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3,709 results for “urbanicity”
PERCEIVE WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels
<p>The data set will contain all the shareable data collected and generated through the different tasks of WP5, that are interdependent. In particular, in Task5.1 we collected a bibliography, which is already included in the dataset. In Task5.2 we collected a large collection of data from different documentary sources and media: EU policies and reports, descriptions and reports created by Local Managing Authorities, newspaper articles, tweets, Facebook posts referred to EU CP policies. During Task5.3 we analyzed these data through Mallet software to elicit topics, as sets of words that co-occur together. The results of the linear regression analysis will be in this data set as well. The results of the analysis will consist of tables of texts and of numerical data.</p> <p>Collected data are only partly available online, therefore our generated data will have a unique value, as there is no comparable public source of data. Data will be helpful for all student and practitioners willing to understand how the concepts of Cohesion Policies, Europe and European identity are shaped in the public sphere.</p>
URBANWASTE - Dataset 1 URBAN_METABOLISM_DATA
<p><strong>General description</strong></p> <p>Within URBANWASTE Work Package 2 data from the pilot cases needed to perform the metabolic analysis was collected. In this sense, mainly data regarding <strong>waste generation and management, tourism (accommodation capacity, tourist flows, tourism economy) and socio-economic data</strong> of each pilot was collected.</p> <p>The indicator sets finally collected were previously cross-checked with the 11 URBANWASTE Pilot Cases regarding data availability on pilot case scale to ensure their suitability and practicability to answer specific URBANWASTE questions. This cross-checking was done by the means of performing a “Survey on data availability” within Task 2.3.</p> <p><strong>Origin, Nature and scale of data</strong></p> <p>For data collection, all pilot cases partners received an empty excel database divided into three thematic areas (waste related data, socio-economic data and tourism related data). In case the pilot case partners did not have access to the requested information, other organisations such as local municipal departments, waste management companies, tourism associations, national statistical agencies etc. were contacted by them for support in data provision.</p> <p>The data collected with these databases mainly represent statistical data. For transparency reasons, data sources were to be specified as well.</p> <p>The spatial scale of the collected data was supposed to be the pilot case area (meaning for the whole city, municipality or metropolitan area). As data on this small scale was not available for all data sets, some of the provided data is on regional or even national level. For ensuring transparency, the spatial scale had to be specified for each data set. According to the type of indicators, the temporal scale varies from annual data to monthly data.</p> <p>For selected data sets, time series data at annual scale were collected for the period 2000 – 2015. For some selected data sets (e.g. waste quantities, tourist arrivals & overnight stays), additionally, also time series on monthly scale were collected for the period 2013 – 2015.</p> <p><strong>Data Format</strong></p> <p>The database prepared to collect the data needed for performing the metabolic analysis was divided into three thematic areas, which are further divided in categories as indicated below:</p> <p><em><strong>Waste related data</strong></em></p> <p>- Waste generation and waste quantities [number]</p> <p>- Waste prevention [text] [number]</p> <p>- Waste management [number] [%] [€]</p> <p><em><strong>Socio-economic data</strong></em></p> <p>- Description of the pilot case [number] [km²]</p> <p>- Economy [number] [%] [€]</p> <p>- Society [number] [%]</p> <p>- Building statistics [%]</p> <p><em><strong>Tourism related data</strong></em></p> <p>- Tourism economy [€]</p> <p>- Accommodation capacity [number]</p> <p>- Tourist flows [number]</p> <p>- Other tourism related information<strong> </strong>[number]</p> <p>Each category contains a lot of indicators, each indicator being identified by a data ID, a unit, and a spatial scale. Data sources had to be specified as well. When needed, the definitions of these indicators were added directly in the database template.</p> <p>In total, 48 data sets (some of them further divided into sub-sets) were collected. Most of the collected data represent quantitative data in the format of [number], [%] [€] or [km²].</p> <p>The data on urban metabolism received from the pilot cases is stored in 1 excel database.</p> <p> </p> <p><strong>Further Information and Contact</strong></p> <p>The <strong>data </strong>on waste generation and management, socio-economic data and tourism data used for all the assessments performed within Work Package 2 and presented in this report <strong>was provided by the URBANWASTE pilot cases</strong>. More detailed information is contained within the database.</p> <p><strong>In case of questions related to this database please contact: abf@boku.ac.at</strong></p> <p> </p> <p>For more information on the <strong>URBANWASTE </strong>project please visit: http://www.urban-waste.eu/</p>
