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FIGURES 15–19 in Nest and Immatures of the South American Anthidiine Bee Notanthidium (Allanthidium) chilense (Urban) (Apoidea: Megachilidae)
FIGURES 15–19. SEM micrographs of larval vestiture of Notanthidium chilense, all to same scale. 15. Cluster of setae of various lengths 16. Long, slender seta. 17. Shorter seta. 18, 19. Setae or spicules? FIGURE 20. Spiracle, external surface.
Fig. 3 in Lymnaeid snails in the city of Salzburg - A malacological and ecological study in the urban area
Fig. 3: Global Marginality Coefficient (GMC; a) and Global Tolerance Coefficient (GTC; b) modeled for the different environmental parameters (Abbreviations: see Table 1).
Fig. 1 in Lymnaeid snails in the city of Salzburg - A malacological and ecological study in the urban area
Fig. 1: Geographic map of the city of Salzburg with all sample locations investigated for the possible colonization by pond snails.
Fig. 2 in Lymnaeid snails in the city of Salzburg - A malacological and ecological study in the urban area
Fig. 2: Urban distribution maps computed for Radix labiata (a) and R. balthica (b). The first gastropod species could be collected at 68% of all sample locations, whereas the second one was restricted to 62% of the studied sites.
Fig. 4 in Histopathology of gills, kidney and liver of a Neotropical fish caged in an urban stream
Fig. 4. Photomicrographs of the liver of P. lineatus caged in Cambé stream. a) normal hepatic tissue, showing hepatocytes with granular cytoplasm (*) and central and round nucleus (arrow); b) hepatocytes with irregular shaped nucleus (black arrows), eosinophilic granules in the cytoplasm (arrowheads) and nuclear hypertrophy (*); c) bile stagnation (arrows); d) nuclear degeneration (arrows) and cytoplasmic degeneration (*); e) melanomacrophages aggregate, close to a vessel (white arrow) and cytoplasmic vacuolation (*); f) hepatic tissue showing focal necrosis (white arrow). Scale bar 10 mm, H.E.
Fig. 3 in Histopathology of gills, kidney and liver of a Neotropical fish caged in an urban stream
Fig. 3. Photomicrographs of the kidney of P. lineatus caged in Cambé stream. a) normal renal corpuscle showing the glomerulus and the Bowman's space well defined (arrow), proximal tubules (*), distal tubules (arrowheads); b) glomerular expansion and absence of the Bowman's space (arrow) and tubule cells with hypertrophied nucleus (arrowheads); c) tubule starting the regeneration process (white arrow), occlusion of the tubular lumen (black arrows) and cloudy swelling degeneration (*); d) detail of 2 tubules with hyaline droplets degeneration (*). Scale bar 10 mm, H.E.
Fig. 1 in Histopathology of gills, kidney and liver of a Neotropical fish caged in an urban stream
Fig. 1. Map showing the region of Londrina city (Paraná State), where the in situ tests were carried out at the reference site (Apertados stream), and the sites at Cambé stream (A, B and C).
