Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,141
datasets available to search
ShareScore release 0.9.0
Dataset results
1,141 results for “primary_productivity”
CMIP6 model vertically-integrated net primary production data
<p>Vertically-integrated net primary production (NPP) data from 12 models that participated in phase six of the Coupled Model Intercomparison Project (CMIP6). All data pulled from the Earth System Grid Federation.</p> <p>All model output was regridded onto a common, regular horizontal grid of 1x1 degrees (360 x 180) in longitude by latitude.</p> <p>Units are mol C per metre squared per second.</p> <p>Models are:</p> <ol> <li>ACCESS-ESM1-5</li> <li>CanESM5</li> <li>CESM2</li> <li>CNRM-ESM2-1</li> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> <li>IPSL-CM6A-LR</li> <li>MIROC-ES2L</li> <li>MPI-ESM1-2-HR</li> <li>MRI-ESM2-0</li> <li>NorESM2</li> <li>UKESM1-0-LL</li> </ol>
CEDAR-GPP: A Spatiotemporally Upscaled Dataset of Gross Primary Productivity Incorporating CO2 Fertilization
<p>Overview:<br>----------<br>CEDAR-GPP is a global Gross Primary Productivity (GPP) data product, including monthly GPP estimates at 0.05º spatial resolution. These datasets were generated via upscaling eddy covariance measurements with machine learning and satellite data. CEDAR-GPP uniquely incorporated the direct CO2 fertilization effect (CFE) using both data-driven and theoretical approaches. GPP estimates were produced from ten different model setups that vary by temporal span, direct CFE incorporation method, and GPP partitioning approaches. CEDAR stands for ups<strong>C</strong>aling <strong>E</strong>cosystem <strong>D</strong>ynamics with <strong>AR</strong>tificial intelligence.</p> <p>CEDAR-GPP consists of GPP estimates from ten model setups, differing by temporal range, methods for quantifying CO2 fertilization effects, and the partitioning methods used to derive GPP from eddy covariance measurements. Users are encouraged to refer to the user manual for a structured approach to selecting the most appropriate dataset.</p> <p> </p> <p>Authors:<br>----------<br>Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, Trevor Keenan</p> <p> </p> <p>File Structure:<br>----------<br>Each zip file contains GPP data from a CEDAR model setup.</p> <p> </p> <p>File Naming Convention:<br>----------<br>All netCDF files follow this naming convention:<br>CEDAR-GPP_<version>_<model-setup>_<YYYYMM>.nc</p> <p>Where:<br><model-setup> comprises of <temporal_span>_<CFE_option>_<GPP_partitioning><br><temporal_span>: ST denotes short-term (2001 to 2020); LT denotes long-term (1982 to 2020)<br><CFE_option>: 'Baseline' indicates no direct CO2 fertilization effect, 'CFE-ML' represents direct CO2 fertilization incorporated by ML, 'CFE-Hybrid' implies direct CO2 fertilization incorporated by theory<br><GPP_partitioning>: 'NT' for night-time GPP partitioning method, 'DT' for day-time GPP partitioning method</p> <p><br>NetCDF characteristics:<br>----------<br>- Spatial Resolution: 0.05 degree<br>- Temporal Resolution: Monthly<br>- Temporal Coverage: Short-term (ST): 2001-2020; Long-term (LT): 1982 - 2020<br>- Image Dimension: Rows: 3600, Columns: 7200<br>- Units: gCm^-2day^-1<br>- Fill Value: -9999<br>- Multiply By Scale Factor: 0.01<br>- Data Type: uint16<br>- File Size: Approximately 99 MB per file</p> <p><br>Data variables:<br>----------<br>- GPP_mean: monthly gross primary productivity (gCm^-2day^-1), mean from 30 model ensemble<br>- GPP_std: standard deviation of 30 model ensemble</p> <p><br>Support Contact:<br>----------<br>For any queries related to this dataset, please contact:</p> <p>Name: Yanghui Kang<br>Email: kangyanghui@gmail.com</p> <p> </p> <p> </p>
Dataset for "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements"
<p>Data of measurements and model output of the publication "Gross primary productivity of four European ecosystems constrained by joint CO<sub>2</sub> and COS flux measurements".</p> <p>Data consists of micrometeorological data, COS and CO<sub>2</sub> flux measurements for 4 sites including filters for the fluxes.</p> <p>The sites include: a managed temperate mountain grassland in Austria (18.06.-21.08.2015), a Mediterranean savanna ecosystem in Spain(29.04.-24.05.2016)), a Temperate beach forest in Denmark(07.06.-03.07.2016) and an agricultural soy bean field in Italy(03.07.-01.08.2017).</p> <p>Version 2: param2950** are now correct (were filled with the same values for all field sites) </p> <p>For additional information please contact: <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a></p>
Monthly, Seasonal and Yearly Net Primary Productivity (NPP) data of India from 2003-2020 modelled using the CASA model
<p>We estimated monthly Net Primary Productivity (NPP) at 1km spatial resolution for India using the Carnegie-Ames-Stanford Approach (CASA) Model. The CASA is a light use efficiency (LUE) based model that simulates NPP driven by remote sensing and meteorological data inputs. NPP is calculated as a product of the light use efficiency (LUE) and absorbed photosynthetically active radiation (APAR). The seasonal and annual data are prepared by aggregating the monthly data. The India Meteorological Department (IMD) recognizes four seasons in India based on climate conditions. The four seasons are defined as Winter (January-February), Pre-monsoon (March-May), Monsoon (June-September), and Post-monsoon (October-December). The seasonal data is prepared according to the above classification of the seasons. </p>
