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TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"

<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time = 3672 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; stations = 6 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; name_strlen = 6 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth = 3 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height = 201 ;<br> variables:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double time(time) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:standard_name = &quot;time&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:long_name = &quot;time of measurement&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:units = &quot;hours since 2016-06-01 00:00:00&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:timezone = &quot;UTC&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:calendar = &quot;proleptic_gregorian&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double lat(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:standard_name = &quot;latitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:long_name = &quot;station_latitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:units = &quot;degrees_north&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double lon(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:standard_name = &quot;longitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:long_name = &quot;station_longitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:units = &quot;degrees_east&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double elev(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:standard_name = &quot;altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:long_name = &quot;station_altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:units = &quot;m ASL&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double height(height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:standard_name = &quot;altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:long_name = &quot;station_altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:units = &quot;m ASL&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double depth(depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:standard_name = &quot;soil_depth&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:long_name = &quot;soil sensor depth&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:units = &quot;cm&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; char station_name(name_strlen, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; station_name:long_name = &quot;station_name&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; station_name:cf_role = &quot;timeseries_id&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double T(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:standard_name = &quot;temperature&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:long_name = &quot;2m air temperature&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:units = &quot;degree_Celsius&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double Q(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:standard_name = &quot;mixing_ratio&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:long_name = &quot;2m mixing ratio&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:units = &quot;g kg-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double ET_i(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:standard_name = &quot;evapotranspiration_intensive&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:long_name = &quot;lysimeter evapotranspiration intensive management&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:units = &quot;g kg-1 h-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double ET_e(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:standard_name = &quot;evapotranspiration_extensive&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:long_name = &quot;lysimeter evapotranspiration extensive management&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:units = &quot;g kg-1 h-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double LvE_cor(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:standard_name = &quot;latent_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:long_name = &quot;energy balance corrected flux tower latent heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double HTs_cor(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:standard_name = &quot;sensible_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:long_name = &quot;energy balance corrected flux tower sensible heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double GHF(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:standard_name = &quot;ground_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:long_name = &quot;flux tower ground heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:positive = &quot;up&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double SW(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:standard_name = &quot;short_wave_radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:long_name = &quot;downward short wave radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double LW(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:standard_name = &quot;long_wave_radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:long_name = &quot;downward long wave radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_25(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:long_name = &quot;DE-Fen SoilNet volumetric water content first quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_50(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:long_name = &quot;DE-Fen SoilNet volumetric water content second quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_75(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:long_name = &quot;DE-Fen SoilNet volumetric water content third quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double T_prof(time, height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:standard_name = &quot;temperature_profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:long_name = &quot;DE-Fen HATPRO spline interpolated temperature profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:units = &quot;K&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:source = &quot;scaleX campaign 2016&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double A_prof(time, height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:standard_name = &quot;humidity_profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:long_name = &quot;DE-Fen HATPRO spline interpolated absolute humidity profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:units = &quot;kg m-3&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:source = &quot;scaleX campaign 2016&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double PRW(time) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:standard_name = &quot;precipitable_water&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:long_name = &quot;DE-Fen HATPRO column precipitable water&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:units = &quot;kg m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:source = &quot;scaleX campaign 2016&quot; ;</p> <p>// global attributes:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :history = &quot;2019-09-12: File created.&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :institution = &quot;Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Contact_person = &quot;Benjamin Fersch (benjamin.fersch@kit.edu)&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Author = &quot;Benjamin Fersch (benjamin.fersch@kit.edu)&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :source = &quot;https://www.tereno.net, https://scalex.imk-ifu.kit.edu&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Conventions = &quot;CF-1.6&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :License = &quot;Creative Commons Attribution Non Commercial Share Alike 4.0 International&quot; ;</p> <p>&nbsp;</p>

opencc-by-nc-sa-4.0Sep 2019View details →
zenodo40/100

Characterization of heteroatom distributions in the polar fraction of North Sea oils using high-resolution mass spectrometry

<p>Supplementary data for&nbsp;<a href="https://doi.org/10.1016/j.petrol.2019.106563">10.1016/j.petrol.2019.106563</a></p> <p>Mass spectra were measured on a Q Exactive HF at 240k@200 m/z resolution in nanospray-ESI direct infusion using a Advion TriVersa NanoMate source. Broadband mass spectra were&nbsp;generated from SIM-segments using dimspy (https://github.com/computational-metabolomics/dimspy). Peaks were annotated using Formularity v.1.0.0 (10.1021/acs.analchem.7b03318) after internal calibration using a homologous CHN series (identified from preliminary KMD/KM plots). All plots were generated using python 3.6.6 and the plotly graphing library (https://plot.ly/python/).</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2019View details →
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Figs 24–27 in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study

Figs 24–27. Transmission electron micrographs of a lorica surface in Eutintinnus angustatus at middle (24) and higher (25–27) resolution. 24 – crystal lattice; 25 – digitally enlarged detail of Fig. 24 (bottom part). The periodicity of the hexagonal structures amounts to ~ 23.7 nm with a resolution of ~ 3 nm for the smallest details; 26 – fast Fourier transform of Fig. 25; 27 – noise filtered bright field image after inverse Fourier transform of Fig. 26, using only the diffraction spots. Due to the enhanced contrast, the ultrastructure of the crystal lattice appears more distinct.

