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.
3,709
datasets available to search
ShareScore release 0.7.1
Dataset results
3,709 results for “urbanicity”
Rural population count at 1 km for 2000-2020 based on WorldPop and GHS-SMOD urbanization level
<p>Rural population count at 1 km grid in EPSG:4326 for 2000-2020 (annual). This is only an estimate of the rural population. This probably misses many rural areas, especially in the tropics. The maps were derived using two data sources:</p> <ol> <li><a href="https://hub.worldpop.org/geodata/listing?id=64">WorldPop population counts at 1 km</a>;</li> <li><a href="https://human-settlement.emergency.copernicus.eu/download.php?ds=smod">GHS-SMOD urbanization levels at 1 km</a>;</li> </ol> <p>Rural population is estimated using the following translation rules for GHS-SMOD (note: these are arbitrary rules based on the GHS-SMOD documentation):</p> <ul> <li>Class 30: “Urban Centre grid cell” = 0% rural</li> <li>Class 23: “Dense Urban Cluster grid cell” = 0.5% rural</li> <li>Class 22: “Semi-dense Urban Cluster grid cell” = 2% rural</li> <li>Class 21: “Suburban or per-urban grid cell” = 15% rural</li> <li>Class 13: “Rural cluster grid cell” = 95% rural</li> <li>Class 12: “Low Density Rural grid cell” = 100% rural</li> <li>Class 11: “Very low density rural grid cell” = 100% rural</li> </ul> <p>The nighttime images are based on: <a href="https://doi.org/10.5281/zenodo.7750174">https://doi.org/10.5281/zenodo.7750174</a></p> <ul> <li>Schiavina, Marcello; Melchiorri, Michele; Pesaresi, Martino (2023): GHS-SMOD R2023A - GHS settlement layers,<br>application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A,<br>multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:<br>10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA PID: <a href="http://data.europa.eu/89h/a0df7a6f-49de-46ea-%209bde-563437a6e2ba">http://data.europa.eu/89h/a0df7a6f-49de-46ea-</a><br><a href="http://data.europa.eu/89h/a0df7a6f-49de-46ea-%209bde-563437a6e2ba">9bde-563437a6e2ba</a></li> </ul>
German image spectral library of urban surface materials
<p>The German image spectral library consists of 5102 labelled image spectra of urban surface materials covering the spectral wavelength range between 455 nm and 2449 nm. The spectra have been extracted from high resolution imaging spectroscopy data (HyMap) acquired over the German cities of Dresden (18/05/1999, 01/08/2000, 20/07/2003), Potsdam (18/05/1999) and Munich (17/06/2007, 25/06/2007). This image data package ensures the collection of the most typical urban surface materials including their variations due to different illumination, alteration, observation conditions, regional specifications and data processing characteristics.</p> <p>The collection was done in two main steps: (1) manual collection of spectrally pure urban surface material pixels from the Dresden and Potsdam data sets including additional information, such as the results of field investigations, a field spectral library and color infrared aerial imagery (Heiden et al., 2007 ) and subsequent reduction for redundant pixel spectra; (2) spectral dissimilarity analysis to include and label meaningful unknow spectra from the Munich data set (Jilge et al. 2017 ). </p> <p>The image spectra are labelled based on three sets of spectra labels: one for EAGLE land cover (EAGLE_LCC, consult the “Explanatory Documentation of the EAGLE Concept” from the Copernicus Land website) , one for generalized material groupings (GENLIB_LCH_BuC_MG) and one for more detailed artificial material type (GENLIB_LCH_BuC_AMT).</p> <p>While every effort was made to ensure accurate information, this data set is presented "as is" without warranties of any kind. The authors accept no liability or responsibility to any person as a consequence of any reliance upon the data presented here. The user assumes all responsibility and risk for the use of this data.</p>
Urban material ground truth data for the 2007 HyMap hyperspectral image of Munich
