Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,618
datasets available to search
ShareScore release 0.9.0
Dataset results
1,618 results for “City”
Urban land expansion in area with decreased urban sprawl at global, national, and city scales during 2000 to 2020
<p>I used calibrated population density thresholds from the year 2000 and 2020 Worldpop population model to measure area and densities for urban and suburban density classes (≥ 250 humans per km<sup>2</sup>) at global and national scales and both broad multi-city agglomerations and fine city cores.</p>
Evaluation of background ionizing radiation dose variation in Ploiesti city area, 2022
<p>Measurements of background ionizing radiation dose rate, done in Ploiesti city area in 2022 (Romania), by the student Tripac Flavius Gabriel under supervision of Prof. Dr. Adrian Iftime as part of a diploma thesis project (C.Davila University of Medicine and Pharmacy). </p> <p>A graphical overview of the values is included (ploiesti_values_2022.png). <br> </p>
Evaluation of background ionizing radiation dose variation in Bucharest city area, 2022
<p>Measurements of background ionizing radiation dose rate, done in Bucharest city area in 2022, by the student Damalan Daria Liana under supervision of prof. Adrian Iftime as part of a diploma thesis project.</p> <p>A graphical overview of the values is included (bucharest_values_2022.png).</p>
Shool drop-out ut in Brazil: rates per city and informations about schools
<p>The dataset presented here is a combination of three databases created by INEP (Brazil), and referes to the years of 2014/2015: </p> <p>- Drop-out rates by city,</p> <p>- Questionnaires to principals about their schools,</p> <p>- Questionnaires about school structure.</p> <p>The original databases and dictionaires are avalilable here:</p> <p>http://portal.inep.gov.br/web/guest/indicadores-educacionais</p> <p>http://portal.inep.gov.br/artigo/-/asset_publisher/B4AQV9zFY7Bv/content/divulgados-os-microdados-do-sistema-nacional-de-avaliacao-da-educacao-basica/21206</p> <p> </p> <p> </p>
Air Quality and Exposure Disparity Results for the Bronx, New York City
<p>This is the dataset accompanying the publication "Big Mobility Data Reveals Hyperlocal Air Pollution Exposure Disparities in the Bronx, New York". It contains mainly three parts: 1. day-to-day air quality prediction maps for exposure estimation; 2. street-level PM2.5 exposure and its disparity modeling results for all populations and for socio-demographic groups; 3. residence- and mobility-based exposure calculation for a sample of Bronx residents.</p>
Interpreting future climate conditions in Brazilian cities – Dashboard and EPW files
<h3>(English)</h3> <h1>1. Introduction</h1> <p>This project aims to address the impacts of climate change on the built environment by developing a set of future Brazilian EPW (Energy Plus Weather Format) files and a dashboard to interpret and evaluate the data. The future climate files were obtained using the Future Weather Generator (FWG) [1] with climate projections for Brazilian cities, integrating these projections into a code pipeline for automation. In this part of the project, thermal comfort indices, such as the Universal Thermal Climate Index (UTCI) and the Discomfort Index (DI), were also evaluated to understand future thermal comfort conditions. The methodology followed the structure available in the <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> repository:</p> <ol> <li>Climate-One-Building (COB) web-scrapping for all available Brazilian EPW files (we recommend doing this carefully so as not to damage the COB infrastructure);</li> <li>Automatic organisation of all EPW files in a folder, extracting them from the ZIP format;</li> <li>Simulation of future climate files using FutureWeatherGenerator [1] in a line of code with default parameters (shown in Table 1);</li> <li>Organisation of all available EPWs (original and simulated) in a single database;</li> <li>Calculation of thermal comfort indices using pythermalcomfort [2].</li> </ol> <p>The main objective is to provide researchers, policymakers and professionals with a comprehensive tool for assessing and mitigating the impacts of climate change in different Brazilian cities, offering accurate data for thermal comfort and energy efficiency modelling. The methodology involves generating future EPW files, validating them against existing literature and visualising the results through a user-friendly dashboard. The study highlights the importance of adaptive and climate-resilient strategies in urban planning and building design. Expected climate changes in Brazil include increased dry bulb temperature and variations in relative humidity, radiation and wind speed in the different bioclimatic zones.</p> <p>The dashboard has been designed to simplify the visualisation of future climate data, focusing on the main climate variables, thermal comfort indices and data visualisation. It allows users to filter by city and automatically calculate all the indices, providing detailed analyses and comparisons of different scenarios. By offering a free, open-access, multi-platform, extensible, customisable and easy-to-maintain tool, the project aims to facilitate continuous updates, new features and corrections. This tool supports decision-making in public policy and urban planning, promoting a more sustainable and resilient built environment in the face of climate change.