Find research datasets worth reusing

opencc-by-4.0Dec 2019View details →

Specific Heat of Holmium in Gold and Silver at Low Temperatures - Data

Data from measurements on the specific heat of a variety of Au:Ho and Ag:Ho alloys. This data is associated with the manuscript: Herbst, M., Reifenberger, A., Velte, C. et al. Specific Heat of Holmium in Gold and Silver at Low Temperatures. J Low Temp Phys 202, 106–120 (2021). https://doi.org/10.1007/s10909-020-02531-1 For information on the motivation, measurement techniques, equipment, and data processing, please refer to this manuscript.

PsPM-AOB: Eye tracker (including pupillometry) measurements from auditory oddball tasks

This dataset includes eye tracker (including pupillometry) measurements from auditory oddball tasks with ITIs of 1, 2, and 3 s (in groups 1, 2, and 3 respectively). Also included are task information, keypress responses, keypress response times and key correctness for each of 66 healthy unmedicated participants (40 females and 26 males aged 24.2+/-3.9 years) participating in auditory oddball tasks. Stimuli consist of sine tones (50-ms length; 10-ms ramp; 440 or 660 Hz).

opencc-by-4.0Dec 2019View details →

GMX_lipid17.ff: Gromacs Port of the amber LIPID17 force field

This is a Gromacs port of the amber LIPID17 force field. To use this force field, the user can construct the lipid bilayer using Charmm-GUI and convert the atom names to the amber atom names using charmmlipid2amber.py. This port has also retained the modular feature of the LIPID17 force field, where the user can customise the head group or acryl chain and use pdb2gmx to construct the topology. The coordinate files for the amber lipids can also be obtained from the `gro` folder. The force field `lipid17.ff`, itp file `lipid17.itp` and a custom PI head group are all included in the attached compressed file. For the details of the generation and validation protocol, please consult the relevant <a href="https://github.com/xiki-tempula/gmx_lipid17.ff">Github</a> page.

Rooftop photovoltaic (PV) potential data for the Swiss building stock

The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in: Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” Applied Energy 262 (March 15, 2020): 114404. In the process of generating this dataset, the following aspects were included: <ul> <li>Meteorological conditions in Switzerland (solar radiation, temperature, snow cover)</li> <li>Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model)</li> <li>Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva)</li> <li>The panel and inverter efficiencies, as a function of the solar radiation and temperature</li> </ul> Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document rooftop_PV_CH_metadata_V1.pdf. Data description: The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided: <ol> <li>Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. </li> <li>Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude</li> </ol> If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly.

Draft genome assembly version 1 of the meadow spittlebug Philaenus spumarius (Linnaeus, 1758) (Hemiptera, Aphrophoridae)

