Find research datasets worth reusing

Effects of intolerance of uncertainty on subjective and psychophysiological measures during threat acquisition and delayed threat extinction

This dataset includes measurements of intolerance of uncertainty (Intolerance of Uncertainty Scale [Freeston et al., 1994]), trait anxiety (State-Trait Anxiety Inventory [Spielberger et al., 1983]), skin conductance response (SCR), fear potentiated startle (FPS) and fear ratings (RAT) acquired in a differential fear conditioning paradigm with habituation and threat acquisition training on one day and extinction training, mood induction (by presenting negative vs. neutral slides), re-extinction training, reinstatement and reinstatement-test 24h later. Overall, 66 participants (female = 44, aged between 18 and 40 years, M = 25.76, SD = 5.82) took part in the study. Several participants had to be excluded due to technical issues (n = 3), non-responding (SCR: n = 2; auditory startle blink: n = 1) and no SCRs to the CSs (n = 1). Visual CSs were two shapes resembling snowflakes. The US consisted of a train of three 2 ms electrotactile square-waves (inter stimulus interval, ISI: 50 ms) and was delivered 7.9 s after each CS+ onset (100% reinforcement rate) during threat acquisition training and three times during reinstatement. The duration of the ITIs ranged from 10 to 13 s (M = 11.5). For SCR measurements, a 1 Hz lowpass filter and a gain of 5 or 10 μΩ were applied. SCR data were scored by using the semi-automatic scoring system Autonomate (Green et al., 2014), down sampled to 10 Hz and scored as the first response within 0.9 to 4 s after CS onset as SCR from trough to peak with a maximum rise time of 5 s. SCRs were square root transformed to reduce skew and z-scored within individuals across trials for day 1 and day 2 separately. To elicit the auditory startle blink, a 95 dB white noise burst was presented simultaneously on both ears. Startle probes were administered 6 or 8 s after the ITI-onset and 6 or 7 s after CS-onset. A gain of 5000 at 1000 Hz and a band-pass filter (28–500 Hz) were applied. Data were rectified and integrated online (averaged over 20 samples) and scored semi-automatically by using a custom-made computer program (EDA View, developed by Prof. Dr. Matthias Gamer, University of Würzburg) as trough to peak 20–120 ms after startle probe onset. For analyses, FPS data was z-scored within individuals across trials for day 1 and day 2 separately. To acquire fear ratings, participants rated throughout the experiment, how much stress, fear, and tension they experienced, when they last saw the CSs. Answers had to be logged in via button press within 7 s on a visual analog scale (VAS) ranging from zero (answer = none) to 100 (answer = maximum). Unlogged ratings were considered as missing values.

Temperature measurements at Saint Thomas and Saint Philip Neri church

This dataset contains temperature measurements in Celsius degrees collected at the church of Saint Thomas and Saint Philip Neri in Valencia (Spain) by multiple wireless sensors. The data were collected from August 2017 to March 2019 and aligned at the same time point using linear interpolation.

H2020 Platone German Demonstrator Use Case 1 Measurement Data

This dataset contains measurement datas collected from mesurements devices in the field (substation, battery storage, etc) during the application of Use Case 1 (Islanding/Maximization of local self-consumption).

Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement (Data)

Origin projects, figures and COMSOL simulation used for the article "Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement", published in Journal of Micromechanics and Microengineering on 05 Nov 2019.

2021 5Genesis Berlin Platform Field Trial RTT and Throughput Measurements

RTT and Throughput measurements from the various measurement endpoints of the 2021 5GENESIS Berlin Platform Field Trials at IHP, in Frankfurt (Oder).