</p> <p> </p> <h1>2. Further details on the methodology</h1> <p>Details on how the indices were selected and how the study was conducted may be found in Vaz et al. [3]. The GitHub repository in <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] also includes details on the step-by-step procedures.</p> <h3>Table 1 - Parameters used in the FWG simulation:</h3> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Data used in the simulations</strong></p> </td> </tr> <tr> <td> <p>Base files</p> </td> <td> <p>578 cities from COB</p> </td> </tr> <tr> <td> <p>CMIP-6 models</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Grid</p> </td> <td> <p>Bilinear interpolation of the four nearest points</p> </td> </tr> <tr> <td> <p>Month transition smoothness</p> </td> <td> <p>72 hours</p> </td> </tr> <tr> <td> <p>Apply variable limits</p> </td> <td> <p>True</p> </td> </tr> <tr> <td> <p>Scenarios</p> </td> <td> <p>A total of nine scenarios: One baseline for 2021 and eight future files (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 for 2050 and 2080)</p> </td> </tr> <tr> <td> <p>Solar hour correction</p> </td> <td> <p>Made by day</p> </td> </tr> <tr> <td> <p>Diffuse irradiation model</p> </td> <td> <p>Engerer, 2015</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. References</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Current version of the dashboard: 1.0.0.</h1> <h1>Available at <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard Comfort.</a></h1> <p>Suggestions for improvements can be made directly in the GitHub repository at <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> or sent to igorcmvaz@gmail.com.</p> <p> </p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p> </p> <h3>(Português-BR)</h3> <h1>1. Introdução</h1> <p>Este projeto tem como objetivo abordar os impactos das mudanças climáticas no ambiente construído, desenvolvendo um conjunto de futuros arquivos EPW (Energy Plus Weather Format) brasileiros e um <em>dashboard</em> para interpretar e avaliar os dados. Os arquivos climáticos futuros foram obtidos com o Future Weather Generator (FWG) [1] com projeções climáticas para cidades brasileiras, integrando essas projeções a um pipeline de código para automação. Nessa parte do projeto, os índices de conforto térmico, como o Universal Thermal Climate Index (UTCI) e o Discomfort Index (DI), também foram avaliados para entender as condições futuras de conforto térmico. A metodologia seguiu a estrutura que está disponível no repositório <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a>:</p> <ol> <li>Web-scrapping do Climate-One-Building (COB) para todos os arquivos EPW brasileiros disponíveis (recomendamos fazer isso com cuidado para não prejudicar a infraestrutura do COB);</li> <li>Organização automática de todos os arquivos EPW em uma pasta, extraindo-os do formato ZIP;</li> <li>Simulação dos arquivos climáticos futuros por meio do FutureWeatherGenerator [1] em linha de código com parâmetros padrão (mostrados na Tabela 1);</li> <li>Organização de todos os EPW disponíveis (originais e simulados) em um único banco de dados;</li> <li>Cálculo dos índices de conforto térmico com o pythermalcomfort [2].</li> </ol> <p>O objetivo principal é fornecer a pesquisadores, formuladores de políticas e profissionais uma ferramenta abrangente para avaliar e mitigar os impactos das mudanças climáticas em diferentes cidades brasileiras, oferecendo dados precisos para modelagem de conforto térmico e eficiência energética. A metodologia envolve a geração de futuros arquivos EPW, validando-os com a literatura existente e visualizando os resultados por meio de um <em>dashboard</em> de fácil utilização. O estudo destaca a importância de estratégias adaptativas e resistentes ao clima no planejamento urbano e no projeto de edificações. As mudanças climáticas esperadas no Brasil incluem o aumento da temperatura de bulbo seco e variações na umidade relativa, radiação e velocidade do vento nas diferentes zonas bioclimáticas.</p> <p>O <em>dashboard</em> foi projetado para simplificar a visualização dos dados climáticos futuros, concentrando-se nas principais variáveis climáticas, índices de conforto térmico e visualização dos dados. Ele permite que os usuários filtrem por cidade e calculem automaticamente todos os índices, fornecendo análises detalhadas e comparações de diferentes cenários. Ao oferecer uma ferramenta gratuita, de acesso aberto, multiplataforma, extensível, personalizável e de fácil manutenção, o projeto visa a facilitar atualizações contínuas, novos recursos e correções. Essa ferramenta apoia a tomada de decisões em políticas públicas e planejamento urbano, promovendo um ambiente construído mais sustentável e resiliente em face das mudanças climáticas.