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Figs 20–23 in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study

Figs 20–23. Transmission electron micrographs of a lorica surface in Eutintinnus angustatus at middle resolution. 20 – a crystalline region ~ 1.5 µm in diameter is shown in the centre of the micrograph. An aggregate of bitter salt (MgSO 4) is attached to the wall (arrow); 21 – crystalline area at higher magnification showing dark spots of sodium and potassium chloride on the lorica wall; 22 – each black spot represents a NaCl or KCl nanocrystal (arrows), which has almost the same size as the unit cells of the crystal lattice (~ 20 nm); 23 – Fourier filtered high-resolution micrograph of a sodium chloride nanocrystal.

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Fig. 15. Energy-dispersive X in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study

Fig. 15. Energy-dispersive X-ray spectrometric (EDX) analysis in the scanning electron microscope, using uncoated material. The analysed area of the Eutintinnus angustatus lorica is marked by a white frame (~ 11 × 9 µm in size). Since this part of the lorica was freely suspended in the vacuum, elemental detection occurred without influence of the carbon substrate.

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Figs 16–19 in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study

Figs 16–19. Transmission electron micrographs of an uncoated lorica surface of Eutintinnus angustatus at different magnifications. 16 – overview of right lorica half. The lorica lies nearly horizontally on the electron transparent holey carbon substrate. The black rectangular structures are the copper bars of the TEM grid; 17 – anterior portion of right lorica half; 18 – apical lorica portion; 19 – lateral lorica portion. The dark crystalline dendritic structures consist of sodium chloride nanocrystals, which probably originate in the sea water.

opencc-by-4.0Dec 2012View details →
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Fig. 1 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes

Fig. 1. Abundance of cercariae by sampling site. Water samples were obtained in mid-June and cercariae abundance was determined using the pan-avian schistosomes qPCR.

opencc-by-4.0Aug 2019View details →
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Fig. 2 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes

Fig. 2. Percent contribution of T. stagnicolae, T. szidati, T. physellae and A. brantae species to each lake. Water samples from different locations and dates were tested using the species-specific qPCR assay and results were pooled by lake to understand the relative contribution overall of each species to each lake. The percent contribution (based on gene copy number) of each species was calculated.

opencc-by-4.0Aug 2019View details →
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Fig. 3 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes

Fig. 3. Lifecycles of T. stagnicolae, A. brantae, T. szidati, and T. physellae. life cycle summary of the avian schistosome species targeted for species-specific qPCR tests designed in this study.

opencc-by-4.0Aug 2019View details →
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Fig. 3 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 3. Phylogenetic tree of seven Trypanosome species and subsequent genotypes constructed with sequences of the amplicons generated by the HRMqPCR primers.

opencc-by-4.0Dec 2020View details →
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Fig. 4 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 4. Amplification plots, melt curves and standard curves of T. copemani, T. vegrandis G7 and T. noyesi G8 prepared from a plasmid containing trypanosome species.

opencc-by-4.0Dec 2020View details →
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Fig. 5. A-D in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 5. A-D: Derivative melt curves showing mock mixed infections generated from plasmid clones containing the following DNA: (A) T. noyesi G8 and T. copemani; (B) T. vegrandis G7 and T. copemani; (C) T. vegrandis G7 and T. noyesi G8; (D) T. vegrandis G7, T. noyesi G8 and T. copemani.

opencc-by-4.0Dec 2020View details →
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Fig. 1 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 1. Multiple sequence alignment of a portion of the 18S rDNA of seven Trypanosome species and subsequent genotypes used to design the HRM-qPCR assays.

opencc-by-4.0Dec 2020View details →
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Figure 2 in High-Resolution Functional Imaging of Native Proteins using Force Distance Curve Based Atomic Force Microscopy

Figure 2. - Juvenile Trachipterus arcticus, 129 mm SL, collected at Faial Island, Azores, 14 May 2014, on the surface. A: Portrait with anterior black facet visible; B: Oblique lateral view with first spines erected; note orange bulbous outgrowths on the prolonged spine; C: Lateral view showing proportions, markings and orientation of fins. Scale bars: A = 1 cm; B, C = 5 cm.

opencc-by-4.0Dec 2015View details →
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Figure 1 in High-Resolution Functional Imaging of Native Proteins using Force Distance Curve Based Atomic Force Microscopy

Figure 1. - Adult Trachipterus arcticus, about 1.8 m long, observed south of Pico Island, Azores, 18 Aug. 2013, 950 m deep.

