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81 results for “water and fluxes”
Carbon, energy, and water flux data from annual and perennial agroecosystems
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Eddy flux measurements and transfer velocities of momentum, water vapor, and sulfur dioxide over the coastal Atlantic ocean
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Soil temperature, volumetric water content and depth of thaw for ITEX CO2 flux survey plots 2003-2009.
Soil temperature, moisture content and thaw depth of the ITEX flux survey plots. Survey plots were located in the Toolik Lake LTER fertilization experiment in Alaska; at Imnavait Creek, Alaska; at Paddus, Latnjajaure and the Stepps site near Abisko in northern Sweden; at various sites in Adventdalen, Svalbard; in the Zackenberg valley, Northeast Greenland; at BEO near Barrow, Alaska and at the Anaktuvuk River Burn in Alaska. Measurements were made during the growing seasons 2003 to 2009.
Surface carbon, water and energy fluxes measured by eddy covariance at 3 sites within the Alaska Peatlands Experiment and Bonanza Creek Experimental Forest 2013-2016
These data are simultaneous and continuous measurements of carbon, water and energy fluxes of the terrestrial landscape. These fluxes are major regulatory drivers of the boreal climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. At the APEX project site, within Bonanza Creek Experimental Forest, this monitoring is repeated across a chronosequence of permafrost degradation; the Black Spruce site is an area of stable permafrost with intact black spruce forest (APEX gamma site), the Thermokarst site is an active thermokarst zone with considerable tree mortality (APEX betaSW site), the Fen site is within a stable treeless fen with deep active layer depth (APEX apexcon,low, and ele sites). The main variables being monitored are the instananeous fluxes of CO2, water vapor and surface energy (shortwave, longwave and net radiation), secondary variables included photosynthetically active radiation (PAR), air and soil temperatures, rainfall, snow depth, soil moisture content, wind direction and speed, and average atmospheric concentrations of CO2 and H2O through the year. Our site naming scheme is as follows: 1) gamma = Black Spruce site = YF_2472, 2) betaSW = Thermokarst site= BC_5166, 3) (apexcon+apexele+apexlow) = Fen site = BC_FEN
Community Land Model version 4.5 (CLM4.5) simulations of water, energy, and carbon fluxes for Saddle vegetation communities, 2008 - 2013
Single point simulations of CLM4.5 that include (1) forcing data that were input to the model and subsequent (2) model output for simulations that approximate conditions in fellfield, dry meadow, moist meadow, wet meadow, and snowbed vegetation communities. Forcing data were generated with observed atmospheric conditions from Tvan, Saddle precipitation, and incoming shortwave radiation measured from the AmeriFlux tower site (US-NR1) from 2008-2013. Wintertime precipitation inputs were modified to approximate average snow depth for each vegetation community observed across the Saddle grid. Land models, like CLM, provide a cohesive framework to investigate biogeophysical and biogeochemical effects of environmental change on ecosystem processes. We used CLM4.5 to investigate if a global-scale model can represent local-scale patterns of water, energy, and carbon fluxes in a heterogeneous mountain environment. Specifically, we were interested in generating testable projections of potential ecosystem responses to climate change. Model output includes half-hourly data on fluxes of energy, water, and carbon, as well as vegetation carbon stocks and edaphic conditions. We also conducted sensitivity analyses to look at ecosystem responses to modifications intended to extend growing season length by decreasing snow albedo and warming air temperatures (black sand and M-A warm, respectively). Information on the variables, units, and data are included as attributed in the network Common Data Form (NetCDF) files for this dataset. For users unfamiliar with using NetCDF files, we have included R scripts that write (forcing data) and read (model output) .nc files include in this data archive. More information about NetCDF files is available at http://www.unidata.ucar.edu/software/netcdf/docs/index.html.
Water column nitrate and ammonium concentrations, sediment oxygen, di-nitrogen (gas), nitrate, nitrite, ammonium, phosphate, and silicate flux from sealed, whole core incubations, Rowley River, Rowley, MA.
Tidal flats are critical components of coastal estuarine ecosystems characterized by high rates of benthic primary productivity and biogeochemical cycling. In order to investigate the impact of anthropogenic nutrient loading on tidal flat biogeochemistry we carried out a two-week fertilization experiment. Throughout the course of the study we conducted two light-dark, whole-core incubations and took measurements of three indicators of microphytobenthos activity in addition to quantifying the resident eastern mud snail (Ilyanassa obsoleta) population.