</p> <p> </p> <h1>2. Mais detalhes sobre a metodologia</h1> <p>Detalhes sobre a seleção dos índices de conforto e como o estudo foi conduzido podem ser encontrados em Vaz et al. [3]. O repositório GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] também inclui detalhes sobre os procedimentos passo a passo.</p> <h3>Tabela 1 - Parâmetros usados na simulação do FWG</h3> <table> <tbody> <tr> <td> <p><strong>Parâmetro</strong></p> </td> <td> <p><strong>Dados utilizados na simulação</strong></p> </td> </tr> <tr> <td> <p>Arquivos base</p> </td> <td> <p>578 cidades do COB</p> </td> </tr> <tr> <td> <p>Modelos CMIP-6</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Malha</p> </td> <td> <p>Interpolação bilinear dos quatro pontos mais próximos</p> </td> </tr> <tr> <td> <p>Suavização da transição mensal</p> </td> <td> <p>72 horas</p> </td> </tr> <tr> <td> <p>Aplicar limites das variáveis</p> </td> <td> <p>Sim</p> </td> </tr> <tr> <td> <p>Cenários</p> </td> <td> <p>Total de nove cenários: Um arquivo base em 2021 e oito arquivos futuros (SSP1-2.6, SSP2-4.5, SSP3-7.0 e SSP5-8.5 para 2050 e 2080)</p> </td> </tr> <tr> <td> <p>Correção de hora solar</p> </td> <td> <p>Feita por dia</p> </td> </tr> <tr> <td> <p>Modelo de radiação difusa</p> </td> <td> <p>Engerer (2015)</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. Referências</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Versão atual do <em>dashboard</em>: 1.0.0. </h1> <h1>Disponível em <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard conforto.</a></h1> <p>As sugestões de melhorias podem ser feitas diretamente no repositório do GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> ou enviadas para igorcmvaz@gmail.com.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Data and Code for the paper 'Indication of long-range correlations governing city size'
<div> <h1>Summary</h1> <p>This repository contains the preprocessed data necessary for constructing the city network as described in the related paper, as well as the code to do the Shortest-path Fluctuation Analysis (SFA) on those networks.</p> <div> <h2>Under the <code>data</code> folder</h2> <ul> <li> <p>In the <code>Node_list</code> folder, each <em><code>CSV</code></em> file has the name convention like <code>AL_1000_node_list.csv</code>, for example, this means the table contains the list of <strong>nodes</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 3 columns with column names, and without row names.</p> <ul> <li> <p>Each row in the table is a record of one node, the <strong><em>node ID</em></strong> is the <code>row_id</code> of the record in the table, the first record has a row_id of <strong>0</strong>.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <em><code>X</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <em><code>Y</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>third</strong> column in the table is the <strong>Size</strong> of the node, i.e., the number of pixels of this city.</p> </li> </ul> </li> <li> <p>In the <code>Edge_list</code> folder, each <em><code>TXT</code></em> file has the name convention like <code>AL_1000_edges.txt</code>, for example, this means the table contains the list of <strong>edges</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 2 columns without column name, without row name.</p> <ul> <li> <p>Each row in the table is a record of one edge that is composed of a pair of nodes, the <strong><em>node ID</em></strong> is the <code>row_id</code> of that node in the <strong>node_list</strong> table.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <strong><em>ID</em></strong> of the node on one side of an edge.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <strong><em>ID</em></strong> of the node on the other side of an edge.</p> </li> </ul> </li> </ul> </div> <div> <h2>Under the <code>code</code> folder</h2> <ul> <li> <p>The <code>SFA_args_LSPT_out.cpp</code> file contains the <strong>C++</strong> code to do the SFA calculation, detailed information can be found in the documentation of the code file.</p> </li> <li> <p>The <code>SFA_config.ini</code> file contains some configuration parameters for running the compiled program, details can also be found in the file. This file has to be put in the same folder as the compiled executable file.</p> </li> </ul> </div> </div>
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>
Asti City Statistics
<p>JSON data related to average statistics on children food habits and physical activities (for the city of Asti)</p>
Milan City Statistics
<p>JSON data related to average statistics on children food habits and physical activities (for the city of Milan)</p>
The Key to the City: Using Digital Tools to Understand Tablet Provenience
<p>These files are those used in the keyness analysis for unprovenienced "Diyala" texts against tablets from Tutub, Eshnunna, Tell Suleimah, Kish and Girsu. Each site has two files: the cleaned atf used to generate a word list, and the word list giving the frequency of each word (based on the lemmatized version of words in the Lemma List and incomplete words in the Stopword List).</p>
EU-CIRCLE - Virtual City Data Sets
