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.
48
datasets available to search
ShareScore release 0.7.1
Dataset results
48 results for “ground temperature”
Ground temperature time series in European mountain permafrost
<p>RELATED PUBLICATION</p> <p>This dataset is related to the following publication:</p> <p><strong>Noetzli J., Isaksen, K., Barnett, J., Chrisitiansen, H.H., Delaloye, R., Etzelmueller, B., Farinotti, D., Gallemann, T., Guglielmin, M., Hauck, C., Hilbich, C., Hoelzle, M., Lambiel, C., Magnin, F., Oliva, M., Paro, L, Pogliotti, P., Riedl, C., Schoeneich, P., M., Valt, M., Vieli A., Philliips, M. (2024). Enhanced permafrost warming in Euro­pean mountains in the 21st century. Nature Communications, 15, 10508, <a href="https://doi.org/10.1038/s41467-024-54831-9">https://doi.org/10.1038/s41467-024-54831-9</a>.</strong></p> <p><strong>==> </strong></p> <p><strong>For information on the measurements, selection criteria, processing information and data providers please refer to the methods, data availability and acknowledgements sections of the related publication ! </strong></p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>CONTENT</p> <p>The dataset includes monthly and annual time series of ground temperatures measured in 64 boreholes in European mountain permafrost areas and corresponding metadata.</p> <p>Temporal coverage: at least 10 years until 2022</p> <p>Spatial coverage: European mountain regions (Svalbard, Scandinavia, Iceland, European Alps, Sierra Nevada)</p> <p>Depth of measurements: at least 10 m; for all boreholes data of the sensors closest to 5, 10 and 20 m depth are included</p> <p>Monthly means are calculated from daily values and annual values are derived from monthly mean values.</p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>DATA COMPILATION</p> <p>The data were compiled to derive 10-year and 20-year warming rates in European mountain permafrost in the study by Noetzli et al. (in review, see above). Data were collected from national permafrost observation networks as well as from individual institutions (e.g, universities, environmental agencies).</p> <p>The aquisition of long time series over decades requires long-term committment from the responsible institutions to maintain instruments and to collect and curate the data. Details on the data source for each time series can be found in the metadata file as well as in the related publication. The main data sources by country are given in the list below.</p> <table> <tbody> <tr> <td><strong>Country</strong></td> <td><strong>Data source (institution or national network)</strong></td> </tr> <tr> <td>Austria</td> <td>GeoSphere Austria</td> </tr> <tr> <td>France</td> <td>Réseau français d'observation du permafrost (PermaFrance, <a href="https://wslch365-my.sharepoint.com/personal/jeannette_noetzli_slf_ch/Documents/PermafrostEurope/permafrance.osug.fr">permafrance.osug.fr</a>)</td> </tr> <tr> <td>Germany</td> <td>Bavarian Environment Agency</td> </tr> <tr> <td>Iceland</td> <td>University of Oslo</td> </tr> <tr> <td>Italy</td> <td>ARPA Piemonte, ARPA Valle d'Aosta, ARPA Veneto, University of Insubria</td> </tr> <tr> <td>Norway</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Spain</td> <td>Universitat de Barcelona</td> </tr> <tr> <td>Svalbard</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Sweden</td> <td>University of Stockholm</td> </tr> <tr> <td>Switzerland</td> <td>Swiss Permafrost Monitoring Network PERMOS (<a href="http://www.permos.ch">http://www.permos.ch</a>)</td> </tr> </tbody> </table> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>FILES AND FORMAT</p> <p>This data set includes three csv-files: <br>1) metadata with information on the measurement location and data provider<br>2) monthly ground temperature time series and <br>3) annual ground temperature time series. </p> <p>The variables in the three files are described below. Data files are in long data format.</p> <p><strong>File 1 – borehole_overview.csv<br></strong>Key information on the boreholes, responsible institutions and contact persons.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Name</td> <td>Name of the borehole (as used in the related study)</td> </tr> <tr> <td>Country</td> <td>Alpha-2 code</td> </tr> <tr> <td>Region</td> <td>Larger region</td> </tr> <tr> <td>First_year</td> <td>First year of data</td> </tr> <tr> <td>Elevation [m asl.]