<p><span>This dataset entails a spectral library file (.sli file with matching .hdr text file) with 12028 labeled spectra derived from the 4m resolution airborne hyperspectral HyMap image of Munich (Germany) that was acquired during the summer of 2007 (June 17 and 25 2007). The labeled image spectra are retrieved from pixels of the HyMap dataset that has been processed to level 2A surface reflectance in 119 bands ranging between the wavelengths of 455 nm and 2496 nm. The preprocessing performed on this image data is explained in Heldens et al. (2008) and Heiden et al. (2012). See the "Related works" section of this data publication.</span></p> <p><span>The ground truth (GT) data have been used in previous research (again, see the "Related works" section) and they were likewise used for the remote sensing-based mapping experiments with a generic urban spectral library performed in the frame of the GENLIB research project. The data set contains reflectance spectra of typical urban surface materials and their spectral variations.</span></p> <p><span>The spectra included in this dataset were sampled from the above mentioned HyMap image by (1) using the methodology described in Jilge et al. (2017) and (2) through the delineation of manually digitized regions of interest. The image spectra are <span> </span>labeled based on the method mentioned above and using ancillary reference data, already published urban spectral libraries, terrain knowledge and some field work. The header of the spectral library contains the various labels that were added to the image spectra. These labels cover:</span></p> <ul> <li><span>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the </span><span><a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener"><span>website of the EAGLE framework</span></a></span><span> for more information.</span></li> <li><span>Material Groups (MG).</span></li> <li><span>Artificial Material Types (AMT).</span></li> </ul> <p><span>The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication.</span></p> <p><span>While considerable efforts have been made to safeguard the accuracy of these data, they are published as is, without any warranty or support. Use at your own discretion.</span></p>
Influence of soil amendment and crop species on nutrient cycling in a St. Paul urban garden, 2017-2023
An experiment was conducted from 2017-2023 at the University of St. Thomas research garden (Saint Paul, MN) to determine rates of nutrient recycling and loss from compost applied to urban gardens. Thirty-two 4 m2 study plots received one of six different soil amendment treatments, with four different crops growing on each plot. Meteorological data includes hourly measurements of rainfall, solar radiation, temperature and relative humidity, and wind speed and direction, from June 2017-October 2023. Hourly soil moisture measurements were recorded at depths of 10 cm, 20 cm, and 30 cm, from June-December 2021, June-October 2022, and June-October 2023. Annual crop harvest totals from each subplot are reported for 2017-2023. Leachate was collected from lysimeters installed in each of the 132 subplots weekly from June-October of each year (2017-2023), recording total volume. Leachate subsamples were analyzed for NO3-N, NH4-N, and PO4-P. Soil samples were collected at the beginning and end of the growing season in 2017, and every two weeks during the growing season from 2018-2023, and analyzed for pH, organic matter, Bray-1 extractable P, available K, nitrate, and ammonium, at the University of Minnesota Analytical Research Laboratory.
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.
Environmental Data for Soil, Leaf, and Root samples Boston Street Trees and Massachusetts Rural and Urban Forests in Summer 2021
This dataset provides detailed environmental and tree-level data and metadata for over 850 samples collected from 91 trees across an urban-to-rural gradient in Massachusetts. The dataset captures key variables characterizing urban environmental gradients, including soil moisture, pH, temperature, and nitrogen availability. Tree-level attributes include species identification, diameter at breast height (DBH), and growth rate based on previous tree census data. Geographic coordinates and site-specific context (urban forest, rural forest, street tree, forest edge, forest interior) are included to enable spatial analyses. The microbial sequence data associated with this environmental metadata can be found in the NCBI SRA under BioProject accession number PRJNA1297772.
Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City (2016-2019)
This dataset incorporates Mexico City related essential data files associated with Beth Tellman's dissertation: Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America. It contains spatio-temporal datasets covering three domains; i) urban expansion from 1992-2015, ii) district and section electoral records for 6 elections from 2000-2015, iii) land titling (regularization) data for informal settlements from 1997-2012 on private and ejido land. The urban expansion data includes 30m resolution urban land cover for 1992 and 2013 (methods published in Goldblatt et al 2018), and a shapefile of digitized urban informal expansion in conservation land from 2000-2015 using the Worldview-2 satellite. The electoral records include shapefiles with the geospatial boundaries of electoral districts and sections for each election, and .csv files of the number of votes per party for mayoral, delegate, and legislature candidates. The private land titling data includes the approximate (in coordinates) location and date of titles given by the city government (DGRT) extracted from public records (Diario Oficial) from 1997-2012. The titling data on ejido land includes a shapefile of georeferenced polygons taken from photos in the CORETT office or ejido land that has been expropriated by the government, and including an accompany .csv from the National Agrarian Registry detailing the date and reason for expropriation from 1987-2007. Further details are provided in the dissertation and subsequent article publication (Tellman et al 2021). The Mexico City portion of these data were generated via a National Science Foundation sponsored project (No. 1657773, DDRI: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City). The project P.I. is Beth Tellman with collaborators at ASU (B.L Turner II and Hallie Eakin). Other collaborators include the National Autonomous University of Mexico (UNAM),
Urban Ecological Infrastructure (UEI) in the greater Phoenix, Arizona metropolitan area and surrounding Sonoran desert region (2010-2017)
Urban ecological infrastructure (UEI) encompasses all infrastructure in a city that supports ecological structure and function, and by extension, provides ecosystem services to urban residents and is a broad, all-encompassing concept for "nature in cities". This idea includes commonly recognized forms of infrastructure, such as parks, residential yards, community gardens, lakes and rivers, and street trees. But UEI also includes less recognized forms, such as vacant lots, agricultural fields, canals, and water retention basins. Despite being widely recognized as important to urban landscapes, the wide variety, and various forms of urban ecological infrastructure are rarely documented in a single source. To address this, we consolidated various aquatic, terrestrial, and wetland UEI throughout the Phoenix Metropolitan area so researchers can incorporate this UEI into project designs and models. Since people’s perceptions of UEI differ not only by the three broad classifications but also by the individual characteristics of UEI, each feature is classified not only as aquatic, terrestrial, or wetlands but also given on of fifteen unique classifications. Incorporation of UEI into both planning and research design can promote practices that increase both biodiversity and human well-being while also possibly limiting negative landscape perceptions.
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Summary statistics and annual trends in chloride concentration in urban Minnesota lakes and streams
These data tables describe statistical summaries of chloride concentration, temporal trends in annual chloride, and projected risk of future chloride pollution in lakes and streams of the 17 most urban counties in Minnesota. Data were summarized separately for lakes and for streams, and include statistics (mean, median, standard deviation, upper and lower confidence intervals, and maximum) over the entire data record, over the warm season (May - October), and over the most recent 5 years (i.e., since 2018). Trends were computed on annual means and medians. For lakes, data were aggregated by lake basin (MN DNR Lake ID, or DOW) as well as by depth of sample (surface and deep). For streams, data were aggregated by individual site level as well as by stream reach (per MN Pollution Control Agency assessment units). Risk of chloride pollution was also determined for sites with longer records (10+ years) based on current concentration, number of exceedances of chronic standards, and projected chloride concentration based on current trends. Raw data were extracted from two sources: (1) the National Water Quality Portal (USGS & EPA) and (2) the Metropolitan Council Environmental Information Management System. The data were originally collected by a large number of entities, including watershed management authorities in the state of Minnesota, tribal groups, the Minnesota Pollution Control Agency, municipalities, university researchers, private consultants, and the Metropolitan Council. Some data records begin as early as the 1950's or 1960's, with many sites still including active data collection. A total of approximately 45,000 observations of chloride were included for lakes and wetlands, and approximately 70,000 observations for streams. Nearly 1600 stream sites and 700 lake/wetland sites were represented in the raw data, with 356 stream sites and 600 lakes represented in the summaries (after filtering out sites with less than 1 year of data collection). Primary data retr
Modeling the effects of lake morphology on chloride retention and salt-driven stratification in two urban lakes in St. Paul, MN
Road salt inputs have caused widespread salinization of urban lakes in northern temperate regions. Watershed characteristics are known to be important drivers of lake chloride concentrations, but there has been less focus on how lake morphometry influences seasonal and interannual dynamics in lake chloride, and how these chloride levels may alter mixing in the water column. We analyzed chloride retention for two urban lakes (Como Lake and Lake McCarrons) in Saint Paul, Minnesota, that are in adjacent watersheds and have similar surface areas, but differ in depth and water residence time. Summer chloride concentrations were negatively related to total summer precipitation for Como Lake (maximum depth 2.2 m), but the relationship was less strong for Lake McCarrons (maximum depth 7.6 m). We used a zero-dimensional model to simulate chloride dynamics in both lakes and tracked the fate of chloride over time. In Como Lake, the mass of chloride in the lake turns over within three years, whereas chloride inputs are retained for >10 years in Lake McCarrons. We then used a one-dimensional hydrodynamic lake model (GLM-AED) to examine how lake depth affects how current chloride loading rates alter lake mixing. Salt inputs significantly extended the duration of summer stratification for simulated lakes with depths of 8 m or more, and salt inputs increased the number of days of hypoxia and anoxia across all depths. These results underscore the importance of considering lake morphometry in understanding the effects of salt inputs on lake ecosystems.