Challenges of high-fidelity air quality modeling in urban environments - PALM sensitivity study during stable conditions (TURBAN)
<h3>Introduction</h3> <p>This dataset contains the PALM model inputs and the source code used to create the simulations for Prague-Legerova scenarios performed in the scope of the <strong>TURBAN</strong> project (<a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>). Detailed description of the simulations is provided in the referencing scientific paper.</p> <h3>List of simulations</h3> <table> <tbody> <tr> <td><strong>Scenario name</strong></td> <td><strong>Days simulated</strong></td> <td><strong>IBC</strong></td> <td><strong>Configuration changes</strong></td> </tr> <tr> <td>legerovas_s6_sens_base</td> <td>13–15 February 2023</td> <td>ICON</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_dtmax</td> <td>13 February 2023</td> <td>ICON</td> <td>dt_max=0.2</td> </tr> <tr> <td>legerovas_s6_sens_heat</td> <td>13 February 2023</td> <td>ICON</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_sgs</td> <td>13 February 2023</td> <td>ICON</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_stg</td> <td>13 February 2023</td> <td>ICON</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_alad</td> <td>13–15 February 2023</td> <td>ALADIN</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_alad_heat</td> <td>13 February 2023</td> <td>ALADIN</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_alad_sgs</td> <td>13 February 2023</td> <td>ALADIN</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_alad_stg</td> <td>13 February 2023</td> <td>ALADIN</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_wrf</td> <td>13–15 February 2023</td> <td>WRF</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_wrf_heat</td> <td>13 February 2023</td> <td>WRF</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_wrf_sgs</td> <td>13 February 2023</td> <td>WRF</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_wrf_stg</td> <td>13 February 2023</td> <td>WRF</td> <td>STG_PROFILES added</td> </tr> </tbody> </table> <h3>Directory structure</h3> <p>The directory inputs contains the model inputs and it is further divided into these subdirectories:</p> <p>- inputs/common: The PALM static driver and the emission drivers for the parent and child domains. These files are common to all simulations</p> <p>- inputs/dynamic/*: These directories contain the dynamic drivers for the parent and child domanis, which contain the initial and boundary conditions (IBC) as well as external radiation data. The three subdirectories aladin, icon and wrf contain IBCs created from the respective mesoscale model outputs. </p> <p>- inputs/legerovas_s6_sens_*: These directories contain the PALM model configuration (p3d) for both domains for each simulation.</p> <p>- inputs/build_config: The included .palm.iofiles configuration file ensures that the files STG_PROFILES are correctly copied from the input directory.</p> <p>The directory palm_sources contains the exact model source used for the simulations. It is derived from the PALM model release 23.04 with additional bugfixes. There are two source archives:</p> <p>- heat.tar.gz: PALM source further modified to include anthropogenic heat from cars, used for the simulations legerovas_s6_sens_*_heat</p> <p>- standard.tar.gz: PALM source used for all other included simulations.</p> <h3>Reproducing the simulations</h3> <p>In order to reproduce the simulations, unpack the respective source code archive and follow the standard installation, configuration and build procedures described in the README.md file within the archive and on the PALM model website http://www.palm-model.org/. Then copy the input files for the respective simulation in the JOBS directory. The common files and the dynamic driver files need to be renamed so that they match the prefix given by the name of the simulation, as is described in the PALM model documentation.</p>
VulneraCity - The urban vulnerability drivers database
<p>VulneraCity is a database of unique urban vulnerability drivers for six different hazards (Coastal flooding, Pluvial flooding, Earthquakes, Heatwaves, Drought, Waterborne diseases), providing descriptions, classifications, and sources. The drivers are collected from over 450 individual studies, based on a systematic literature review. For more info, please see our accompanying paper (please cite this when using VulneraCity in your own work): </p> <p><strong>Stolte, T. R., Koks, E. E., De Moel, H., Reimann, L., Van Vliet, J., De Ruiter, M. C., & Ward, P. J. (2024). VulneraCity–drivers and dynamics of urban vulnerability based on a global systematic literature review. <em>International Journal of Disaster Risk Reduction, 108,</em> 104535. <a href="https://doi.org/10.1016/j.ijdrr.2024.104535">https://doi.org/10.1016/j.ijdrr.2024.104535</a> </strong></p> <p> </p>
Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020
<p>Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".</p> <p> </p> <p>Please cite the original paper:</p> <p>Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” <em>Applied Geography </em> 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.</p> <p><br>WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).</p> <p>All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.</p> <p>Three WUI maps were generated using structures built on of before the years indicated below:</p> <p>2000 - "CA_WUI_2000.tif"</p> <p>2010 - "CA_WUI_2010.tif"</p> <p>2020 - "CA_WUI_2020.tif" </p> <p> </p> <p>Acknowledgments -</p> <p>We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.</p>
Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation
<p>This data archive provides simulated hourly heating and cooling building energy demand for current and future RCP85 climate for 8 representative cities for a single-family and small office building archetype.</p> <p>The data forms part of the following publication:</p> <p><em>Eggimann S.; Fiorentini M. (2024): Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.114348</em></p> <p><strong>Attributes</strong></p> <ul> <li>ID_origin: City ID of source city</li> <li>ID_destination: City ID of target city</li> <li>Signature_Cooling: Cooling demand determined by the signature approach</li> <li>Model_Cooling: Cooling demand determined by EnergyPlus</li> <li>Absolute_Diff: Absolute difference</li> <li>Percentage_Diff: Relative difference</li> <li>Daily_Tout: Average daily dry-bulb ambient temperature</li> </ul> <p><strong>Instruction</strong></p> <p>To obtain the simulation and energy signature-based results, it is required to filter the dataset and set the source ID to the destination ID. The city IDs are provided in the file city_table_ID.</p> <p><strong>Source</strong></p> <p>The archetypes are provided by the Office of Energy Efficiency & Renewable Energy: https://www.energycodes.gov/prototype-building-models</p>
Modelled urban climate island during the record-breaking 2022 heatwave in London
<p>This record is created as a data supplement for the manuscript "Estimated mortality attributable to the urban heat island during the record-breaking 2022 heatwave in London".</p> <p>These data were produced using the Weather Research Forecasting model with BEP-BEM. The model setup is described in Brousse et al (2023) <a href="doi.org/10.1175/JAMC-D-22-0142.1">10.1175/JAMC-D-22-0142.1</a>. These data cover the period 2022-07-10 to 2022-07-25, during which temperatures exceding<strong> </strong>40 °C were recorded in London for the first time.</p> <p>The data comprise two NetCDF files. One is labelled "Urb" one "Nourb". In the "Nourb" file, the urban tile is removed from the model and the land surface replaced by the nearest natural tile. This can be used to estimate the influence of the urban tile on the local climate.</p> <p>Variables included in the file are T2 (temperature at 2 m elevation in Kelvin), V10 and U10 (winds at 10 m elevation in metres per second), PSFC (surface level pressure in Pascal), RAINNC (rain in mm), TH2 (potential temperature at 2m elevation in Kelvin), and Q2 (specific humidity at 2 m elevation, which is dimensionless). All variables are provided at hourly timestep.</p> <p>Queries about this dataset can be directed to o.brousse@ucl.ac.uk</p>
Georg Urban (u0205)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Georg Urban<br><u>musiXplora-ID</u>: u0205<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/u0205">https://musixplora.de/mxp/u0205</a><br><u>Gender</u>: m<br><u>Date of Death</u>: 1965<br><u>Place of Death</u>: Undefined<br><u>First Mentioned</u>: 1925<br><u>Sectors</u>: Instrumentenbau<br><u>Professions (Musical)</u>: Holzblasinstrumentenbauer<br><u>Other Places of Activity</u>: Hamburg<br><br><br><u>Institutionen:</u><br><table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Shackleton Collection</td><td><a href="https://musixplora.de/mxp/3080397">3080397</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>New Langwill Index 1993</td><td>The New Langwill Index. A Dictionary of Musical Wind-Instrument Makers and Inventors. NLI</td><td><a href="https://musixplora.de/mxp/5001112">5001112</a></td></tr><tr><td>Related</td><td>Fricke 2007</td><td>Clarinets. Catalogue of the Sir Nicholas Shackleton Collection. Historic Musical Instruments in the Edinburgh University Collection</td><td><a href="https://musixplora.de/mxp/5001374">5001374</a></td></tr></tbody></table><br><u>Ereignisse:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6021794">6021794</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Johann Urban (u0127)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Johann Urban<br><u>musiXplora-ID</u>: u0127<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/u0127">https://musixplora.de/mxp/u0127</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1817<br><u>Sectors</u>: Instrumentenbau, Kirche<br><u>Professions (Musical)</u>: Orgelbauer<br><u>Main Place of Activity</u>: Obergriesbach<br><br><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Bernhard 2003a</td><td>Orgeldatenbank Bayern</td><td><a href="https://musixplora.de/mxp/5001131">5001131</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Franz Urban Stoß (s1943)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Franz Urban Stoß<br><u>musiXplora-ID</u>: s1943<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/s1943">https://musixplora.de/mxp/s1943</a><br><u>Gender</u>: m<br><u>Confessions</u>: römisch-katholisch<br><u>Date of Birth</u>: 25 May 1711<br><u>Place of Birth</u>: Füssen<br><u>Date of Death</u>: 21 August 1783<br><u>Place of Death</u>: Füssen<br><u>First Mentioned</u>: 1737<br><u>Sectors</u>: Hof, Instrumentenbau, Stadt<br><u>Professions (Historical)</u>: Chelifex<br><u>Professions (Musical)</u>: Geigenbauer, Lautenmacher, Musikalienhändler<br><u>Professions (Non-Musical)</u>: Verwalter<br><u>Main Place of Activity</u>: Füssen<br><u>Other Places of Activity</u>: Innsbruck, Paris<br><br><br><u>Herkunftsfamilie:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Eltern</td><td>Sohn</td><td>Hermann Joseph Stoß</td><td><a href="https://musixplora.de/mxp/s3011">s3011</a></td></tr><tr><td>Geschwister</td><td>Bruder</td><td>Joseph Anton Stoß</td><td><a href="https://musixplora.de/mxp/s3015">s3015</a></td></tr></tbody></table><br><u>Ausbildung:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>LehrerInnen und AusbilderInnen</td><td>Lehrer</td><td>Johann Stephan Maldoner</td><td><a href="https://musixplora.de/mxp/m1613">m1613</a></td></tr></tbody></table><br><u>Portfolio:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Gambe</td><td><a href="https://musixplora.de/mxp/2001529">2001529</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Layer 1978</td><td>Die Allgäuer Lauten- und Geigenmacher. Ein Kapitel schwäbischer Kulturleistung für Europa</td><td><a href="https://musixplora.de/mxp/5001129">5001129</a></td></tr><tr><td>Related</td><td>Lütgendorff 1990</td><td>Die Geigen- und Lautenmacher vom Mittelalter bis zur Gegenwart. Teil 3: Ergänzungsband. Erstellt von Thomas Drescher. Lüt3</td><td><a href="https://musixplora.de/mxp/5002060">5002060</a></td></tr><tr><td>Related</td><td>Schlagmann 1980</td><td>Die Bürger von Füssen. [10 Teile]. . Teil 1: Alt Füssen 1980, 44–52. Teil 2a: Alt Füssen 1981, 28–53. Teil 2b: Alt Füssen 1982, 57–71. Teil 3: Alt Füssen 1983, 69–105. Teil 4: Alt Füssen 1985, 45–84. Teil 5: Alt Füssen 1986, 85–124. Teil 6: Alt Füssen 1987, 75–127. Teil 7: Alt Füssen 1988, 58–61. Teil 8: Alt Füssen 1988, 82–109. Teil 9: Alt Füssen 1989, 69–115</td><td><a href="https://musixplora.de/mxp/5033648">5033648</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Julius Urban Kreutzbach (k3211)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Julius Urban Kreutzbach<br><u>musiXplora-ID</u>: k3211<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/k3211">https://musixplora.de/mxp/k3211</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 29 November 1845<br><u>Place of Birth</u>: Borna<br><u>Date of Death</u>: 22 September 1913<br><u>Place of Death</u>: Leipzig<br><u>First Mentioned</u>: 1874<br><u>Sectors</u>: Hof, Klavierbau<br><u>Professions (Historical)</u>: Begründer und Inhaber der Hof-Pianofortefabrik-Leipzig Julius Kreutzbach, Großherzoglich Weimarischen Hoflieferant, Herzoglich Anhaltischen Hoflieferant<br><u>Professions (Musical)</u>: Klavierbauer<br><u>Main Place of Activity</u>: Leipzig<br><br><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Urban Heusler (h0872)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Urban Heusler<br><u>musiXplora-ID</u>: h0872<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/h0872">https://musixplora.de/mxp/h0872</a><br><u>Gender</u>: m<br><u>Confessions</u>: römisch-katholisch<br><u>Date of Birth</u>: 1584<br><u>Place of Birth</u>: Aichach<br><u>Date of Death</u>: 1617<br><u>Place of Death</u>: Undefined<br><u>First Mentioned</u>: 1584<br><u>Last Mentioned</u>: 1609<br><u>Sectors</u>: Hof, Instrumentenbau, Kirche<br><u>Professions (Historical)</u>: Instrumentenmacher, Orgelmacher<br><u>Professions (Musical)</u>: Orgelbauer, Zupfinstrumentenbauer<br><u>Main Place of Activity</u>: München<br><u>Other Places of Activity</u>: Aichach<br><br><br><u>Schwägerschaft:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Schwiegereltern</td><td>Schwiegervater</td><td>Leonhard Kurz</td><td><a href="https://musixplora.de/mxp/k1178">k1178</a></td></tr></tbody></table><br><u>Ausbildung:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>LehrerInnen und AusbilderInnen</td><td>Lehrer</td><td>Leonhard Kurz</td><td><a href="https://musixplora.de/mxp/k1178">k1178</a></td></tr></tbody></table><br><u>Arbeitsumfeld:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>KollegInnen</td><td>Kollege</td><td>Leonhard Kurz</td><td><a href="https://musixplora.de/mxp/k1178">k1178</a></td></tr><tr><td>VorgängerInnen</td><td>Vorgänger</td><td>Hans Lechner</td><td><a href="https://musixplora.de/mxp/l0271">l0271</a></td></tr></tbody></table><br><u>Personal:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Arbeitsplatz</td><td>Mitarbeiter</td><td>Bayerische