Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

The dataset for the paper "Ambient noise tomography reveals asymmetric impact damage zone beneath Lonar crater, India: Implications for oblique impact cratering in heterogeneous basalt with planetary applications"

These datasets are used to produce the results and generate figures and tables in the manuscript titled as "Ambient Noise Tomography Reveals Asymmetric Impact Damage Zone Beneath Lonar Crater, India: Implications for Oblique Impact Cratering in Heterogeneous Basalt with Planetary Applications" submitted to JGR-Planets.

opencc-by-4.0Jul 2024View details →

High resolution shallow structure of Ebao basin revealed with DAS ambient noise tomography and its relation to earthquake ground motion

EBAO dataset: fig2b, fig3a, fig9, figS5

opencc-by-4.0Jul 2024View details →

Overhead Measurement Noise in Different Runtime Environments

This dataset contains the data for the SSP paper "Overhead Measurement Noise in Different Runtime Environments". In particular, it contains data MooBench performance measurement data for GH actions, an i7-4770 (Desktop PC from 2016), a Raspberry Pi 4B, and an Ryzen 5700G (Desktop PC from 2024). These data are structured as follows: <ul> <li>$server.tar.xz and $server-2.tar.xz for 1 and 2 threads</li> <li>additionalData.tar.xz for the summary data for 4, 8 and 12 threads (which were not possible to upload to Zenodo)</li> <li>maui.se.informatik.uni-kiel.de.tar.xz: The Uni Kiel CI MooBench execution results</li> <li>Analysis scripts for plotting the result data (common.sh, jenkins.sh, analyzeGH.sh, analyzeRaw.sh, analyzeThreads.sh)</li> <li>threads.pdf displaying the growth of response time for the different execution environments</li> <li>moobench-6833aade.tar.xz: The MooBench repositories version that was used for the experiments</li> </ul>

opencc-by-4.0Jul 2024View details →

Quantitatively Monitoring of Seasonal Frozen Ground Freeze-thaw Cycle Using Ambient Seismic Noise Data

This is the electronic supplemental data for the publication entitled  "Quantitatively Monitoring of Seasonal Frozen Ground Freeze-thaw Cycle Using Ambient Seismic Noise Data" submitted to Seismological Research Letters (SRL).  The names of the compressed files represent the experiment number and station number. For example, "1_2" indicates data collected from the second station during the first experiment. Each compressed file contains seismic raw data in the ".SAC" format. The filenames include the UTC end time of data collection. For instance, "453003616.00000001.2021.10.20.06.40.22.000.z.sac" indicates that data collection ended at 06:40:22 on October 20, 2021. Each complete .sac file contains 4 days of data with a sampling interval of 0.002 seconds.

opencc-by-4.0Mar 2018View details →

Task dialog by native-Danish talkers in Danish and English in both quiet and noise

The zip files contain recorded conversations between 19 pairs of normal-hearing native-Danish talkers taking part in an experiment in the lab of the Hearing Systems Group at The Technical University of Denmark during October-November 2016. Each participant pair conversed in each of four conditions: - In their first language (Danish) without background noise     (L1-quiet) - In their first language (Danish) with background noise        (L1-noise) - In their second language (English) without background noise   (L2-quiet) - In their second language (English) with background noise      (L2-noise) The conversations were recorded in three blocks with a break between each block. Each block consisted of a conversation from each of the four conditions (in a random order). In each conversation, the pairs found 10 differences between two almost identical pictures (DiapixUK; Baker and Hazan, 2011). The pictures were randomized across conditions. The background noise was a mixed-gender 6-talker babble-like vocoded noise (ICRA 7, Dreschler et al. (2001)). The pairs were seated in separate sound booths and could not see each other.  Each talker wore a Shure WH20 microphone (close mic) along with a pair of Sennheiser HD650 headphones. An operator sat outside the door monitoring the conversations and had the opportunity to talk to the participants through an operator microphone. In the headphones, both talkers heard a mix of themselves, their conversational partner, and the background noise (in the noise conditions). The gains were set and held constant such that the speech level presented by the headphones was the same as the level in the soundbooth 1 m away from the talker. In total four channels were recorded using an RME Fireface 802 soundcard at 24 bit, 48 kHz, with Matlab 2016a:  Channel 1: the microphone of Talker 1 Channel 2: the microphone of Talker 2 Channel 3: the operator microphone Channel 4: The mix sent to the headphones Prior to recording, all 20 pairs provided written consent for the recordings to be used in the conversation experiment. After the recordings had been completed, we recognized that these recordings might be useful for others. Thus we approached the participants and asked if they would be willing to make the recordings publicly available. 19 out of the 20 pairs gave written consent to make the recordings publicly available. However, pair 12 asked that one of their conversations not be made publicly available (the third replicate of the conversation in Danish in quiet). Thus, this conversation and the conversations from the pair that did not provide written consent to make the recordings public (pair 9) are not included here.  The file "Recordings_4channel_48kHz_24bit.zip" contains the original recordings. When uncompressed, the wav files are approximately 42 GB in total. The file "Recordings_2channel_22050Hz_16bit.zip" contains only channels 1 and 2 (the microphones from both talkers) from the recordings, which have been downsampled to 22050 Hz and rescaled as 16 bit wav files. When uncompressed, the wav files are approximately 6.4 GB in total. In both zip files, there are individual wav files for each conversation and the file names are structured as follows: TalkerPair_Condition_Replicate.wav. For any questions, please contact  Anna Josefine Sørensen: ajs@elektro.dtu.dk or Ewen MacDonald: emcd@elektro.dtu.dk References: R. Baker and V. Hazan. DiapixUK: task materials for the elicitation of multiple spontaneous speech dialogs. Behavior Research Methods, 43(3):761–770, 2011. ISSN 1554-3528. doi: 10.3758/s13428-011-0075-y. W. Dreschler, H. Verschuure, C. Ludvigsen, and S. Westermann. ICRA Noises: Artificial Noise Signals with Speech-like Spectral and Temporal Properties for Hearing Instrument Assessment: Ruidos ICRA: Señates de ruido artificial con espectro similar al habla y propiedades temporales para pruebas de instrumentos auditiv. International Journal of Audiology, 40(3):148–157, 2001. ISSN 1499-2027. doi: 10.3109/00206090109073110.

