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540 results for “data normalization”
Normalized Difference Vegetation Index (NDVI) derived from 2019 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices—Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2019 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, and 2017 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP study area boundary.
Normalized Difference Vegetation Index (NDVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include NDVI data with SAVI data presented in a companion dataset that is also available through the EDI.
Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization"
<p>Data for the article "Computational micromagnetics based on normal modes: Bridging the gap between macrospin and full spatial discretization".</p> <p>Link to publisher: https://www.sciencedirect.com/science/article/abs/pii/S0304885321009197</p> <p>Link to Arxiv preprint: https://arxiv.org/abs/2105.08829</p>
Normalized Difference Vegetation Index (NDVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Normalized Difference Vegetation Index (NDVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Monthly Normalized MAVEN Data (2014-2021)
<p>These data were obtained from the magnetometer (MAG), Neutral Gas and Ion Mass Spectrometer (NGIMS), and Solar Wind Ion Analyzer (SWIA) onboard the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. These two data sets were derived from these three instruments' data products and utilized in the article <strong>Influence of Magnetic Fields on Precipitating Solar Wind Hydrogen at Mars</strong>, which we plan to submit to Geophysical Research Letters.</p> <p>The backscattered and downward propagating penetrating proton data have been separated into two data files titled respectively. The contents of each file are as follows:</p> <p>1. Time [s]: epoch time (seconds since Jan. 1, 1970) for each penetrating proton measurement</p> <p>2. Orbit [#]: MAVEN's orbit number</p> <p>3. LSM [degrees]: Martian solar longitude</p> <p>4. Flux [eV/(eV s sr cm^2)]: peak angle averaged differential energy flux for each 4-s spectrum in electronvolt per electronvolt x second x steradian x square centimeters.</p> <p>5. Normalized Flux: monthly normalized peak flux (unitless)</p> <p>6. Energy [eV]: energy at which peak flux occurred for each 4-s spectrum found from Gaussian fit in electronvolts</p> <p>7. FWHM [eV]: full width at half maximum of 4-s spectrum found from Gaussian fit in electronvolts</p> <p>8. Altitude [km]: spacecraft altitude in kilometers </p> <p>9. SZA [degrees]: solar zenith angle in degrees </p> <p>10. X_MSO [km]: X position of MAVEN in the Mars-Sun-Orbit (MSO) coordinate system in kilometers</p> <p>11. Y_MSO [km]: Y position of MAVEN in the MSO coordinate system in kilometers</p> <p>12. Z_MSO [km]: Z position of MAVEN in the MSO coordinate system in kilometers</p> <p>13. Latitude [degrees]: Martian planetary latitude</p> <p>14. Longitude [degrees]: Martian planetary longitude</p> <p>15. Bx [nT]: X component of magnetic field measured by MAG in MSO coordinates in nanotesla </p> <p>16. By [nT]: Y component of magnetic field measured by MAG in MSO coordinates in nanotesla</p> <p>17. Bz [nT]: Z component of magnetic field measured by MAG in MSO coordinates in nanotesla</p> <p>18. Elevation Angle [degrees]: local magnetic field elevation angle measured relative to a vector tangent to the Martian surface during each penetrating proton measurement. 90 degree elevation angle corresponds to radial configurations while 0 degree elevation angle corresponds to horizontal configurations.</p> <p>19. Column Density [cm^-2]: CO<sub>2</sub> column density derived from NGIMS measurements in inverse square centimeters. All NaN values were used to fill for times during which NGIMS inbound verified data were not available, and the column density could not be determined.</p>
Python Time Normalized Superposed Epoch Analysis (SEAnorm) Example Data Set
<p>Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for <a href="https://github.com/samwalton7645/SEA_Code">SEAnorm</a>, a time normalized superposed epoch analysis package in python.</p> <p>Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format. </p> <p>The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence. </p> <p>The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.</p> <p> </p>
Data to Three-Dimensional Binocular Eye-Hand Coordination in Normal Vision and with Simulated Visual Impairment
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G., Kwon, M. & Bex, P.J. (2018) Three-dimensional binocular eye--hand coordination in normal vision and with simulated visual impairment. <em>Experimental Brain Research</em>. https://doi.org/10.1007/s00221-017-5160-8</p>
Data on eye movements in people with glaucoma and peers with normal vision
<p>Eye movements were recorded from 44 elderly glaucoma patients and 32 age-similar healthy vision controls whilst watching three separate small video clips.</p>