Staatsoper</td><td><a href="https://musixplora.de/mxp/3020001">3020001</a></td></tr></tbody></table><br><u>Institutionen:</u><br><table><tbody><tr><th>Role</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Heusler</td><td><a href="https://musixplora.de/mxp/3030571">3030571</a></td></tr><tr><td>Related</td><td>St. Michael</td><td><a href="https://musixplora.de/mxp/3050003">3050003</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Bernhard 2003a</td><td>Orgeldatenbank Bayern</td><td><a href="https://musixplora.de/mxp/5001131">5001131</a></td></tr><tr><td>Related</td><td>Fischer & Wohnhaas 1994</td><td>Lexikon süddeutscher Orgelbauer</td><td><a href="https://musixplora.de/mxp/5001133">5001133</a></td></tr><tr><td>Related</td><td>Sandberger 1895</td><td>Beiträge zur Geschichte der bayerischen Hofkapelle unter Orlando die Lasso. In drei Büchern. Drittes Buch: Dokumente. Habilitationsschrift</td><td><a href="https://musixplora.de/mxp/5001428">5001428</a></td></tr><tr><td>Related</td><td>Kinsky 1910</td><td>Besaitete Tasteninstrumente, Orgeln und orgelartige Instrumente, Friktionsinstrumente. Katalog des Musikhistorischen Museums von Wilhelm Heyer in Cöln. Erster Band</td><td><a href="https://musixplora.de/mxp/5002021">5002021</a></td></tr></tbody></table><br><u>Ereignisse:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6003510">6003510</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6012612">6012612</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6012613">6012613</a></td></tr><tr><td>Hersteller</td><td></td><td>Herstellung</td><td><a href="https://musixplora.de/mxp/6012614">6012614</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Survey data on behaviours and attitudes towards green food consumption of participants of the SmartFood Urban Living Lab in Warsaw, Poland
<p>In this dataset, we present raw data of a survey on behaviours and attitudes towards green food consumption, conducted between June 2023 and April 2024 among a group of 21 households from Warsaw, participating in a SmartFood Urban Living Lab (ULL). The dataset is complemented with results collected from two control groups. The SmartFood Urban Living Lab was an intervention aimed at providing residents of urban blocks of flats with a novel technology for growing their own food. The ULL served as an experimental ground for testing and refining innovations such as hydroponic cabins, rainwater management systems, solar energy systems, and insect farming units. Residents actively participated in the lab, providing valuable insights into the practical challenges and benefits of urban farming, which helped refine and adapt the technologies for broader application. After each month of the intervention, a survey was conducted to check participants' behaviours and attitudes towards green food consumption</p>
Raw data of the study: Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a northern Andean city
<p>This datasheet contains raw data on bird count records made from 2016 and 2019. Data were taken in urban and adjacent non-urban areas of Medellín, Colombia. It was part of a collaborative sampling effort during environmental assessments and personal research, summarizing systematic information on 139 sampling points (124 within the city and 15 in adjacent non-urban areas). All points were sampled under the same protocol in order to facilited data for research; in all cases, sampling was in charge of ornithologist with at least 4 years of previous experience in bird surveys. This protocol consisted in sampling during 10 minutes, four times per point (i.e., repetitions), using a fixed radius of 25 m. </p> <p>Information on bird surveys (Count_Data within the corresponding datasheet tab) contains the ID of each site; whether corresponded to a urban or non-urban site; in what category of urban development the site was located, based on 1000, 500 and 200 m buffers (from the observer during bird counts: moderate, low or high); the taxonomic information of each species (order, family, scientific name); the number of recorded individuals; the repetition or number of the visit (1, 2, 3, or 4); the name of the project; the name of the observer, and the date of sampling. </p> <p>Information on categorization of bird species (Categorization within the corresponding datasheet tab) represents additional information on altitudinal ranges, trophic guilds, distribution, and others. In addition, information on frequency for each bird species is given, according to the location of each sampling site and the way it was grouped. This information was the base for categorizing bird species as urban avoider, utilizer, or dweller, under the calculations and decision rules that are also given within the corresponding cells of the datasheet.</p> <p>Any further information or questions about this data could be ask directly, writing to the e-mails: jgarizabal@unal.edu.co or njmacer@unal.edu.co.</p> <p> </p>
A spatio-temporal dataset for ecophysiological monitoring of urban trees
