Histograms of the DARDAR product in the North Atlantic, stratified by the low-level instability parameter. The data are complemented with two Python functions that generate some statistical analysis figures.

This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A25,3A26Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.This is the GPM-like formatted TRMM Precipitation Radar (PR) monthly gridded data, first released with the "V8" TRMM reprocessing. The TRMM radar Level 3 grids are now consistent with the GPM Dual-frequency Precipitation Radar (DPR). The closest ancestor of this dataset was the monthly radar statistics 3A25.This product consists of monthly statistics of the PR measurements at 0.25x0.25 degrees, and monthly histograms and statistics at 5x5 degrees, horizontal resolution.The objective of the algorithm is to calculate various daily statistics from the level 2 PRoutput products. Four types of statistics are calculated:1. Probabilities of occurrence (count values)2. Means and standard deviations3. Histograms4. Correlation coefficientsIn all cases, the statistics are conditioned on the presence of rain or some other quantity suchas the presence of stratiform rain or the presence of a bright-band. For example, to computethe unconditioned mean rain rate, the conditional mean must be multiplied by the probabilityof rain which, in turn is calculated from the ratio of rain counts to the total number ofobservations in the box of interest.The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. The low resolution 5x5 deg grid covers 70S to 70N. Areas beyond the ±40 degrees of latitudes are padded with empty grid cells.

This is the new (GPM-formated) TRMM product. It replaces the old TRMM_3A25,3A26Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.This is the GPM-like formatted TRMM Precipitation Radar (PR) monthly gridded data, first released with the "V8" TRMM reprocessing. The TRMM radar Level 3 grids are now consistent with the GPM Dual-frequency Precipitation Radar (DPR). The closest ancestor of this dataset was the monthly radar statistics 3A25.This product consists of monthly statistics of the PR measurements at 0.25x0.25 degrees, and monthly histograms and statistics at 5x5 degrees, horizontal resolution.The objective of the algorithm is to calculate various daily statistics from the level 2 PRoutput products. Four types of statistics are calculated:1. Probabilities of occurrence (count values)2. Means and standard deviations3. Histograms4. Correlation coefficientsIn all cases, the statistics are conditioned on the presence of rain or some other quantity suchas the presence of stratiform rain or the presence of a bright-band. For example, to computethe unconditioned mean rain rate, the conditional mean must be multiplied by the probabilityof rain which, in turn is calculated from the ratio of rain counts to the total number ofobservations in the box of interest.The grids are in the Planetary Grid 2 structure matching the Dual-frequency PR on the core GPM observatory that covers 67S to 67N degrees of latitudes. The low resolution 5x5 deg grid covers 70S to 70N. Areas beyond the ±40 degrees of latitudes are padded with empty grid cells.

Students are introduced to concepts of sampling distributions, p-values, and hypothesis testing. Using both simulated and real data for methylmercury level in fish populations, students will determine whether observations fall within government safety guidelines for safe consumption.

This data release includes the data used to generate histograms that compared total watershed pollutant removal efficiency (TWPRE) in the two study watersheds Crystal Rock (traditional watershed) and Tributary (Trib.) 104 low impact development (LID watershed) to determine if LID BMP design offered an improved water quality benefit. Input/calibrants data used in the model (Monte Carlo) are described in the manuscript as mentioned in the list below: -BMP Name and Type: references in the manuscript -BMP Connectivity: Proprietary (derived from Montgomery County GIS Data) -BMP Drainage Areas: Proprietary (derived from Montgomery County GIS Data) -BMP Efficiency Ranges: referenced in manuscript -Baseline Pollutant Loadings: referenced in manuscript Stormwater runoff and associated pollutants from urban areas in the Chesapeake Bay Watershed represent a serious impairment to local streams and downstream ecosystems, despite urbanized land comprising only 7% of the Bay watershed area. Excess nitrogen, phosphorus, and sediment affect local streams in the Bay watershed by causing problems ranging from eutrophication and toxic algal blooms to reduced oxygen levels and loss of biodiversity. Traditional management of urban stormwater has primarily focused on directing runoff away from developed areas as quickly as possible. More recently, stormwater best management practices (BMPs) have been implemented in a low impact development (LID) manner on the landscape to treat stormwater runoff closer to its source.The objective of this research was to use a modeling approach to compare total watershed pollutant removal efficiency (TWPRE) of two watersheds with differing spatial patterns of SW BMP design (traditional and LID), and determine if LID SW BMP design offered an improved water quality benefit.

