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130 results for “urban environment”
Fig. 5 in Larval Development And Habitat Usage Of Stream-Breeding Fire Salamanders In An Urban Environment
Fig. 5. Mean number of salamander larvae detected per a year in the "releasing" 2–6 upper segments (continuous bold line), in the "strong collector" middle segments: 7–9 (dashed line) and the "weak collector" lower segments: 10–13 (dotted line) during surveys every 10 days in "Hűvös-ér" stream between 2011–2014. Data from segment 1 have not been plotted because larvae were present at only one time point
Fig. 1 in Larval Development And Habitat Usage Of Stream-Breeding Fire Salamanders In An Urban Environment
Fig. 1. Segments of "Hűvös-ér" stream, where Salamandra salamandra larvae were surveyed. (Numbers indicate individual stream segments, bold meandering line = main branch of the stream, thin branch- ing line = tributaries of the stream, straight lines = segment boundaries, four-pointed stars at segment boundaries and in the stream bed = water steps, double line = main road between Budapest and Solymár, P = "Paprikás"-stream)
Supplementary Material - Marine animal forests in turbid environments are overlooked seascapes in urban areas
<p>Figures S1, S2, and S3 of the manuscript "Marine animal forests in turbid environments are overlooked seascapes in urban areas" accepted in the open-access journal Ocean and Coastal Research (Soares et al. 2023)</p>
Dataset from: Ultraviolet refractive index values of organic aerosol extracted from deciduous forestry, urban and marine environments
<p>The refractive index values of atmospheric aerosols are required to address the large uncertainties in the magnitude of atmospheric radiative forcing and measurements of the refractive index dispersion with wavelength of particulate matter sampled from the atmosphere are rare over ultraviolet wavelengths. An ultraviolet-optimized spectroscopic system illuminates optically-trapped single particles from a range of tropospheric environments to determine the particle’s optical properties. Aerosol from remote marine, polluted urban, and forestry environments is collected on quartz filters, and the organic fraction is extracted and nebulized to form micron-sized spherical particles. The radius and the real component of refractive index dispersion with wavelength of the optically trapped particles are determined to a precision of 0.001 µm and 0.002 respectively over a near-ultraviolet-visible wavelength range of 0.320–0.480 µm. Remote marine aerosol is observed to have the lowest refractive index (n=1.442 (λ=0.350 µm)), with above-canopy rural forestry aerosol (n=1.462–1.481 (λ=0.350 µm)) and polluted urban aerosol (n=1.444–1.485 (λ=0.350 µm)) showing similar refractive index dispersions with wavelength. In-canopy rural forestry aerosol is observed to have the highest refractive index value (n=1.508 (λ=0.350 µm)). The study presents the first single particle measurements of the dispersion of refractive index with wavelength of atmospheric aerosol samples below wavelengths of 0.350 µm. The Cauchy dispersion equation, commonly used to describe the visible refractive index variation of aerosol particles, is demonstrated to extend to ultraviolet wavelengths below 0.350 µm for the urban, forestry, and atmospheric aerosol water-insoluble extracts from these environments. A 1D radiative-transfer calculation of the difference in top-of-the-atmosphere albedo between atmospheric core-shell mineral aerosol with and without films of this material demonstrates the importance of organic films forming on mineral aerosol.</p> <p>The raw experimental spectra collected and analysed in this study are provided, as well as files for the calibrated wavelengths.</p>
Bees need larger brains to thrive in urban environments
<p>The rapid conversion of natural habitats to anthropogenic landscapes is threatening insect pollinators worldwide, raising concern on the negative consequences for their fundamental role as plant pollinators. However, not all pollinators are negatively affected by habitat conversion, as certain species find in anthropogenic landscapes appropriate resources to persist and proliferate. The reason why some species thrive in anthropogenic environments while most find them inhospitable remains poorly understood. The cognitive buffer hypothesis, widely supported in vertebrates but untested in insects, offers a potential explanation. This theory suggests that species with larger brains have enhanced behavioural plasticity, enabling them to confront and adapt to novel challenges. To investigate this hypothesis in insects, we measured brains for 89 bee species, and evaluated the association between brain size and habitat preferences. Our analyses revealed that bee species that prefer urban habitats had larger brains relative to their body size than those who prefer forested or agricultural habitats. Additionally, urban bees exhibited larger body sizes and, consequently, larger absolute brain sizes. Our results provide the first empirical support for the cognitive buffer hypothesis in invertebrates, suggesting that a large brain in bees could confer behavioural advantages to tolerate urban environments.</p>
Urban environments promote adaptation to multiple stressors
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Habitat occupancy of the critically endangered Chinese pangolin (Manis pentadactyla) under human disturbance in an urban environment: Implications for conservation
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Host-parasite relationship in urban environments: A network analysis of hemoparasite infections in Nasua nasua Linnaeus (South American coati)
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Brain size predicts bees’ tolerance to urban environments
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Ecophysiological and behavioral adaptations of birds to rapid urbanization of a desert environment in central Arizona-Phoenix, from 2006 to 2008.
