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.

3,709

datasets available to search

ShareScore release 0.7.1

Dataset results

3,709 results for “urbanization”

Learn how ShareScore rates datasets ↗

zenodo44/100

Dataset for the preprint: "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich"

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". Meteorological data 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. Data format: comma separated values (csv) Time step: 60 min (aggregated) Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period Period: 01/2022–09/2023 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. Further information on the dataset can be found in the submitted manuscript.  Data format: comma separated values (csv) Time step: Monthly (mean) Time stamp: yyyy-MM-dd  Period: 11/2012–09/2023 <table> <tbody> <tr> <td> Variable </td> <td> Acronym </td> <td> Units </td> <td> Height above ground </td> <td> Location </td> <td> Geographic location </td> </tr> <tr> <td> Global radiation </td> <td> G </td> <td> W m-2 </td> <td> 27 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Air temperature </td> <td> Tair </td> <td> Degrees Celsius </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Relative humidity </td> <td> RH </td> <td> % </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Air pressure </td> <td> P </td> <td> hPa </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Wind speed </td> <td> u </td> <td> m s-1 </td> <td> 35 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Precipitation </td> <td> R </td> <td> mm </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Downward longwave radiation </td> <td> LW </td> <td> W m-2 </td> <td> 110 m </td> <td> Hardau II, ERA-5 </td> <td> 8°30'/47°23' </td> </tr> <tr> <td> Soil temperature </td> <td> Tsoil </td> <td> Degrees Celsius </td> <td> -0.15 m </td> <td> Parks </td> <td> - </td> </tr> <tr> <td> Soil water content </td> <td> SWC </td> <td> m3 m-3 </td> <td> -0.15 m </td> <td> Parks </td> <td> - </td> </tr> <tr> <td> Atmospheric CO2 concentration </td> <td> CO2 </td> <td> ppmv </td> <td> 2 m </td> <td> Hardau II, Beromunster, ERA-5 </td> <td> 8°30'/47°23', 8°10'/47°11 </td> </tr> </tbody> </table>   In-situ ecophysiological data In-situ ecophysiology measurements performed on park trees and lawns in the city of Zurich during the ICOS-Cities project. LAI (leaf area index) was measured in dense Platanus sp. tree stands, found only in Bullingerhof and Hardaupark, during sunny conditions using a ceptometer (SS1 SunScan, Delta-T Devices). Sap flow was measured at six trees (Platanus sp., Tilia 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. 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). Further information on the dataset can be found in the submitted manuscript. Data format: comma separated values (csv) Time stamp: yyyy-MM-dd Period: 04/2022–09/2023   Land cover map Land cover map of part of Zurich urban area. Datasets used to derive this map: <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> Further information on the dataset can be found in the submitted manuscript.   Data specifications: CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected Spatial Extent: 461972.1, 5246490.4 : 463972.1, 5248490.4 Temporal Extent: 2023 Units: meters Width: 2000 Height: 2000 Bands: 1 Pixel Size: 1,-1 Data type: Float32 - Thirty two bit floating point GDAL Driver Description: GTiff GDAL Driver Metadata: GeoTIFF   Legend: 30           Grass 40           Crops 50           Paved 60           Buildings 70           Deciduous trees 80           Water   CO2 fluxes 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. 