Abstract A method is described and implemented which reduces the size of a histograms data base arising from a large set of histograms. The program compresses the histogram data before storage, avoiding significant loss of information and can be adapted to sample more densely areas of interest. The method has been applied to image processing where histograms were numbers of counts for the 256 gray levels of a subsampled image of millions of pixels.

Title of program: NGRC, CRSUP, RES-FIT, SPEC-FIT Catalogue Id: ADCE_v1_0

Nature of problem The program NGRC was written to implement a gamma response function interpolation scheme for a 5" x 5" NaI(Tl) spectrometer. The program takes a library of response functions that were measured at specified energies and constructs response functions at any intermediate energy. These response functions are used in the unfolding of continuous gamma spectra measured by the same spectrometer. The number of response functions to be constructed is input by the user and NGRC places their centroids so t ...

Versions of this program held in the CPC repository in Mendeley Data ADCE_v1_0; NGRC, CRSUP, RES-FIT, SPEC-FIT; 10.1016/0010-4655(95)00129-8

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

The VIIRS/SNPP Cloud Properties Level 3 monthly, 1x1 degree grid product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.

This dataset contains daily histograms of wind speed at 100m ("WS100"), wind direction at 100 m ("WD100") and an atmospheric stability proxy ("STAB") derived from the ERA5 hourly data on single levels [1] accessed via the Copernicus Climate Change Climate Data Store [2]. The dataset covers six geographical regions (illustrated in regions.png) on a reduced 0.5 x 0.5 degrees regular grid and covers the period 1994 to 2023 (both years included). The dataset is packaged as a zip folder per region which contains a range of monthly zip folders following the convention of zarr ZipStores (more details here: https://zarr.readthedocs.io/en/stable/api/storage.html). Thus, the monthly zip folders are intended to be used in connection with the xarray python package (no unzipping of the monthly files needed).Wind speed and wind direction are derived from the U- and V-components. The stability metric makes use of a 5-class classification scheme [3] based on the Obukhov length whereby the required Obukhov length was computed using [4]. The following bins (left edges) have been used to create the histograms:Wind speed: [0, 40) m/s (bin width 1 m/s)Wind direction: [0,360) deg (bin width 15 deg)Stability: 5 discrete stability classes (1: very unstable, 2: unstable, 3: neutral, 4: stable, 5: very stable)Main Purpose: The dataset serves as minimum input data for the CLIMatological REPresentative PERiods (climrepper) python package (https://gitlab.windenergy.dtu.dk/climrepper/climrepper) in preparation for public release).References:[1] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)[2] Copernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)'[3] Holtslag, M. C., Bierbooms, W. A. A. M., & Bussel, G. J. W. van. (2014). Estimating atmospheric stability from observations and correcting wind shear models accordingly. In Journal of Physics: Conference Series (Vol. 555, p. 012052). IOP Publishing. https://doi.org/10.1088/1742-6596/555/1/012052[4] Copernicus Knowledge Base, ERA5: How to calculate Obukhov Length, URL: https://confluence.ecmwf.int/display/CKB/ERA5:+How+to+calculate+Obukhov+Length, last accessed: Nov 2024

This data was illustarted section histogram of Baingoin locality, based on result of geological survey on Tibetan Plateau in recent years. The thickness of stratigraphic level was measured artificially, rock character was identified by well-experienced geological worker. Fossils were discovered and clearly marked in the section. Stratigraphic and lithologic data obtained from geological survey was organized systematically after field work, adding relevant text. The content of data is very detailed, with significance in geological and topographic research in Baingoin locality and Northern Tibetan Plateau, especially in tectonics in plateau uplift and paleo-altimetry.

The MISR Level 3 Daily Component Global Albedo Product contains a statistical summary of column albedo 555 nanometer optical depth, and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 albedo parameters of interest averaged over a day and reported on a geographic grid, with resolution of 1 degree by 1 degree and 5 degree by 5 degree. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.

