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4,725 results for “Normalization”
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>
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p>The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation.</p> <p> </p> <p>The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies.</p> <p> </p> <p>Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022). <strong>We strongly recommend an adequate use of the quality control (QC) layer in the product. </strong>Please refer to the Readme file for more details. <strong>We also recommend removing sparse vegetation by a threshold (e.g., 0.1) in trend analysis (Zhou et al., 2001; Liu et al., 2016)</strong></p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (December 15, 2022):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 17, 2023):</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· The data files have been re-organized on a decade basis.</p> <p> </p> <p>Version 1.2 (August 17, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate NDVI values of EBF during the periods of 1982−1984 and all October to April, when the Landsat NDVI samples were relatively scarce.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN</p> <p>Projection: Geographic</p> <p>Spatial Resolution: 1/12 degree</p> <p>Temporal Resolution: Half month</p> <p>Temporal Coverage: January 1982 to December 2022</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: unitless</p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-1000</p> <p>Scale Factor: 0.001</p> <p>File Format: TIFF(.tif)</p> <p>File Size: ~8Mb each file</p> <p> </p> <p><strong>References:</strong></p> <p>Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, <a href="https://doi.org/10.5194/essd-15-4181-2023">https://doi.org/10.5194/essd-15-4181-2023</a>, 2023.</p> <p>Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biology, 22, 3702–3711, <a href="https://doi.org/10.1111/gcb.13311">https://doi.org/10.1111/gcb.13311</a>, 2016.</p> <p>Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106, 20069–20083, <a href="https://doi.org/10.1029/2000JD000115">https://doi.org/10.1029/2000JD000115</a>, 2001.</p> <p> </p>
The effect of normal stress oscillations on fault slip behavior near the stability transition from stable to unstable motion
<p>Tectonic fault zones are subject to normal stress variations with a wide range of spatio-temporal scales. Stress perturbations cover a wide range of frequencies and amplitudes from high frequency seismic waves generated by earthquakes to low frequency transients associated with solid Earth tides. These perturbations can reactivate critically stressed faults and trigger earthquakes. Here, we describe lab experiments to illuminate the physics of such changes in friction and the mechanics of earthquake triggering and fault reactivation. Friction tests were done in a double direct shear configuration for conditions near the stability transition from stable to unstable motion. We studied simulated fault gouge composed of quartz powder and conducted experiments at reference normal stress from 10 to 13.5 MPa. After shearing to steady state sliding, we applied sinusoidal normal stress oscillations of amplitude 0.5 to 2 MPa, and period of 0.5 to 50 s. We performed numerical simulations using measured values of rate/state friction (RSF) parameters to assess our data. Our results show that low frequency stress oscillations cause a Coulomb-like response of shear strength that transitions from stable slip to slow lab earthquakes as frequency increases. At the critical frequency predicted by RSF we observe periodic stick-slip behavior. Perturbations of high amplitude and short period weaken the fault, while lower amplitudes strengthen the fault. We find that a modified RSF formulation is able to accurately match our laboratory data. Our findings highlight the complex effects of stress perturbations for fault strength and the mode of fault slip.</p> <p>The data are uploaded are structured as follow:</p> <p>1) For each experiment a .txt file of the datafile that is recorded from the machine (raw data) and a binary file containing the elaborated data (data_rp). The experiments information are listed in experiment_info.txt</p> <p>2) The folder <a href="https://zenodo.org/api/files/89fe30fb-a2cb-4fcb-b9df-db80583fc652/codes_results.zip">codes_results.zip</a> contain the codes of the data analysis and the related results </p> <p>The data are analyzed using rawPy that can be found at <a href="https://github.com/marcoscuderi/rawPy">https://github.com/marcoscuderi/rawPy</a></p> <p>For any additional information please do not hesitate to contact the corresponding author Federico Pignalberi at federico.pignalberi@uniroma1.it</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 and Leaf area index of tussocks from reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon, Alaska 2016
Normalized difference vegetation index (NDVI) and Leaf area index (LAI) data from tussocks in the reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon in 2016.