</td> <td>Elevation of the borehole</td> </tr> <tr> <td>Lat [° N]</td> <td>Latitude</td> </tr> <tr> <td>Lon [° E]</td> <td>Longitude</td> </tr> <tr> <td>Depth [m]</td> <td>Total depth of the borehole</td> </tr> <tr> <td>DZAA [m]</td> <td>Depth of the Zero Annual Amplitude (uppermost sensor with annual amplitude ≤0.1)</td> </tr> <tr> <td>Phase lag</td> <td>Phase lag at 10 m depth compared to surface in months</td> </tr> <tr> <td>Morphology</td> <td>Main morphology of the site</td> </tr> <tr> <td>Surface_cover</td> <td>Main surface cover at the site</td> </tr> <tr> <td>Lithology</td> <td>Main lithology of the site</td> </tr> <tr> <td>Ice_content</td> <td>Basic classification by ground ice content at the site (no ice, ice-poor, ice-bearing, ice-rich), see publication for details</td> </tr> <tr> <td>Institution</td> <td>Responsible institution (in the year 2024)</td> </tr> <tr> <td>Contact_person</td> <td>Contact person (in the year 2024)</td> </tr> <tr> <td>Special_remarks</td> <td>Remarks on location, e.g. horizontal borehole</td> </tr> </tbody> </table> <p> </p> <p><strong>File 2 – permafrost_temperatures_european_mountains_monthly_2022.csv<br></strong>Time series of monthly mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY-MM-DD]</td> <td>Date</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Monthly mean ground temperature (aggregated from daily values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum daily ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum daily ground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of daily values available to calculate monthly mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table> <p> </p> <p><strong>File 3 – permafrost_temperatures_european_mountains_annual_2022.csv<br></strong>Time series of annual mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY]</td> <td>Year</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Annual mean ground temperature (aggregated from monthly values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum monthly ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum monthlyground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of monthly values available to calculate annual mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>CONTACT</p> <p>For question related to this dataset please contact the corresponding author: jeannette.noetzli@slf.ch. <br>For questions related to a specific time series, see metadata for contact information.</p>
An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)
<p>This is a multiannual ground temperature dataset covering sixteen high elevation sites (3493-4377 m a.s.l.) in the Bale Mountains, southern Ethiopian Highlands</p> <p>The dataset is described in detail in the corresponding data paper by Groos et al. 2021 (https://doi.org/10.5194/essd-2021-268)</p> <p>The repository contains a readme file ("readme.txt"), a GeoPackage ("Data_Logger_Location.gpkg"), a thermal infrared time-lapse video ("thermal_infrared_time-lapse_video.mp4"), a metadata file for the video ("video_metadata.txt"), and two sub-folders: "raw_data" and "processed_data"</p> <p>The GeoPackage provides information on the location and environmental setting of each logger and can be easily opened and displayed in a Geographic Information System. The coordinate reference system is WGS84 / Geographic (EPSG code: 4326).</p> <p>The thermal infrared time-lapse video (<a href="https://vimeo.com/676294827">https://vimeo.com/676294827</a>) visualises the phenomenon of nocturnal cold air drainage and ponding in the Bale Mountains (for more information see the metadata file and Appendix C in the corresponding data paper).</p> <p>The folder "raw_data" contains the original logfiles of all GT and TM data loggers (see Table 1) in a tab-delimited text format with the logger ID and download date encoded in the file name. The date format of the GT data loggers is YYYY.MM.DD hh:mm:ss East Africa Time (EAT). The date format of the TM data loggers is DD.MM.YYYY hh:mm:ss EAT.</p> <p>The folder "processed_data" contains the followings two files:</p> <p>"Information_Sheet_Data_Gap-Filling.ods": An overview table with relevant information regarding the filling of (longer) data gaps in the ground temperature time series. The gap-filling procedure based on simple linear regression models is described individually for each logger.</p> <p>"Hourly_Ground_Temperatures.csv": Compilation of hourly ground temperature data from all GT and TM data loggers installed in the Bale Mountains (see Table 1 in the data paper). The dataset covers the period from 1 January 2017 to 31 January 2020, but individual time series may be shorter or contain data gaps (see Fig. 3 in the data paper). We use the international date format (ISO 8601): YYYY-MM-DD hh:mm:ss EAT. The following numerical indices (or a combination of them) in the columns starting with "Flag_*" are used to provide additional information on the post-processing of each hourly measurement of each time series:</p> <p>0 no data available<br> 1 original data (no post-processing)<br> 2 data interpolated to full hour<br> 3 erroneous data corrected<br> 4 erroneous data removed<br> 5 data gap-filled</p> <p>The meteorological data from the ten automatic weather stations in the Bale Mountains, which are operated since 2017, are currently post-processed and analysed in the framework of the DFG Research Unit 2358 "The Mountain Exile Hypothesis". The data will be made publicly available at some point in the future. However, individual access to the weather station data may be granted before on request to the coordination board of the research unit (bale@staff.uni-marburg.de).</p>
