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30,334 results for “Responsiveness”
An Integrated Structural Model of the DNA Damage Responsive H3K4me3 Binding WDR76:SPIN1 Complex with the Nucleosome
<p>Serial Capture Affinity Purification (SCAP) is a powerful method to isolate a specific protein complex. When combined with cross linking mass spectrometry (XL-MS) and computational approaches one can build an integrated structural model of the isolated complex. Here, we applied SCAP to dissect a subpopulation of WDR76 in complex with SPIN1, a histone marker reader that specifically recognizes trimethylated histone H3 lysine4 (H3K4me3). In contrast to a previous SCAP analysis of the SPIN1:SPINDOC complex, histones and the H3K4me3 mark were copurified with the WDR76:SPIN1 complex. Next, interaction network analysis of copurifying proteins and microscopy analysis revealed a potential role of the WDR76:SPIN1 complex in the DNA damage response. Since we detected an extensive number of cross-linked sites were found between WDR76, SPIN1, and histones, we first built an integrated structural model of the complex which revealed that SPIN1 recognized the H3K4me3 epigenetic mark while interacting with WDR76. Finally, we then used the powerful Integrative Modeling Platform to build a structural model of WDR76 and SPIN1 bound to the nucleosome.</p>
Changes in above- versus belowground biomass distribution in permafrost regions in response to climate warming
<p>Permafrost regions contain approximately half of the carbon stored in land ecosystems and have warmed at least twice as much as any other biome. This warming has influenced vegetation activity, leading to changes in plant composition, physiology, and biomass storage in aboveground and belowground components, ultimately impacting ecosystem carbon balance. Yet, little is known about the causes and magnitude of long-term changes in the above- to belowground biomass ratio of plants (η). Here, we analyzed η values based on 3,013 plots and 26,337 plant-specific measurements representing eight sites across the Tibetan Plateau from 1995 to 2021. Our analysis revealed distinct temporal trends in η for three vegetation types: a 17% increase in alpine wetlands, and a decrease of 26% and 48% in alpine meadows and alpine steppes, respectively. These trends were primarily driven by temperature-induced growth preferences rather than shifts in plant species composition. Our findings indicate that in wetter ecosystems climate warming promotes aboveground plant growth, while in drier ecosystems, such as alpine meadows and alpine steppes, plants allocate more biomass belowground. Four process-based biogeochemical models failed to simulate the observed changes in η, which highlights the importance of improved process understanding of the processes driving the response of biomass distribution to climate warming, which is crucial for predicting the future carbon trajectory of permafrost ecosystems.</p>
Classification and frequency of climate change drivers and responses of small-scale fishers found in literature review
<p>Climate change hazards were classified into resource availability and fishing operations or both following the framework proposed by Cheung et al (2012). Response units were firstly classified into overarching responses and then categorized as suggested by the adaptive-transformative framework of Barnes et al.<sup> </sup>(2020). Adaptation units that did not represent an active adaptation response were classified as remaining. More than one hazard could be attributed to each fishers' response.</p>
Dataset: Greenland environmental drivers and biota responses
<p>This dataset includes data on sea surface temperatures, sea ice concentration, sea ice seasonality, salinity, runoff form the Greenland ice sheet, cholorophyll a, and a litterature review. The data is divided into six regions around Greenland stretching 200 km of the coastline. Each region spans 9 degrees latitude.</p>
Evaluation of transcription factor knockout impact on paclitaxel response for Triple Negative Breast Cancer
