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3,709 results for “urbanization.”
FIGURE 1 in A new and threatened species of Trichaptum (Basidiomycota, Hymenochaetales) from urban mangroves of Santa Catarina Island, Southern Brazil
FIGURE 1. Best-scored tree from the ML analyses, showing the phylogenetic relationships of Trichaptum species inferred from ITS sequences. Values higher than 70% and 0.95 for Bootstrap and Posterior probability, respectively, are shown above the branches (BS/PP).
Figure 1 in Urban heronry birds tolerate human presence more than its conspecific rural birds
Figure 1. Flight initiation distance of Ardeidae birds (4 species) in urban (N = 253) and rural habitats (N = 217) was significantly different.
Urban forests host rich polypore assemblages in a Nordic metropolitan area
<p>This dataset contains data for a manuscript titled 'Urban forests host rich polypore assemblages in a Nordic metropolitan area' where we have studied associations between the diversity of dead-wood inhabiting fungi and urbanization. The included spreadsheets contain data used in the analyses, i.e. polypore species occurrences, species richness and environmental data, accompanied by annotations about the characteristics of the study sites and individual dead-wood units. Data is given at two spatial scales on two separate spreadsheets: one at the level of dead-wood units and the other at the level of forest stands. Variable explanations are provided in separate text files. For further details regarding data collection and processing, please see the published article (https://doi.org/10.1016/j.landurbplan.2021.104222).</p>
Tourism and urban development as drivers for invertebrate diversity loss on tropical islands
<p>Oceanic islands harbour a disproportionately high number of endemic and threatened species. Rapidly growing human populations and tourism are posing an increasing threat to island biota, yet the ecological consequences of these human land uses on small oceanic island systems have not been quantified. Here, we investigated and compared the impact of tourism and urban island development on ground-associated invertebrate biodiversity and habitat composition on oceanic islands. To disentangle tourism and urban land uses, we investigated Indo-Pacific atoll islands, which either exhibit only tourism or urban development, or remain uninhabited. Within the investigated system, we show that species richness, abundance, and Shannon diversity of the ground-associated invertebrate community are significantly decreased on islands used for tourism and on islands with urban development, relative to uninhabited islands. Remote-sensing-based spatial data suggests that habitat fragmentation and a reduction in vegetation density are having significant effects on biodiversity on urban islands, whereas land use/ cover changes could not be linked to the documented biodiversity loss on tourist islands. This offers first direct evidence for a major terrestrial invertebrate loss on remote oceanic islands due to different human land uses with yet unforeseeable long-term consequences for the stability and resilience of oceanic island ecosystems.</p>
Fine-scale variation within urban landscapes affects marking patterns and gastrointestinal parasite diversity in red foxes
<ol> <li>Urban areas are often considered to be a hostile environment for wildlife as they are highly fragmented and frequently disturbed. However, these same habitats can contain abundant resources, while lacking many common competitors and predators. The urban environment can have a direct impact on the species living there but can also have indirect effects on their parasites and pathogens. To date, relatively few studies have measured how fine-scale spatial heterogeneity within urban landscapes can affect parasite transmission and persistence.</li> <li>Here we surveyed 237 greenspaces across the urban environment of Edinburgh (UK) to investigate how fine-scale variation in socio-economic and ecological variables can affect red fox (<i>Vulpes vulpes</i>) marking behaviour, gastrointestinal (GI) parasite prevalence and parasite community diversity,</li> <li>We found that the presence and abundance of red fox faecal markings was non-uniformly distributed across greenspaces, and instead was dependent on the ecological characteristics of a site. Specifically, common foraging areas were left largely unmarked, which indicates that suitable resting and denning sites may be limiting factor in urban environments. In addition, the amount of greenspace around each site was positively correlated with overall GI parasite prevalence, species richness and diversity, highlighting the importance of greenspace (a commonly used measure of landscape connectivity) in determining the composition of the parasite community in urban areas.</li> <li>Our results suggest that fine scale variation within urban environments can be important for understanding the ecology of infectious diseases in urban wildlife and could have wider implication for the management of urban carnivores.</li> </ol>
SONYC-Backgrounds: a collection of urban background recordings from an acoustic sensor network