Ground temperature profiles from DVDP borehole 11 at Explorers Cove, McMurdo Dry Valleys, Antarctica (2020-2025, ongoing)
The Dry Valley Drilling Project (DVDP) drilled multiple boreholes throughout Antarctica’s McMurdo Dry Valleys in the early 1970s, several of which remain open and accessible. DVDP borehole 11, with a total depth of 327.86 m, is located adjacent to the Explorers Cove Meteorological Station (EXEM), operated by the McMurdo Dry Valleys Long Term Ecological Research program (MCM LTER). In January 2020, the MCM LTER instrumented this borehole with a string of thermistors to monitor ground temperatures through the permafrost. Sensors were installed at depths of 1, 2, 3, 4, 5, 10, 20, and 30 m, providing ongoing measurements of subsurface thermal conditions at Explorers Cove.
Spectral albedo and summer ground temperature of herbaceous and shrub tundra vegetation at Bylot Island, Canadian High-Arctic
<p>These data are in support of a preprint: </p><p>Comparing spectral albedo and NDVI of herbaceous and shrub tundra vegetation at Bylot Island, Canadian High-Arctic</p><p>Florent Domine, Maria-Belke-Brea, Ghislain Picard, Laurent Arnaud, and Esther Lévesque</p><p>To be submitted in 2023. </p><p>The spectral albedo of several vegetation assemblages on Bylot Island and in Mala River valley on nearby Baffin Island were recorded between 10 and 18 July 2015. The spectral range covered was 346 to 2400 nm. Surfaces were classified according to the main vegetation types. Classes used are graminoids, moss, Salix arctica, soil, and Salix richardsonii. S. richardsonii is the only truly erect species on Bylot Island. Transmission spectra of radiation through the S. richardsonii canopy were also recorded. S. richardsonii spectra were different depending on the location where they were measured and we present spectra for sites in active parts of an alluvial fan (Salix-G2), an inactive part of an alluvial fan (Salix-D1) and in a mesic area on Mala River Valley (Salix-M). We also present typical relative solar irradiance spectra recorded at Bylot Island during the campaign, under clear and overcast conditions. In conjunction with spectral albedo data, these irradiance spectra allow the calculation of the broadband (BB) albedo of the vegetation types and to compare BB albedo values under identical irradiance conditions. 83 spectra were recorded: 39 for S. richardsonii and 44 for low vegetation and soil. 17 transmission spectra under S. richardsonii were recorded. We present here only averages for each vegetation type. We also present averages for all low vegetation types and for all S. richardsonii spectra, to allow the calculation of the radiative impact of erect shrubs at Bylot Island. </p><p>We also present soil temperature data at 15 cm depth for the spots GRASS (mostly Salix Arctica), TUNDRA (Mostly moss), SALIX-D1 (Salix richardsonii) and SALIX-F (Salix richardsonii). SALIX-F is similar to SALIX-G2. The data are during summer 2020. </p><p>The locations of the various spots investigated are: </p><p><strong>Spot name Latitude Longitude Vegetation types found</strong></p><p>TUNDRA 73.150° -80.004° Humid and moist polygons with low vegetation dominated by mosses, graminoids, S. arctica and S. herbacea.</p><p>PLAINE 73.167° -79.915° Low vegetation and bare soil patches caused by cryoturbation (mudboils) with mosses, graminoids and S. arctica.</p><p>GRASS 73.158° -79.907° Low vegetation between patches of S. richardsonii dominated by S. arctica, with litter, mosses, graminoids and occasional bare soil. </p><p>SALIX-D1 73.158° -79.907° Scattered patches of S. richardsonii <35 cm tall. Understory is mosses, graminoids, litter, S. arctica and bare soil.</p><p>SALIX-M 73.006° -80.685° Mesic area with patches of S. richardsonii 35 to 40 cm tall. Understory includes moss, graminoids and litter. Between patches: herb tundra with graminoids and mosses. The area is not within an alluvial fan.</p><p>SALIX-G2 73.168° -79.812° Extended area in an alluvial fan with S. richardsonii >40 cm. Understory includes litter, mosses, graminoids, bare soil, S. arctica and S. reticulata.</p><p>SALIX-F 73.182° -79.745° Similar to SALIX-G2. Ground temperature is monitored there. No spectral data were recorded at that site. </p><p> </p><p> </p>
Satellite-based measurements of brightness temperatures (AMSR2 sensor) colocated to MOSAiC ground measurements