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11238552</div> <div> </div> <div>Two experimental formats included:</div> <div>'fixed' prefix: data from terminal time point of siRNA screen applied to HCC1143, HCC1806, and MDA-MB-468 Triple Negative Breast Cancer cell lines.</div> <div>'live' prefix: data from live-cell imaging of cell cycle reporter (HDHB-mClover/NLS-mCherry) HCC1143 Triple Negative Breast Cancer cell line.</div> <div>Note: 'live' level 1 data is available upon request (heiserl@ohsu.edu, calistri@ohsu.edu).</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate whether siRNA knockdown of transcription factors elevated during paclitaxel response impact cell count, cell morphology or cycling dynamics.</div> <div> </div> <div>Methods:</div> <div>siRNA Knockdown: Cells were plated in 90ul of serum free media per well of a 96 well plate. 24 hours later, siRNA knockdown mixture was prepared using a cell-line optimized concentration of Lipofectamine RNAiMAX (cat 13778075-075, Invitrogen) and siRNA (Horizon Discovery ON-TARGETplus) following RNAiMAX recommended protocol. The final concentration of siRNA per well was 1pmol and the final volume of RNAiMAX per well was 75nL for HCC1143, and 37.5nL for HCC1806 or MDA-MB-468 in 100uL of cell containing volume. 24 hours after siRNA transfection cells were treated with an addition of 100uL complete media containing either DMSO vehicle control or paclitaxel. </div> <div> </div> <div>Fixed-cell assays: Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask Green in PBS (H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 50, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>HDHB reporter live-cell assays: siRNA knockdown and drug treatment was performed as described above, and then the plate was loaded on an Incucyte S3 (Sartorious) and cells imaged every 15 minutes for 72 hours post drug treatment. At each timepoint 4 fields of view were captured at 20x magnificantion in each well using the phase, red and green channels. A cytoplasmic mask was computed from the mean of normalized red/green channel (cellpose parameters: diameter = 57, chan = mean(normalized(red), normalized(green)), and a nuclear mask was computed from the red channel (cellpose parameters: diameter = 30, chan = DAPI) using custom trained Cellpose models. Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0). An additional perinuclear ring mask was computed as the 11 pixel dilation from the nuclear mask, but still bound by the cytoplasmic mask. To determine mClover localization thresholds for cell cycle assignment, 250 cell images were randomly selected and manually assigned to the G1, S/G2 or M cell cycle state based on mClover localization. The mClover intensity ratios were then used to determine thresholds for automated cell cycle phase calling which was applied to the rest of the data set (Supplemental Figure 5A). Mononuclear cells with a Perinuclear:Nuclear mean intensity ratio greater than 0.8 and Nuclear:Cytoplasmic total intensity less than 0.5 were assigned to the S/G2 phase. Mononuclear and Multinuclear cells with a Nuclear:Cytoplasmic total intensity ratio greater than 0.8 and Perinuclear:Nuclear mean intensity ratio less than 0.8 were assigned to the ‘M’ phase. The remainder of mononuclear cells were assigned ‘G1’, and the remainder of multinucleated cells were assigned ‘Multinucleated’. </div> <div> </div> <div>Included files:</div> <div>fixed_level_1-plate_#.zip : Six .zip archives containing the raw images (DAPI/CellMask/Brightfield) from fixed-cell experiments.</div> <div>plate 1: HCC1143 cells treated with plate A schema</div> <div>plate 2: HCC1143 cells treated with plate B schema</div> <div>plate 3: HCC1806 cells treated with plate A schema</div> <div>plate 4: HCC1806 cells treated with plate B schema</div> <div>plate 5: MDA-MB-468 cells treated with plate A schema</div> <div>plate 6: MDA-MB-468 cells treated with plate B schema</div> <div>fixed_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>fixed_level_3: Data from 'fixed_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>fixed_incell_to_cellpose.rmd: R markdown code for converting original incell files (fixed_level_1) to RGB images for cellpose segmentation</div> <div>fixed_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (fixed_level_1)</div> <div>fixed_cellpose_models.zip: Archive including cellpose models used for fixed experiment</div> <div>live_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>live_level_3: Data from 'live_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>live_level_4: Data from 'live_level_3.csv' collapsed to the single condition level summarizing the number, multinucleation status and phase of cells at each time point.</div> <div>live_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (live_level_1).</div> <div>l ive_incu_archive2rgb.rmd: R markdown code for converting incucyte archive formatted data into RGB images, where the blue channel is the arithmetic mean of the min-max (0-1) normalized red and green channels.</div> <div>live_cellpose_models.zip: Archive including cellpose models used for live experiment.</div> <div> </div> <div> </div>