We sequenced the genome of the meadow spittlebug, Philaenus spumarius (Linnaeus, 1758), the main insect vector of Xylella fastidiosa Wells et al. 1987 in Europe (Saponari et al., 2014), using 10x Chromium linked-reads. A single P. spumarius adult female from Portugal (Fontanelas, Sintra; GPS location: 38°50'15.75"N; 9°25'20.77"W), collected in September of 2018, was selected for genome sequencing. This population was initially surveyed for colour polymorphism in 1988 (Quartau & Borges, 1997) and was later included in phylogeographic and population genomic studies of this species (Rodrigues et al., 2014; Seabra et al., unpublished). It is also geographically close to the population from which the individual used for the first partial genome assembly was collected (Rodrigues et al., 2016). The availability of this previous genetic information contributed to the choice of this population as the source of genomic material for whole genome sequencing. A subset of males from the same collection date were analysed for genitalia morphology to confirm species identification, as the best diagnostic characters are the appendages of the aedeagus (Drosopoulos & Quartau, 2002). The genomic DNA of the P. spumarius adult from Sintra was extracted using Illustra Nucleon Phytopure kit according to the manufacturer’s instructions (GE Healthcare). We assessed the quality and concentration of the DNA using Femto fragment analyser (Agilent). 10x Chromium library preparation and Illumina genome sequencing (HiSeq X, 150bp paired-end) were performed by Novogene Bioinformatics Technology Co, Beijing, China, in accordance with standard protocols. To create the de novo 10x Chromium assembly we ran Supernova 2.1.1 (Weisenfeld et al., 2017) on the 10x Chromium linked-read data with default parameters, using 1.0 billion reads corresponding to 56X coverage. To improve the initial supernova assembly, we performed iterative scaffolding using all of the 10x raw data (2.3 billion of reads). We ran two rounds of Scaff10x (https://github.com/wtsi-hpag/Scaff10X), followed by mis-assembly detection and correction with Tigmint (Jackman et al., 2018). This was followed by a final round of scaffolding with ARCS (Yeo et al., 2018). The assembly was checked for contamination using the BlobTools pipeline (version 0.9.19; Laetsch and Blaxter 2017; Kumar et al., 2013) and k-mer content was analysed with the KAT comp tool (Mapleson et al., 2017). In order to perform these analyses, it was necessary to remove the 10x linked barcodes from the reads with the script process_10xReads.py (https://github.com/ucdavis-bioinformatics/proc10xG). We assessed the quality of our draft genome assembly by searching for conserved, single copy, arthropod genes (n=1,066) with Benchmarking Universal Single-Copy Orthologs (BUSCO) v3.0 (Waterhouse et al., 2018). With the above assembly procedure, we obtained a final assembly of 2.7 Gb, having a scaffold N50 length of 116 Kb (contig N50 = 18 Kb) and the longest scaffold was 3.7 Mb. The length of the assembly was consistent with the genome size estimated by flow cytometry (Rodrigues et al., 2016). The k-mer distribution indicated high heterozygosity, estimated at 2.3%. BlobTools analyses revealed the presence of contigs assigned to Sodalis spp. (Enterobacteriaceae), a symbiont in members of tribe Philaenini (Koga et al., 2013). These contigs were filtered from the final assembly. Gene completeness assessment shows that 956 (89.6%) among 1,066 BUSCOs were  found as complete copies, with only 26 (2.4%) missing. Of the BUSCOs that were detected, 878 (82.4%) were complete and single-copy, 78 (7.3%) were complete and duplicated and 84 (7.9%) were fragmented. In conclusion, due in part to high (2.3%) heterozygosity levels, the P. spumarius version 1 genome assembly is highly fragmented. Nonetheless, the assembly is considered complete and is likely to contain the majority of the gene content of P. spumarius.

MiRoR15-P1-Tools used to assess the quality of peer review reports: a methodological systematic review

Database, data extraction form, R codes and protocol related to: Superchi C, González JA, Solà I, Cobo E, Hren D, Boutron I. Tools used to assess the quality of peer review reports: a methodological systematic review. BMC Med Res Methodol. 2019;19(48):1–14. DOI: <a href="https://doi.org/10.1186/s12874-019-0688-x">https://doi.org/10.1186/s12874-019-0688-x</a>

opencc-by-4.0Jan 2019View details →

Simulated NGS read datasets for bacterial pathogenic potential prediction

## Predicting pathogenic potentials from NGS reads: novel bacterial species This repository contains simulated Illumina read datasets for bacterial pathogenic potential prediction and associated metadata extracted from the IMG Database (https://img.jgi.doe.gov/). The reads are 250bp long and were simulated with Mason (https://www.seqan.de/apps/mason/) from genomes downloaded from NCBI. The training-validation-test split was done on the species level to ensure "novelty" of validation and test species. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. Additional, imbalanced training sets contain 2.5 million "nonpathogenic" and 17.5 million "pathogenic" reads, keeping the mean covarage constant for all species. The temporal benchmark test set contains reads from 3 additional pathogenic species in the Pantoea genus. ## Predicting pathogenic potentials from NGS reads: novel strains of known species The BacPaCS datasets contain reads simulated from the dataset compiled by Barash et al. (https://doi.org/10.1093/bioinformatics/bty928). It this case, the training-validation-test split was done on the strain level (so different strains of the same species may be present in all three sets).

250MBaud Gaussian CV-QKD in coexistence with 8x200G PM-16QAM

This dataset comprises the simulation data of the VPItoolkit™ QKD application example "250MBaud Gaussian CV-QKD in coexistence with 8x200G PM-16QAM". This demonstrates the possibility of coexisting Gaussian CV-QKD and two times four classical channels. The classical channels (32 GBaud) are simulated with 8 samples per symbol. The modulation variance is swept to illustrate how excess noise and secret fraction depend on the modulation variance.

