialClusterName "Berlin"

spatialClean "Berolini"

spatialRaw "Berolini"

mediatype "monograph"

subject "Historische Drucke"

publisher "Kallius"

lat "52.5170365"

lng "13.3888599"

textCluster "45"

creatorCluster "5040"

titleImage "titlepages/PPN57346250X.jpg"

SPECPRsplib07 This compressed archive includes: 1) Files, in SPECPR format, containing spectral data and associated metadata descriptions: - measured spectra (splib07a) - spectra interpolated to a higher number of more finely-spaced channels (splib07b) - spectra convolved to other spectrometers, for example * Analytical Spectral Devices standard resolution (s07_ASD) * AVIRIS-Classic 2014 characteristics (s07_AV14) * Hyperspectral Mapper (HyMap) 2014 characteristics (s07_HY14) * and others - spectra resampled to multispectral sensors: * ASTER * Landsat 8 OLI * Sentinel-2 MSI * Worldview-3 2) Folders containing information linked to from the metadata descriptions in the SPECPR files: - README: contains a HTML version of the USGS Data Series publication, linked to this data release, that describes this spectral library (Kokaly and others, 2017). The folder also contains an HTML version of release notes. - photo_images: contains full resolution images of photos of samples and field sites. - photo_thumbs: contains low-resolution thumbnail versions of photos of samples and field sites. 3) A folder (alternativeSPECPR) contains SPECPR files that store just the spectral data, without the associated metadata descriptions of the spectra and samples. These files have consistent data record numbering in all files. GENERAL LIBRARY DESCRIPTION This data release provides the U.S. Geological Survey (USGS) Spectral Library Version 7 and all related documents. The library contains spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns). Laboratory samples of specific minerals, plants, chemical compounds, and man-made materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically-constructed as well as mathematically-computed mixtures. Measurements of rocks, soils, and natural mixtures of minerals have also been made with laboratory and field spectrometers. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. The related U.S. Geological Survey Data Series publication, "USGS Spectral Library Version 7", describes the instruments used, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements (Kokaly and others, 2017). Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of ASD field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Two fundamental spectrometer characteristics significant for interpreting and utilizing spectral measurements are sampling position (the wavelength position of each spectrometer channel) and bandpass (a parameter describing the wavelength interval over which each channel in a spectrometer is sensitive). Bandpass is typically reported as the Full Width at Half Maximum (FWHM) response at each channel (in wavelength units, for example nm or micron). The linked publication (Kokaly and others, 2017), includes a comparison plot of the various spectrometers used to measure the data in this release. Data for the sampling positions and the bandpass values (for each channel in the spectrometers) are included in this data release. These data are in the SPECPR files, as separate data records, and in the American Standard Code for Information Interchange (ASCII) text files, as separate files for wavelength and bandpass. Spectra are provided in files of ASCII text format (files with a .txt file extension). In the ASCII files, deleted channels (bad bands) are indicated by a value of -1.23e34. Metadata descriptions of samples, field areas, spectral measurements, and results from supporting material analyses – such as XRD – are provided in HyperText Markup Language HTML formatted ASCII text files (files with .html file extension). In addition, Graphics Interchange Format (GIF) images of plots of spectra are provided. For each spectrum a plot with wavelength in microns on the x-axis is provided. For spectra measured on the Nicolet spectrometer, an additional GIF image with wavenumber on the x-axis is provided. Data are also provided in SPECtrum Processing Routines (SPECPR) format which packages spectra and associated metadata descriptions into a single file (see the linked publication, Kokaly and others, 2017, for additional details on the SPECPR format and freely-available software than can be used to read files in SPECPR format). The data measured on the source spectrometers are denoted by the “splib07a” tag in filenames. In addition to providing the original measurements, the spectra have been convolved and resampled to different spectrometer and multispectral sensor characteristics. The following list specifies the identifying tag for the measured and convolved libraries and gives brief descriptions of the sensors. splib07a – this is the name of the SPECPR file containing the spectra measured on the Beckman, ASD, Nicolet and AVIRIS spectrometers. The data are provided with their original sampling positions (wavelengths) and bandpass values. The prefix “splib07a_” is at the beginning of the ASCII and GIF files pertaining to the measured spectra. splib07b – this is the name of the SPECPR file containing a modified version of the original measurements. The results from using spectral convolution to convert measurements to other spectrometer characteristics can be improved by oversampling (increasing sample density). Thus, splib07b is an oversampled version of the library, computed using simple cubic-spline interpolation to produce spectra with fine sampling interval (therefore a higher number of channels) for Beckman and AVIRIS measurements. The spectra in this version of the library are the data used to create the convolved and resampled versions of the library. The prefix “splib07b_” is at the beginning of the ASCII and GIF files pertaining to the oversampled spectra. s07_ASD – this is the name of the SPECPR file containing the spectral library measurements convolved to standard resolution ASD full range spectrometer characteristics. The standard reported wavelengths of the ASD spectrometers used by the USGS were used (2151 channels with wavelength positions starting at 350 nm and increasing in 1 nm increments). The bandpass values of each channel were determined by comparing measurements of reference materials made on ASD spectrometers in comparison to measurements made of the same materials on higher resolution spectrometers (the procedure is described in Kokaly, 2011, and discussed in Kokaly and Skidmore, 2015, and Kokaly and others, 2017). The prefix “s07ASD_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV95 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1995 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV95_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV96 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1996 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV96_” is at the beginning of the ASCII, and GIF files. s07_AV97 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1997 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV97_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV98 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1998 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV98_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV99 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1999 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV99_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV00 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2000 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV00_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV01 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the