Data for "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots"
<p>This dataset contains all the data and CAD models needed to replicate the study presented in "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots".</p>
Extended Room Transition Dataset (ERTD) of room impulse responses in coupled rooms
<p>This dataset accompanies the publication</p> <blockquote> <div> <div> <div> <p>Georg Götz, Sebastian J. Schlecht, and Ville Pulkki. Common-slope modeling of late reverberation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, pp. 3945–3957, September 2023. doi: <a href="https://doi.org/10.1109/TASLP.2023.3317572" target="_blank" rel="noopener">10.1109/TASLP.2023.3317572</a></p> </div> </div> </div> </blockquote> <div> <div> <div> <p> </p> <div> <div> <div> <p>The dataset contains finite-difference time-domain (FDTD) simulations. It extends the Room Transition Dataset (RTD) by McKenzie et al (doi: <a href="https://doi.org/10.5281/zenodo.4636068" target="_blank" rel="noopener">10.5281/zenodo.4636068</a>). More precisely, it extends the “meeting room to hallway” transition, by additional receiver positions in both rooms. The ERTD contains 2833 RIRs in total. For all RIRs, the sound source position was chosen according to the “source in meeting room, no line-of-sight” (NLOS) configuration of the RTD. While the RTD only features receiver positions on a straight line between the rooms, the Extended Room Transition dataset samples the entire room geometry using a uniform grid with 0.2 m resolution. All simulations are performed on the horizontal plane at 1.55 m height.</p> <p>We simulated two variants of the dataset. In variant ERTD1, the wall absorption properties were assigned to approximately match the corresponding measurements. The absorption coefficient was assigned uniformly to all walls in the respective rooms, where the meeting room walls exhibited an absorption coefficient alphaR1 = 0.12, while the hallway was modeled less absorbent with alphaR2 = 0.042. In variant ERTD2, a significantly higher absorption coefficient alphaR3 = 0.48 is assigned to the right wall of the hallway, while all other surfaces are modeled analogous to ERTD1. We use ERTD2 in our evaluation to demonstrate that the common-slope model can also be used in environments with very non-uniform absorption distributions.</p> <p> </p> <p>___________</p> <div> <div> <div> <p>We acknowledge the computational resources provided by the Aalto Science-IT project.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div>
German Student Responses to Probability Theory and Statistics Bachelor Course (WuS24): Evaluated with Rubrics
<p><strong>Description:</strong></p> <p>This dataset contains questions and answers from an introductory computer science bachelor course on statistics and probability theory at Hochschule Bonn-Rhein-Sieg. The dataset includes three questions and a total of 90 answers, each evaluated using binary rubrics (yes/no) associated with specific scores.</p> <p> </p> <p><strong>Dataset Components:</strong></p> <ol> <li><em>questions.csv</em>: Contains the details of the three questions. <ul> <li>Columns: <ul> <li><em>question_id</em>: Unique identifier for each question</li> <li><em>question</em>: The text of the question</li> <li><em>solution</em>: The reference answer for the question</li> <li><em>max_score</em>: The maximum score for this question</li> </ul> </li> </ul> </li> <li><em>rubrics.csv</em>: Contains the grading rubrics for each question. <ul> <li>Columns: <ul> <li><em>question_id</em>: Unique identifier for each question</li> <li><em>rubric_id</em>: Unique identifier for each rubric within a question</li> <li><em>rubric</em>: The rubric phrased as a question</li> <li><em>score</em>: The score associated with fulfilling the rubric</li> </ul> </li> </ul> </li> <li><em>answers.csv</em>: Contains 90 student answers to the questions. <ul> <li>Columns: <ul> <li><em>answer_id</em>: Unique identifier for each answer</li> <li><em>question_id</em>: Unique identifier of the question that is answered</li> <li><em>answer</em>: The text of the student's answer</li> <li><em>score</em>: The score associated with fulfilling the rubric</li> </ul> </li> </ul> </li> <li><em>answer_rubrics.csv</em>: Contains the evaluations of rubrics for each answer.