Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

Pulse Profiles and Times of Arrival Measurements from a Rotating Radio Transient Census with the Irish LOFAR station

The reduced data produced as a part of a census of rotating radio transients (RRATs) with the Irish LOFAR station.   This deposit contains: <ul> <li>Metadata regarding observed data</li> <li>A copy of RFI-zapped, single pulse archives</li> <li>A copy of the time-flattened periodic emission archives</li> <li>A copy of the measured pulse times of arrival</li> <li>Ephemerides used and produced as a part of the work</li> </ul> Additional data can be made available on request to the author.

MEaSUREs blue band total column water vapor sample data for the Ozone Monitoring Instrument

This dataset contains the MEaSUREs OMI Total Column Water Vapor (TCWV) data and their related data used in the paper titled “Development of the MEaSUREs blue band water vapor algorithm – Towards a long-term data record” by Wang et al. (2023). The unzipped archive contains the following three directories.  <ol> <li>OMI-H2O-L2/ contains the MEaSUREs Level 2 data (in molecules/cm2) in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included in each file.</li> <li>OMI-H2O-L3/ contains MRaSUREs Level 3 data (0.25 degree by 0.25 degree, in molecules/cm2) generated using the standard filtering criteria in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included.</li> <li>Model3_ncresult/ contains netCDF4 formatted files for the MEaSUREs OMI TCWV data (in mm), the AMSR_E TCWV data sampled onto the corresponding OMI pixel locations, and the LightGBM model 3 predictions for the OMI pixels.</li> </ol> The linux command ‘ncdump -h filename’ can be used to examine the contents of netCDF4 files. Due to the current size limit of Zenodo, only a small subset of the MEaSUREs data is archived here. The full dataset will be released elsewhere, e.g., NASA EARTHDATA GES DISC.

opencc-by-4.0Feb 2023View details →

Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"

Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"   For more information please refer to the data descriptor available at: https://www.sciencedirect.com/science/article/pii/S2352340924003251 Please cite as: Klus, L., Klus, R., Lohan, E.S., Nurmi, J., Granell, C., Valkama, M., Talvitie, J., Casteleyn, S. and Torres-Sospedra, J., 2024. TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density. Data in Brief, p.110356.

Building measured data for model validation

Measured indoor/outdoor temperatures, solar radiation and heating load of a 103-m2 building in Athens, Greece. Data include measurements of two weeks, one without heating delivery to the building and another with heating delivery (with fan coils).

opencc-by-4.0Jan 2023View details →

Datasets for "Cryogenic sensor enabling broad-band and traceable power measurements"

Python code and data used to generate the plots in "Cryogenic sensor enabling broad-band and traceable power measurements".  <table> <tbody> <tr> <td>File/Folder</td> <td>Description</td> </tr> <tr> <td> Article_Plots_25Jan2023.ipynb </td> <td>main code to analyze and plot the data</td> </tr> <tr> <td>Data</td> <td>sub-folder that includes data analyzed by the code</td> </tr> <tr> <td>Figures</td> <td>sub-folder for figures generated by the code</td> </tr> </tbody> </table> The code requires an installation of <a href="https://qcodes.github.io/Qcodes/start/index.html">QCoDeS</a> to run.

opencc-by-4.0Apr 2019View details →

FT7 Anonymous (8) 3-key fagottino: measurements, photos, endoscopic video

Dataset of FT7 Anonymous (8) 3-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. Note: Better quality photos will be available at a later date.

Time series measurements of nitrogen fixation in the subtropical North Pacific (extended through 2019) (Reformatted)

Rates of N2 fixation were measured using the 15N2 isotopic tracer technique. Sampling occurred during near-monthly Hawaii Ocean Time-series cruises. Whole seawater samples from six discrete depths (5, 25, 45, 75, 100, and 125 m) were subsampled into acid-washed 4.3 L polycarbonate bottles.  The 15N2 gas was first dissolved into seawater and 100 mL of the resulting 15N2-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of 15N2-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the 15N2 tracer were attached to a free-drifting array and incubated at the discrete depths from which samples had been collected. The array was deployed before dawn and samples were incubated at in situ light and temperature for 24 h. After recovery of the array, the entire volume from each bottle was filtered onto a pre-combusted glass microfiber filter (Whatman 25 mm GF/F) and filters were placed onto pre-combusted pieces of foil in Petri dishes and stored frozen at -20°C. Filters were dried for 24 h at 60°C, pelleted, and the total mass of N and its isotopic signature on each filter were analyzed on an elemental analyzer-isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with ThermoFinnigan Delta S). Dataset has been reformatted to meet submission requirements for Simons CMAP.

Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling

Self-discharge data related to the manuscript entitled: 'Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling', submitted to Energy Reports on 26 April 2023.

THE StellaR PAth WP1: Sun-as-a-star plasma Emission Measure Distributions

This folder contains a set of plasma Emission Measure Distributions (EMDs) vs. temperature, derived from observations of the solar corona with the Soft X-ray Telescope (SXT) on board the solar satellite Yohkoh, and the prescription to build EMDs for coronae of solar-type stars with different activity levels, including both quiescent and flaring components. For details read the Description PDF file.

opencc-by-4.0Apr 2022View details →

Supporting Data Ferguson, Camenzind, et al., "Measurement-induced induced population switching", Phys. Rev. Research 5, 023028 (2023)

This repository contains data for the publication "Measurement-induced population switching", Phys. Rev. Research 5, 023028 (2023) by Ferguson, Camenzind, et al. Abstract Quantum information processing is a key technology in the ongoing second quantum revolution, with a wide variety of hardware platforms competing toward its realization. An indispensable component of such hardware is a measurement device, i.e., a quantum detector that is used to determine the outcome of a computation. The act of measurement in quantum mechanics, however, is naturally invasive as the measurement apparatus becomes entangled with the system that it observes. This always leads to a disturbance in the observed system, a phenomenon called quantum measurement backaction, which should solely lead to the collapse of the quantum wave function and the physical realization of the measurement postulate of quantum mechanics. Here we demonstrate that backaction can fundamentally change the quantum system through the detection process. For quantum information processing, this means that the readout alters the system in such a way that a faulty measurement outcome is obtained. Specifically, we report a backaction-induced population switching, where the bare presence of weak, nonprojective measurements by an adjacent charge sensor inverts the electronic charge configuration of a semiconductor double quantum dot system. The transition region grows with measurement strength and is suppressed by temperature, in excellent agreement with our coherent quantum backaction model. Our result exposes backaction channels that appear at the interplay between the detector and the system environments, and opens new avenues for controlling and mitigating backaction effects in future quantum technologies.

HotBin composter temperature measurements

This is a CSV data table with temperature measurements of a HotBin composter, as displayed by the thermometer on its top. Environment temperature, rain is also recorded. A home-made heater unit is installed near the lid of the HotBin, this is a simple kettle that boils the water when a button is pressed, then heats up the water to boiling temperature and does no more heating until the button is pressed again. The composter is fed mainly with kitchen waste, occasionally with garden waste.

Data presented in González-Flórez et al. 2023 "Insights into the size-resolved dust emission from field measurements in the Moroccan Sahara", Atmos. Chem. Phys.

