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20 results for “Public spaces”
Results: Predicted cooling effect, deaths prevented and associated economic value from public green spaces in Paris V2
<p>This dataset represents results predicting the cooling effect, deaths prevented and associated economic value for public green spaces in Paris for 40 hot days above the minimum mortality threshold in 2019. </p> <p>This is version 2. The value of a statistical life (VSL) has been corrcted and all values adjusted. </p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the Variable_name csv file for description of the variable names. </p> <p>The (non-reproducible) code is available at https://github.com/j-k-garrett/REGREEN_Paris_heat</p> <p>These results are from the submitted (September 2025) paper entitled:</p> <p><strong><span>Nature-Based Solutions for Urban Heat: Health and Economic Value of Paris’s Public Green Spaces</span></strong></p> <p>Authored by:</p> <p>Joanne K. Garrett<sup>1</sup>, David Neil Bird<sup>2</sup>, Timothy J. Taylor<sup>1</sup>, Elizabeth McCarthy<sup>3</sup>, David H. Fletcher<sup>4</sup>, Benedict W. Wheeler<sup>1</sup>, Marianne Zandersen<sup>5</sup>, Laurence Jones<sup>3</sup></p> <p><sup>1</sup>European Centre for Environment and Human Health, University of Exeter, Penryn, Cornwall, UK</p> <p><sup>2 </sup>Institute for Climate, Energy and Society, JOANNEUM RESEARCH, Graz, Austria</p> <p><sup>3</sup> Department of Environmental Studies, Schiller Institute for Integrated Science and Society, Boston College, USA</p> <p><sup>4</sup> UK Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd, Wales, UK</p> <p><sup>5 </sup>Department of Environmental Science, iClimate Interdisciplinary Centre for Climate Change, Aarhus University, Denmark</p> <p> </p>
TDA4ContextualEmbeddings - Public - Debug Data for the codebase of the publication "Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction"
<p>Debug dataset for testing the <a href="https://gitlab.cs.uni-duesseldorf.de/general/dsml/tda4contextualembeddings-public">codebase</a> of the paper <a href="https://doi.org/10.18653/v1/2024.sigdial-1.31">“Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction”</a> published at the 25th Meeting of the Special Interest Group on Discourse and Dialogue, Kyoto, Japan (SIGDIAL 2024).</p>
Dataset for "Beating 1 Sievert: Optimal Radiation Shielding of Astronauts on a Mission to Mars" publication in Space Weather journal
<p>Datasets in .fig Matlab format and figures in .jpg format published in Space Weather journal</p> <p>effectiveDoseRF.mat contains the effective dose "response functions" and an example (how2useDoseResponceFunctions.m) of how to use them to assess GCR dose.</p>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
Spectral data associated to the publication: "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" by R. Cerubini et al. (Planetary and Space Science 211, 2022)
<p>This is the complete set of experimental NIR reflectance data collected by R. Cerubini and co-authors for the article "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" published in Planetary and Space Science 211 (2022). doi: https://doi.org/10.1016/j.pss.2021.105391.</p> <p>The article itself is published in open-access and provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces.</p> <p>The data are contained in ASCII files (columns separated by comma). The first column is the wavelength (in micrometers) and the other columns contain the reflectance data (in unit of reflectance factor). The different compositions are indicated in the filenames and correspond directly to the figures in the published paper.</p> <p> </p> <p> </p>
Data for a publication "Labyrinth Seal Design for Space Applications"
<p>The presented dataset contains raw and processed data from evaporation tests, simulations, and surface analyses aimed at optimizing labyrinth seal configurations for space applications. It includes experimental measurements from a custom-built evaporation test rig, simulation data from COMSOL Multiphysics (using Molecular Flow and Particle Tracing Modules), and surface roughness analyses from a 3D Optical Profilometer. Supporting MATLAB scripts for data processing, along with detailed subfolders for experimental and simulation data, provide a comprehensive resource for evaluating labyrinth seal performance and minimizing lubricant evaporation.</p>
Public Attitudes Towards the European Space Agency
<p>The data inherent in this dataset were collected between 11 February 2020 to 1 March 2020 as part of a public survey of German residents. The survey looked into attitudes and (hypothetical) behaviours related to the European Space Agency and European space activities. Convenience sampling and snowball sampling were employed over email, messengers and social media.</p> <p>This is a multiple imputation dataset, including an imputation variable, a weighting variable, as well as composite variables where applicable.</p> <p>Additional files include a generic codebook for the dataset and a print version of the online survey for a rough overview of the survey design. The survey content in the latter is in German.</p>
Dataset from publication "Predicting context-sensitive urban green space quality to support urban green infrastructure planning"