<p><strong>Created by</strong></p> <p>Aurora Cramer <sup>(1, 2)</sup>, Mark Cartwright <sup>(3)</sup>, Fatemeh Pishdadian <sup>(4)</sup>, Juan Pablo Bello <sup>(1,2,5,6)</sup></p> <p> 1. Music and Audio Research Lab, New York University<br> 2. Department of Electrical and Computer Engineering, New York University<br> 3. Department of Informatics, New Jersey Institute of Technology<br> 4. Interactive Audio Lab, Northwestern University<br> 5. Center for Urban Science and Progress, New York University<br> 6. Department of Computer Science and Engineering, New York University</p> <p><br> <strong>Publication</strong></p> <p>If you use this data in your work, please cite the following paper, which introduced this dataset:</p> <p>[1] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021. [<a href="https://arxiv.org/pdf/2105.02911">pdf</a>]</p> <p><br> <strong>Description</strong></p> <p>SONYC-Backgrounds is an open dataset of recordings of urban background noise obtained from the <a href="https://wp.nyu.edu/sonyc/">SONYC</a> acoustic sensor network [2]. This dataset was developed with the goal of synthesizing soundscapes with a diverse set of realistic sounding background activity, for use in developing and evaluating machine listening systems in urban settings.</p> <p><br> <strong>Data acquisition</strong></p> <p>The provided audio has been acquired using the <a href="https://wp.nyu.edu/sonyc/">SONYC</a> acoustic sensor network for urban noise pollution monitoring [2]. Over 50 different sensors have been deployed in New York City. All recordings are 10 seconds and were recorded with identical microphones at identical gain settings.</p> <p><br> <strong>Recording selection</strong></p> <p>From the large collection of audio recordings acquired in 2017, we obtain a much smaller subset of likely background recordings. We first process the dataset using a sensor fault detector to filter out recordings with artifacts caused by hardware failures in the sensors. The sensor fault detector is a random forest, trained with a small collection of audio examples using active learning [3].</p> <p>We then determine if a recording is background or not using an urban sound classifier trained to detect the presence of sources of interest to urban noise pollution monitoring [4, 5]. We use the classifier to find recordings that *do not* contain the sound classes of interest. The classifier model is a multi-layer perception with two hidden layers, which takes as input an OpenL3 embedding [6] for a 1 s clip of audio and produces multi-label prediction probabilities for each class. This model is nearly identical to the one used for the <a href="http://dcase.community/challenge2019/task-urban-sound-tagging">DCASE 2019 Challenge Urban Sound Tagging Task</a> baseline model, aside from the addition of an extra hidden layer.</p> <p>Predictions for entire recordings are obtained by max-pooling the predictions for each class across time. A recording is considered background if the probabilities of the target classes fall below their respective detection thresholds, i.e. no target classes are detected. The classifier was trained on the SONYC-UST v1 dataset [4], and the detection thresholds for each class were tuned to correspond to 70% <em>negative</em> recall (true negative rate) on the test set to increase the likelihood that recordings are background.</p> <p>After this selection process, we obtain 441 background clips.</p> <p> </p> <p><strong>Metadata</strong></p> <p>To maintain privacy, the recordings in this release have been distributed in time and location, and recording times have been quantized to the hour. Sensor IDs are consistent with those SONYC-UST dataset [4]. The corresponding location of the sensors can be found in the SONYC-UST v2 dataset [5], though these locations have been mapped to the "block" level to maintain privacy. See the <a href="http://dcase.community/challenge2020/task-urban-sound-tagging-with-spatiotemporal-context">DCASE 2020 Challenge Urban Sound Tagging with Spatiotemporal Context Task page</a> for more information on the metadata.</p> <p><br> <strong>Data splits</strong></p> <p>The dataset is partitioned into a train/valid/test split of roughly 60/20/20, using a simple greedy method to assign sensors to subsets.</p> <p><br> <strong>Files</strong></p> <p>The dataset directory contains the directories `train`, `valid`, and `test` for each of the respective data subsets. Each directory contains recordings, with the file format: `<sensor-id>_<year>-<month>-<day>_<hour>_<instance-num>.wav`, where `<instance-num>` is used to distinguish recordings from the same sensor occurring during the same hour. Aside from `<year>`, each of these fields in the format are lead zero padded to two places (i.e. `printf` format `"%02d"`).</p> <p><br> <strong>Conditions of use</strong></p> <p>Dataset created by Aurora Cramer, Mark Cartwright, Fatemeh Pishdadian, and Juan Pablo Bello.</p> <p>The SONYC-Backgrounds dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license: <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>The dataset and its contents are made available on an “as is” basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, New York University is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the SONYC-Backgrounds dataset or any part of it.