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3,709 results for “urbanization.”
Fig. 1 in Molecular surveillance of piroplasms in ticks from small and medium-sized urban and peri-urban mammals in Australia
Fig. 1. Bayesian phylogenetic reconstruction of the novel Babesia and Theileria spp. identified in ticks from brushtail possums and bandicoots using a multiple nucleotide alignment of 1701 bp at the 18S locus. Inset trees were produced on the basis of a shorter alignment (856 bp) with the inclusion of (a) B. macropus and (b) T. penicillata, T. brachyuri, and T. fuliginosus. Bold represents sequences identified in this study. GenBank accession numbers are shown in parentheses. Node labels represent posterior probabilities.
Fig. 1 in A report of 26 unrecorded bacterial species in Korea, isolated from urban streams of the Han River watershed in 2018
Fig. 1. Map of sampling stations. Open circles and arrows indicate the sampling stations where freshwater and sediment samples were collected in the Han River watershed.
Fig. 2 in A report of 26 unrecorded bacterial species in Korea, isolated from urban streams of the Han River watershed in 2018
Fig. 2. Neighbor-joining phylogenetic tree based on 16S rRNA gene sequences showing the relationship between the strains isolated in this study and their closest bacterial species. Bootstrap values over 70% are shown at nodes for neighbor-joining, maximum parsimony, and maximum likelihood methods, respectively. Filled circles indicate that the corresponding node was also recovered in the trees reconstructed with both the maximum parsimony and maximum likelihood algorithms, while open circles indicate that the corresponding node was recovered in the tree generated with only one of these algorithms. Scale bar = 0.05 substitutions per nucleotide position.
Urban Traffic Speed Dataset of Guangzhou, China
<p>This is an urban traffic speed dataset which consists of 214 anonymous road segments (mainly consist of urban expressways and arterials) within two months (i.e., 61 days from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected in Guangzhou, China. In practice, it can be used to conduct missing data imputation, short-term traffic prediction, and traffic pattern discovery experiments.</p> <p>According to the spatial and temporal attributes, we can easily derive a third-order tensor as <span class="math-tex">\(\mathcal{X}\in\mathbb{R}^{214\times 61\times 144}\)</span> and its dimensions include road segment, day and time window (see the file <strong>tensor.mat</strong>). The total number of speed observations (or non-zero entries of the tensor <span class="math-tex">\(\mathcal{X}\)</span>) is <span class="math-tex">\(1,855,589\)</span>. If the dataset is complete, then we have <span class="math-tex">\(214\times 61\times 144=1,879,776\)</span> observations, therefore, the original missing rate of this dataset is <span class="math-tex">\(1.29\%\)</span>.</p> <p>Note that the file <strong>traffic_speed_data.csv</strong> is the original traffic speed data with four columns including road segment attribute, day attribute, time window attribute, and traffic speed value. The file <strong>day_information_table.csv</strong> is a table referring to the specific date, and the file <strong>time_information_table.csv</strong> is a table expressing time window with start time and end time information.</p> <p>Feel free to email me with any questions: <a href="#">chenxy346@mail2.sysu.edu.cn</a> (author: Xinyu Chen).</p> <p><strong>Acknowledgement</strong>: Mr. Weiwei Sun (affiliated with Sun Yat-Sen University) also provided insightful suggestion and help for publishing this data set. Thank you!</p>
Reportatge: Bioplàstics fets a partir de residus urbans
<p>In the laboratory of environmental biotechnology of department of Chemical Engineering and Analytical Chemistry of University of Barcelona, here is where we are working on the European Union project, RES URBIS, to produce bioplastics from organic fraction of municipal solid waste.</p>
URBAN-SED