Supporting data for "Measurable fractional spin for quantum Hall quasiparticles on the disk"

Supporting data for the manuscript "Measurable fractional spin for quantum Hall quasiparticles on the disk", by T. Comparin, A. Opler, E. Macaluso, A. Biella, A. P. Polychronakos, L. Mazza. If you use these numerical results in a scientific work, please cite the corresponding article [<a href="https://link.aps.org/doi/10.1103/PhysRevB.105.085125">Phys. Rev. B 105, 085125 (2022)</a>]. For additional details, please contact Tommaso Comparin (tommaso.comparin@ens-lyon.fr). We computed the density profile rho(r) for the Laughlin state (with filling 1/m, for m=2,3,4) and for the Halperin 221 state, by means of Monte Carlo simulations. All data are in units of the magnetic length (that is, with lB=1). When present, labels "QH0", "QH1" and "QH2" in the filenames correspond to the case with q=0, q=1 or q=2 quasiholes localized at the origin. Folders: <ul> <li>Data_Laughlin contains the Laughlin density-profile data used to compute the spin values in Fig. 1.</li> <li>Data_Halperin221 contains the Halperin 221 density-profile data used to compute the spin values in Fig. 2. Filenames include a label for the type and number of quasiholes: "A" stands for Gamma=A and q=1; "AA" stands for Gamma=A and q=2; "AB" stands for Gamma=AB and q=1; "AABB" stands for Gamma=AB and q=2.</li> <li>Data_Laughlin_boundary contains the Laughlin density-profile data shown in Fig. 6.</li> <li>Data_Halperin221_boundary_A contains the Laughlin density-profile data shown in Fig. 7, for Gamma=A.</li> <li>Data_Halperin221_boundary_AB contains the Laughlin density-profile data shown in Fig. 7, for Gamma=AB.</li> </ul>

Reference Measurements of Pavement Samples

Data for MDPI Sensors article Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete (doi: <a href="https://doi.org/10.3390/s21248190">10.3390/s21248190</a>). Contains data related to Sections 2.3 and 3.1 of the paper. Data describe reference measurements of pavement samples. Data are registered point clouds in ASCII text XYZ format in an arbitrary coordinate system.

opencc-by-4.0Oct 2021View details →

Datasets for Ecohydrological Model for Grassland Lacking Historical Measurements

We proposed the distributed dynamic process model (DDPM) and here are the example datasets of our model. Datasets I : Eva_module_datasets.zip: Here are the datasets for running the DDPM Eva module. Datasets II: RP&FLC_module_datasets.zip: Here are the datasets for running the DDPM RP & FLC module.   Also, you can find our model at https://github/myli1993/DDPM_ver1.0. You can watch my report on MODSIM 2021 at https://www.bilibili.com/video/BV1vR4y1s7vc. ----------------------------------------------------------------------------------------- When using this dataset, please cite: [1] Li, MY.; Liu, TX.; Duan, LM.; et al. Confluence simulations based on dynamic channel parameters in the grasslands lacking historical measurements. Journal of Hydrology, 2023, Volume 627, 130425. <a href="https://doi.org/10.1016/j.jhydrol.2023.130425">https://doi.org/10.1016/j.jhydrol.2023.130425</a> [2] Li, MY.; Liu, TX.; Duan, LM.; et al. A novel evapotranspiration downscaling approach based on dynamic sensitive parameters and deep learning in the grassland lacking historical measurements. Ecological Indicators, 2025, Volume 178, 113839. https://doi.org/10.1016/j.ecolind.2025.113839 ----------------------------------------------------------------------------------------- What's new: Version 1.01: Here, we added the datasets for Eva module, and renamed the datasets for RP & FLC module. If you have downloaded the datasets in Version 1.0, you don't have to download the RP&FLC_module_datasets.zip for another time.

MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles (Data)

Origin project and figures used for the article "MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles", published in Chemosensors on 04 Aug 2021.

Measurement of energies and intensities of multiple ionization satellite (MIS) excited in light elements by helium ion beams