<p>For achieving the goals of EU-CIRCLE project, a virtual datasets were created base on a reference region, enriched with bibliography. The datasets are related to Critical Infrastructure data, Forest Fire and Flood Hazards and a number of supplementary data for supporting the Hazard and Risk modelling procedures.</p>
District heating network data for the city of Flensburg from 2014-2016
<p>The data package contains flow temperatures and the overall heat load for the district heating network of Flensburg, Germany for the years 2014-2016.</p>
Testing Smart City environmental monitoring technology using small scale temporary cities
<p>This is the data used for:</p> <blockquote> <p>S. J. Johnston <em>et al</em>., "Testing Smart City environmental monitoring technology using small scale temporary cities," <em>2019 IEEE 5th World Forum on Internet of Things (WF-IoT)</em>, 2019, pp. 578-583, doi: 10.1109/WF-IoT.2019.8767274.</p> </blockquote> <p> </p> <p><em><strong>Abstract:</strong></em></p> <p>Exposure to particulate matter has been identified as a major health problem worldwide. Established measurement<br> techniques require equipment costing many thousands of dollars and specialist expertise to maintain. Ongoing research<br> is investigating the use of low cost <$300 sensors to enable greater temporal-spatial density of readings to be taken. There<br> are questions about the suitability and reliability of these low-cost sensors, queries which can be addressed by deploying<br> and evaluating the sensors in a real world application. We propose festival site as small scale cities to enable a short term<br> deployments and evaluation of sensors. We present data from these devices and experiences gained from using a festival site as a substitute for a city.</p> <p><em><strong>Files:</strong></em></p> <p>1 - timeLapse: mp4 file presenting a time lapse of the measurements realised during the festival<br> 2 - sensor_data: csv file containing the 5 min averaged data from all the sensors deployed and their coordinates used to generate the graph and the maps in the paper</p> <p>3 - workshop.pdf: instructions to run the workshop and code used for the workshop</p>
Dataset for the preprint: "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich"
<p>Dataset supporting the submission of the manuscript titled "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich" to the to the international journal "Biogeosciences".</p> <p><strong>Meteorological data</strong></p> <p>Hourly aggregated meteorological dataset for the urban area of Zurich, originating from two urban stations: Kaserne (8°32'/47°23'), which is a station of the Swiss national air pollution monitoring network NABEL, and Hardau II (8°30'/47°23'), which is a station established for the ICOS-Cities project. Zurich Kaserne is located in a large courtyard. Wind and global radiation are measured on top of a four-storey building. Wind is measured at 35 m and global radiation at 27 m above ground. Hardau II station is established on the top of a high-rise building (110 m a.g.l.). Meteorological observation gaps were filled using Copernicus ERA5-Land data.</p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <p>Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period</p> <p>Period: 01/2022–09/2023</p> <p>Monthly mean atmospheric CO2 concentration data derived from the ICOS-Cities Hardau II station (07/2022–09/2023) and the Beromunster station (11/2012–02/2022). Observation gaps were filled using Copernicus ERA5-Land data.</p> <p>Further information on the dataset can be found in the submitted manuscript. </p> <p>Data format: comma separated values (csv)</p> <p>Time step: Monthly (mean)</p> <p>Time stamp: yyyy-MM-dd </p> <p>Period: 11/2012–09/2023</p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Acronym</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Height above ground</strong></p> </td> <td> <p><strong>Location</strong></p> </td> <td> <p><strong>Geographic location</strong></p> </td> </tr> <tr> <td> <p>Global radiation</p> </td> <td> <p>G</p> </td> <td> <p>W m-2</p> </td> <td> <p>27 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Air temperature</p> </td> <td> <p>Tair</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Relative humidity</p> </td> <td> <p>RH</p> </td> <td> <p>%</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Air pressure</p> </td> <td> <p>P</p> </td> <td> <p>hPa</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Wind speed</p> </td> <td> <p>u</p> </td> <td> <p>m s-1</p> </td> <td> <p>35 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Precipitation</p> </td> <td> <p>R</p> </td> <td> <p>mm</p> </td> <td> <p>2 m</p> </td> <td> <p>Kaserne</p> </td> <td> <p>8°32'/47°23'</p> </td> </tr> <tr> <td> <p>Downward longwave radiation</p> </td> <td> <p>LW</p> </td> <td> <p>W m-2</p> </td> <td> <p>110 m</p> </td> <td> <p>Hardau II, ERA-5</p> </td> <td> <p>8°30'/47°23'</p> </td> </tr> <tr> <td> <p>Soil temperature</p> </td> <td> <p>Tsoil</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>-0.15 m</p> </td> <td> <p>Parks</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Soil water content</p> </td> <td> <p>SWC</p> </td> <td> <p>m3 m-3</p> </td> <td> <p>-0.15 m</p> </td> <td> <p>Parks</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Atmospheric CO2 concentration</p> </td> <td> <p>CO2</p> </td> <td> <p>ppmv</p> </td> <td> <p>2 m</p> </td> <td> <p>Hardau II, Beromunster, ERA-5</p> </td> <td> <p>8°30'/47°23', 8°10'/47°11</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>In-situ ecophysiological data</strong></p> <p>In-situ ecophysiology measurements performed on park trees and lawns in the city of Zurich during the ICOS-Cities project.