Meteorological, dust and saltation data used in González-Flórez et al., 2023. Data are based on measurements taken during an intensive dust field campaign conducted in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The campaign took place in September 2019 in a small ephemeral lake, locally named "L'Bour", located in the Lower Drâa Valley in Morocco. The description of the data is provided below: - t.nc: time series of temperature measured with four aspirated shield temperature sensors (Campbell Scientific 43502 fan-aspirated shield with 43347 RTD Temperature probe) placed at heights of 1m, 2m, 4m and 8m. - t005.nc time series of temperature measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height. - rh005.nc: time series relative humidity measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height. - wspd.nc: time series of wind speed measured with five 2-D sonic anemometers  (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m. - sdir.nc: time series of wind direction measured with five 2-D sonic anemometers  (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m. - radout.nc: time series of outgoing long wave radiation measured with a four-component net radiometer  (Campbell Scientific NR01-L radiometer) placed at 1.5m height. - p015.nc: times series barometric pressure measured with a barometer (Campbell Scientific CS106) at around 1.5m height. - u_star_law.nc: time series friction velocity calculated through the law of the wall method. - z0_law.nc: time series of roughness length calculated through the law of the wall method. - zeta_law.nc: time series of dimensionless height, zref/L, where zref is the reference height (zref=2m) and L is the Obukhov length  calculated through the law of the wall method. - psd_lower_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~1.8m height. - psd_upper_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~3.5m height and corrected for systematic bias based on an intercomparison between the two Fidas at the end of the campaign. - diff_flux_nb_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number diffusive flux calculated using the flux-gradient method. - q_15avg.nc: time series of 15-min average saltation flux calculated based on measurements with optical gate devices at heights of 0.05m, 0.15m and 0.3m as part of the Standalone AeoliaN Transport Real-time Instrument (SANTRI, Desert Research Institute). - geometric_diameters_integrated_size_bins.csv: containing the minimum, maximum and mean logarithmic optical diameter of the integrated size bins. - optical_diameters_integrated_size_bins: containing the minimum, maximum and mean logarithmic geometric diameter of the integrated size bins. SANTRI data were processed by Martina Klose (<a href="mailto:martina.klose@kit.edu">martina.klose@kit.edu</a>) and the rest by Cristina González Flórez (<a href="mailto:cristina.gonzalez@bsc.es">cristina.gonzalez@bsc.es</a>). Please, cite González-Flórez et al. (2023, ACP) if you use these data. If the data become the key main component of a paper then co-authorship may be offered. Contact Carlos Pérez García-Pando (<a href="mailto:carlos.perez@bsc.es">carlos.perez@bsc.es</a>) if more details are needed. This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051, FRAGMENT).

Non-identical moire twins in bilayer graphene revealed by valley Hall effect measurements

The superlattice obtained by aligning a monolayer graphene and boron nitride (BN) inherits from the hexagonal lattice a sixty degrees periodicity with the layer alignment. It implies that, in principle, the properties of the heterostructure must be identical for 0$^{\circ}$ and 60$^{\circ}$ of layer alignment. Here, we demonstrate, using dynamically rotatable van der Waals heterostructures, that the moir\'e superlattice formed in a bilayer graphene/BN has different electronic properties at 0$^{\circ}$ and 60$^{\circ}$ of alignment. Although the existence of these non-identical moir\'e twins is explained by different relaxation of the atomic structures for each alignment, the origin of the observed valley Hall effect remains to be explained. A simple Berry curvature argument do not hold to explain the hundred and twenty degrees periodicity of this observation. Our results highlight the complexity of the interplay between mechanical and electronic properties on moir\'e structure and the importance of taking into account atomic structure relaxation to understand its electronic properties.

