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1,481 results for “data processing”
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
A Bioconductor workflow for processing, evaluating and interpreting expression proteomics data
<p>Files for users of the workflow "A Bioconductor workflow for processing, evaluating and interpreting expression proteomics data". Files include Proteome Discoverer (v2.5) processing and consensus workflows for both TMT and LFQ expression proteomics data. Also provided are the output .txt files of a corresponding Proteome Discoverer identification search, as required for users to follow the workflow themselves. For raw data please refer to PRIDE. Appendix is provided as a PDF.</p>
Processed Data for "Improving Gene Regulatory Network Inference using Dropout Augmentation"
<p>Here are the processed dataset that are used in the manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation"</p>
Processed data used in transcriptome- metabolome-wide association study
<p>Processed_RNASeq_RPKM.txt contains RPKM levels for 45484 genes quantified in 555 individuals from RNA-Seq of lymphoblastoid cell lines (LCLs).</p> <p>Processed_NMRpeaks_baseline.txt contains binned, normalized and standardised (z-scored) NMR peak intensities for 1276 bins quantified in 555 individuals from urine samples taken at baseline. NMR spectra were acquired at 300 K on a Bruker 16.4 T Avance II 700 MHz NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) using a standard 1H detection pulse sequence with water suppression. The spectra were referenced to the TSP signal and phase and baseline corrected.</p> <p>Processed_NMRpeaks_followup.txt contains binned, normalized and standardised (z-scored) NMR peak intensities for 1289 bins quantified in 315 individuals from urine samples taken during follow-up. NMR spectra were acquired with an Avance III HD 600 NMR spectrometer. Spectra were referenced to the TSP signal and phase and baseline corrected.</p> <p>More details on the data set can be found in Sönmez Flitman et al. (doi: https://doi.org/10.1101/2020.05.22.110197).</p>
Processed data for the manuscript, entitled "Substantial increase in heavy precipitation events preceded by moist heatwaves over China during 1961–2019"
<p>This is the dataset on annual frequency of heatwaves, heavy precipitation, and heatwave-heavy precipitation events during 1961-2019 at 1776 stations across China. This dataset is the processed results based on daily observations that are provided by the National Meteorological Science Data Center (<a href="http://data.cma.cn/en">http://data.cma.cn/en</a>). In this processed dataset, the "HW_HI" column is the annual frequency of heat-index-based heatwaves; "HW_TW" column is the annual frequency of wet-bulb temperature based heatwaves; "HP" is the annual frequency of heavy precipitation with taking the 95<sup>th</sup> percentile of non-zero precipitation at the threshold. "HWHP_HI"/"HWHP_TW" column indicate annual frequency of heavy precipitation preceded by HW_HI/HW_TW. All the results shown in the manuscript entitled "<strong>Substantial increase in heavy precipitation events preceded by moist heatwaves over China during 1961</strong>–<strong>2019</strong>", are obtained based on this processed dataset.</p>
Processed EUVM and NGIMS data from the MAVEN spacecraft
<p>The .csv files contain EUVM and NGIMS data that have been interpolated onto a uniform altitude grid and time scale. For every orbit, the data are interpolated onto a uniform altitude scale. The time at for the average of these points is used to interpolate the EUVM data. the density is recorded in the 'density' variable and the EUVM data are recorded for four spectral bands: (0-7 nm = da, 17-22 nm = db, 0-45 nm = dc, 117-125 nm = dd). Auxiliary parameters of local solar time (LST), longitude (lon), latitude (lat), solar zenith angle (SZA), and the distance between maven and the sun (Rs), are also provided.</p> <p>The .p file contains the same data, but with additional fields for the relative changes in both the EUVM and NGIMS data as well as the slope and r value for a linear fit inside of a rolling 27 day bin. The relative change is defined as the 5 day running mean of the 27-day residuals. Additionally, days with high flares are removed as these data may be affected. The .p file is a pickle file, made with python. </p>
Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease, J Neurophysiol (2021): Data