</p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any questions, comments, or concerns, please direct correspondence to Aurora Cramer (aurora (dot) linh (dot) cramer (at) gmail (dot) com).</p> <p><br> <strong>References and Links</strong></p> <p>[1] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021.</p> <p>[2] Bello, J. P., Silva, C., Nov, O., Dubois, R. L., Arora, A., Salamon, J., C. Mydlarz, and Doraiswamy, H. (2019). Sonyc: A system for monitoring, analyzing, and mitigating urban noise pollution. Communications of the ACM, 62(2), 68-77.</p> <p>[3] Wang, Y., Mendez, A.E.M., Cartwright, M., and Bello, J.P. Active Learning for Efficient Audio Annotation and Classification with a Large Amount of Unlabeled Data. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.</p> <p>[4] Cartwright, M., Mendez, A.E.M., Cramer, A., Lostanlen, V., Dove, G., Wu, H., Salamon, J., Nov, O., and Bello, J.P. SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) , 2019.</p> <p>[5] Cartwright, M., Cramer, A., Mendez, A.E.M., Wang, Y., Wu, H., Lostanlen, V., Fuentes, M., Dove, G., Mydlarz, C., Salamon, J., Nov, O., and Bello, J.P. SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020.</p> <p>[6] Look, Listen and Learn More: Design Choices for Deep Audio Embeddings<br> Cramer, A., Wu, H.-H., Salamon J., and Bello. J.P. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.</p> <p><br> <strong>Acknowledgements</strong></p> <p>We would like to thank <a href="https://wp.nyu.edu/sonyc/people/">all those involved in the SONYC project</a>. This work is partially supported by National Science Foundation <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633259">award 1633259</a> and <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544753">award 1544753</a>.</p> <p> </p>
Supplementary material 2 from: Szlavecz K, Vilisics F, Tóth Z, Hornung E (2018) Terrestrial isopods in urban environments: an overview. In: Hornung E, Taiti S, Szlavecz K (Eds) Isopods in a Changing World. ZooKeys 801: 97-126. https://doi.org/10.3897/zookeys.801.29580
List of terrestrial isopod species records in urban areas : Explanation note: Species name have been cross-checked with the world catalog by Schmalfuss (2003). Synonyms have been changed to valid species names. In some cases the species was not listed in the catalog. Notes regarding uncertainties are also from Schmalfuss (2003).
Supplementary material 1 from: Szlavecz K, Vilisics F, Tóth Z, Hornung E (2018) Terrestrial isopods in urban environments: an overview. In: Hornung E, Taiti S, Szlavecz K (Eds) Isopods in a Changing World. ZooKeys 801: 97-126. https://doi.org/10.3897/zookeys.801.29580
List of cities with terrestrial isopod records : Explanation note: Publications older than 70 years are not included. Localities listed here are mapped on Fig. 2 in the text. Source of geographical coordinates and population data: https://latitude.to
Supplementary material 1 from: Hornung E, Kásler A, Tóth Z (2018) The role of urban forest patches in maintaining isopod diversity (Oniscidea). In: Hornung E, Taiti S, Szlavecz K (Eds) Isopods in a Changing World. ZooKeys 801: 371-388. https://doi.org/10.3897/zookeys.801.22829
Geographical location and habitat characterization (urbanization intensity, soil and vegetation characteristics) of study sites in Buda, Hungary :
TAU Urban Acoustic Scenes 2019, Leaderboard dataset
<p>TAU Urban Acoustic Scenes 2019, Leaderboard 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>
TAU Urban Acoustic Scenes 2019 Openset, Leaderboard dataset
<p>TAU Urban Acoustic Scenes 2019 Openset leaderboard 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 scenes are provided</p>
TAU Urban Acoustic Scenes 2019 Mobile, Leaderboard dataset
<p>TAU Urban Acoustic Scenes 2019 Mobile, Leaderboard 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>
Measurement report: Springtime reactive Volatile Organic Compounds (VOCs) and impacts on ozone in urban areas of Yunnan-Guizhou Plateau, China: a PTR-TOF-MS study
<p>Time-varying sequence and day-to-day concentration after treatment. It includes 18 observed major VOCs [acetaldehyde, 2-acrolein, acetone, methyl ethyl ketone (MEK), methyl vinyl ketone (MVK), methacrolein (MACR), methyl isobutyl ketone (MIK), 2-pentanone, ethyl acetate, isoprene, α-pinene, benzene, toluene, styrene, C8 aromatic hydrocarbons, C9 aromatic hydrocarbons, 1,3-dichlorobenzene, naphthalene, and acetonitrile].</p>
Informing the design of urban green and blue spaces through an understanding of European's usage and preferences
