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opencc-by-4.0Aug 2024View details →

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-4.0Sep 2024View details →

A Dataset with Synthetic Landing Trajectories for Zurich Airport

The archive contains synthetic datasets in npy format generated using a TimeGAN-based model designed to capture a range of aircraft landing trajectories at Zurich airport across different operational scenarios and environmental conditions. Each dataset consists of multiple groups representing distinct patterns or behaviors in the trajectory data. The trajectories are segmented in clusters, and to some of them a smoothing filter was applied. The datasets incorporate a range of variables critical for modeling aircraft landing behaviors. Continuous variables such as longitude, latitude, and altitude exhibit multimodal distributions, capturing different operational phases and conditions within each cluster. The data is stored in an array format with dimensions (number of samples, sequence length, feature dimensions). Here, the number of samples corresponds to the total number of recorded flight trajectories included in the dataset, while the sequence length represents the duration or the number of time steps over which each trajectory is recorded. The feature dimensions denote the various variables (state vector) measured at each time step, consisting of longitude, latitude and altitude. Categorical variables, such as runway identifiers and cluster labels, follow distributions that reflect operational frequencies, with certain clusters or runways being more common under specific conditions.  The archive contains the following files: - 5clust0.npy, 5clust1.npy, 5clust2.npy, 5clust3.np & 5clust4.npy (5 clusters of landing trajectories separated) - ma_5clust0.npy, ma_5clust1.npy, ma_5clust2.npy, ma_5clust3.np & ma_5clust4.npy (5 clusters of landing trajectories separated, moving average filter applied) - ma_3clust0.npy, ma_3clust1.npy & ma_3clust2.npy (3 clusters  of landing trajectories separated, moving average filter applied) - run28_syn.npy & run24_syn.npy (groups of landing trajectories per runway) - ma_run28_syn.npy & ma_run24_syn.npy (groups of landing trajectories per runway, moving average filter applied) - go_around_synthetic.npy (go-around landing trajectories on runway 14)

opencc-by-4.0Nov 2022View details →

Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.

This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022. Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich. Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.

opencc-by-4.0Aug 2023View details →

Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data

General description of wind turbine: The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system. Location of site: The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E. Control and measurement systems and signals: The turbine is regulated via a variable-speed and collective variable pitch control system. SHM Motivation: Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes. Data Description: The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout. The measurements/instrumentation setup, type and layout is provided in the pdf files. The data: the data is provided in zip files corresponding to four use-cases as follows: <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows: <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> Additional data is available upon request, please contact: <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> For further details or questions, please contact: Prof. Dr. Eleni Chatzi Chair of Structural Mechanics & Monitoring ETH Zürich <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a>

opencc-by-4.0Aug 2024View details →

Digitized Copies of the Zurich Overnight Visitor Logs ("Nachtzedel") after 1780

<div> <div>This dataset contains digital copies - image files and metadata - of the "Zürcher Nachtzedel" (overnight visitor logs = Fremdenliste, 1780 to 1784).</div> </div>

opencc-zeroJan 2024View details →

Linked collectors and determiners for: United Herbaria of the University and ETH Zurich.

Natural history specimen data linked to collectors and determiners held within, "United Herbaria of the University and ETH Zurich". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/064508e2-255e-4d82-9f13-05d73476cc03">https://bionomia.net/dataset/064508e2-255e-4d82-9f13-05d73476cc03</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/064508e2-255e-4d82-9f13-05d73476cc03">https://gbif.org/dataset/064508e2-255e-4d82-9f13-05d73476cc03</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Jul 2019View details →

Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network

Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network This dataset contains ozone (O3) and carbon monoxide (CO) concentration measurements collected by the OpenSense (<a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a>) mobile senor network over the course of 4.5 years (2012/02-2016/09). The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland. In particular, the dataset contains:  <ol> <li>Ozone (O3) data: 2012/02 - 2016/09 (19.9 Mio samples)</li> <li>Carbonmonoxide (CO) data: 2014/03 - 2016/09 (49.7Mio samples)</li> </ol> Hardware: -------------- <ol> <li>Ozone sensor: SGX (former e2V) MiCS-OZ-47 Ozone Sensing Head with Smart Transmitter PCB</li> <li>Carbon monoxide sensor: Alphasense CO-B4</li> <li>GPS receiver: u-blox EVK-6p</li> </ol> Data files format:  ------------------------- co_data_*:  <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>WE_CHANNEL_SENSOR_1_MV: The voltage [in mV] at the working electrode of the electrochemical sensor (see Alphasense CO-B4 datasheet for more details)</li> </ol> o3_data_*:  <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Ozone [ppb]: On-device calibrated (according to manufacturer) ozone measurement [in parts-per-billion] </li> <li>Temperature [in °C]</li> <li>Relative Humidity [in %]</li> </ol> Data quality: ------------------ The data has NOT been post-processed! In order to achieve high data quality, the data needs to be cleaned (e.g. outlier filtering) and, most importantly, the sensors need to be individually calibrated. Reference data can be obtained from <a href="http://www.ostluft.ch">www.ostluft.ch</a>, the official air quality monitoring network in eastern Switzerland, which operates multiple monitoring stations in the city of Zurich. Plot Coverage Map (MATLAB): -------------------------------------------- The provided MATLAB script plot_data_coverage.m plots the locations of the collected samples onto the map of Zurich (map_zurich.png).  References: ----------------- The dataset (and related aspects) has partly been used and is described in more detail in the following publications: <ol> <li>Balz Maag et al. SCAN: Multi-Hop Calibration for Mobile Sensor Arrays. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol.1, No.2 (IMWUT), 2017.</li> <li>Olga Saukh et al. Reducing Multi-Hop Calibration Errors in Mobile Sensor Networks. In IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN), 2015. Best Paper Award!</li> <li>Olga Saukh et al. Route Selection for Mobile Sensor Nodes on Public Transport Networks. In Journal of Ambient Intelligence and Humanized Computing, 5(3), Springer, 2014.</li> <li>Olga Saukh et al. On Rendezvous in Mobile Sensing Networks. In Proceedings of the 5th Workshop on Real-World Wireless Sensor Networks (RealWSN), 2013.</li> <li>Jason Jingshi Li  et al. Sensing the Air we Breathe – The OpenSense Zurich Dataset. In Proceedings of the 26th International Conference on Artificial Intelligence (AAAI), 2012.</li> <li>Olga Saukh et al. Route Selection for Mobile Sensors with Checkpointing Constraints. In Proceedings of the 8th International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS, in conjunction with IEEE PerCom), March 2012.</li> <li>David Hasenfratz et al. On-the-fly Calibration of Low-Cost Gas Sensors. In Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN), 2012. </li> </ol> For further information, visit:  <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a>

opencc-by-4.0Oct 2024View details →

Zurich Participatory Budgeting Digital Voting Experiment

This release contains the dataset from the study Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting. The experiment, conducted in March 2023 with 180 participants from ETH Zurich and the University of Zurich, explores how different voting input formats and aggregation methods (Greedy and MES) affect citizens' perceptions of fairness and trustworthiness in Participatory Budgeting (PB). The dataset includes: <ul> <li>Voting data across six formats (A-F).</li> <li>Participants' perceptions of fairness and voting ease.</li> <li>Responses to simulated voting outcomes.</li> </ul> This research is part of a Swiss National Science Foundation (SNSF) project (NRP 77 Digital Transformation, project no. 187249).  <h4>Citation:</h4> If you use the data, please cite the study as follows: Joshua C. Yang, Carina I. Hausladen, Dominik Peters, Evangelos Pournaras, Regula Hänggli Fricker, and Dirk Helbing. 2024. Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting. ACM Digital Government: Research and Practice. <a href="https://doi.org/10.1145/3665332" target="_new" rel="noopener">https://doi.org/10.1145/3665332</a>

opencc-by-4.0May 2023View details →

All cause mortality and morbidity from Influenza in the City and the Canton of Zurich, 1910-1970