Annual seasonality trends of Normalized Difference Vegetation Index (NDVI), Maricopa County, Arizona, 2001-2018
Description: A dataset with 18 years (2001-2018) of consistent, spatial and temporal patterns of Normalized Difference Vegetation Index (NDVI) values in Maricopa County AZ Abstract: This dataset consists of 18 years (2001-2018) of consistent, spatial, and temporal patterns of vegetation indices, as expressed by the Normalized Difference Vegetation Index (NDVI), in Maricopa County, AZ. I download and process images, at 250m resolution, from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS uses the atmospherically-corrected reflectance in the red, near-infrared, and blue wavebands to calculate vegetation indices. In the last decades, vegetation indices have been widely used for monitoring the seasonal variation of vegetation, document vegetation structure, productivity, overall health, and land cover changes and measure vegetation productivity in desert landscapes. NDVI is also of high interest to investigate the potential distribution of plant and animal species and to investigate the effect of climate change on vegetation productivity. MODIS data were obtained from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. Spatial Extent: Maricopa County, AZ, USA.
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>
Numerical modeling of the seismic cycle for normal and reverse faulting earthquakes in Italy
<p>Results of the numerical models expressed in terms of nodal stresses, strains and displacements.</p> <p>Data Set S1. Nodal values of the modelled displacements for the L’Aquila 2009 earthquake.</p> <p>Data Set S2. Nodal values of the modelled strain tensor for the L’Aquila 2009 earthquake.</p> <p>Data Set S3. Nodal values of the modelled stress tensor for the L’Aquila 2009 earthquake.</p> <p>Data Set S4. Nodal values of the modelled displacements for the Norcia 2016 earthquake.</p> <p>Data Set S5. Nodal values of the modelled strain tensor for the Norcia 2016 earthquake.</p> <p>Data Set S6. Nodal values of the modelled stress tensor for the Norcia 2016 earthquake.</p> <p>Data Set S7. Nodal values of the modelled displacements for the Emilia 2012 earthquake.</p> <p>Data Set S8. Nodal values of the modelled strain tensor for the Emilia 2012 earthquake.</p> <p>Data Set S9. Nodal values of the modelled stress tensor for the Emilia 2012 earthquake.</p>
The "Castelluccio-Amatrice" low-angle normal fault seismic dataset
<p>Seismology data (from INGV database) for idetification the geometry of “Castelluccio-Amatrice” low-angle normal fault. The seismic data are the same of central Italy sesmic sequence in 2014-2017 period. </p>
Dexmedetomidine Effects On Shivering and Core Temperature in Awake Normal Subjects
<p>Ten normal adult human subjects received a rapid intravenous infusion of two liters of cold (4ºC) isotonic saline on two separate test days, and we measured their core body temperature, shivering, hemodynamics and sedation for two hours. On one test day, fluid infusion was preceded by placebo infusion. On the other test day, fluid infusion was preceded by 1.0 µg/kg bolus of dexmedetomidine over 10 minutes.</p>
Spatial Evolve Algorithm Results for Erdos Renyi Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Watts Strogatz Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Watts Strogatz small world network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Erdős Rényi Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been the Erdős Rényi random network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the strategies list, deterministic and non and on the sample size. </p>
Spatial Evolve Algorithm Results for Complete Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topology used for the spatial tournaments has been a complete network. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Minimum Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the minimum normalized rank. Two files are contained here based on the sample size. All 132 strategies of the Axelrod have been used. </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).
Figs 39-46 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 39-46: sixth visible abdominal sternite, tergite and sternite of the male genital segment, aedeagus (bar scale: 0,1 mm) of Metolinus manfei nov.sp. (39-42); sixth visible abdominal sternite, tergite and sternite of the male genital segment, aedeagus (bar scale: 0,1 mm) of Metolinus nabanhe nov.sp. (43-46).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.