<p><strong>DESCRIPTION</strong></p> <p>URBAN-SED is a dataset of 10,000 soundscapes with sound event annotations generated using scaper (github.com/justinsalamon/scaper).</p> <p>A detailed description of the dataset is provided in the following article:</p> <p><strong>Scaper: A Library for Soundscape Synthesis and Augmentation</strong><br> J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello.<br> In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2017.<br> (PDF: https://goo.gl/RsfRhP)</p> <p>A summary is provided here:</p> <ul> <li>The dataset includes 10,000 soundscapes, totals almost 30 hours and includes close to 50,000 annotated sound events</li> <li>Complete annotations are provided in JAMS format, and simplified annotations are provided as tab-separated text files</li> <li>Every soundscape is 10 seconds long and has a background of Brownian noise resembling the typical "hum" often heard in urban environments</li> <li>Every soundscape contains between 1-9 sound events from the following classes: <ul> <li>air_conditioner, car_horn, children_playing, dog_bark, drilling, engine_idling, gun_shot, jackhammer, siren and street_music</li> </ul> </li> <li>The source material for the sound events are the clips from the UrbanSound8K dataset (https://serv.cusp.nyu.edu/projects/urbansounddataset/)</li> <li>URBAN-SED comes pre-sorted into three sets: train, validate and test: <ul> <li>There are 6000 soundscapes in the training set, generated using clips from folds 1-6 in UrbanSound8K</li> <li>There are 2000 soundscapes in the validation set, generated using clips from folds 7-8 in UrbanSound8K</li> <li>There are 2000 soundscapes in the test set, generated using clips from folds 9-10 in UrbanSound8K</li> </ul> </li> <li>Further details about how the soundscapes were generated including the distribution of sound event start times, durations, signal-to-noise ratios, pitch shifting, time stretching, and the range of sound event polyphony (overlap) can be found in Section 3 of the scaper paper: https://goo.gl/RsfRhP</li> <li>The scripts used to generated URBAN-SED using scaper can be found here: https://github.com/justinsalamon/scaper_waspaa2017/tree/master/notebooks</li> </ul> <p><strong>AUDIO FILES INCLUDED</strong></p> <p>* 10,000 synthesized soundscapes in single channel (mono), 44100Hz, 16-bit, WAV format.<br> * The files are split into a training set (6000), validation set (2000) and test set (2000).</p> <p><strong>ANNOTATION FILES INCLUDED</strong><br> <br> The annotations list the sound events that occur in every soundscape. The annotations are "strong", meaning for every <br> sound event the annotations include (at least) the start time, end time, and label of the sound event. Sound events <br> come from the following 10 labels (categories):<br> * air_conditioner, car_horn, children_playing, dog_bark, drilling, engine_idling, gun_shot, jackhammer, <br> siren, street_music</p> <p>There are two types of annotations: full annotations in JAMS format, and simplified annotations in <br> tab-separated txt format.</p> <p>JAMS Annotations<br> -------------------------<br> * The full annotations are distributed in JAMS format (https://github.com/marl/jams).<br> * There are 10,000 JAMS annotation files, each one corresponding to a single soundscape with the same filename (other than the extension)<br> * Each JAMS file contains a single annotation in the scaper namespace format - jams >=v0.3.2 is required in order to load the annotation into python with jams:<br> import jams <br> jam = jams.load('soundscape_train_bimodal0.jams').<br> * The value of each observation (sound event) is a dictionary storing all scaper-related sound event parameters:<br> * label, source_file, source_time, event_time, event_duration, snr, role, pitch_shift, time_stretch.<br> * Note: the event_duration stored in the value dictionary represents the specified duration prior to any time <br> stretching. The actual event durtation in the soundscape is stored in the duration field of the JAMS observation.<br> * The observations (sound events) in the JAMS annotation include both foreground sound events and the background(s).<br> * The probabilistic scaper foreground and background event specifications are stored in the annotation's sandbox, allowing<br> a complete reconstruction of the soundscape audio from the JAMS annotation (assuming access to the original source material)<br> using scaper.generate_from_jams('soundscape_train_bimodal0.jams').