Taking a Memory Out for a Walk. Urban Auscultation
<p>In a group of ten-fifteen people maximum (adjusting to covid rules), we will go on a walk at the surroundings of Ars Electronica; guided by a leader that will give instructions on the data collections to make; take careful notes about what happens and produce a series of experimental inventories or archives and a final cosmogram. Each spot introduces approaches to accounting data in the city realm: from public memory, metaphors, senses, and ecologies, and ways to bridge our inter/dependences with our environment. Accompanied by our urban sketcher we are documenting visually.</p> <p>Spot 1 – Collect examples of “algorithm” histories.<br> Spot 2 – Collect examples of “data” uncertainties.<br> Spot 3 – Collect examples of “data” extraction.<br> Spot 4 – Collect examples of the olfactory/ tactile/ sonic dimensions<br> Make an inventory / Dealing with an archive<br> Create a “cosmogram” mapping out the different elements involved.</p>
Scientific data: Laboratory modelling of urban flooding
<p>These datasets include experimental data obtained in the hydraulic laboratory. A detailed description of the datasets is available in the article. </p>
Habitat specialization by wildlife reduces pathogen spread in urbanizing landscapes
<p>Urban areas are expanding globally, with far-reaching ecological consequences, including for wildlife-pathogen interactions. Wildlife show tremendous variation in their responses to urbanization; even within a single population, some individuals can specialize on urban or natural habitat types. This specialization could alter pathogen impacts on host populations via changes to wildlife movement and aggregation. Here, we build a mechanistic model to explore how habitat specialization in urban landscapes affects interactions between a mobile host population and a density-dependent specialist pathogen that confers no immunity. We model movement on a network of resource-stable urban sites and resource-fluctuating natural sites, where hosts are either urban specialists, natural specialists, or generalists that use both patch types. We find that, for generalists, natural and partially urban landscapes produce the highest infection prevalence and mortality, driven by high movement rates at natural sites and high densities at urban sites. However, habitat specialization protects hosts from these negative effects of partially urban landscapes by limiting movement between patch types. These findings suggest that habitat specialization can benefit populations by reducing infectious disease transmission, but by reducing movement between habitat types could also carry the cost of reducing other movement-related ecosystem functions such as seed dispersal and pollination.</p>
Estimates of high tide flooding on roadways within urban areas along the United States Atlantic coast
<p>Estimates of high tide flooding (HTF) on roadways in urban areas along the US Atlantic Coast. These estimates were calculated using NOAA HTF areal extent estimates, OpenStreetMap roadway data, and 2010 census-designated urban area and census block data. See the corresponding manuscript (Gold et al., 2021 - link coming soon) and <a href="https://github.com/acgold/HTF-on-roads">GitHub repository</a> for additional information about these data.</p>
Potentials and perspectives of food self-sufficiency in urban areas – data and code
<p>Script and input data files (consumption and production data) for the publication "Potentials and perspectives of food self-sufficiency in urban areas – a case study from Leipzig". The script provides a tool for 1) the calculation of the self-sufficiency level (ssl), regional and non-regional agricultural area demand for the region of interest and 2) the calculation of share of area demand and total consumption of single commodities and food groups. Input data files include production yields and consumption quantities for single commodities.</p>
Synthetic noisy urban soundscapes: a dataset of synthetic soundscapes with real urban backgrounds
