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30,334 results for “Responsiveness”
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. (continued).
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. (continued).
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. (continued).
Mars nightside ionospheric response during the disappearing solar wind event: First Results
<p>The impact of a rarest solar wind phenomenon [disappearing solar wind (DSW) event during 26-28 December 2022] on the Martian nightside ionosphere is investigated.</p>
In-Car McVAMPIRE – In-Car Multichannel Varying Mouth Position Impulse Response Dataset
<p>This dataset contains impulse responses (IRs) that were recorded in a minivan with eight seats arranged in three seat rows. The IRs were captured with 14 distributed overhead microphones using a mouth simulator at eight passenger seat positions with eleven orientations each. In addition, the dataset contains IRs measured with six built-in car loudspeakers as well as driving noise recordings at various speeds.</p> <p>This dataset supplements the <a href="https://doi.org/10.5281/zenodo.12818781">Anechoic McVAMPIRE</a> dataset which was captured with an identical microphone setup in an anechoic chamber. 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>
Response of a fringing reef coastline to the direct impact of a tropical cyclone
<p>The data contains observations from the impact of Tropical Cyclone Olwyn to northwest Western Australia in March 2015. A cross-shore array of 5 pressure sensors were deployed to measure wave heights and water levels at Point Jurabi (~5 km north of Tantabiddi), Ningaloo Reef; beach morphology was measured pre- and post-cyclone using RTK-DGPS; and a two-way coupled model was developed using Delft3D and SWAN. </p> <p> </p> <p>The data sets contains the raw pressure sensor measurements (.rsk), beach morphology grids (.txt), and model input/output files. Please see the 'Cuttler_etal_2017_Metadata.pdf', 'ReadMe.txt' file or contact michael.cuttler@uwa.edu.au for further information</p>
Binaural Impulse Responses with and without HTC Vive HMD
<p>Impulse responses (IRs) were recorded with a single 20s long sweep with a B&K 4128 HATS with ear canals. The room is an anechoic chamber with 64 loudspeakers (KEF LS50) arranged in a full sphere. The locations of the loudspeakers are shown in the files loudspeakerLocations (azimuth, elevation in radians). The distance of the loudspeaker to the centre of the artificial head is 2.4m. The loudspeaker were equalized beforehand.</p> <p>The files including the term "HMD" (head mounted display) or "vive" were measured with the B&K 4128 HATS wearing the HTC Vive HMD.</p> <p>The sampling frequency is 48 kHz.</p> <p>The IRs were truncated to allow at least 256 samples (5.3ms) after the main peak.</p>
Time-generalized multivariate analysis of EEG responses reveals a cascading architecture of semantic mismatch processing
<p>This entry includes the required data to conduct the analysis from our paper. It includes recordings, a look-up csv for target word onsets and montage file. Github page for analysis code: https://github.com/heikele/GAT_n4-p6</p> <p> </p> <p>Abstract from submitted paper:</p> <p>Event-related brain potentials have a strong impact on neurocognitive models, as they inform about the temporal sequence of cognitive processes. Nevertheless, their value for deciding among alternative cognitive architectures is partly limited by component overlap and the possibility of ambiguity regarding component identity. Here, we apply temporally-generalized multivariate pattern analysis – a recently-proposed machine learning method capable of tracking the evolution of neurocognitive processes over time – to constrain possible alternative architectures underlying the processing of semantic incongruency in sentences. In a spoken sentence paradigm, we replicate established N400/P600 correlates of semantic mismatch. Time-generalized decoding indicatesthat early vs. late mismatch-sensitive processes are (i) distinct in their neural substrate, arguing against recurrent or latency-shifted single process architectures, and (ii) partially overlapping in time, inconsistent withpredictions of strictly serial models. These results are in accordance withan incremental-cascading neurocognitive organization of semantic mismatch processing. We propose time-generalized multivariate decoding as a valuable tool for neurocognitive language studies.</p> <p> </p> <p>Keywords: EEG; ERP; semantic mismatch; N400; P600; multivariate pattern analysis; generalization across time decoding</p>
Dataset for the tutorial: "Response properties of embedded molecules through the polarizable embedding model"
<p>Dataset for the tutorial "<em>Response properties of embedded molecules through the polarizable embedding model</em>". The abstract is the following:</p> <blockquote> <p>"<em>The polarizable embedding (PE) model is a fragment-based quantum–classical approach aimed at accurate inclusion of environment effects in quantum-mechanical response property calculations. The aim of this tutorial is to give insight into the practical use of the PE model. Starting from a set of molecular structures and until you arrive at the final property, there are many crucial details to consider in order to obtain trustworthy results in an efficient manner. To lower the threshold for new users wanting to explore the use of the PE model, we describe and discuss important aspects related to its practical use. This includes directions on how to generate input files and how to run a calculation.</em>"</p> </blockquote> <p>The dataset contains files that can be used in direct connection with the tutorial (<em>getting-started.zip</em>), and an additional set of files that can be used for further exploration (<em>peqm-inputs.zip</em>). </p>
Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines
<p>Online Supplementary Dataset of the manuscript "Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines"</p>
WSSSPE5.1 Attendee Survey - Responses
<p>The responses to the WSSSPE5.1 Attendee Survey, conducted via an online form.</p>
Mechanisms behind the low-cloud optical depth response to temperature in ARM site observations - datasets and scripts
<p>Datasets used in the analysis of the article Mechanisms behind the low-cloud optical depth response to temperature in ARM site observations, which was submitted to and subsequently published in Journal of Geophysical Research-Atmosphere. DOI: 10.1029/2018JD029359</p>
Dataset: Methods for computing the maximum performance of computational models of fMRI responses.