We used Sonoran desert species that have adapted to urbanization to various degrees to investigate relations between endocrine parameters, in particular plasma corticosterone in response to acute stress, and aspects of the immune system, parasite infections, and body condition. Desert residents generally suppressed their endocrine response to acute stress during the breeding season and underwent marked seasonal changes in body condition. By contrast, conspecific urban residents tended to maintain an acute stress response throughout the year and showed subdued seasonal changes in body condition. We surmise that this difference is related to differences between desert and urban environments in food resources that birds use to sustain themselves. The goal of upcoming studies will be to test this hypothesis. In addition, we are carrying out studies aimed at elucidating the neuroendocrine mechanisms that mediate differential stress responses seen in birds residing in rural vs. urban habitats. We are also developing experiments examining relation between the stress response and the activity of the immune and reproductive system. The data that we have already collected, and those to be collected in the coming year, will provide the most extensive set of data to date on effects of urbanization on wild vertebrates and on the mechanisms that are responsible for these effects.
Everyday urban environment (urbArki-2009)
<p><strong>Data description:</strong></p> <p>This data is collected as part of the Urban Everyday (Urbaani Arki) project and includes the following datasets: Home locations, everyday errand points, suggestion points, and appeal points.</p> <p><strong>Who collected (person/organization):</strong></p> <p>The dataset is collected in department of Built Environment, Aalto University, Finland, in Prof. Marketta Kyttä’s research team.</p> <p><strong>when collected the data:</strong></p> <p>Data is collected in 2009</p> <p><strong>Data characteristics: </strong></p> <p>Age range: Data is collected from individuals aged 15-74</p> <p>Geographical area: Tampere, Finland</p>
Characterizing the nectar microbiome of the non-native tropical milkweed, Asclepias curassavica, in an urban environment
<p>In increasingly urban landscapes, the loss of native pollen and nectar floral resources is impacting ecologically important pollinators. Increased urbanization has also brought about the rise of urban gardens which introduce new floral resources that may help replace those the pollinators have lost. Recently, studies have shown that the microbial communities of nectar may play an important role in plant-pollinator interactions, but these microbial communities and the floral visitors in urban environments are poorly studied. In this study we characterized the floral visitors and nectar microbial communities of <i>Ascelpias curassavica</i>, a non-native tropical milkweed commonly, in an urban environment. We found that the majority of the floral visitors to <i>A. curassavica</i> were honey bees followed closely by monarch butterflies. We also found that there were several unique visitors to each site, such as ants, wasps, solitary bees, several species of butterflies and moths, Anna's hummingbird, and the tarantula hawk wasp. Significant differences in the nectar bacterial alpha and beta diversity were found across the urban sites, although we found no significant differences among the fungal communities. We found that the differences in the bacterial communities were more likely due to the environment and floral visitors rather than physiological differences in the plants growing at the gardens. Greater understanding of the impact of urbanization on the nectar microbiome of urban floral resources and consequently their effect on plant-pollinator relationships will help to predict how these relationships will change with urbanization, and how negative impacts can be mitigated through better management of the floral composition in urban gardens.<br> </p>
Uncertainties associated with microwave link rainfall estimates in an urban environment
<p>These dataset were collected from a dedicated microwave link setup between Mt View Reservoir (T) and 33 Lakeside Burwood (R). There were two OTT1 disdrometers installed at both ends of the microwave link complemented by 3 tipping bucket rain gauges. </p>
Red and white clover provide food resources for honeybees and wild bees in urban environments
<p>Pollination is a key ecological process both in wild plant species and in economically important crops. Global land use change and urbanization are known to alter plant-pollinator interactions, but our understanding of how the local (i.e. size of green area, food resource availability) and landscape (surrounding green area) context affect pollinators in urban landscapes remains understudied. We selected two co-occurring clover species, Trifolium pratense and T. repens. to assess whether mixed stands of common wildflowers provide resources for a diverse pollinator assemblage by supporting differently adapted/specialized pollinator species. We further wanted to test how environmental factors (flower diversity, resource availability, size and percentage of green area) alter plant-pollinator interactions in urban environments. We studied the pollinator assemblage and visitation rate of pollinators in 1 m² plots in 21 green areas of different sizes in the city of Vienna (Austria). In addition, we assessed the surrounding landscape context by estimating the percentage of green area in perimeters of 100 m, 500 m and 1000 m around each study plot and measured local flower resource availability. We found that proportions of pollinator taxa differed significantly between white and red clover, with T. repens mainly pollinated by Apis mellifera, and T. pratense primarily pollinated by different bumblebee species. Visitation frequency was positively correlated to local resource availability (number of anthetic Trifolium inflorescences in each plot), but independent of the surrounding landscape context (i.e. percentage of green area). We conclude that the establishment and maintenance even of small patches of different common wildflowers help maintain a diverse bee community in urban environments. Particularly large-flowered species may be important for supporting long-tongued, late emerging pollinators such as certain bumblebee species.</p>
Data from: Living in the city: urban environments shape the evolution of a native annual plant
Urban environments are warmer, have higher levels of atmospheric CO2, and altered patterns of disturbance and precipitation than nearby rural areas. These differences can be important for plant growth and are likely to create distinct selective environments. We planted a common garden experiment with seeds collected from natural populations of the native annual plant Lepidium virginicum, growing in five urban and nearby rural areas in the northern United States to determine whether and how urban populations differ from those from surrounding rural areas. When grown in a common environment, plants grown from seeds collected from urban areas bolted sooner, grew larger, had fewer leaves, had an extended time between bolting and flowering, and produced more seeds than plants grown from seeds collected from rural areas. Interestingly, the rural populations exhibited larger phenotypic differences from one another than urban populations. Surprisingly, genomic data revealed that the majority of individuals in each of the urban populations were more closely related to individuals from other urban populations than they were to geographically proximate rural areas – the one exception being urban and rural populations from New York which were nearly identical. Taken together our results suggest that selection in urban environments favors different traits than selection in rural environments and that these differences can drive adaptation and shape population structure.