opencc-by-4.0Sep 2019View details →

Physical and biogeochemical oceanography data from Conductivity, Temperature, Depth (CTD) rosette deployments during the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract This data set contains measurements from various sensors mounted on the Conductivity, Temperature, Depth (CTD) rosette that was deployed in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE). 63 CTD casts were carried out during three legs in the period 21st December 2016 to 16th March 2017, including one test cast and one failed cast, for which no data is available. Data include temperature, salinity, pressure, dissolved oxygen, oxygen saturation, chlorophyll-a concentration, backscatter, and photosynthetically active radiation (PAR) and reported are also the computed variables density, depth, and sound velocity. All data has been quality controlled and post-cruise calibrated, except for the oxygen data. Data is provided at 1 dbar pressure intervals for the up- and down-casts separately and as a merged bottle file when Niskin bottles were closed. This circumpolar data set provides insights into the circumpolar hydrography and biogeochemistry of the Southern Ocean during one austral summer season. Dataset contents For transparency, the raw files and files produced at the intermediate stages of data processing have been provided, in addition to the final processed files. Raw data files: <ul> <li>ace_ctd_raw_files.zip - includes raw files direct from instrument and XMLCON configuration files</li> </ul> Intermediate files: <ul> <li>files output at each stage of the SeaBird processing</li> </ul> Processed data files: <ul> <li>ace_ctd_CTD20200406CURRSGCMR - one final set of files for the complete sensor data;</li> <li>ace_ctd_BOTTLE20200406CURRSGCMR_hy1.csv - a merged bottle file extracted from the sensor data is also provided</li> </ul> Metadata: <ul> <li>range of files describing the CTD deployments, sensors, water sampling; quality-checking and processing of the files.</li> </ul> Dataset license This physical and biogeochemical oceanography dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/ Change log v1.1 Quick summary of issues addressed in CTD DOI Update - Resolved discrepancies between upcast and downcast MLD estimates - ‘Bad’ datapoints in file dACE201601_002_ct1.csv which were not flagged with ‘4’ Bad measurement - CTDFLUOR1, CTDFLUOR1Q, CTDFLUOR2, CTDFLUOR2Q ‘dark’ correction was not applied consistently in first processing and should have been applied to all fluorescence variables - CTDFLUOR1Q, CTDFLUOR2Q quenching correction needed to be recalculated and reapplied after update to MLD and dark correction - Limit the number of decimal places for fluorescence, PAR and backscattering variables according to the instrument sensitivity limits (which is 4 decimal places except for backscattering which is 6) - Changed file names described in data_file_header.txt Additional details on ‘Issues’ and resolutions Mixed layer depth estimates - Large discrepancy in MLD estimates from upcast and downcasts at the same station was due to differences in the ‘reference’ depth i.e. depth other than 10 m was used when there were no datapoints at 10 m. - Note: influence of time between casts was also checked and was not the driver of the discrepancies. - Issue was resolved by setting the MLD for any cast where the reference depth was not 10 m to NaN. Bad data flagging - 22 ‘bad’ datapoints for variables salinity, density, temperature and sound at the end of the downcast file dACE201601_002_ct1 were missed during the visual inspection of the first CTD processing and hence were not flagged as bad. - The bad datapoints are now flagged as ‘4’ bad measurement Fluorescence - In the first processing, dark correction was only applied to the files where quenching correction was needed, and only to the quenched corrected fluorescence variable, but should have been applied to all fluorescence variables in all files. This has been corrected - In the first processing, the upcast MLD was used as the MLD estimate in quenching correction for both the upcast and downcast file. This has been changed so that the MLD from the same cast is used i.e. downcast estimate for the downcast file and upcast estimate for the upcasts file, unless the MLD estimate is NaN (because the reference depth was not 10 m), in that case the either the downcast or upcast estimate is used - whichever exists. Decimal places - The number of decimal places for the fluorescence, PAR and backscattering variables far exceeded the sensitivity limits of the respective sensors - for the fluorescence and backscattering variables this was due to the additional calculations and corrections applied. For the PAR variable it was the output from the Seabird processing. Updated files list The following files have been updated: Folder: ace_bottle_BOTTLE20200406CURRSGCMR (all files within) Folder: ace_ctd_CTD20200406CURRSGCMR (all files within) ace_ctd_mld_CURRSSRGCMR20200405.csv ace_ctd_visual_inspection_v2.csv README.txt data_file_header.txt ace_physical_biogeochemical_oceanography_ctd_change_log.txt (new file) v1.0 - Initial release of physical and biogeochemical oceanography data set.

