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.
70
datasets available to search
ShareScore release 0.9.0
Dataset results
70 results for “urban water”
Long-term monitoring of stormwater runoff and water quality in urbanized watersheds of the greater Phoenix metropolitan area, ongoing since 2008
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Stormwater sampling is conducted at numerous locations. The longest running sampling location is near the outflow of the IBW ~0.6 km above its confluence with the Salt River. The sampling location coincides with a permanent USGS gauging sta
Water quality in restored urban streams in Lexington, KY, USA
Stream-water grab samples were collected periodically from sampling locations upstream and downstream of restored stream reaches in Lexington, KY, USA, and analyzed for a suite of water quality characteristics including nitrate, cations, and pH. Study sites included streams receiving riparian reforestation or other conservation, as well as streams restored using a natural channel design approach. The goal of this sampling program was to evaluate to what extent stream restoration interventions can influence stream-water quality in an urban context.
Data for manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)'
<p>The uploaded zip-file entails the data obtained through four field campaigns performed in the province of Azuay (Ecuador) in the period July 2023 - May 2024, which is used as a basis for the manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)' that was submitted to a Special Issue in the journal Water in 2024. The study aimed at illustrating the impact of urbanisation and drought on the abiotic water conditions of the rivers passing through the studied urban areas.</p> <p>The data includes a subfolder with data obtained from an external website (https://generacioncsr.celec.gob.ec/graficasproduccion/) and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (to be added when the manuscript is accepted). The data file only contains the baseline data, while results can be obtained through running the R-scripts in the GitHub-repository. Additional comments on the analyses are also provided in the analysis scripts.</p> <p><strong>DATA COLLECTION</strong></p> <p>Information on the locations was collected prior to the first field campaign (July 2023) and confirmed in the field (and corrected when necessary). The following variables were registered: Date & Time, Coordinates (latitude and longitude, in WGS84 format), Altitude (in meters above mean sea level), Distance (to a fixed location downstream; being the province border), and Category (River or Stream).</p> <p>Information on the physicochemical conditions was collected directly in the field with a <strong>Horiba U-52</strong> multiprobe. The following variables were registered: Temperature, pH, Electrical conductivity (reference at 25 °C), Oxygen level (as concentration), and Turbidity (in NTU).</p> <p>At each site, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. The multiprobe was rinsed with this sample water and then submerged in the bucket, followed by continuous stirring (to avoid a decrease of the oxygen levels) until the readings stabilised. After stabilisation, readings were recorded on a separate data sheet prior to being digitalised.</p> <p>Information on the nutrient levels was obtained through the collection of water samples in the field and the subsequent analysis in the laboratory. The following nutrients were selected: ammonium, nitrate, nitrite, and orthophosphate. For the analyses, <strong>Merck test kits</strong> (equivalent to USEPA analyses) were used in combination with a Genesys UV-VIS spectrophotometer (Thermofisher).</p> <p><strong>In the field</strong>, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. A polyethylene syringe was rinsed thrice with sample water and subsequently filled prior to being fitted with a 0.45 µm PES filter. About 100 mL of sampled water was filtered and collected in a 250 mL polyethylene bottle that was rinsed with the first 5 mL of filtered water. The bottle was stored in a cooling box and transported to the laboratory.