<p><strong>Publication</strong></p> <p> </p> <p>If you use this data in your work, please cite the following paper, which introduced this dataset:</p> <p> </p> <p>[1] Pishdadian, F., Wichern, G., & Le Roux, J. (2020). Finding strength in weakness: Learning to separate sounds with weak supervision. IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP). [<a href="https://arxiv.org/pdf/1911.02182">pdf</a>]</p> <p>[2] 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>Created by</strong></p> <p>Fatemeh Pishdadian (1), Gordon Wichern (2), Jonathan Le Roux (2), Aurora Cramer (3, 4), Mark Cartwright (5), and Juan Pablo Bello (3,4,6,7)</p> <p> 1. Interactive Audio Lab, Northwestern University<br> 2. Mitsubishi Electric Research Laboratory<br> 3. Music and Audio Research Lab, New York University<br> 4. Department of Electrical and Computer Engineering, New York University<br> 5. Department of Informatics, New Jersey Institute of Technology<br> 6. Center for Urban Science and Progress, New York University<br> 7. Department of Computer Science and Engineering, New York University</p> <p><br> <strong>Description</strong></p> <p>Synthetic noisy urban soundscapes (SNUSS) is a dataset of synthetic soundscapes with real urban background noise meant to mimic urban soundscapes. This dataset contains 30,000 10 second mixtures, their isolated components, and auto-generated annotations. 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>Mixture generation</strong></p> <p>We generate synthetic mixtures using a collection of isolated sound events with class annotations, as well as a collection of urban background noise. Audio mixtures are 4 seconds long (at 16kHz). We generate a foreground sub-mixture using a subset of clips from <a href="https://urbansounddataset.weebly.com/urbansound8k.html">UrbanSound8K</a> [3] from the <em>car horn</em>, <em>dog bark</em>, <em>gun shot</em>, <em>jackhammer</em> and <em>siren</em> classes. These clips range from 0.5 s to 4s. The number of events per mixture is sampled from a zero-truncated Poisson distribution with a rate parameter of 5. The class for each event is chosen uniformly at random from the five target classes. The particular sound event is chosen uniformly at random from the available clips for that class. The start time is chosen uniformly throughout the clip such that the entire clip is contained in the 4 second mixture. A brief fade-in and fade-out is applied to the clip to avoid discontinuities. Each clip is set to a sound level sampled uniformly at random in the range -30 to -20 dB LUFS.</p> <p>For the background audio, we use urban background recordings from the SONYC-Background dataset [2, 4], containing 441 10 second recordings of urban background noise in New York City. For more information, see the <a href="https://doi.org/10.5281/zenodo.5129078">SONYC-Backgrounds page</a>. For each mixture, a random background clip is chosen from which we extract a uniformly chosen 4 second segment.</p> <p>We create datasets using an foreground-to-background SNRs of -50, -20 -0 dB LUFS, (`n50dB`, `n20dB`, and `0dB` respectively), in addition to a noiseless dataset (`none`). The datasets are generated such that the only difference between them is the relative loudness between the foreground and background.</p> <p>For the training set, we generate 20,000 mixtures using folds 1-6 of UrbanSound8K and the training set of SONYC-Background. For the validation set, we generate 5,000 examples using folds 7-8 of UrbanSound8K and the validation set of SONYC-Background. For the test set, we generate 5,000 examples using folds 9-10 of UrbanSound8K and the test set of SONYC-Background.</p> <p>For additional details on the foreground mixture generation process, please refer to [1]. For additional details on generating the soundscapes with background, please refer to [2].</p> <p><br> <strong>Files</strong></p> <p>The dataset files are split into the following compressed archives:</p> <ul> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-none.tar.gz` - Noiseless mixtures</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-n50dB.tar.gz` - Mixtures with -50 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-n20dB.tar.gz` - Mixtures with -20 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-0dB.tar.gz` - Mixtures with 0 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_isolated_events.tar.gz` - Isolated sound events for each mixture</li> </ul> <p><br> <em>Preparing the files</em></p> <ol> <li>Download each of the tar.gz files to a new folder. You need at the `isolated_events` and one of the mixture datasets.</li> <li>Decompress all of the tar.gz files.</li> <li>Merge the contents of the extracted `isolated_events` folder into the extracted mixture folders. This makes sure the corresponding isolated events for each mixture are placed in its `XXXXX_events` folder.</li> </ol> <p><br> <em>File structure</em></p> <p>The mixture dataset folder for the desired background condition should have the format `synthetic-noisy-urban-soundscapes_mixtures-bkgr-<condition>/<split>`. Within each split folder are the mixture files, which are identified by an integer (padded with leading zeros up to 5 places). For a mixture `00001`, the mixture audio is `00001.wav` and the annotation file (in JAMS [5] format) is `00001.jams`. The isolated events can be found in the `00001_events` folder, where foreground events have the format `foreground<fg-event-num>_<class-name>.wav` and the background recording (if used) is called `background0_2017.wav`.