North Temperate Lakes LTER: Patterns of Soil Phosphorus Across an Urbanizing Agricultural Landscape 2000 - 2001
Understanding the magnitude and location of soil phosphorus (P) accumulation in watersheds is a critical step toward managing runoff of this pollutant to aquatic ecosystems. Here, we examined the usefulness of urban-rural gradients (URGs), an emerging paradigm in urban ecology, for predicting soil P concentrations across a rapidly urbanizing agricultural watershed in southern Wisconsin. We compared several measures of an urban-rural gradient to predictors of soil P such as soil type, slope, topography, land use, land cover, and fertilizer and manure use. Most of the factors that were expected to drive differences in soil P concentrations were not found to be good predictors of soil P; while there were several significant relationships, most explained only a small proportion of the variation. There was a significant relationship between soil P concentration and each of the urban-rural gradients, but these relationships explained only a small amount of the variation in soil P concentrations. Soil P concentration, unlike some other ecosystem properties, is not well predicted by urban-rural gradients Additional Chemical Analyses: These additional analyses were done to provide comparisons to Bray-1 P. Specifically, we wanted to know whether, in Dane County, there was a consistent relationship between total P and Bray-1 P. For sample sites on private property, specific site location information, such as GPS coordinates, is not included in these datasets. If you have a need for this information, please get in touch with the contact person listed above Number of sites: 334; 20 of these sites with additional chem analyses
Data for: The circular economy potential of urban organic waste streams in low- and middle-income countries
<p>This dataset includes the research data and supporting information for the publication "The circular economy potential of urban organic waste streams in low- and middle-income countries" which was published in the Journal of Environment, Development and Sustainability (DOI: 10.1007/s10668-021-01487-w).</p> <p>This dataset and the associated publication are the basis upon which the REVAMP (Resource Value Mapping) tool has been developed. See more info about the REVAMP tool here: https://www.sei.org/revamp</p> <p> </p>
Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present
<p>This dataset contains data described and used in the research article <strong>"The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan"</strong>. </p> <p>The longitudinal dataset contains urban form data on nearly 17,000 individual plots (parcels) in Midtown Manhattan, documented through four subsequent time frames: 1890, 1920, 1956 and 2021. The data was compiled from historical cartographic resources and open-access geospatial datasets listed in the ReadMe file. </p> <p>The dataset includes an array of quantitative descriptions of plots, buildings and streets central to the field of urban morphology, and the binary information of physical change (1: change, 0: no change) identified via diachronic comparison of each time frame at the scale of plots.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The dataset presented in this repository has been generated as part of a PhD research conducted at the University of Melbourne, Faculty of Architecture, Building and Planning and funded by the University of Melbourne - Melbourne Research Scholarship: </p> <p><strong>Tümtürk, O</strong>. (2024). <strong>A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics</strong>. Unpublished PhD Thesis. The University of Melbourne, Australia. </p>
Data: Disentangling drivers of temporal changes in urban pond macroinvertebrate diversity
<p>Data for: (i) presence and abundance of Odonata and Trichoptera (larvae), and Coleoptera and Hemiptera (larvae and adults) species in ponds in Stockholm, Sweden, in 2014 and 2019, (ii) environmental data 2014 and 2019 (pond data like water chemistry, and land-change data), (iii) coordinates of ponds and pond area, (iv) and R script to reproduce analyses presented in Granath et al. 2024 (Urban Ecosystems, https://doi.org/10.1007/s11252-023-01500-2). A meta-data file with descriptions of the data files is also included.</p>
Satellite remote sensing dataset for urban climate in Bergen and Prague (TURBAN-D09)
<p><span>Shared dataset contains remote sensing data necessary for a land surface temperature (LST) calculation. Layers were processed for two cities; Bergen (Norway) and Prague (Czech Republic). Original data were downloaded from the U.S. Geological Survey (https://doi.org/10.5066/P975CC9B). For a LST calculation, a land surface emissivity (LSE) algorithm was used.</span></p> <h3><span>Processing of LANDSAT-8 and LANDSAT-9 data</span></h3> <ol> <li><span>Reading metadata file for each scene (*MTL.txt)</span></li> <li><span>Reprojection of scene (note: Bergen scenes have two UTM Zones; 31N and 32N)</span></li> <li><span>Cloud cover raster (see folder 01_CloudCover)</span></li> <li><span>Calculation Top-Of-Atmosphere (TOA) reflectance for bands 10 and 11 (TB_10 and TB_11), saving to folder 02_TOA-reflectance</span></li> <li><span>Calculating of NDVI and Fractional Vegetation Cover (FVC), saving to folder 03_FVC-NDVI</span></li> <li><span>Calculating of LSE for both bands, same as different and mean LSE (folder 04_LSE)</span></li> <li><span>Calculating of LST</span></li> <li><span>Saving of metadata file (see *metadata.txt)</span></li> </ol>