</p> <p><strong>In the laboratory</strong>, the 250 mL bottle was stored at 4 °C until analysis. Within 36 hours, concentrations of ammonium, nitrate, nitrite, and orthophosphate were determined <strong>in triplicate</strong>. More specifically, the following test kits were used to determine said nitrogen and phosphorus concentrations (with quantification range between brackets):</p> <ul> <li>Ammonium: 1.14752.0001 (0.05-3.00 mgN/L)</li> <li>Nitrate: 1.14773.0001 (2-20 mgN/L)</li> <li>Nitrite: 1.14776.0001 (0.02-1.00 mgN/L)</li> <li>Orthophosphate: 1.14848.0001 (0.05-5.00 mgP/L)</li> </ul> <p>Regarding the <strong>spectrophotometric determination</strong>, all analyses were complemented with a blank and a standard with a known concentration of each individual nutrient component. For each nutrient, a specific wavelength was used and the resulting absorbance was converted to the associated nutrient concentration through known factors (similar to the use of calibration curves), after setting the absorbance of the blank as reference absorbance (i.e. a concentration of 0 mg/L). All of the analyses were performed with plastic 1-cm cuvettes during the first campaign, while 5-cm cuvettes were used in the remaining three campaigns due to low nutrient levels (except for nitrate, for which an analysis through 5-cm cuvettes is not supported by the used test kits).</p>
Water-soluble organic matter and nutrients from stormwater control measure and urban wetland soils
Water-soluble organic matter (WSOM) represents organic matter that has the potential to be readily released from soils. WSOM has been understudied in urban, engineered soils relative to natural soils. To understand the potential for organic matter and nutrient release, we extracted WSOM from the soils of stormwater control measures (SCM) and urban wetlands. In February 2022, we sampled soils from 20 SCMs and natural wetlands in the Rappahannock River watershed of the mid-Atlantic United States. The SCMs reflected a variety of design configurations including bioretention, rain gardens, wet ponds, and swales. We also sampled naturally occurring floodplain wetlands that are located in this urban watershed. Soils were sampled to a depth of approximately 40 cm. If there was standing water present in the SCMs and wetlands at the time of sampling, we also collected surface water samples. If present, grab samples of leaf litter or biomass were collected. Soil characteristics, such as pH, bulk density, soil moisture, soil organic matter, and cation exchange capacity were also determined for each site. WSOM was extracted from soils and biomass in the laboratory and analyzed for organic matter concentration (dissolved organic carbon) and composition (absorbance and fluorescence metrics), along with dissolved nutrient concentrations (total dissolved nitrogen, total dissolved phosphorus, nitrate, ammonium, and orthophosphate). In addition to the 20 sites in the Rappahannock watershed, soils from 2 additional bioretention SCMs on the Virginia Tech campus were sampled on a monthly basis from February 2022 to February 2023 to explore temporal variability in WSOM. To characterize changes in soil hydrologic conditions during monthly SCM sampling, we applied a Thornthwaite-type monthly water balance model. Finally, we performed a simple scaling exercise to WSOM results based on SCM area, sample depth, and soil bulk density to estimate potential SCM contributions of organic matter.
Greenhouse gas and water chemistry data from urban ponds in Madison, Wisconsin during the summer and under-ice period of 2021-2022
Stormwater ponds are common features in urbanized landscapes and can suffer from rapid oxygen depletion when thermally stratified or ice-covered. To investigate under-ice oxygen dynamics and drivers of bottom water oxygen saturation, we sampled 20 stormwater ponds in Madison, Wisconsin, USA during the summer of 2021 and winter 2022. The urban ponds ranged in age, shape, size, and depth. We repeatedly took YSI profiles of water temperature, oxygen, and specific conductance 7 times in the summer and 3 times in the winter. Water chemistry variables were collected in the surface waters, habitat surveys were conducted in the summer, and ice/snow thickness was recorded in the winter. We also measured the concentration of greenhouse gases in the surface waters as a consequence to oxygen depletion using the headspace equilibrium method.
UWSCatCH: Urban Water Supply Catchment Contributions and Hydrological Statistics for large cities of the conterminous United States.