<p>The file contains measurements of brightness temperatures of satellite overpasses of the research vessel Polarstern during the MOSAiC expedition from October 26, 2019 - May 26, 2020 as well as co-located measurements of different parameters. For every overpass of Polarstern, the satellite measurement closest to the hourly position of Polarstern is taken.</p> <p>The satellite sensor is AMSR2 (six frequencies between 6.9 and 89 GHz and both polarizations) and we use the Level 1R (<em>Madea et al., 2016)</em> product available at JAXA <a href="https://gportal.jaxa.jp/gpr/">https://gportal.jaxa.jp/gpr/</a></p> <p>The co-located parameters are liquid water path, total water vapor, sea ice concentration, multi-year ice fraction, snow depth, snow-air interface temperature, snow-ice interface temperature, wind speed and sea surface temperature. In addition to the co-located parameters as ground truth, the dataset also contains their “uncertainties” given as temporal and/or spatial variability.</p> <p>Note: The dataset contains <strong>only</strong> satellite overpasses where co-located data is available.</p> <p>More information on the parameters are found below and they are described in more detail in <em>Rückert et al., 2023</em><em> </em>and the references given therein.</p> <ul> <li> <p><strong>scantime</strong>: time of satellite observation as included in the satellite data from JAXA</p> </li> <li> <p><strong>lon</strong>: longitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>lat</strong>: latitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA</p> </li> <li> <p><strong>distance</strong>: distance to the hourly Polarstern position</p> </li> <li> <p><strong>TB6.9V, TB6.9H, TB10.7V, TB10.7H, TB18.7V, TB18.7H, TB23.8V, </strong><strong>T</strong><strong>B23.8H, TB36.5V, TB36.5H, TB89V, TB89H</strong>: Brightness temperatures (TB) measured by AMSRE2, the name includes the frequency in GHz and the polarization (either H for horizontal or V for vertical polarization), e.g, TB6.9V is the brightness temperature at 6.9 GHz and vertical polarization</p> </li> <li> <p><strong>LWP</strong>: liquid water path in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al., 2022</em>), averaged within +/- 10 minutes of the satellite observations</p> </li> <li> <p><strong>sigma_LWP</strong>: temporal variability of liquid water path (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>TWV</strong>: total water vapor (integrated water vapor) in kg/m² measured by a radiometer onboard the ship (<em>Walbröl et al. (2022)</em>), averaged within +/- 10 minutes satellite observation time</p> </li> <li> <p><strong>sigma_TWV</strong>: temporal variability of total water vapor (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>WSP</strong>: wind speed in m/s from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_WSP</strong>: temporal variability of wind speed (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SST</strong>: sea water temperature in K from the vessel’s meteorological observatory (<em>Schmithüsen et al., 2021</em>), averaged within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>sigma_SST</strong>: temporal variability of sea water temperature (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time</p> </li> <li> <p><strong>SND</strong>: snow depth in m obtained from the median of daily snow depth from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (<em>Lei et al., 2021</em><em>a</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_SND</strong>: spatial variability of snow depth (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>Tsi:</strong> Snow-ice interface temperature in K obtained from the median of daily measurements from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (e.g. <em>Lei et al., 2021b</em>, for references of all buoys the reader is referred to the references given in <em>Rückert et al., 2023</em>) in the proximity of Polarstern.</p> </li> <li> <p><strong>sigma_Tsi:</strong> spatial variability of snow-ice interface temperature (see previous point) given as standard deviation of all buoys available on that day.</p> </li> <li> <p><strong>MYIF:</strong> multi-year ice fraction (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_MYIF:</strong> estimated (constant) uncertainty of multi-year ice fraction (see previous point).</p> </li> <li> <p><strong>SIC</strong>: sea ice concentration (from 0 to 1) based on classified TerraSAR-X scenes (<em>Guo et al., 2023</em>) in the proximity of Polarstern, interpolated to daily values.</p> </li> <li> <p><strong>sigma_SIC:</strong> estimated (constant) uncertainty of sea ice concentration (see previous point).</p> </li> <li> <p><strong>Tsa</strong>: Snow-air interface temperature in K based on infrared thermometer data (<em>Cox et al., 2023 a)-d)</em>) installed at four positions in the proximity of Polarstern, averaged within +/- 20 minutes of the satellite observation time.</p> </li> <li> <p><strong>sigma_Tsa:</strong> spatial variability of snow-ice interface temperature (see previous point), given as spatial (4 sites) and temporal (within +/- 20 minutes of the satellite observation time) standard deviation.</p> </li> </ul>
Ground surface temperature data 2007-2021 at different sites of the PERMATHERMAL monitoring network in Livingston and Deception Islands, SouthShetland Archipelago, Antarctica.
