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 0.9.0
Dataset results
3,709 results for “urbanization.”
TUT Urban Acoustic Scenes 2018 Mobile, Evaluation dataset
<p>TUT Urban Acoustic Scenes 2018 Mobile evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>Recordings were made with three devices that captured audio simultaneously. The dataset contains in total 42 hours of audio.</p>
TUT Urban Acoustic Scenes 2018, Evaluation dataset
<p>TUT Urban Acoustic Scenes 2018 evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 10 hours of audio.</p>
Last species standing: loss of Pocilloporidae corals associated with coastal urbanization in a tropical city state
<p>DNA sequence alignment</p>
Prof. Mauro Majone - An urban biorefinery to convert organic waste into bio-base plastics
<p>Prof. Mauro Majone Full professor in Department of Chemistry, Sapienza University of Rome "An urban biorefinery to convert organic waste into bio-base plastics" Workshop Sapienza – Sony CSL 2018, Sapienza University of Rome, Rome April the 6th 2018</p>
Supporting dataset for: "Technical note: Laboratory modelling of urban flooding: strengths and challenges of distorted scale models"
<p>Li, X., Erpicum, S., Bruwier, M., Mignot, E., Finaud-Guyot, P., Archambeau, P., Pirotton, M., Dewals, B., (2019). Laboratory modelling of urban flooding: strengths and challenges of distorted scale models. Hydrology and Earth System Sciences, 2019.</p> <p>Included in this repository the data used in:</p> <p>- Figure 3: Upscaled water depth profiles in prototype in street 4 of Araud (2012).</p> <p>- Figure 4: Spatial distributions and scatter plot of the differences between the upscaled water depths</p> <p>- Figure 6: Upscaled outflow discharge in each street compared to the corresponding value deduced from the observations in the less distorted model.</p>
TAU Urban Acoustic Scenes 2019 Mobile, Development dataset
<p>TUT Urban Acoustic Scenes 2019 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>Recordings were made with three devices that captured audio simultaneously. Each acoustic scene has 1440 segments (240 minutes of audio) recorded with device A (main device) and 108 segments of parallel audio (18 minutes) each recorded with devices B and C. The dataset contains in total 46 hours of audio.</p>
TAU Urban Acoustic Scenes 2019, Development dataset
<p>TAU Urban Acoustic Scenes 2019 development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>Each acoustic scene has 1440 segments (240 minutes of audio). The dataset contains in total 40 hours of audio.</p>
TAU Urban Acoustic Scenes 2019 Openset, Development dataset
<p>TAU Urban Acoustic Scenes 2019 Openset development dataset is designed for researching open set classification problem in acoustic scene classification. In this problem, the test recording may be from a different environment than the 10 target classes. The dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>In addition, material for Unknown scene `unknown` is provided from 4 scenes:</p> <ul> <li>Beach - <em>beach</em>, extracted from TUT Acoustic scenes 2017 dataset</li> <li>Library - <em>library</em>, extracted from TUT Acoustic scenes 2017 dataset</li> <li>Office - <em>office</em>, extracted from TUT Acoustic scenes 2017 dataset</li> <li>Public event - <em>public_event</em></li> </ul>
Plane-wave propagation path data from wideband MIMO channel sounding in an urban microcellular scenario
<p>We provide plane-wave propagation path data from a wideband MIMO radio channel sounding measurement in an urban microcell scenario. The binary Matlab file includes: direction of departure (DOD: variables "par.PhiTx" and "par.ThetaTx" in [rad]), direction of arrival (DOA: "par.PhiRx" and "par.ThetaRx" in [rad]), delay ("par.Tau" to be multiplied with 8.3ns, the tab length), and complex polarimetric path gain ("par.Alpha" is a 2x2 matrix, where element [1,1]=TXtheta ->RXtheta, [1,2]=TXphi ->RXtheta, [2,1]=TXtheta->RXphi, and [2,2]=TXphi->RXphi), for the 30 strongest signal paths (from TX to RX) at each of the 4574 RX locations along the route described below. In the element names above, the term "theta" refers to the vertically polarised component, and accordingly the term "phi" referes to the horizontally polarised component.<br> Note #1: The exact RX location for each individual measured radio channel was NOT recorded (see route description below). <br> Note #2: The complex path gain (par.Alpha) is NOT calibrated, but depends on the initially fixed AGC level in the receiver, which was chosen to provide the best dynamic range for the given mobile (RX) route.<br> Both these limitations are seen reasonable since this dataset is meant for the realistic *statistical comparison* of the performance of different RX antennas in a microcell environment (and not to determine the actual received power at each exact location of the measured route).<br> Background information: The provided dataset is processed and is based on a radio channel sounder measurement at 5.3 GHz, carried out in downtown Helsinki, Finland, in April 2004. The uniform rectangular transmit (TX) array was placed at 10 m height in Aleksanterinkatu-street (an approx. 15-m wide street canyon), in front of the Nordea building, broadside pointing westwards (towards Stockmann building). The semishperical receive (RX) array was moved at 1.6-m height and for about 50 m along Aleksanterinkatu-street in line-of-sight (LOS), i.e. from in front of Kluuvi shopping centre westwards just across the crossing of Kluuvikatu-street. The TX and RX arrays cover the relevant azimuth and elevation ranges, so that this plane wave propagation path data can directly be combined with the polarimetric directional radiation pattern(s) of an antenna (array).</p>