<p>A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured <em>in situ</em> and in the laboratory; (ii) plant area density measured <em>in situ</em> under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.</p> <p>The repository comprised 3 files : </p> <ul> <li><strong>DATASET_PART1.csv</strong> : This file contains leaf trait measurements</li> <li><strong>DATASET_PART2.csv</strong> : This file contains plant area density measurements </li> <li><strong>DATASET_PART3.gpkg</strong> : This file contains two spatial vector layers: (1) <em>CROWN_EXTENT </em>that is<em> </em>a polygon layer describing tree crowns and (2) <em>TRUNK_LOCATION</em> that is a point layer describing tree location.</li> </ul> <p>More details on the study site, protocols and data can be found in the following reference:</p> <p>Théo Le Saint, Jean Nabucet, Cécile Sulmon, Julien Pellen, Karine Adeline, Laurence Hubert-Moy, A spatio-temporal dataset for ecophysiological monitoring of urban trees, Data in Brief, Volume 57, 2024, 111010, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.111010.</p>
Raw images (photographs) of urban text scenes for camera-based Thai text recognition
<p>Raw image collection of city scenes in Thailand with text content.<br> Text is photographed from diffeent angles. Also morning and evening<br> photographs were taken in order to capture different lighting<br> conditions. The material, 309 images, was photographed in 2013 by<br> Bowornrat Sriman and volunteers.</p> <p>Example EXIF:<br> JPEG image data, Exif standard: [TIFF image data, little-endian,<br> direntries=13, height=2448, manufacturer=SAMSUNG, model=GT-I9300,<br> orientation=upper-right, xresolution=220, yresolution=228,<br> resolutionunit=2, software=I9300XXEMA2, datetime=2013:03:14 18:17:49,<br> GPS-Data, width=3264], baseline, precision 8, 3264x2448, frames 3</p> <p>The images are not labeled. The orientation (landscape/portrait) is<br> not corrected yet. This material was used in preparation of the publication:</p> <p>Sriman, B. & Schomaker, L. (2015).<br> Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT,<br> Proceedings of the International Conference on Pattern Recognition Applications and<br> Methods: ICPRAM 2015. De Marsico, M., Figueiredo, M. & Fred, A.<br> (Eds.). Lisbon, Portugal: SciTePress, Vol. 1, p. 304-311 8 p.</p> <p>Please cite this publication when using these data.</p>
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
Data sets for simulation of urban construction consolidation centres
<p>Data from SUCCESS H2020 project used as input in the simulation activities of the Work Package 4</p> <p>This data sets is a public version of the data used in the simulation activities of the workpackage 4 in the SUCCESS project.</p> <p>It can be used to simulate the options of using one, several or no construction consolidation centers in an urban area.</p> <p>The dataset is composed of 7 distinct CSV files. All CSV files have headers.</p> <ol> <li>CCC_options_data.csv</li> <li>construction sites_data.csv</li> <li>material_demand.csv</li> <li>material_demand_periods.csv</li> <li>origin_destination.csv.csv</li> <li>suppliers_data.csv</li> <li>trucks_data.csv</li> </ol> <p><strong>construction sites_data </strong>file</p> <p>This file contains descriptions of construction sites that would be candidate to use the services of a Construction Consolidation Center (CCC).</p> <p>This file contains 99 observations of 11 fields :</p> <ol> <li> <p>site_id (<em>String</em>)<br> a unique identifier of the construction site, composed of:</p> <ul> <li> <p>one letter, </p> </li> <li> <p>an underscore, and </p> </li> <li> <p>3 digits.</p> <p>The letter represents the success pilot that provided the data. The digits sequence is the numeric identifier for the pilot.</p> </li> </ul> </li> <li> <p>private_public (<em>String</em>)<br> The mention whether the site builds a public building, a private building or a mixed building (both public and private)</p> </li> <li> <p>site_profile (<em>String</em>)<br> The profile of the building under construction:</p> <ul> <li> <p><strong>Profile I</strong> is an <strong>apartments building</strong>,</p> </li> <li> <p><strong>Profile II </strong>is an <strong>offices building</strong>,</p> </li> <li> <p><strong>Profile III </strong>is a <strong>leisure </strong>construction,</p> </li> <li> <p><strong>Profile IV</strong> is a <strong>specific building </strong>like an hospital</p> </li> </ul> </li> <li> <p>Y1 (<em>Integer</em>)<br> the turnover of the construction site on the first year of operations in EUR</p> </li> <li> <p>Y2 (<em>Integer</em>)<br> the turnover of the construction site on the second year of operations in EUR</p> </li> <li> <p>Y3 (<em>Integer</em>)<br> the turnover of the construction site on the third year of operations in EUR</p> </li> <li> <p>start (<em>Date</em>)<br> The start date of the construction project</p> </li> <li> <p>end (<em>Date</em>)<br> The end date of the construction project</p> </li> <li> <p>duration (<em>Integer</em>)<br> The duration of the construction project in months</p> </li> <li> <p>total_value_eur (<em>Integer)</em><br> The total value of the construction project in EUR</p> </li> <li> <p>size_sqm (<em>Integer</em>)<br> The size of the construction project in square meters</p> </li> </ol> <p><strong>CCC_options_data </strong>file</p> <p>This file contains descriptions of Construction Consolidation Centers that could service construction sites.