opencc-by-4.0Sep 2018View details →

Representation of model error in convective-scale data assimilation: additive noise, relaxation methods and combinations

ModelError_1_dpsdt.tar.xz for Fig. 11 ModelError_1_innovstat.tar.xz for Fig. 4, 10 and 13 ModelError_1_KESpectrum.tar.xz for Fig. 1 ModelError_1_pre_veri.tar.xz for Fig. 8 and 9 ModelError_1_precipobs_tau.tar.xz for Fig. 2 ModelError_1_refl_veri.tar.xz  for Fig. 6, 12 and 14 For plotting, MATLAB (R2017b) and python are used

opencc-by-4.0Apr 2019View details →

Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures

Folder and Data Description for dataset of: Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures Ghada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O’Driscoll, and Karolina Kluk Please site the paper when using this dataset (DOI: 10.1177/2331216519848297)   Shared dataset is as follows: <ul> <li>Behavioral data is in the excel spread sheet entitled: “BinKhamis_et_al_behavioral_data .xlsx”  </li> <li>Speech-ABRs (raw EEG (speech-ABR) data) are contained within the five 'zip' folders.</li> </ul> Description of the “Speech-ABRs” folders, subfolders, and raw EEG files: “Speech-ABRs” Folder Information: <ul> <li>Each Speech_ABR folder contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number 1 to participant number 20)</li> <li>Subfolders: <ul> <li>Each subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the background condition: noise or quiet</li> <li>Next is whether recordings were: aided or unaided</li> </ul> </li> <li>Example subfolder names: <ul> <li>HA1 noise aided: participant number 1, aided speech-ABRs in background noise</li> <li>HA4 noise unaided: participant number 4, unaided speech-ABRs in background noise</li> <li>HA55 quiet aided: participant number 55, aided speech-ABRs in quiet</li> <li>HA97 quiet unaided: participant number 97, unaided speech-ABRs in quiet</li> </ul> </li> <li>Each participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise) <ul> <li>Each subfolder contains four ‘.mat’ files, ‘.mat’ file names: <ul> <li>Each ‘.mat’ file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the stimulus: 40 da</li> <li>Next is ‘unaided’ only if recordings were without HA</li> <li>Next is ‘noise’ only if the background condition was noise</li> <li>Next is the stimulus polarity: <ul> <li>‘Pos’ for positive/standard</li> <li>‘Neg’ for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally the test ear and recording number for that polarity <ul> <li>R1 is the first recording from the right ear, R2 is the second recording from the right ear</li> <li>L1 is the first recording from the left ear, L2 is the second recording from the left ear</li> </ul> </li> <li>Example ‘.mat’ file name: <ul> <li>HA1 40 da Neg Noise R1.mat: participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.</li> <li>HA4 40 da unaided Pos Noise L2.mat: participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.</li> <li>HA7 40 da Neg R2.mat: participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.</li> <li>HA10 40 da unaided Pos Noise L1.mat: participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.</li> </ul> </li> </ul> </li> </ul> </li> </ul> File Information: Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks): Each ‘.mat’ file is a structure that contains the following fields: <ul> <li>The first nine fields are informational, for example:</li> <li>xunits: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li>start: ‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li>points: 2200 is the number of sample points</li> <li>chans: 2 is the number of channels <ul> <li>Right ear: channel 2, Left ear: channel 1</li> </ul> </li> <li>frames: 2500 is the number of epochs</li> <li>The last filed ‘values’ is what contains the raw EEG data (2200x2x2500) <ul> <li>2200 is the number of samples</li> <li>2 is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li>2500 is the number of epochs <ul> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> <li>Data are in Volts; conversion to μVolts (multiply by 1000) is required.</li> </ul> </li> </ul> </li> </ul> Date of data collection: October 2017 to July 2018

