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30,334 results for “Response”
Predicting gene expression using morphological cell responses to nanotopography
<p>This dataset contains the raw files, results files and R workspace files (.RData) associated with the paper:</p> <p>Predicting gene expression using morphological cell responses to nanotopography</p> <p>Please note that this dataset is separated according to the Figure presented in the published and peer-reviewed version of the manuscript. Particular folders contain its own README file to facilitate reproduction/replication of results and figures. </p>
Acclimation to water restriction implies different paces for behavioral and physiological responses in a lizard species
<p>Raw data of the article "Acclimation to Water Restriction Implies Different Paces for Behavioral and Physiological Responses in a Lizard Species" by Rozen-Rechels D. et al., published in Physiological and Biochemical Zoology 93(2):160-174 in 2020 (https://doi.org/10.1086/707409). These data are freely available in csv format. See the readme file for metadata explanation.</p> <p>Data were formatted by the first author David Rozen-Rechels and collected according to standards and procedures described in the companion journal article.</p> <p> </p>
CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia: Dataset
<p>This repository is linked to the paper "CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia" submitted to Annals of Forest Science and written by Louis DE WERGIFOSSE (corresponding author), Frédéric ANDRE, Hugues GOOSSE, Steven CALUWAERTS, Lesley DE CRUZ, Rozemien DE TROCH, Bert VAN SCHAEYBROECK and Mathieu JONARD.</p> <p>The files stored in the repository are the input files that should be used in the model HETEROFOR to retrieve the results displayed in the study and the corresponding results themselves. The source code of the model HETEROFOR can be freely accessed and downloaded (https://doi.org/10.5281/zenodo.3591348). Additional information on the model can be found in the following description papers: Jonard et al., 2020 (https://doi.org/10.5194/gmd-13-905-2020) and de Wergifosse et al., 2020 (https://doi.org/10.5194/gmd-13-1459-2020).</p> <p>The repository contains three directories. The first (HETEROFOR_input_files) comprises the additional files to those in the model repository presented in the previous paragraph needed to run the model for the purpose of this study. The second directory (Simulation_outputs_raw) contains the data directly provided by the model without any processing. The third directory (Simulation_outputs_raw) includes the model outputs after processing.</p> <p>The directory "HETEROFOR_input_files" is constituted of two directories called "Climate_files" and "Stand_files". "Climate_files" is subdivided in three sub-directories. Sub-directory "Original_downscaled_CORDEX_timeseries" contains the climate projections of the four sites and scenarios described in the study. These downscaled timeseries have been produced by the Royal Meteorological Institute of Belgium under the program CORDEX.be, which is part of EURO-CORDEX. A bias correction has been further applied to these climate timeseries that are stored in the "Bias_corrected_timeseries" sub-directory. The files of these two sub-directories should be used in HETEROFOR as "Meteorological data" input files. The "CO2_concentrations" sub-directory includes the yearly averaged projected concentrations for the three RCP scenarios described in the paper. In HETEROFOR, they should be put as input in the "Atmospheric CO2 concentration" part after selecting the option "Variable over time". The second directory called "Stand files" contain the six inventory files described in the study for which a thinning has been applied. They should be used in HETEROFOR as "Inventory data" input files.</p> <p>The directory "Simulation_outputs_raw" is divided similarly to the study into two simulation experiments. The "First simulation experiment" directory is further subdivided into constant and time-dependent CO2 concentrations like in the study and contains one file for the regular modality and one for the thinning modality. All the files are constructed the same way with, for each tree and site (or stand, soil and climate), annual values of Net Primary Production (NPP) in kg of carbon, transpiration and potential transpiration in L under the different climate scenarios. In addition, the "Phenology" directory contains, for each day and under all climate scenarios, the green proportion (proportion of green leaves comprised between 0 and 1) for the two tree species considered in the study (Common oak and European beech).</p> <p>Finally, the directory "Simulation_outputs_processed" is constructed similarly to "Simulation_outputs_raw" but all the data are integrated in one file at the yearly time step. However, the units change with the NPP expressed in gC/m2 and transpiration and potential transpiration in mm (or L/m2) while the vegetation period is averaged according to the percentage of species occurrence<br> in the different stands.</p> <p><br> For more information concerning this repository or the study, please do not hesitate to contact Louis DE WERGIFOSSE (louis.dewergifosse@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
mom6 cobalt model result for oceanic carbon response to El Niños