<p>Data set accompanying the publication:</p> <p>Engel, D., Student, J., Schwenk, J., Morris, A. P., Waldthaler, J., Timmermann, L., & Bremmer, F. (2021). Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease. <em>Journal of neurophysiology</em>, <em>126</em>(4), 1076–1089. https://doi.org/10.1152/jn.00183.2021</p>
Data sharing and processing infrastructures
<p>These illustrations depict a variety of options for sharing and processing large (research) datasets typically containing human (sometimes personal) data. They were inspired in part by the research article "Bringing Code to Data: Do Not Forget Governance"*, and make use of source material available openly at <a href="https://undraw.co/">https://undraw.co/</a>.</p> <p>When using any of these images, please credit it with:</p> <p>"This image was created by Stephan Heunis and is used under a CC-BY licence."</p> <p>The use and re-use of these images are encouraged, including remixing the images for example changing the colours or merging them together with additional (openly licensed) images.</p> <p><em>*Suver C, Thorogood A, Doerr M, Wilbanks J, Knoppers B. Bringing Code to Data: Do Not Forget Governance. J Med Internet Res 2020;22(7):e18087. DOI: <a href="https://www.jmir.org/2020/7/e18087">10.2196/18087</a></em></p>
Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer - pre-processed data
<p>A multicentre, prospective observational study was conducted of colorectal cancer (CRC) patients undergoing primary surgical resection in the United Kingdom and Czech Republic. Analysis was performed using metataxonomics (microbiome) and ultra-performance liquid chromatography mass spectrometry (UPLC-MS, metabolomics). Both datasets were pre-processed as described in the methods section of the main article. The data here were used as the input to the data analysis workflows available from <a href="https://github.com/jmp111/CRC">Github</a>.</p>
Processed Synthetic Real-World Data for tristate modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset.</p> <p>The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is a tristate quantized version of the health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Processed Synthetic Real-World Data for binary modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Raw and post-processed data for the microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
<p>The current dataset consists of three main folders:</p> <ul> <li><strong>01-Stimuli/</strong>: Contains the three sets of noises (white noise, bump noise, MPS noise) for the 12 study participants (S01 to S12).</li> <li><strong>02-Raw-data/fastACI/</strong>: Contains the raw data as obtained for each participant, which are also available within the GitHub repository of the fastACI toolbox, using the same directory tree. The results for each (anonymised) participant (under: <strong>publ_osses2022b/data_SXX/1-experimental_results/</strong>) include their audiometric thresholds (folder: <strong>audiometry</strong>), the results for the Intellitest speech test (folder: <strong>intellitest</strong>), and for the phoneme-in-noise test /aba/-/ada/ for the three noises (savegame files in MAT format).</li> <li><strong>02-Raw-data/ACI_sim/</strong>: Contains the raw data as obtained for the artificial listener, i.e., the model osses2022a.m (available within the fastACI toolbox). Twelve sets of simulations (using the waveforms of participants S01 to S12) were run for the three types of test noises. The results of the simulations of the phoneme-in-noise test are stored in the savegame MAT files. The template derived from 100 repetitions of /aba/ and /aba/ at an SNR=-6 dB in white noise is also included (template-osses2022a-speechACI_Logatome-abda-S43M-trial-1-v1-white-2022-7-15-N-0100.mat). The same template was used in all simulations.</li> <li><strong>03-Post-proc-data/ACI_exp/</strong>: Auditory classification images (ACIs) derived from the participants' data (folder: <strong>ACI_exp</strong>) and from the simulations (folder: <strong>ACI_sim</strong>). For each participant (or artificial listener) there are three ACIs (MAT files) for each of the corresponding noises. Cross predictions are also included with performance predictions across 'participants' (Crosspred.mat, 12 cross predictions for each noise) or across 'noises' (Crosspred-noise.mat, 3 cross predictions for each participant). The cross predictions all have the same names but are stored in dedicated directories.