opencc-by-4.0Dec 2015View details →
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High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data

<p>We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m<sup>2</sup> for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.</p> <p>For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40<a> European countries,&nbsp;</a>forming a continuous coverage of the western part of the European continent.</p> <p>A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to&nbsp;<a>73 %&nbsp;</a>the South-Eastern area.</p> <p>The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.</p> <p>The author list is sorted by last name except for the first and last authors who also serve as corresponding authors.</p> <p>Corresponding authors: <a href="mailto:Jukka.Miettinen@vtt.fi">Jukka.Miettinen@vtt.fi</a>, <a href="mailto:Johannes.Breidenbach@nibio.no">Johannes.Breidenbach@nibio.no</a></p>

opencc-by-4.0Jul 2024View details →
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Dataset for the publication "Identification of plasticity-induced crack closure by using high-resolution digital image correlation"

<p>This repository publishes the data generated in the article "Identification of plasticity-induced crack closure by using high-resolution digital image correlation" (see arxiv preprint <a href="https://arxiv.org/html/2409.02560v1">Plasticity-induced crack closure identification during fatigue crack growth in AA2024-T3 by using high-resolution digital image correlation (arxiv.org)</a>)</p> <p>This repository is structured with the following subfolders:</p> <ul> <li><strong>0_fe_data:&nbsp;</strong>contains the displacement field of the free surface of the 3D finite element model that were used to determine the crack opening curves and, in the following, the crack opening value Kop</li> <li><strong>1_hrdic_data:&nbsp;</strong>contains the high-resolution DIC displacement field data at a crack length of 27.8 mm at different load levels, starting from minimum load 1.5 kN to maximum load 15 kN</li> </ul>

opencc-by-4.0Sep 2024View details →
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(Magnetic dataset) High-Resolution Magnetic Investigation of a Hydrothermal System in a Volcanic-Evaporitic Environment

<p>This is the ground magnetic dataset collected in hydrothermal vent field sites (called Yellow Lake Fissure and the surrounding area near Dallol Dome) in the Danakil Depression, Ethiopia.</p> <p>The dataset consists of 12 profiles and 2 grided data. and useful geological locations.</p> <p>The dataset is in Excel spreadsheet format and consists of 15 sheets (12 profiles, 2 grid data and other useful points).</p> <p>The format of each profile and grid consists of 8 columns and is formatted as below</p> <p>Latitude(dd&deg;mm.mmmm'), Longitude(ddd&deg;mm.mmmm'), Altitude(meter), Date(yyyy-mm-dd), UTC_time(hhmmss), MagneticField(nT), MagneticAnomaly(nT), Signal_Quality</p> <p>&nbsp;</p>

opencc-by-4.0Sep 2024View details →
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High-resolution DEMs and ortho images of Langtang village post-2015 Gorkha earthquake in Nepal

<p>Datasets related to the article "Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-seismic Avalanche in the Himalayas." The data were collected around Langtang village, which was destroyed by snow and ice avalanches triggered by the 2015 Gorkha earthquake. The datasets include digital elevation models (DEMs) and orthoimages, gathered using three types of UAVs equipped with different cameras in October 2015.<br><br>Description of files:<br>-a7_DEM_50cm.tif: 0.5 m resolution DEM derived from a quadcopter UAV equipped with a Sony &alpha;7R (36.3-megapixel sensor).<br>-a7_ortho_9cm.tif: 0.09 m resolution orthoimage derived from the same data as in a7_DEM_50cm.tif.<br>-ebee_DEM_50cm.tif: 0.5 m resolution DEM derived from a fixed-wing UAV equipped with a Canon IXUS 125HS (16-megapixel sensor).<br>-ebee_ortho_15cm.tif: 0.15 m resolution orthoimage derived from the same data as in ebee_DEM_50cm.tif.<br>-gr_DEM_50cm.tif: 0.5 m resolution DEM derived from a fixed-wing UAV equipped with a Ricoh GR (14.2-megapixel sensor).<br>-gr_ortho_12cm.tif: 0.12 m resolution orthoimage derived from the same data as in gr_DEM_50cm.tif.<br><br></p> <p>Please refer to the related journal article for more details on the datasets.</p> <p>Sunako S, Fujita K, Yamaguchi S, Inoue H, Immerzeel WW, Izumi T and Kayastha RB (2024) Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-Seismic Avalanche in the Himalayas. J. Disaster Res. 19(5), 865&ndash;873 (doi:10.20965/jdr.2024.p0865)</p>

opencc-by-4.0Sep 2024View details →
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High-resolution digital elevation model of Klados Gorge, Crete, Greece

<p>High-resolution digital elevation model constructed from photogrammetric processing of drone images taken at Klados Gorge, Crete, Greece in 2017. The authors used <em>Agisoft</em> PhotoScan for the photogrammetric processing and to generate 3D spatial data for further use.</p>

opencc-by-4.0Feb 2021View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record