Simulated daily weather dynamics and gross primary production in 3 locations for 100,000 years
<p>IMPORTANT NOTE: The data in version 1 of this record, due to an error of units in the precipitation, had a non-physical vegetation growth and gross primary production. This has been fixed in version 2 of the record/dataset. Further, version 2 of the dataset contains 3 locations because the sites called "Grassland" and "Temperate" site produced the same type of vegetation (just grasses) in version 2, that contains therefore only a "Temperate" site.</p> <p>-------------------------------------------------</p> <p>The dataset reports daily temperature, precipitation, radiation and gross primary production in 3 different geographic locations (denoted as Temperate, Boreal and Tropical), representative of different climates and vegetation distributions, for 100,000 years. Each of the .nc files contains the dataset corresponding to one particular site.</p> <p>The weather data was produced using the AWE-GEN stochastic weather generator model ( Fatichi et al., Water Resources, 34(4):448–467 (2011) ). Vegetation dynamics and gross primary production are simulated via the dynamic global vegetation model LPX-Bern v1.4 ( Lienert and Joos, Biogeosciences, 15(9):2909–2930 (2018) ). The foliar projective cover is also reported at an annual scale.</p> <p> </p>
Global gross primary production (GPP) product generated by data fusion based on random forest
<p>Improving the ability of gross primary production (GPP) estimates to capture extreme climate perturbations and reduce the uncertainty of GPP response processes to extreme climate is a new challenge. Based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. DFRF-GPP is highly reliable and can be used as a valuable data source for various applications, especially in high-temperature and drought-related studies.</p>
8-day, 500-m Gross Primary Production for Europe from 2001 to 2016(YEAR_doy: 2001001 to 2004329)
<p>This is the 8-day average GPP (g C m<sup>-2</sup> d<sup>-1</sup>) for Europe estimated by the Farquhar GPP Model. The multisource remote sensing datasets used to drive the FGM in this study included the 500 m 500 m yearly land use and land cover data from the MOD12Q1 C6 product (2001-2016), the 500 m 500 m 8-day LAI data from the GLASS V5 product (2001-2016), the 500 m 500 m 8-day clumping index data in 2006 derived from MODIS bidirectional reflectance distribution function (BRDF) data, the 500 m 500 m 8-day photosynthetic capacity (V<sub>cmax</sub>) data derived from leaf chlorophyll content (2001-2016), and the 5 km 5 km daily downward shortwave radiation (DSR) data from the GLASS V5 product (2001-2016). We obtained the 0.5° 0.5° 6-hr climate data (including air temperature and relative humidity) from the Climatic Research Unit-NCEP (CRUNCEP) V7 (2001-2016). The vapor pressure deficit (VPD) was calculated from atmospheric pressure, the minimum and maximum air temperature, and relative humidity. Meteorological data used to drive FGM included the mean air temperature and VPD. In addition, site-level daily ambient CO<sub>2 </sub>concentrations observed at the Mauna Loa Observatory (MLO) site (2001 to 2016) were used to drive the FGM. DSR data were resampled to a spatial resolution of 500 m 500 m with bilinear interpolation. Climate data were aggregated to a targeted temporal resolution of 8 days and downscaled to a spatial resolution of 500 m 500 m with bilinear interpolation. </p>
Data for "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion"
<p>Data used in generating results for the paper <a href="https://doi.org/10.1029/2023JG007457">"Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion."</a></p> <ol> <li>VIIRS_MOD16_MOD17_tower_site_drivers_v9.h5</li> <li>MOD17_5km_global_simulation.zip</li> <li>VNP17_5km_global_simulation.zip</li> </ol> <p>[1] is an HDF5 file containing surface meteorological drivers, MODIS/ VIIRS vegetation fPAR and LAI, and other data necessary for calibrating and validating the MOD17 and VNP17 GPP models at FLUXNET towers. It also contains driver data and field-based NPP data for calibrating and validating MOD17/ VNP17 NPP models.</p> <p>[2] and [3] are the global, 5-km GPP and NPP simulations using the updated MOD17 parameters and new VNP17 model parameters. Other than their 5-km resolution, these global, annual GeoTIFF files are formatted the same as MOD17A3H data; <a href="https://lpdaac.usgs.gov/products/mod17a3hgfv061/">see the User Guide</a> for more information. The same scale factors apply to recover geophysical units: multiply the values by 0.0001 to obtain [kg C m-2 year-1].</p> <p>Please cite the peer-reviewed paper:</p> <blockquote> <p>Endsley, K.A., M. Zhao, J.S. Kimball, S. Devadiga. 2023. "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion." <em>Journal of Geophysical Research: Biogeosciences</em> 128(9).</p> </blockquote>
Dataset - Drying out fish ponds, for an entire growth season, as an agroecological practice: maintaining primary producers for fish production and biodiversity conservation
