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.
68
datasets available to search
ShareScore release 0.9.0
Dataset results
68 results for “water sources”
Indicators of Contaminant Sources, PFAS, and Water Quality in Ellerbe Creek and New Hope Creek, NC (2019-2022)
Thousands of chemical contaminants are found in urban stream globally. This is a dataset of water quality measures of (1) compounds that are indicative of specific contaminant sources, (2) common water quality measures [trace metals, major ions, nutrients], and (3) PFAS. Sampling was conducted in Ellerbe Creek and New Hope Creek in the Durham and Orange counties of North Carolina. Biweekly and synoptic sampling was undertaken to explore spatial and temporal variation in water concentrations.
Plant community data at water sources, Mpala Research Centre, Kenya (2015-2017)
Data package contains four datasets of plant measurements taken at Mpala Research Centre, Laikipia County, Kenya from November 2015-September 2017. Additional code for data analysis is also provided as part of the publication `The effects of herbivore aggregations at water sources on savanna plants differ across soil and climate gradients`.
Data sources for the groundwater depletion manuscript in Water Resources Research
<p>Here, you can access the source files for the figures (and tables) of the publication (see reference).</p> <p>Basically, you find the model output (WaterGAP 2.2a) for global scaled groundwater storage, total water storage, baseflow, groundwater recharge (diffuse and below surface water bodies) and a table where location of grid cell and belonging continental area (e.g. to convert values into km³) is given. In addition, an Excel-File for the diagram of HPA (Figure 2) is accessible.</p> <p>First part of the file name represents the model variant (IRR100, IRR100_S, IRR70_S, NOUSE_S, for details see the manuscript), then the variable name and unit is given (Total Water Storages [mm], groundwater storage [mm], Qb (baseflow) [mm], Rg (diffuse groundwater recharge) [mm], Rg_swb (groundwater recharge below surface water bodies) [mm]). File format is a zipped netCDF. The table "lat_lon_cont_area.txt" contains the ArcID (internal grid cell number), coordinates and the continental area which is used for WaterGAP calculations.</p> <p>Original data description: https://www.uni-frankfurt.de/49903932/6__GW_depletion</p>
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Tracing and visualisation of contributing water sources in a model of flood inundation: video supplement
<p>These are video supplement files to Wilson & Coulthard (2021), produced using version 1.8f-WS of CAESAR-Lisflood software, <a href="https://doi.org/10.5281/zenodo.5541122">available on Zenodo here</a>. For a full description of the methodology and case studies, please refer to the paper which is available here: <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>.</p> <p>Video animations (no audio) for the following case studies are included:</p> <p>1. <strong>Carlisle, United Kingdom</strong> (carlisleanimation-sourcetracing.avi and carlisleanimation-depthonly.avi):</p> <ul> <li>Simulation of the January 2005 flood event at the confluence of the Rivers Caldew, Petteril and Eden, using a 5 m grid.</li> <li>Both water source tracing and depth only versions are provided.</li> <li>In the water tracing version, blue colours represent flows from the River Eden, reds are from the River Petteril and greens are from the River Caldew; darker shades represent deeper water. Available on YouTube here: <a href="https://youtu.be/xOtOi06cXvA">https://youtu.be/xOtOi06cXvA</a></li> <li>In the depth only version, darker shades of blue represent deeper water, with no information about the water source in a grid cell. Available on YouTube here: <a href="https://youtu.be/aFz-sPRGHVE">https://youtu.be/aFz-sPRGHVE</a></li> </ul> <p>2. <strong>Avon-Heathcote estuary in Christchurch, New Zealand</strong> (avonheathcoteanimation.avi):</p> <ul> <li>Simulation for July 2017, which included a high flow event on 22 July, using a model grid of 10 m.</li> <li>Blue colours represent flows from tide, reds are from the River Avon and greens are from the Heathcote River; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/Fczr5tczzXU">https://youtu.be/Fczr5tczzXU</a></li> </ul> <p>3. <strong>Amazon </strong>(amazonanimation.avi):</p> <ul> <li>Simulation at the confluence of the Solimões (mainstem Amazon) and Purus rivers in the central Amazon, Brazil, for the period of 1 October 2013 through December 2014, using a ~270 m model grid.</li> <li>Red colours are from the Solimões, green colours are from the Purus; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/PknAL_8fd1I">https://youtu.be/PknAL_8fd1I</a></li> </ul> <p>4. <strong>Planar slope</strong> (planaranimation.avi):</p> <ul> <li>A simple test case consisting of a 2000 x 1000 m planar slope (0.001 m/m), with walls added at 250 m intervals across the slope, each of which has several gaps through which water can flow. Model grid was 5 m.</li> <li>Eight water sources were traced in total, with three visualised in the animation: red = source 2, green = source 4, blue = source 6. Depths are shown in the middle plot.</li> <li>Available on YouTube here: <a href="https://youtu.be/DTw8ysJtx8o">https://youtu.be/DTw8ysJtx8o</a></li> </ul> <p>Please feel free to use these animations, under the terms of the CC-BY-4.0 license. Please provide a link back to this site and a citation to Wilson & Coulthard (2021).</p> <p>Reference:</p> <p>Wilson, M. D. and Coulthard, T. J.: Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation, Geosci. Model Dev. Discuss. [preprint], <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>, in review, 2021</p>
Oxygen isotope in plants and source water between 2018 and 2020, along the Santa Clara River, CA
This data package includes two data tables: 1. the oxygen stable isotope values for plant xylem, soil, groundwater, precipitation; and 2. the environmental conditions between 2018-2020, including precipitation, groundwater depth, potential evapotranspiration, and soil moisture. Isotope data were collected monthly at three sites along the Santa Clara River, CA between 2018 and 2020.