Data from: Drivers of nocturnal water flux in a tallgrass prairie
1. Nocturnal transpiration can impact water balance from the local community to earth-atmosphere fluxes. However, the dynamics and drivers of nocturnal transpiration among coexisting plant functional groups in herbaceous ecosystems are unknown. 2. Here, we addressed the following questions: (1) How do nocturnal (Enight) and diurnal (Eday) transpiration vary among coexisting grasses, forbs, and shrubs in a tallgrass prairie? (2) What environmental variables drive Enight and do these differ from the drivers of Eday? (3) Is Enight associated with daytime physiological processes? 3. We measured diurnal and nocturnal leaf gas exchange on perennial grass, forb, and woody species in a North American tallgrass prairie. Measurements were made periodically across two growing seasons (May-August 2014-2015) on three C4 grasses (Andropogon gerardii, Sorghastrum nutans, and Panicum virgatum), two C3 forbs (Vernonia baldwinii and Solidago canadensis), one C3 sub-shrub (Amorpha canescens) and two C3 shrubs (Cornus drummondii and Rhus glabra). 4. By extending our study to multiple functional groups we were able to make several key observations: (1) Enight was variable among co-occurring plant functional groups, with the highest rates occurring in C4 grasses, (2) Enight and Eday exhibited different responses to vapor pressure deficit and other environmental drivers, and (3) rates of Enight were strongly related to predawn leaf water potential for grasses and woody species, and likely modulated by small-scale changes in soil moisture availability. 5. Our results provide novel insight into an often-overlooked portion of ecosystem water balance. Considering the high rates of Enight observed in C4 grasses, as well as the widespread global occurrence of C4 grasses, nocturnal water loss might constitute a greater proportion of global evapotranspiration than previously estimated. Additionally, future predictions of nocturnal water loss may be complicated by stomatal behavior that differs between during the day and at night. Finally, these data suggest a water-use strategy by C4 grasses wherein the high rates of Enight occurring during wet periods may confer a competitive advantage to maximize resource consumption during periods of availability.
Bridging the flux gap: sap flow measurements reveal species-specific patterns of water-use in a tallgrass prairie
<p>Predicting the hydrological consequences following changes in grassland vegetation type (i.e., woody encroachment) requires an understanding of water flux dynamics at high spatiotemporal resolution for predominant species within grassland communities. However, grassland fluxes are typically measured at the leaf or landscape scale, which inhibits our ability to predict how individual species contribute to changing ecosystem fluxes. We used external heat balance sap flow sensors and a hierarchical Bayesian state-space modeling approach to bridge this "flux-gap" and estimate continuous species-level water flux in common tallgrass prairie species. Specifically, we asked: 1) How do diurnal and nocturnal water fluxes differ among woody and herbaceous plants? (2) How sensitive are woody and herbaceous species to environmental drivers of diurnal and nocturnal water flux? We highlight three results: (1) <i>Cornus drummondii</i>, the primary woody encroacher in this grassland, exhibited the greatest canopy-level water loss, (2) nocturnal transpiration was a large component of the water lost in this ecosystem and was driven primarily by C<sub>4</sub> grasses and <i>C. drummondii</i>, and (3) the sensitivity of canopy transpiration to environmental drivers varies among plant functional types and throughout a 24-hour period. Our data reveal important insights regarding the water-use strategies of woody versus herbaceous species in tallgrass prairies, and about the potential hydrological consequences of ongoing woody encroachment. We suggest that the high, static flux rates observed in woody species will likely deplete deep water stores over time, potentially creating hydrological deficits in grasslands experiencing woody encroachment and concomitantly increasing the vulnerability of these ecosystems to drought.</p>
Carbon, water and energy fluxes at the subtropical forest in Kaziranga National Park in India
<p>This dataset contains measured and modeled records of gross primary productivity (GPP), sensible heat flux and latent heat flux from 2016 to 2018 at the Kaziranga National Park, India. The measured fluxes are obtained from eddy covariance technique at the flux tower established by the Indian Institute of Tropical Meteorology (IITM) Pune as part of the MetFlux India network funded by the Ministry of Earth Sciences (MoES), the Government of India, whereas the Integrated Science Assessment Model (ISAM) at the Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Illinois, USA is used to simulate these fluxes. Additionally, the leaf area index (LAI) and meteorological measurements used as the model input are also included in this dataset. </p>
