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392 results for “streets”
Outsourcing asylum reception Street-level organizations and the privatization of state action in Switzerland
<p>By approaching outsourcing through an ethnographic lens,we focus on the policies that enable public authorities to deal with the many uncertainties of refugee reception. We investigate how cantonal (subnational) governments in Switzerland remodel these uncertainties by implementing the policy of reception through private intermediaries, namely actors, instruments, and rationalities from the private sphere.</p> <p>Codebook along DDI standard in pdf and xml formats.</p>
Ultrafine particle number concentration observations perpendicular to a main street in Berlin, Germany, in summer 2017
<p>The data set was recorded in summer 2017 in Berlin, Germany. It was the basis of the paper:</p> <p>Fritz, S., Schubert, S., and Schneider, C. (2021). Measurements of spatial variability of sub-micron particle number concentrations perpendicular to a main road in a built-up area. metz. 30, 315–331. doi:10.1127/metz/2021/1058</p> <p>The measurement setup, study site, and devices used are described more detailed in the paper.</p> <p>The data sets uploaded include observations of particle number concentrations, wind direction and wind speed at ground level as well as traffic count data. The time stamp is in local time for Berlin/Germany.</p> <p>The data sets:</p> <p>coordinates.csv contains the coordinates of the measurement points used in file obs_pnc_wind. The column ‘distance’ links these two files. The values in the column ‘distance’ are the distance from the curb of the main road. The distance ‘-10’ represents the measurement point on the central strip of the main road. The two columns ‘latitude’ and ‘longitude’ provide the coordinates of the measurement points.</p> <p>obs_pnc_wind contains the observation data for particle number concentration (PNC), wind speed, and wind direction. PNC was recorded in #/cm³ with a TSI 3007 CPC at a 1-second resolution. Wind speed `ws’ [m/s] was recorded with a KESTREL 5000 with a 2-second resolution. Wind direction ‘wdir’ [°] was manually recorded as the most prominent wind direction within the 3-minute measurements per measurement point in angles of 10°. Wind direction was changed to NA (wdir_comment > 0), if it could not be determined either due to too calm wind (wdir_comment = 1) or with continuously changing wind direction (wdir_comment = 2). Along the measurement route, sometimes additional sources occurred and were recorded in the variable ‘source_type’ at the time they passed by the measurement device. Variable ‘source_comment’ provides additional information about ‘source_type’ where necessary.</p> <p>traffic_count contains the vehicles counted on the main street (Straße des 17. Juni) for each of the 72 runs. It was calculated as [vehicles/hour] based on 5-minute traffic counts. During the traffic counts, we differentiated between smaller (light duty) vehicles (<3.5 t, cars, vans, motorcycles, scooters) and bigger (heavy duty) vehicles (> 3.5 t, trucks and busses).</p>
Perceived safety and attractiveness of city streets in Frankfurt, Germany: ratings and explanations
<p>How safe or attractive do different people perceive streets to be and why?</p> <p>In this repository we share the data we collected and analysed for the paper "Is it safe to be attractive? Disentangling the influence of streetscape features on the perceived safety and attractiveness of city streets".</p> <p>The data contain ratings of perceived safety and attractiveness (using a 5-point Likert scale) coming from 403 participants who were asked to virtually navigate city streets in Frankfurt, Germany, through a sequence of street-level images. Moreover it contains their explanations of the ratings (in their own words).</p> <p>In total we have collected data for 753 locations. In particular:</p> <ul> <li>7989 rating pairs of perceived safety and attractiveness</li> <li>19114 keywords used to explain the safety ratings</li> <li>18232 keywords used to explain the attractiveness ratings</li> </ul>
Environmetal layers of the street network of Tallinn
