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767 results for “switzerland”
Isotopic analysis of extracted water from a larch (Larix decidua) stand in a high mountain watershed (Vallon de Nant - Switzerland)
<p>A total of 185 samples of soil and trees were taken from a stand of larch (Larix decidua) spanning from 1500 to 1600 m.a.s.l. in the Vallon de Nant in the Swiss canton of Vaud. Twenty individual trees and soil were sampled along two transects perpendicular to the main river channel of the Avançon de Nant at approximately midday on seven days in the foliage season between July 2017 and June 2018.</p><p>The data consists of two data (csv) and one document (pdf) files. The sample data file includes the date and estimated time of sampling, the type of sample (vegetation or soil), the transect and tree ID for look-up in the tree metadata file, the soil depth in centimeters, and the determined mean and standard deviation of deuterium, oxygen-18, and oxygen-17. The tree metadata file includes the transect (north or south), the tree ID number, the latitude, longitude, elevation (meters above sea level), and tree height and diameter at breast height in centimeters. Finally, a document describing the methods in more detail is included. </p>
LIAS light – A Database for Rapid Identification of Lichens – Subset Switzerland p. pte.
<p>This subset of the LIAS light database focuses on lichens found in Switzerland, providing comprehensive data for ecological research and taxon identification purposes. Not all taxa and only categorical (but 2 numerical) characters (descriptors) of the recorded taxa are covered. Updates and additional data will be published subsequently.</p>
Technical potential of ground-source heat pumps for Western Switzerland
<p>This dataset contains an estimation of the technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 200 x 200 m<sup>2</sup>. The technical potential is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to <strong>avoid the over-exploitation</strong> of the heat capacity of the ground. We consider GSHPs with <strong>vertical closed-loop borehole heat exchangers</strong> (BHE) installed at depths of 50 - 200 m. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains.</p> <p>The estimated potential accounts for:</p> <ul> <li>Norms for geothermal installations set by the Swiss Society of Engineers and Architects (SIA 384/6)</li> <li>Thermal interferences between neighbouring boreholes and their impact on the temperature change in the ground</li> <li>Topographic Landscape data to assess the available area for BHE installation</li> </ul> <p>The methodology used to generate the data is described in:</p> <p>Walch, Alina, Nahid Mohajeri, Agust Gudmundsson, and Jean-Louis Scartezzini. ‘Quantifying the Technical Geothermal Potential from Shallow Borehole Heat Exchangers at Regional Scale’. <em>Renewable Energy</em> 165 (2021): 369–80. <a href="https://doi.org/10.1016/j.renene.2020.11.019">https://doi.org/10.1016/j.renene.2020.11.019</a>.</p> <p><strong>Dataset description</strong></p> <p>As the data is targeted to large-scale applications and potential studies, it is shared in the format of <strong>pixels of 200 x 200 m<sup>2</sup></strong>. Upon request it can be provided at different aggregation levels, as it is generated at the resolution of individual building units (parcels). The potential is provided as <strong>annual</strong> <strong>values</strong>, and it can be converted to monthly values using the provided heating degree weights. For each pixel of 200 x 200 m<sup>2</sup>, we provide the following variables:</p> <ul> <li>Annual total technical heat extraction potential (in MWh)</li> <li>Potential heat delivered <em>to buildings </em>(heat pump output), assuming a heat pump performance (COP) of 4.5 (in MWh)</li> <li>Available area for GSHP installation (in m<sup>2</sup>)</li> <li>Number of installed boreholes </li> <li>Average heat extraction rate (in W/m)</li> <li>Average borehole depth (in m)</li> <li>Average borehole spacing within the parcels located in the pixel (in m)</li> <li>Heating degree weights (i.e. heat demand variation) for each month</li> </ul> <p>A description of the metadata is provided in the document <em>gshp_VD_GE_metadata_V1.pdf.</em></p> <p>This work is part of the PhD Thesis of Alina Walch. </p>
A 2-minute rainfall (12 locations) and discharge time series at the Vallon de Nant catchment, Switzerland, for 2018 summer seasons
