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486 results for “water use”
Data for the paper: Water-Food-Energy nexus: Learning from global cities using machine learning algorithms
<p>This data respository contains the datasets used to produce the "<strong>Water-Food-Energy nexus: Learning from global cities using machine learning algorithms" </strong>article. </p>
Water Droplet Evaporation Profiles Calculated Using SADKAT Model
<p>Evaporation profiles of pure water droplets calculated using SADKAT model.</p> <p>Initial size: 25 µm</p> <p>Temperature range: 278.15 - 343.15 K</p> <p>RH range: 0 - 100 %</p> <p>Particle motion is ignored (gravity = 0 m^2/s) in these calculations.</p> <p>Evaporation profiles are saved individually as comma separated .txt files.</p> <p>Data is also saved as .npy files, containing lists of evaporation profiles. These may be accessed using Python 3 with the Numpy and Pandas libraries.</p>
Data from journal article "Amplifying effects of recurrent drought on the dynamics of tree growth and water use in a subalpine forest"
<p>This dataset contains all data used in the above mentioned publication (DOI: tba). The data was obtained in a field drought experiment in a subalpine forest in the Austrian Central Alps, where adult trees were exposed to either ambient conditions or recurrent summer drought. The experiment was conducted from 2016-2018. All data were measured on individual tree level. All dataset contains the following, pre-processed data (processed as described in the corresponding journal article):<br> - Micrometeorological data<br> - Dendrometer data<br> - Tree ring data<br> - Sapflow data<br> - Stem electrical resistivity data<br> - Non-structural carbohydrate (NSC) and soluble sugar carbon isotopic composition (d13C) data</p> <p>The metadata.xlsx file contains all metadata, including units and descriptions of the variables.</p>
Global terrestrial water storage reconstruction using cyclostationary empirical orthogonal functions (1979 - present)
<p>The dataset consists of a 1979-present reconstruction of Terrestrial Water Storage (TWS) anomalies from Gravity Recovery and Climate Experiment (GRACE) using common modes of variability between TWS and terrestrial precipitation and land surface temperatures. </p>
Satellite-derived chlorophyll-a concentrations for Western Water Treatment Plant (Melbourne, Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of the Western Water Treatment Plant (Melbourne, Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.</p>
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning
<p>This is the source code and data for the publication "Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning". Preprint - https://arxiv.org/abs/2112.09182</p>
Standardized Dataset of the Ecosystem's Water Use Efficiency, Gross Primary Productivity and the Evapotranspiration Deficit Index for 1982–2017 over the Middle East
<p>This data aimed to investigate the spatial-temporal variability of Standardized Actual Evapotranspiration (sAET), Gross Primary Productivity (sGPP) and Water Use Efficiency (WUE) anomalies series, and the Standardized Evapotranspiration Deficit Index (SEDI). The Middle East (ME), was selected as a case study to monitoring drought events as one of the major natural disasters for the ecosystem. To this end, the yearly gross primary production of GLASS, GIMMS, FloxCom, and VPM datasets for the study area spanning 1982–2017 was used to develop the sGPPR data. On the other hand, the Global Land Evaporation Amsterdam Model (GLEAM-version (v3.3a)), which estimated the several components of terrestrial evaporation (annual actual and potential evaporation (AET, PET)) was used for the same period this aimed to detect the variability of the SEDI.<br> This version of the yearly GLASS-sGPPR dataset (1982–2017) is available for the ME at 0.05° spatial resolution, as the original data of the GPP-GLASS products, While, sGPPR dataset of GIMMS, FloxCom, and VPM are also at annual temporal resolution, and at 0.5 degree spatial resolution spanning 1982–2016 for GIMMS, FloxCom, and 2000-2016 for VPM (Excel wrokbook .xlsx). The SEDI data are also available at 0.25 degree spatial resolution for 1980–2018 ( Raster files (TIFF)). For more details about Standardization of the GPP and evapotranspiration deficit data see: <strong>Alsafadi, K., Al-Ansari, N., Mokhtar, A., Mohammed, S., Elbeltagi, A., Sammen, S. S., & Bi, S. (2021). An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East. <em>Environmental Research Letters</em>. <a href="http://dx.doi.org/10.1088/1748-9326/ac4765">10.1088/1748-9326/ac4765</a></strong></p>
Increased water use by sugar maple trees in the presence of a dense beech understory layer
<p>The formation of a recalcitrant understory vegetation layer is a phenomenon observed in various forests around the world, which can limit tree regeneration and in the long term, modify the composition, succession process and water balance of forests. In temperate forests of southern Quebec (Canada), the proliferation of American beech (<em>Fagus grandifolia</em>) in stands dominated by sugar maple (<em>Acer saccharum</em>) can be related to the recalcitrant vegetation phenomenon. With a projected increase in the severity and duration of droughts, a better understanding of its effect on water fluxes is crucial to understand the trajectory of impacted forests. The objective of this study was to understand how recalcitrant-type vegetation, in this case beech proliferation in sugar maple stands, influence tree water use. We compared transpiration i) by overstory trees (i.e. sugar maples) and ii) by understory saplings (i.e. American beech) in sites with and without beech understory dominance. At each of the six sites, we measured sap flux density (F<sub>d</sub>) of two sugar maple trees (diameter at breast height > 9 cm) and one beech sapling (1 cm < diameter at breat height ≤ 9 cm) with thermal dissipation sensors during the growing season. At tree level, F<sub>d</sub> of sugar maple trees was significantly larger in beech-dominated sites compared to control ones, indicating greater water consumption by sugar maple when understory is dominated by beech. At stand scale, total transpiration varied between 140 and 296 mm for the study period, with no significant difference between beech-dominated and control sites. We provide two hypotheses to explain our results at tree scale: i) reduced cover by forest floor vegetation would limit transpiration through this layer, thus allowing increased availability of water resources to supply tree transpiration or ii) increased tree transpiration rate would be a mechanism to satisfy nutrient requirements in beech-dominated stands often associated with lower soil fertility. Further research is needed to better understand the mechanisms explaining tree water use given beech proliferation.</p>
Data used for the article "Global evaluation of the dry gets drier and wet gets wetter paradigm from terrestrial water storage changes perspective".