<p>These datasets contain the spatial layers of environmental variables of the street network of Tallinn. They are the result of a research assessment conducted to evaluate the environmental quality of the Tallinn street network. Environmental layers have been assigned to each street segment using the Zonal Statistics tool within a 25-metre buffer zone from street segments.</p> <p>The datasets are provided in the GeoPackage (gpkg) file format, ensuring compatibility with Geographic Information System (GIS) software and integration into existing spatial databases.</p> <p>The datasets include the following environmental layers:</p> <ul> <li>ndvi_segment_tallinn: This layer represents greenery for street segments derived from the Normalised Difference Vegetation Index (NDVI), ranging between 0 and 1, from 18th June 2021.</li> <li>pm25_segment_tallinn: Annual PM2.5 (particulate matter with a diameter of 2.5 micrometres or smaller) mean street segment values for the year 2020.</li> <li>pm10_segment_tallinn: Annual PM10 (particulate matter with a diameter of 10 micrometres or smaller) mean street segment values for the year 2020.</li> <li>no2_segment_tallinn: Annual NO2 (nitrogen dioxide) mean street segment values for the year 2020.</li> <li>noise_segment_tallinn: Mean noise (Lden) street segment values from the year 2019.</li> </ul> <p>These datasets serve as resources for analysing and understanding the environmental characteristics of the Tallinn street network. Researchers and urban planners can utilise these data. </p>
Results of the expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps
<p>This is the repository for the results of the 'expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps'.</p> <p>Note: check the most recent version in the sidebar</p> <table> <tbody> <tr> <td>Current version</td> <td>v.0.2</td> </tr> <tr> <td>Date</td> <td>2024/01/10</td> </tr> <tr> <td>Respondants</td> <td>30</td> </tr> </tbody> </table> <p><strong>Available files:</strong></p> <table> <tbody> <tr> <td>File</td> <td>Type</td> <td>Description</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_public.csv">responses_v01_public.csv</a></td> <td>CSV table</td> <td>Survey raw results (anonymous)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_stats.csv">responses_v01_stats.csv</a></td> <td>CSV table</td> <td>Questions statistics</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_mean_sd.jpg">responses_v01_mean_sd.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (mean and standard deviation)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_bands.jpg">responses_v01_bands.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (uncertainty bands)</td> </tr> </tbody> </table> <p>The column descriptions in the statistical table are as follows:</p> <p>Prefixes:</p> <ul> <li>HABITAT: habitat suitability score</li> <li>WEIGHT: Threat weight</li> <li>MAX_DIST: Maximum distance of negative influence (impact)</li> </ul> <p>Suffixes:</p> <ul> <li>mean: Average</li> <li>std: Standard deviation</li> <li>min: Minimum value</li> <li>p05: 5th percentile</li> <li>p25: 25th percentile</li> <li>p50: 50th percentile (median)</li> <li>p75: 75th percentile</li> <li>p95: 95th percentile</li> <li>max: Maximum value</li> </ul> <p>These prefixes and suffixes describe various statistical measures used to analyze the environmental modeling data.</p>
NYU FloodSense street sign mounted flood depth sensor
<p>Water depth level in mm from a sensor mounted on a street sign post at the corner of 5th Street and Hoyt, Brooklyn, NY (40.676640, -73.994595). The sensor is designed to detect flood water that fills the street and blocks vehicle and pedestrian traffic, as well as depositing micro-organisms on the street. Ultrasonic technology is used to detect flood water depth.</p> <p>The sensor transmits its data via LoRaWAN and is equipped with a solar panel for continuous operation.</p> <p>Depth data is collected at ~5min intervals. Time fields are in local time (New York). Date format is: 2020-10-04 20:11:45.742594232-04:00</p> <p>Two flood events have been observed in this dataset between these date ranges:</p> <ol> <li> <p>"2020-11-15 19:37:00.000000000-05:00" to "2020-11-16 00:30:00.000000000-05:00"</p> </li> <li> <p>"2020-11-30 10:20:00.000000000-05:00" to "2020-11-30 13:30:00.000000000-05:00"</p> </li> </ol> <p>Erroneous data has been observed:</p> <ul> <li>There are ~1% decreases in depth measures on days with sun which suggests that the distance sensor is affected by direct sunlight</li> </ul> <p>This data is preliminary and is for prototyping purposes. </p> <p>This dataset will be updated when more data is collected.</p> <p>Please see our github org for sensor information and build instructions: <a href="https://github.com/floodsense">github.com/floodsense</a></p>