</p> <p><br> <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> </p> <p><strong>References</strong></p> <p>[1] Pishdadian, F., Wichern, G., & Le Roux, J. (2020). Finding strength in weakness: Learning to separate sounds with weak supervision. IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP).</p> <p>[2] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J. P. (2021). Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).</p> <p>[3] Salamon, J., Jacoby, C., and Bello, J.P. (2014). A dataset and taxonomy for urban sound research. In 2014 ACM International Conference on Multimedia.</p> <p>[4] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. (2021). SONYC-Backgrounds: a collection of urban background recordings from an acoustic sensor network (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5129078</p> <p>[5] Humphrey, E. J., Salamon, J., Nieto, O., Forsyth, J., Bittner, R. M., and Bello, J.P. (2014). JAMS: A JSON Annotated Music Specification for Reproducible MIR Research. In 2014 International Society for Music Information Retrieval Conference (ISMIR)</p> <p><br> <strong>Acknowledgements</strong></p> <p>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>
Ecological dataset from: 'Rats and the city: implications of urbanization on zoonotic disease risk in Southeast Asia'
<p>This dataset includes the ecological and environmental data, a description of the analysis steps, and the related R code associated with the manuscript: "Rats and the city: implications of urbanization on zoonotic disease risk in Southeast Asia" by Kim R. Blasdell, Serge Morand, Susan G.W. Laurance, Stephen L Doggett, Amy Hahs, David Perera, and Cadhla Firth, available at: https://www.pnas.org/doi/abs/10.1073/pnas.2112341119</p> <p>This dataset also relates to the preprint: "Rats in the city: implications for zoonotic disease risk in an urbanizing world" available at: https://www.biorxiv.org/content/10.1101/2021.03.18.436089v1</p> <p>A detailed description of the files can be found in README.txt</p>
Survey Content Urban Waste Tourism Workers
<p>This dataset includes data from a survey developed by URBANWASTE in the framework of WP3. In particular this dataset includes the answer from tourism workers surveyed in 11 pilot cases - Florence (IT), Nice (FR), Lisbon (PT), Syracuse (IT), Copenhagen (DK), Kavala (GR), Santander (ES), Nicosia (CY), Ponta Delgada (PT), Dubrovnik – Neretva county (HR), Tenerife (ES)- in Europe to investigate the efficiency of waste management system and structure in their pilot case and to understand the possible influence of tourism on waste management and production.</p>
Dataset for the paper "Data for Distribution of Vascular Plants (Tracheophytes) of urban forests and floodplains in the Tyumen city (Western Siberia)"
<p>Dataset associated with the manuscript “Data for Distribution of Vascular Plants (Tracheophytes) of urban forests and floodplains in the Tyumen city (Western Siberia)” submitted to the journal Data.</p>
FuSA: recording of urban sounds in Valdivia, Chile, between 23 May and 6 June 2022
<p>This dataset includes 115 audio recordings and corresponding metadata. The audio recordings were obtained through two monitoring stations installed in different parts of Valdivia (city ubicated at the south of Chile).</p> <p>The two monitoring stations listened to a grand total of 30,240 minutes during two-week and one-week operation periods, respectively. From this dataset only 115 minutes were flagged as surpassing the established sound pressure level.</p> <p>The FuSA system (<a href="https://www.acusticauach.cl/fusa/">https://www.acusticauach.cl/fusa/</a>) was then used to obtain event predictions for the 115 minutes subset.<br> The file metadata.csv includes predictions for each audio recordings and their corresponding probabilities.</p>
SCoRe - Prototyp 2.2 - Erprobung des Forschungsszenarios "Urbane Grünflächen" - UGF-2