</p> <p>LAI (leaf area index) was measured in dense <em>Platanus</em> sp. tree stands, found only in Bullingerhof and Hardaupark, during sunny conditions using a ceptometer (SS1 SunScan, Delta-T Devices).</p> <p>Sap flow was measured at six trees (<em>Platanus</em> sp., <em>Tilia</em> sp.), at Bullingerhof, Hardaupark and Fritschiwiese, with heat pulse sap flow sensors (3 x 3 cm probes, Implexx Sense), providing continuous measurements at 10-min sampling intervals. Daily aggregated sap flux densities (cm3 cm−2 d−1) were calculated from the 10-min data using the sensor inner thermistors, averaged for the six sampled trees.</p> <p>Soil and grass respiration were measured using a portable CO2 soil efflux system equipped with a 20 cm diameter survey chamber (LI-8200-01S, LI-COR Biosciences) and a CO2/H2O analyser (LI-870, LI-COR Biosciences). The observations originate from a total of 10 soil collars (Bullingerhof, Hardaupark, Fritschiwiese, Heiligfeld) separated to undisturbed grass collars (Reco, μmol CO2 m-2 s-1) and collars where the aboveground grass was clipped (Rsoil, μmol CO2 m-2 s-1).</p> <p>Further information on the dataset can be found in the submitted manuscript.</p> <p>Data format: comma separated values (csv)</p> <p>Time stamp: yyyy-MM-dd</p> <p>Period: 04/2022–09/2023</p> <p> </p> <p><strong>Land cover map</strong></p> <p>Land cover map of part of Zurich urban area. Datasets used to derive this map:</p> <ul> <li>· Land Use Cadastre of the Canton of Zurich (https://www.geolion.zh.ch/geodatensatz/show?gdsid=443)</li> <li>· Urban Atlas (https://doi.org/10.2909/fb4dffa1-6ceb-4cc0-8372-1ed354c285e6)</li> <li>· Vegetation Height Model (VHM) from the Swiss federal forest inventory (https://opendata.swiss/de/dataset/vegetationshohenmodell-lfi)</li> <li>· Forest Mixture from the Swiss Federal Forest Inventory (https://opendata.swiss/de/dataset/waldmischungsgrad-lfi)</li> </ul> <p>Further information on the dataset can be found in the submitted manuscript.</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 461972.1, 5246490.4 : 463972.1, 5248490.4</p> <p>Temporal Extent: 2023</p> <p>Units: meters</p> <p>Width: 2000</p> <p>Height: 2000</p> <p>Bands: 1</p> <p>Pixel Size: 1,-1</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p> <p> </p> <p>Legend:</p> <p>30 Grass</p> <p>40 Crops</p> <p>50 Paved</p> <p>60 Buildings</p> <p>70 Deciduous trees</p> <p>80 Water</p> <p> </p> <p><strong>CO2 fluxes</strong></p> <p>Hourly mean CO2 fluxes estimated by the models diFUME, JSBACH, SUEWS and VPRM for the trees and lawns of the Zurich urban parks: Bullingerhof, Hardaupark, Fritschiwiese and Heiligfeld. GPP stands for gross primary productivity, Reco for ecosystem respiration and NEE for net ecosystem exchange. All fluxes are in units: μmol CO2 m-2 s-1.</p> <p>The parameter sets used by each model are presented in the Tables below. Further information on the dataset can be found in the submitted manuscript.</p> <p>Parameters used by diFUME model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>A_max</p> </td> <td> <p>15</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>maximum leaf gross photosynthetic rate</p> </td> </tr> <tr> <td> <p>a</p> </td> <td> <p>0.045</p> </td> <td> <p>mol CO<sub>2</sub> mol<sup>-1</sup> PAR</p> </td> <td> <p>quantum yield for CO2 assimilation</p> </td> </tr> <tr> <td> <p>a_1</p> </td> <td> <p>25</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient in Leuning (1995) model</p> </td> </tr> <tr> <td> <p>b_</p> </td> <td> <p>0.65</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient in β-factor formula</p> </td> </tr> <tr> <td> <p>b_1</p> </td> <td> <p>5</p> </td> <td> <p>N/A</p> </td> <td> <p>empirical coefficient</p> </td> </tr> <tr> <td> <p>D_o</p> </td> <td> <p>0.3</p> </td> <td> <p>kPa</p> </td> <td> <p>empirically determined coefficient for the VPD scalar inside Leuning (1995) model</p> </td> </tr> <tr> <td> <p>D_sc</p> </td> <td> <p>1</p> </td> <td> <p>N/A</p> </td> <td> <p>daylight scalar for dark respiration inhibition during day (1: no inhibition)</p> </td> </tr> <tr> <td> <p>E_0</p> </td> <td> <p>487.75</p> </td> <td> <p>K</p> </td> <td> <p>temperature sensitivity parameter for soil respiration</p> </td> </tr> <tr> <td> <p>g_o</p> </td> <td> <p>0.01</p> </td> <td> <p>mol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>residual stomatal conductance for CO2 (g_s when Anet = 0, PAR = 0).