Laboratory measurements of wind, waves, and turbulence in hurricane conditions in the ASIST wind-wave facility

Laboratory measurements of wind, waves, and turbulence in hurricane conditions, collected in September and October of 2018 and January of 2019 in the ASIST wind-wave facility, in the SUSTAIN laboratory at the University of Miami. This dataset includes two experiments, one with fresh water ("fresh") and another with seawater ("salt"), each in 10-m winds from 0 to approximately 42 m/s. Data include: <ul> <li>3-dimensional wind velocity at 20 Hz sampling frequency from Campbell Scientific IRGASON sonic anemometer (collected in 2018)</li> <li>2-dimensional (along-tank and vertical) wind velocity at 1000 Hz sampling frequency from TSI IFA-300 hot film anemometer (collected in 2018)</li> <li>1-dimensional (along-tank) wind velocity at 10 Hz sampling frequency from a pitot anemometer (collected in 2018 and 2019)</li> <li>3-dimensional water velocity in the bottom 5 cm of the tank at 100 Hz sampling velocity from Nortek Vectrino velocimeter. (collected in 2018)</li> <li>Water elevation at 20 Hz sampling frequency at 6 locations in the tank from Senix Toughsonic 30 ultrasonic distance meters (collected in 2019)</li> <li>Along-tank static air pressure difference at 10 Hz sampling frequency from Baratron MKS 226 differential pressure transducer (collected in 2019)</li> </ul> All data is in NetCDF4 format. Experiment set up and positions of instruments are documented in more detail in Curcic and Haus (2020), Revised estimates of ocean surface drag in strong winds, Geophysical Research Letters, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL087647</a>. Produced as part of the National Science Foundation Award #1745384, titled "Air-Sea Momentum Transfer in Extreme Wind Conditions". Contact: Milan Curcic <mcurcic@miami.edu>

P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)

The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019). The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019. The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3). The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N): Xmin = 655000, Xmax = 670000 Ymin = 2168000, Ymax = 2185000 Zmin = -3000, Zmax = 4000

opencc-zeroApr 2020View details →

Transcriptomic response of human cells to SARS-CoV-2, RSV and H1N1 (STAR + StringTie)

These data represent results from: <ol> <li>Processing reads from 20 experiments (part of GSE147507) by following a standard approach, which includes using STAR to align the reads to GRCh38 and StringTie to calculate the (raw) counts per experiment. These results depict the transcriptomic response of human cells to SARS-CoV-2, RSV and H1N1, and enrichment analyses based on genes differentially expressed in SARS-CoV-2 but not in RSV or H1N1. (Authors: V.A.-P., M.G.F. and A.G.)</li> <li>Aligning to SARS-CoV-2 and quantifying reads by using HISAT2 and StringTie. (Author: C.R.-A.)</li> </ol> Disclaimer: These results were obtained during the virtual BioHackathon 2020. As such, they are subject to ongoing research and have thus NOT yet undergone any scientific peer-review. That is, none of the contents can be considered to be free of errors and must be taken with caution!

Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"

This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>. The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.

PsPM-HRM1-2: SCR and ECG measurements in response to white noise sounds and an auditory oddball task

This dataset includes skin conductance response (SCR) and electrocardiogram (ECG) for each of 61 healthy unmedicated participants (28 males, 32 females, 1 unassigned, aged 25.8 +/- 4.6 years) in response to 20 broadband white noise sounds (HRM1) or 10 oddball tones in a oddball task (HRM2). Some participants did not did not complete HRM1 or HRM2 or were excluded from analysis such that there are 56 recordings for HRM1 and 58 recordings for HRM2. White noise sounds in HRM1 were 1 s long with 10 ms on- and offset ramp and presented at ~85 dB. Oddball and standard sounds in HRM2 were 50 ms long, with 10 ms on- and offset ramp, and presented at ~75 dB. Sound frequency was 440 Hz or 460 Hz, randomly balanced per participant to oddballs and standards. SOA between white noise sounds and oddball tones was selected randomly on each trial from 30 s, 35 s or 40 s. All stimuli were presented in one block. There is a marker for each sound onset (including standard tones) in the windaq files.

Sky irradiance over photosynthetically active radiation wavelengths (400-700 nm) recorded shipboard during the Antarctic Circumnavigation Expedition (ACE) during the Austral Summer of 2016/2017.