<p>Dieses Datenset enthält Materialien (Videos, Protokolle und Fallbeschreibungen) aus der zweiten prototypischen Durchführung des Forschungsszenarios "Urbane Grünflächen" im Teilprojekt <a href="http://www.360total.de/score/">SCoRe-VideoLearning</a> des <a href="https://scoreforschung.com/ueber/">Score-Projektes</a> ..</p> <p>Hierin finden sich drei exemplarische Fälle von Studierenden, welche sich videografisch forschend mit urbanen Grünflächen auseinandersetzten und dabei die Merkmale der Grünfläche hinsichtlich urbanen Nutzungsmöglichkeiten und der biologischen Vielfalt untersuchten. Dazu wurden die Grünflächen zunächst ausgewählt und in Bezug auf verschiedene vorgegebene Ordnungskriterien beschrieben und bewertet (Fallbeschreibung). Zur Produktion der Videoforschungsdaten - als Basismaterial der empirischen Untersuchung - waren die Studierenden angehalten ein Produktionsprotokoll während aller drei Produktionsphasen der Videografie (Vorproduktion, Produktion im Feld sowie Nachproduktion) auszufüllen und somit für sich sowie andere analysierende Studierende die Entscheidungsprozesse zur Gestaltung der Videoforschungsdaten zu explizieren und zu dokumentieren.. Diese Protokolle bilden entsprechend die Grundlagen für Gütekriterien qualitativer Forschungsdaten: Transparenz und intersubjektive Nachvollziehbarkeit (vgl. <a href="https://scoreforschung.files.wordpress.com/2022/03/score-vl-wirkungsbericht-3-zur-summativen-evaluation-des-prototypen-3_mhh-2.pdf">Wirkungsbericht 3</a>).</p>
Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan
<p>#############################################################################################################<br> Datasets supporting the publication:<br> Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:<br> a case study of Bishkek, Kyrgyzstan.<br> <a href="https://doi.org/10.3390/rs14225790">https://doi.org/10.3390/rs14225790</a></p> <p>-Please refer to the publication for details on the production of each dataset.<br> -Datasets are ordered following the publication figures.<br> -Please cite the publication and this dataset repository when using the data.<br> #############################################################################################################</p> <p>------------------------<br> Structure:<br> File ID<br> -[fields:] description<br> ------------------------</p> <p>KH9_1979_builtup.shp<br> -KH9 1979 built-up area classification</p> <p>S2_2021_builtup.tif<br> -Sentinel-2 2021 built-up area classification.</p> <p>S2_2021_corine_land_cover_class.tif<br> -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes.</p> <p>S2_KH9_DN_change_aggregated.shp<br> -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up.</p> <p>building_characteristics.shp<br> -build_count: building count in 500 m square grid cell.<br> -mean_area: mean building size (m^2) in 500 m square grid cell.<br> -median_area: median building size(m^2) in 500 m square grid cell.<br> -cell_coverage: %building coverage of 500 m square grid cell.</p> <p>pleiades_buildings_all.shp<br> -All building detections from Pleiades data. Confidence values are output from the deep learning model.</p> <p>pleiades_buildings_heights.shp<br> -Building detections from the Pleiades data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>wv2_buildings_all.shp<br> -All building detections from WorldView-2 data. Confidence values are output from the deep learning model.</p> <p>wv2_buildings_heights.shp<br> -Building detections from the WorldView-2 data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>trained_rcnn.zip<br> -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.</p>
Urban nature-based solutions to climate change adaptation database
<p>This dataset is the result of a systematic mapping of the application of nature-based solutions (NbS) to climate change adaptation in urban areas across the world. We screened 823 potential urban NbS to climate adaptation, which resulted in the inclusion of 216 interventions worldwide from 130 cities in 55 countries within our dataset. We analysed each of the NbS according to key characteristics in terms of how these interventions are helping cities confront the grave climate change, biodiversity, and related social challenges they are facing. We further analyse the capacity for each NbS to affect change in the city it is implemented within, which ranges from incremental (shallow) change to reformistic (middle ground) and finally transformative (deep) change. The full range of climate, biodiversity, and social challenges, as well as further discussion on the meaning of these different levels of change, is described in the attached file in the coding template tab. </p> <p>The results and analysis of this database (v 1.0.0) appear in the following article:</p> <p>Goodwin, S., M. Olazabal, A. Castro, U. Pascual. "Global mapping of urban nature-based solutions for climate change adaptation". <em>Nature Sustainability. doi: <a href="http://doi.org/10.1038/s41893-022-01036-x">10.1038/s41893-022-01036-x </a></em></p> <p><strong>A read-only version of this article can be found online for free <a href="https://rdcu.be/c4tjk">here</a>.</strong></p> <p>For any use of this dataset, please cite this dataset along with the associated publication in Nature Sustainability. Please report any errors or omissions to Sean Goodwin.</p>
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.