<p><strong>Data Description:</strong></p><p>This dataset presents the spatial outcome of an analysis modelling perceived green space quality across the city of Espoo, Finland. The analysis relies on data gathered through the My Espoo on the Map survey (<i>Mun Espoo kartalla</i>) in the autumn of 2020 as part of the NordForsk-funded research project NORDGREEN. A comprehensive account of the analytical process and potential applications of the dataset is available in the associated publication, "<i>Predicting context-sensitive urban green space quality to support urban green infrastructure planning</i>" (open access: <a href="https://doi.org/10.1016/j.landurbplan.2023.104952">https://doi.org/10.1016/j.landurbplan.2023.104952</a>).</p><p><strong>Data Processing:</strong></p><p>This dataset results from an analysis that integrates both primary and secondary sources of geospatial data. The primary data were collected with an online public participation GIS (PPGIS) survey directed for the adult inhabitants of Espoo. The data collection took place in September-October 2020 and was executed in collaboration with Aalto University and the City of Espoo. For a detailed overview of the data collection process, please refer to the related publication.</p><p><strong>Data characteristics:</strong></p><p>Format: Shapefile (50m x 50m grid)</p><p>Geographical area: Espoo, Finland</p><p>Spatial reference: EUREF FIN TM35FIN</p><p>Note: Only grid cells intersecting with greenspace have been included in the dataset. For the employed definition of green areas, please consult the related publication.</p><p><strong>Data attributes and their descriptions:</strong></p><p><strong>"</strong><i>P_PROB"</i>: Probability (P), positive perceived quality</p><p><i>"N_PROB"</i>: Probability (P), negative perceived quality</p><p><strong>Funding: </strong></p><p>This research was funded by NordForsk, Sustainable Urban Development and Smart Cities Programme, Project Smart Planning for Healthy and Green and Nordic Cities – NORDGREEN, under Grant Number: 95322.</p>
Preliminary results: Predicted cooling effect, lives saved and associated economic value from public green spaces in Paris
<p><span>This dataset represents preliminary results predicting the cooling effect from public green spaces in Paris for the 10th, 22nd and 24th July 2019, the lives saved and associated economic valuation.</span></p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the README file for description of the variable names. </p> <p>Full results will be shared in due course, alongside all code and a detailed description of the method. If you would like to know more about the method or would like to be informed when the full results and method are published, please contact Jo Garrett at j.k.garrett@Exeter.ac.uk</p>
Radiation Effects on Satellites during Extreme Space Weather Events (pre-publication dataset)
<p>Data for submitted paper entitled "Radiation Effects on Satellites during Extreme Space Weather Events".</p> <p>Submitted to AGU Space Weather.</p> <p> </p>
Household accessibility to heat refuges: Residential air conditioning, public cooled space, and walkability
Access to air conditioned space has been identified as a critical protective element that can mitigate the adverse health effects of heat waves. However, there is limited knowledge of where this resource exists in cities. While many cities have deployed networks of sponsored cooling centers their location is likely to be inadequately informed with respect to the location of existing resources. This study explores the distribution of in-home air-conditioning and household access to public cooling resources in Maricopa County, AZ. There are significant variations in the distribution of private and public air conditioned space and there are areas where access to these spaces is limited which could increase health risks during periods of extreme heat.
Crowdsourcing public perceptions of urban green space quality: A case study of Rembrandt park in Amsterdam
<p><strong><em>City-dwellers are realizing the benefits of green spaces and are flocking to urban parks. City planners face the challenge of ensuring that urban green spaces are functional for all citizens. To make informed choices they need the right information and that is where the Mijn Park app can help. </em></strong></p> <p>Research shows that when considering the social functions of urban green spaces, quality is just as important as quantity. It is easy enough to map how much green spaces there are, but how do we measure their quality? How do city planners ensure that the city’s green areas are attractive, accessible and inclusive – for everyone? The Vrije Universiteit Amsterdam in collaboration with the International Institute for Applied Systems Analysis developed Mijn Park, a mobile application that will help city planners do just that. As part of the LandSense Citizen Observatory, the ‘Mijn Park’ (My Park) app asks respondents to go to several locations in a park and give subjective responses to those locations. They are then further questioned about how they use the whole park and how much they would like to see certain changes made in the park. This information provides information that can help to inform decisions about any renovations or improvements to the park.</p> <p>A pilot campaign was conducted in the summer of 2018 in Rembrandt park in Amsterdam and insights from the citizen-driven observations were shared with the Department of Planning and Sustainability of Amsterdam.</p> <p>This dataset includes responses and photographs collected by 129 unique volunteers providing 377 observations in select locations across Rembrandt Park. The following files are available:</p> <ul> <li>_Preview MijnPark-Amsterdam-LandSense.png</li> <li>Attributes-MijnPark-Amsterdam-LandSense.csv</li> <li>MijnPark-Amsterdam-LandSense.csv</li> <li>MijnPark-Amsterdam-LandSense.geoJSON</li> <li>README.txt</li> </ul> <p> </p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://www.vu.nl/">Vrije Universiteit</a> (VU), Amsterdam and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a> (IIASA).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Exploring and promoting green urban spaces in Vienna - can data about public perception help drive change?