<br> <ul> <li>Columns: <ul> <li><em>answer_id</em>: The identifier of the answer.</li> <li><em>question_id</em>: The identifier of the question.</li> <li><em>rubric_id</em>: The identifier of the rubric for that question.</li> <li><em>label</em>: Indicates if the rubric crierion is fulfilled for the specific answer (true / false).</li> </ul> </li> </ul> </li> </ol> <pre><strong><br>Working with the Dataset:</strong> The easiest way to work with this dataset is using the class `RubricsDataset` defined in the file `dataloader.py`. Example:<br><br></pre> <pre><code>from dataloader import RubricsDataset<br><br>dataset = RubricsDataset.from_directory("data")<br> dataset.get_question(1) # Get a dictionary containing info about the first question, including rubrics dataset.get_answers(1) # Get all the answers for the first question as a list. Each answer is a dictionary with answer, score, rubrics.</code></pre>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Image-derived indicators of phytoplankton community responses to Pseudo-nitzschia blooms
<p>Data associated with the manuscript "Image-derived indicators of phytoplankton community responses to <em>Pseudo-nitzschia</em> blooms" submitted to the journal <em>Harmful Algae</em>. There is an additional R script that calculates an interaction metric as described in the paper. </p>
Anechoic McVAMPIRE – Anechoic Multichannel Varying Mouth Position Impulse Response Dataset
<p>This dataset contains impulse responses (IRs) that were recorded in an anechoic room. The recording setup imitates the geometry of a minivan with eight seats arranged in three seat rows. The IRs were captured with 14 overhead microphones positioned in the imaginary car roof using a built-in mouth simulator of a head and torso simulator (HATS) at eight passenger seat positions with eleven orientations each. In addition, the dataset contains IRs measured with four lateral loudspeakers imitating door loudspeakers, as well as a noise floor recording.</p> <p>This dataset supplements the <a href="https://doi.org/10.5281/zenodo.12806684">In-Car McVAMPIRE</a> dataset which was captured with an identical microphone setup in a real car. Both datasets can be used to simulate speech in a car from different seats with different speaker orientations including the loudspeaker-enclosure-microphone (LEM) system under anechoic or realistic, reverberant conditions.</p>
Data for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect
<p>Dataset for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect, in final revision for Nature Communications.</p> <p>Includes two files - one behavioral data from uniquely identified worker bumblebees, and the second containing metadata for the colonies from which these data were generated (including experimental treatments, locations, sizes, etc.).</p>
Sources used for content analysis in Crespy, A. and Szabo, I. : Healthcare reforms and fiscal discipline in Europe: Responsibility or responsiveness? forthcoming: European Policy Analysis
<p>Sources used for content analysis in Crespy, A. and Szabo, I. : Healthcare reforms and fiscal discipline in Europe: Responsibility or responsiveness? forthcoming: European Policy Analysis. Data from the ENLIGHTEN project (H2020 #649456).</p>
Supplementary data for "Heterometallic perovskite-type metal-organic framework with an ammonium cation: structure, phonons, and optical response"
<p>Optimised structures of [NH<sub>4</sub>][Na<sub>0.5</sub>M<sub>0.5</sub>(COOH)<sub>3</sub>] (M = Al, Cr)</p> <p>Phonon output for [NH<sub>4</sub>][Na<sub>0.5</sub>Cr<sub>0.5</sub>(COOH)<sub>3</sub>]</p> <p>Gif of the T’(NH<sub>4</sub><sup>+</sup>) mode (no. 23). The c-axis is the vertical direction.</p> <p>For further information please see the associated publication.</p>
Data accompanying the master thesis: A neuronal model for visually evoked startle responses in schooling fish
<p>This dataset contains data that was generated and analyzed for the master thesis "A neuronal model for visually evoked startle responses". All related material, including analysis code, of the master thesis can be found at https://github.com/awakenting/master-thesis.</p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
A multiple model high-resolution head-related impulse response database for aided and unaided ears (HDF5 format)
<p>The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation). The data here is stored in HDF5 files, the SOFA files are published in a separate dataset <a href="https://zenodo.org/record/2582553">doi:10.5281/zenodo.2582553</a>.</p>