<p>The data set contains rainfall time series within the experimental 13.4 km² Vallon de Nant catchment, Switzerland (Michelon et al., 2020), from June 30th to September 23rd 2018 at 12 locations. A network of <em>Pluvimate</em> drop-counting raingauges (www.driptych.com) measured continuously the rainfall intensity at a 2-minute resolution. Operation and characteristics of the raingauges are detailed in Benoit et al. (2018) and Michelon et al. (2020), and the rating curve is described by Ceperley et al. (2018).</p> <p>Description of the files:</p> <ul> <li><em><strong>data.csv</strong></em> contain the rainfall intensities for the observation period, along with the main river discharge measured at the <a href="https://map.geo.admin.ch/?lang=fr&topic=ech&bgLayer=ch.swisstopo.pixelkarte-farbe&layers=ch.swisstopo.zeitreihen,ch.bfs.gebaeude_wohnungs_register,ch.bav.haltestellen-oev,ch.swisstopo.swisstlm3d-wanderwege,KML%7C%7Chttps:%2F%2Fpublic.geo.admin.ch%2FaLKDanGXRPGMpB_D51f2Tg&layers_visibility=false,false,false,false,true&layers_timestamp=18641231,,,,&E=2574619.27&N=1122462.26&zoom=8">outlet</a> over the same 2-minutes time step as the rainfall intensity. We also provide areal rainfall intensity aggregated over the whole catchment:<br> Columns: <ul> <li>year [-]</li> <li>month [-]</li> <li>day [-]</li> <li>hour [-]</li> <li>minute [-]</li> <li>specific discharge 95% inf. [mm/day]: inferior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge 95% sup. [mm/day]: superior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge mean [mm/day]: mean value of the specific discharge over 2 minutes</li> <li>specific discharge median [mm/day]: median value of the specific discharge over 2 minutes</li> <li>P St. #X [mm]: rainfall amount measured at the station X over 2 minutes</li> <li>P stochastic mean [mm/h]: rainfall amount interpolated over the whole catchment over 2 minutes</li> <li>P stochastic std [mm/h]: standard deviation of the stochastic rainfall interpolation, over 2 minutes</li> </ul> </li> <li><strong><em>stations.csv</em></strong> describes the raingauge locations.<br> Columns: <ul> <li>Station ID [-]</li> <li>lon [WGS84]: decimal longitude of the station into WGS84</li> <li>lat [WGS84]: decimal latitude of the station into WGS84</li> <li>E [CH1903]: east coordinate into Swiss Coordinate System</li> <li>N [CH1903]: north coordinate into Swiss Coordinate System</li> <li>elevation [m asl]: altitude of the station in meters above the sea level</li> <li>data in 2017 [-]: flag if the station was working over the 2017 observation period</li> <li>data in 2018 [-]: flag if the station was working over the 2018 observation period</li> </ul> </li> <li><em><strong>rainfall_viewer.m</strong></em> is a <em>MatLab</em> script (created with <em>MatLab 2017b</em>) which allows the joint visualization of the rainfall intensities and river discharge. It produces a composite figure with the following plots: <ul> <li>On top the general hydrograph over the whole observation period [mm/day]. The red dashed lines mark out period that the other plots are focus on. The shaded orange areas correspond to when the river stage data was not available.</li> <li>Below, the zoomed hydrogram show a detailed view of the river discharge (and uncertainty). In case a river reaction is associated, the discharge event is marked out by red dashed lines. Between these vertical lines is drawn a line joining the initial and final baseflow, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff part.</li> <li>In the middle a zoomed magnification of the hydrograph that shows a detailed view of the discharge in the river [mm/day]. When a river response is associated, the discharge event is marked with dashed red lines. Between these vertical lines a line joining the initial and final baseflow is drawn, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff.</li> <li>At the bottom are shown the rainfall recorded by each of the 12 rain gauges (the y-axis scale between 2 stations is about 20 mm/h). The rainfall event is marked out by green dashed lines.</li> <li>Above is shown the rainfall amount (and uncertainty) interpolated over the catchment using the stochastic method. The rainfall event is marked out by green dashed lines.</li> <li>On the left, a map with the 12 raingauge locations show the total amount of rainfall recorded by each station during the event (a red cross shows missing data).<br> <br> It is possible to zoom in the plots by clicking with the left and right mouse buttons to define respectively the starting and ending of the visualization window. The middle button defines a third time reference used to identify rainfall intensity peaks or discharge peaks. Statistics concerning the visualization period are displayed on the MatLab console.<br> Pressing [enter] will save the figure into a PNG file named with the starting and ending dates of the visualization window.</li> </ul> </li> <li><strong><em>Q_stats.m </em></strong>is a MatLab function used by the main code rainfall_viewer.m</li> <li><strong><em>print_figure.m </em></strong>is a MatLab function used by the main code rainfall_viewer.m</li> <li><strong>data.mat</strong> is a MatLab data file with all data required by the main code rainfall_viewer.m</li> </ul>