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. Parameters used by diFUME model <table> <tbody> <tr> <td> Parameter </td> <td> Value </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> A_max </td> <td> 15 </td> <td> μmol CO2 m-2 s-1 </td> <td> maximum leaf gross photosynthetic rate </td> </tr> <tr> <td> a </td> <td> 0.045 </td> <td> mol CO2 mol-1 PAR </td> <td> quantum yield for CO2 assimilation </td> </tr> <tr> <td> a_1 </td> <td> 25 </td> <td> N/A </td> <td> empirical coefficient in Leuning (1995) model </td> </tr> <tr> <td> b_ </td> <td> 0.65 </td> <td> N/A </td> <td> empirical coefficient in β-factor formula </td> </tr> <tr> <td> b_1 </td> <td> 5 </td> <td> N/A </td> <td> empirical coefficient </td> </tr> <tr> <td> D_o </td> <td> 0.3 </td> <td> kPa </td> <td> empirically determined coefficient for the VPD scalar inside Leuning (1995) model </td> </tr> <tr> <td> D_sc </td> <td> 1 </td> <td> N/A </td> <td> daylight scalar for dark respiration inhibition during day (1: no inhibition) </td> </tr> <tr> <td> E_0 </td> <td> 487.75 </td> <td> K </td> <td> temperature sensitivity parameter for soil respiration </td> </tr> <tr> <td> g_o </td> <td> 0.01 </td> <td> mol CO2 m-2 s-1 </td> <td> residual stomatal conductance for CO2 (g_s when Anet = 0, PAR = 0). </td> </tr> <tr> <td> Q_10 </td> <td> 1.85 </td> <td> N/A </td> <td> temperature sensitivity of leaf respiration </td> </tr> <tr> <td> R_(l,ref) </td> <td> 1.53 </td> <td> μmol CO2 m-2 s-1 </td> <td> reference leaf respiration at Tair = 25 °C </td> </tr> <tr> <td> R_(S,ref) </td> <td> 2.49 </td> <td> μmol CO2 m-2 s-1 </td> <td> reference soil respiration at Tsoil = 10 °C </td> </tr> <tr> <td> T_opt </td> <td> 23 </td> <td> °C </td> <td> optimum air temperature for gross photosynthesis </td> </tr> <tr> <td> T_0 </td> <td> -46 </td> <td> °C </td> <td> low-temperature limit for soil respiration </td> </tr> <tr> <td> T_(ref,S) </td> <td> 10 </td> <td> °C </td> <td> reference soil temperature for soil respiration </td> </tr> <tr> <td> T_(ref,l) </td> <td> 25 </td> <td> °C </td> <td> reference air temperature for leaf respiration </td> </tr> <tr> <td> W </td> <td> 10 </td> <td> °C </td> <td> width of the bell-shape curve at f(T_air )  = 0.5 </td> </tr> <tr> <td> θ_ref </td> <td> 0.4 </td> <td> m3 m-3 </td> <td> saturated soil volumetric water content  </td> </tr> <tr> <td> θ_g </td> <td> 0.1 </td> <td> m3 m-3 </td> <td> minimum soil volumetric water content, limit to stomatal conductance </td> </tr> <tr> <td> θ_0 </td> <td> 0.04 </td> <td> m3 m-3 </td> <td> minimum soil volumetric water content, limit to soil respiration </td> </tr> </tbody> </table>   Parameters used by JSBACH model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> J_max </td> <td> 104.5 </td> <td> 148.6 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Maximum electron transport rate at 25 °C </td> </tr> <tr> <td> T_alt </td> <td> 4.0–4.5 </td> <td>   </td> <td> °C </td> <td> Alternation temperature </td> </tr> <tr> <td> θ_cap </td> <td> 0.32– 0.39 </td> <td> 0.32– 0.34 </td> <td> m m-1 </td> <td> Volumetric soil field capacity </td> </tr> <tr> <td> θ_pwp </td> <td> 0.13– 0.21 </td> <td> 0.135–0.165 </td> <td> m m-1 </td> <td> Volumetric wilting point </td> </tr> <tr> <td> V_max </td> <td> 55.0 </td> <td> 78.2 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Maximum carboxylation rate at 25 °C </td> </tr> <tr> <td> z_root </td> <td> 0.5 </td> <td> 0.12 </td> <td> m </td> <td> Root depth </td> </tr> <tr> <td> CC </td> <td> 1.25 </td> <td> 1.25 </td> <td> N/A </td> <td> Relative cost to produce one carbon </td> </tr> <tr> <td> f_faeces </td> <td> 0.3 </td> <td> 0.3 </td> <td> N/A </td> <td> Fraction of carbon from herbivore faeces that goes into the green litter pool </td> </tr> <tr> <td> f_leaf </td> <td> 0.4 </td> <td> 0.4 </td> <td> N/A </td> <td> A fixed fraction of canopy maintenance respiration that makes up the dark respiration </td> </tr> <tr> <td> k </td> <td> 0.1 </td> <td> 0.09 </td> <td> N/A </td> <td> LAI growth rate during growth phase </td> </tr> <tr> <td> LAI_max </td> <td> 3.6–4.1 </td> <td> 3.0 </td> <td> m2 m-2 </td> <td> Maximum leaf area index </td> </tr> <tr> <td> p </td> <td> veg: 0.004 rest: 0.1 </td> <td> growth: 0.1 dry: 0.015 </td> <td> N/A </td> <td> LAI shedding rate (trees: vegetative and rest phase; grass: growth and dry season) </td> </tr> <tr> <td> r_d </td> <td> 0.605 </td> <td> 0.8602 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Dark respiration at 25 °C, fraction of Vmax </td> </tr> </tbody> </table>   Parameters used by SUEWS model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> f_i </td> <td> 0.21 </td> <td> 0.18 </td> <td> N/A </td> <td> Fraction of each vegetation type i </td> </tr> <tr> <td> F_(pho,max,i) </td> <td> 8.3 </td> <td> 8.92 </td> <td> μmol m-2 s-1 </td> <td> Maximum potential photosynthesis </td> </tr> <tr> <td> LAI_(max,i) </td> <td> 4.8 </td> <td> 3 </td> <td> m2 m-2 </td> <td> Full leaf-on summertime value </td> </tr> <tr> <td> LAI_(min,i) </td> <td> 0.66 </td> <td> 1.6 </td> <td> m2 m-2 </td> <td> Leaf-off wintertime value </td> </tr> <tr> <td> T_L </td> <td> -10 </td> <td> -10 </td> <td> °C </td> <td> Lower air temperature limit </td> </tr> <tr> <td> T_H </td> <td> 55 </td> <td> 55 </td> <td> °C </td> <td> Upper air temperature limit </td> </tr> <tr> <td> G_5 </td> <td> 30 </td> <td> 30 </td> <td> °C </td> <td> Parameter related to temperature dependence </td> </tr> <tr> <td> G_3 </td> <td> 0.66 </td> <td> 0.538 </td> <td> N/A </td> <td> Parameter related to VPD dependence </td> </tr> <tr> <td> G_4 </td> <td> 0.89 </td> <td> 0.87 </td> <td> N/A </td> <td> Parameter related to VPD dependence </td> </tr> <tr> <td> G_6 </td> <td> 0.36 </td> <td> 0.55 </td> <td> mm-1 </td> <td> Parameter related to soil moisture dependence </td> </tr> <tr> <td> G_2 </td> <td> 477 </td> <td> 263.5 </td> <td> W m-2 </td> <td> Parameter related to  dependence </td> </tr> <tr> <td> Δθ_WP </td> <td> 132.5 </td> <td> 143 </td> <td> mm </td> <td> Wilting point deficit </td> </tr> <tr> <td> K_(↓max) </td> <td> 1200 </td> <td> 1200 </td> <td> W m-2 </td> <td> Maximum incoming shortwave radiation </td> </tr> <tr> <td> a_i </td> <td> 0.78 </td> <td> 1.7 </td> <td> N/A </td> <td> Empirical soil and vegetation respiration coefficient </td> </tr> <tr> <td> b_i </td> <td> 0.08 </td> <td> 0.06 </td> <td> N/A </td> <td> Empirical soil and vegetation respiration coefficient </td> </tr> <tr> <td> ω_(1,GDD,i) </td> <td> 0.04 </td> <td> 0.04 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(2,GDD,i) </td> <td> 0.0005 </td> <td> 0.0005 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(1,SDD,i) </td> <td> -1.5 </td> <td> -1.5 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(1,SDD,i) </td> <td> 0.0025 </td> <td> 0.0025 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> GDD </td> <td> 300 </td> <td> 300 </td> <td> days </td> <td> The growing degree days (GDD) needed for full capacity of the leaf area index </td> </tr> <tr> <td> SDD </td> <td> -300 </td> <td> -300 </td> <td> days </td> <td> The senescence degree days (SDD) needed to initiate leaf off </td> </tr> <tr> <td> T_(base,GDD) </td> <td> 5 </td> <td> 5 </td> <td> °C </td> <td> Base Temperature for initiating growing degree days (GDD) for leaf growth </td> </tr> <tr> <td> T_(base,SDD) </td> <td> 10 </td> <td> 10 </td> <td> °C </td> <td> Base temperature for initiating senescence degree days (SDD) for leaf off </td> </tr> </tbody> </table>   Parameters used by VPRM model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> λ </td> <td> <div> -0.16 </div> </td> <td> <div> -0.13 </div> </td> <td> <div> μmol CO2 m-2 s-1 </div> </td> <td> <div> light use efficiency </div> </td> </tr> <tr> <td> PAR_0 </td> <td> <div> 356.99 </div> </td> <td> <div> 545.61 </div> </td> <td> <div> μmol m-2 s-1 </div> </td> <td> <div> half-saturation value </div> </td> </tr> <tr> <td> α </td> <td> <div> 0.22 </div> </td> <td> <div> 0.40 </div> </td> <td> <div> μmol CO2 m-2 s-1 /0C </div> </td> <td> <div> empirical coefficient </div> </td> </tr> <tr> <td> β </td> <td> <div> 1.09 </div> </td> <td> <div> 0.42 </div> </td> <td> <div> μmol CO2 m-2 s-1 </div> </td> <td> <div> empirical coefficient </div> </td> </tr> <tr> <td> T_max </td> <td> <div> 40 </div> </td> <td> <div> 40 </div> </td> <td> <div> °C </div> </td> <td> <div> maximum temperature for photosynthesis </div> </td> </tr> <tr> <td> T_min </td> <td> <div> 0 </div> </td> <td> <div> 2 </div> </td> <td> <div> °C </div> </td> <td> <div> minimum temperature for photosynthesis </div> </td> </tr> <tr> <td> T_opt </td> <td> <div> 20 </div> </td> <td> <div> 18 </div> </td> <td> <div> °C </div> </td> <td> <div> optimal temperature for photosynthesis </div> </td> </tr> <tr> <td> T_low </td> <td> <div> 0 </div> </td> <td> <div> 0 </div> </td> <td> <div> °C </div> </td> <td> <div> to account for the persistence of soil respiration in winter </div> </td> </tr> </tbody> </table>   Data format: comma separated values (csv) Time step: 60 min (aggregated) Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period Period: 01/2022–09/2023