<br> * The annotation sandbox also includes additional metadata such as the total number of foreground sound events, the <br> maximum polyphony (sound event overlap) of the soundscape and its gini coefficient (a measure of soundscape complexity).</p> <p>Simplified Annotations<br> ------------------------------<br> * The simplified annotations are distributed as tab-separated text files.<br> * There are 10,000 simplified annotation files, each one corresponding to a single soundscape with the same filename (other than the extension)<br> * Each simplified annotation has a 3-column format (no header): start_time, end_time, label.<br> * Background sounds are NOT included in the simplified annotations (only foreground sound events)<br> * No additional information is stored in the simplified events (see the JAMS annotations for more details).</p> <p><strong>Please acknowledge this dataset in academic research</strong></p> <p>We would highly appreciate it if scientific publications of work partly based on URBAN-SED and/or scaper cite the aforementioned publication.</p> <p>The creation of this dataset was supported by NSF award 1544753.</p> <p><strong>Version 2.0.0</strong><br> - Audio files generated with scaper v0.1.0 (identical to audio in URBAN-SED 1.0)<br> - Jams annotation files generated with scaper v0.1.0 and updated to comply with scaper v1.0.0 (namespace changed from "sound_event" to "scaper")<br> - NOTE: due to updates to the scaper library, regenerating the audio from the jams annotations using scaper >=1.0.0 will result in audio files that are highly similar, but not identical, to the audio files provided. This is because the provided audio files were generated with scaper v0.1.0 and have been purposely kept the same as in URBAN-SED v1.0 to ensure comparability to previously published results.</p>
Urbane Feldforschung: Pashto
<p>"Urbane Feldforschung" ist ein Projekt des Instituts für deutsche Sprache und Linguistik der Humboldt-Universität zu Berlin. Es fand im Sommersemster 2018 im Rahmen eines gleichnamigen Masterseminars unter der Leitung von Dr. Frank Seifart statt. Inhalt des Projekts war die Dokumentation kleiner Sprachen in Berlin. Dabei wurden sowohl Wortlisten erhoben als auch spezifische grammatische Phänomene der einzelnen Sprachen durch Elizitation näher untersucht. Die im Rahmen des Projekts dokumentierten Sprachen sind: Dazaga, Akan, Kirgisisch, Georgisch und Pashto.</p> <p>Das Teilprojekt Pashto widmet sich der zentralen Variante der pashtunischen Sprache. Im Rahmen des Teilprojekts wurden zwei Aufnahmen angefertigt: Eine Wortliste (40 ASJP-Wörter aus der Swadesh-Liste) und eine Elizitation zur Adjektivkomparation.</p> <p> </p>
Everyday risks and access to water and sanitation in Lilongwe urban and peri-urban areas
<p>The household survey INHAbIT Cities - UNHIDE (Investigating Natural, Historical and Institutional Transformations in Cities and Uncovering Hidden Dynamics in Slum Environments) focuses on urban risks and sanitation in Lilongwe. The aim was to assess access to basic services and risks perception of urban dwellers living in areas characterised by different conditions of access to water and sanitation and other basic services. Lilongwe was a small town of less than 20,000 inhabitants in 1966 and only started growing after it became the capital in 1975. Its population has reached approximately 1 million inhabitants, living in 58 administrative units, called areas. Infrastructures and service provision is concentrated in the central areas – where parliament, ministries, government offices, embassies, hotels and the commercial area were located - while low income areas suffer the most from infrastructure and basic services deficits. To illustrate, while some areas access water through in-house connections, others are served through water kiosks, characterised (in some areas) by high rates of discontinuity. Similarly, everyday risks are unevenly distributed across urban spaces: as shown in the survey perception of risks varies drastically from neighbourhood to neighbourhood and depending on the quality and availability of services provided. Data for this survey were collected between February and April 2015 by a team of local researchers, who administered the questionnaire in local language.