</p> </td> </tr> <tr> <td> <p>Q_10</p> </td> <td> <p>1.85</p> </td> <td> <p>N/A</p> </td> <td> <p>temperature sensitivity of leaf respiration</p> </td> </tr> <tr> <td> <p>R_(l,ref)</p> </td> <td> <p>1.53</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>reference leaf respiration at Tair = 25 °C</p> </td> </tr> <tr> <td> <p>R_(S,ref)</p> </td> <td> <p>2.49</p> </td> <td> <p>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>reference soil respiration at Tsoil = 10 °C</p> </td> </tr> <tr> <td> <p>T_opt</p> </td> <td> <p>23</p> </td> <td> <p>°C</p> </td> <td> <p>optimum air temperature for gross photosynthesis</p> </td> </tr> <tr> <td> <p>T_0</p> </td> <td> <p>-46</p> </td> <td> <p>°C</p> </td> <td> <p>low-temperature limit for soil respiration</p> </td> </tr> <tr> <td> <p>T_(ref,S)</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>reference soil temperature for soil respiration</p> </td> </tr> <tr> <td> <p>T_(ref,l)</p> </td> <td> <p>25</p> </td> <td> <p>°C</p> </td> <td> <p>reference air temperature for leaf respiration</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>width of the bell-shape curve at f(T_air ) = 0.5</p> </td> </tr> <tr> <td> <p>θ_ref</p> </td> <td> <p>0.4</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>saturated soil volumetric water content </p> </td> </tr> <tr> <td> <p>θ_g</p> </td> <td> <p>0.1</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>minimum soil volumetric water content, limit to stomatal conductance</p> </td> </tr> <tr> <td> <p>θ_0</p> </td> <td> <p>0.04</p> </td> <td> <p>m<sup>3</sup> m<sup>-3</sup></p> </td> <td> <p>minimum soil volumetric water content, limit to soil respiration</p> </td> </tr> </tbody> </table> <p> </p> <p>Parameters used by JSBACH model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Trees</strong></p> </td> <td> <p><strong>Lawn</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>J_max</p> </td> <td> <p>104.5</p> </td> <td> <p>148.6</p> </td> <td> <p>μmol(CO<sub>2</sub>) m<sup>-2</sup>(leaf) s<sup>-1</sup></p> </td> <td> <p>Maximum electron transport rate at 25 °C</p> </td> </tr> <tr> <td> <p>T_alt</p> </td> <td> <p>4.0–4.5</p> </td> <td> <p> </p> </td> <td> <p>°C</p> </td> <td> <p>Alternation temperature</p> </td> </tr> <tr> <td> <p>θ_cap</p> </td> <td> <p>0.32– 0.39</p> </td> <td> <p>0.32– 0.34</p> </td> <td> <p>m m<sup>-1</sup></p> </td> <td> <p>Volumetric soil field capacity</p> </td> </tr> <tr> <td> <p>θ_pwp</p> </td> <td> <p>0.13– 0.21</p> </td> <td> <p>0.135–0.165</p> </td> <td> <p>m m<sup>-1</sup></p> </td> <td> <p>Volumetric wilting point</p> </td> </tr> <tr> <td> <p>V_max</p> </td> <td> <p>55.0</p> </td> <td> <p>78.2</p> </td> <td> <p>μmol(CO<sub>2</sub>) m<sup>-2</sup>(leaf) s<sup>-1</sup></p> </td> <td> <p>Maximum carboxylation rate at 25 °C</p> </td> </tr> <tr> <td> <p>z_root</p> </td> <td> <p>0.5</p> </td> <td> <p>0.12</p> </td> <td> <p>m</p> </td> <td> <p>Root depth</p> </td> </tr> <tr> <td> <p>CC</p> </td> <td> <p>1.25</p> </td> <td> <p>1.25</p> </td> <td> <p>N/A</p> </td> <td> <p>Relative cost to produce one carbon</p> </td> </tr> <tr> <td> <p>f_faeces</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>N/A</p> </td> <td> <p>Fraction of carbon from herbivore faeces that goes into the green litter pool</p> </td> </tr> <tr> <td> <p>f_leaf</p> </td> <td> <p>0.4</p> </td> <td> <p>0.4</p> </td> <td> <p>N/A</p> </td> <td> <p>A fixed fraction of canopy maintenance respiration that makes up the dark respiration</p> </td> </tr> <tr> <td> <p>k</p> </td> <td> <p>0.1</p> </td> <td> <p>0.09</p> </td> <td> <p>N/A</p> </td> <td> <p>LAI growth rate during growth phase</p> </td> </tr> <tr> <td> <p>LAI_max</p> </td> <td> <p>3.6–4.1</p> </td> <td> <p>3.0</p> </td> <td> <p>m2 m-2</p> </td> <td> <p>Maximum leaf area index</p> </td> </tr> <tr> <td> <p>p</p> </td> <td> <p>veg:</p> <p>0.004</p> <p>rest:</p> <p>0.1</p> </td> <td> <p>growth:</p> <p>0.1</p> <p>dry:</p> <p>0.015</p> </td> <td> <p>N/A</p> </td> <td> <p>LAI shedding rate (trees: vegetative and rest phase; grass: growth and dry season)</p> </td> </tr> <tr> <td> <p>r_d</p> </td> <td> <p>0.605</p> </td> <td> <p>0.8602</p> </td> <td> <p>μmol(CO2) m-2(leaf) s-1</p> </td> <td> <p>Dark respiration at 25 °C, fraction of Vmax</p> </td> </tr> </tbody> </table> <p> </p> <p>Parameters used by SUEWS model</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Trees</strong></p> </td> <td> <p><strong>Lawn</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>f_i</p> </td> <td> <p>0.21</p> </td> <td> <p>0.18</p> </td> <td> <p>N/A</p> </td> <td> <p>Fraction of each vegetation type i</p> </td> </tr> <tr> <td> <p>F_(pho,max,i)</p> </td> <td> <p>8.3</p> </td> <td> <p>8.92</p> </td> <td> <p>μmol m<sup>-2</sup> s<sup>-1</sup></p> </td> <td> <p>Maximum potential photosynthesis</p> </td> </tr> <tr> <td> <p>LAI_(max,i)</p> </td> <td> <p>4.8</p> </td> <td> <p>3</p> </td> <td> <p>m<sup>2</sup> m<sup>-2</sup></p> </td> <td> <p>Full leaf-on summertime value</p> </td> </tr> <tr> <td> <p>LAI_(min,i)</p> </td> <td> <p>0.66</p> </td> <td> <p>1.6</p> </td> <td> <p>m<sup>2</sup> m<sup>-2</sup></p> </td> <td> <p>Leaf-off wintertime value</p> </td> </tr> <tr> <td> <p>T_L</p> </td> <td> <p>-10</p> </td> <td> <p>-10</p> </td> <td> <p>°C</p> </td> <td> <p>Lower air temperature limit</p> </td> </tr> <tr> <td> <p>T_H</p> </td> <td> <p>55</p> </td> <td> <p>55</p> </td> <td> <p>°C</p> </td> <td> <p>Upper air temperature