opencc-by-4.0Aug 2024View details →

zenodo44/100

Dateset on 'Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity'

The data was collected a part of the baseline survey on household socio-economics among the Bengalurian along the rural-urban interface.

opencc-by-4.0Aug 2024View details →

zenodo44/100

Urban Vegetation Data - Canopy Height Model (Brussels Capital Region, 2021)

This GIS dataset was created for the following scientific publication, as part of the EU-funded <a href="https://coolschools.eu/">Cool Schools</a> research project (under Grant Agreement No. 101003758) : Gallez, E., Canters, F., Gadeyne, S., & Baró, F. (2024). <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000846?via%3Dihub">A multi-indicator distributive justice approach to assess school-related green infrastructure benefits in Brussels - ScienceDirect</a>. Ecosystem Services, 70, 101677. https://doi.org/10.1016/j.ecoser.2024.101677.  Very-High Resolution Canopy Height Model (resolution : 25cm), distinguishing between 4 vegetation types (trees, high shrubs, low shrubs and grass) in the Brussels Capital Region. Coordinate system : Lambert_Belge_72. The CHM was built on : <ul> <li>VHR aerial orthophotos (visible RGB and NIR) (“UrbIS-Ortho N-S, 2021”) for the Brussels Capital Region, of 5x5cm resolution  Source: Paradigm. (2021). UrbIS-Ortho N-S. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/fec72767-d6b6-41b9-a767-616df2779aae#access">https://datastore.brussels/web/urbisdownload</a>.  and;</li> <li>digital terrain models (DSM and DTM) of 50x50cm, captured on 22/09/2021. Paradigm.Brussels. Source: Paradigm. (2021). DSM / DTM. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/1d7bd49d-fe83-4388-af85-6f5dc8ec7909#access">https://datastore.brussels/web/urbisdownload.</a></li> </ul> Both the orthophotos and digital terrain models were resampled to a 25x25cm resolution, using a bilinear interpolation method. The Canopy Height Model was then created by selecting NDVI values of 0.2 and higher, - a commonly used threshold value to distinguish vegetated land from built land (Hashim et al., 2019) -, and vegetation height thresholds of < 0.5m (for grass), 0.5 - 2m (for low shrubs), 2 - 5m (for high shrubs), and > 5m (for trees) (Derkzen et al., 2015; Sankey et al., 2018). Green roofs were excluded.The CHM raster was then converted to polygon features.  Classification : <ul> <li>From 0 to 0.5 m (nDSM value) : gridcode 1 = grass</li> <li>From 0.5 to 2 m (nDSM value): gridcode 2 = low shrubs</li> <li>From 2 to 5 m (nDSM value): gridcode 3 = high shrubs</li> <li>From 5 to 113.96 m (nDSM value): gridcode 4 = trees</li> </ul>

opencc-by-4.0Sep 2024View details →

zenodo44/100

Dataset for Method for Delivery Planning in Urban Areas with Environmental Aspects

The purpose of this dataset is to enable the replication of the research results presented in the article: Michał Lasota, Aleksandra Zabielska, Marianna Jacyna, Piotr Gołębiowski, Renata Żochowska, Mariusz Wasiak. Method for Delivery Planning in Urban Areas with Environmental Aspects. Sustainability 2024, 16(4), 1571. https://doi.org/10.3390/su16041571 - published online: 2024-02-13, which a method of large-criteria decision-making support was developed in the field of urban supply planning, taking into account the minimization of harmful compound emissions. Dataset contains: <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data in the model. The data is presented in three tables.</li> <li>OutputOptimization.xlsx: Contains the output optimization data. The data is prsented in four tables.</li> <li>OutputSummary.xlsx: Contains contains the final results of the aggregated variable.</li> </ul> The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service). Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.  E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden). - The Chinese part of the project is funded by National Natural Science Foundation of China. - The Swedish part of the project is funded by Swedish Energy Agency. - The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).

opencc-zeroOct 2024View details →

zenodo44/100

Dataset for Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs

The purpose of this dataset is to enable the replication of the research results presented in the article: Kłodawski Michał, Jachimowski Roland, & Chamier-Gliszczyński Norbert, 2024. &bdquo;Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs”. Energies 17: 1–24. https://doi.org/10.3390/en17050985 - published online: 2024-02-20, which discusses the application of simulation in solving the problem of the overhead crane energy consumption using different container loading strategies in Urban Logistics Hubs. Dataset contains: <ul> <li>Readme.txt: description of the dataset.</li> <li>Data_Crane.xlsx: Contains the input data used in the model for estimating crane energy consumption.</li> <li>Results_01.csv: Contains output data - Simulation results of energy consumption, and total average energy recovery for each scenario.</li> <li>Results_02.csv: Contains output data - Simulation results - mean values from the results of all scenario replications.</li> </ul> The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service). Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.  E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden). - The Chinese part of the project is funded by National Natural Science Foundation of China. - The Swedish part of the project is funded by Swedish Energy Agency. - The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).