</p> <p>Publications linked to this survey are:</p> <p>Rusca M., Alda Vidal C., Hordijk M., Kral N., (2017) Bathing without water, and other stories of everyday hygiene practices and risk perception in urban low-income areas: the case of Lilongwe, Malawi, Environment and Urbanisation Vol 29, Issue 2, pp. 533 – 550. </p> <p>Tiwale S., Rusca M<strong>.</strong>, Zwarteveen M., The power of pipes: mapping urban water inequities through the material properties of networked water infrastructures. The case of Lilongwe, Malawi, Water Alternatives, Water Alternatives 11(2): 314-335.</p> <p>Rusca M. (2018): Visualising urban inequalities: the ethics of videography and documentary filmmaking in water research, <em>Wires Water</em>, <a href="https://doi.org/10.1002/wat2.1292">https://doi.org/10.1002/wat2.1292</a></p>
Monitoring of urban areas on Sentinel-1
<p>Data from Sentinel-1 SLC product was used to determine the extent of the urbanised area. Such a solution is necessary in the case of rapidly developing cities, as in the case of the capital of India - New Dehli.</p> <p>Links to the presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/Urban-area-on-S-1.pdf</p> <p> </p>
Simulations of urban heat island effect in Paris Region during various types of heatwaves, and in different adaptation scenarios
<p><strong>Content</strong><br> - These data present air temperature, in the shade, 2m above grounds in Paris Region (projection: RGF93/Lambert 93, EPSG:2154) at different times of the day, for various heat waves conditions, and in different prospective scenarios for the built-up evolution and adaptation actions implementations.<br> - more information can be found here : https://www.umr-cnrm.fr/ville.climat/spip.php?rubrique45</p> <p><strong>Classification of the data</strong><br> - the first 5 letters (e.g. "CDFFA") present the prospective scenario<br> - the 4 following letters (e.g. "HW34") present the type of heat wave<br> - the following 2 letters (e.g. "D8") present the length of the heat wave (number of days after the beginning of the heat wave)<br> - the final letters (e.g. H15) represent the time (UTC : one hour should be added for French time) of the day</p> <p><strong>Prospective scenarios</strong><br> - the first letter is always C<br> - the second letter represents the expansion scenario. They are presented here : Lemonsu, A., Viguié, V., Daniel, M., Masson, V., 2015. Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in Paris (France). Urban Climate 14, 586–605.<br> - D stands for "dense development"<br> - F for business as usual scenario ("fil de l'eau" in French)<br> - V for a scenario with 10% more parks<br> - the third letter represents the building evolution scenario<br> - F stands for business as usual scenario<br> - V for a scenario with more insulation and reflective roofs<br> - the third letter represents AC use<br> - F stands for strong AC use<br> - M for moderate AC use<br> - N for no AC use<br> - the fourth letter represents vegetation watering<br> - N stands for no watering<br> - A for watering</p> <p><strong>Heat waves</strong><br> - the figure (e.g. "34" in "HW34") represents the intensity class, in °C of the heat wave. It is more precisely the maximum daily temperature observed without the impact of the urban heat island effect. (Tmax=34, 38, 42, or 46°C).</p> <p><strong>Other information</strong><br> - see the file "aggregated data.xls" for more information and data about energy consumption for AC, and averages of temperatures in the city over the entire day.</p> <p> </p>
Coastal plain stream nutrient export across a gradient of urbanization
<p>This dataset contains nutrient concentration, nutrient export, and stream discharge data from five coastal plain streams in North Carolina, USA across a gradient of urbanization. See the spreadsheet "metadata.csv" for information about units and missing data. Methodology and site descriptions will be made available once the manuscript using these data is published (currently in review) or by request.</p>
WASHTREET. Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.