<p>Ground Surface Temperature (GST) corrected data adquired between 2007 and 2021 at different stations of the PERMATHERMAL monitoring network at Livingston and Deception Islands, South Shetland Archipelago, Antarctica.</p> <p>(To be completed)</p>
A-ERT and ground temperature data- 2010- Deception Island/Antarctica
<p>Climate induced warming of permafrost soils is a global phenomenon, with regional and site-specific variations, which are not fully understood. In this context, a 2D automated electrical resistivity tomography (A-ERT) system was installed for the first time in Antarctica at Deception Island, associated to the existing Crater Lake site of the Circumpolar Active Layer Monitoring Network (CALM-S). This set-up aims to I) monitor subsurface freezing and thawing processes on a daily and seasonal basis and to map the spatial and temporal variability of thaw depth, and to II) study the impact of short-lived extreme meteorological events on active layer dynamics. In addition, the feasibility of installing and running autonomous ERT monitoring stations in remote and extreme environments such as Antarctica was evaluated for the first time. Measurements were repeated at 4-hour intervals during a full year, enabling the detection of seasonal trends, as well as short-lived resistivity changes reflecting individual meteorological events. The latter is important to distinguish between (1) long-term climatic trends and (2) the impact of anomalous seasons on the ground thermal regime.<br> The A-ERT.txt file contains all ERT surveys which were performed using Wenner electrode configuration. 20 copper plates, which are connected by buried cables to the active boxes, with an electrode spacing of 0.5 m were used in this experiment. This setup yields 56 individual data points for each monitoring data set at six data levels. All data were saved in the Res2Dinv format.<br> The temperature. xlsx file contains air and ground temperatures data during the experiment period. The Air temperature was measured at 160 cm above the surface and ground temperatures in the shallow borehole S3,3 were measured with ibutton-sensors at depths 2.5, 5, 10, 20, 40, 80 and 160 cm.</p>
Ecosystem fluxes and ground temperatures - Abisko tundra site
<p>Observational data from a tundra site near Abisko in Northern Sweden (68°21’N, 18°49′E) consisting of NEE, daily GPP, ER, and ground temperature (5 cm depth), for the growing seasons 2011 to 2012.</p> <p>Finderup Nielsen T., Ravn N. R., Michelsen A. (2019) Increased CO2 efflux due to long-term experimental summer warming and litter input in subarctic tundra – CO2 fluxes at snowmelt, in growing season, fall and winter. Plant and Soil 444:365-382 https://doi.org/10.1007/s11104-019-04282-9</p> <p> </p>
ERA5 overviews complementing temperature measurements of ground-based Rayleigh lidars for the investigation of gravity waves generated by moving sources
<p>ERA5 overviews to associate stratospheric gravity waves in temperature measurements from vertically staring (zenith-pointing) ground-based Rayleigh lidars with atmospheric processes. Animations are for a virtual lidar location over the Southern Ocean during research flight RF25 of the DEEPWAVE campaign (July 17 to 19, 2014) and for the location of the COmpact Rayleigh Autonomous Lidar (CORAL) in the lee of the southern Andes. Here, the first overview is for the CORAL measurement from June 22 to 23, 2018. The second one is for the nightly measurements between August 7 and 9, 2020.</p> <p>(a) and (b) emulate the measurement of a vertically staring ground-based lidar and show temperature perturbations after subtracting a temporal running mean of 12h (a) and the mean absolute temperature profile (b). Panels (c) and (d) are vertical sections of stratospheric 𝑇′ along sectors of the latitude circle (c) and meridian (d) of the virtual lidar location. (e) and (f) are corresponding vertical sections of thermal stability 𝑁2 (10−4 s−2, color-coded), potential temperature (K, thin grey lines), and potential vorticity (1, 2, 4 PVU: black, 2 PVU: green). Thin black lines in the vertical sections are zonal (d, f) and meridional (c, e) wind components (solid: positive, dashed: negative). Panel (g) is a horizontal section of the height of the 2 PVU surface (km, color-coded), geopotential height (m, solid lines) and wind barbs at the 850 hPa level. The black vertical line in (a) marks the time for (c)-(g) and dashed lines in (c)-(g) highlight the location of the virtual lidar and profiles in (a) and (b).