opencc-zeroOct 2024View details →

zenodo44/100

Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"

City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics. To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: Escherichia coli, the emerging pathogen Streptococcus suis, and the sewer-inhabiting Arcobacter cryaerophilus. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin). Using these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.   Usage Users can perform the analysis by running the R script (TC3D.R) after the installation of all package mentioned in the preamble,  This folder contains: - 3 datasets with OD measures for the 3 species:               * data_acrya.xlsx               * data_ecoli.xlsx               * data_ssuis.xlsx - 1 excel files with metadata (plate, well, species, environmental conditions)               * map_plate_all.xlsx - 4 datasets giving time series of the flow and several measures including  conductivity and   temperaturefor 2 sewers of Barcelona obtained from sample cabines  set during the implementation of SCOREWATER (ID:820751)               * carmel_flow.csv               * carmel_quality.csv               * poblenou_flow.csv               * poblenou_quality.csv - 1 C++ script compiled and run with the R TMB package:               * fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.

restrictedcc-by-4.0Jul 2024View details →

zenodo44/100

Prevalence of Multimorbidity among Urban–Rural Older Adults in Mongolia: A Cross-Sectional Study

A face-to-face, questionnaire-based cross-sectional study was conducted with 800 valid participants aged ≥60 years in Mongolia from June to September 2023.

opencc-by-4.0Oct 2024View details →

zenodo44/100

Schools Weather and Air Quality (SWAQ) –Metadata – Urban Network, Sydney (NSW)

Schools Weather and Air Quality (SWAQ) is a citizen science project funded by the Department of Industry, Innovation and Science as part of its Inspiring Australia - Citizen Engagement Program. SWAQ is equipping public schools across Sydney with research-grade meteorology and air quality sensors, enabling students to collect and analyse research quality data through curriculum-aligned classroom activities. The network includes twelve automatic weather stations and seven automatic air quality stations, stretched from -33.5995° to -34.0424° latitude and from 150.6918° to 151.2706° longitude. The average spacing is 10.2 km and the average installation height is 2.5 m above ground level. Six meteorological parameters (dry-bulb temperature, relative humidity, barometric pressure, rain, wind speed, and wind direction) and six air pollutants (SO2, NO2, CO, O3, PM2.5, and PM10) are recorded via Vaisala WXT 536 and Vaisala AQT 420 with a 20 minutes sampling frequency.  SWAQ data provides urban canopy layer observations of the intra-urban heterogeneity and inter-parameter dependency of all major urban climate and air quality variables, valuable across diverse urban disciplines. SWAQ stations are located where there are gaps in existing government networks, and focus on Sydney’s western suburbs, where the highest urbanization rate is taking place, to better inform future urban planning. QC procedures are designed to ensure observations of extreme episodes are not excluded. Beyond research purposes, SWAQ is a citizen-centered network, conceived to promote valuable STEM (science, technology, engineering, mathematics) skills among citizens and students. This collection includes the metadata files for all SWAQ stations, in pdf. Metadata describe the site (type, geographic coordinates, elevation, orographic setting, representativeness, local climatic zone, dominant land use, percent land cover, mean tree and building heights, proximity to water/heat and pollutants sources/sinks, estimation of Davenport Roughness, traffic density, sky view factor), and the instrumentation (variables, models, manufacturers, calibration and installation dates). Site characteristics are described at three radial scales: 20 km, 500 m, and 50 m. Graphical representations include: satellite images, street-view maps, cardinal direction photographs, panoramic photos, and close-up photos of sensors, solar panels, and connections. Optimum site allocation was determined by undertaking a multi-criteria weighted overlay analysis to ensure data representativeness and quality. All SWAQ sensors are installed: <ul> <li>in homogenous urban regions, without sections of anomalous variation in the regional urban makeup and aspect-ratio, and without large, concentrated heat/pollution sources or sinks;</li> <li>in areas falling into the WMO Class 4 with no electromagnetic sources that could have distorted the transmission;</li> <li>at a constant height of  2 - 3.5 m above ground level.</li> </ul> The actual data is available from the Australian Terrestrial Ecosystem Research Network (TERN) <a href="https://https://portal.tern.org.au/schools-weather-air-sydney-nsw/22077">data portal</a> and is regularly updated. The data available from TERN has undergone a rigorous quality check routine before upload. <ul> <li>Calibration: Sensors and gateways are calibrated and tested by Vaisalain controlled conditions </li> <li>Quality Assurance: Annual maintenance log</li> <li>Quality control: continuity tests, fixed range tests (on both physical and instrumental limits), dynamic range and step tests (both performed on a monthly basis), internal consistency tests (on known atmospheric relations) and persistence tests. </li> </ul> The files are in csv format. On the <a href="https://www.swaq.org.au">SWAQ website</a> a non quality controlled subset is available for educational purposes only. This project was funded by an Australian Government Department of Industry, Innovation and Science, Inspiring Australia – Science Engagement Program: Citizen Science Grants (CSG56028). It was also part of the Centre of Excellence for Climate Extremes research project "Attribution & Risk". More information is available in the readme file, in particular a full list of the variables and a legend for the quality flags used.