Elevation modulates the phenotypic responses to light of four co-occurring Pyrenean forest tree species
<p>Data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient. The dataset contains three files:</p> <ol> <li><strong>Biomass.txt: </strong>Data on plant biomass per fraction (leaf, stem and roots) 4 years after plantation. Included variables:<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant in that plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (factor): code to identify uniquely each plant<br> - GLI (num): Global Light Index, the amount of irradiance that receives each seedling<br> - Code (factor): code to identify uniquely each plant<br> - PLB (numeric): total plant biomass (g)<br> - LFB (numeric): leaf biomass (g)<br> - STB (numeric): stem biomass (g)<br> - RTB (numeric): root biomass (g)<br> - LMF (numeric): leaf mass fraction (LFB/PLB)<br> - SMF (numeric): stem mass fraction (STB/PLB)<br> - RMF (numeric): root mass fraction (RTB/PLB)<br> - SLA (numeric): specific leaf area<br> - H (numeric): plant height (mm)<br> - D (numeric): plant diameter at root collar (mm)<br> - PB2 (numeric): total plant biomass without considering leaves (g)<br> - SF2 (numeric): stem mass fraction without considering leaves (STB/PB2)<br> - RF2 (numeric): root mass fraction without considering leaves (RTB/PB2)</li> <li><strong>init_biomass.txt:</strong> for biomass at the moment of plantation<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - N (numeric): number of plant<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (numeric): code to identify uniquely each plant<br> - PB (numeric): total plant biomass (g)<br> - LB (numeric): leaf biomass (g)<br> - SB (numeric): stem biomass (g)<br> - RB (numeric): root biomass (g)</li> <li><strong>WaterPot.txt</strong>: data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient during a period of intense drought<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant <br> - Parcela (factor): identifier ofthe plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Estacion (factor): the moment for the measurement. One level: September<br> - GLI (numeric): global light index, the ration of total irradiance received by the plant at the moment of plantation<br> - WPt (numeric): water potential (bars)</li> </ol>
METU SPARG Eigenmike em32 Acoustic Impulse Response Dataset v0.1.0
<p><strong>DESCRIPTION</strong></p> <p>This dataset includes acoustic impulse response (AIR) measurements made using an Eigenmike em32 and the room impulse response measurements carried out at the same position using an Alctron M6 measurement microphone. The measurements were made in classroom S05 at the METU Graduate School of Informatics on 23 January 2018. </p> <p><strong>LICENSE</strong></p> <p>The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).</p> <p><strong>MEASUREMENTS</strong></p> <p>The classroom in which the measurements were made has a high reverberation time (T60 ≈ 1.12 s) when empty. The room is approximately rectangular and has the dimensions 6.5 × 8.3 × 2.9 m. AIR measurements were made at 240 points on a rectilinear grid of 0.5 m horizontal and 0.3 m vertical resolution surrounding the array. The array was positioned at a height of 1.5 m. The measurement planes were positioned at the heights of 0.9, 1.2, 1.5, 1.8 and 2.1 m from the floor level. These positions cover the whole azimuth range and an elevation range of approximately ±50◦ above and below the horizontal plane.</p> <p>The sound source was a Genelec 6010A two-way loudspeaker whose acoustic axis pointed at the vertical axis of the array at all measurement positions. Logarithmic sine sweep method was used for the AIR measurements. </p> <p><strong>FILE FORMAT</strong></p> <p>The AIRs and RIRs are provided as 16-bit signed integer WAVE files. The sampling rate is 48 kHz.</p> <p><strong>NAMING CONVENTION</strong></p> <p>There are two folders: em32 and alctron. The former includes AIR measurements, and the latter includes the RIR measurements. Each of these folders include 244 subfolders where each subfolder is named as ABC, from 000 through to 664. See documentation.pdf for details.</p> <p>Note that the measurements right above and below the array are not ideal since the acoustic axis of the loudspeaker did not face the array. Therefore, these measurements are considered unfit and were not used in the publications given below.