Contact: PD Dr. Kaspar Staub <a href="mailto:kaspar.staub@iem.uzh.ch">kaspar.staub@iem.uzh.ch</a> For the <a href="http://leaddata.ch">LEAD Hub</a> we digitized and analyzed the following historical demographic and epidemiological data for the city and the canton of Zurich the first time: Since the end of the 19th century, the Federal Health Office (Eidgenössisches Gesundheitsamt) published a <a href="https://swisscovery.slsp.ch/permalink/41SLSP_NETWORK/gp9v4j/alma991049771079705501">weekly bulletin</a> on vital statistics, newly reported cases of notifiable infectious diseases, and hospitalisations. For the period January 1910 to December 1970, we have digitized and transcribed the following weekly series:  <ol> <li>Weekly deaths for residents and non-residents of the city of Zurich. The quality of these historical vital statistics is assessed to be very good in the literature, incompleteness and migration are no longer a problem as compared to earlier years. However, age-, sex- and cause-specific death numbers were not available on the weekly level. </li> <li>Weekly newly reported cases of influenza-like-illness for the canton and the city of Zurich. This series begins with the introduction of the reporting obligation for influenza in the canton of Zürich in mid-July 1918. As these figures do not include mild cases not treated by a doctor and misdiagnoses, they are probably underestimates, but can still track pandemic and seasonal waves. The reporting system and obligation did not change in the observed time period.</li> <li>Weekly new hospitalisation due to influenza in the canton of Zurich. This series ends in 1938. </li> </ol> The original data format in the weekly bulletins are printed, aggregated tables that have been converted into PDFs using a professional book scanner. Transcription of the data was performed by student assistants using a software and running extended quality-controls. The original tables were in German and French, the digitised data set was annotated in English. The digitized data are organized as a spreadsheet and stored in csv format. The data are organized as rows (representing reporting weeks) and columns (see variable list below). For a few weeks, information in the original sources was missing (indicated by 1 in the “interpolated” variable). In these cases, the missing values were interpolated by averaging the numbers of the week before and the week afterwards.  Codebook: Worksheet "Data" <ul> <li>StartReportingPeriod = Start date of the reporting week (dd.mm.yyyy)</li> <li>EndReportingPeriod = End date of reporting week (dd.mm.yyyy)</li> <li>Interpolated: 1=value for this week has been interpolated; 0=not interpolated</li> <li>CityDeathsTotal = Total absolute number of deaths (all-causes) in the City of Zurich (residents and non-residents)</li> <li>CityDeathsResidents = Absolute number of deaths (all-causes) in the City of Zurich for residents</li> <li>CityDeathsNonresidents = Absolute number of deaths (all-causes) in the City of Zurich for non-residents</li> <li>CantonCases = Absolute number of reported new influenza-like-illness cases by physicians in the Canton of Zurich (including the City)</li> <li>CityCases = Absolute number of reported new influenza-like-illness cases by physicians in the City of Zurich</li> <li>CantonHospitalisationsFluInfections = Absolute number of new hospitalisations due to influenza-like-illness in the Canton of Zurich (including the City)</li> </ul> Worksheet "Population" <ul> <li>Yearly population numbers for the City and the Canton of Zurich (<a href="https://hsso.ch">source</a>)</li> </ul>

opencc-by-4.0Jun 2023View details →

The morphologically glossed Rigveda - The Zurich annotation corpus revised and extended. Hosted by VedaWeb - Online Research Platform for Old Indic Texts.