Tabulated statistics of road networks at the level of intersections and for built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical road networks developed by Johannes Uhl. See Burghardt et al. (2022) for details on the data processing.

Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Keith A. Burghardt, USC Information Sciences Institute, USA

Codebook: these CBSA statistics are stratified by degree of aggregation. - CBSA_stats_diffFrom1950: Change in CBSA-aggregated patch statistics between 1950 and 2015 - CBSA_stats_by_decade: CBSA-aggregated patch statistics for each decade from 1900-2010 plus 2015 - CBSA_stats_by_decade: CBSA-aggregated cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics. - Patch_stats_by_decade: Individual patch statistics for each decade from 1900-2010 plus 2015 - Patch_stats_by_decade: Individual cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics.

The statistics are the following:

msaid: CBSA code id: (if patch statistics) arbitrary int unique to each patch within the CBSA that year year: year of statistics pop: population within all CBSA counties patch_bupr: built up property records (BUPR) within a patch (or sum of patches within CBSA) patch_bupl: built up property l (BUPL) within a patch (or sum of patches within CBSA) patch_bua: built up area (BUA) within a patch (or sum of patches within CBSA) all_bupr: Same as above but for all data in 2015 regardless of whether properties were in patches all_bupl: Same as above but for all data in 2015 regardless of whether properties were in patches all_bua: Same as above but for all data in 2015 regardless of whether properties were in patches num_nodes: number of nodes (intersections) num_edges: number of edges (roads between intersections) distance: total road length in km k_mean: mean number of undirected roads per intersection k1: fraction of nodes with degree 1 k4plus: fraction of nodes with degree 4+ bearing: histogram of different bearings between intersections entropy: entropy of bearing histogram mean_local_gridness: Griddedness used in text mean_local_gridness_max: Same as griddedness used in text but assumes we can have up to 3 quadrilaterals for degree 3 (maximum possible, although intersections will not necessarily create right angles)

Code available at https://github.com/johannesuhl/USRoadNetworkEvolution.

References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems 95: 101803.
doi: 10.1016/j.compenvurbsys.2022.101803

description: Coastal storms and other meteorological phenomenon can have a significant impact on how high water levels rise and how often. The inundation analysis program is extremely beneficial in determining the frequency (or the occurrence of high waters for different elevations above a specified threshold) and duration (or the amount of time that the specified location is inundated by water) of observed high waters (tides). Statistical output from these analyses can be useful in planning marsh restoration activities. Additionally, the analyses have broader applications for the coastal engineering and mapping community, such as, ecosystem management and regional climate change. Since these statistical outputs are station specific, use for evaluating surrounding areas may be limited. Products The data input for this tool is 6-minute water level data time series and the tabulated times and heights of the high tides over a user specified time period, relative to a desired tidal datum or user-specified datum. The data output of this tool provides summary statistics, which includes the number of occurrences of inundation above the threshold (events) and length of duration of inundation of each events above the threshold elevation for a specified time period. In addition to summary statistics, graphical outputs are provided using three plots. The first plot is a histogram of frequency of occurrence relative to the threshold elevation, the second plot is a histogram of the frequency of duration of inundation, and the third plot is an X-Y plot of frequency of elevation versus duration of inundation for each event. Input data time series are presently limited to the verified data from a set of operating and historical tide stations in the NOAA CO-OPS data base.; abstract: Coastal storms and other meteorological phenomenon can have a significant impact on how high water levels rise and how often. The inundation analysis program is extremely beneficial in determining the frequency (or the occurrence of high waters for different elevations above a specified threshold) and duration (or the amount of time that the specified location is inundated by water) of observed high waters (tides). Statistical output from these analyses can be useful in planning marsh restoration activities. Additionally, the analyses have broader applications for the coastal engineering and mapping community, such as, ecosystem management and regional climate change. Since these statistical outputs are station specific, use for evaluating surrounding areas may be limited. Products The data input for this tool is 6-minute water level data time series and the tabulated times and heights of the high tides over a user specified time period, relative to a desired tidal datum or user-specified datum. The data output of this tool provides summary statistics, which includes the number of occurrences of inundation above the threshold (events) and length of duration of inundation of each events above the threshold elevation for a specified time period. In addition to summary statistics, graphical outputs are provided using three plots. The first plot is a histogram of frequency of occurrence relative to the threshold elevation, the second plot is a histogram of the frequency of duration of inundation, and the third plot is an X-Y plot of frequency of elevation versus duration of inundation for each event. Input data time series are presently limited to the verified data from a set of operating and historical tide stations in the NOAA CO-OPS data base.