<p><span>In light of global climate change and the biodiversity crisis, making cities more resilient through an adjusted design of urban green and blue spaces is crucial. Nature-based solutions help address these challenges while providing opportunities for nature experiences, and providing cultural ecosystem services that support public health. The COVID-19 pandemic and its associated stressors highlighted the interrelated socio-ecological services provided by nature-based solutions like urban green and blue spaces. </span><span>This pan-European study therefore aimed to enhance the socio-ecological understanding of green and blue spaces to support their design and management. Using an online survey, green and blue space preferences, usage, and pandemic-related changes in greenspace visit and outdoor recreation frequencies were examined. </span><span>Greenspace visit and outdoor recreation frequencies were associated with respondents' (N=584 from 15 countries) geographic location, dominant type of neighborhood greenspace, and greenspace availability during the pandemic, but not greenspace perceptions or sociodemographic background. </span><span>Greenspace visit and outdoor recreation frequencies were generally high, however Southern Europeans reported lower greenspace visit and outdoor recreation frequencies both before and during the pandemic than Northern Europeans. Many Southern Europeans also reported having few neighborhood greenspaces and low greenspace availability during the pandemic. </span><span>The most common outdoor recreational activity among respondents before the pandemic was walking or running with the most frequently stated purpose of time spent outdoors being restorative in nature (i.e. relaxing or calming down). Most Europeans had positive perceptions of green and blue spaces with preferences for structurally diverse and natural or unmanaged green elements. </span><span>This highlights the importance of accessible green and blue spaces both in everyday life and during times of crisis. Stakeholders, their preferences, and regional and cultural differences should be included in the co-design of urban green and blue spaces to maximize their potential for both people and nature.</span></p>
Dataset for "Relationships of the Hygroscopicity of HULIS with Their Degrees of Oxygenation and Sources in the Urban Atmosphere, J. Geophys. Res. Atmos."
<p>The hygroscopicity of humic-like substances (HULIS) from Beijing aerosols and its relationship with the degrees of oxygenation and sources were investigated. The dataset in the Excel sheet contains the measured hygroscopicity parameter (<em>κ</em>), elemental ratios, and estimated intrinsic<em> κ</em> for HULIS, and the mass-based source contributions to HULIS, the <em>κ</em> estimated for total organic aerosol and the high-polarity fraction of water-soluble organic matter, and other data for Zhou et al. in Journal of Geophysical Research: Atmospheres. The concentrations and elemental ratios of HULIS and the source contributions to HULIS (both in µg/m<sup>3</sup> and percentage) were taken from Zhou et al. (2021). The dataset is based on the work supported by JSPS KAKENHI JP19H04253 and the National Natural Science Foundation of China (grant no. 41625014).</p> <p>Ruichen Zhou, Yange Deng, Bhagawati Kunwar, Qingcai Chen, Jing Chen, Lujie Ren, Kimitaka Kawamura, Pingqing Fu, and Michihiro Mochida: Relationships of the hygroscopicity of HULIS with their degrees of oxygenation and sources in the urban atmosphere, J. Geophys. Res. Atmos.</p> <p>Ruichen Zhou, Qingcai Chen, Jing Chen, Lujie Ren, Yange Deng, Petr Vodicka, Dhananjay K.Deshmukh, Kimitaka Kawamura, Pingqing Fu, and Michihiro Mochida: Distinctive sources govern organic aerosol fractions with different degrees of oxygenation in the urban atmosphere, Environ. Sci. Technol., 55, 4494–4503, 2021.</p>
High Urban NOx Triggers a Substantial Chemical Downward Flux of Ozone
<p>These data and code correspond to the manuscript referenced above.</p>
Emergency-line calls as an indicator to assess human-wildlife interaction in urban areas
<p><span>Human-wildlife interactions (HWI) are increasingly common as human disturbance and development continue to remove wildlife habitats. Documenting HWIs is critical for management agencies to develop strategies and management decisions that meet the needs of both people and wildlife. However, evaluating the frequency and types of HWI at broad spatial scales (e.g., national or regional level) can be costly and difficult to implement by managers. In this study, we apply a novel method for evaluating the patterns of HWI in urban areas using publicly available data from emergency calls placed by inhabitants of Romania's >300 urban areas. We used information from 4,601 emergency calls (Romanian National Emergency Call System 112), consisting of (1) wildlife species, (2) spatial location, (3) date and time, and (4) a short description of the emergency. Out of the 318 analyzed cities, 300 cities documented emergency calls on HWI between 2015–20120, with roe deer and brown bear as the most frequent species. There was an increasing trend in HWI-related emergency calls in 73% of the urban areas. We mapped the large-scale distribution of HWI by species and type of interactions, capturing variations at the national level, and further, we analyze social and biophysical factors influencing the occurrence and frequency of HWI. The results showed that social factors have the same positive or negative effect on all species, while the effect of the biophysical factors varied between species. Particularly, the presence of large natural habitats, represented by forests, influenced the number of calls only for brown bears. Seminatural landscapes with agricultural land have a different influence in terms of effect and significance for the considered species. Our results suggest that publicly available data from emergency calls can be used for the rapid assessment of HWI and for evaluating trends and predictors of HWI at broad spatial scales.</span></p>