This data set contains landscape mosaic images, created as a product for the NOAA Coral Reef Conservation Program’s (CRCP) National Coral Reef Monitoring Program (NCRMP). Orthorectified mosaic images of six reef plots were captured of a coral reef near Salt River in St. Croix, US Virgin Islands. Each reef plot is made up of a 10m x 10m transect area (100 m2). Landscape mosaics are a composite of many underwater images stitched together. These mosaics have the clarity and pixel size of the individual pictures but collectively produce a “landscape view†of the coral reef community within each transect. A scuba diver holds the mosaic rig, containing two separate cameras, above the transect while swimming in a lawnmower pattern creating a crosshatching design. The diver takes these underwater images about one to two meters above the seabed at a rate of one image per second per camera. This swimming technique allows the mosaic rig to gather 1500-3000 images which are then merged into a single “landscape mosaic image†file via Agisoft Photoscan® software. A total of six TIFF (.tif) files are included in this dataset, one TIFF file corresponds to one reef transect plot area.

This data set contains landscape mosaic images, created as a product for the NOAA Coral Reef Conservation Program’s (CRCP) National Coral Reef Monitoring Program (NCRMP). Orthorectified mosaic images of six reef plots were captured of a coral reef in East Flower Garden Banks National Marine Sanctuary. Each reef plot is made up of a 10m x 10m transect area (100 m2). Landscape mosaics are a composite of many underwater images stitched together. These mosaics have the clarity and pixel size of the individual pictures but collectively produce a “landscape view†of the coral reef community within each transect. A scuba diver holds the mosaic rig, containing two separate cameras, above the transect while swimming in a lawnmower pattern creating a crosshatching design. The diver takes these underwater images about one to two meters above the seabed at a rate of one image per second per camera. This swimming technique allows the mosaic rig to gather 1500-3000 images which are then merged into a single “landscape mosaic image†file via Agisoft Photoscan® software. A total of six TIFF (.tif) files are included in this dataset, one TIFF file corresponds to one reef transect plot area.

The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Canopy height (CH) describes the average height of the top of the canopy for a stand. A spatially-explicit map of canopy height supplies information for fire behavior models such as FARSITE (Finney 1998) to determine the starting point for embers in the spotting model, calculate wind reductions, and compute the volume of crown fuels. In FARSITE, canopy characteristics are used to compute shading for fuel moisture calculations, wind reduction factors, spotting distances, crown fuel volume, spread characteristics of crown fires and incorporate the effects of ladder fuels for transitions from a surface to crown fire. Canopy characteristics refer to the tree canopy. Where there are tree canopies, i.e. existing vegetation types that are forest and woodland, LANDFIRE has attributed the grid with canopy characteristics with some exceptions. There will be no canopy characteristics in fuel types where the tree canopy is considered a part of the surface fuel and the surface fire behavior fuel model is chosen as such. This is because LANDFIRE assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model parameters. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. CH is derived from Existing Vegetation Height (EVH) using the Landfire Total Fuel Change (LFTFC) ArcGIS toolbar. Forested EVH values are reclassified from the five Landfire EVH codes to represent the midpoint of the classification. CH values are represented in meters times 10 starting with 25 and ending at 500. Where EVH is not forested or the tree height is considered to be part of the surface fuel CH receives a value of 0. Certain types of agriculture that a deemed burnable get a value added by LFTFC based on region and type of vegetation. Field plot data contributed either directly or indirectly to this LANDFIRE National data product. Go to https://landfire.gov/participate_refdata_sub.php for more information regarding contributors of field plot data. LANDFIRE 2014 (lf_1.4.0) and used LANDFIRE 2012 (lf_1.3.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape after 2012. Specific examples of disturbance are: fire, vegetation management, wind, and insect and disease. Disturbance data used in the updating is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE events data call. Vegetation growth was modeled where disturbance occurred. Metadata and Downloads

This data set contains landscape mosaic images, created as a product for the NOAA Coral Reef Conservation Program’s (CRCP) National Coral Reef Monitoring Program (NCRMP). Orthorectified mosaic images of five reef plots were captured of a coral reef in Flower Garden Banks National Marine Sanctuary. Due to deteriorating weather, this research cruise was cut short and transect 4 was not able to be photographed. Each reef plot is made up of a 10m x 10m transect area (100 m2). Landscape mosaics are a composite of many underwater images stitched together. These mosaics have the clarity and pixel size of the individual pictures but collectively produce a “landscape view†of the coral reef community within each transect. A scuba diver holds the mosaic rig, containing two separate cameras, above the transect while swimming in a lawnmower pattern creating a crosshatching design. The diver takes these underwater images about one to two meters above the seabed at a rate of one image per second per camera. This swimming technique allows the mosaic rig to gather 1500-3000 images which are then merged into a single “landscape mosaic image†file via Agisoft Photoscan® software. A total of five TIFF (.tif) files are included in this dataset, one TIFF file corresponds to one reef transect plot area.