opencc-by-4.0Jun 2021View details →

zenodo44/100

Urban bicycle networks, existing and synthetically grown

This data set contains all data used and generated in the study Growing Urban Bicycle Networks. The data contain, for 62 cities: original data of bicycle and street networks acquired from OpenStreetMap using OSMnx, processed simplified and merged data of these networks and snapped points of interests of rail and metro stations and grids, results and metrics of simulated synthetic bicycle networks, plots of results, plots of existing and synthetic networks, videos of simulated growing synthetic bicycle networks.

opencc-by-4.0Jul 2021View details →

zenodo44/100

Qualitative dataset - Social justice-oriented narratives in European urban food strategies: Bringing forward redistribution, recognition and representation (Smaal et al., 2021)

This qualitative dataset contains the English translations of the plain texts of the urban food strategy documents or webpages of 16 European medium-sized cities: Basel [CH]; Bristol [UK]; Bruges [BE]; Cordoba [ES]; Donostia - San Sebastián [ES]; Ede [NL]; Geneva [CH]; Ghent [BE]; Grenoble [FR]; Groningen [NL]; Montpellier [FR]; Nantes [FR]; Rennes [FR]; Tours [FR]; Uppsala [SE]; and Vitoria-Gasteiz [ES]. The search for and translation of the urban food strategy documents and webpages have been performed in early 2019. The files have been analysed in NVivo (qualitative data analysis software). The upload also includes figures and a table with the authors' assessments connected to the resources and services codes and radar diagram visualisations presented in the following paper:  Smaal, S. A. L., Dessein, J., Wind, B. J., & Rogge, E. (2021). Social justice-oriented narratives in European urban food strategies: Bringing forward redistribution, recognition and representation. Agriculture and Human Values, 38(3), 709–727. <a href="http://doi.org/10.1007/s10460-020-10179-6">https://doi.org/10.1007/s10460-020-10179-6</a>  Abstract: More and more cities develop urban food strategies (UFSs) to guide their efforts and practices towards more sustainable food systems. An emerging theme shaping these food policy endeavours, especially prominent in North and South America, concerns the enhancement of social justice within food systems. To operationalise this theme in a European urban food governance context we adopt Nancy Fraser’s three-dimensional theory of justice: economic redistribution, cultural recognition and political representation. In this paper, we discuss the findings of an exploratory document analysis of the social justice-oriented ambitions, motivations, current practices and policy trajectories articulated in sixteen European UFSs. We reflect on the food-related resource allocations, value patterns and decision rules these cities propose to alter and the target groups they propose to support, empower or include. Overall, we find that UFSs make little explicit reference to social justice and justice-oriented food concepts, such as food security, food justice, food democracy and food sovereignty. Nevertheless, the identified resources, services and target groups indicate that the three dimensions of Fraser are at the heart of many of the measures described. We argue that implicit, fragmentary and unspecified adoption of social justice in European UFSs is problematic, as it may hold back public consciousness, debate and collective action regarding food system inequalities and may be easily disregarded in policy budgeting, implementation and evaluation trajectories. As a path forward, we present our plans for the RE-ADJUSTool that would enable UFS stakeholders to reflect on how their UFS can incorporate social justice and who to involve in this pursuit. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765389.  Project webpage: <a href="https://recoms.eu/">https://recoms.eu/</a>

opencc-by-4.0Aug 2021View details →

zenodo44/100

Qualitative dataset - Socially just urban food policy implementation: a case study in Groningen (NL)

This qualitative dataset contains the transcripts of 43 interviews that have been conducted with members of social food initiatives (e.g. community gardens and orchards, food assistance, social restaurants, food education projects, social employment trajectories, fair trade campaigns, and so on) in the city of Groningen, as well as the interview guide and the information sheet and consent form that have been used during data collection. In addition, the upload includes the interview guide, posters, assessment table, information sheet and consent form that have been used in a two-part focus group with 3 food policy coordinators of the municipality of Groningen. The data was collected from November 2019 till March 2020. The transcripts have been analysed in NVivo (qualitative data analysis software).The link to and abstract of the paper based on this dataset will be provided when our manuscript gets published. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765389.  Project webpage: <a href="https://recoms.eu/">https://recoms.eu/</a>