limit</p> </td> </tr> <tr> <td> <p>G_5</p> </td> <td> <p>30</p> </td> <td> <p>30</p> </td> <td> <p>°C</p> </td> <td> <p>Parameter related to temperature dependence</p> </td> </tr> <tr> <td> <p>G_3</p> </td> <td> <p>0.66</p> </td> <td> <p>0.538</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter related to VPD dependence</p> </td> </tr> <tr> <td> <p>G_4</p> </td> <td> <p>0.89</p> </td> <td> <p>0.87</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter related to VPD dependence</p> </td> </tr> <tr> <td> <p>G_6</p> </td> <td> <p>0.36</p> </td> <td> <p>0.55</p> </td> <td> <p>mm<sup>-1</sup></p> </td> <td> <p>Parameter related to soil moisture dependence</p> </td> </tr> <tr> <td> <p>G_2</p> </td> <td> <p>477</p> </td> <td> <p>263.5</p> </td> <td> <p>W m<sup>-2</sup></p> </td> <td> <p>Parameter related to dependence</p> </td> </tr> <tr> <td> <p>Δθ_WP</p> </td> <td> <p>132.5</p> </td> <td> <p>143</p> </td> <td> <p>mm</p> </td> <td> <p>Wilting point deficit</p> </td> </tr> <tr> <td> <p>K_(↓max)</p> </td> <td> <p>1200</p> </td> <td> <p>1200</p> </td> <td> <p>W m<sup>-2</sup></p> </td> <td> <p>Maximum incoming shortwave radiation</p> </td> </tr> <tr> <td> <p>a_i</p> </td> <td> <p>0.78</p> </td> <td> <p>1.7</p> </td> <td> <p>N/A</p> </td> <td> <p>Empirical soil and vegetation respiration coefficient</p> </td> </tr> <tr> <td> <p>b_i</p> </td> <td> <p>0.08</p> </td> <td> <p>0.06</p> </td> <td> <p>N/A</p> </td> <td> <p>Empirical soil and vegetation respiration coefficient</p> </td> </tr> <tr> <td> <p>ω_(1,GDD,i)</p> </td> <td> <p>0.04</p> </td> <td> <p>0.04</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(2,GDD,i)</p> </td> <td> <p>0.0005</p> </td> <td> <p>0.0005</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(1,SDD,i)</p> </td> <td> <p>-1.5</p> </td> <td> <p>-1.5</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>ω_(1,SDD,i)</p> </td> <td> <p>0.0025</p> </td> <td> <p>0.0025</p> </td> <td> <p>N/A</p> </td> <td> <p>Parameter for LAI calculation</p> </td> </tr> <tr> <td> <p>GDD</p> </td> <td> <p>300</p> </td> <td> <p>300</p> </td> <td> <p>days</p> </td> <td> <p>The growing degree days (GDD) needed for full capacity of the leaf area index</p> </td> </tr> <tr> <td> <p>SDD</p> </td> <td> <p>-300</p> </td> <td> <p>-300</p> </td> <td> <p>days</p> </td> <td> <p>The senescence degree days (SDD) needed to initiate leaf off</p> </td> </tr> <tr> <td> <p>T_(base,GDD)</p> </td> <td> <p>5</p> </td> <td> <p>5</p> </td> <td> <p>°C</p> </td> <td> <p>Base Temperature for initiating growing degree days (GDD) for leaf growth</p> </td> </tr> <tr> <td> <p>T_(base,SDD)</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>°C</p> </td> <td> <p>Base temperature for initiating senescence degree days (SDD) for leaf off</p> </td> </tr> </tbody> </table> <p> </p> <p><span>Parameters used by VPRM model</span></p> <table> <tbody> <tr> <td> <p><strong><span>Parameter</span></strong></p> </td> <td> <p><strong><span>Trees</span></strong></p> </td> <td> <p><strong><span>Lawn</span></strong></p> </td> <td> <p><strong><span>Units</span></strong></p> </td> <td> <p><strong><span>Description</span></strong></p> </td> </tr> <tr> <td> <p><span>λ</span></p> </td> <td> <div> <p><span>-0.16</span></p> </div> </td> <td> <div> <p><span>-0.13</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>light use efficiency</span></p> </div> </td> </tr> <tr> <td> <p><span>PAR_0</span></p> </td> <td> <div> <p><span>356.99</span></p> </div> </td> <td> <div> <p><span>545.61</span></p> </div> </td> <td> <div> <p><span>μmol m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>half-saturation value</span></p> </div> </td> </tr> <tr> <td> <p><span>α</span></p> </td> <td> <div> <p><span>0.22</span></p> </div> </td> <td> <div> <p><span>0.40</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup> /<sup>0</sup>C</span></p> </div> </td> <td> <div> <p><span>empirical coefficient</span></p> </div> </td> </tr> <tr> <td> <p><span>β</span></p> </td> <td> <div> <p><span>1.09</span></p> </div> </td> <td> <div> <p><span>0.42</span></p> </div> </td> <td> <div> <p><span>μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup></span></p> </div> </td> <td> <div> <p><span>empirical coefficient</span></p> </div> </td> </tr> <tr> <td> <p><span>T_max</span></p> </td> <td> <div> <p><span>40</span></p> </div> </td> <td> <div> <p><span>40</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>maximum temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_min</span></p> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>2</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>minimum temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_opt</span></p> </td> <td> <div> <p><span>20</span></p> </div> </td> <td> <div> <p><span>18</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>optimal temperature for photosynthesis</span></p> </div> </td> </tr> <tr> <td> <p><span>T_low</span></p> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>0</span></p> </div> </td> <td> <div> <p><span>°C </span></p> </div> </td> <td> <div> <p><span>to account for the persistence of soil respiration in winter</span></p> </div> </td> </tr> </tbody> </table> <p><span> </span></p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <p>Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period</p> <p>Period: 01/2022–09/2023</p>