UFLUX European ensemble 0.25deg daily carbon, water, and energy fluxes from 2000 - 2020
<h3>UFLUX Ensemble Europe025ddaily (European 0.25° Daily)</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European daily fluxes at 0.25° spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based vegetation proxies </strong>— including MODIS NIRv, GOME-2 SIF, and OCO-2 SIF — with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The dataset includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX global ensemble 0.25deg monthly carbon, water, and energy fluxes from 2001 - 2021
<p> </p> <h3>UFLUX Ensemble Globe025dmonthly (Global 0.25° Monthly, 13 Members)</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> provides <strong>global monthly fluxes at 0.25° spatial resolution</strong>, incorporating <strong>13 ensemble members</strong> derived from different combinations of satellite-based vegetation proxies and climate reanalysis data. The dataset includes five key ecosystem flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Ensemble Members:</strong><br>Each member combines unique satellite vegetation indices with climate datasets:</p> <ol> <li> <p>MODIS-NIRv-CFSV2</p> </li> <li> <p>MODIS-NIRv-ERA5</p> </li> <li> <p>OCO-2-CSIF-ERA5</p> </li> <li> <p>GOME-2-SIF-ERA5</p> </li> <li> <p>GOSAT-755-SIF-ERA5</p> </li> <li> <p>GOSAT-772-SIF-ERA5</p> </li> <li> <p>MODIS-NDVI-ERA5</p> </li> <li> <p>MODIS-EVI2-ERA5</p> </li> <li> <p>AVHRR-NIRv-ERA5</p> </li> <li> <p>AVHRR-NDVI-ERA5</p> </li> <li> <p>AVHRR-EVI2-ERA5</p> </li> <li> <p>MODIS-NIRv-ERA5-WY</p> </li> <li> <p>MODIS-NIRv-ERA5-NT</p> </li> </ol> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
Dataset for 'Can restoring water and sediment fluxes across a mega-dam cascade alleviate a sinking river delta?'
<p>Please cite this dataset and corresponding manuscript at <a href="https://doi.org/10.1126/sciadv.adn9731">10.1126/sciadv.adn9731</a> if data were used in any way.</p> <p>Correspondence to Prof Lu Xi Xi at geoluxx@nus.edu.sg</p>
Dataset Mayen et al_Temporal variations of water carbon and atmospheric carbon dioxide fluxes in a temperate salt marsh and influence of aquatic metabolism
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Re-assessment of the climatic controls on the carbon and water fluxes of a boreal aspen forest over 1996-2016: changing sensitivity to long-term climatic conditions
<p>Recent evidence suggests that the relationships between climate and boreal tree growth are generally non-stationary; however, it remains uncertain whether the relationships between climate and carbon (C) fluxes of boreal forests are stationary or have changed over recent decades. In this study, we used continuous eddy-covariance and microclimate data over 21 years (1996-2016) from a 100-year-old trembling aspen stand in central Saskatchewan, Canada to assess the relationships between climate and ecosystem C and water fluxes. Over the study period, the most striking climatic event was a severe, 3-year drought (2001-2003). Gross ecosystem production (GEP) showed larger interannual variability than ecosystem respiration (<em>R</em><sub>e</sub>) over 1996-2016, but <em>R</em><sub>e</sub> was the dominant component contributing to the interannual variation in net ecosystem production (NEP) during post-drought years. The inter-annual variations in evapotranspiration (ET) and C fluxes were primarily driven by temperature and secondarily by water availability. Two-factor linear models combining precipitation and temperature performed well in explaining the inter-annual variation in C and water fluxes (<em>R</em><sup>2</sup>>0.5). The temperature dependence of all three C fluxes (NEP, GEP and <em>R</em><sub>e</sub>) declined over 1996-2015 (<em>p</em><0.05), and as a result, the phenological controls on annual NEP weakened. The decreasing temperature sensitivity of the C fluxes over 1996-2015 may reflect changes in forest structure, related to the over-maturity of the aspen stand at 100-years of age and exacerbated by high tree mortality following the severe 2001-2003 drought. These results may provide an early warning signal of driver shift or even an abrupt status shift of aspen forest dynamics. They may also imply a universal weakening in the relationship between temperature and GEP as forests become over-mature, associated with the structural and compositional changes that accompany forest ageing.</p>
Temporal dynamics of canopy properties and carbon and water fluxes in a temperate evergreen angiosperm forest