</li> </ul> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:</li> </ol> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1','zenodo'); publ_osses2022b_JASA_figs('fig2a','zenodo'); publ_osses2022b_JASA_figs('fig2b','zenodo'); publ_osses2022b_JASA_figs('fig3','zenodo'); publ_osses2022b_JASA_figs('fig4','zenodo'); publ_osses2022b_JASA_figs('fig5','zenodo'); publ_osses2022b_JASA_figs('fig6','zenodo'); publ_osses2022b_JASA_figs('fig7','zenodo'); publ_osses2022b_JASA_figs('fig8','zenodo'); publ_osses2022b_JASA_figs('fig8b','zenodo'); publ_osses2022b_JASA_figs('fig9','zenodo'); publ_osses2022b_JASA_figs('fig9b','zenodo'); publ_osses2022b_JASA_figs('fig10','zenodo');</code></pre> <p>To generate the figures of the supplementary materials (Appendix in the BioRxiv preprint):</p> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1_suppl','zenodo'); publ_osses2022b_JASA_figs('fig2_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5b_suppl','zenodo');</code></pre> <p><strong>References:</strong></p> <ul> <li><strong>Preprint</strong>: Alejandro Osses, Léo Varnet. "A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception." BioRxiv.</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset
<p><strong>Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset</strong><br> </p> <p>We curated a dataset of post-processed <em>in situ</em> evapotranspiration (ET) measurements, primarily from eddy covariance flux towers, from stations located within the contiguous United States. The dataset includes daily and monthly aggregated ET, energy balance metrics, and micrometeorological data that were post-processed from 148 flux towers, 4 weighing lysimters, and 8 Bowen Ration stations. Original data was retrieved from the <a href="https://ameriflux.lbl.gov/">AmeriFlux</a> network and other networks and partners. The dataset is oriented towards ET and includes both ET that has been corrected for energy balance closure error as well as the uncorrected values. Energy balance components (latent and sensble heat flux, soil heat flux, and net radiation) were subject to limited gap-filling and latent energy (ET) was subject to additional visual quality control. Other meteorological measurements such as air temperature, precipitation, humidity, etc. are included for most stations depending on availability, and some additional variables were calculated. Interactive graphics of most post-processed data are also included. The dataset has many potential uses including evaluation of regional hydrologic and atmospheric models, energy balance analysis, and more.</p> <p><br><strong>Description of the Data and file structure</strong></p> <p>The dataset is in a compressed (zipped) archive titled "flux_ET_dataset", so first it needs to be downloaded and extracted. Once extracted there are four major components within: </p> <p>1. A collection of time series files with daily aggregated data (one for each station), these are in the directory named "daily_data_files" and are in CSV format.<br>2. A similar collection of time series files for monthly aggregated data in "monthly_data_files". <br>3. Interactive graphic files (HTML format) for each station which are in the "graphical_files" directory. <br>4. Two additional tables in the root directory, including a metadata file named "station_metadata.xlsx" with site information such as site ID, coordinates, land cover type, principal investigator information, etc. The other table named "variable_explanation.xlsx" lists all variables that were post-processed in the flux dataset and gives a short description of each as well as their units. </p> <p>Each data and plot file starts with the station's ID or site ID which are listed in the station_metadata.xlsx file. </p> <p>Here is a visual of the file structure:</p> <blockquote> <p><br>flux_ET_dataset<br>│ README.md<br>│ variable_explanation.xlsx<br>│ station_metadata.xlsx<br>│<br>└───daily_data_files<br>│ │ [site ID]_daily_data.csv<br>│ │ ...<br>└───monthly_data_files<br>│ │ [site ID]_monthly_data.csv<br>│ │ ...<br>└───graphical_files<br>│ │ [site ID]_plots.html<br>│ │ ...<br>```</p> </blockquote> <p>The variable names in the daily and monthly data files as well as the graphics all follow the same naming scheme which are defined in the variable_explanation.xlsx file. For example, LE stands for latent energy flux and is in units of W/m<sup>2</sup>. </p> <p><br><strong>Sharing/access Information</strong></p> <p>Currently, this repository is the only location where the data are hosted. Original data, prior to post-processing, were retrieved from multiple providers listed below:</p> <p>* AmeriFlux network (https://ameriflux.lbl.gov/) </p> <p>* California State University, Monterey Bay, Seaside, CA, USA </p> <p>* Desert Research Institute, Reno, NV, USA </p> <p>* gridMET, Northwest Knowledge Network at the University of