Research data from the survey on Smart Cities professional profiles for the Article "Modelling and analyzing the availability of technical professional profiles for the success of Smart Cities projects in Europe"
<p>The file includes the complete version of data collected through the surrvey on recommended profile for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician. It complements the previous version focused on IoT implementation stired at <a href="../doi/10.5281/zenodo.7492254">https://zenodo.org/doi/10.5281/zenodo.7492254</a></p>
National Checklists 2017: Vatican City Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Vatican City collected using effechecka and geonames polygons
National Checklists 2019: Vatican City Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Vatican City collected using effechecka and geonames polygons
Hrycyna et al. 2022 - Satellite observations of NO2 indicate legacy impacts of Redlining in US Midwestern cities
<p>This dataset contains remotely sensed estimates of nitrogen dioxide (NO2, via TROPOMI accessed via Google Earth Engine) for HOLC neighborhoods in 11 US Midwestern cities, and corresponding coarse geographic and demographic data of those cities. NO2 data is reported daily for the entire calendar year of 2019, geographic and demographic variables are fixed for each city for the entire year. Each HOLC-graded neighborhood included in this dataset was filtered to be greater than 2 km2. The number of pixels used to calculate the area-weighted mean of NO2 is also reported, as is the area of the neighborhood. The dataset has also been filtered for observations that did not pass quality filters for L3 TROPOMI data. The cities included in the study are: Chicago IL, Milwaukee WI, Saint Paul MN, Minneapolis MN, Indianapolis IN, Cleveland OH, Wichita KS, Greater Kansas City KS and MO, Columbus OH, Detroit MI, and Omaha NE. HOLC neighborhood shapefiles were obtained from the Mapping Inequality project website, hosted by the University of Richmond, and resulting polygons used in analysis were created by dissolving shared boundaries in Google Earth Engine. City populations and population density were obtained from the US 2010 Census data. All data was collected and organized to assess if current day NO2 levels varied with HOLC grades in these major cities.</p> <p> </p> <p>Data was used in the study: Hrycyna et al. (2022) <em>Elementa</em> 10(1):00027 </p> <div> <div><a href="https://doi.org/10.1525/elementa.2022.00027" target="_blank" rel="noopener">https://doi.org/10.1525/elementa.2022.00027</a></div> </div> <p>Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. https://dsl.richmond.edu/panorama/redlining/#loc=5/39.1/-94.58&text=downloads</p> <p><strong>Dataset for all analyses presented in Hrycyna et al. Columns described below:</strong></p> <p>HOLC_grade: A, B, C, D (neighborhood grade categories obtained from Mapping Inequality project, indicate historic HOLC designations of neighborhoods).</p> <p>HOLCAreaKm2: continuous area value in km2 of the HOLC neighborhood polygon, which may be more than one HOLC designated polygon merged from the shapefiles downloaded from Mapping Inequality.</p> <p>pixelcount: integer values of the number of TROPOMI NO2 pixels used to produce the area-weighted mean NO2 value.</p> <p>NO2_mol_m2: area-weighted mean value of TROPOMI NO2 for that HOLC neighborhood polygon in mol m-2</p> <p>system.index: designated date and time boundary of the observation collected via TROPOMI</p> <p>date: date of observation</p> <p>month: month of observation</p> <p>City: city in the US Midwest</p> <p>State: state for the city of focus</p> <p>Population: urban population obtained from 2010 census</p> <p>PopDensity: urban population density obtained from 2010 census, based on modern city boundaries (in people per square miles)</p> <p>CityArea_mi2: Area of the city of interest, in square miles.</p> <p>ln_NO2: natural log transformed NO2 values in mol m-2</p> <p>NO2_DU: NO2 value converted from mol m-2 to DU (Dobsons Units, converted by multiplying 2241.15)</p> <p>NO2_lnDU: natural log transformed NO2 values in DU<br><br></p>
Accommodation Facilities and Ratings on Booking for 10 European Cities
<p>This dataset, collected on 11/11/2024, includes data from 4,806 accommodations across 10 European cities: Barcelona, Reykjavík, Amsterdam, Prague, Sofia, Porto, Edinburgh, Berlin, Dubrovnik, and Innsbruck. With prices corresponding to the 16/07/2025 night (2 adults and 1 room), it features global ratings, and category scores like cleanliness, comfort, staff, and location, along with details on available amenities and accommodation types. The dataset also covers check-in/check-out policies, pet policies, exact addresses, and review counts. It is designed to analyze key factors to a positive accommodation experience, and it allows comparisons across cities and types of accommodations.</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)
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.