Data from: Normalizing gas-chromatography–mass spectrometry data: method choice can alter biological inference
<p>Gas-Chromatography Mass Spectrometry data from European badger (<em>Meles meles</em>) sub-caudal gland secretion used in:</p> <p>Noonan, M.J., Tinnesand, H.V.,<sup> </sup>and Buesching, C.D. (2018). Normalizing gas-chromatography–mass spectrometry data: method choice can alter biological inference. BioEssays, 40(6): 0-0. DOI: 10.1002/bies.201700210.</p>
MERFISH data of the developing mouse visual cortex under normal- and dark-rearing
<p>MERFISH data for the manuscript "Spatial profiling of the interplay between cell type- and vision-dependent transcriptomic programs in the visual cortex"</p>
Data and scripts for reproducing "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer", submitted to Flow, Turbulence and Combustion.</p>
Data to: "Despite impaired binocular function, binocular disparity integration across the visual field is spared in normal aging and glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello G., & Kwon, M. (in press) Despite impaired binocular function, binocular disparity integration across the visual field is spared in normal aging and glaucoma. IOVS</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2022.11.28.518250</p>
Weekly Normalized Difference Vegetation Index (NDVI) data from Roche Moutonnee, Toolik Field Station, Imnavait, and Sag river DOT sites, in the northern foothills of the Brooks Range, Alaska, summer 2010-2014.
Weekly Normalized Difference Vegetation Index (NDVI) data from Roche Moutonnee, Toolik Lake Field Station, Imnavait Creek and Sagavanirktok River DOT sites in the northern foothills of the Brooks Range, Alaska. Located south of the Arctic LTER and Toolik Lake Field Station. Data collected from May to July 2010-2014. Methods and further data published in Ecography by Rich, et al. 2013.
Normalized difference vegetation index data for Saddle snowfence, 1994.
A snowfence was built in 1993 on the Niwot Ridge Saddle grid to determine the effects of changes in snowpack on a number of variables, one of which was vegetation greenness (measured through the normalized difference vegetation index). The study area of the snowfence was 60m x 125m. Spectral data in the red and near infra- red bands were recorded at sixty plots (points) in four rows located approximately at 10, 25, 45, and 75 meters east of the fence (the drift area). The plots were marked with thin wire stakes put into the ground and flagged (for visibility) and given aluminum tags with their plot identification code. All the plots were located in Kobresia myosuroides communities. In each row there were ten plots located in control areas just to the north and south of the area affected by the snowfence -- five to the north and five to the south -- and five plots located in the drift area of the snowfence. The individual plots were broken into groups of five according to their location (i.e., their distance from the fence (row), and location within their row (north, south, or within the snowfence area)). Within each of these groups, the plots were given a number starting with one and going to five. The plots were then given unique codes made up of plot type (c for control and sf for snowfence), location within a row (for the control plots only - n for north and s for south), the distance in meters of the row from the snowfence (10, 25, 45, or 75), and the number that uniquely identifies each plot within a group (one through five). For example, the code cn10_1 represents plot number one of the group of control plots north of the snowfence and 10 meters east of the snowfence. The location of each plot was measured as set of coordinates within the snowfence area. The southern terminus of the snowfence was used as the origin, with the y-axis running along the snowfence and the x-axis running perpendicular to the snowfence at it's southern end. The first coordinate was t
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Figs 140-145 in New data on the Xantholinini from China. 24. New genus, new species and new records of the Shanghai Normal University collection (Coleoptera, Staphylinidae) 244° contribution to the knowledge of the Staphylinidae
Figs 140-145: male genital segment, sternite of the same, aedeagus of Atopolinus leigong nov.sp (140-142); tergite, sternite of the male genital segment, aedeagus of Atopolinus guiheshang nov.sp. (143-145) (bar scale: 0,1 mm).
Figs 108-115 in New data on the Xantholinini from China. 24. New genus, new species and new records of the Shanghai Normal University collection (Coleoptera, Staphylinidae) 244° contribution to the knowledge of the Staphylinidae
Figs 108-115: aedeagus (bar scale: 0,5 mm) of Atopolinus hanmi nov.sp. (108); male genital segment, sternite of the same, aedeagus (bar scale: 0,1 mm) of Atopolinus xizang nov.sp. (109- 111); sixth visible abdominal tergite and sternite, tergite and sternite of the male genital segment of Atopolinus tangi nov.sp. (112-115).
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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