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83 results for “GPP”
Global Monthly GPP from an Improved Light Use Efficiency Model, 1982-2016
This dataset provides global monthly average gross primary productivity (GPP; g carbon/m2/d) modeled at 8 km spatial resolution for each of the 35 years from 1982-2016. GPP is based on the well-known Monteith light use efficiency (LUE) equation but was improved with optimized spatially and temporally explicit LUE values derived from selected FLUXNET tower site data. Optimized LUE was extrapolated to a consistent 8 km resolution global grid using multiple explanatory variables representing climatic, landscape, and vegetation factors influencing LUE and GPP. Global gridded long-term daily GPP was derived using the optimized LUE, Global Inventory Modeling and Mapping Studies (GIMMS3g) canopy fraction of photosynthetically active radiation (FPAR), and Modern-Era Retrospective analysis for Research and Applications, Version 2, (MERRA-2) meteorological information. These data will improve satellite-based estimation and understanding of GPP using a refined LUE model framework.
MODIS-based GPP, PAR, fC4, and SANIRv estimates from SLOPE for CONUS, 2000-2019
This dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution. There are three distinct features of the GPP estimation algorithm: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR, (2) SLOPE couples gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset, and (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two parameters to estimate GPP, along with a quantitative uncertainty, on a per-pixel and daily basis. The slope GPP product has an R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1.
Meteorology, MAIDENiso parameter files and observational data for Tungsten, Caniapiscau, and associated GPP stations
<p>This repository contains three datasets:</p> <p>1) Meteorology files needed to run the ecophysiological model of forest growth MAIDENiso at the dendrological sites of Tungsten and Caniapiscau and the eddy covariance flux stations of Uaf and Chibougamau.</p> <p>2) Parameter files needed to run the ecophysiological model of forest growth MAIDENiso at the dendrological sites of Tungsten and Caniapiscau and the eddy covariance flux stations of Uaf and Chibougamau.</p> <p>3) Observational dataset of snow, and d18O concentration at the dendrological sites, and GPP at the eddy covariance flux stations. In addition, observations of river discharge in the Caniapiscau basin (Quebec).</p>
PEM GPP
<p>The PEM GPP is the average GPP from ensemble of three PEMs forced with two climate data sets (GMAO MERRA-2 and ECMWF ERA-5). The PEM GPP covers the period from 2001 to 2021 year, with spatial resolution of 0.05 degrees for global vegetation area and temporal resolution of month. The unit of PEM GPP in this version is gC m-2 month-1.</p>
CorEvitas Generalized Pustular Psoriasis (GPP) Drug Safety and Effectiveness Registry
ClinicalTrials.gov study NCT06100991. IPD Sharing: NO. Countries: 1. Publications: 0.
Efficacy and Safety of HB0034 in Patients with Generalized Pustular Psoriasis (GPP)
ClinicalTrials.gov study NCT06231381. IPD Sharing: NO. Countries: 1. Publications: 0.
Special Drug Use-results Survey to Evaluate Safety and Efficacy of Cosentyx in Pediatric Patients With PsV, PsA, or GPP
ClinicalTrials.gov study NCT05215561. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Test Whether Spesolimab Helps People With Generalized Pustular Psoriasis (GPP) Who Need Treatment for Repeated Flares
ClinicalTrials.gov study NCT06013969. IPD Sharing: NO. Countries: 17. Publications: 0.
Study to Investigate Efficacy and Safety of Adalimumab in Japanese Subjects With Generalized Pustular Psoriasis (GPP)
ClinicalTrials.gov study NCT02533375. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
A Clinical Study of TQH2929 Injection in Treatment With Generalized Pustular Psoriasis (GPP)
ClinicalTrials.gov study NCT06433531. IPD Sharing: Not stated. Countries: 1. Publications: 0.
HB0034 in Patients With Generalized Pustular Psoriasis (GPP)
ClinicalTrials.gov study NCT05512598. IPD Sharing: NO. Countries: 1. Publications: 0.
PMS of Spesolimab I.V. in GPP Patients With Acute Symptoms
ClinicalTrials.gov study NCT05670821. IPD Sharing: NO. Countries: 1. Publications: 0.
GPP model output from PHACE experiment
<p>Gross Primary Production model output data from teh PHACE experiment.</p>
Transcriptome analysis of Generalized Pustular Psoriasis (GPP) versus Palmoplantar Pustular Psoriasis (PPP) uncovers raised potential markers of severity, atopy and anxiety in GPP
GEO Series GSE293996. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic analysis of low-density neutrophils (LDNs) in acute generalized pustular psoriasis (GPP)
GEO Series GSE200836. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.
Predicted GPP
Open the record for dataset details and reuse information.
North America (SW) Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</p>
Amazon Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Amazon </p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Europe Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Europe</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publications: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</p>
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International Brain Laboratory public data
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OpenNeuro
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