</p> <p><strong>HOW TO CITE</strong></p> <p>The dataset has the DOI number 10.5281/zenodo.2635758 and can be cited as:</p> <p><strong>Orhun Olgun, & Huseyin Hacihabiboglu. (2019). METU SPARG Eigenmike em32 Acoustic Impulse Response Dataset v0.1.0 (Version 0.1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2635758</strong></p> <p>The data presented here were used in one journal article and two conference papers as of the time of writing this document. Please also consider citing these papers if you find this dataset to be useful in your research:</p> <p>[1] Coteli, M. B., Olgun, O., and Hacihabiboglu, H. (2018). Multiple Sound Source Localization With Steered Response Power Density and Hierarchical Grid Refinement. IEEE/ACM Trans. Audio, Speech and Language Process., 26(11), 2215-2229.</p> <p>[2] Olgun, O. and Hacihabiboglu, H., (2018) "Localization of Multiple Sources in the Spherical Harmonic Domain with Hierarchical Grid Refinement and EB-MUSIC". In Proc. 2018 16th Int. Workshop on Acoust. Signal Enhancement (IWAENC-18) (pp. 101-105), Tokyo, Japan.</p> <p>[3] Coteli, M. B., and Hacihabiboglu, H., (2019), "Multiple Sound Source Localization with Rigid Spherical Microphone Arrays Via Residual Energy Test", Proc. 2019 IEEE Int. Conf. on Acoust., Speech and Signal Process., (ICASSP-19), Brighton, UK.</p>
Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve
<p>Dataset containing the recorded and simulated data reported in the manuscript "Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve" published in the Journal of the Association for Research in Otolaryngology, JARO (<a href="https://doi.org/10.1007/s10162-019-00721-7">https://doi.org/10.1007/s10162-019-00721-7</a>):</p> <ol> <li>RECORDED Envelope Following Responses (EFR) in normal-hearing (NH) threshold and hearing-impaired (HI) human listeners using deeply (m = 85%) and shallowly (m = 25%) modulated sinusoidally amplitude modulated (SAM) tones.</li> <li>SIMULATED EFRs using the auditory nerve (AN) model by Zilany et al. (2009, 2014).</li> </ol> <p>Files content and structure:</p> <p><strong>Recorded EFRs</strong></p> <p><strong>Fig. 2:</strong></p> <ul> <li><em>fig2__recorded_efr.csv</em>: <br> EFR recordings as a function of stimulus level (EFR magnitude-level functions) for the NH and HI listeners using two modulation depths.</li> </ul> <p>The file containing the recorded EFR data have the following columns:</p> <ul> <li><em>lvl</em>: Stimulation level</li> <li><em>m85_ok: </em>EFR magnitude (dB re to 1 µV) using m = 85%. Significant responses (F-test = 1)</li> <li><em>m85_ko: </em>EFR magnitude (dB re to 1 µV) using m = 85%. Non-significant responses (F-test = 0)</li> <li><em>m85_bkg: </em>Estimated background noise magnitude (dB re to 1 µV) for the recordings when m = 85%</li> <li><em>m25_ok: </em>EFR magnitude (dB re to 1 µV) using m = 25%. Significant responses (F-test = 1)</li> <li><em>m25_ko: </em>EFR magnitude (dB re to 1 µV) using m = 25%. Non-significant responses (F-test = 0)</li> <li><em>m25_bkg: </em>Estimated background noise magnitude (dB re to 1 µV) for the recordings when m = 25% </li> <li><em>subj: </em>Listener id</li> <li>hearing: Hearing group (nh | hi) of the listener</li> </ul> <p><strong>Simulated EFRs:</strong></p> <p><strong><em>Files with the simulation results summing across frequency and SR fiber type</em></strong></p> <p><strong>Fig. 4:</strong></p> <ul> <li><em>fig4a__simul_efr__nh_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4a).</li> <li><em>fig4b__simul_efr__nh_slp_thres_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of OHC loss (Fig. 4b).</li> <li><em>fig4c__simul_efr__nh_slp_thres_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of IHC loss (Fig. 4c).</li> <li><em>fig4d__simul_efr__hi_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4e__simul_efr__hi_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of OHC loss (Fig. 4e).</li> <li><em>fig4f__simul_efr__hi_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of IHC loss (Fig. 4f).</li> <li><em>fig4g__simul_efr__hi_slp_thres_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI listeners (average) assuming sloping threshold at EHF and 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4h__simul_efr__hi_slp_thres_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF and all of OHC loss (Fig. 4e).</li> <li><em>fig4i__simul_efr__hi_slp_thres_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF and and all of IHC loss (Fig. 4f).