<p>Dataset and code for the Manuscript "<span>Temporal dynamics of canopy properties and carbon and water fluxes in a temperate evergreen angiosperm forest"</span></p>
Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis
<p>This dataset includes estimates of transpiration, evaporation, soil respiration, and plant net photosynthesis obtained using five partitioning approaches. Flux components are available at 47 NEON sites over a period of five years. Additional meteorological inputs and water-use efficiency data are also included.</p>
Water and energy fluxes measurements over a riparian Tamarix spp. stand in the lower Tarim River basin, northwestern China
<p>This dataset includes water and energy fluxes measurements over a riparian <em>Tamarix spp.</em> stand in the lower Tarim River basin, northwestern China. Details of field site and measurements can be found in the paper: Yuan, G., P. Zhang, M.-a. Shao, Y. Luo, and X. Zhu (2014), Energy and water exchanges over a riparian Tamarix spp. stand in the lower Tarim River basin under a hyper-arid climate, Agricultural and Forest Meteorology, 194(0), 144-154.</p> <p>This dataset also accompanies the published paper in the Water Resources Research: Implementing Dynamic Root Optimization in Noah‐MP for Simulating Phreatophytic Root Water Uptake. Water Resources Research 54(3), 1560-1575. With this dataset, we tested the Noah-MP land surface model with implementation of a soil moisture-responsive root dynamics scheme (VOM-ROOT). </p>
Quantifying vertical fluxes near the sediment water interface
<p>Field and laboratory observations used in GRL article "<a href="http://onlinelibrary.wiley.com/doi/10.1002/2017GL076789/abstract?campaign=wolacceptedarticle">Determining near-bottom fluxes of passive tracers in aquatic environments</a>" DOI: 10.1002/2017GL076789.</p>
Porphyry copper formation driven by water-fluxed crustal anatexis during flat-slab subduction
<p><span>Supplemtary Data used in the study entitled 'Lamont et al. (2024) Porphyry copper formation driven by water-fluxed crustal anatexis during flat-slab subduction, Nature Geoscience'.</span></p> <p><span>A compilation of geochronology (U–Pb and Ar-Ar and K-Ar) for intrusive and extrusive igneous rocks, mineralization and porphyry copper deposits, and timing of shortening and extension is provided in Supplementary Table S1. Whole rock geochemical data from igneous rocks in Arizona is provided in Supplementary Table 2. A compilation of Nd, Pb and Hf in zircon isotopes, and two-stage model ages from the SW USA and NW Mexico is provided in Supplementary Table S3. Electron-probe microanalysis data for samples TLAZ22-08 and TLAZ22-167 are in Supplementary Table S4. Thermobarometry Results are provided in Supplementary Table S5. In-situ Rb-Sr Geochronology Results are provided in Table S6, whereas U–Th–Pb Monazite Geochronology Results are provided in Supplementary Table S7. <span> </span></span></p>
Data in support of "Antarctic Bottom Water sensitivity to spatio-temporal variations in Antarctic meltwater fluxes"
<p> <em>This data contains the unprocessed output from the five equilibrium simulations used in the paper "Antarctic Bottom Water sensitivity to spatio-temporal variations in Antarctic meltwater fluxes". Simulations were run using the ocean and sea-ice components of CESM1, and the atmosphere was data-driven, and based on ERA5 reanalysis from 1958 until 1980. All data is in netcdf, and includes the description of flag values, and units. The five simulations differ in the freshwater flux scheme as described below:</em></p> <p><br> <em>Simulation UNIF - files: ga1.f09_g16.308<br> Forced with freshwater fluxes from the Antarctic Ice Sheet (AIS), uniformly distributed around the Antarctic coast. Total freshwater flux from AIS: 2075 Gt/yr<br> <br> Simulation BM - files: ga1.f09_g16.208<br> Forced with asymmetric zonal freshwater fluxes from the AIS.<br> Total freshwater flux from AIS: 2075 Gt/yr<br> <br> Simulation VARI - files: ga1.f09_g16.108<br> Forced with asymmetric zonal and meridional freshwater fluxes from the AIS. Total freshwater flux from AIS: 2075 Gt/yr<br> <br> Simulation CV - files: ga1.f09_g16.408<br> Forced with asymmetric meridional freshwater fluxes from the AIS, to mimic iceberg melting. Total freshwater flux from AIS: 934 Gt/yr<br> <br> Simulation VARI120% - files: ga1.f09_g16.x18<br> Same as VARI, but the total freshwater fluxes were increased by 20%. Total freshwater flux from AIS: 2490 Gt/yr</em><br> </p> <p> </p> <p>[30S 90S] , [180W 180E]</p> <p> </p> <p><strong>Variables:</strong> Ocean Temperature [Celius], Salinity [PSU], Sea ice Fraction [fraction], Salt flux from sea ice [kg/m2/s], and ocean overturning [SV]</p> <p><em>For more information, check:<a href="https://doi.org/10.1002/essoar.10512610.1">https://doi.org/10.1002/essoar.10512610.1</a></em></p>
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.