<p><strong>WASHTREET</strong><strong> - </strong><strong>Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.</strong></p> <p>This dataset contains raw data and surface elevations results from the application of the Structure from Motion (SfM) photogrammetric technique in a 36 m<sup>2</sup> full-scale urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). This work is part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The accurately measurement of the surface elevations is needed for a proper representation of surface flow, which is key in the detachment and transport of solids in the model surface. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>)</p> <p>A detailed description of experimental procedure and data collected can be consulted in ‘<em>1_ExperimentalProcedure.pdf’</em>. Raw images taken as input for the SfM software are included in ‘<em>2_RawImages.zip’</em>. Then, the point cloud resulted is provided in ‘<em>3_SFM_RawPointCloud.ply</em>’. This point cloud was processed and the final elevation map with a resolution of 5 mm is included in ‘<em>4_SfM_ElevationMap(m).xyz</em>’.</p> <p>Further details of the physical model and hydraulic and sediment transport experiments can be consulted in the dataset <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET - Hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques are provided in the dataset <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13.<a href="http://doi.org/10.1038/s41597-020-0384-z"> https://doi.org/10.1038/s41597-020-0384-z</a></li> </ul>
Urban Food Riots in Late Ottoman Bilad al-Sham as a 'Repertoire of Contention'
<p>This repository holds supplementary material such as maps, data sets, and code for my essay ‘Urban Food Riots in Late Ottoman Bilād Al-Shām as a “Repertoire of Contention”’, in <em>Crime, Poverty and Survival in the Middle East and North Africa: The ‘Dangerous Classes’ since 1800</em>, ed. Stephanie Cronin (London: I.B. Tauris, 2019), 157–76. All materials are licensed as <a href="http://creativecommons.org/licenses/by-nd/4.0/">cc by-nd 4.0</a>.</p> <p>Material is organised in the following folders</p> <ul> <li><code>data/</code>: data sets on food prices as CSV.</li> <li><code>maps/</code>: maps/geo-located data for the cities of Damascus, Hama, and Homs - mostly as GeoJSON.</li> <li><code>plots/</code>: plots of time series of various food prices across <em>Bilād al-Shām</em>.</li> <li><code>publication/</code>: the final plots used for the publication.</li> <li><code>r/</code>: R scripts for plotting the price data on a timeline.</li> </ul>
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>
PERCEIVE: WP4: Spatial determinants of policy performance and synergies: Task4.4: Cohesion Policy vs Urban and Rural policies to address spatial discrepancies in EU territorial policy
<p>This dataset consists of data addressing the relationship between territorial cohesion objectives and the problems perceived by citizens. In particular a comparative analysis between the case study regions will generate data useful for identifying best practices in mixing the EU policy instruments for a better achievement of regional needs. Data that will be generated via focus groups interviews among representatives of LMA (local management authorities) in 2 Polish regions: Dolnośląskie and Warmińsko-Mazurskie . Interview transcripts and report was used to address how territorial cohesion objectives match the “real problems” of regions. The focus groups were built around the following main topics: 1) governance of the Cohesion Policy projects, in order to understand how different authorities at different levels cooperate and share the responsibilities for the implementation of the Cohesion Policy; 2) level of citizen engagement, in order to understand whether a bottom-up approach is used; 3) how the media inform on the Cohesion Policy programmes, in order to appreciate the discrepancies (if any) about the aims of Cohesion Policy and its construction on the public discourse. Comparing current and past programming periods, we investigate how the policy performs in reducing the gap between territorial cohesion objectives and “real problems” defined by LMAs and citizens. Because it is project-specific data and reflects the concept and methodology of the study under PERCEIVE they are perceived as unique - similar data does not exist. Potential users are be Regional Policy’s European/National/Local policy makers and practitioners, European networks and associations looking to data on LMA opinions on cohesion policy implementation in Poland to be used in policy recommendation, studies and policy making process; next group of potential data users are researchers working on assessment of Cohesion Policy, data may be used as a source for topic-related studies, case studies, comparisons.</p> <p>The dataset is made up of 5 files: 2 files consist of reports from the workshops, 2 files consist in transcripts of interviews to practitioners, beneficiaries and targets of the Cohesion Policy projects in the Polish selected case‐study regions. Interviewees are asked to provide their views and perceptions on the multilevel governance system, on the communication activities of the Operational Programmes and on the effectiveness of Cohesion Policy. 1 readme file is included.</p>
