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 spaces”
Terrorism and the city: affect, space and violence in urban Europe - Dataset
<p>Dataset for SNF Ambizione project "Terrorism and the city: affect, space and violence in urban Europe"</p>
Codes, raw predation data on dummy caterpillars, and environmental data about urban green spaces across 26 Brazilian state capitals
<p>Data collected by the Urban Predation Risk Network about predation pressure on dummy caterpillars exposed for eight days in 26 small green spaces and 24 forest patches across 26 Brazilian state capitals. The raw predation data describes the type of predator and the day in which each caterpillar was found with marks, as well as the sites' coordinates. The environmental data provides information for each site about precipitation, annual mean temperature, temperature seasonality, elevation, and coordinates. This database also provides summarized information about predation at each site, such as the total number of attacked caterpillars and the number of attacked caterpillars by each predator group. The data files contain a metadata sheet with a description of each column.</p> <p>The code provided was used to assess the local and large-scale drivers of predation pressure on dummy caterpillars in small green spaces and forest patches across 26 Brazilian cities.</p>
Dataset for "Space use by animals on the urban fringe: interactive effects of sex and personality"
Open the record for dataset details and reuse information.
Data from: What determines how we see nature? Perceptions of naturalness in designed urban green spaces
Open the record for dataset details and reuse information.
Keep it real: Selecting realistic sets of urban green space indicators - Supplementary data
<p>This excel sheet contains, for each of the four studied cities, the conceptual framework that is described in the paper "Keep it real: Selecting realistic sets of urban green space indicators".</p> <p>Each of the cities first listed all possible indicators that they could think of. Next, they indicated how each of those indicators relates to each of the KPI; i.e. whether the indicator can not at all (0), somewhat (1) or perfectly measure (2) the KPI. Lastly, the city authorities indicated whether the indicators are implemented of not, and scored some measures of indicator quality (relevance, feasibility, clarity, and credibility)</p>
Datasets used in: Modelling eye-level visibility of urban green space: Optimising city-wide point-based viewshed computations through prototyping
<p>Research data supporting our publication. Full workflows using the R programming language have been provided on <a href="https://github.com/STBrinkmann/protoVS">GitHub</a>. Here we provide external data that has been used for our research, as well as the resulting Viewshed Greenness Visibility Index (VGVI) raster.</p> <p><strong>Datasets</strong></p> <p>Digital Terrain Model (DTM):</p> <ul> <li>Spatial Resolution: 1 m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DTM_1m.tif<br> </li> </ul> <p>Digital Surface Model (DSM):</p> <ul> <li>Spatial Resolution: 1 m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DSM_1m.tif<br> </li> </ul> <p>Landuse</p> <ul> <li>Spatial Resolution: 2 m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_LULC_2m.tif<br> </li> </ul> <p>VGVI map</p> <ul> <li>Spatial Resolution: 5 m</li> <li>Source: Resulting dataset from our analysis</li> <li>Licence: MIT License</li> <li>File name: vgvi_van.tif</li> </ul>
SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release
The SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release, part of the SDGI collection, measures the average share of the built-up area of a city that is open space for public use for all. UN SDG 11 is "make cities and human settlements inclusive, safe, resilient and sustainable". Aside from environmental benefits, public space can also help improve public health, bolster commUnity, and encourage economic exchange. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.7.1. The indicator was computed by measuring both the proportion of OpenStreetMap (OSM) public space within a given urban center and the proportion of WorldPop gridded population within 400 meters to Open Public Space (OPS). Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (GHS-UCDB). The SDG indicator 11.7.1 data set provides estimates of the average share of the built-up area of cities that is open space for public use for all for 8,873 urban centers across 180 countries.
Urban Landsat: Cities from Space
The Urban Landsat: Cities from Space data set contains images for 66 urban areas and the raw, underlying data for 28 of these places. Each image shows a Landsat false color composite in UTM projection. The R/G/B layers correspond to TM/ETM+ bands 7/4/2. Each pixel is 30x30 meters in area and most images are 30x30 km in area. A 2% linear stretch has been applied to the images. The Landsat data files contain six reflected bands of calibrated exoatmospheric reflectance stored in ENVI band sequential (BSQ) format. Geographic coordinates are included in the header files. The data files contain 1000x1000x6 4 byte floating point numbers as indicated in the header files.
R.CULT.HEA - URban Environment, CULTural Social Use of Space and HEAlth / Well-being Effect on Population
ClinicalTrials.gov study NCT02426528. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Constructing Urban Tourism Space Digitally — Supplementary Material
<p>Over the past decade, Airbnb has emerged as the most popular platform for renting out single rooms or whole apartments. The impact of Airbnb listings on local neighborhoods has been controversially discussed in many cities around the world. The platform's widespread adoption led to changes in urban life, and in particular urban tourism. In this paper, we argue that urban tourism space can no longer be understood as a fixed, spatial entity. Instead, we follow a constructivist approach and argue that urban tourism space is (re-)produced digitally and collaboratively on online platforms such as Airbnb. We relate our work to a research direction in the CSCW community that is concerned with the role of digital technologies in the production and appropriation of urban space and use the concept of representations as a theoretical lens for our empirical study. In that study, we qualitatively analyzed how the two Berlin neighborhoods <em>Kreuzkölln</em> and <em>City West</em> are digitally constructed by Airbnb hosts in their listing descriptions. Moreover, we quantitatively investigated to what extend mentioned places differ between Airbnb hosts and <em>visitBerlin</em>, the city's destination management organization (DMO). In our qualitative analysis, we found that hosts primarily focus on facilities and places in close proximity to their apartment. In the traditional urban tourism hotspot <em>City West</em>, hosts referred to many places also mentioned by the DMO. In the neighborhood of <em>Kreuzkölln</em>, in contrast, hosts reframed everyday places such as parks or an immigrant food market as the must sees in the area. We discuss how Airbnb hosts contribute to the discursive production of urban neighborhoods and thus co-produce them as tourist destinations. With the emergence of online platforms such as Airbnb, power relations in the construction of tourism space might shift from the DMOs towards local residents who are now producing tourism space collaboratively.</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.