Code and data to support 'Street view imagery for built environment auditing: a systematic review'
<p>Code and data to support the manuscript entitled 'Street view imagery for built environment auditing: a systematic review'</p>
FREE-ROAMING DOGS DETECTED USING GOOGLE STREET VIEW
<p>Datasets to count free-roaming dogs using Google Street View, and compare with population of free-roaming dog from surveys in Arequipa, Peru.</p>
High-fidelity simulation of the effects of street trees, green roofs and green walls on the distribution of thermal exposure in Prague-Dejvice
<p>Archive with PALM simulation results. All data were used in paper <a href="https://doi.org/10.1016/j.buildenv.2022.109484">https://doi.org/10.1016/j.buildenv.2022.109484</a></p>
First Street Foundation Flood Model Hazard Layers V1.3
<p>Up to 15 different hazard layers are available, representing 3 different time periods (2021, 2036, 2051) and 4-5 different return periods from the 2-year (coastal only) to the 500-year intervals.</p> <p>Data is delivered in GeoTIFF format and at a 3 meter resolution with each pixel representing depth of flooding in centimeters. This high resolution dataset allows you to visualize flood extents at multiple return periods both today and in the future.</p> <p>The hazard inundation layers are emailed through a clickable link that automatically starts the download of the datasets. The Version 1.3 hazards are available for the contiguous United States.</p> <p>You can download a sample of the hazard layers generated from First Street's Flood Model on this page. You can request access to the hazard layers for areas within the contiguous United States on the First Street website<a href="https://firststreet.org/data-access/paid-access/?utm_source=Hazard_Layers&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component"> here</a>. You can find the data dictionary which breaks down the data that is available with each hazard layer purchase<a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Hazard_Layers&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo"> here</a>. If you are also interested in the flood risk statistics, you can find more information <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/data-dictionary/?utm_source=Hazard_Layers&utm_medium=Data_Dictionary&utm_campaign=Zenodo">here</a>.</p>
Mapillary Annotated Street Level Images
<p>This dataset contains street-level images from the Mapillary platform, for the year of 2017, in the <a href="https://zenodo.org/api/files/e945988e-2c3d-4cc6-9cd2-ee7761a2f15a/mapillary.zip?versionId=86c5f856-324c-45a6-aa9d-03a0f661a0b2">mapillary.zip</a> file. These images have been matched with crop type labels from the freely available Land Parcel Identification System (LPIS) of the Netherlands. The name of each .jpf file corresponds to a unique identifier.</p> <p>Supported by the CALLISTO (No. v) project, which has been funded by EU Horizon 2020 programs.</p>
Data used in manuscript Carbon sequestration potential of street tree plantings in Helsinki
<p>Data and model runs used in manuscript "Carbon sequestration potential of street tree plantings in Helsinki". This data set includes model runs for the Surface Urban Energy and Water balance Scheme (SUEWS) and soil carbon model Yasso.</p> <p><br> The data files are:</p> <p><strong>Met_Gapfilling</strong></p> <ul> <li>ConvertMeteorologyInput.m (MATLAB) is the main file and functions gapfilling.m (with other measurements) and gapfillingfill.m (with interpolations) are used in the gap filling</li> <li>Includes files for meteorological measurement data <ul> <li>Airport: Data from Helsinki-Vantaa airport; airportdata.m, where data is cleaned</li> <li>Precipitation: Data from multiple locations; Pres_Gap.m for gap filling precipitation and function PrecipitationGap.m</li> <li>Roof: Data from rooftop</li> <li>SMEARIII: Monthly meteorological data from Kumpula (2003-2016)</li> </ul> </li> <li>SUEWS_met file for the final gap filled meteorological files for SUEWS </li> </ul> <p><strong>Fits</strong></p> <ul> <li>Includes FitCO2_parameter.m for fitting CO2 parameters for SUEWS</li> <li>Includes functions Pho6.m and Resp0.m that have the function forms</li> <li>Includes data files for measurement