Sewer mining modular unit at the Athens Urban Tree Nursery
<p>The Athens Urban Tree Nursery is part of the Goudi Park, an area in the process of redevelopment and regeneration to become the key metropolitan park of the capital. The area, which lies in the heart of Athens, is a mixed-use area, comprising of urban green and urban agriculture spaces as well as administration and residential uses. The regeneration is an effort to boost both the local economy and improve quality of life for the 4.000.000 citizens of the Attica Region.</p> <p>The nursery comprises 4 ha of vegetation, supplys all urban parks and green spaces of Athens with plant material and uses potable water from Athens’s Water Supply and Sewerage Company (EYDAP) for its irrigation. The city is seeking alternative water sources leveraging circular economy solutions to achieve environmental, social and financial benefits for the city.</p> <p>The installation of a sewer mining modular unit for urban green irrigation at the point of demand would be of direct benefit for the sustainability of the new metropolitan park. Additionally, compost-based eco-engineered growing media products will be reused as an onsite fertilizer, as part of a portfolio of autonomous, decentralized water, energy and materials circular solutions for cities in water scarce area. Finally, thermal energy recovery schemes will be investigated to minimize the pilot’s environmental footprint.</p>
Fruits of the city: The nature, nurture and future of urban foraging
<p><span>Urban foraging is a global informal phenomenon that has been investigated in the Global North more than other parts of the world. Characterising the nature of urban foraging in the Global South is imperative given the rapid urbanisation and sustainable development priorities in the region. </span><span>In this study, we interviewed 80 urban foragers in four cities in the eastern coastal region of South Africa, with an aim to understand the nature of urban foraging in a developing nation context. We asked foragers about their initiation to and motivations for foraging, their logistics, yields, and associated activities, descriptions of their foraging grounds, and if and how they had changed, and what they envisage as an ideal future for foraging. </span><span>Many foragers started foraging in their childhood, in the company of friends and family, and, as in the Global North, regarded it as a cultural and recreational activity. Foraging was mostly done within a five-kilometre radius of home, on a weekly or fortnightly basis, and very few foragers processed or sold their foraged products. </span><span>Unlike the Global North, formal green spaces were not foraged in and were perceived to be poorly provisioned in urban planning. Forests and roadsides were equally used by the foragers, and very few had been discouraged from foraging. Most foragers were enthusiastic about the possibility of more people foraging, having designated spaces for foraging, and foraging-based businesses such as processed products and ecotourism. </span><span>We recommend that policymakers and land managers recognise and encourage foraging as a potentially sustainable use for stewardship of urban green spaces. To this end, we list the main wild edible fruit species used by foragers in the area, which could be planted in public spaces. We also suggest harnessing foragers</span><span>' </span><span>knowledge of useful species and spaces to develop green spaces and foraging-based supply chains. </span></p>
Identifying environmentally-comparable cities across the globe for urban ecology studies
<p>Defining a comprehensive and quantitative framework for comparing urban areas is a priority for conducting research in urban ecology and related fields. In this study, we use unsupervised learning on a matrix of climate- and human-related features to estimate the number of groups explaining the characteristics of ~6,000 cities across the globe. Using estimates of city-level stability within clusters, we estimate that cities can be clustered in 5–7 clusters, with 6 being the most likely and stable number of groups. Groups of cities are primarily defined by climatic and geographical features (e.g. elevation), with population-level and land use parameters being less relevant for structuring cities. City clusters generally correspond with a continental-based partitioning. However, geographic distances between cities do not necessarily reflect their position in the examined multivariate space. The analytical framework presented in this paper can be extended to accounting for alternative features to describing cities and their characteristics. We implement an online web application for comparing cities across the globe based on the results presented in this study. This application is expected to inform decisions on where to sample populations or species in cities with either similar or divergent climatic conditions.</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.