S64 | NATOXAQ | NaToxAq: Natural Toxins and Drinking Water Quality - From Source to Tap
<p>This is the collection associated with list S64 NaToxAq on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p><strong>NaToxAq: Natural Toxins and Drinking Water Quality - From Source to Tap</strong></p> <p>NaToxAq (<a href="https://natoxaq.ku.dk">https://natoxaq.ku.dk</a>) is a European Training Network (ETN) funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 722493, to produce knowledge about natural toxins in aquatic environments. This is a list of all NaToxAq chemicals registered in MassBank within the project, provided by Tobias Schulze, UFZ and hosted on the NORMAN Suspect List Exchange (<a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a>). DOI: 10.5281/zenodo.3695174</p> <p> </p>
Datasets for the article "Moisture source controls on water isotopes in Antarctic precipitation - insights from water tracers in ECHAM6-wiso"
<p>This is the dataset used for the manuscript Qinggang Gao, Louise C Sime, Alison J Mclaren, et al. Moisture source controls on water isotopes in Antarctic precipitation -insights from innovative water tracers in ECHAM6-wiso. <em>ESS Open Archive .</em> December 10, 2024. DOI: 10.22541/essoar.173386109.93218804/v1.</p> <p>The corresponding code used for the manuscript can be found at https://github.com/l975421700/a_basic_analysis.</p>
Water-soluble iron in PM2.5 in winter over six Chinese megacities: distributions, sources, and environmental implications
<p>We collected winter PM2.5 samples simultaneously in six Chinese megacities for analyzing ws‐Fe contents and speciation.</p>
Open source measurement data of the ground water physio-chemical parameters in the Peshawar District, Pakistan
<p>This excel file provides the measurement data of the physio-chemical parameters in the Peshawar district, Pakistan. The physio-chemical parameters include pH, electrical conductivity, total dissolved solids, Ca hardness, Mg hardness, Total hardness, Turbidity, Nitrate and Chloride. The data also include the latitude and longitude and location and depth to groundwater level (water table). The data was collected and sample were analyzed in June-August 2012.</p> <p>The full data collection and case studies are described in Adnan and Iqbal 2014, Adnan et al. 2018 and Adnan et al. 2019.</p>
Data archive: Niche overlap between a cold-water coral and an associated sponge for isotopically-enriched particulate food sources
<p>Data belonging to the paper: </p> <p>Dick van Oevelen, Christina E. Mueller, Tomas Lundälv, Fleur C. van Duyl, Jasper M. de Goeij, Jack J. Middelburg<span> </span>(In press) <strong>Niche overlap between a cold-water coral and an associated sponge for isotopically-enriched particulate food sources</strong>. PLOS ONE</p>
Fig. 3 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources
Fig. 3. Partial regressions testing the effects of water depth (left) and distance from colonizing source (right) on fish species richness collected in 22 plots in Site of Long-Term Sampling (SLTS). Only statistically significant relationships are shown.
Fig. 1 in Spatial pattern of a fish assemblage in a seasonal tropical wetland: effects of habitat, herbaceous plant biomass, water depth, and distance from species sources
Fig. 1. Geographical location of the study area and the Site of Long-Term Sampling (in the area). The system is installed in the Pantanal, Brazil.