data <ul> <li>CO2Data: Canopy photosynthesis and canopy respiration estimated with SPP model (KumpulaX.out for Tilia site and Kumpula2X.out for Alnus site)</li> <li>Met_2016: Meteorology from Kumpula for June to August in 2016</li> <li>SWCdata: Soil water content from two streets and three soil types</li> </ul> </li> </ul> <p><strong>ModelRuns</strong></p> <ul> <li>SUEWS model runs separately for Alnus and Tilia sites <ul> <li>Includes input and output files and model codes</li> <li>Alnus site includes both the Baserun and Finalrun</li> </ul> </li> <li>Yasso model runs <ul> <li>Model run in file yasso.f90</li> <li>Output files: DecRate...txt includes three soil types and values for each month from 2002 to 2016</li> <li>Yasso_meteorology_month.m creates meteorological input files for Yasso (Clim_month_xx.txt) using meteorology from SUEWS</li> <li>Lifetimerun: 30 year simulations that includes estimations for leaves and pruned branches</li> </ul> </li> </ul> <p><strong>FigCodes</strong></p> <ul> <li>Includes MATLAB codes for figures and statistics</li> <li>Includes measurement data for CO2, sap flow and SWC</li> </ul> <p> </p>
Citizen science for traffic counts: WeCount project dataset for inner-city streets of Ljubljana
<p>The Horizon 2020 project WeCount is a citizen science project that involves citizens in all steps from problem definition to data collection and analysis. This is currently one of the most common methods of citizen participation. The ethical criteria that such a project must meet in order to be classified as citizen science, and the form of transparency or informed consent that should be a necessary part of the ethical conduct of citizen science projects, were on Telraam platform for examination at the international and national level during the collection of data on traffic flows for WeCount Ljubljana. Engaged citizens were given low-cost sensors which they placed on the inside of the windowpane in their home or office facing the street at different distances (from 3 to 15 meters). </p>
Code and Data for the Study "A User-Centric Model of Connectivity in Street Networks"
<p>This resource contains the code and results used in the paper:</p> <p>Corcoran, P. and R. Lewis (Pending) “A User-Centric Model of Connectivity in Street Networks”</p> <p>Please consult <strong>UserGuide.pdf</strong> for further information. </p>
Dataset: Rimini Street, Inc. (RMNI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Newbury Street Acquisition Corporation (NBSTW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Newbury Street Acquisition Corporation (NBSTU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Newbury Street Acquisition Corporation (NBST) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Linked collectors and determiners for: Nightmare on Dendropanax Street: What is Dendropanax arboreus (Araliaceae)?.
Natural history specimen data linked to collectors and determiners held within, "Nightmare on Dendropanax Street: What is Dendropanax arboreus (Araliaceae)?". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/5fc15e32-a9cc-4dd5-b192-1c75252e7415">https://bionomia.net/dataset/5fc15e32-a9cc-4dd5-b192-1c75252e7415</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5fc15e32-a9cc-4dd5-b192-1c75252e7415">https://gbif.org/dataset/5fc15e32-a9cc-4dd5-b192-1c75252e7415</a>. Formatted as a Frictionless Data package.
Comparing first street foundation and PRIMo flood hazard data across the Los Angeles metropolitan region
<p>Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national-scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. The data in this repository supports an assessment of the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S.. For the analysis, FSF data was compared to PRIMo-Drain, a flood hazard model that resolves drainage infrastructure and fine resolution drainage dynamics.</p> <p>In the linked journal manuscript, using the case of Los Angeles, California, we find that FSF and PRIMo-Drain estimates of population and property value exposed to 1%- and 5%-annual-chance hazards diverge at finer scales of governance, for example by 4- to 18-fold at the municipal scale. FSF and PRIMo-Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ~1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national-scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models.</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.