</p> <p>This file contains 25 observations of 40 fields :</p> <ol> <li>ccc_id (<em>String</em>)<br> a unique identifier of the CCC, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits.<br> The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>capacity_sqm (<em>Integer</em>)<br> The storage area capacity of the CCC in square meters</li> <li>capacity_cubic_meters (<em>Integer</em>)<br> The storage volume capacity of the CCC in cubic meters</li> <li>Activation_Cost (<em>Integer</em>)<br> The CCC activation costs in EUR</li> <li>Accessories (<em>Integer</em>)<br> The storage capacity for Accessories</li> <li>Bitumen (<em>Integer</em>)<br> The storage capacity for Bitumen</li> <li>Bricks (<em>Integer)</em><br> The storage capacity for Bricks</li> <li>Cement (Integer)<br> The storage capacity for Cement</li> <li>Coating (<em>Integer)</em><br> The storage capacity for Coating</li> <li>Electrical (Integer)<br> The storage capacity for Electrical</li> <li>Epoxi (<em>Integer)</em><br> The storage capacity for Epoxi</li> <li>External_Doors (<em>Integer</em>)<br> The storage capacity for External_Doors</li> <li>Fences (<em>Integer</em>)<br> The storage capacity for Fences</li> <li>Fire_Doors (<em>Integer</em>)<br> The storage capacity for Fire_Doors</li> <li>Gabions (<em>Integer</em>)<br> The storage capacity for Gabions</li> <li>Garden_Equipment (<em>Integer</em>)<br> The storage capacity for Garden_Equipment</li> <li>Geotexil (<em>Integer</em>)<br> The storage capacity for Geotexil</li> <li>Glass_wool (<em>Integer</em>)<br> The storage capacity for Glass_wool</li> <li>Hydraulic (<em>Integer</em>)<br> The storage capacity for Hydraulic</li> <li>Internal_Doors (<em>Integer</em>)<br> The storage capacity for Internal_Doors</li> <li>Lift (<em>Integer</em>)<br> The storage capacity for Lift</li> <li>Metal (<em>Integer</em>)<br> The storage capacity for Metal</li> <li>Metal_1 (<em>Integer</em>)<br> The storage capacity for Metal_1</li> <li>Paint (<em>Integer</em>)<br> The storage capacity for Paint</li> <li>Parquet (<em>Integer</em>)<br> The storage capacity for Parquet</li> <li>Pipes (<em>Integer</em>)<br> The storage capacity for Pipes</li> <li>Plants (<em>Integer</em>)<br> The storage capacity for Plants</li> <li>Plaster (<em>Integer</em>)<br> The storage capacity for Plaster</li> <li>Polystyrene (<em>Integer</em>)<br> The storage capacity for Polystyrene</li> <li>Precasted_Concrete (<em>Integer</em>)<br> The storage capacity for Precasted_Concrete</li> <li>Roof (<em>Integer</em>)<br> The storage capacity for Roof</li> <li>Scaffolding (<em>Integer</em>)<br> The storage capacity for Scaffolding</li> <li>Signals (<em>Integer</em>)<br> The storage capacity for Signals</li> <li>Steel (<em>Integer</em>)<br> The storage capacity for Steel</li> <li>Stone (<em>Integer</em>)<br> The storage capacity for Stone</li> <li>Store_Equipment (<em>Integer</em>)<br> The storage capacity for Store_Equipment</li> <li>Tar (<em>Integer</em>)<br> The storage capacity for Tar</li> <li>Tiles (<em>Integer</em>)<br> The storage capacity for Tiles</li> <li>Windows (<em>Integer</em>)<br> The storage capacity for Windows</li> <li>Wood (<em>Integer</em>)<br> The storage capacity for Wood</li> </ol> <p> </p> <p><strong>suppliers_data</strong> file</p> <p> </p> <p>This file contains description of suppliers that provide materials to the above construction sites.</p> <p>This file contains 407 observations of 3 fields :</p> <ol> <li>supplier_id (<em>String</em>)<br> an identifier of the supplier, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits.<br> The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>Material_delivered (<em>String</em>)<br> the material delivered by the supplier</li> <li>Truck (<em>Integer</em>)<br> the truck identifeir of the usual truck used by the supplier to deliver the material</li> </ol> <p><strong>trucks_data</strong> file</p> <p>This file contains description of truck used by suppliers to deliver construction sites.</p> <p>This file contains 5 observations of 6 fields:</p> <ol> <li>Truck_id (<em>Integer</em>)<br> a unique identifier for the truck</li> <li>Vehicle (<em>String</em>)<br> description of the vehicle (including the number of axles)</li> <li>Capacity_(kg) (<em>Integer</em>)<br> the material transport capacity of the truck in kilograms</li> <li>Capacity_(m3) (<em>Integer</em>)<br> the material transport capacity of the truck in cubic meters</li> <li>FlagFirstEchelon (<em>String)</em><br> a flag indicating if the truck is used in 1st echelon</li> <li>FlagSecondEchelon (<em>String</em>)<br> a flag indicating if the truck is used in 2nd echelon</li> </ol> <p><strong>origin_destination </strong>file</p> <p>This file contains the quantitative data of distance and time to travel from construction sites, suppliers, and CCCs to construction sites, suppliers and CCCs using a delivery truck.