Hail Event on 2022-06-28 in Locarno-Monti (TI), Switzerland: Drone Photogrammetry Imagery, Mask R-CNN Model and Analysis Data of Hailstones
<p>This hail data collection belongs to a drone hail survey performed on 2022-06-28 in Locarno-Monti (TI, Switzerland). The supercell reached the location around 07:50 UTC in the morning. Only one photogrammetry flight could be performed and thus no estimation of the hail melting process is available. The orthophoto is masked to ignore parts where detection of hail is unwanted.</p> <p> </p> <p>Expert 1 (lai, mlainer), Expert 2 (jtm), Expert 3 (por, jportmann)</p>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
Scenarios of technical and useful ground-source heat pump potential for building heating and cooling in Western Switzerland
<p>This dataset contains an estimation of the useful and technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 400 x 400 m<sup>2</sup>. The <strong>technical potential</strong> is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to <em>avoid the over-exploitation</em> of the heat capacity of the ground. We consider GSHPs with <em>vertical closed-loop borehole heat exchangers</em> (BHE) installed at depths of 50 - 200 m. The <strong>useful potential</strong> is defined as the potential that could be delivered to building heating and cooling systems via a water-to-water heat pump.</p> <p>The datasets contains future scenarios of heating and cooling demand, space cooling equipment deployment (service sector only) and climate change models and considers the potential use of DHC. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains.</p> <p>The data package contains information on the available area for GSHP systems, the heating and cooling demand as well as the resulting technical and useful potentials for all simulated scenarios of future cooling demand (200 Monte Carlo runs), for the case of <strong>direct heat supply</strong> (per pixel of 400 x 400 m<sup>2</sup>) as well as for <strong>district heating and cooling</strong> (DHC). In scenarios without DHC (direct heat supply), the results are summarized by pixel of 400 x 400 m<sup>2</sup>. In scenarios with DHC, the results of potentials <em>within</em> DHCs are summarized by DHC (see <em>*_in_dhc.csv</em>) while potentials <em>outside</em> of DHCs are summarized by pixel (see <em>*_outside_dhc.csv</em>).</p> <p>For details on the methodology applied to obtain the results provided in the data package, please refer to the above-mentioned research articles. A description of all files is provided in<em> Dataset documentation.pdf</em> and metadata is provided in <em>Datapackage.json.</em></p>
Weather dataset from Otemma glacier forefield, Switzerland (from 14 July 2019 to 18 November 2021)
<p>Weather data collected in the Otemma forefield (Switzerland) from 14 July 2019 to 18 November 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:<br> tom.muller.1@unil.ch<br> bettina.schaefli@giub.unibe.ch</p> <p><strong>Description of data : </strong>WeatherData.csv</p> <p>Time span of data : 14 July 2019 to 18 November 2021<br> Time step : homogenized 10 minutes-averaged data</p> <p>Location of data (Coordinate in SWISS LV95 (EPSG:2056)<br> - Glacier snout Station : 2598615 / 1087375<br> - Glacier center Station : 2600495 / 1088631<br> - Floodplain Station : 2598096 / 1087087</p> <p>STRUCTURE OF DATA : tidy dataframe with following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> Air_humidity : Air humidity in percent of air saturation [%]<br> Air_temperature : Air temperature in [°C]<br> Atm_pressure : Atmospheric pressure in [hPa]<br> Incoming_radiation : Incoming shortwave radiation in [W/m2]<br> Precipitation : Liquid precipitation measured [mm]<br> - <em>name </em>: location of data (see coordinates above)<br> - <em>dateUTC </em>: date with UTC timezone</p> <p>Device used for data acquisition :<br> - Air_humidity/Air_temperature/Atm_pressure : Decagon VP-4<br> - Incoming_radiation : Apogee Instruments SP-11<br> - Precipitation : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)</p> <p> </p> <p><strong>Description of data : </strong>RainComposite_Otemma_Arolla.csv</p> <p>Time span of data : 01 July 2019 to 18 November 2021<br> Time step : homogenized 10 minutes-averaged data</p> <p>The dataset compiles the measured rain during summer at the closest weather station (Glacier snout).