Idaho (https://thredds.northwestknowledge.net/) </p> <p>* United States Geological Survey Nevada Water Science Center, Carson City, NV, USA </p> <p>* Delta-Flux network, Arkansas, Louisiana, MS, USA </p> <p>* United States Department of Agriculture Agricultural Research Service (USDS-ARS): </p> <p> * Sustainable Water Management Research Unit, Stoneville, MS, USA </p> <p> * US Salinity Laboratory, Agricultural Water Efficiency and Salinity Research Unit, Riverside, CA, USA </p> <p> * Conservation & Production Research Laboratory, Bushland, TX, USA </p> <p> * US Arid-Land Agricultural Research Center, Maricopa, AZ, USA </p> <p> * Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA </p> <p>Further contact information for each station as well as DOI's for original AmeriFlux data are included in the "station_metadata.xlsx" file. </p> <p><br><strong>Code/Software</strong></p> <p>All files that comprise this dataset were generated using the "flux-data-qaqc" open-source Python package version 0.1.6. The package is hosted on <a href="https://github.com/Open-ET/flux-data-qaqc">GitHub</a> and <a href="https://pypi.org/project/fluxdataqaqc/">PyPI</a>, it also has <a href="https://flux-data-qaqc.readthedocs.io/en/latest/">online documentation</a> including an in depth user tutorial. </p>
Processed data for "Model identification of neural encoding (MINE)" publication
<p>This dataset contains mouse and zebrafish data processed by MINE. These datafiles were used to generate the publication figures for the mouse cortical dataset [m<em>usall.hdf5</em>] (Figure 5) and the zebrafish whole-brain [<em>main_analysis.hdf5</em>] (Figures 6 and 7) and reticulospinal datasets [r<em>s_analysis.hdf5</em>] (Figure 6).</p> <p> </p> <p><em>Musall.hdf5 </em>contains reordered data from "Musall, S., Kaufman, M.T., Juavinett, A.L. <em>et al.</em> Single-trial neural dynamics are dominated by richly varied movements. <em>Nat Neurosci</em> <strong>22</strong>, 1677–1686 (2019)."</p> <p>The contents of each dataset are described in <em>DataContent_xxx.pdf</em></p>
Vibration Sensor and Process Data from Ferrosilicon Production
<p><strong>Elkem Facility</strong></p> <p>The facility specializes in producing ferrosilicon (FeSi) and ferrosilicon magnesium (FSM) master alloys. Elkem Bjølvefossen is among the world’s largest producers of FSM. Three reduction furnaces deliver the base metal which is then alloyed and refined to the right quality of FeSi or FSM. These alloys are important additives in the manufacturing of steel products. Silicon in the form of FeSi is used to remove oxygen from the steel and as an alloying element to improve the final quality of the steel. Silicon increases strength and wear resistance, elasticity, i.e., spring steels, scale resistance, and heat resistant steels and lowers electrical conductivity and magnetostriction.</p> <p>After tapping and refining, the ferro-alloys are crushed to grains ranging from 1 mm to 25 mm in size. Consumers of FeSi and FSM have strict requirements for particle size, related mainly to the chemical kinetics of their refining and alloying processes. For this reason, the crushed material is separated in sieves and packaged by particle size before shipment. Two lattice gratings inside the Mogensen shaker separate the material according to required particle size. </p> <p>The subject of the present study is a mechanical shaker platform containing one or more such sieves. The shaker is a <a href="https://www.mogensen.se/">Mogensen S0556</a> that was installed in 1996 in Bjølvefossen and is no longer produced in this type. This device is powered by two counter-rotating 1.2-horsepower AC motors operating at 960 RPM. Together with the spring suspension, these cause an elliptical motion that both transports and scatters the incoming material across the sieve. The shaker is engineered so that the motion transitions from a slanted ellipse at the in-feed to nearly linear at the output.</p> <p><strong>Vibration Data</strong></p> <p>Vibration data from two sensors. Each sensor measures acceleration in three axes with three different ADCs. </p> <p><strong>ERP and MES data</strong></p> <p>Manufacturing Execution System (MES) data as well as process data from the Enterprise Resource Planning (ERP) is given. It is providing information about the material that is currently being produced as well as the data from the scales from the material packing station where the bags with completed production were packed. MES data has a resolution of 5 seconds and the process data from ERP has a resolution of roughly 10 minutes. This operational data is meant to provide insight into the current state and throughput of the facility and will serve as labels for the correlation analysis with the vibration data. </p>
