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.
300
datasets available to search
ShareScore release 0.9.0
Dataset results
300 results for “Urban area”
Fig. 6 in Distribution Of The Carabid Species (Coleoptera, Carabidae) In Woodlands Of The Protected And Urban Areas (North Of Ukraine)
Fig. 6. Plot ordination of detrending correspondent analysis (66 % of the variance) of the forest species compositions in forests of the Nature Reserve and the urban parks during the study years. Symbols represent: plus — species which found only in parks, cross — species which found only in Reserve and column — shared species in both territories. Site name abbreviations are the same as in fig. 3.
Fig. 5 in Distribution Of The Carabid Species (Coleoptera, Carabidae) In Woodlands Of The Protected And Urban Areas (North Of Ukraine)
Fig. 5. Plots of the distribution of carabids species (A) and their density (B) according to dispersal ability (B — brachypterous, D — dimorphic, M — macropterous) in three habitat affinity groups in the Polisky Nature Reserve and in the urban parks.
Fig. 2 in Distribution Of The Carabid Species (Coleoptera, Carabidae) In Woodlands Of The Protected And Urban Areas (North Of Ukraine)
Fig. 2. Estimated species richness of carabid beetles collected in the investigated territories using rarefaction analysis (bars are 95 % CI).
Fig. 1 in Distribution Of The Carabid Species (Coleoptera, Carabidae) In Woodlands Of The Protected And Urban Areas (North Of Ukraine)
Fig. 1. Geographic location of the investigated areas is shown in arrows (indicating the position of the Polisky Nature Reserve (PNR) and the urban parks in Kyiv City, in the Ukraine). The study area is marked by the red flags.
Data from: Balanced spatial distribution of green areas creates healthier urban landscapes
<div> <span>The benefits of green infrastructure on human well-being in urban areas are already well established, with strong evidence of the positive effects of the amount and proximity to green areas. However, the understanding of how the spatial distribution and type of green areas affect health is still an open question. <br>Here, we explore how different spatial configurations of green and built-up areas, through a land sharing and sparing framework, and how different types of green areas affect cardiovascular and respiratory hospitalizations in São Paulo city, Brazil. <br>Sharing/sparing indicators were selected as the main explanatory factors in the control of all groups of diseases. Land sharing appeared as a favourable spatial condition to prevent cardiovascular hospitalization, while land sparing and arboreal vegetation were relevant to reduce hospitalization by lower respiratory diseases. <br>For upper respiratory diseases, forests seem to provide a disservice, once they were associated with increased rates of hospitalization by respiratory allergies causes.<br>Considering that hospitalization rates and severity of cardiovascular diseases are substantially higher than those of upper respiratory ones, dense vegetation tends to provide more services than disservices. The land sharing configuration, which is characterized by green areas spread throughout the urban network (in streets, gardens, small squares, or parks), should lead to higher exposure and use of the benefits of green areas, which may then explain the greater prevention of cardiovascular diseases. <br>These novel results indicate that a more balanced distribution of green areas across built-up areas creates healthier urban spaces, and thus can be used as an urban planning strategy to leverage the health benefits provided by green infrastructure. <br>Policy implications: </span><span><span>Aiming to reduce hospitalizations by cardiovascular and pulmonary causes, urban planning should promote the spreading of green areas across the cities, in order to increase daily contact with natural attributes, giving preference to distribution over total quantity of green in urban landscape.</span></span> </div>
The unequal impact of the COVID-19 pandemic in 2020 on life expectancy across urban areas in Chile: A cross-sectional demographic study
<p>Data accompanying the paper The unequal impact of the COVID-19 pandemic in 2020 on life expectancy across urban areas in Chile: A cross-sectional demographic study, currently at https://www.medrxiv.org/content/10.1101/2021.12.08.21267475v1</p>
Dataset: On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas
<p>Dataset containing the geodata needed to replicate the analysis and the results related the research article: <a href="https://doi.org/10.1109/TNSM.2022.3190634">"On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas"</a> published on Transactions of Network and Service Management.</p> <p> </p> <p>This dataset contains all the data used in the research article: "On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas" published on Transactions of Network and Service Management.</p> <p>It is divided into three main archives:</p> <ul> <li>The first archive, called <strong>data.zip</strong>, contains the Data Surface Model (DSM) used to generate the intervisibility graphs. These maps have been aggregated from different sources and they are all released under a CC-BY-SA 4.0 license. The files follow the naming format {area}_{type}.tif, where type can be one of the followings: <ul> <li>'<strong>buildings_mask</strong>': contains the buildings' shapes in raster format. These have been obtained by rasterizing the OpenStreetMap vectorial buildings data.</li> <li>'<strong>roads_mask</strong>': contains the roads' shapes in raster format. These have been obtained by expading the OpenStreetMap road graph by a given a mount of meters and rasterizing the result.</li> <li>'<strong>dtm</strong>': the Data Terrain Model in raster format</li> <li>'': the polished version of the Data Surface Model, where the values of the DSM are used only for the buildings (to map the roofs) and outside of the buildings the values from the dtm are used. In this way trees and unmapped buildings are not considered.</li> </ul> </li> <li>The second archive, called <strong>results.zip</strong>, contains the outcome of our algorithm for the optimal BS locations. The folder format is the following: 'results/{area}/threestep/{sa_id}/{ranking_function}/{k}/{ratio}/{lambda} : <ul> <li>'area': corresponds to the macro area used for that specific run</li> <li>'sa_id': corresponds to the subarea id (from 0 to 4) of a specific block of that area</li> <li>'ranking_function': corresponds to the ranking function used by the algorithm for that specific result (see the research article for more information)</li> <li>'ratio': corresponds to the percentage of buildings used (see the research article for more information)</li> <li>'lambda': corresponds to the density of Base Stations deployed (see the research article for more information).</li> </ul> </li> </ul> <p>The data are licensed as follows:</p> <p>The DSM and DTM are licensed depending on the area:</p> <ul> <li><strong>Trento</strong>: The data are released by <a href="https://www.provincia.tn.it/">Provincia Autonoma di Trento</a> under a <a href="https://creativecommons.org/licenses/by/2.5/">CC-BY 2.5 License</a></li> <li><strong>Firenze</strong>: The data are released by <a href="https://www.regione.toscana.it/">Regione Toscana</a> under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0 License</a></li> <li><strong>Napoli</strong>: The data are released by <a href="https://cittametropolitana.na.it/">Città Metropolitana di Napoli</a> under a <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA 4.0 License</a></li> </ul> <p>The OpenStreetMap data have been obtained by <a href="https://download.geofabrik.de/">geofabrik.de</a> and are released under an <a href="https://opendatacommons.org/licenses/odbl/">Open Data Commons Open Database License</a></p> <p> </p> <p>All the results can be replicated using our code, available on <a href="https://github.com/UniVe-NeDS-Lab/TrueBS">Github</a>.</p> <p> </p> <p>In order to cite this dataset please cite the original research article:</p> <p> </p> <pre><code>@article{9828530, author={Gemmi, Gabriele and Cigno, Renato Lo and Maccari, Leonardo}, journal={IEEE Transactions on Network and Service Management}, title={On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas}, year={2022}, volume={}, number={}, pages={1-1}, doi={10.1109/TNSM.2022.3190634} } </code></pre> <p> </p>
FIGURES 20–38. Associated diatom flora. Figs 20–21, 33 in A new diatom (Bacillariophyta) species from Indonesian urban areas, description of Microcostatus labrisicus sp. nov.
FIGURES 20–38. Associated diatom flora. Figs 20–21, 33. Eunotia isabelensis Lange-Bertalot, Bąk & Kociolek. Fig. 22. Microcostatus stapputana Lange-Bertalot & Wydrzycka. Figs 22–23, 35–36. Placoneis geitleri (Hustedt) Vishnjakov. Figs 25–27, 34. Humidophila contenta (Grunow) Lowe et al. Figs 29–31, 37, 38. Luticola muticoides (Hustedt) D.G. Mann. Fig. 32. Luticola sp. Scale bar represents 10 µm (Figs 1–13), 5 µm (Figs 16, 17, 19), 3 µm (Fig. 15), 2 µm (Fig. 18).
FIGURES 1–19 in A new diatom (Bacillariophyta) species from Indonesian urban areas, description of Microcostatus labrisicus sp. nov.
FIGURES 1–19. Microcostatus labrisicus sp. nov. Figs 1–13. Light micrographs of the holotype population, fig. 7 represents the holotype specimen. Figs 14–17. Scanning electron microscopic (SEM) images of external view. Fig. 14. External view of the entire valve. Fig. 15. External detail of the central area and central parts of longitudinal depressions. Figs 16–17. External detail of the distal raphe endings and microcostae. Fig. 18. Internal detail of the central area and proximal raphe endings. Fig. 19. Internal valve view. Scale barrepresents 5 µm (Figs 1–13), 4 µm (Fig. 14), 3 µm (Fig. 19), 1 µm (Figs 15–18).