<br> For the winter period (and few gaps during the summer), the solid precipitations (snow) from the closest MeteoSwiss weather stations (SwissMetNet) were used.<br> Gaps where filled with 1) MeteoSwiss Otemma and if data were still missing filled with 2) MeteoSwiss Arolla<br> <br> Location of data (Coordinate in SWISS LV95 (EPSG:2056)<br> - Glacier snout Station : 2598615 / 1087375<br> - Otemma camp Station : 2597508 / 1086653<br> - MeteoSwiss Station : 2596476 / 1085864<br> - MeteoSwiss Arolla : 2603507 / 1095832</p> <p>STRUCTURE OF DATA : tidy dataframe with following headers :<br> - date : local date (UTC+01 with dst)<br> - variable : parameter of interest<br> Precipitation : Liquid and solid precipitation [mm]. Composite dataset composed of melted snow (snow Water Equivalent, in mm, from MeteoSwiss station) and Rain (in mm from Glacier station).<br> - Location : location of data (see above)<br> - dateUTC : date in UTC timezone<br> <br> Device used for data acquisition :<br> - Glacier snout Station : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)<br> - Otemma camp Station : Double tipping buckets rain gauge, Spectrum WatchDog 1120 (resolution 0.25 mm)<br> - MeteoSwiss : see <a href="https://www.meteoswiss.admin.ch/home/measurement-and-forecasting-systems/land-based-stations/automatisches-messnetz.html">SwissMetNet</a> project</p> <p> </p> <p><strong>Description of data :</strong> Otemma_weather_Plot_alldata.html</p> <p>An interactive plot generated with python plotly (open in web browser) containing all above described data.</p>
Stream discharge, stage, electrical conductivity & temperature dataset from Otemma glacier forefield, Switzerland (from July 2019 to October 2021)
<p>Stream data collected in the Otemma forefield (Switzerland) from July 2019 to Ocober 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> <li>floreana.miesen@unil.ch</li> </ul> <p><strong>Description of data </strong></p> <p>A detailed description of the dataset is provided in the <strong>data_description_analysis.pdf</strong> file. In particular, the methodology and stage-discharge rating curves are provided in this file. Stream data were measured in three locations from glacier snout (Station 1); after the outwash plain (Station 2) and at the end of the glacier forefield (Station 3) (<strong>see overview_GS.png</strong>). A <strong>shapefile </strong>is also provided (coordinate system LV95).</p> <p>2 datasets are available in the data.zip file:</p> <ul> <li> <p><strong>River_2019_2021_10T.csv</strong> : contains the measured River Electrical conductivity (EC) [μS/cm], Stage [meters] and Temperature [°C] data for all stations in a tidy data format (see pdf for detailed description), with a 10 minutes timestep.</p> </li> <li> <p><strong>Discharge2020_10T.csv </strong>&<strong> Discharge2021_10T.csv </strong>: contains the estimated discharge [m<sup>3</sup>/s] at Station 1 and Station 2 from July 2020 to October 2021 and estimated error (2 standard deviations) in a tidy data format (see pdf for detailed description), with a 10 minutes timestep.</p> </li> </ul> <p>Additionally, the point discharge measurements covering peak summer discharge to minimal winter baseflow are provided in the <strong>Point_discharge_measurements_2020_2021.xlsx</strong> file.</p> <p>Plots of river parameters and discharge are also provided in data.zip for vizualisation.</p> <p> </p>
Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021
<p><strong>Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>9 piezometers consisting of fully screened plastic tubes were installed at an averaged depth of 1.5 to 2m in the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). They cover four transects perpendicular to the stream from downstream (A) to upstream to (D).</p> <p>Water table elevation and temperature were recorded in each well at a 10 minute interval using SparkFun MS5803-14BA pressure sensors. Sensor resolution is 1 mm and 0.01 °C, sensor accuracy is ± 2 cm and ± 0.8°C. Sensor bias was verified and corrected by bi-monthly manual groundwater stage measurements. Water temperature was not manually corrected and may be subject to some unidentified bias.