Hydraulic mixing cell simulation of sources of surface water in the Weierbach catchment
<p>The dataset contains the meteorological forcing data and results of the hydraulic mixing cell (HMC) simulation presented and discussed in the research article: </p> <p><em>Sources of surface water in space and time: Identification of delivery processes and geographical sources with hydraulic mixing-cell modeling</em> (DOI:10.1029/2021WR030332)</p> <p>The simulation was performed with the model HydroGeoSphere and the HMC modelling was used to identify the delivery processes and geographical sources of surface water at specific points of interest (POI) within the riparian-stream continuum of the Weierbach catchment (Luxembourg). Detailed information on the model setup, the location of the POIs, the considered source types and source areas, and the processing we applied to the raw simulation output is given in the research article.</p>
Irrigation water source matters: Saline groundwater irrigation lowers date palm root-associated fungal richness and alters their community patterns
<p>Saline groundwater irrigation is predominantly used for date palm (<em>Phoenix dactylifera</em>) cultivation in the arid regions of the Middle East and North Africa without knowing its impact on root-associated fungal (RAF) communities. We investigated the impact of irrigation water sources (freshwater <em>vs.</em> saline groundwater) on date palm RAF diversity, communities, and their assembly processes. Summarily, we show that water chemistry (pH and EC) mainly structures RAF communities, whereas water pH shapes saprotrophic and pathotrophic communities, and the higher abundance of saprotrophic RAF (i.e. <em>Acrocalymma vagum</em>) under saline groundwater irrigation, despite the higher importance of the drift process, indicates their potential role in nutrient cycling and plant growth promotion under saline conditions in arid agroecosystems.</p>
Datasets and R source code of manuscript "No evidence for an effect of chronic boat noise on the fitness of reared water fleas"
<p>Datasets and R source code of manuscript "No evidence for an effect of chronic boat noise on the fitness of reared water fleas"</p> <p>Experiments : exposition of Daphnia magna to boatnoise or silence along all their life. Measure of survival and clonal reproduction.</p>
Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco Dataset
<p>Here you can find the official data used for the publication "Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco"</p> <p>If you want to run the model, please update line 11 in the run.jl file, to select the dataset from the scenario you want to look at. It is also recommended to change the result path, to a directory that corresponts to the current model run in order to find the results files quicker.</p> <p> </p> <p>To create a new plot a file called newPlot.jl can be found, that already take care of most data handling, only lines 136 and 141 need to be changed, in order to read in the result files of the results you want to investigate.</p>
Dataset for combined influences of sources and atmospheric bleaching on light absorption of water-soluble brown carbon aerosols
<p>This dataset provides the mass-absorption cross section at 365 nm and the corresponding absorption Ångström exponent, carbon isotope (13C and 14C) signature of water-soluble organic carbon, and OC, EC and WS-BrC concentration for aerosol samples collected in East Asia. The PM2.5 samples were collected simultaneously during the winter period (January 2014) from the representative hotspot regions of BrC emissions in E. Asia, including in the Beijing-Tianjin-Hebei (BTH) area, Yangtze River Delta (YRD), Pearl River Delta (PRD), Sichuan (SC) province and SE Yellow Sea regional receptor site — the Korea Climate Observatory at Gosan (KCOG). The earlier published data in E. and S. Asia, such as KCOG, urban city Delhi, Bangladesh Climate Observatory at Bhola Island (BCOB) in the outflow region of the Indo-Gangetic Plain and Maldives Climate Observatory at Hanimaadhoo Island (MCOH) in the Indian Ocean are from corresponding references (see the annotation in each sheet).</p> <p>Please cite Wenzheng Fang, August Andersson, Meehye Lee, Mei Zheng, Ke Du, Sang-Woo Kim, Henry Holmstrand and Örjan Gustafsson (2023): Combined influences of sources and atmospheric bleaching on light absorption of water-soluble brown carbon aerosols. npj Climate and Atmospheric Science. </p>
Physical and biogeochemical properties of California Current upwelled source waters
<p>Coastal upwelling variability in the California Current region, one of the four main eastern boundary current upwelling systems, is controlled by processes acting over a wide range of spatial and temporal scales. While the ensuing ecosystem response depends strongly on upwelled water properties, determining their exact physical and biogeochemical characteristics is notoriously difficult as it requires tracking water masses backward in space and time from the moment they upwell near the coast to their subsurface origin. Adjoint model simulations have been used successfully to track water masses in coastal upwelling systems and the work presented here extends these applications to determining the co-variability of physical and biogeochemical properties of source waters at spatial scales that resolve the known alongshore variability of coastal upwelling dynamics in the region. Notably, the results identify that the modulation of coastal upwelling efficiency by onshore/offshore geostrophic meanders is the dominant mechanism explaining alongshore variability in source depth and properties of upwelled waters. The simulations also reveal that source water properties vary seasonally in response to different balances between coastal upwelling intensity and biogeochemical processes. During spring, interannual variability of physical and biogeochemical properties is directly tied to the intensity of upwelling-favorable alongshore winds, whereas, during summer, biogeochemical properties respond more strongly to biological activity and subsequent organic matter remineralization at depth. Overall, the present work provides important insight into the mechanisms responsible for the alongshore mosaic and seasonal variation upwelled source water properties in the central California Current region.</p>
Effects of water source and technology on energy use and environmental impacts of rice production in northern Iran
<p>To analyze the energy flow and greenhouse gas emissions, a total of 200 paddy fields were selected based on the water source utilized for irrigation (river as the surface water source and well as the underground water source), transplanting methods (traditional and mechanical) and grain yield (low- and high-yielding rice cultivars) as a factorial experimental design (2×2×2=8 conditions). Data were collected in 2020 from 200 rice growers in northern Iran using a face-to-face questionnaire survey.</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.