dMRI data from the PRIME-DE processed by mrHARDIflow
<p>This dataset contains processed diffusion MRI data of primate subjects, DTI<sup>[1]</sup>, DIAMOND<sup>[2]</sup> and fODF<sup>[3]</sup> reconstruction and metrics. Subjects included in the dataset come from 3 sites of the PRIME-DE<sup>[4]</sup> database, specifically Aix-Marseille, UC-Davis and Mount Sinai - Philips. The data was processed using mrHARDIflow<sup>[5]</sup>, a pipeline tailored to process high resolution diffusion data. Please, cite the paper, as well as the PRIME-DE paper, if you use any data available here in your research.</p> <ol> <li>LeBihan, D., Mangin, J.-F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., et al. (2001). Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging 13, 534–546. doi:<a href="http://doi.org/10.1002/jmri.1076">10.1002/jmri.1076</a></li> <li>Scherrer, B., Schwartzman, A., Taquet, M., Sahin, M., Prabhu, S. P., and Warfield, S. K. (2016). Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (diamond). Magnetic Resonance in Medicine 76, 963–977. doi:<a href="http://doi.org/10.1002/mrm.25912">10.1002/mrm.25912</a></li> <li>Tournier, J.-D., Calamante, F., and Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion mri: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472. doi:<a href="http://doi.org/10.1016/j.neuroimage.2007.02.016">10.1016/j.neuroimage.2007.02.016</a></li> <li>Milham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., et al. (2018). An open resource for non-human primate imaging. Neuron 100, 61–74. doi:<a href="http://doi.org/10.1016/j.neuron.2018.08.039">10.1016/j.neuron.2018.08.039</a></li> <li> <p>Valcourt Caron, A., Shmuel, A., Hao, Z., and Descoteaux, M. (2021). mrHARDIflow : A pipeline tailored for the preprocessing and analysis of Multi-Resolution High Angular diffusion MRI and its application to a variability study of the PRIME-DE database. bioRxiv. doi:<a href="http://doi.org/10.1101/2021.11.22.469616">10.1101/2021.11.22.469616 </a></p> </li> </ol> <p> </p>
Processed data to regenerate figures in Noecker et al, "Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta"
<p>This archive contains the processed source data for the publication by Noecker et al, "Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe <em>Eggerthella lenta</em>" (2023, in review). Data tables underlying each figure panel are included, except for the following panels:</p> <ul> <li>Figure S1A: Source data is in Table S1 of the publication</li> <li>Figure 6D: Source data can be found at NCBI GEO accession GSE212420 (supplementary counts data matrix)</li> </ul> <p>Raw metabolomics data can also be found at Metabolomics Workbench accession PR001620.</p> <p>Methods used to summarize these data and generate the figures are described in the manuscript Materials and Methods and figure captions. Code to generate all figures is also available at www.github.com/turnbaughlab/2022_Noecker_ElentaMetabolism and 10.5281/zenodo.7779454.</p>
Raw and post-processed data for the study of prosodic cues to word boundaries in a segmentation task using reverse correlation
<p>The current dataset provides all the stimuli (folder <strong>../01-Stimuli/</strong>), raw data (folder <strong>../02-Raw-data/</strong>) and post-processed data (<strong>../03-Post-proc-data/</strong>) used in a prosody reverse correlation study with the title "prosodic cues to word boundaries in a segmentation task using reverse correlation" by the same authors. The listening experiment was implemented using one-interval trials with target words of the structure l'aX (option 1) and la'X (option 2). The experiment was designed and implemented using the <a href="https://github.com/aosses-tue/fastACI">fastACI toolbox</a> under the name 'segmentation'. A between-subject design was used with a total of 47 participants, who evaluated one of five conditions, LAMI (N=16), LAPEL (N=18), LACROCH (N=5), LALARM (N=5), and LAMI_SHIFTED (N=3). More details are given in the related publication (to be submitted to JASA-EL in May 2023).</p>
ALO Meteor Radar Processed Data in Jan 2022
<p>Meteor radar data include:</p> <p>Horizontal wind (vel) and detected meteors (met). Both original format and MatLab format (mat) files are included. Wind data are in three temporal resolutions, 1-hr, 30-hr, and 15-min.</p>
Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox. </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.