<p>Citizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues.</p> <p>The Horizon 2020 project, <a href="https://landsense.eu/">LandSense</a>, is building a modern citizen observatory for Land Use & Land Cover (LULC) monitoring, by engaging citizens to transform current approaches to environmental decision making. Citizen Observatories are community-driven mechanisms to complement existing environmental monitoring systems and can be fostered through mobile and web applications, allowing citizens to play a key role in environmental monitoring. Within this project, the City Oases mobile application, focused on the city of Vienna, has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. <a href="https://play.google.com/store/apps/details?id=com.iiasa.cityoases">City Oases</a> was launched in March 2019.</p> <p>Where are the best places for a romantic date? Where can you skate? Where is it cool on a hot summer’s day? Open urban spaces can be used in many ways. Pick an activity in the CityOases app and we show the spots where you can do them, including the rating of previous users and pictures from the location. If you visit the spot you can rate it as well based on a few selected subjective criteria. If you know a cool spot that is openly accessible but not marked in our map yet? Just add it with a list of activities and some pictures. Additionally, you can input your perceptions including whether it is noisy, clean or if the infrastructure is attractive. The picture module within the application promotes you to take photos in the four cardinal directions. Users can search for specific activities, visit one of the points indicated on the map and then evaluate this point.</p> <p>Time period of data collection: 19/03/2019 - 10/11/2019</p> <p>Types of contributors: General Public, Students<br> Number of contributors: 50<br> Number of observations: 788<br> Number of photos: 1846</p> <p>Associated files: City Oases Vienna 2019.csv, City Oases Vienna 2019.geoJSON, City Oases Vienna 2019 Attributes.csv</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://www.umweltbundesamt.at/en/">Umweltbundesamt</a> (Environment Agency Austria) and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Dataset for publication: 'Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design'
<p>This is the dataset the publication "Search Space Representation and Reduction Methods to Enhance Multi-Objective Water Supply Monitoring Design" is based on.</p>
Supporting data of the publication: Conquering chemical spaces in the billion range: is docking a computational alternative to DNA-encoded libraries?
<p>Raw data of the publication: "Conquering chemical spaces in the billion range: is high-throughput docking a computational alternative to DNA-encoded libraries?" by Levente M. Mihalovits, Tibor V. Szalai, Dávid Bajusz and György Miklós Keserű. Figures of the manuscript and the supporting information were created using these data.</p>
Ecosystem services, nature preferences, and valuation of urban green and blue spaces - Pre-publication data
<p>Data used in the analysis for the manuscript: "Ecosystem services, nature preferences, and valuation of urban green and blue spaces".</p> <p>The data was collected in a web-based survey of Tel Aviv-Yafo, Israel residents between March 21, 2021 and May 5, 2021. </p>
Analysis of the model of integration of multicultural media in the network's space of public opinion
<p>Supplementary data of the manuscript published on Transinformação, which title is Analysis of the model of integration of multicultural media in the network’s space of public opinion.</p>
Post-Pandemic Perception of Public Space in Singapore
ClinicalTrials.gov study NCT04394039. IPD Sharing: NO. Countries: 1. Publications: 0.
SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release
The SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release, part of the SDGI collection, measures the average share of the built-up area of a city that is open space for public use for all. UN SDG 11 is "make cities and human settlements inclusive, safe, resilient and sustainable". Aside from environmental benefits, public space can also help improve public health, bolster commUnity, and encourage economic exchange. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.7.1. The indicator was computed by measuring both the proportion of OpenStreetMap (OSM) public space within a given urban center and the proportion of WorldPop gridded population within 400 meters to Open Public Space (OPS). Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (GHS-UCDB). The SDG indicator 11.7.1 data set provides estimates of the average share of the built-up area of cities that is open space for public use for all for 8,873 urban centers across 180 countries.
Public space for public health: a call to action
<p>Korsakow film</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)
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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.