Time-at-depth data (to generate histograms) from tagged jumbo squid from R/V R4107, R/V Pacific Storm, Chartered Vessels, R/V cruises in the Monterey Bay vicinity and Gulf of California from 2004-2009 access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=All data were collected with Mk10-PAT tags (Wildlife Computers, Redmond, WA) attached to living Humboldt squid (Dosidicus gigas) as described elsewhere (Gilly et al. 2006). Tags were programmed to sample at 0.5 Hz or 1 Hz. Tags deployed in Monterey Bay (CCS-1through CCS-6; deployed during OCE-0850839) were programmed to transmit time series data (75 s intervals = 0.01333 Hz) for depth, temperature and light to the Argos satellite system. Tags deployed in the Gulf of California (GOC-1 through GOC-6; deployed during OCE-0526640) were physically recovered, and the data were subsampled to match the 75 s interval of the CCS tags. This procedure was also carried out for tag CCS-6 that was recovered but never reported to Argos.

Mk10 PAT tags measure depth from 0 to 2000 m with a resolution of 0.5 m and temperature from 0 to +40 degrees C with a resolution of 0.05 degree C. The tags were used as supplied by the manufacturer without additional calibration.

References:
Gilly, W.F., Zeidberg, L.D., Booth, J.A.T, Stewart, J.S., Marshall, G., Abernathy, K., and Bell, L.E. 2012. Locomotion and behavior of Humboldt squid, Dosidicus gigas, in relation to natural hypoxia in the Gulf of California, Mexico. The Journal of Experimental Biology, 215, 3175-3190. doi: 10.1242/jeb.072538.
Gilly, W.F., Markaida, U., Baxter, C.H., Block, B.A., Boustany, A., Zeidberg, L., Reisenbichler, K., Robinson, B., Bazzino, G., and Salinas, C. 2006. Vertical and horizontal migrations by the jumbo squid Dosidicus gigas revealed by electronic tagging. Marine Ecology Press Series, 324, 1-17. doi: 10.3354/meps324001.
Stewart, J.S., Field, J.C., Markaida, U., and Gilly, W.F. 2013. Behavioral ecology of jumbo squid (Dosidicus gigas) in relation to oxygen minimum zones. Deep Sea Research Part II: Topical Studies in Oceanography, 95, 197-208. doi: 10.1016/j.dsr2.2012.06.005. awards_0_award_nid=55203 awards_0_award_number=OCE-0850839 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0850839 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 awards_1_award_nid=55226 awards_1_award_number=R/OPCFISH-06 awards_1_funder_name=California Sea Grant awards_1_funding_acronym=CASG awards_1_funding_source_nid=402 awards_2_award_nid=471705 awards_2_award_number=OCE-0526640 awards_2_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0526640 awards_2_funder_name=NSF Division of Ocean Sciences awards_2_funding_acronym=NSF OCE awards_2_funding_source_nid=355 awards_2_program_manager=David L. Garrison awards_2_program_manager_nid=50534 cdm_data_type=Other comment=Jumbo squid (Dosidicus gigas) time-at-depth data from MK10 PAT tags California Current System (CCS) & Gulf of California (GOC) PI: William Gilly (Stanford University) Version: 22 Nov 2013