<p>This is the data used in the HESS paper "Global evaluation of the dry gets drier and wet gets wetter paradigm from terrestrial water storage changes perspective".</p>
Deep potential model and datasets used in the publication "Plastic Deformation of Superionic Water Ices"
<p>The file training_data_for_deepmd.zip contains the training set and deep potential model for superionic ices used in the publication "Plastic Deformation of Superionic Water Ices". The deep potential (DP) neural-network model was generated using the DeepMD-kit package. The training set was generated using AIMD/CP2K calculations for both undeformed and deformed states of the fcc superionic phase at temperatures of 2500, 3000, 3500 and 4000 K. </p> <p> </p> <p>The file cp2k_shear.zip contains instructions to modify the CP2K code to calculate the shear vs strain deformation in continuous mode. </p> <p> </p> <p>The file lammps_dislocation.zip contains the files needed to run the dislocation mobility calculation. </p> <p> </p>
Datasets used for developing sulfuric acid-water neutral and ion-induced particle formation parameterizations
<p>Supporting data for "New parameterizations for neutral and ion-induced sulfuric acid-water particle formation in nucleation and kinetic regimes" by Määttänen et al. (2017). A README file describing the datasets is included.</p> <p>Five datasets used for developing sulfuric acid-water neutral and ion-induced particle formation parameterizations, published in Määttänen et al. 2017, JGR Atmospheres (submitted) and at https://doi.org/10.5281/zenodo.1044366.</p> <p>Two datasets that have been used to produce Fig 8. of the manuscript: these datasets contain global model output of nucleation rates and total aerosol number concentrations.</p>
Figure 1 in Shallow-water polychaete assemblages in the northwestern Mediterranean Sea and its possible use in the evaluation of good environmental state
Figure 1. (Upper left graph) Map of the studied zone. Blue circles represent sampled stations from the Gulf of Lions and red circles from the Northern Mediterranean Spanish coast. (Lower graph) Schematic diagram showing the distribution of the four studied communities in the mesoscale studied area
Figure 1 in Effect of degree of water stress on growth and fecundity of velvetleaf (Abutilon theophrOsti) using soil moisture sensors
Figure 1. Soil moisture content in pots was measured using (A) Meter Group 5TM moisture sensors and (B) Em50 data loggers to determine degree of water stress on Abutilon threophrasti in a greenhouse study conducted at the University of Nebraska–Lincoln.
Dataset for "Meteorological drivers of vineyard water vapour loss and water use efficiency during dry days" [corrected version]
<p>Dataset for "Meteorological drivers of vineyard water vapour loss and water use efficiency during dry days".</p>
Fig. 6 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 6. DNA damage scores (mean ± SEM, n = 8) in erythrocytes of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).
Fig. 2 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 2. Activity (mean ± SEM, n = 8) of glutathione S-transferase in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).
Fig. 3 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 3. Activity (mean ± SEM, n = 8) of catalase in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).
Fig. 4 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 4. Content (mean ± SEM, n = 8) of glutathione in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P<0.05).
Figure 1 in The influence of land use-impacted tributaries on water quality and phytoplankton in the Mooi River, North West Province, South Africa
Figure 1: Catchment area of the Mooi River from its source to the confluence with the Vaal River, showing the position of sampling sites in the main stream (1–8) and in the tributaries (WFS, GM and WS). Different types of land use activities in the catchment are also indicated on the map.
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.