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3,709 results for “urbanization.”
DAS Urban Mobility Pattern Database
<p>DISCONTINUED!!!! New version: https://doi.org/10.5281/zenodo.8068608</p> <p>This database is based on the MINIDAS format designed by the Incorporated Research Institutions for Seismology (IRIS) with the objective of storing information captured by distributed acoustic systems. These files correspond to the hdf5 type, which facilitates their reading.</p> <p>In this same dataset you can find the official script to work with this kind of data (for more information see official repository, https://github.com/DAS-RCN/RCN_DASformat).</p> <p> </p>
Dataset on 'Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India'
<p>This dataset includes the raw data of a survey of 649 residents of 61 villages along a rural-urban gradient in Bengaluru, India. It presents socio-demographic characteristics (village, village rural-urban state, age, gender, level of education, caste, and sources of household income) and Likert-scale answers (ranging from 1, i.e., not important to 5, i.e., very important) to rank the perceived importance of ecosystem services and disservice from five types of small-scale green infrastructure: Domestic trees (DT), Farm trees (FT), Street trees (ST), Platform trees (PT), Temple trees (TT).</p> <p>The complete method is described in Thapa, P., Torralba, M., Bhaskar, D., Nagendra, H., Plieninger, T. (2023): Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India. Ecosystems and People, in press.</p>
Supplementary material 3 from: Zhang Z-Y, Li X, Chen W-H, Liang J-D, Han Y-F (2023) Culturable fungi from urban soils in China II, with the description of 18 novel species in Ascomycota (Dothideomycetes, Eurotiomycetes, Leotiomycetes and Sordariomycetes). MycoKeys 98: 167-220. https://doi.org/10.3897/mycokeys.98.102816
The concatenated sequences dataset
Supplementary material 1 from: Zhang Z-Y, Li X, Chen W-H, Liang J-D, Han Y-F (2023) Culturable fungi from urban soils in China II, with the description of 18 novel species in Ascomycota (Dothideomycetes, Eurotiomycetes, Leotiomycetes and Sordariomycetes). MycoKeys 98: 167-220. https://doi.org/10.3897/mycokeys.98.102816
Strain numbers and sequence accession numbers of new isolates
Supplementary material 2 from: Zhang Z-Y, Li X, Chen W-H, Liang J-D, Han Y-F (2023) Culturable fungi from urban soils in China II, with the description of 18 novel species in Ascomycota (Dothideomycetes, Eurotiomycetes, Leotiomycetes and Sordariomycetes). MycoKeys 98: 167-220. https://doi.org/10.3897/mycokeys.98.102816
Strains used in this study
Fig. 3A-B in The impact of urban warfare on the structure of ant assemblages on trees (Hymenoptera: Formicidae)
Fig. 3A-B – Average number of ants / 2 min on trees of different damage classes. A – for all trees, regardless of species; B – on lindens and oaks of different damage classes from 0 (no damage)) to 4 (heavy damage). Tc – Tilia cordata, Qr – Quercus robur.
Datasets for Gong Modeling walking accessibility to urban parks in Hong Kong
<p>Datasets (ArcMap format) for Gong Modeling walking accessibility to urban parks in Hong Kong</p>
Dataset and figures for "Nationwide urban ground deformation in Japan for 15 years detected by ALOS, Sentinel-1, and LiCSBAS"
<p>This dataset contains the LiCSBAS results and figures (LOS velocities, decomposed vertical and EW velocities, optical images, SB network, mask) mentioned in the paper “Nationwide urban ground deformation in Japan for 15 years detected by ALOS, Sentinel-1, and LiCSBAS”.</p>
The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material
<p>These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. </p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. </li> <li><strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.</li> <li><strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) </li> <li><strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.</li> <li><strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.</li> <li><strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022</li> </ul>
Questionnaire data for assessing what determines urban residents' engagement in activities for the protection of urban green spaces (UGS)