Urban Fabric Types in Osaka-Kobe Metropolitan Area
<p>This upload provides the processed results of Multiple Fabric Assessment (Araldi and Fusco, 2019) performed on a hyper-urbanized region of 2,500 km<sup>2</sup> in Japan and including Osaka and Kobe municipalities. The scale of analysis are the areas surrounding urban streets at close distance, which are named proximity bands. Outputs are made available using a geospatial vector data format (GeoPackage - WGS 84/UTM zone 53N) in order to be visualized in a geographic information system software. Attribute data contain the Bayesian probability assignment of each proximity band for the nine urban fabric types that have been identified in the Osaka-Kobe metropolitan area, namely, (1) High-rise and discontinuous modern fabric (2) Discontinuous mid-to-high-rise fabric of mixed land use (3-4) Peripheral low-to-mid-rise discontinuous mixed fabric (5) Industrial and logistic techno-fabrics (6) Residential hyper-compact continuous fabric (7) Residential compact continuous fabric (8) suburban planned single-house residential fabric (9) ex-urban irregular fabric with natural spaces. “MostProb” variable provides the higher probability of each proximity band, which is the main output cross analyzed with field observations in Perez <em>et al.,</em> 2019. Results are based upon the processing of morphological indicators calculated using the following datasets: 2013/14 Zmap-TOWN II (ZENRIN Residential Maps) for building coverage and Digital Road Map Database extended version 2015.</p> <p>Perez J., Araldi A., Fusco G., Fuse T. (2019) “The Character of Urban Japan: Overview of Osaka-Kobe’s Cityscapes”, <em>Urban Science</em>, 3(105), pp 1-22. https://www.mdpi.com/2413-8851/3/4/105</p> <p>Araldi A., Fusco G. (2019) “From the built environment along the street to the metropolitan region. Human perspective approach in urban fabric analysis<em>”. Environment and Planning B: Urban Analytics and City Science</em>, 46(7), pp. 1243-1263.</p>
Urban-rural life tables for Scotland, 1861-1910
<p>This data set contains the life tables the were computed for the study presented in: Torres, C., V. Canudas-Romo, and J. Oeppen (2019) 'The contribution of urbanization to changes in life expectancy in Scotland, 1861–1910', <em>Population Studies</em>, 73:3, 387-404, DOI: 10.1080/00324728.2018.1549746</p> <p>The life tables are by sex and urban-rural category. For reasons explained in the paper, the tables cover periods of different lengths, from 1861 to 1910.</p> <p><strong>Example of how to load the data in R:</strong></p> <p>LT <- read.table("Urban-Rural-LifeTables-Scotland-1861-1910.txt", header = T, sep = ";")</p> <p><strong>Description of each column:</strong><br> Period: time-interval, including the first and excluding the last indicated years (e.g., [1861,1866) corresponds to the years from 1861 to 1865). Available periods: 1861-1865, 1866-1870, 1871-1874, 1875-1877, 1878-1880, 1881-1885, 1886-1890, 1891-1892, 1893-1896, 1897-1900, 1901-1905, 1906-1910.<br> Population: Rural, Semi-Urban, Urban, or Total population (see definitions in Torres et al. 2019)<br> Sex: Female or Male<br> x : Age (from 0 to 110+, by single ages)<br> nmx: Death rate in the age interval [x, x+n)<br> nax: average number of person-years lived in the age interval [x, x+n) by those who die in that interval<br> nqx: Probability of dying in the age interval [x, x+n)<br> lx: number of survivors at exact age x, or probability of surviving until exact age x<br> ndx: Life-table deaths in the age interval [x, x+n)<br> nLx: Person-years lived in the age interval [x, x+n)<br> Tx: Person-years lived above age x<br> ex: Remaining life expectancy at age x</p> <p>For more information about life tables in general, see: Preston, S., Heuveline, P., and Guillot, M. (2001). <em>Demography: Measuring and Modeling Population Processes</em>. Wiley-Blackwell</p>
Fig. 3 in Ixodid Ticks (Acari, Ixodidae) In Urban Landscapes. A Review
Fig. 3. Diagram of indexes of references of mass hard tick species that adapted to European urban landscapes (including Russia).
Figure 2 in Spatial segregation between the native Tropical mockingbird and the invader Chalk-browed mockingbird (Passeriformes: Mimidae) along a Neotropical natural-urban gradient
Figure 2. Abundance (Punctual Abundance Index) of Tropical mockingbird (Mimus gilvus, closed circle and continuous line) and Chalk-browed mockingbird (M. saturninus, open circle and dashed line) regarding urbanization index (Normalized Difference Built-up Index) in a coastal region of southeastern Brazil. Urbanization increases toward a higher urbanization index.
Figure 1 in Spatial segregation between the native Tropical mockingbird and the invader Chalk-browed mockingbird (Passeriformes: Mimidae) along a Neotropical natural-urban gradient
Figure 1. Sampling design (transects) in the municipalities of Vila Velha and Guarapari, state of Espírito Santo, southeastern Brazil.
ScienceDex guides
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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.