First time-resolved measurement of infrared scintillation light in gaseous xenon

Repository with supplemental data to: First time-resolved measurement of infrared scintillation light in gaseous xenon. Piotter, M., Cichon, D., Hammann, R., Jörg, F., Hötzsch, L., Marrodán Undagoitia, T. Eur. Phys. J. C 83, 482 (2023). A pre-print of the article is available on arXiv: <a href="https://arxiv.org/abs/2303.09344">2303.09344</a> Note: When re-using the data, please make sure to cite the article (and not only the dataset)   The files contain all data related to the observed IR scintillation in gaseous xenon presented in the paper. This comprises the IR time profiles obtained via single photon counting and the measured pressure dependence of the IR light yield for the three extrapolation methods: <ul> <li>waveform_before.csv, waveform_during.csv, waveform_after.csv: These files contain the IR time profiles before, during, and after the purification of the gas (presented in figure 8 in the publication). The column dt is given in nanoseconds relative to the UV pulse and counts corresponds to counts per nanosecond per 100 UV events.</li> <li>light_yield_ir.csv: This file contains the IR light yield as a function of pressure obtained with the three extrapolation models together with the respective statistical and systematic uncertainties. The data is presented in figure 9 in the publication and all values are given in units of photons per MeV.</li> <li>waveform_495.csv, waveform_742.csv, waveform_1047.csv: These files contain the IR time profiles for xenon gas pressures of 495.0 mbar, 742.5 mbar, and 1047.0 mbar, respectively (presented in figure 10 in the publication). The column dt is given in nanoseconds relative to the UV pulse and counts corresponds to counts per nanosecond per 100 UV events.</li> </ul>   Code examples for plotting the data: The following Python code reproduces figure 9 in the publication: <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': df = pd.read_csv("light_yield_ir.csv") color_pairs = [("#365898", "#B7D0FF"), ("#AB123B", "#F0B5C5"), ("#E1992E", "#F1DAB9")] fig, ax = plt.subplots(1, figsize=(4, 3)) for fit_func_str, cs in zip(["Recombination model fit", "Exponential fit", "Linear fit"], color_pairs): # Plot systematic error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Syst. uncertainty ({fit_func_str} fit)"], ls="", elinewidth=3, capsize=0, ecolor=cs[1] ) # Plot estimator with statistical error ax.errorbar(df["Pressure"], df[f"IR Light Yield ({fit_func_str} fit)"], yerr=df[f"Stat. uncertainty ({fit_func_str} fit)"], ls="", c=cs[0], ecolor=cs[0], elinewidth=1, capsize=1, marker=".", label=fit_func_str) # Cosmetics ax.set_xlabel("Pressure [mbar]") ax.set_ylabel("IR light yield [ph / MeV]") ax.set_ylim(1200, 12_500) ax.legend(frameon=False, loc="upper left") plt.show()</code></pre>   The IR time response of figure 8 can be redrawn as follows: <pre><code class="language-python">import pandas as pd import matplotlib.pyplot as plt if __name__ == '__main__': fig, ax = plt.subplots(1, figsize=(4, 3)) for label in ["before", "during", "after"]: df = pd.read_csv(f"waveform_{label}.csv") ax.step(df["dt"], df["counts"], label=label) # Cosmetics ax.set_xlabel("$\Delta t$ between IR and UV signal [ns]") ax.set_ylabel("Counts per 1 ns per 100 UV events") ax.legend(frameon=False, loc="upper right") plt.show()</code></pre>