<p>This dataset contains the core data used to investigate what determines Munich’s residents’ engagement in activities for the protection of urban green spaces (UGS). We conducted an exploratory factor analysis and a structural equation modelling based on the data from an online and in-person questionnaire.</p> <p><strong>List of data and content</strong></p> <ol> <li>Questionnaire_English: The content of the questionnaire in English (*.pdf)</li> <li>Questionnaire_RawData_ Munchen_German: Core data from the questionnaire in German (*.xlsx)</li> <li>InputEFA1: Core data for the first exploratory factor analysis with the input for analysis in Mplus (*.xlsx)</li> <li>InputEFA2: Core data for the second exploratory factor analysis with the input for analysis in Mplus (*.xlsx)</li> <li>InputSEM: Core data for the structural equation modelling with the input for analysis in Mplus (*.xlsx)</li> <li>ResultsSEM: Results of the structural equation modelling (*.xlsx)</li> <li>InputCFA1: Core data for the first confirmatory factor analysis with the input for analysis in Mplus (*.xlsx)</li> <li>ResultsCFA1: Results of the confirmatory factor analysis (*.xlsx)</li> <li>InputCFA2: Core data for the second confirmatory factor analysis with the input for analysis in Mplus (*.xlsx)</li> <li>ResultsCFA2: Results of the confirmatory factor analysis (*.xlsx)</li> <li>Acronyms_Shortings: Acronyms and shortings used in the uploaded files (*.pdf)</li> </ol> <p><strong>Data processing</strong></p> <p>The software for performing the exploratory factor analysis and structural equation modelling was Mplus 8.8 (Muthén and Muthén, 1998). The details on the methodological steps are available in the published publication. To quickly understand the <a href="https://www.statmodel.com/HTML_UG/chapter4V8.htm">EFA</a>,<a href="https://www.statmodel.com/download/usersguide/Chapter5.pdf"> CFA and SEM model and settings</a> the details are provided in the hyperlinks.</p> <p><strong>Acknowledgments</strong></p> <p>The authors thank to all participants for their participation in the online and in-person questionnaires. Data processing and analysis would not have been possible without the help of Tomomi Saito.</p> <p>This work was supported by a grant from the Alexander von Humboldt Foundation and by the Leibniz Best Minds Competition, Leibniz-Junior Research Group, Grant J76/2019.</p>
Terrorism and the city: affect, space and violence in urban Europe - Dataset
<p>Dataset for SNF Ambizione project "Terrorism and the city: affect, space and violence in urban Europe"</p>
Urban nature visitation, accessibility, and impact of travel distance for sustainable cities - Data
<p>Data used in the analysis for: Talal, M.L., Gruntman, M. Urban nature visitation, accessibility, and impact of travel distance for sustainable cities. <i>Sci Rep</i> <strong>13</strong>, 17808 (2023). https://doi.org/10.1038/s41598-023-44861-6</p><p>The data was collected in a web-based survey of Tel Aviv-Yafo residents between March 21, 2021 and May 5, 2021. It includes information for 421 visitors of urban nature sites, such as visit frequency per year before and during COVID-19 and distance from home (km) category. </p>
Codes, raw predation data on dummy caterpillars, and environmental data about urban green spaces across 26 Brazilian state capitals
<p>Data collected by the Urban Predation Risk Network about predation pressure on dummy caterpillars exposed for eight days in 26 small green spaces and 24 forest patches across 26 Brazilian state capitals. The raw predation data describes the type of predator and the day in which each caterpillar was found with marks, as well as the sites' coordinates. The environmental data provides information for each site about precipitation, annual mean temperature, temperature seasonality, elevation, and coordinates. This database also provides summarized information about predation at each site, such as the total number of attacked caterpillars and the number of attacked caterpillars by each predator group. The data files contain a metadata sheet with a description of each column.</p> <p>The code provided was used to assess the local and large-scale drivers of predation pressure on dummy caterpillars in small green spaces and forest patches across 26 Brazilian cities.</p>
Depression-Diabetes Mechanisms: Urban African Americans
ClinicalTrials.gov study NCT00209170. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Promoting CRC Screening in an Urban Minority Population
ClinicalTrials.gov study NCT02392143. IPD Sharing: Not stated. Countries: 0. Publications: 4.
Prospective, Randomized Controlled Comparison of TERUMO SurFlash Plus Versus BD Insyte Autoguard in an Urban ED
ClinicalTrials.gov study NCT02769442. IPD Sharing: NO. Countries: 1. Publications: 0.
Quality of Tuberculosis Care in Urban Patna, India
ClinicalTrials.gov study NCT02874716. IPD Sharing: Not stated. Countries: 0. Publications: 1.
An Intuitive, Non-intrusive, Approach to Reduce Patient Harm From Inappropriate Dosing of High-risk Drugs in Older Adult Patients Across an Urban Safety Net Hospital System
ClinicalTrials.gov study NCT05218343. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Home-Based Management of Fever in Urban Ugandan Children
ClinicalTrials.gov study NCT00115921. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Treating Violence-related PTSD and Substance Risk in Low-income, Urban Adolescents
ClinicalTrials.gov study NCT03737383. IPD Sharing: YES. Countries: 1. Publications: 0.
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.