This file contains morphological and lexicographic annotations for the Rigveda. It was created in the DFG-funded research project Vedaweb and used as source data for the linguistic research platform <a href="https://vedaweb.uni-koeln.de">vedaweb.uni-koeln.de</a>. Prof. Dr. Paul Widmer and Dr. Salvatore Scarlata from the "Institut für Vergleichende Sprachwissenschaft" (Universität Zürich) provided the VedaWeb project a Filemaker file that was later transformed in Cologne into an Excel file. This data contained a version of the Rigveda by Prof. Dr. A. Lubotsky ("Indo-European Linguistics", Leiden University) that had been morphosytactically annotated over the course of more than 10 years at the University of Zurich. It also contained for each token, if available, a reference to an entry in Grassmann's dictionary for the Rigveda.   Modifications made by Jakob Halfmann and Natalie Korobzow to the data in 2020: Disambiguation of the relevant categories, if unspecified in Zurich data, according to the Grassmann dictionary (updates from 6th edition partially included up to page 274): <ul> <li>case, gender and number for nouns, pronouns (columns G–I)</li> <li>number, person, mood, tense and voice for verbs (columns I–M) up to line 109216</li> <li>case, gender, number, tense and voice for participles (columns G–I, L–M) up to line 109216</li> <li>absolutives are marked as Abs. in columns N and V</li> <li>Inconsistencies between the original file from Zurich and the Grassmann dictionary as well as internal inconsistencies in Grassmann are noted in column AE, whenever they were noticed.</li> <li>Zurich data was overwritten by conflicting Grassmann data in columns G–M but retained elsewhere.</li> <li>Verb classes according to Whitney (1885) and Jamison (1983) for class 10 in column Y, differences in root spelling between Whitney and Grassmann are noted in column Z. All potential verb classes provided by Whitney are given for every occurrence of the root.</li> <li>Local particles and verbal forms containing them are marked as LP in column AF.</li> <li>Comparatives and superlatives are marked as such in column X and desideratives as Des. in column Y.</li> </ul>   Modifications made by Anna Fischer (data transformation, technical realisation) to the data: New structure of data table for linguistic annotations with new column titles: <ul> <li>A - "VERS_NR": renamed column (from "belege::stelleMMSSSRR")</li> <li>B - "PADA_NR": renamed column (from "belege::pada")</li> <li>C - "PADA_TEXT_LUBOTSKY": renamed column (from "belege::lubotskypada")</li> <li>D - "TOKEN_NR_VERS": renamed column (from "belege::wortnummer rc")</li> <li>E - "TOKEN_NR_PADA": renamed column (from "belege::wortnummer pada")</li> <li>F - "FORM": renamed column (from "belege::form")</li> <li>G - "KASUS": renamed column (from "belege::kasus")</li> <li>H - "GENUS": renamed column (from "belege::genus")</li> <li>I - "NUMERUS": renamed column (from "belege::numerus")</li> <li>J - "PERSON": renamed column (from "belege::person")</li> <li>K - "TEMPUS": moved and renamed column (from L "belege::tempus")</li> <li>L - "PRAESENSKLASSE": created new column for present stem class for each form</li> <li>M - "LEMMA_PRAESENSKLASSEN": created column for present stem classes of respective lemma: Moved and renamed column (from Y "formen::zusätzliche merkmale verb"), moved values "Abs." and "Inf." to column P "INFINIT", moved values "Prek." and "si-Ipv." to column N "MOOD", moved value "Des." to column Q "ABGELEITETE_KONJUGATION": moved value "se-Form" to column W "WEITERE_WERTE"</li> <li>N - "MODUS": moved and renamed column (from K "belege::modus")</li> <li>O - "DIATHESE": moved and renamed column (from M "belege::diathese")</li> <li>P - "INFINIT": created new column for infinite forms "Abs.", "Inf.", "Ptz.", "ta-Ptz.", "na-Ptz."</li> <li>Q - "ABGELEITETE_KONJUGATION": created new column for secondary conjugation "Des.", "Int.", "Kaus."</li> <li>R - "GRADUS": created new column for degree: "Comp.", "Sup."</li> <li>S - "LOKALPARTIKEL": moved and renamed column (from AF "LP")</li> <li>T - "LEMMA_ZÜRICH": moved and renamed column (from AA "lemmata klassisch::lemma")</li> <li>U - "LEMMA_ZÜRICH_LEMMATYP": moved and renamed column (from AB "lemmata klassisch::lemmatyp")</li> <li>V - "LEMMA_ZÜRICH_BEDEUTUNG": moved and renamed column (from AC "lemmata klassisch::bedeutung")</li> <li>W - "WEITERE_WERTE": created new column for all miscellaneous values: e.g. "Hyperchar.", "n-haltig", "se-Form"</li> <li>X - "KOMMENTAR": created new column merging former columns Z "formen::HELPformbestimmung", AD "lemmata klassisch::HELPbedeutung" and AE "anmerkungen abweichungen"</li> </ul> Columns that were removed due to redundant information: <ul> <li>"formen::zusätzliche merkmale nomen": values "superlative" And "comparative" were renamed "sup." and "comp." and moved to new column R "GRADUS", all other values were moved to new column for miscellaneous W "WEITERE_WERTE"</li> <li>"belege::belegbestimmung summe simpel": values "Ptz.", "ta-Ptz." and "na-Ptz." were moved to new new column P "INFINIT"</li> <li>"belege::kasus bestof"</li> <li>"belege::genus bestof"</li> <li>"belege::numerus bestof"</li> <li>"belege::person bestof"</li> <li>"belege::modus bestof"</li> <li>"belege::tempus bestof"</li> <li>"belege::diathese bestof"</li> <li>"belege::belegbestimmung bestof summe sophistiziert"</li> </ul>   Revisions and additions made by Antje Casaretto to the data in 2023: <ul> <li>F-T: - revision and correction (wherever necessary) of all annotations (books 1-7)</li> <li>G,H,I - disambiguation of case forms, reg. pronouns and nominal forms, if unspecified in Zurich data (books 1-7)</li> <li>L - disambiguation of present stem classes (book 7 and book 1 up to line 21050 vers 01.125.01)</li> <li>M - disambiguation of denominal verbs from primary verbs of the 10th class (books 1-10)</li> <li>N - disambiguation of precative and optative forms wherever possible (books 1-7)</li> <li>Q - new annotations for "Int." (intensives) and "Kaus." (causatives) (books 1-7)</li> </ul>   Revisions and additions made by Antje Casaretto to the data in 2024 with support in data modeling and automation by Anna Fischer: <ul> <li>F-T: revision and correction (wherever necessary) of all annotations (books 8-10)</li> <li>G,H,I: disambiguation of case and gender forms in nominal and pronominal forms, if unspecified in Zurich data (books 8-10)</li> <li>L, M: disambiguation of present stem classes (books 1-10)</li> <li>N: disambiguation of precative and optative forms wherever possible (books 8-10)</li> <li>P: new annotations for "Gdv." (gerundives)</li> <li>Q: new annotations for "Den." (denominatives) (books 1-10) and further annotations of “Kaus.” (causatives) and “Int.” (intensives) (books 8-10)</li> <li>T: revision of lemmatization (books 1-10)</li> <li>V: update of meanings according to revised lemmatization; minimal revision</li> <li>W: revised annotation of ending -se (“se-Form”) (books 1-10); no systematic revision</li> <li>X: no systematic revision</li> <li>A-U: general revision of formal inconsistencies and typing errors (book 1-10)</li> </ul>   Revisions made by Natalie Korobzov and Pascal Coenen to the data in 2024 with computational support by Anna Fischer: <ul> <li>Y - "LEMMA_GRASSMANN_ID": new column for references to Grassmann dictionary (books 1-10) and revision of Grassmann references</li> </ul>