Urban Riparian Wetland Hydrology Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA
<p>This is the initial release of a <strong>hydrology</strong> 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 (<em>Castor canadensis</em>)</strong>. This dataset contains data specific to the hydrology of Walnut Creek, 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 22, 2023 through January 30, 2024</strong>. </p> <p>The core of the dataset is water stage measured in five surface pond sites and six groundwater monitoring wells within Walnut Creek Wetland Park. This data was collected using synchronized Solinst Levelogger pressure transducer sensors at 15-minute intervals, compensated with corrections for barometric pressure measured locally using a Solinst Barologger sensor. In addition to this data collected by the authors, this dataset also includes publicly available stream stage and precipitation data obtained from the <strong>US Geological Survey,</strong> and weather and soils data from the <strong>North Carolina State Climate Office</strong>. In total, this dataset aims to provide a comprehensive view of surface and subsurface hydrology in the studied wetlands as it connects with precipitation events, antecedent moisture conditions, directed stormwater flows and overbank flood events. </p> <p>This hydrology dataset is intended to accompany the <u>separate</u> <strong>water quality dataset</strong> published on Zenodo at URL: <a href="https://doi.org/10.5281/zenodo.10888463">https://doi.org/10.5281/zenodo.10888463</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.</p> <p>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". </p> <p>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.</p> <p>Special thanks to <strong>Raleigh Parks</strong> and <strong>Walnut Creek Wetland Park</strong> for making this work possible.</p>
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
Identifying the mechanisms by which irrigation can cool urban green spaces in summer
<p>This dataset contains the measured soil moisture and microclimate data from two (2021 and 2022) urban green space irrigation experiments conducted in Burnley, Melbourne, Australia. The experiments consisted of two treatments, irrigated turf and unirrigated turf. The purpose of the experiments was to provide testing (2021) and evaluation (2022) data for an urban ecohydrological model, UT&C. </p> <p><br>After evaluating the performance of UT&C in modelling soil moisture and microclimate, UT&C was used to model the surface energy balance and evapotranspiration processes of the irrigated and unirrigated turf. This dataset also contains the modelled soil moisture, microclimate, surface energy balance and evapotranspiration data, as well as the measured background climate data at the reference climate station and the forcing data for the model.</p> <p><br>The aims of this study were to:<br>i) identify the proportional contribution of different evapotranspiration processes to irrigation cooling effect, and <br>ii) quantify the impacts of different irrigation amounts (from 2 to 30 mm/d) on the cooling effect of irrigating turfgrass in Melbourne, Australia during normal summer conditions.</p> <p>This study was published in:<br>Pui Kwan Cheung, Naika Meili, Kerry A. Nice, Stephen J. Livesley (2024). Identifying the mechanisms by which irrigation can cool urban green spaces in summer. Urban Climate. 55,101914. https://doi.org/10.1016/j.uclim.2024.101914.</p>
Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR
<p>Dataset for demographic, psychological and physiological data obtained for RESTORE (Experiment 1 WP1). Study results are published in the article titled "Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR" in the Journal of Environmental Psychology. Explanations on all variables (column names) in the datasets are given either in the second spreadsheet in each Excel file or in the csv files appended with _legend.csv (see latest version of the dataset). File 'Psychophysiological_participant_data_aggregated' is aggregated per participant (single or mean values), and the file 'Restoration_EDA_baseline-corrected_aggregated' contains EDA data aggregated per time point per restoration setting (Nature vs Urban) and prior cognitive demand condition. Methodological details on how the data was obtained and processed are given in the Open Access article.</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.