<p>UWSCatCH extends and enhances the Urban Water Blueprint (McDonald et al., 2014) for a selection of 116 cities (population > 150,000) and their associated surface water supply catchments in the conterminous United States. The two major enhancements to the Urban Water Blueprint are: [1] estimates of the relative contributions of each surface water catchment to each city's average water supply (as well as updated estimates of any contributions from groundwater); [2] NHDplusV2 reach codes for each water supply intake stream location and associated average flow estimates (regulated and unregulated) (local upstream USGS gage IDs are also provided). UWSCatCH also features a raster file with spatially distributed (1/24° grid) runoff (average of 1980 - 2012 reanalysis simulation) which is masked to watershed polygons (included as a shapefile) to explore spatial distribution of average runoff generation affecting each city. UWSCatCH is designed for use in the R package "gamut" (https://github.com/IMMM-SFA/gamut), and may be applied in a variety of regional and national scale research studies concerning drinking water supply to major US cities.</p>
An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona
<p><em><strong>The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets.</strong></em></p> <p><strong>How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a></strong></p> <p>This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions.</p> <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> <p>note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index</p> <p>We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is :</p> <p>VV_new_perNN_LSTWW</p> <p>where:</p> <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> <p>Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65. </p> <p> </p>
High-frequency water quality data for three urban streams in Boston, MA (USA), 2021-2022
This dataset contains high-frequency water quality data for three urban stream locations in the great Boston, Massachusetts metropolitan area. Multiparameter sondes with sensors to measure temperature, pH, specific conductivity, optical dissolved oxygen (DO), turbidity, colored dissolved organic matter (CDOM), and optical brighteners (OB) were deployed from 23 November 2021 to 20 December 2022. Data were collected at 15-minute intervals.
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
[Database] Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets
<p>This file contains the complete catalog of datasets and publications reviewed in: Di Mauro A., Cominola A., Castelletti A., Di Nardo A.. <em>Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets.</em> Water 2021.The <strong>complete catalog</strong> contains:</p> <ul> <li>92 state-of-the-art water demand datasets identified at the district, household, and end use scales;</li> <li>120 related peer-reviewed publications;</li> <li>57 additional datasets with electricity demand data at the end use and household scales.</li> </ul> <p>The following <strong>metadata</strong> are reported, for each <strong>dataset</strong>:</p> <ul> <li>Authors</li> <li>Year</li> <li>Location</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Time Sampling Resolution</li> <li>Access Policy.</li> </ul> <p>The following <strong>metadata </strong>are reported, for each <strong>publication</strong>:</p> <ul> <li>Authors</li> <li>Year</li> <li>Journal</li> <li>Title</li> <li>Spatial Scale</li> <li>Type of Study: Survey (S) / Dataset (D)</li> <li>Domain: Water (W)/Electricity (E)</li> <li>Time Sampling Resolution</li> <li>Access Policy</li> <li>Dataset Size</li> <li>Time Series Length</li> <li>Location</li> </ul> <p><strong>Authors:</strong><br> Anna Di Mauro - Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | <a href="mailto:anna.dimauro@unicampania.it">anna.dimauro@unicampania.it</a>;<br> Andrea Cominola - Chair of Smart Water Networks | Technische Universität Berlin - Einstein Center Digital Future (Germany) | <a href="mailto:andrea.cominola@tu-berlin.de">andrea.cominola@tu-berlin.de</a>; <br> Andrea Castelletti - Department of Electronics, Information and Bioengineering | Politecnico di Milano (Italy) | <a href="mailto:andrea.castelletti@polimi.it">andrea.castelletti@polimi.it</a><br> Armando Di Nardo -Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | <a href="mailto:armando.dinardo@unicampania.it">armando.dinardo@unicampania.it</a></p> <p><strong>Citation and reference:</strong></p> <p>If you use this database, please consider citing <a href="https://www.mdpi.com/2073-4441/13/1/36">our paper</a> </p> <p>Di Mauro, A., Cominola, A., Castelletti, A., & Di Nardo, A. (2021). Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water, 13(1), 36, https://doi.org/10.3390/w13010036</p> <p><strong>Updates and Contributions:</strong></p> <p>The catalogue stored in this public repository can be collaboratively updated as more datasets become available. The authors will periodically update it to a new version. </p> <p>New requests can be submitted to the authors, so that the dataset collection can be improved by different contributors. Contributors will be cited, step by step, in the updated versions of the dataset catalogue.</p> <p><strong>Updates history:</strong></p> <ol> <li>March 1st, 2021 - Pacheco, C.J.B., Horsburgh, J.S., Tracy, J.R. (Utah State University, Logan, UT - USA) --- The dataset associated with paper <a href="https://doi.org/10.3390/s20133655">Bastidas Pacheco, C.J.; Horsburgh, J.S.; Tracy, R.J.. A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters. Sensors 2020, 20, 3655.</a> is published in the HydroShare repository, where it is available as an OPEN dataset. Data can be found here: <a href="https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51">https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51</a></li> </ol>
Urban Riparian Wetland Water Quality Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA
<p><span>This is the initial release of a </span><strong><span>water quality</span></strong><span> dataset pertaining to the <strong>riparian floodplain wetlands</strong> alongside Walnut Creek in Raleigh, North Carolina USA. Walnut Creek is the main drainage channel in an <strong>urbanized watershed</strong> (HUC-12: 030202011101) in central North Carolina. There are several riparian floodplain wetlands along the creek which are largely supplied by <strong>urban stormwater</strong> runoff including directed <strong>storm sewer flows</strong> and regular <strong>overbank flooding</strong> events. In many of these wetlands local water retention and residence time in the surface ponds is mediated by the damming activity of <strong>North American beavers (</strong><em><strong>Castor canadensis</strong></em><strong>)</strong>. This dataset contains data specific to the water quality values of <strong>Walnut Creek</strong>, its tributary <strong>Little Rock Creek</strong>, and the surface ponds and groundwater at the <strong>Walnut Creek Wetland Park</strong> which is actively influenced by resident beavers. The period of this dataset is from <strong>January </strong></span><strong><span>5</span><span>, 2023 through </span></strong><strong><span>October 28</span><span>, 2023</span></strong><span>. </span></p> <p><span>This dataset includes a variety of common <strong>water quality parameters</strong> measured in situ by use of a <strong>YSI Pro water quality meter</strong>, as well as <strong>dissolved nutrient values</strong> determined by <strong>laboratory analysis</strong> of collected water samples.<span> </span>YSI data was collected on a <strong>weekly</strong> basis and water samples were collected for laboratory analysis on a <strong>monthly</strong> basis. Additional measurements and collection took place during <strong>six large rainfall events</strong> to allow comparison between baseflow and stormflow conditions across the site.<span> </span>This dataset aims to provide a comprehensive look at the water quality of Walnut Creek in comparison with the surface ponds and groundwater in the Walnut Creek Wetland Park, which are all ultimately sourced from <strong>urban stormwater runoff</strong>. </span></p> <p><span>This water quality dataset is intended to accompany the <u>separate</u> <strong>hydrology dataset</strong> published on Zenodo at URL: <a href="https://doi.org/10.5281/zenodo.10709630">https://doi.org/10.5281/zenodo.10709630</a>. Together, these datasets are meant to support an improved understanding of the water availability and water quality found in connection with beaver-mediated stormwater capture in an urbanized watershed in the North Carolina Piedmont.</span></p> <p><span> </span><span>This dataset resulted from research supported with a Graduate Student Research Grant awarded by the <strong>North Carolina Water Resources Research Institute (WRRI)</strong>, under Project Number 23-10-W: "Stormwater Diversion, Storage, and Treatment by Beaver-enhanced Floodplain Wetlands in Piedmont Urban Watersheds". </span></p> <p><span>This material is based upon work supported by the <strong>National Science Foundation (NSF)</strong> Graduate Research Fellowship Program (GRFP) under Grant No. (DGE 2137100). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.</span></p> <p><span>Special thanks to <strong>Raleigh Parks</strong> and <strong>Walnut Creek Wetland Park</strong> for making this work possible.