</p> <p>The provided NETCDF files contain the corresponding CORAL temperature measurements for the two periods with CORAL measurements in 2018 and 2020.</p>
Ground temperature timeseries (2014-2021) and cryostratigraphy from Villum Research Station, Station Nord, eastern North Greenland (81° N)
<p>This dataset contains ground temperature timeseries (2014-2021) and cryostratigraphy data from two 20 m deep boreholes located at Villum Research Station (VRS), Station Nord, eastern North Greenland. The cryostratigraphy data includes split permafrost core photographs and values for the following parameters: gravimetric moisture content, salinity, and freezing point depression. A complete sample inventory and information on sample quality and recovery is also included. Please read the file "Readme_Strandetal2021_V2" for the necessary background information and overview of the dataset contents (file structure and description of each file). This is the second version of the dataset; the ground temperature timeseries in this version are 2.5 years longer than in the first version, and meteorology data from the same period as the ground temperature data is provided.</p>
Soil temperature, moisture, and ground heat flux measurements at LPTEG-TREES-1 site, 2019/07/01-2019/09/09
<p>This dataset includes the original measurements of soil temperature, moisture, and surface ground heat flux reconstructed from heat flux plate measurements at the LPTEG-TREES-1 site (N66°53’55’’, E66°45’27’’). Soil temperature (T_soil, °C) was measured at 2 cm below the peat layer surface. Soil liquid water content (theta_liq, m<sup>3</sup>/m<sup>3</sup>) was measured 2 cm below the mineral soil layer surface. Observation for ground heat flux at the soil surface (G_obs, W/m<sup>2</sup>) was reconstructed from the heat flux plate (buried 6 cm below the mineral soil surface) measurement plus the energy storage above the heat flux plate calculated based on soil temperature and soil heat capacity.</p>
Laser cooling a membrane-in-the-middle system close to the quantum ground state from room temperature
<p>This dataset contains processed data corresponding to the figures in the main text of our paper "Laser cooling a membrane-in-the-middle system close to the quantum ground state from room temperature"</p> <p>The dataset consists of 10 .csv files and a jupyter notebook for generating the figures in the main text.</p>
Ground temperature at and near I-Minus-2 thermokarst sites around Toolik Lake Field Station, Alaska, Summer 2009-Summer 2012
Ground temperatures were measured hourly at ~20-50cm intervals below the ground surface inside and adjacent to thermokarst features in the region around Toolik Field Station. Ground temperatures were measured using Hobo thermistors. Temperatures at 0 and 20cm depths were measured directly in the ground whereas 40cm and deeper measurements were logged from dry wells installed in summer 2009. IM2_GT01dot06_temp is located inside of the I-Minus-2 Gulley thermokarst, downslope.
Ground temperature at and near NE 14 thermokarst sites around Toolik Lake Field Station, Alaska, Summer 2009-Summer 2012
Ground temperatures were measured hourly at ~20-50cm intervals below the ground surface inside and adjacent to thermokarst features in the region around Toolik Field Station. Ground temperatures were measured using Hobo thermistors. Temperatures at 0 and 20cm depths were measured directly in the ground whereas 40cm and deeper measurements were logged from dry wells installed in summer 2009. NE14_TS02dot02_temp is located in the old NE14 thermokarst, upslope.