</p> <p>This file contains 38640 observations of 4 fields:</p> <ol> <li>origin (<em>String</em>)<br> A composite identifier of the origin location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>destination (<em>String</em>)<br> A composite identifier of the destination location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>meters (<em>Integer</em>)<br> The drive distance from origin to destination in meters</li> <li>seconds (<em>Integer</em>)<br> The driving time from origin to destination in seconds</li> </ol> <p><strong>material demand </strong>file</p> <p>This file contains the qualitative data representing the material demand of construction sites per construction site profile .</p> <p>This file contains 1277 observations of 7 fields:</p> <ol> <li>demand_id (<em>Integer</em>)<br> a unique identifier for the material demand</li> <li>profile (<em>String)</em><br> the profile of the construction site for such demand</li> <li>start_date (<em>Date</em>)<br> the start date of the activity</li> <li>end_date (<em>Date</em>)<br> the end date of the activity</li> <li>number_of_days (<em>Integer</em>)<br> the duration of the activity in days</li> <li>material (<em>String</em>)<br> the type of material requested</li> <li>supplier_id (<em>String</em>)<br> the identifier of the supplier providing the material</li> </ol> <p><strong>material_demand_periods </strong>file</p> <p>This file contains the quantitative demand data per demand and per period. Units of periods are weeks.</p> <p>This file contains 93663 observations of 5 fields:</p> <ol> <li>demand_id (<em>Integer</em>)<br> the identifier for the material demand</li> <li>profile (<em>String</em>)<br> the profile type of construction for the demand</li> <li>period (<em>Integer</em>)<br> the period of the construction project during which the material has to be delivered (in number of weeks from the beginning of the construction project)</li> <li>demand_m3 (<em>Integer)</em><br> the volume of material to be delivered during the period</li> <li>demand_kg (<em>Integer)</em><br> the weight of material to be deliverd during the period</li> </ol>
Data for: Generation of sanitation system options for urban planning considering novel technologies
<p>This data has been used (1) to quantify the appropriateness of a set of sanitation technologies for a small town (Katarnyia) in Nepal and (2) to generate sanitation system options from the appropriate technologies as an input into strategic sanitation planning using a structured decision making process. For (1), the appropriateness is quantified based on a set of criteria, also called screening criteria. These criteria include technical, socio-demographic, climatic, and institutional aspects and are quantified using uncertainty functions in order to account for the quality and quantity of available input information.</p> <p>The data contains raw data as well as modelling results. The raw data is a compilation of information collected from literature, information collected through a household survey in the small town, field observations. They are all used to describe the screening criteria for the studied sanitation technologies and the small town. Results include: (1) the outcome of the technology appropriateness assessment (technology appropriateness scores); and (2) the sanitation system options (all possible sanitation systems built from the appropriate technologies, and a smaller set of divers and highly appropriate sanitation system options as an input into decision-making).</p>
PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels
<p>This data set contains all the shareable data collected and generated through the different tasks of WP5, which are interdependent.</p> <p>In particular, in Task5.1 we collected a bibliography, which is the basis for our theoretical work.</p> <p>In Task5.2 we collected a large collection of data from different documentary sources and media: EU policies and reports, descriptions and reports created by Local Managing Authorities, newspaper articles, tweets, Facebook posts referred to EU CP policies. We don’t have the permission to share these data (as they are protected by copyright), but all the sources are described in Deliverable 5.2, which is public (see <a href="http://doi.org/10.6092/unibo/amsacta/5726">http://doi.org/10.6092/unibo/amsacta/5726</a> or <a href="http://doi.org/10.5281/zenodo.1318184">http://doi.org/10.5281/zenodo.1318184</a>).</p> <p>During Task5.3 we analyzed the textual content of data listed in Task5.2, to construct a database of discursive topics in Task5.4. Data set includes the description of topics (results of topic modeling), clusters of topics obtained both interpretively and algorithmically, and the relevant data regarding sentiment and semantic analyses.</p> <p>Task5.5 regards a statistical analysis linking public discourse and different definitions of being Europeans on the one hand with European identification on the other hand. The data set contains the measures of variables used to run the regression test, and the results of the test in tabular form.</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.