opencc-by-4.0Aug 2021View details →

zenodo44/100

City boundary and urban district boundaries, Vienna, 1920

This data repository includes geospatial datasets on city and urban district boundaries as well as background data. In detail, the following datasets are included: <ol> <li>City boundary 1920 (CB_1920.shp)</li> <li>Urban district boundaries 1920 (UDB_1920.shp)</li> <li>Background data <ol> <li>Vienna and surroundings map (VSM.tif). Retrieved from <a href="http://wais.wien.gv.at//archive.xhtml?id=Stueck++00000461ma8KartoSlg#Stueck__00000461ma8KartoSlg">Municipal and provincial archives of Vienna</a>. Georeferenced by the authors.</li> <li>Building age map 1920 (BAM.tif). Retrieved from <a href="http://wais.wien.gv.at//archive.xhtml?id=Stueck++A629C2CD-82BD-43FE-B474-D49161ADF381#Stueck__A629C2CD-82BD-43FE-B474-D49161ADF381">Municipal and provincial archives of Vienna</a>. Georeferenced by the authors.</li> <li>City boundary (raw data). Complementary to CB_1920.shp</li> <li>Urban district boundaries (raw data). Complementary to UDB_1920.shp</li> <li>City boundary 2020 (CB_2020.shp). Dataset " Verwaltungsgrenzen (VGD) - Stichtagsdaten Wien" retrieved from Federal Office of Metrology and Surveying via <a href="https://www.data.gv.at">Open Data Österreich</a>. Modified by the authors.</li> </ol> </li> </ol>

opencc-by-4.0Dec 2020View details →

zenodo44/100

Dataset for simulation of a low-carbon urban energy system using the Backbone model

The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy. The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).

opencc-by-4.0Sep 2021View details →

zenodo44/100

Data: The Role of Urban Trees in Reducing Land Surface Temperatures in European Cities

Data on the LST differences between urban fabric, urban trees and urban green spaces for each city and the LST differences between urban fabric, rural forests and rural pastures (for hot days and JJA (June, July and August) average). In addition, estimates of the evapotranspiration of forests and pastures of each city and albedo estimates of urban fabric and forests are provided. The description of the column names is provided in the readme file.

opencc-by-4.0Dec 2020View details →

zenodo44/100

Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient - Data and code

Dataset and code used in the article "Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient", by A. Fisogni et al., published in Landscape and Urban Planning (2022, 226:104512, <a href="https://www.sciencedirect.com/science/article/pii/S016920462200161X?via%3Dihub">https://doi.org/10.1016/j.landurbplan.2022.104512</a>)

opencc-by-4.0May 2021View details →

zenodo44/100

A High-Resolution Dataset of Global Urban Fraction for Mesoscale Urban Modelling

Coupled urban-atmospheric models are extensively used to understand the urban environment and its impact on atmospheric processes. A common requirement of these models is information about the “urban fraction” (fraction of model grid covered by impervious surface area (ISA)). The European Space Agency (ESA) WorldCover product provides a global land cover map for the base year of 2020 and 2021 at a spatial resolution of 10 m. The dataset is based on Sentinel-1 and Sentinel-2 data with an overall accuracy of 74.4% (2020) and 76.7% (2021). In this study we process the WorldCover dataset and provide a ready-to-use “urban fraction” that can be incorporated in urban modelling systems. The dataset contains GeoTIFF and Weather Research and Forecasting Pre-processing System (WRF-WPS) format files for 1, 0.5, 0.25, 0.009 (~1 km), 0.0027 (~300 m), and 0.0009 (~100 m) degree spatial resolutions. The GeoTIFF files can be converted to other urban mesoscale modelling systems. Please check the README.txt for more information on using the dataset. Note: version 2.0.0 uses WorldCover 2021 v200 dataset for processing of urban fractions, while version 1.0.0 uses WorldCover 2020 v100 dataset. For more information please see here: <a href="https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4">https://1drv.ms/w/s!Ai5IcIuv5U4DioElr0E8CEq0DE3CSw?e=Wdp9i4</a>

opencc-by-4.0Aug 2022View details →

zenodo44/100

SCoRe- Prototyp 2 - Erprobung des Forschungsszenarios "Urbane Grünflächen" – UGF-1

Dieses Datenset enthält Materialien (Videos, Protokolle und Fallbeschreibungen) aus der ersten prototypischen Durchführung des Forschungsszenarios "Urbane Grünflächen" im Teilprojekt <a href="http://www.360total.de/score/">SCoRe-VideoLearning</a>  des <a href="https://scoreforschung.com/ueber/">Score-Projektes</a> .. Hierin finden sich drei exemplarische Fälle von Studierenden, welche sich videografisch forschend mit urbanen Grünflächen auseinandersetzten und dabei die Merkmale der Grünfläche hinsichtlich urbanen Nutzungsmöglichkeiten und der biologischen Vielfalt untersuchten. Dazu wurden die Grünflächen zunächst ausgewählt und in Bezug auf verschiedene vorgegebene Ordnungskriterien beschrieben und bewertet (Fallbeschreibung). Zur Produktion der Videoforschungsdaten - als Basismaterial der empirischen Untersuchung - waren die Studierenden angehalten ein Produktionsprotokoll während aller drei Produktionsphasen der Videografie (Vorproduktion, Produktion im Feld sowie Nachproduktion) auszufüllen und somit für sich sowie andere analysierende Studierende die Entscheidungsprozesse zur Gestaltung der Videoforschungsdaten zu explizieren und zu dokumentieren.. Diese Protokolle bilden entsprechend die Grundlagen für Gütekriterien qualitativer Forschungsdaten: Transparenz und intersubjektive Nachvollziehbarkeit.