Ground temperature at and near Toolik River thermokarst sites around Toolik Lake Field Station, Alaska, Summer 2009-Summer 2012
Ground temperatures were measured hourly at ~20-50cm intervals below the ground surface inside and adjacent to thermokarst features in the region around Toolik Field Station. Ground temperatures were measured using Hobo thermistors. Temperatures at 0 and 20cm depths were measured directly in the ground whereas 40cm and deeper measurements were logged from dry wells installed in summer 2009. TRTK_GT01dot05_temp is located outside the TRTK thermokarst, midslope.
Ground temperature & ground water content (20, 40, 60cm depth) in Katterjokk fen site, 2019-2020
<p>Sub-hourly ground temperature (GT) and water content (GWC) collected in a fen site in Katterjokk, northern Sweden, for 2019 and 2020. Depths are 20cm (Port 3), 40 cm (Port 2) and 60cm (Port 1).</p> <p>The data was collected with Teros 12 sensors, and is provided as raw data and calibrated (configured.xlsx file) for organic soils as described by the seller. (<a href="https://www.metergroup.com/en/meter-environment/products/teros-12/teros-12-resources">https://www.metergroup.com/en/meter-environment/products/teros-12/teros-12-resources</a><br><br><br></p>
Ground-state dataset "Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states"
<h1>2D QUANTUM EDWARDS-ANDERSON GROUND-STATE DATASET:</h1> <p>The dataset contains coupling and energy data for 50 instances of a 2D quantum Edwards-Anderson model at Gamma (transverse field) = 1.8, featuring N=LxL=100 spins on a square lattice of side-length L=10 with periodic boundary conditions. The couplings are sampled from a Gaussian distribution with zero mean and unit variance.<br>The dataset consists of two text files containing coupling values and the corresponding ground-state energies.</p> <h2>Coupling Data (`coup_dataset.txt`)</h2> <p>The file `coup_dataset.txt` contains fifty sets of coupling data. Each set consists of three columns representing the indices `i`, `j`, and the coupling value `J_ij`, respectively.<br>The spin indices range from 1 to 100, ordered progressively by rows. Each set of coupling data is separated by two empty lines.</p> <h2>Energy Data (eng_dataset.txt)</h2> <p>The file `eng_dataset.txt` contains fifty rows of energy data corresponding to the coupling sets in `coup_dataset.txt`. Each row contains two columns representing the energy value and its associated statistical error-bar, rounded to the fifth decimal digit.</p>
Ground surface temperature measurements at grazed and ungrazed plots in Central Mongolia
<p>Ground surface temperature measurements from two sites with different topographic aspect in Central Mongolia. The dataset includes both grazed and ungrazed plots, and covers ca. 14 months from May 2022 to August 2023.</p>
Satellite-ground synchronous in-situ dataset of water optical parameters and surface temperature for typical lakes in China
<p>Remote sensing technology has the potential to significantly enhance the lakes large-scale and long-term dynamic monitoring capabilities. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of water optical parameters. This dataset provides satellite-ground synchronized in-situ data on water optical parameters for typical lakes in China spanning the period between 2020 and 2023. The dataset includes quality-checked remote sensing reflectance ( ) data and water optical parameter data for chlorophyll-a (Chl-a), total suspended matter (TSM), Secchi disk depth (SDD), andwater surface temperature (WST). It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights: Firstly, synchronous observations from multiple satellites are coordinated during the data collection process, effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of and various water optical parameters. This dataset will be continuously updated, thereby making a substantial contribution to enhancing regional and global lake monitoring capabilities through satellite remote sensing data.</p>
Data from: Statistical forecasting of current and future circum-Arctic ground temperatures and active layer thickness
Mean annual ground temperature (MAGT) and active layer thickness (ALT) are key to understanding the evolution of the ground thermal state across the Arctic under climate change. Here a statistical modeling approach is presented to forecast current and future circum-Arctic MAGT and ALT in relation to climatic and local environmental factors, at spatial scales unreachable with contemporary transient modeling. After deploying an ensemble of multiple statistical techniques, distance-blocked cross-validation between observations and predictions suggested excellent and reasonable transferability of the MAGT and ALT models, respectively. The MAGT forecasts indicated currently suitable conditions for permafrost to prevail over an area of 15.1 ± 2.8 × 106 km2. This extent is likely to dramatically contract in the future, as the results showed consistent, but region-specific, changes in ground thermal regime due to climate change. The forecasts provide new opportunities to assess future Arctic changes in ground thermal state and biogeochemical feedbacks.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.