FIGURE 2 in Doryphoribius chetumalensis sp. nov. (Eutardigrada: Isohypsibiidae) a new tardigrade species discovered in an unusual habitat of urban areas of Mexico
FIGURE 2. Doryphoribius chetumalensis sp. nov.. Habitus A) Dorsal view (holotype). B) Lateral view. C) Ventral view (arrow = eye spot). Scale bars = 50 µm.
FIGURE 4 in Doryphoribius chetumalensis sp. nov. (Eutardigrada: Isohypsibiidae) a new tardigrade species discovered in an unusual habitat of urban areas of Mexico
FIGURE 4. Doryphoribius chetumalensis sp. nov.. Claws. A) Claws on second and third legs. B) Claw of second pair of legs. C) Claws of the fourth pair of legs. D) Claws of the third pair of legs. E) Claws of the fourth pair of legs. F) Claws of the fourth pair of legs. Arrows = bases of the external or posterior claws in the pair of claws in a leg. Scale bars = 10 µm.
FIGURE 3 in Doryphoribius chetumalensis sp. nov. (Eutardigrada: Isohypsibiidae) a new tardigrade species discovered in an unusual habitat of urban areas of Mexico
FIGURE 3. Doryphoribius chetumalensis sp. nov.. Buccal-pharyngeal apparatus. A) Macroplacoids in lateral view, stylet support and stylet furca (arrow = eye spot). B) Macroplacoids in dorsal view. C) Lateral view (arrow = ventral lamina). Scale bars = 10 µm.
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>
FIGURE 1 in Phylloporia minuta sp. nov. (Basidiomycota, Hymenochaetales): a remarkable species discovered in a small protected urban area of Atlantic Forest
FIGURE 1. Best-scored tree from the ML analyses, showing the phylogenetic relationships of Phylloporia species inferred from 28S sequences. The tree was rooted with Inonotus cuticularis and I. hispidus. New species is highlighted in bold. Thickened branches represents bootstrap (BS) and posterior probability (PP) support values greater than or equal to 75% or 0.95, respectively. Support values are shown above those branches (BS/PP).
FIGURE 2. Phylloporia minuta. A in Phylloporia minuta sp. nov. (Basidiomycota, Hymenochaetales): a remarkable species discovered in a small protected urban area of Atlantic Forest
FIGURE 2. Phylloporia minuta. A. Basidioma in situ indicated by a white arrow growing on Doliocarpus schottianus (FLOR 63064 –paratype); B. Detail of the sulcate surface of the basidioma (FLOR 63063–paratype); C. Minute dried basidiomata, showing the hymenophore (SP–paratype); D. Detail of the basidiomata, showing the lower context and the upper tomentum separated by a thin black line indicated by a white arrow (FURB 55088–holotype); D. Generative hyphae from the hymenophoral trama (SP –paratype); E. Mass of collapsed basidiospores, observed in some specimens (SP–paratype); F. Basidiospores (FURB 55088–holotype). Bars: A, B = 10 mm; C, D = 2 mm, E, F = 20 μm. Photos by F. Bittencourt.
FIGURES 8–11. 8. Cosmarium rostafinskii. 9. C. benedictum. 10. C. amstelodamense. 11. C in Some new and interesting desmids (Streptophyta, Desmidiales) from ephemeral puddles in the urban and industrial areas of Amsterdam (Netherlands)
FIGURES 8–11. 8. Cosmarium rostafinskii. 9. C. benedictum. 10. C. amstelodamense. 11. C. benedictum, zygote. Scalebar = 10μm.
FIGURES 12–18. 12–14. Cosmarium notabile. 15–16. C. galeatum. 17. C. fruticosum. 18. C in Some new and interesting desmids (Streptophyta, Desmidiales) from ephemeral puddles in the urban and industrial areas of Amsterdam (Netherlands)
FIGURES 12–18. 12–14. Cosmarium notabile. 15–16. C. galeatum. 17. C. fruticosum. 18. C. scutiforme. Scalebar = 10μm.
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here is the data and original code for the paper titled <em>Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning</em>. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</p>
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here are the relevant data and preliminary code for the paper titled 'Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning' for your reference. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</p>
Figure 3 in Relationship between environmental conditions and hostseeking activity of Ochlerotatus albifasciatus (Diptera: Culicidae) in an agroecosystem and in an urban area in Chubut, Central Patagonia, Argentina
Figure 3. Relationship between biting activity rate of Ochlerotatus albifasciatus (number of mosquitoes captured on the bait during 20 minutes of exposition) and (A) air temperature, (B) air environmental RH, (C) wind speed and (D) sunshine degree. Continuous lines correspond to trials conducted in the evening, and dashed lines correspond to trials conducted in the morning and afternoon (pooled). Adjusted functions were obtained from a generalised linear model with negative binomial error distribution and log link function (see Methods for details).
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.