</span></p> <p><span>Laboratory analysis support for evaluation of dissolved nutrients (nitrate+nitrite, TKN, total phosphorus, and total organic carbon) was provided by the <strong>NC State Environmental and Agricultural Testing Services (EATS)</strong> laboratory, Department of Crop and Soil Sciences.</span></p> <p><span> </span><span>Additional laboratory analysis support for evaluation of dissolved nutrients (TKN and total phosphorus) was provided by the <strong>NC State Environmental Analysis Laboratory (EAL)</strong>, Department of Biological and Agricultural Engineering (BAE).</span></p> <p><span> </span><span>Usage of and technical support for the YSI Pro water quality meter used in this study was made possible by the <strong>Osburn Lab</strong>, Department of Marine, Earth and Atmospheric Sciences (MEAS), NC State University.</span></p>
Turner et al., 2021, urban water supply contributions and GAMUT output data
<p>Output from gamut (Geospatial Analytics for Multisectoral Urban Teleconnections) model supporting Turner et al. (2021) - https://www.nature.com/articles/s41467-021-27509-9</p> <p> </p> <p> </p>
Flower production and water infiltration in LandKlif experimental urban grassland plots
<p><span>Mixtures of grassland communities (three different compositions varying in composition of grasses and forbs) were sown in field experimental areas (56 plots, 2 x 4 m each) in Munich and Weihenstephan in 2020. In June and August 2021, flower production of dicotyledonous, richness of dicotyledonous species in bloom, and soil water infiltration were measured in the plots, as a proxy to ecosystem functions related to resource offer to pollinators and water regulation. For integrity of the database, all field experimental units of this study are associated to Plot ID 7835_1_U, but the variable PlotID is deprecated. </span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Рис. 1. Карта-схема р. Амазар. Цифрами обозначены: I — места Αобычи россыпного зоΛота; II — участки иссΛеΑования в 2018–2019 гг.: 1 — реки Амазар и БоΛьшая Чичатка в районе пгт. Амазар, 2 — воΑохраниΛище, 3 — р. Крестовая, 4 — р. Амазар в нижнем течении Fig. 1. Schematic map of the Amazar River. Legend: I — placer gold mining areas; II — survey areas in 2018–2019: 1 — the Amazar and the Bolshaya Chichatka Rivers in the area of Amazar urban-type settlement, 2 — water storage reservoir, 3 — the Krestovaya River, 4 — the lower reaches of the Amazar River in Dynamics and current status of the Amazar River ichthyofauna after the construction of the PPM «Polyarnaya» hydroelectric complex
Рис. 1. Карта-схема р. Амазар. Цифрами обозначены: I — места Αобычи россыпного зоΛота; II — участки иссΛеΑования в 2018–2019 гг.: 1 — реки Амазар и БоΛьшая Чичатка в районе пгт. Амазар, 2 — воΑохраниΛище, 3 — р. Крестовая, 4 — р. Амазар в нижнем течении Fig. 1. Schematic map of the Amazar River. Legend: I — placer gold mining areas; II — survey areas in 2018–2019: 1 — the Amazar and the Bolshaya Chichatka Rivers in the area of Amazar urban-type settlement, 2 — water storage reservoir, 3 — the Krestovaya River, 4 — the lower reaches of the Amazar River
Fig. 2 in Not only pond sliders: freshwater turtles in the water bodies of the Milan northern urban area (Italy)
Fig. 2 - Distribution maps of the species found in the study area. Circled letters: species records; when the position is approximated, the circle is dashed. P. subrufa records are omitted because the species was recovered far from the wetlands; also T. scripta is not shown, because the species was excluded from the study. Letters indicate the wetlands as in Fig. 1 (modified from https://d-maps.com/ and GeoPortale Regione Lombardia). / Mappa di distribuzione delle specie rinvenute nell'area di studio. Lettera cerchiata: specie presente; quando la posizione è approssimativa, il cerchio è tratteggiato. Il dato per P. subrufa è omesso in quanto la specie è stata rinvenuta lontano dalle zone umide; la distribuzione di T. scripta non è indicata poiché la specie non è oggetto del presente studio. Le aree umide sono indicate da lettere secondo la nomenclatura usata in Fig. 1 (modificato da https://d-maps.com/ e GeoPortale Regione Lombardia).
Fig. 1 in Not only pond sliders: freshwater turtles in the water bodies of the Milan northern urban area (Italy)
Fig. 1 - Study area (Lombardy region, Northern Italy). Letters indicate each studied wetland (modified from www.d-maps.com and GeoPortale Regione Lombardia). / Area di studio (Lombardia, Italia Settentrionale). Ogni lettera identifica un'area umida indagata (modificato da https://d-maps.com/ e GeoPortale Regione Lombardia).