opencc-by-4.0Mar 2003View details →

Zurich Natural Image Database

Zip-File containing a set of 128 natural images that have been used in various eye-tracking studies. Thumbnails.jpg provides an overview. Images were captured with a 3.3 Mega pixel colour mosaic CCD camera (Nikon Coolpix 995, Tokyo, Japan) in RGB and have a resolution (WxH) of 2048 x 1536 pixels.You are free to use these images for scientific purposes, provided at least one of the following papers is appropriately cited:Einhäuser, W., & König, P. (2003). Does luminance‐contrast contribute to a saliency map for overt visual attention?. European Journal of Neuroscience, 17(5), 1089-1097. <a href="https://doi.org/10.1046/j.1460-9568.2003.02508.x">https://doi.org/10.1046/j.1460-9568.2003.02508.x </a>[used the first 8 images in grayscale]Einhäuser, W., Kruse, W., Hoffmann, K. P., & König, P. (2006). Differences of monkey and human overt attention under natural conditions. Vision Research, 46(8-9), 1194-1209. <a href="https://doi.org/10.1016/j.visres.2005.08.032">https://doi.org/10.1016/j.visres.2005.08.032 </a>[used the first 108 images in grayscale]Frey, HP., König, P. & Einhäuser, W. The role of first- and second-order stimulus features for human overt attention. Perception & Psychophysics, 69, 153–161 (2007). <a href="https://doi.org/10.3758/BF03193738">https://doi.org/10.3758/BF03193738 </a>[used the images in color]

opencc-by-4.0Apr 2024View details →

Wedding Fair, Zurich, 8 January 2023 (Wedding fair_02_CH_080123)