</p> <p>Piezometer location can be visualized in<strong><em> overview_piezo.jpg</em> </strong></p> <p>Piezometer coordinates are available in<strong> </strong>shapefile <strong><em>GPS_piezometers.zip</em></strong> (coordinate system: swiss LV95 (EPSG:2056))</p> <p>Piezometers name matches the labelling used in Müller et al. 2022 (A1,A2 to D1,D2). Two additionnal piezometers (BinjUp & BinjDown) used for a specific salt tracing analysis (see <em>ERT_timelapse_salt_tracer.gif </em>at <a href="https://zenodo.org/record/6342767#.YjBRmTXjJlh">https://zenodo.org/record/6342767#.YjBRmTXjJlh</a>) are also available. Finally an additional piezometer (B1-2) located between B1 and B2 is also available although it was not used in Müller et al. 2022.</p> <p><strong>Data Structure</strong></p> <p><strong>df_piezometers.csv</strong> is formated as a tidy dataframe with 10 minute interval and following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> w_elevation: Water table elevation [m. asl]<br> w_temperature : Groundwater temperature [°C]<br> - <em>name </em>: name of piezometer (see coordinates in GPS_piezometers.zip)<br> - <em>dateUTC </em>: date with UTC timezone</p> <p>Data can be quickly vizualized in<strong> plot_piezo.png</strong> or interactively in a browser using <strong>plot_piezo_interactive.html</strong></p>
GNSS refractometry data from Davos Weissfluhjoch, Switzerland in 2016/17
<p>This data is collected for the feasibility study on snow water equivalent (SWE) retrieval using the GNSS refractometry method, described in Steiner et al. (2020). </p> <p>GNSS Rinex data (1s sampling interval, multi-system, multi-frequency) from two high-end geodetic receivers (base=WJLR and rover=WJL0) are available for the complete 2016/17 season for the Swiss Alpine test sites Davos Weissfluhjoch, operated by the WSL Institute for Snow and Avalanche Research SLF (WSL SLF), Switzerland. SWE reference data from a snow pillow, snow scale, and manual observations were provided by the WSL SLF. The data processing and the SWE estimation results are revealed in Steiner et al. (2018, 2020, 2022).</p>
GNSS refractometry data from Davos Laret, Switzerland in 2021/22
<p>This data is collected for the feasibility study on (near) real-time snow water equivalent (SWE) retrieval using the GNSS refractometry method, described in Steiner et al., submitted to Sensors, 2022. </p> <p>GNSS baseline solutions, stored in zipped binary python pickle format (ENU.pkl) are available from real-time kinematic (RTK) GNSS processing for the season 2021/22 for the Swiss Alpine test sites Davos Laret, operated by the WSL Institute for Snow and Avalanche Research SLF (WSL SLF), Switzerland. Additionally, the raw GNSS Rinex data (1s sampling interval, multi-system, multi-frequency) are available for the base and rover receiver from the same setup. SWE reference data from a snow scale and manual observations were provided by the WSL SLF. The data processing and the SWE estimation results are revealed in Steiner et al. 2020 and 2022.</p>
Cup-marked stone, Zermatt-Hubelwäng, Switzerland - imagery and photogrammetrically derived 2.5D data, 3D data and orthophoto of stone slab no. 3920-01
<p>Imagery and derived 2.5D data, 3D data and orthophoto of cup-marked stone slab No. 3920-01 (http://www.ssdi.ch/), Zermatt-Hubelwäng, Switzerland.</p> <p>Supplemental data for: J. Reinhard, Was in den Rucksack passt… In: Chr. Rinne et al. (ed.), Vom Bodenfund zum Buch - Archäologie durch die Zeiten. Festschrift für Andreas Heege. Historische Archäologie Sonderband 1 (Bonn 2017), 503-520. URL: <a href="http://www.histarch.uni-kiel.de/sonderband01.htm">http://www.histarch.uni-kiel.de/sonderband01.htm</a>, DOI:<a href="https://doi.org/10.18440/ha.2017.101"> https://doi.org/10.18440/ha.2017.101</a> (original paper and additional poster contained in the upload). See <a href="http://skfb.ly/6sxJT">https://skfb.ly/6sxJT</a> for an online visualization of the data on Sketchfab.</p> <p> </p> <p>Contents:</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_503.pdf?versionId=2c4ebd68-da57-42da-b49a-b95d10a9f4f8">HASB2017_130_503.pdf</a>: PDF of Reinhard 2017 (cited above).