Dataset abstract This dataset contains high resolution records of sky irradiance over photosynthetically active radiation wavelengths (PAR; 400-700 nm) recorded shipboard during the Antarctic Circumnavigation Expedition (ACE) Leg 1-3. A hemispherical PAR sensor was fixed to the bow of the RV Akademik Tryoshnikov ship at approximately 2 metres height above the main deck and continuously recorded the irradiance over PAR wavelengths (400-700 nm) at 1 minute intervals from 21st December 2016 to the 16th March 2017. This data provides high resolution information on the diel cycle in sky irradiance and absolute sky irradiance over PAR wavelengths (400-700 nm) along the ship track of the ACE expedition. Dataset contents <ul> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> <li>ace_par_20200526CURRSGCMR.csv, data file, comma-separated values</li> </ul>

Particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition of seawater sampled during the Antarctic Circumnavigation Expedition (ACE) during the Austral Summer of 2016/2017.

Dataset abstract This dataset contains particulate organic carbon and particulate organic nitrogen concentrations and stable isotope composition (delta 13C and delta 15N) sampled during the Antarctic Circumnavigation Expedition (ACE) Leg 1-3. Water samples were collected from the underway seawater supply every 3 hours, filtered onto pre-combusted glass fibre filters, acidified to remove inorganic compounds and analysed for both elements on the same filter using an elemental analyser. These samples provide an estimate of the organic carbon and organic nitrogen concentration and carbon and nitrogen stable isotope composition of living and detrital particles > 0.7 micrometres in size. Dataset contents <ul> <li>README.txt, metatdata, text</li> <li>data_file_header.txt, metadata, text</li> <li>ace_uw_poc_pon_blanks_20200512CURRSGCMR.csv, data file, comma-separated values</li> <li>ace_uw_poc_pon_20200512CURRSGCMR.csv, data file, comma-separated values</li> </ul>

Physical and biogeochemical oceanography data from underway measurements with an AquaLine Ferrybox during the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract This data set contains measurements from various sensors installed on the Aqualine Ferrybox system that was connected to the underway seawater supply in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE). Data was collected continuously except for periods when the pump of the underway system was switched off or the system was turned off. Data collection covers all three cruise legs in the period 24th December 2016 to 18th March 2017. Data collected with the CTG MiniPack CTD-F are temperature, salinity, pressure, and turbidity. Data collected by the Aanderaa oxygen optode include dissolved oxygen and oxygen saturation. An SBE 18 sensor measured pH. The CTG UniLux fluorometer measured chlorophyll-a concentration. All data has been quality controlled and post-cruise calibrated. Data is provided at 1-minute intervals along the cruise track. In addition, we provide satellite data (sea-surface temperature, sea-surface height, geostrophic velocity, sea-ice concentration) that was interpolated to the cruise-track and an estimate of frontal positions to supplement this underway data set where data was missing or for additional information. This circumpolar data set provides insights into the circumpolar surface ocean conditions and biogeochemistry of the Southern Ocean during one austral summer season. Note on version 1.0: The first version of this data set only contains temperature, salinity, pressure, and potential density in the post-processed file, since post-processing and quality control for turbidity, chlorophyll-a, dissolved oxygen, oxygen saturation, and pH have not been finalized. These variables will be added to the post-processed data file in a future release. Dataset license This dataset of physical and biogeochemical oceanography underway measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

PsPM-HRM5: SCR, ECG and respiration measurements in response to positive/negative IAPS pictures, and neutral/aversive sounds

This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements for each of 19 healthy unmedicated participants (8 males and 11 females aged 26.1 +/- 5.4 years) in response to 65 dB and 85 dB sounds as well as the 60 most arousing negative and the 60 most arousing positive (excluding explicit nude) IAPS pictures, presented for 1 s each. ITI was between 4-16 s.

opencc-by-4.0Apr 2020View details →

MPU9250 MEMS IMU Sine wave acceleration excitation along the Z axis

MPU9250 MEMS IMU Sine wave acceleration excitation along the Z axis The file Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep_ADC.dump contains a dump of the ADC protbuff messages recorded by the Met4FoF dataaqusition unit during the calibration measurement. The ADC is sampled synchronously to the data ready signals of the MPU9250. The file Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep_Sensor.dump contains a dump of the MPU9250 protbuff messages recorded by the Met4FoF dataaqusition unit during the calibration measurement. Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.xlsx contains the accelerations recorded by the PTB refferenzsystem for each measurement run. The phase is referred to the analog reference values in the channel Data_11  Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.csv contains the values from the excel table in panda readable form. 1FE4_AC_CAL.zip contains various measurements of the ADC transfer function as JSON files.