<p>This dataset is based on samples taken from fish ponds in the Dombes region between 2007 and 2014. It includes sediment and water physio-chemistry data, as well as primary producer diversity, benthic invertebrate density and fish yield for 85 different ponds. All these data are linked to the distance to the last dry-out, a major practice in extensive fish farming in this region.</p> <p>There are two .tab and .csv files:<br> One containing the dataset<br> One containing the description of the different variables (Metadata)</p>
Net primary production (NPP) and climate data from Sevilleta LTER core and control sites in desert grassland and shrubland ecosystems, 1999 - 2017
This dataset and R code were used to create the figures, tables and statistical analyses for the following publication: Rudgers, JA et al. 2018. Climate sensitivity functions and net primary production: A framework for incorporating climate mean and variability. Ecology. Data were collected by the Sevilleta LTER program, which is located in the Sevilleta National Wildlife Refuge (SNWR), New Mexico. These are long-term, continuing data sets. Data collection started in 1999 at the black grama grassland and creosote shrubland, and in 2002 for blue grama grassland. Meteorological stations started recording data as early as 1989. The study abstract from Rudgers et al. 2018 is: Understanding controls on net primary production (NPP) has been a long-standing goal in ecology. Climate is a well-known control on NPP, although the temporal differences among years within a site are often weaker than the spatial pattern of differences across sites. Climate sensitivity functions describe the relationship between an ecological response (e.g., NPP) and both the mean and variance of its climate driver (e.g., aridity index), providing a novel framework for understanding how climate trends in both mean and variance vary with NPP over time. Nonlinearities in these functions predict whether an increase in climate variance will have a positive effect (convex nonlinearity) or negative effect (concave nonlinearity) on NPP. The influence of climate variance may be particularly intense at ecosystem transition zones, if species reach physiological thresholds that create nonlinearities at these ecotones. Long-term data collected at the confluence of three dryland ecosystems in central New Mexico revealed that each ecosystem exhibited a unique climate sensitivity function that was consistent with long-term vegetation change occurring at their ecotones. Our analysis suggests that rising temperatures in drylands could alter the nonlinearities that determine the relative costs and benefits of varia
Dataset for: Freshwater biogeochemical hotspots: High primary production and ecosystem respiration in shallow waterbodies
This data accompanies the manuscript: "Freshwater biogeochemical hotspots: High primary production and ecosystem respiration in shallow waterbodies" Ponds, wetlands, and shallow lakes (collectively “shallow waterbodies”) are among the most biogeochemically active freshwater ecosystems. Measurements of gross primary production (GPP), respiration (R), and net ecosystem production (NEP) are rare in shallow waterbodies compared to larger and deeper lakes, which can bias our understanding of lentic ecosystem processes. In this study, we calculated GPP, R, and NEP in 26 small, shallow waterbodies across temperate North America and Europe. We observed high rates of GPP (mean 8.4 g O2 m-3 d-1) and R (mean -9.1 g O2 m-3 d-1), while NEP varied from net heterotrophic to autotrophic. Metabolism rates were affected by depth and aquatic vegetation cover, and the shallowest waterbodies had the highest GPP, R, and the most variable NEP. The shallow waterbodies from this study had considerably higher metabolism rates compared to deeper lakes, stressing the importance of these systems as highly productive biogeochemical hotspots.
SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/325/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1980
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1980.
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1979
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1979.
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1978
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1978.
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1977
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1977.
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1976
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1976.
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1975
Primary production data for Toolik Lake and surrounding lakes near the Arctic LTER site during the summer of 1975.
Sediment primary productivity, respiration and productivity by irradiance curves from lakes near Toolik Field Station 2009 - 2010
Dataset includes rates of benthic gross primary productivity (GPP) in mmol O2/m2/d by irrandiance (I) in uE/m2/s curves and benthic respiration rates in mmol/m2/d from lakes E-5, E-6, Toolik, Fog Lake 2, Horn, Perched and Luna during the summer of 2009-2010.
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Net Primary Production (NPP) for black spruce (Picea mariana) in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes net primary production (NPP) data for black spruce (Picea mariana) in the Caribou-Poker Creeks Research Watershed. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
ScienceDex guides
Understand access before you commit
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.