TNA project number: 19001708-ST Measurement of energies and intensities of multiple ionization satellite (MIS) excited in light elements by helium ion beams Scientific background: Over 1600 X-ray spectra have been collected from the alpha particle X-ray spectrometers on Mars rovers including the present Curiosity rover. The spectra are excited by the radionuclide 244Cm, which emits 5 MeV He ions for PIXE and Pu L X-rays for XRF. These spectra provide elemental analysis of rocks, soils and dust as part of the quest to identify formerly habitable (water-bearing) environments. Applicant is a member of the Curiosity APXS team within NASA’s Mars Science Laboratory. Excitation of K X-rays by 5 MeV He ions produces also energy-shifted satellites due to 1, 2 or 3 L-shell spectator vacancies. These cause significant distortion of the diagram lines and worsen the quality of spectrum fits by the GUPIX(Mars) code. An MIS database is needed to support a correction procedure that is already devised. This work will increase analytical accuracy and will also support more accurate terrestrial PIXE analysis using alpha beams, especially when partnered with RBS; this could lead to increased use of these two IBA methods in a complementary manner.   Measurements performed within TNA project: The wavelength-dispersive in-vacuum x-ray spectrometer of J. Stefan Institute (Ljubljana, Slovenia) [1] have been used to record high energy resolution KaLN X-ray spectra of Ca and Cr induced in collisions with MeV alpha particles. The targets used were metallic Cr, Cr2O3, and CaF2. The KaLN X-ray spectra of Cr and Cr2O3 were measured using three different energies of He ions, namely 3 MeV, 4 MeV and 5 MeV. For CaF2 we have collected only spectra induced with 5 MeV He beam. The main purpose of the experiment was to record KaLN spectra with good enough statistics to determine precisely energy/intensity of the corresponding satellite lines. The results for the energy shifts and relative intensities of the groups will be incorporated in the MIS database providing an empirical means for inclusion of one peak per satellite group when modelling energy-dispersive spectra (GUPIX(Mars) code). [1] M. Kavčič, M. Budnar, A. Mühleisen, F. Gasser, M. Žitnik, K. Bučar, R. Bohinc, Design and performance of a versatile curved-crystal spectrometer for high-resolution spectroscopy in the tender x-ray range, Rev. Sci. Instr. 83, 033113 (2012). <a href="http://dx.doi.org/10.1063/1.3697862">http://dx.doi.org/10.1063/1.3697862</a>   Data files: We are sharing the detector files (a series of single exposure 2D raw images) collected by the Andor DX438-BV CCD camera (770 × 1152 pixels with pixel size 22.5×22.5 μm2) after the diffraction on the crystal analyzer. The horizontal axis of the detector corresponds to the dispersion axis and diffracted photons are detected at different horizontal positions according to their wavelength, the vertical axis of the detector serves mainly to accumulate more statistics. The final emission spectra are obtained from the corresponding detector files using the home-written data processing software.

QAH potential profile measurements

Dataset and analysis code accompanying the manuscript "Measured potential profile in a quantum anomalous Hall system suggests bulk-dominated current flow," available at <a href="https://arxiv.org/abs/2112.13123">https://arxiv.org/abs/2112.13123</a>. Simulation code available at <a href="https://github.com/itrosen/hall-solver">https://github.com/itrosen/hall-solver</a>.

opencc-by-4.0Mar 2020View details →

Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution - Dataset

This repository contains the data associated with the paper: Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution. Bulot, F.M.J.; Russell, H.S.; Rezaei, M.; Johnson, M.S.; Ossont, S.J.J.; Morris, A.K.R.; Basford, P.J.; Easton, N.H.C.; Foster, G.L.; Loxham, M.; Cox, S.J. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution. Sensors 2020, 20, 2219. <a href="https://doi.org/10.3390/s20082219">https://doi.org/10.3390/s20082219</a>  It contains: - DHT22.csv measurements from the DHT22 humidity and temperature sensor - dusttrak.csv measurements from the DustTrak - ops.csv measurements from the OPS TSI 3330 - sensors.csv measurement from the low-cost PM sensors - sensors_blank.csv measurements from the low-cost PM sensors during the blank test   sensors_blank.csv contains the following variables: <ul> <li>Bin1 to Bin15: particle numbers for different bin sizes reported by the Alphasense OPCR1, as defined by its user's manual available here https://www.alphasense.com/products/optical-particle-counter/</li> <li>SamplingPeriod: sampling period of the Alphasense OPCR1 in seconds</li> <li>SFR: sampling flow rate of the Alphasense OPCR1 in ml/s</li> <li>PM1, PM25, PM4, PM10: PM concentrations reported by the sensors in ug/m3.</li> <li>gr03um to gr100um: particle number concentrations for different bin sizes for the Plantower PMS5003, in particle per 100ml, as defined by its user's manual https://aqicn.org/air/view/sensor/spec/pms5003-manual_v2-3</li> <li>n05 to n10: particle number concentrations for different bin sizes for the Sensirion SPS30, in particles per cm3, as defined by its user's manual: https://www.sensirion.com/fileadmin/user_upload/customers/sensirion/Dokumente/9.6_Particulate_Matter/Datasheets/Sensirion_PM_Sensors_Datasheet_SPS30.pdf</li> <li>humidity and temperature: relative humidity (%) and temperature (Celsius) recorded by the SHT35 sensors</li> <li>sensor: sensor identifier</li> <li>site: name of the air quality monitor containing the sensors</li> <li>exp: name of the experiment considered</li> <li>source: source of PM used</li> <li>variation: peak or stable concentration</li> <li>date: date and time of the experiment</li> </ul>

Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples

Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are widely used in industry for process monitoring and control. One factor that limits their accuracy is the dramatic decrease in the insulation resistance at temperatures above about 600 °C which results in temperature measurement errors due to electrical shunting. In this work the insulation resistance of a cohort of representative MI thermocouples was characterised at temperatures up to 1160 °C, with simultaneous measurements of the error in indicated temperature by in situ comparison with a reference Type R thermocouple. Intriguingly, there appears to be a systematic relationship between the insulation resistance and the error in the indicated temperature. At a given temperature, as the insulation resistance decreases, there is a corresponding increasingly negative error in the temperature measurement. Although the measurements have a relatively large uncertainty (up to about 1 °C in temperature error and up to about 10 % in insulation resistance measurement), the trend is apparent at all temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation disappears at temperatures below about 600 °C, which is consistent with the well-established diminution of insulation resistance breakdown effects below that temperature. This raises the intriguing possibility of using the as-new MI thermocouple calibration as an indicator of insulation resistance breakdown: large deviations of the electromotive force (emf) in the negative direction could indicate a correspondingly low insulation resistance.

opencc-by-4.0Feb 2021View details →

Reactive nitrogen fluxes over peatland (Bourtanger Moor) and forest (Bavarian Forest National Park) using micrometeorological measurement techniques

Within the framework of the research projects NITROSPHERE and FORESTFLUX, field campaigns were carried out to investigate the biosphere-atmosphere exchange of reactive nitrogen compounds. We applied novel fast-response instruments in eddy-covariance setups for continuous determination of surface ammonia (NH3) and total reactive nitrogen (\(\Sigma\)Nr) fluxes using two different analytical devices. While high-frequency measurements of ammonia were measured with a quantum cascade laser absorption spectrometer (QCL), a custom-built converter called TRANC coupled to a chemiluminescence detector was used for the determination of total reactive nitrogen. High-resolution data of surface-atmosphere fluxes of reactive compounds are still scarce, but highly desired for testing and validating local inferential and larger scale models. We provide access to campaign data including concentrations, fluxes and ancillary measurements of meteorological data. Campaigns were conducted in natural (forest) and semi-natural (peatland) ecosystem types. The published datasets stress the importance of recent advancements in laser spectrometry and help improve our understanding of the temporal variability of surface-atmosphere exchange in different ecosystems, thereby providing validation opportunities for inferential models simulating the exchange of reactive nitrogen.

AMS and FTIR measurements and the corresponding codes for their statistical combination

This dataset includes the post-processed FTIR and AMS data for the particulate phase obtained by Yazdani et al., https://doi.org/10.5194/amt-2021-186 form wood and coal burning experiments in the PSI environmental simulation chamber. It also contains the codes for the statistical combination of AMS and FTIR measurements to estimate the high-time-resolution functional group composition of organic aerosols.

Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit and Effects on the Point Cloud

This data repository contains vibration tests of an Ouster OS1-64 lidar including a docker based python environment and documentation. It consists of movement data, Fotos, IMU data, pointclouds and a ground truth measurements with an Riegl VZ6000 laser scaner. For further details see the linked publication and the example.ipynb notebook. Quick start <ul> <li> download the repo </li> <li> unzip the archive </li> <li> install VS code with the remote development extension </li> <li> install docker desktop </li> <li> open the folder in a new VS code window </li> <li> Say "yes" to open the folder inside a docker container </li> <li> wait for the container to start </li> <li> open the example jupyter notebook </li> </ul> Structure <blockquote> <pre>├── .devcontainer                       .... VS code devcontainer (arm64 and amd64)</pre> <pre>├── Acceleromenter_A_z_deflection       .... Accelerometer data</pre> <pre>├── Foto                               .... Fotos of the setup</pre> <pre>│   ├── VZ6000                         .... Fotos of the ground truth measurements</pre> <pre>│   └── test_setup                     .... Fotos of the test setup</pre> <pre>├── Notebook                           .... A jupyter notebook with examples</pre> <pre>├── OS1_64_IMU                         .... Ouster IMU data</pre> <pre>├── OS1_64_pointcloud                   .... point cloud data</pre> <pre>├── VZ6000_groundtruth                 .... Ground truth data from Riegl VZ6000</pre> <pre>   └── targets                         .... contains 2 different sets of reference</pre> <pre>│       ├── scene_aligned_by_reflectors</pre> <pre>│       └── targets_aligned</pre> <pre>└── files.csv                           .... an overview of the files and meta data</pre> </blockquote>

Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest

This data set is connected to the article:  Jokinen, T., Lehtipalo, K., Thakur, R. C., Ylivinkka, I., Neitola, K., Sarnela, N., Laitinen, T., Kulmala, M., Petäjä, T., and Sipilä, M.: Measurement report: Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest, Atmos. Chem. Phys., 2022

opencc-by-4.0Sep 2021View details →

Radar measurements on drones, birds and humans with a 77GHz FMCW sensor.

This data set contains radar measurements on birds, humans and six different drones with a total of 75868 samples. The sensor was a  frequency modulated continuous wave (FMCW) radar operating at 77 GHz with a mechanically scanning antenna. The 'ReadMe.txt' file contains a detailed description of the data. The data set is used in [1] where only FM-sweeps corresponding to azimuth index 54 to 203 are used (out of the provided 256), or 150 sweeps.  When using this data set please refer to: [1] A. Karlsson, M. Jansson and M. Hämäläinen, "Model-Aided Drone Classification Using Convolutional Neural Networks," 2022 IEEE Radar Conference (RadarConf22), 2022, pp. 1-6, doi: 10.1109/RadarConf2248738.2022.9764194. The data in version 1.0 and 2.0 is identical apart from the format, ".mat" in 1.0 and ".npy" in 2.0

opencc-by-4.0Feb 2022View details →

Low energy MeV SIMS yield measurements of various inorganic samples

The low energy range (a few 100 keV to a few MeV) primary ion mode in MeV Secondary Ion Mass Spectrometry (MeV SIMS) and its potential in exploiting the capabilities of conventional (keV) SIMS and MeV SIMS simultaneously was investigated. The aim is to see if in this energy range both types of materials, inorganic and organic, can be simultaneously analyzed. A feasibility study was conducted, first by analyzing the dependence of secondary ion yields in Indium Tin Oxide (ITO – In2O5Sn) and leucine (C6H13NO2) on various primary ion energies and charge states of Cu beam, within the scope of equal influence of electronic and nuclear stopping. Expected behavior was observed for both targets (mainly nuclear sputtering for ITO and electronic sputtering for leucine). MeV SIMS images of samples containing separate regions of Cr and leucine were obtained using both keV and MeV primary ions. Based on the image contrast and measured data, the benefit of a low energy beam is demonstrated by Cr+ intensity leveling with leucine [M+H]+ intensity, as opposed to a significant contrast at higher energy. It is estimated that by lowering the energy, leucine [M+H]+ yield efficiency lowers roughly 20 times as a price for gaining about 10 times larger efficiency of Cr+ yield, while leucine [M+H]+ yield still remains sufficiently pronounced.