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.
392
datasets available to search
ShareScore release 0.9.0
Dataset results
392 results for “streets”
Figure 2 in Detection of enteroparasites in foliar vegetables commercialized in street- and supermarkets in Aparecida de Goiânia, Goiás, Brazil
Figure 2. Protozoa detected in samples of lettuce and collard greens from street market and supermarkets in Aparecida de Goiânia, Goiás. (A) Cyst; (B) coccid oocyst with multiple sporocysts; (C) oocyst from Cystoisospora sp. resemble the C. belli; (D) oocyst of C. canis, (E) and (F) oocyst of Eimeriidae.
Dakar land use map at street block level
<p>This datatset contains a land use classification of Dakar (Senegal) at the street block level. It was created following the methodology presented in [1].</p> <p>Description of the files:</p> <ul> <li>"Dakar_landuse_shapefile.zip" : Shapefile of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table.</li> <li>"Dakar_landuse_style.zip" : Files for style of the shapefile.</li> </ul> <p>Attribute table content:</p> <ul> <li>"CAT", "GID" : ID of the street block</li> <li>"PROB_ACS" : Probability to belong to class ACS</li> <li>"PROB_AGRI" : Probability to belong to class AGRI</li> <li>"PROB_BARE" : Probability to belong to class BARE</li> <li>"PROB_DEPR" : Probability to belong to class DEPR</li> <li>"PROB_PLAN" : Probability to belong to class PLAN</li> <li>"PROB_VEG" : Probability to belong to class VEG</li> <li>"FIRST_LABE" : Class with the highest classification probability</li> <li>"SEC_LABEL" : Class with the second highest classification probability</li> <li>"FIRST_PROB" : Value of the highest classification probability</li> <li>"SEC_PROB" : Value of the second highest classification probability</li> <li>"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"</li> <li>"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)</li> <li>"MAP_LABEL" : Final classification label with uncertainty and different density classes</li> </ul> <p>Legend classes label:</p> <ul> <li>"AGRI" : Agricultural vegetation</li> <li>"VEG" : Natural vegetation</li> <li>"BARE" : Bare soils</li> <li>"ACS" : Non-residential built-up (administrative, commercial, services, etc.)</li> <li>"PLAN" : Planned residential built-up</li> <li>"PLAN_LD" : Planned residential low density built-up</li> <li>"DEPR" : Deprived residential built-up</li> <li>"UNCERT" : Uncertain classification</li> </ul> <p>References:</p> <p>[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. <a href="https://doi.org/10.3390/ijgi7070246">https://doi.org/10.3390/ijgi7070246</a> </p> <p>[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. <a href="https://doi.org/10.5281/zenodo.1290637">https://doi.org/10.5281/zenodo.1290637</a>.</p> <p>Funding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Excitation waves propagating on the streets of Barcelona. Numerical integration of Oregonator equations.
<p><a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_050.mov?versionId=285ba608-3f27-422f-b9f2-72137a5525a7">Barcelona_Raval_phi_0_050.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.050$<br> <br> <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_065.mov?versionId=30d2c271-9058-4587-a1ec-b5fdda78482b">Barcelona_Raval_phi_0_065.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.065$<br> <br> <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_074.mov?versionId=d0a4c922-b29c-46da-b8fd-618a404b482f">Barcelona_Raval_phi_0_074.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.074$</p> <p>Barcelona_Gracia_phi_0_0666.mov: Gracia. Initial perturbation site is Sagrada Familia. $\phi=0.0666$</p> <p>====== Description of the model ====</p> <p>Two fragments of Barcelona street map --- Gracia and Raval, were mapped onto a grid of 2500 by 2500 nodes. Nodes of the grid corresponding to streets are considered to be filled with a Belousov-Zhabotinsky medium, i.e. excitable nodes, other nodes are non-excitable. We use two-variable Oregonator equations~\cite{field1974oscillations} adapted to a light-sensitive <br> Belousov-Zhabotinsky (BZ) reaction with applied illumination~\cite{beato2003pulse}:</p> <p>\begin{eqnarray}<br> \frac{\partial u}{\partial t} & = & \frac{1}{\epsilon} (u - u^2 - (f v + \phi)\frac{u-q}{u+q}) + D_u \nabla^2 u \nonumber \\<br> \frac{\partial v}{\partial t} & = & u - v <br> \label{equ:oregonator}<br> \end{eqnarray}</p> <p>The variables $u$ and $v$ represent local concentrations of an activator, or an excitatory component of BZ system, and an inhibitor, or a refractory component. Parameter $\epsilon$ sets up a ratio of the time scale of variables $u$ and $v$, $q$ is a scaling parameter depending on rates of activation/propagation and inhibition, $f$ is a stoichiometric coefficient. </p> <p> We integrated the system using Euler method with five-node Laplace operator, time step $\Delta t=0.001$ and grid point spacing $\Delta x = 0.25$, $\epsilon=0.02$, $f=1.4$, $q=0.002$. We varied value of $\phi$ from the interval $\Phi=[0.05,0.08]$.</p> <p>To generate excitation waves we perturb the medium by square solid domains of excitation, $20 \times 20$ sites in state $u=1.0$, site of the perturbation is shown by red discs in <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Gracia.png?versionId=cfeb3a2a-1f8a-427d-a2b1-41f9893a4d66">Barcelona_Gracia.png </a> and <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval%20point.png?versionId=c0ad4d24-ff59-4287-a817-2c7bf72f509c">Barcelona_Raval point.png</a>. Time-lapse snapshots provided in the paper were recorded at every 150\textsuperscript{th} time step, we display sites with $u >0.04$; videos supplementing figures were produced by saving a frame of the simulation every 50\textsuperscript{th} step of numerical integration and assembling them in the video with play rate 30 fps. All figures in this paper show time lapsed snapshots of waves, initiated just once from a single source of stimulation; these are not trains of waves following each other.</p>
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</p>
Figure 1. A in Diversity of sarcosaprophagous dipterans (Insecta: Diptera) associated with street markets in the semiarid of northeastern Brazil
Figure 1. A. Location of the municipality of Toritama in the state of Pernambuco, Brazil. B. Jeans Fair held in Toritama, Pernambuco, Brazil. Source: Adapted from Google Maps and Agreste NotÍcia (2020). / A. Ubicación del municipio de Toritama en el estado de Pernambuco, Brasil. B. Feria del Jeans realizada en Toritama, Pernambuco, Brasil. Fuente: Adaptado de Google Maps y Agreste NotÍcia (2020).
Figure 4 in Diversity of sarcosaprophagous dipterans (Insecta: Diptera) associated with street markets in the semiarid of northeastern Brazil
Figure 4. Species accumulation curve for the assemblage of flies in an urban environment of Toritama city, Pernambuco state, Brazil. / Curva de acumulación de especies para el ensamblaje de moscas en un ambiente urbano de la ciudad de Toritama, estado de Pernambuco, Brasil.
Figure 3 in Diversity of sarcosaprophagous dipterans (Insecta: Diptera) associated with street markets in the semiarid of northeastern Brazil
Figure 3. Sex ratio of specimens sampled in an urban environment of Toritama city, Pernambuco state, Brazil, by treatments (before the fair and after the fair). / Proporción de sexos de ejemplares muestreados en un ambiente urbano de la ciudad de Toritama, estado de Pernambuco, Brasil, por tratamientos (antes y después de la feria).
Point clouds from terrestrial laser scanning of 30 trees along Malet Street, London
<p>Point clouds of 30 street trees scanned along <a href="https://goo.gl/maps/7x3dutn6vHcxVPdC6">Malet Street, London, UK</a>. </p> <p>Tree species is predominantly London Plane (<em>Platanus × hispanica</em>).</p> <p>Data was captured on 8/2/2017 (leaf-off) with a RIEGL VZ-400 terrestrial laser scanner. 24 scans were conducted from 12 positions along the street. The weather was good, with little to no noticeable wind.</p> <p>Data is a binary PLY format with <em>xyz</em> fields in an arbitrary coordinate system. Trees have been extracted from the global point cloud and have been "cleaned" to remove the ground and neighbouring trees (however there may be some errors). Data has been downsampled to a voxel size of 0.04 m.</p> <p>Raw data can be accessed from here.</p> <p>Please acknowledge the data set authors if using this data.</p>
Benefits and limitations of environmental magnetism for completing citizen science on air quality: a case study in a street canyon.