NOTE: 1 count represents a 75-second interval (in count_night and count_day columns) Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.471977.1 Easternmost_Easting=-111.22 geospatial_lat_max=37.91 geospatial_lat_min=27.34 geospatial_lat_units=degrees_north geospatial_lon_max=-111.22 geospatial_lon_min=-123.48 geospatial_lon_units=degrees_east geospatial_vertical_max=1950.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/471977 institution=BCO-DMO instruments_0_acronym=MK10 PAT instruments_0_dataset_instrument_description=Mk10-PAT tags (Wildlife Computers, Redmond, WA) were programmed to sample at 0.5 Hz or 1 Hz. Tags deployed in Monterey Bay (CCS-1through CCS-6) were programmed to transmit time series data (75 s intervals = 0.01333 Hz) to the Argos satellite system. Tags deployed in the Gulf of California (GOC-1 through GOC-6) were physically recovered. Mk10 PAT tags measure depth from 0 to 2000 m with a resolution of 0.5 m and temperature from 0 to +40 degrees C with a resolution of 0.05 degree C. The tags were used as supplied by the manufacturer without additional calibration. instruments_0_dataset_instrument_nid=471984 instruments_0_description=The Pop-up Archival Transmitting (Mk10-PAT) tag, manufactured by Wildlife Computers, is a combination of archival and Argos satellite technology. It is designed to track the large-scale movements and behavior of fish and other animals which do not spend enough time at the surface to allow the use of real-time Argos satellite tags. The PAT can be configured to transmit time-at-depth and time-at-temperature histograms, depth-temperature profiles, and/or light-level curves. The histogram duration (1 to 24 hours) and bin ranges can also be configured. PAT archives depth, temperature, and light-level data while being towed by the animal. At a user-specified date and time, the PAT actively corrodes the pin to which the tether is attached, thus releasing the PAT from the animal. The PAT then floats to the surface and transmits summarized information via the Argos system. Argos also uses the transmitted messages to provide the position of the tag at the time of release. instruments_0_instrument_name=Wildlife Computers Mk10 Pop-up Archival Tag (PAT) instruments_0_instrument_nid=471924 instruments_0_supplied_name=MK10 PAT metadata_source=https://www.bco-dmo.org/api/dataset/471977 Northernmost_Northing=37.91 param_mapping={'471977': {'lon_start': 'flag - longitude', 'depth_m': 'flag - depth', 'lat_start': 'flag - latitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/471977/parameters people_0_affiliation=Stanford University people_0_person_name=William Gilly people_0_person_nid=51715 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI BCO-DMO people_1_person_name=Shannon Rauch people_1_person_nid=51498 people_1_role=BCO-DMO Data Manager people_1_role_type=related project=Jumbo Squid Physiology,Jumbo Squid Vertical Migration projects_0_acronym=Jumbo Squid Physiology projects_0_description=This project concerns the ecological physiology of Dosidicus gigas, a large squid endemic to the eastern Pacific where it inhabits both open ocean and continental shelf environments. Questions to be addressed include: 1) How does utilization of the OML by D. gigas vary on both a daily and seasonal basis, and how do the vertical distributions of the OML and its associated fauna vary? 2) What behaviors of squid are impaired by conditions found in the OML, and how are impairments compensated to minimize costs of utilizing this environment? and 3) What are the physiological and biochemical processes by which squid maintain swimming activity at such remarkable levels under low oxygen conditions? The investigators will use an integrated approach involving oceanographic, acoustic, electronic tagging, physiological and biochemical methods. D. gigas provides a trophic connection between small, midwater organisms and top vertebrate predators, and daily vertical migrations between near-surface waters and a deep, low-oxygen environment (OML) characterize normal behavior of adult squid. Electronic tagging has shown that this squid can remain active for extended periods in the cold, hypoxic conditions of the upper OML. Laboratory studies have demonstrated suppression of aerobic metabolism during a cold, hypoxic challenge, but anaerobic metabolism does not appear to account for the level of activity maintained. Utilization of the OML in the wild may permit daytime foraging on midwater organisms. Foraging also occurs near the surface at night, and Dosidicus may thus be able to feed continuously. D. gigas is present in different regions of the Guaymas Basin on a predicable year-round basis, allowing changes in squid distribution to be related to changing oceanographic features on a variety time scales. This research is of broad interest because Dosidicus gigas has substantially extended its range over the last decade, and foraging on commercially important finfish in invaded areas off California and Chile has been reported. In addition, the OML has expanded during the last several decades, mostly vertically by shoaling, including in the Gulf of Alaska, the Southern California Bight and several productive regions of tropical oceans, and a variety of ecological impacts will almost certainly accompany changes in the OML. Moreover, D. gigas currently supports the world's largest squid fishery, and this study will provide acoustic methods for reliable biomass estimates, with implications for fisheries management in Mexico and elsewhere. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This is a Collaborative Research project encompassing three NSF-OCE awards. Background Publications: Stewart, J.S., Field, J.C., Markaida, U., and Gilly, W.F. 2013. Behavioral ecology of jumbo squid (Dosidicus gigas) in relation to oxygen minimum zones. Deep Sea Research Part II: Topical Studies in Oceanography, 95, 197-208. doi:10.1016/j.dsr2.2012.06.005. Gilly, W.F., Zeidberg, L.D., Booth, J.A.T, Stewart, J.S., Marshall, G., Abernathy, K., and Bell, L.E. 2012. Locomotion and behavior of Humboldt squid, Dosidicus gigas, in relation to natural hypoxia in the Gulf of California, Mexico. The Journal of Experimental Biology, 215, 3175-3190. doi: 10.1242/jeb.072538. Related Project:

MI3DAER_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Component Global Aerosol Regional public Product covering a day version 2. It contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product is complete. The data are for distinct regions associated with associated field campaigns. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.

The MISR Level 3 Quarterly (seasonal) Component Global Albedo Product contains a statistical summary of column albedo 555 nanometer optical depth, and a monthly aerosol compositional type frequency histogram. This data product is a global summary of the Level 2 albedo parameters of interest averaged over a quarter and reported on a geographic grid, with resolution of 1 degree by 1 degree and 5 degree by 5 degree. The seasons are winter (December from previous year, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November).The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.

The Multi-angle Imaging SpecrtroRadiometer (MISR) Level 3 Component Global Aerosol Product covering a day subset for the UAE region V004 contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aersosol compositional type frequency histogram. This data product is a regional summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm.

The MISR Level 3 Component Global Aerosol Product covering a day contains a statistical summary of column aerosol 555 nanometer optical depth, and a monthly aersosol compositional type frequency histogram. This data product is a global summary of the Level 2 aerosol parameters of interest averaged over a day and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree.The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm.

How to cite this article: Fernandez-Lozano, C. et al. Texture analysis in gel electrophoresis images using an integrative kernel-based approach. Sci. Rep. 5, 19256; doi: 10.1038/srep19256 (2015).

    In order to generate the dataset, ten 2-DE images of different types of tissues and different experimental conditions were used. These images are from the dataset owned by G.-Z Yang (Imperial College of Science, Technology and Medicine, London) and have been used in several publications. For each image, two different clinicians agreed on 100 regions of interest manually segmented that were selected to build a training set with 1000 samples and 274 textural variables.

We considered six groups of textural features: Histogram-based (first-order statistical texture features), Absolute Gradient, Run-length Matrix (high-order statistical texture features), Co-occurrence Matrix (second-order statistical texture features), Autoregressive Model and Wavelet. These features are based on image histogram, co-occurrence matrix (information about the grey level value distribution of pairs of pixels), image gradients (spatial distribution of grey level values), auto-regressive models (description of texture based on statistical correlation between pairs of pixels) and wavelet analysis (information about image frequency at different scales). We calculated those features with a specialized software called Mazda. Various approaches have demonstrated the effectiveness of this software, extracting textural features in different types of medical images

The new equivalent for this dataset should be searched for as "GPM_3GPROFTRMMTMI_CLIM". The TMI Gridded Oceanic Rainfall Product, also known as TMI Emission, consists of 5 degree by 5 degree monthly oceanic rainfall maps using TMI Level 1 data as input. Statistics of the monthly rainfall, including number of samples, standard deviation, goodness-of-fit (of the brightness temperature histogram to the lognormal rainfall distribution function) and rainfall probability are also included in the output for each grid box. TMI brightness temperature histograms at 1 degree intervals are generated based on the 19, 21 and 19-21 GHz combination channels obtained from the Level 1B (calibrated brightness temperature) TMI product. Monthly rainfall indices over the ocean are derived by statistically matching monthly histograms of brightness temperatures with model calculated rainfall Probability Distribution Functions (PDF) using the 19-21 GHz combination data. Retrieved monthly rainfall data must pass a quality test based on the quality of the PDF fit.

Confusion matrix of CW classification based on SVM.