</li> </ul> <p> </p> <p><strong>Fig. 5:</strong></p> <ul> <li><em>fig5a__simul_efr__nh__cs_ms_ls_100p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming cochlear synaptopathy (CS) of a 100% of loss of only medium- and low-spontaneous rate (SR) AN fibers (Fig. 5a).</li> <li><em>fig5b__simul_efr__nh09_cs__approx</em>.<em>csv</em>: <br> Simulated EFR magnitude-level function to approximate the data for the NH threshold listener NH09 including CS (Fig. 5b).</li> <li><em>fig5c__simul_efr__hi04_cs__approx</em>.<em>csv</em>: <br> Simulated EFR magnitude-level function to approximate the data for the HI listener HI04 including CS (Fig. 5c).</li> </ul> <p> </p> <p><strong>Fig. 6:</strong></p> <p>Files with the simulation results for the NH threshold listener in different characteristic frequency (CF) bands and SR fiber types</p> <ul> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m12.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 12%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m25.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 25%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m50.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 50%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m85.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 85%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m100.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 100%.</li> </ul> <p> </p> <p><strong>Fig. 7:</strong></p> <ul> <li><em>fig7a__simul_efr__bw_analys__32oct_[20, 40, 60, 80, 100]p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 3/2-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7a).</li> <li><em>fig7b__simul_efr__bw_analys__1oct_[20, 40, 60, 80, 100]p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 1-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7b).</li> <li><em>fig7c__simul_efr__bw_analys__13oct_[20, 40, 60, 80, 100]p.csv</em>:<br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 1/3-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7c).</li> </ul> <p> </p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p>The structure of the .csv files that contain the EFR simulations is:</p> <ul> <li><em>lvl</em>: Stimulation level</li> <li><em>mgn_mxxx: </em>Simulated EFR magnitude (a.u. in dB) for each modulation depth (100%, 85%, 50%, 25% and 12%)</li> <li><em>bkg_mxxx: </em>Estimate of the background noise floor (a.u. in dB) for each modulation depth. </li> <li><em>ftest_mxxx: </em>Result of the F-test statistical test (0 or 1) for each modulation depth.</li> </ul> <p> </p> <p>The structure of the .mat files that contain the EFR simulations is:</p> <ul> <li><em>exper_type</em>: Name of the simulated experiment</li> <li><em>species</em>: Species used in the AN model (in this study is always 2: human)</li> <li><em>species_age</em>: (Not used in this work). Age of the animal when species is 4: mouse</li> <li>modulation<em>: </em>Modulation depth of the SAM tone used in the simulation <em>(100%, 85%, 50%, 25% or 12%)</em></li> <li><em>f_on: </em>Center frequency of the on-frequency band (<em>2000 Hz</em>)</li> <li><em>f_off_hf: </em>Center frequency of the first off-frequency band (<em>3000 Hz</em>)</li> <li><em>f_off_vhf: </em>Center frequency of the second off-frequency band (<em>7000 Hz</em>) </li> <li><em>f_off_uvhf: </em>Center frequency of the third off-frequency band (<em>12000 Hz</em>)</li> <li><em>lvl_vect: </em>Stimulus level vector <em>(from 5 to 100 dB SPL, in steps of 5 dB)</em></li> <li><em>simul_efr </em> Structure with the simulated data <ul> <li>The data structure contains many fields which are matricies of size 3x20. The columns are the 20 stimulus levels defined in <em>lvl_vect</em>, and the first row is the simulated EFR, the second row is the estimates background noise floor in the simulation, and the third row is the output of the F-test statistics.</li> <li>The structure fields can be divided in 4 groups: <ul> <li><em>ihc_</em> Responses from the IHC (not shown in the paper)</li> <li><em>an_hs_</em> Responses from the High-SR fibers in the AN</li> <li><em>an_ms_</em> Responses from the Medium-SR fibers in the AN</li> <li><em>an_ls_</em> Responses from the Low-SR fibers in the AN</li> </ul> </li> <li>Each group has 5 responses corresponding to the on-frequency band (<em>_on</em>) and the three off-frequency bands (<em>_off_hf</em>, <em>_off_vhf</em>, <em>_off_uvhf</em>); and the sum across frequencies (<em>_across_f</em>)</li> </ul> </li> </ul>
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