Wedding fair in Zurich 8 January 2022 Videos and photos

opencc-by-4.0Jan 2022View details →

Zurich Summer Dataset

The "Zurich Summer v1.0" dataset is a collection of 20 chips (crops), taken from a QuickBird acquisition of the city of Zurich (Switzerland) in August 2002. QuickBird images are composed by 4 channels (NIR-R-G-B) and were pansharpened to the PAN resolution of about 0.62 cm GSD. We manually annotated 8 different urban and periurban classes : Roads, Buildings, Trees, Grass, Bare Soil, Water, Railways and Swimming pools. The cumulative number of class samples is highly unbalanced, to reflect real world situations. Note that annotations are not perfect, are not ultradense (not every pixel is annotated) and there might be some errors as well. We performed annotations by jointly selecting superpixels (SLIC) and drawing (freehand) over regions which we could confidently assign an object class. The dataset is composed by 20 image - ground truth pairs, in geotiff format. Images are distributed in raw DN values. We provide a rough and dirty MATLAB script (preprocess.m) to: i) extract basic statistics from images (min, max, mean and average std) which should be used to globally normalize the data (note that class distribution of the chips is highly uneven, so single-frame normalization would shift distribution of classes). ii) Visualize raw DN images (with unsaturated values) and a corresponding stretched version (good for illustration purposes). It also saves a raw and adjusted image version in MATLAB format (.mat) in a local subfolder. iii) Convert RGB annotations to index mask (CLASS \in {1,...,C}) (via rgb2label.m provided). iv) Convert index mask to georeferenced RGB annotations (via rgb2label.m provided). Useful if you want to see the final maps of the tiles in some GIS software (coordinate system copied from original geotiffs). Some requests from you We encourage researchers to report the ID of images used for training / validation / test (e.g. train: zh1 to zh7, validation zh8 to zh12 and test zh13 to zh20). The purpose of distributing datasets is to encourage reproducibility of experiments. Acknowledgements We release this data after a kind agreement obtained with DigitalGlobe, co. This data can be redistributed freely, provided that this document and corresponding license are part of the distribution. Ideally, since the dataset could be updated over the time, I suggest to distribute the dataset by the official link from which this archive has been downloaded. We would like to thank (a lot) Nathan Longbotham @ DigitalGlobe and the whole DG team for his / their help for granting the distribution of the dataset. We release this dataset hoping that will help researchers working in semantic classification / segmentation of remote sensing data in comparing to other state-of-the-art methods using this dataset as well in testing models on a larger and more complete set of images (with respect to most benchmarks available in our community). As you can imagine, it has been a tedious work in preparing everything. Just for you.   If you are using the data please cite the following work <ul> <li> Volpi, M. & Ferrari, V.; <a href="https://drive.google.com/file/d/0B9xP9Y5JKJz0RUZjVzRFTDF4U0k/view?usp=sharing">Semantic segmentation of urban scenes by learning local class interactions</a>, In IEEE CVPR 2015 Workshop "Looking from above: when Earth observation meets vision" (EARTHVISION), Boston, USA, 2015. </li> </ul>

opencc-by-4.0Jul 2022View details →

Bibliographic Metadata from Zurich University of Teacher Education (PHZH) 2021

The data for this set were extracted from unstructured bibliographic metadata in the online personal portraits of PHZH staff and from the PHZH repository. The two sources were merged, cleaned, and supplemented with relevant parameters regarding a comprehensive Open Access monitoring. The set provides the PHZH data basis for the national Open Access monitoring (<a href="https://oam.oamonitor.ch/">https://oam.oamonitor.ch/</a>) by swissuniversitis/CSAL.

opencc-byMay 2021View details →

Fountain 238 Zurich

Fountain No 238 Zurich, built ca. 1940, I think. I cross it every day on my way to work. I don't know the name of the artist of the fountain figure or the architect. Modeled with blender, textured with substance painter and substance designer Source: Objaverse 1.0 / Sketchfab