Everyday risks and access to water and sanitation in Lilongwe urban and peri-urban areas
<p>The household survey INHAbIT Cities - UNHIDE (Investigating Natural, Historical and Institutional Transformations in Cities and Uncovering Hidden Dynamics in Slum Environments) focuses on urban risks and sanitation in Lilongwe. The aim was to assess access to basic services and risks perception of urban dwellers living in areas characterised by different conditions of access to water and sanitation and other basic services. Lilongwe was a small town of less than 20,000 inhabitants in 1966 and only started growing after it became the capital in 1975. Its population has reached approximately 1 million inhabitants, living in 58 administrative units, called areas. Infrastructures and service provision is concentrated in the central areas – where parliament, ministries, government offices, embassies, hotels and the commercial area were located - while low income areas suffer the most from infrastructure and basic services deficits. To illustrate, while some areas access water through in-house connections, others are served through water kiosks, characterised (in some areas) by high rates of discontinuity. Similarly, everyday risks are unevenly distributed across urban spaces: as shown in the survey perception of risks varies drastically from neighbourhood to neighbourhood and depending on the quality and availability of services provided. Data for this survey were collected between February and April 2015 by a team of local researchers, who administered the questionnaire in local language.</p> <p>Publications linked to this survey are:</p> <p>Rusca M., Alda Vidal C., Hordijk M., Kral N., (2017) Bathing without water, and other stories of everyday hygiene practices and risk perception in urban low-income areas: the case of Lilongwe, Malawi, Environment and Urbanisation Vol 29, Issue 2, pp. 533 – 550. </p> <p>Tiwale S., Rusca M<strong>.</strong>, Zwarteveen M., The power of pipes: mapping urban water inequities through the material properties of networked water infrastructures. The case of Lilongwe, Malawi, Water Alternatives, Water Alternatives 11(2): 314-335.</p> <p>Rusca M. (2018): Visualising urban inequalities: the ethics of videography and documentary filmmaking in water research, <em>Wires Water</em>, <a href="https://doi.org/10.1002/wat2.1292">https://doi.org/10.1002/wat2.1292</a></p>
Data Sets: An Assessment of Water Trusts: Drinking Water Quality and Provision in Six Low-income, Peri-urban Communities of Lusaka, Zambia
<p>Data set associated with the manuscript published in GeoHealth titled An Assessment of Water Trusts: Drinking Water Quality and Provision in Six Low-income, Peri-urban Communities of Lusaka, Zambia. This includes bacterial, nitrate, specific conductance, and Water Trust survey data collected from Lusaka, Zambia in 2013, 2014, 2016, an 2019.</p>
Surface Water Loss map and Urbanization map
<p>Surface water are severly affected by human activities, and here we defined two novel datasets, both derived from remote sensing data, to investigate the influence of urban areas on the spatial distribution of surface water loss locations across the watersheds in the United States: the Surface Water Loss map and the Urbanization map.</p> <p>The Surface Water Loss map is a binary map that identifies the geographical location of surface water depletion hotspots. It was obtained from the Surface Water Transitions layer of the Global Surface Water dataset (Pekel et al., 2016). Pixels values in the map are as follows: 0 = No surface water loss, 1 = Surface water loss hotspots.</p> <p>The Urbanization map is a binary map that shows the presence of built-up areas. It was generated from the GHS-BUILT layer from the Global Human Settlement dataset (Corbane et al., 2019). Pixels values in the map are as follows: 0 = No urban area, 1 = Urban area.<br><br><em>References:</em><br>Corbane et al. (2019). Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data 3, 140-169, https://doi.org/10.1080/20964471.2019.1625528</p> <p>Pekel et al. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418-422, https://doi.org/10.1038/nature20584</p>
Data: On the role of water table depth, and urban and vegetation cover on groundwater dry period susceptibility
Open the record for dataset details and reuse information.
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.