</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup1.zip?versionId=87498b06-3e60-4dc1-a182-0157df73ad80">HASB2017_130_sup1.zip</a>: dense point cloud (full resolution, .ply)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup2.zip?versionId=951d3131-b09b-4efb-b768-adbd29e55e91">HASB2017_130_sup2.zip</a>: orthophoto (5 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup3.zip?versionId=8f5ac851-e596-4a31-b724-5f056a4940eb">HASB2017_130_sup3.zip</a>: DEM (1 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup4.zip?versionId=29043ecd-1bea-4fbd-8264-38e99c583691">HASB2017_130_sup4.zip</a>: orthophoto (1 mm resolution, GeoTIFF)</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/HASB2017_130_sup5.zip?versionId=ae05c230-c25a-4a29-801f-202823648ade">HASB2017_130_sup5.zip</a>: 3D model (full resolution, .obj/.mtl/.jpg)</p> <p><a href="https://zenodo.org/record/3373713/files/Image-based_modeling_report.pdf?download=1">Image-based_modeling_report.pdf</a>: Image-based modeling report generated by Agisoft PhotoScan</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/In_Rock_We_Trust_Poster_EAA_Bern_2019-09-07.pdf?versionId=7c003048-b261-45a6-803f-6affc3c48721">In_Rock_We_Trust_Poster_EAA_Bern_2019-09-07.pdf</a>: poster presented at the EAA annual conference 2019 in Bern</p> <p><a href="https://zenodo.org/record/3373713/files/Notes_on_image-based_modeling.pdf?download=1">Notes_on_image-based_modeling.pdf</a>: Notes on the image-based modeling process including scaling information</p> <p><a href="https://zenodo.org/api/files/166f08c2-3b28-4c6d-8fca-dcd6fadd8ef5/Photos.zip?versionId=833aaf74-8c0e-48a5-8459-28d47af02d2e">Photos.zip</a>: complete set of images used in this project, taken in april 2016</p> <p> </p>
Source apportionment of highly time-resolved elements during a firework episode from a rural freeway site in Switzerland
<p>Data to accompany "Source apportionment of highly time-resolved elements during a firework episode from a rural freeway site in Switzerland" publication in Atmospheric Chemistry and Physics. This repository contains measurement data in Härkingen, Switzerland, a permanent station of the Swiss National Air Pollution Monitoring Network (NABEL). Sampling was performed from 23 July to 13 August 2015. This repository has excel file (all data.xlsx) for all the raw data measured during campaign. In addition, it has data corresponding to each figures presented in main text published version.</p>
Data and code for the analysis in "Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland"
<p>Data and code used for the analysis in <em>Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland</em> (Lemaitre et al., Swiss Medial Weekly 2020).</p>
Research Data of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland
<p>The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.</p> <p>This 2014 Census data set represents the data collected in three different ways:</p> <ul> <li>qualitative analysis of the open access repository websites</li> <li>automatic validation of the metadata via OAI-PMH using the DINI-Validator [2] </li> <li>online survey of repository operators</li> </ul> <p>As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.</p> <p> </p> <p>[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. <em>D-Lib Magazine</em>, 19. http://dx.doi.org/10.1045/november2013-vierkant </p> <p>[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml</p> <p>[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. fig<strong>share</strong>. <br /> http://dx.doi.org/10.6084/m9.figshare.677099</p>
Landslides from Space - Val Strem Rockslide, Switzerland (14th March 2016)
<p>In the night of 14th of March 2016 a large rockslide appeared in the Swiss Alps. It run down the valley for more than one kilometre and stopped about 800 metres ahead of the village Sedrun/Tavetsch.</p> <p>The pre-event acquisition is from 29th August 2015 (Sentinel-2) and the post-event acquisition is from 5th May 2016 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2015-2016)</em></p>
Hybrid Solar Panel Real Measurements in Switzerland
<h3>Measurements of a hybrid solar panel</h3> <p>Datasheet of the hybrid PV panel (<a title="Datasheet" href="https://cdn.enfsolar.com/Product/pdf/Crystalline/55adc587c2506.pdf" target="_blank" rel="noopener">Here</a>)</p> <p>These measurements have taken place in two places in Valais, Switzerland.</p> <p>One is "Granges" in longitude: 7.4649965° and latitude: 46.2647793° and the other is "Sion" in longitude: 7.364832° and latitude: 46.227372°.</p> <p> </p>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Soil characteristics and spectral reflectance data of six agricultural fields in Switzerland
<p>Soil characteristics and spectral reflectance data of six agricultural fields in Switzerland collected within the EJP Soil project STEROPES. The data in the .csv files is organized as relational database with the database schema depicted in DB_Schema.pdf. The file headers (marked with #) contain metadata.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.