<p>GEOS_Chem atmospheric transport model, monthly, 2.5 degree resolution in tropical Pacific Ocean (120-300E, 30N-30S)<br> 1. GEOS_Chem_tropical_pacific_monthly_clim_1992_2017.nc<br> 2. GEOS_Chem_tropical_pacific_monthly_iav_1992_2017.nc<br> 1 and 2 are GEOS_Chem atmospheric transport model results</p> <p>MOM6 COBALT model results<br> Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1<br> Note2: if the file name has a label "_detrend_deseason", this file is detrended and deseasonized using full value in 1982-2018 with CDO<br> "cdo -ymonsub -detrend $fin -ymonmean -detrend $fin $fout"<br> Note3: ocean/sea surface is the first layer which is 1 meter deep<br> Note4: MLD_001_temp/salt/dic/alk/kd is the vertical mean value in the mixing layer depth (criterial of 0.01 kg/m3)</p> <p>MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface)<br> MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018.nc<br> (delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface)<br> MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized air-sea CO2 flux, positive to ocean)<br> MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018.nc<br> (air-sea CO2 flux, positive to ocean)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_1982_2018.nc<br> (Mixing layer depth with a density criterial of 0.01 kg/m3)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_1982_2018_detrend_deseason.nc<br> (first compute vertical mean alkilinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_zgradient_1982_2018_detrend_deseason.nc<br> (first compute vertical mean alkilinity gradient (dalk/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_1982_2018_detrend_deseason.nc<br> (first compute vertical mean dissolved inorganic carbon (DIC) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_zgradient_1982_2018_detrend_deseason.nc<br> (first compute vertical mean DIC (ddic/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_Kd_interface_1982_2018_detrend_deseason.nc<br> (first compute vertical mean Diapycnal diffusivity at interfaces layers (kd_interface) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_salt_1982_2018_detrend_deseason.nc<br> (first compute vertical mean salinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_temp_1982_2018_detrend_deseason.nc<br> (first compute vertical mean temperature in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_001_u_1982_2018_detrend_deseason.nc<br> (first compute vertical mean velocity u in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_v_1982_2018_detrend_deseason.nc<br> (first compute vertical mean velocity v in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_003_1982_2018.nc<br> (Mixing layer depth with a density criterial of 0.03 kg/m3)<br> MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized sea surface pCO2 (ocean pCO2))</p> <p>MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018.nc<br> (sea surface pCO2 (ocean pCO2))<br> MOM6_COBALT_tropical_pacific_monthly_sfc_chl_1982_2018.nc<br> (sea surface chlorophyll)<br> MOM6_COBALT_tropical_pacific_monthly_sfc_dic_1982_2018.nc<br> (sea surface dissolved inorganic carbon (DIC))<br> MOM6_COBALT_tropical_pacific_monthly_sfc_no3_1982_2018.nc<br> (sea surface nitrate, NO3)<br> MOM6_COBALT_tropical_pacific_monthly_sfc_po4_1982_2018.nc<br> (sea surface phosphate, PO4)<br> MOM6_COBALT_tropical_pacific_monthly_SSS_1982_2018.nc<br> (sea surface salinity)<br> MOM6_COBALT_tropical_pacific_monthly_SST_1982_2018.nc<br> (sea surface temperature)<br> MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized wind stress in zonal direction)<br> MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018.nc<br> (wind stress in zonal direction)<br> MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized wind stress in meridional direction)<br> MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018.nc<br> (wind stress in meridional direction)<br> MOM6_COBALT_tropical_pacific_monthly_tc_depth_1982_2018.nc<br> (thermocline depth defined as depth where the temperature equals 20oC)</p> <p>JRA_rain_tropical_pacific_monthly_prrn_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized JRA rainfall)<br> </p> <p>Budget terms based MOM6 COBALT model results<br> Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1<br> Note2: this is the vertical mean result in the mixing layer depth (criterial of 0.01 kg/m3) after detrend and deseasonalize</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_001_pco2w_budget_1982_2018_detrend_deseason.nc<br> (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> pco2_hadv_hdif: horizontal transport term, H_circ;<br> dpco2_hadvx: zonal advection term;<br> dpco2_hadvy: meridional advection term;<br> dpco2_hdif: horizontal diffusivity term;<br> dpco2_vadv_vdif: vertical transport term;<br> dpco2_dic_vdif_vdif: vertical transport term induced by DIC;<br> dpco2_alk_vdif: vertical transport term induced by Alk;<br> dpco2_bio: biological term<br> dpco2_rain: surface freshwater term<br> dpco2_sst: thermal term<br> dpco2_dt: pco2 response term<br> dpco2_flux: CO2 flux response term<br> </p>
Analysis of human humoral responses in a typhoid vaccine efficacy trial used for SIMON analysis
<p>The VAST dataset contains data from 72 individuals enrolled in the clinical study to evaluate humoral responses in a typhoid vaccine efficacy trial in a controlled human <em>Salmonella </em>Typhi infection model (see original publication: <a href="https://doi.org/10.3389/fimmu.2019.02582">https://doi.org/10.3389/fimmu.2019.02582</a>). Only day 0 (day of the challenge) log-transformed data were used in the SIMON analysis, as described in the publication (<a href="https://doi.org/10.1101/2020.08.16.252767">https://doi.org/10.1101/2020.08.16.252767</a>). Individuals were vaccinated with either a purified Vi polysaccharide (Vi-PS) vaccine (35 individuals) or the Vi tetanus toxoid conjugate (Vi-TT) vaccine (37 individuals) one month prior to oral challenge with live <em>Salmonella </em>Typhi. Out of 72 individuals, 26 developed an acute typhoid infection following the challenge.</p>
Kotliarov 2020 Vaccine Responsiveness PBMC dataset for Besca
<p>Kotliarov, Y., Sparks, R., Martins, A.J. <em>et al.</em> Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. <em>Nat Med</em> <strong>26, </strong>618–629 (2020). https://doi.org/10.1038/s41591-020-0769-8. We reprocessed the dataset using the Besca package (<a href="https://github.com/bedapub/besca">https://github.com/bedapub/besca</a>). The original gene expression data are available from <a href="https://doi.org/10.35092/yhjc.c.4753772">https://doi.org/10.35092/yhjc.c.4753772</a>.</p>
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances (MAT-Version)