<p>Inside a street canyon in Montpellier (France) a total of 72 deposimeters were deployed in 29 households for a period of 3 months to measure local air quality. This street canyon was chosen because dwellers were already mobilized against the street traffic, and because they were in conflict on this issue with policy makers. The project aimed to include all the stakeholders through co-construction. The closure of the street during the metrological campaign and the absence of agreement curbed their involvement and motivation. However, the feedbacks from the citizen partners promote the fact that this study supported their claims and brought them a deeper understanding on the micro-scale air quality monitoring. Indeed, it is increasingly difficult for citizens, who seemed specifically interested in what is happening right outside their front door, to understand this measure with the emergence of ever more low-cost sensors. For that reason, we examined the citizen’s degree of confidence in magnetic monitoring of air quality and how can this technique be useful in their claims. The results show that magnetism can be a measurement technique favorable to citizen participation because it provides a large amount of data at the micro-scale of the street level, while the data from the certified associations for monitoring air quality requires a spatial interpolation to map variations on a neighborhood scale. In this study, we proposed a magnetic air quality index to standardize and democratize the magnetic monitoring of air quality to facilitate the dialogue with all stakeholders.</p>
Mapillary POI-Neighborhood Street-Level Images (MPOINSLI)
<p><em><strong>Dataset Name:</strong></em> MPOINSLI Mapillary POI-Neighborhood Street-Level Images </p> <blockquote> <p>This is a repository of Mapillary street-view images of New York City that include any portion of POIs in their field of view. The repository is the outcome of a paper, the abstract of which is provided below. Please use the below citatin for using this dataset:</p> <p> </p> </blockquote> <p><strong>Citation: </strong></p> <p>N. Zarbakhsh and G. McArdle, "Points-of-Interest from Mapillary Street-level Imagery: A Dataset For Neighborhood Analytics," 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW), Anaheim, CA, USA, 2023, pp. 154-161, doi: 10.1109/ICDEW58674.2023.00030.</p> <p><strong>Abstract: </strong></p> <p>The Sustainable Development Goals of the United Nations promote sustainable urban development to make cities more economically and socially liveable. Points of Interest (POIs) such as commercial properties and healthcare facilities are significant markers for these goals. Street-view images are becoming increasingly important for capturing cities' streetscapes. Existing studies provide city-level images, while there are few studies that provide images in the vicinity of certain POIs. Therefore, this paper develops a framework for filtering images so that a portion of a given POI is visible in their field of view (FOV). We contribute with Mapillary POI-Neighborhood Street-Level Images (MPOINSLI) dataset, a large street-view image of POIs and their neighborhood in New York City. First, all the images within a 35-meter radius of certain POIs are filtered. Then, the intersection technique is utilized to determine if the cameras' FOV triangular polygons intersect the POIs' polygons. Using 11,126 POIs from SafeGraph's Geometry and Place datasets in conjunction with 875,592 Mapillary images, we demonstrate the effectiveness of our approach. MPOINSLI contains 167,743 Mapillary street-view images of 6,732 unique POIs, defined by the standard identifiers (Placekeys) which are further classified into 23 general functionalities categories (top-categories) and 67 more specific categories (sub-categories) of the POIs. MPOINSLI provides an open-source repository that contains metadata such as raw and post-processed camera-related parameters, the Harvesian distance between the camera and the POI's coordinates, and the intersection area. MPOINSLI could provide promising future applications for both smart cities and computer vision, including scene recognition across POI neighborhoods and fine-grained land-use classification.</p>
Dataset from 'Influence of anthropogenic emissions on the composition of highly oxygenated organic molecules in Helsinki: a street canyon and urban background station comparison'
<p>This dataset supplements the following manuscript:</p> <p>Okuljar, M., Garmash, O., Olin, M., Kalliokoski, J., Timonen, H., Niemi, J. V., Paasonen, P., Kontkanen, J., Zhang, Y., Hellén, H., Kuuluvainen, H., Aurela, M., Manninen, H. E., Sipilä, M., Rönkkö, T., Petäjä, T., Kulmala, M., Dal Maso, M., and Ehn, M.: Influence of anthropogenic emissions on the composition of highly oxygenated organic molecules in Helsinki: a street canyon and urban background station comparison, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-524, 2023.</p>
pLitterStreet - Street Level Plastic Litter Detection Dataset
<p><strong>pLitterStreet</strong> dataset comprises of more than <em>13,000 images</em>. These images were captured using <em>vehicle-mounted cameras</em> that were strategically positioned to focus on the sides of streets. The primary objective of this dataset is to facilitate research related to street litter and its impact on the environment.</p> <p>Annotations for the images are provided in the widely-used Microsoft COCO JSON format. Image in the dataset is fully annotated, enabling the identification and categorization of various types of litter found along urban and rural streets. These annotations include precise labeling of litter items, making the dataset an invaluable resource for developing and evaluating object detection and image recognition models.</p>
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
Comparing first street foundation and PRIMo flood hazard data across the Los Angeles metropolitan region
Open the record for dataset details and reuse information.