Data from: Nitrogen fertilization differentially enhances nodulation and host growth of two invasive legume species in an urban environment
Invasive plants negatively impact native communities by altering ecosystem processes and reducing species diversity. Plants in the legume family are overrepresented among invasive taxa and establish in disturbed environments common in urban ecosystems. Mutualisms with rhizobia and anthropogenic activities, such as nitrogen fertilization, may be key mechanisms driving legume invasions of urban habitats. Moreover, legume species and genetic lineages within species may vary in their responses to nitrogen fertilization, making some more likely to invade than others. Despite this threat, it remains unclear whether nitrogen fertilization impacts mutualism and plant growth traits of invasive legumes in urban environments, and whether these effects depend on the genetic origin of invaders. We conducted a common garden experiment using two widespread, invasive legume species, Medicago sativa and Trifolium pratense, to test the effects of species, genetic origin of lineages within species, and nitrogen fertilization on the mutualism. Soil nitrogen was manipulated and effects on traits associated with the mutualism (nodule traits) and plant growth were quantified. Nitrogen fertilization improved nodule traits and host growth for both species, but M. sativa and certain genetic lineages of this species benefited more from fertilization than any of the tested T. pratense lineages. This work reveals how anthropogenic activities alter mutualism traits and plant growth in urban environments, potentially facilitating invasions by leguminous taxa. Because species and lineages varied in the strength of their responses to fertilization, over time some invading legumes may outcompete other plant species or lineages, leading to differential impacts on native communities in urban environments.
Self-Learning Vehicle Detection Dataset for Urban Environments
<p>This dataset was collected as part of a research study aimed at enhancing vehicle detection algorithms through a self-learning approach tailored for urban environments. The primary objective was to minimize dependency on extensive manual labeling and improve adaptability and effectiveness in dynamic urban conditions. The study utilized urban camera infrastructures to gather real-time traffic data, focusing on a diverse range of vehicle types.</p> <p>The dataset includes images captured from traffic cameras situated at the intersection of Calle de Alcalá and Calle de Velázquez in Madrid, Spain, operated by the Madrid City Council. Data collection spanned from November 30, 2023, to December 6, 2023, covering daytime traffic between 8:30 hours and 18:00 hours. A total of 770 images were captured at approximately 5-minute intervals.</p> <p>This dataset specifically targets five vehicle types: buses, cars, motorcycles, trucks, and vans, chosen to encompass a wide range of vehicle sizes, shapes, and functionalities commonly encountered in city traffic. A subset of 134 images was manually labeled, into sets for training, validation (fine-tuning phase), and validation (self-training phase). The remaining 653 images were labeled automatically via the self-learning process proposed in the research.</p>
Figure 1 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 1. Map of the sampling localities in Georgia.
Outdoor NB-IoT and 5G coverage and channel information data in urban environments
<p>This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).</p> <p>Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a> </p> <p>The dataset includes a metadata file providing the following information for each campaign: </p> <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> <p>Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.</p> <p>The dataset contains the following data for NB-IoT:</p> <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> <p>The dataset contains the following data for 5G:</p> <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> <p>In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:</p> <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> <p>A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:</p> <p>L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. <span>DOI: </span><a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener"><span>10.3390/s23094266</span></a>.</p> <p>The second interpolation approach is instead introduced and described in:</p> <p>L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.</p> <p>Positioning using the 5G data was furthermore in investigated in: </p> <p>K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," <span>IEEE Communications Magazine, Volume 62, Issue 5, pp</span><span>. 44-49, May</span><span> 202</span><span>4</span><span>. DOI: </span><a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener"><span>10.1109/MCOM.011.2200707</span></a><span>.</span></p> <p><span>G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener"><span>10.1109/OJCOMS.2024.3499370</span></a>.</span></p> <p>Please refer to the above publications when using and citing the dataset. </p>
Dataset: Ambient BTEX concentrations during the COVID-19 lockdown in a peri-urban environment (Orléans, France)
<p>The dataset of the manuscript "Ambient BTEX concentrations during the COVID-19 lockdown in a peri-urban environment (Orléans, France)" are presented here</p>
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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.