opencc-by-4.0Apr 2019View details →

Data: Trans-saccadic integration is dominated by early, independent noise

Data for "Trans-saccadic integration is dominated by early, independent noise"

openOct 2023View details →

Data accompanying: Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures

These are the data accompanying the publication titled: Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures The repository contains: <ul> <li>FigurePlotting.ipynb : JupyterNotebook used to load the data and plot the Figures.</li> <li>FigureData : folder that contains the raw/analysed data needed for plotting the Figures.</li> </ul> The JupyterNotebook is organised in sections corresponding to the Figures and Supplementary Figures of the last version (published) of the paper. For each Figure/SuppFigure the needed data (stored in .txt or .pkl files in the FigureData folder) is loaded and plotted.

Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2011 & 2014 AUDIO DATA

Description of the data and file structure This record contains all 2011 & 2014 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:  Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. Global Change Biology. In press. The data include the following: the 2011 & 2014 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13333019 - all 2009 & 2010 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above. These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.   Description of audio data files The data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags . Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them. Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software). We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .

Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2009 & 2010 AUDIO DATA

Description of the data and file structure This record contains all 2009 & 2010 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:  Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. Global Change Biology. In press. The data include the following: the 2009 & 2010 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13328931 - all 2011 & 2014 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above. These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.   Description of audio data files The data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags . Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them. Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software). We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .

The seismic signature and geothermal potential of the Schwechat Depression in the Vienna Basin, Austria, from ambient noise tomography

This folder contains the cross-correlation functions, the Love and Rayleigh dispersion measurements, the Love and Rayleigh2D group velocity maps, the 3D shear-wave velocity model and a Paraview file for 3D visualization of the Vs model. For further information refer to "C. Esteve, Y. Lu, J. M. Gosselin, R. Kramer, Y. Aiman, G. Bokelmann, 2024, The seismic signature and geothermal potential of the Schwechat Depression in the Vienna Basin, Austria, from ambient noise tomography" published in Geothermics.

opencc-by-4.0Jun 2024View details →

Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks

The datasets used to evaluate Enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) against predicting the pro-longevity or anti-longevity effect of model organism gene. This repo also includes the pretrained encoders that obtained the best predictive performance for each organism (see Table 2).

DATA:Signal-Power Dependent Features Causing Non-Gaussian Noise in Natural Electromagnetic Signals Revealed by Magnetotelluric Arrays

The data set mainly consists of three parts, which are respectively the longitude and latitude of all stations, signal power and noise power, and residual error obtained by different methods. Each part is divided into three folders by region.

Data for "Technical reports: Methods - The Effects of Noise Magnitude and Measurement Resolution on Groundwater Tidal Analysis"

This data for the technical note submitted to Water Resources Research.

openSep 2024View details →

The data for "Noise-Resilient Forward Transient Analysis for Pipe Burst Localization in Complex Pipe Networks: Theoretical Framework"

The data is for supporting our research paper titled "Noise-Resilient Forward Transient Analysis for Pipe Burst Localization in Complex Pipe Networks: Theoretical Framework".

Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise

Video of submitted paper entitled "Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise"

Monitoring water content variations from seismic noise in a controlled laboratory experiment: PART 2 [Dataset]

This dataset has been obtained with an original laboratory experiment aimed at assessing the sensitivity of passive seismic interferometry imaging (PII) to controlled fluctuations in water content. Multiple controlled cycles of water imbibition and draining at the base of the sandbox produce significant variations in the seismic wavefield and especially in dominant surface waves. PART 2: seismic measurements from 1501 to 2700minutes

Monitoring water content variations from seismic noise in a controlled laboratory experiment: PART 1 [Dataset]

This dataset has been obtained with an original laboratory experiment aimed at assessing the sensitivity of passive seismic interferometry imaging (PII) to controlled fluctuations in water content. Multiple controlled cycles of water imbibition and draining at the base of the sandbox produce significant variations in the seismic wavefield and especially in dominant surface waves. PART 1: seismic measurements from 0001 to 1500minutes

Data - Ultralow noise preamplified optical receiver using conventional single wavelength transmission

This dataset contains measurement data and processing code for the results published in "Ultralow noise preamplified optical receiver using conventional single wavelength transmission". Raw measurement batches from the BER-measurement can be made available upon request, at rasmus.larsson@chalmers.se. This work was funded by the Swedish Research Council (grant VR-2015-00535).