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>This entry stores the measurements in the MAT format for use in Matlab/Octave. The measurements are identical to the once stored in the SOFA format available at <a href="https://doi.org/10.5281/zenodo.55418">https://doi.org/10.5281/zenodo.55418</a></p>
Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability
<p>The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EI<sub>R</sub>) between consecutive years (e.g. HSi= |∆ EI<sub>R</sub> /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016. A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years. For each map, where HSi >1, regions are classified as <strong><em>Sensitive</em></strong> and for HSi ≤1, <strong><em>Resilient</em></strong>. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive. </p> <p><strong>This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against latitudinal change.</strong> We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees.</p> <p><strong>Contents:</strong></p> <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> <p>Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names.</p> <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>
Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Arabidopsis thaliana images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Arabidopsis thaliana</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Oryza sativa images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Oryza sativa</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Solanum lycopersicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Solanum lycopersicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Ocimum basilicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Ocimum basilicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
exopop: Response to Referee
<p>These are the results consistent with the version of our paper submitted in response to the referee.</p>
Binaural room impulse responses of an apartment-like environment
<p>Measured Binaural Room Impulse Responses (BRIR) of the ADREAM Laboratory, LAAS-CNRS, Toulouse, France. The measurements are described in detail in this publication:</p> <p>F. Winter, H. Wierstorf, A. Podlubne, T. Forgue, J. Manhès, M. Herrb, S. Spors, A. Raake, and P. Danès, "Database of binaural room impulse responses of an apartment-like environment," Proc. of 140th Aud. Eng. Soc. Conv., Paris, 2016</p> <p>Abstract of the Publication:</p> <p>We present a database of measured binaural room impulse responses (BRIRs) captured in an apartment-like environment. The BRIRs were measured for four different sound source positions, each combined with four listener positions with a head-orientation varying in the range of +-78° with 2° resolution. Additionally, BRIRs for 20 listener positions along a trajectory connecting two of the four positions were measured, each with a fixed head-orientation. The data is provided in the Spatially Oriented Format for Acoustics (SOFA) and it is freely available under Creative Commons (CC-BY-4.0). It can be used to simulate complex acoustic scenes in order to study the process of auditory scene analysis for humans and machines.</p>
Head-related impulse responses of a loudspeaker array
<p>Head-related impulse responses measured with a KEMAR dummy head of a loudspeaker array consisting of 13 loudspeakers. The dummy head was placed at three different positions, which allows to combine the measurements to an loudspeaker array consisting of 35 loudspeakers.</p> <p>The measurement equipment was exactly the same as in http://dx.doi.org/10.5281/zenodo.55418</p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>For details have a look at README.md.</p> <p>The same measurement can be downloaded as MAT files at <a href="https://doi.org/10.5281/zenodo.4459911">https://doi.org/10.5281/zenodo.4459911</a></p> <p>This dataset is further described in (see the PDF file)</p> <p>H. Wierstorf, M. Geier, A. Raake, S. Spors - A Free Database of Head-Related<br> Impulse Response Measurements in the Horizontal Plane with Multiple Distances.<br> In 130th AES Conv. 2011, eBrief 6.</p> <p> </p>
Skin response measurements
<p>The objective of this study is to investigate the possibility to detect mental and light physical stress through the measurement of skin reflectance in the mm-wave/sub-THz band. Two frequency bands have been considered, 75-110 GHz (Band-I) and 325-500 GHz (Band-II), while the measurements have been performed in the three different locations, the arm, the dorsal side of the hand and the fingertip.</p> <p>Each transmitter and receiver set is equipped with a matched standard horn antenna operated at the selected frequency band.</p> <p>The transmitter and receiver used during the test are active mixers. To operate each active module a local oscillator (LO) is required. The PXA and the Agilent generators are used as LO for transmitter (TX) and receiver (RX) accordingly (Fig. 2). The output signal from the receiver (RX) is gathered by a spectrum analyzer and then post-processed. The entire lab bench is connected through GPIB and both LO generators are synchronized (10MHz).</p> <p>The measured reflected signal amplitude is recorded and post-process on a personal computer.</p> <p>The dataset refers to Laboratory test. The skin reflectance was measured during rest and after mental and physical stress. Physical stress was provoked using a dynamometer under 15 N of force for 5 minutes. Provocation of mental stress was achieved with the use of the Stroop Test [11] for 15 minutes. A stress measurement was always preceded by a resting period of at least 15 minutes. Three hand locations have been considered, (a) arm, (b) hand and (c) finger.</p> <p>The datasets are related to the open accessible publication "<strong>Human Physical Condition RF Sensing at THz range".</strong></p>
ScienceDex guides
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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.