opencc-by-4.0Jun 2023View details →

Measured data and calculations of pilot RES system for a 103-m2 building in Athens, Greece

This dataset contains complete measurements of an energy system for a building in Athens, Greece (temperatures, flow rates, power, solar radiation, etc.). This system includes a vapour compression heat pump, 4 PVT collectors, a virtual BTES (emulated via a tank with controllable temperature) and three water tanks. A winter and summer day are included. The system operated for space cooling and hot water during the summer day and for space heating and hot water during the winter day. An in-house Python code of NCSR Demokritos has been applied to simulate the energy system operation during these two days for validation purposes. The calculated results are also given in this dataset.

opencc-by-4.0Jun 2023View details →

'Danum Revised' forest field plot data for thirty-five 0.28 hectare field plots, measured in 2020

Permanent forest plot data from Sabah, Malaysia. Danum Valley Conservation Area. Here we provide a brief description of the permanent plot data collected following the same protocols utilized within the Kuamut forest reserve.Twenty circular field plots with a 30 m radius that were established across the protected area as part of a collaboration between SEARRP and the Carnegie Airborne Observatory (CAO) in 2017. See <a href="https://doi.org/10.5281/zenodo.8042050">'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017</a>In 2020 we added an additional 15 plots as part of work for Keller et al. 'Biodiversity consequences of long-term active forest restoration in selectively-logged tropical rainforests', and so this dataset is the full 35 field plots measured in 2020.These plots were surveyed following the same protocols as those described by the Kuamut conservation project. The field protocol is provided. The original 20 Field plot locations where selected using airborne LiDAR to specifically target both high Aboveground Carbon Density (ACD) areas, and also areas with different ACD predictions based on a draft set of carbon mapping  models. See Asner et al. (2017) and Jucker et al. (2018) for further details.In 2020 we selected existing these additional 15 plot points using a high resolution biomass map (see: Asner et al. 2017; Asner et al. 2021). In order to represent the entirety of the area sufficiently, we chose plot locations to represent the average biomass value and variation around it of the majority of the managed area (<a href="https://doi.org/10.1126/science.aay4490">cf. Philipson et al. (2020) for regional values</a>). The plot location further must have fulfilled the following criteria: (1) plots had to be at least 100 m away from noticeable features such as roads to avoid edge effects (but for logistic reasons they nevertheless had to be in the vicinity of roads); (2) plots had to be spread out as much as logistically feasible; (see Keller et al. (submitted), for full details). Note Keller et al. (submitted), used 30 plots for a balanced design between naturally regenerating and actively restored areas. Here we present all 35 plots measured in 2020. Plot locations where additionally updated using an SXblue GPS differential with SBAS augmentation. These locations are provided.Use of these data require citation of this dataset, the associated publication (Keller et al. submitted), as well as the <a href="https://doi.org/10.5281/zenodo.8042050">2017 dataset</a> (which describes the full set up process),.  We also require that you inform us of the use of the data would also appreciate the opportunity to be involved in work making use of this data. The required citations are follows:Philipson, Christopher D., Brodrick, Philip G., Milne, Sol, Murus, Remmy, Rasion, Joulu, Reynolds, Glen, Siew Yee, Hii, Ulok, Philip Ak, & Asner, Gregory P. (2023). 'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017 (1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8042050">https://doi.org/10.5281/zenodo.8042050</a>Keller et al. (submitted). 'Biodiversity consequences of long-term active forest restoration in selectively-logged tropical rainforests',This work would not be possible without the incredibly hard work and dedication of the entire SEARRP field team, including Philip Ak Ulok; Hii Siew Yee; Remmy Bin Murus; Alexander Karolus; Andy Brian Karolus; Frederica Karolus; Zidey Fulgentius; Welday Bin Girang; Mohamad Taufiq Bin Sumin, Mohd Fadil Bin Abd Karim, Joulu Rasion, and Japin Bin Rasion.  We are very grateful to the help of all the field staff not specifically mentioned here. ReferencesAsner, Gregory P., Philip G. Brodrick, Christopher Philipson, Nicolas R. Vaughn, Roberta E. Martin, David E. Knapp, Joseph Heckler, et al. 2017. "Mapped Aboveground Carbon Stocks to Advance Forest Conservation and Recovery in Malaysian Borneo." Biological Conservation 217 (June 2017): 289–310. <a href="https://doi.org/10.1016/j.biocon.2017.10.020">https://doi.org/10.1016/j.biocon.2017.10.020</a>Jucker, Tommaso, Gregory P. Asner, Michele Dalponte, Philip G. Brodrick, Christopher D. Philipson, Nicholas R. Vaughn, Yit Arn Teh, et al. 2018. "Estimating Aboveground Carbon Density and Its Uncertainty in Borneo's Structurally Complex Tropical Forests Using Airborne Laser Scanning." Biogeosciences 15 (12): 3811–30. <a href="https://doi.org/10.5194/bg-15-3811-2018">https://doi.org/10.5194/bg-15-3811-2018</a>Keller, Nadine, Pascal A Niklaus, Jaboury Ghazoul, Tobias Marfil, Elia Godoong, and Christopher D Philipson. 2023. "Biodiversity Consequences of Long-Term Active Forest Restoration in Selectively-Logged Tropical Rainforests." Forest Ecology and Management 549: 4. <a href="https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fdoi.org%2f10.1016%2fj.foreco.2023.121414&c=E,1,CfW3xjatfMP0AonRryjLaKUmg50KQQtF2-n0UPlyW_NATChUbrP9yczm8IQKqWaiNSMtm0g3HuVHyW8N7CKr84VGIQokuIogx-7OG-AAY5epGc0i&typo=1">https://doi.org/10.1016/j.foreco.2023.121414</a>.Philipson, Christopher D., Mark E.J. Cutler, Philip G. Brodric, Gregory P. Asne, Doreen S. Boy, Pedro Moura Costa, Joel Fiddes, et al. 2020. "Active Restoration Accelerates the Carbon Recovery of Human-Modified Tropical Forests." Science 369 (6505): 838–41. <a href="https://doi.org/10.1126/science.aay4490">https://doi.org/10.1126/science.aay4490</a>Philipson, Christopher D., Brodrick, Philip G., Milne, Sol, Murus, Remmy, Rasion, Joulu, Reynolds, Glen, Siew Yee, Hii, Ulok, Philip Ak, & Asner, Gregory P. (2023). 'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017 (1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8042050">https://doi.org/10.5281/zenodo.8042050</a>

opencc-by-4.0Jun 2023View details →

Dataset for Measurement report: Ion clusters as indicator for local new particle formation

Data for Measurement report: Ion clusters as indicator for local new particle formation. There are two files, negative_ion_concentrations.csv and positive_ion_concentrations.csv. The former (latter) includes absolute number concentrations for 1.87, 2.16, 2.49, and 2.88 nm negative (positive) ions. The unit for these concentrations is #/cm-3. Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.