Simulated trajectories of city population: Levy walks along Buffalo, NY, street network
<p>Simulated trajectories of the full 2000 census population of Buffalo, New York. Each simulated person was placed randomly along a road within his or her census tract of residence and then independently performed road-network-constrained, truncated Lévy walks for 8 hours of simulated time, moving at 4 km per hour. The trajectories were then sampled at 30 minute intervals. The original dataset was created for studying indexes of activity-space segregation. It is archived at <a href="https://zenodo.org/record/2865830#.XpYviKsza00">https://zenodo.org/record/2865830#.XpYviKsza00</a> and described more fully in Palmer (2013), Activity-Space Segregation: Understanding Social Divisions in Space and Time (http://arks.princeton.edu/ark:/88435/dsp01k643b130h).</p> <p>This version has been created to aid in testing contact-tracing apps and mobility analysis tools. The fields are:</p> <p>latitude: latitude</p> <p>longitude: longitude</p> <p>time: UNIX time in milliseconds</p> <p>ID: random ID assigned to each individual</p>
Simulated trajectories of city population: Levy walks along Utica, NY, street network
<p>Simulated trajectories of the 2000 census population of Utica, New York. Each of 53,971 simulated people was placed randomly along a road within his or her census tract of residence and then independently performed road-network-constrained, truncated Lévy walks for 8 hours of simulated time, moving at 4 km per hour. The trajectories were then sampled at 30 minute intervals. The original dataset was created for studying indexes of activity-space segregation. It is archived at <a href="https://zenodo.org/record/2865830#.XpYviKsza00">https://zenodo.org/record/2865830#.XpYviKsza00</a> and described more fully in Palmer (2013), Activity-Space Segregation: Understanding Social Divisions in Space and Time (http://arks.princeton.edu/ark:/88435/dsp01k643b130h).</p> <p>This version has been created to aid in testing contact-tracing apps and mobility analysis tools. The fields are:</p> <p>latitude: latitude</p> <p>longitude: longitude</p> <p>time: UNIX time in milliseconds</p> <p>ID: random ID assigned to each individual</p>
Floristic monitoring of the 1,324 alignment tree bases of 15 streets in the district of Bercy, Paris, France, from 2009 to 2018
<p>Floristic monitoring of the 1,324 alignment tree bases of 15 streets in the district of Bercy, Paris, France, from 2009 to 2018.</p> <p>Nathalie Machon (CESCO, MNHN-CNRS-Sorbonne-Université)</p> <p>Data collectors : Noëlie Maurel, Marion Noualhaguet, Marion Dubois, Sébastien Julliard, Ambre Zéléla Bouvard, Paul Haenel, Florence Devers, Hélène Beaugeard, Gwendoline Chastel, laure Schneider-Maunoury and Mona Omar</p> <p>Centre d’Ecologie et des Sciences de la Conservation, Muséum national d’Histoire naturelle, 61 rue Buffon, 75005 Paris, nathalie.machon@mnhn.fr</p> <p>In cities, trees planted along streets host at their base a high number of spontaneous plants. Thus, they may provide shelters and corridors across the urban matrix.</p> <p>From 2009, we monitor urban tree bases in streets of Paris, France. Our objective is to follow the dynamics of these plant communities (Omar et al. 2018, 2019).</p> <p> </p> <p><strong>Study area and floristic inventories</strong></p> <p>The monitoring was performed in the 12th administrative district of Paris (Postal code: 75012; France; 48°50′26.91″N, 2°23′17.46″E),</p> <p>We monitored the 1,324 tree bases distributed along the 15 streets or avenues which contained at least 30 alignment trees in the district.</p> <p>Tree bases (TB) were for some of them covered by metal grills (grill/soil) to prevent soil compaction to preserve tree roots.</p> <p>The present file gives the list of all wild vascular plant taxa observed in each tree base, in May or June, each year from 2009 to 2018 except in 2013 because of a lack of observers. The taxonomic reference is the French Flora Reference TAXREF v8.0 (Gargominy et al., 2016).</p>
25 Carlos Street, Woodbrook
This late 19th century house was destroyed by fire in 2018 and was located Adam Smith Square. Source: Objaverse 1.0 / Sketchfab
Liege Street Art
Photogrammetry Street-art by Fabrizio Borrini ( https://www.facebook.com/atelierfabrizioborrini/ ) Liège - Avenue Maurice Destenay - Chiroux https://goo.gl/maps/6AvYsWmqhyG2 Source: Objaverse 1.0 / Sketchfab
A home on Chingiz Mustafayev Street in Sovetski
The exterior of a home on Chingiz Mustafayev Street in the Sovetski neighborhood of Baku, Azerbaijan. Two commemorative plaques hang on the facade; one is for Chingiz Mustafayev himself, a journalist who covered the Karabagh War, and another for a soidier who died in the same war. The home is shaded by a large tree. Next to the tree an alley runs along side the home towards more residences. Source: Objaverse 1.0 / Sketchfab
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.