Dataset: Impact of tall buildings and building-height variability on flow within and above a complex urban canopy in London - Part I
<p>This dataset contains the first part of a series of wind-tunnel flow measurements in a 1:200 scale model of a part of the London Borough of Southwark. The data published here accompanies analyses in Hertwig et al. (2019):</p> <p>D. Hertwig, H.L. Gough, S. Grimmond, J.F. Barlow, C.W. Kent, W.E. Lin, A.G. Robins, P. Hayden (2019), "Wake Characteristics of Tall Buildings in a Realistic Urban Canopy", Boundary-Layer Meteorology, DOI: 10.1007/s10546-019-00450-7.</p> <p>Flow measurements (based on 2D Laser Doppler Anemometry) were carried out in the boundary-layer wind-tunnel facility of the Environmental Flow (EnFlo) Research Centre, University of Surrey, UK, using the EPSRC <a href="http://www.magic-air.uk/">MAGIC</a> project model domain and scale model (<a href="https://www.tandfonline.com/doi/full/10.1080/09613218.2018.1468158">Song et al., 2018</a>).</p> <p>This repository contains:</p> <ol> <li>wt_ts_part_I_readme.pdf: Meta information and description of data in 'wt_ts_part_I_data.zip'</li> <li>wt_ts_part_I_data.zip: Flow measurements (time series and statistics)</li> </ol>
TAU Urban Acoustic Scenes 2019, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2019 evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 20 hours of audio.</p>
TAU Urban Acoustic Scenes 2019 Mobile, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2019 Mobile, Evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>The dataset contains in total 30 hours of audio.</p>
TAU Urban Acoustic Scenes 2019 Openset, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2019 Openset evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul> <p>In addition, material for Unknown scene <em>unknown</em> is provided.</p> <p>The dataset contains in total 20 hours of audio.</p>
Atmospheric processing of loess dust particles in a polluted urban area of China
<p>STATEMENT</p> <p> </p> <p>This file is for the upload of data for 2018JD029956; the basic data for the article are available. The research data follows the FAIR Data Principles and demonstrate compliance with international standards for data repositories.</p> <p>Please contact Prof. Yang Chen (<a href="mailto:chenyang@cigit.ac.cn">chenyang@cigit.ac.cn</a>) if you have any questions.</p>
Path Planning for Autonomous Bus Driving in Urban Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Urban Environments".</pre>
Dataset used in "Urban Seismic Site Characterization by Fiber-Optic Seismology" by Spica et al. in Journal of Geophysical Research: Solid Earth
<p>Spica et al. (2019, Journal of Geophysical Research: Solid Earth). This repository contains continuous waveforms from DAS for one day and for the stations mentioned in the article (Fig. 8).</p> <p>Files: all traces in miniseed format. Traces stats in the headers and in the article.</p>
kongdd/ChinaHW_data: Data of "Contributions of anthropogenic warming and urbanization to intensification of heatwaves over China"
<p>No description provided.</p>
FIGURE 4. A–C. Phyllanthus niruri. A in A new species of Phyllanthus (Phyllanthaceae) hiding in the shades in an urban Atlantic Forest in Rio de Janeiro State, Brazil
FIGURE 4. A–C. Phyllanthus niruri. A. Branches with pistillate flowers and fruits, B. Staminate flowers, C. Pistillate flowers. D–H. P. augustinii. D. Habit, E–F. Staminate flower. G. Pistillate flower. H. Fruits. (Fotos: A. Otávio Marques, B–C. Joyce Assunção; D–H. Jone Mendes).
Remarks on sustainable disposal and potential reuse of greywater in an informal urban settlement, Cape Town, South Africa
<p><span>The disposal of greywater is a challenge for informal urban settlements that do not have a reticulation system to collect the wastewater they generate. G</span><span>reywater, which includes domestic wastewater, represents a potential resource that can alleviate water stress if efficiently managed. This study aimed to provide sustainable disposal and potential reuse of greywater produced using informal settlements in Cape Town, South Africa.</span><span> <span>Greywater has the potential to contribute significantly to food security in such settlements by providing a source of both irrigation water and toilet flushing. A combination of desktop research and interviews was employed to gather and analyze the data. <a name="_Hlk148373731"></a>A total of 69 questionnaires were administered in the study, and </span></span><span>greywater management practices were investigated.</span><span><span> </span></span><span>The findings showed deficiencies in the sustainable disposal of greywater from households. <a name="_Hlk148373893"></a>To understand the volumes of greywater generated from a household, a model has been proposed in the study, as there</span> has been a lack of focus on making predictions that quantify the volume of greywater recovered from these households. <span>In the context of sustainable development, water management, and considering the scarcity of water resources, particularly in semi-arid regions, the use of treated greywater is an alternative method that can reduce the burden of withdrawals.</span></p>
Fig. 3 in Discoderus LeConte Species Exhibit Episodic Emergences in both Urban and Natural Environments of Arizona, USA (Coleoptera: Carabidae)
Fig. 3. Syntype specimens of Discoderus species described by Casey from Arizona. All scale bars 5 mm. A) D. aequalis Casey, 1914, B) D. congruens Casey, 1914, C) D. obsidianus Casey, 1914, D) D. papagonis Casey, 1924, E) D. hesperus Casey, 1914, junior subjective synonym of D. parallelus, F) D. pinguis Casey, 1884, G) D. subviolaceus Casey, 1914, H) D. symbolicus Casey, 1914.
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.