opencc-by-4.0Jul 2023View details →

Dataset for the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects"

Datasets to accompany the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects". Written by J. Elgy, P. D. Ledger, J. L. Davidson, T. Özdeğer and A. J. Peyton. The article has been submitted to "Engineering Computations" (2023). The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch). The datasets also include measurement data for real world objects courtesy of The University of Manchester. J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1. J. L. Davidson and A. J. Peyton are grateful for the financial support received from an Innovate UK Grant (reference number 39814). T. Özdeğer and A. J. Peyton are grateful for the financial support received from EPSRC, U.K. through the research grant EP/R002177/1.

opencc-by-4.0Jul 2023View details →

Satellite-based measurements of brightness temperatures (AMSR2 sensor) colocated to MOSAiC ground measurements

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. The satellite sensor is AMSR2 (six frequencies between 6.9 and 89 GHz and both polarizations) and we use the Level 1R (Madea et al., 2016) product available at JAXA <a href="https://gportal.jaxa.jp/gpr/">https://gportal.jaxa.jp/gpr/</a> 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. Note: The dataset contains only satellite overpasses where co-located data is available. More information on the parameters are found below and they are described in more detail in Rückert et al., 2023 and the references given therein. <ul> <li> scantime: time of satellite observation as included in the satellite data from JAXA </li> <li> lon: longitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA </li> <li> lat: latitude in decimal degrees (DD) of satellite observations as included in the satellite data from JAXA </li> <li> distance: distance to the hourly Polarstern position </li> <li> TB6.9V, TB6.9H, TB10.7V, TB10.7H, TB18.7V, TB18.7H, TB23.8V, TB23.8H, TB36.5V, TB36.5H, TB89V, TB89H: 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 </li> <li> LWP: liquid water path in kg/m² measured by a radiometer onboard the ship (Walbröl et al., 2022), averaged within +/- 10 minutes of the satellite observations </li> <li> sigma_LWP: temporal variability of liquid water path (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time </li> <li> TWV: total water vapor (integrated water vapor) in kg/m² measured by a radiometer onboard the ship (Walbröl et al. (2022)), averaged within +/- 10 minutes satellite observation time </li> <li> sigma_TWV: temporal variability of total water vapor (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time </li> <li> WSP: wind speed in m/s from the vessel’s meteorological observatory (Schmithüsen et al., 2021), averaged within +/- 10 minutes of the satellite observation time </li> <li> sigma_WSP: temporal variability of wind speed (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time </li> <li> SST: sea water temperature in K from the vessel’s meteorological observatory (Schmithüsen et al., 2021), averaged within +/- 10 minutes of the satellite observation time </li> <li> sigma_SST: temporal variability of sea water temperature (see previous point) given as standard deviation within +/- 10 minutes of the satellite observation time </li> <li> SND: snow depth in m obtained from the median of daily snow depth from available Snow and Ice Mass Balance Apparatus (SIMBA) buoys (Lei et al., 2021a) in the proximity of Polarstern. </li> <li> sigma_SND: spatial variability of snow depth (see previous point) given as standard deviation of all buoys available on that day. </li> <li> Tsi: 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. Lei et al., 2021b, for references of all buoys the reader is referred to the references given in Rückert et al., 2023) in the proximity of Polarstern. </li> <li> sigma_Tsi: spatial variability of snow-ice interface temperature (see previous point) given as standard deviation of all buoys available on that day. </li> <li> MYIF: multi-year ice fraction (from 0 to 1) based on classified TerraSAR-X scenes (Guo et al., 2023) in the proximity of Polarstern, interpolated to daily values. </li> <li> sigma_MYIF: estimated (constant) uncertainty of multi-year ice fraction (see previous point). </li> <li> SIC: sea ice concentration (from 0 to 1) based on classified TerraSAR-X scenes (Guo et al., 2023) in the proximity of Polarstern, interpolated to daily values. </li> <li> sigma_SIC: estimated (constant) uncertainty of sea ice concentration (see previous point). </li> <li> Tsa: Snow-air interface temperature in K based on infrared thermometer data (Cox et al., 2023 a)-d)) installed at four positions in the proximity of Polarstern, averaged within +/- 20 minutes of the satellite observation time. </li> <li> sigma_Tsa: 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. </li> </ul>