opencc-by-4.0Mar 2022View details →

zenodo44/100

Data files for: The Urban Lightning Effect Revealed with Geostationary Lightning Mapper Observations

Warm season (June, July, August; JJA) Geostationary Lightning Mapper (GLM) observations from 2018-2021. Original processing of 20-second Level 2 GLM packets into 5-min files and quality control performed by CPTEC/INPE. Complete description provided by Oda et al. (2022). Further processing conducted locally to isolate GLM flash data for the Southeast U.S., accumulate the 5-minute files into yearly and 4-year bins, and to derive total flash count ("flash"), flash days ("fday"), and average flashes per flash day ("fpfd"). Included files: <ul> <li>GOES-16 Full Disk <ul> <li>Yearly files containing all GLM data classes (flash, group, and event) with 5-minute timesteps</li> <li>Yearly files containing only GLM flash data with 5-minute timesteps</li> </ul> </li> <li>Southeast (lat-lon bounds: -96.00, -74.00, 41.00, 24.00)  <ul> <li>Final 4-year aggregate file containing derived total lightning metrics ready for analysis in GIS</li> </ul> </li> </ul>

opencc-by-4.0Nov 2022View details →

zenodo44/100

An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona

The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets. How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a> This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions. <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is : VV_new_perNN_LSTWW where: <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65.

opencc-by-4.0Dec 2022View details →

zenodo44/100

Dataset of IEEE 802.11 probe requests from an uncontrolled urban environment

Introduction The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates. This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania. It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.   Related dataset Same authors also produced the <a href="https://zenodo.org/record/7503594">Labeled dataset of IEEE 802.11 probe requests</a>  with same data layout and recording equipment. Measurement setup   The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device). Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel. The following information about each received PR is collected:  - MAC address  - Supported data rates  - extended supported rates  - HT capabilities  - extended capabilities  - data under extended tag and vendor specific tag  - interworking  - VHT capabilities  - RSSI  - SSID  - timestamp when PR was received. The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package to collect, preprocess, and transmit the data. Data preprocessing The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database. For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure: <pre><code class="language-json">PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }</code></pre> Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.   Missing IE fields in the captured PR are not included in PR_IE_DATA. When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure: <pre><code class="language-json">{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },</code></pre> where PR_data is structured as follows: <pre><code class="language-json">{ 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.</code></pre>   This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored. The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval. If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended. If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key. The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the Single_PR_capture_example.json file.   Folder structure For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period. Each folder contains four files, each containing samples from that device. The folders are named after the start and end time (in UTC). For example, the folder [2022-09-22T22-00-00_2022-09-23T22-00-00](2022-09-22T22-00-00_2022-09-23T22-00-00) contains samples collected between 23th of September 2022 00:00 local time, until 24th of September 2022 00:00 local time. Files representing their location via mapping: - 1.json -> location 1 - 2.json -> location 2 - 3.json -> location 3 - 4.json -> location 4 Environments description   The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset. As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system. Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided. Four Raspbery Pi-s were used: - location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano) - location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo - location 3 -> nothernmost window in the building of Via Etnea near Piazza Università - location 4 -> first window top the right of the entrance of the University of Catania Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access) Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.   Known dataset shortcomings Due to technical and physical limitations, the dataset contains some identified deficiencies. PRs are collected and transmitted in 10-second chunks. Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred. Every 20 minutes the service is restarted on the recording device. This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond. For this reason, up to 20 seconds of data will not be recorded in each 20-minute period. The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.      Location 1 - Piazza del Duomo - Chierici  The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period.  Its location is constant and is not disturbed, dataset seems to have complete coverage.      Location 2 - Via Etnea - Piazza del Duomo  The device is located inside the building.  During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed.  As the device was moved back and forth, power outages and internet connection issues occurred.  The last three days in the record contain no PRs from this location.      Location 3 - Via Etnea - Piazza Università  Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building.  Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall  when no people are present.  This device appears to have been collecting data throughout the whole dataset period.        Location 4 - Piazza Università  This location is wirelessly connected to the access point.  The device was placed statically on a windowsill overlooking the square.  Due to physical limitations, the device had lost power several times during the deployment.  The internet connection was also interrupted sporadically. Recognitions The data was collected within the scope of <a href="https://www.resilocproject.eu/">Resiloc project</a> with the help of City of Catania and project partners.

opencc-by-4.0Jan 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record