<p>Accompanying data for manuscript: Methods for computing the maximum performance of computational models of fMRI responses. written by Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino, submitted for publication in Plos Computational Biology, July 2018.</p> <p>This dataset provide the Betas for subcortical and a subset of the cortical voxels for three subjects in matlab format.</p> <p>The field bTest refers to the Beta coefficients for every voxel in the test data. The fields beta1 and beta2 refer to the split-half partitions of the bTest coefficients. The field varBparam refers to the parametric variances of the Beta coefficients and the field varBBootstrap refers to the variances of the Betas computed with bootstrap. </p>
RAW DATA form - Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs
<p><strong>Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs</strong></p> <p>Ghada BinKhamis, Agnès Léger, Steven L. Bell, Garreth Prendergast, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>doi: 10.1097/AUD.0000000000000648</strong></p> <p><em>(<strong>Please site above article)</strong></em></p> <p> </p> <p><strong>Description of raw EEG (speech-ABR) data main folder, subfolders, and raw EEG files</strong></p> <p><strong>Folder Information</strong></p> <p><strong>Main folder:</strong></p> <ul> <li>Contains 144 subfolders with raw data from 12 participants</li> </ul> <p><strong>Subfolder names:</strong></p> <ul> <li>Each subfolder starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12</li> </ul> </li> </ul> <ul> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>And finally the background condition <ul> <li>quiet, noise</li> </ul> </li> </ul> <p><strong>Example subfolder names:</strong></p> <ul> <li><em>S01 40ms da noise:</em>Participant number 1, speech-ABRs in response to the 40ms [da] in background noise</li> <li><em>S07 170ms ga quiet:</em>Participant number 7, speech-ABRs in response to the 170ms [ga] in quiet</li> </ul> <p><strong>Each participant has 12 subfolders:</strong></p> <ol> <li>S__ 40ms da quiet </li> <li>S__ 40ms da noise</li> <li>S__ 50ms da quiet</li> <li>S__ 50ms da noise</li> <li>S__ 50ms ba quiet</li> <li>S__ 50ms ba noise</li> <li>S__ 50ms ga quiet</li> <li>S__ 50ms ga noise</li> <li>S__ 170ms da quiet</li> <li>S__ 170ms da noise</li> <li>S__ 170ms ba quiet</li> <li>S__ 170ms ga quiet</li> </ol> <p><strong>Each participant subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong></p> <ul> <li>Each ‘.mat’ file starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12 </li> </ul> </li> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>Next is ‘noise’ if background condition was noise</li> <li>Next is the stimulus polarity <ul> <li>Pos for positive/standard</li> <li>Neg for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally is the recording number for that polarity <ul> <li>R1 is the first recording</li> <li>R2 is the second recording</li> </ul> </li> </ul> <p><strong>Example ‘.mat’ file name:</strong></p> <ul> <li><em>S04 50 ba Neg R1:</em>Participant number 4, speech-ABR in response to the 50ms [ba] in quiet, reversed polarity stimulus, recording number one </li> <li><em>S02 40 da noise Pos R2:</em>Participant number 2, speech-ABR in response to the 40ms [da] in background noise, standard/positive stimulus, recording number two</li> </ul> <p> </p> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example: <ul> <li>xunits: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li>start: ‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li>points: <strong>1800</strong>is the number of sample points for speech-ABRs to the 40ms da, this number will be <strong>2200</strong>for the speech-ABRs to the 50ms stimuli (ba, da, ga), and <strong>4600</strong>for the speech-ABRs to the 170ms stimuli (ba, da, ga)</li> <li>chans: 2 is the number of channels (channel 2 is the ipsilateral channel)</li> <li>frames: 3000 is the number of epochs</li> </ul> </li> <li>The last filed <strong>‘values’</strong>is what contains the raw EEG data (1800x2x3000) <ul> <li><strong>1800 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>3000 </strong>is the number of epochs</li> <li>The field <strong>‘values’ </strong>for speech-ABRs to the 50ms stimuli is <strong>2200x2x3000 </strong>and for speech-ABRs to the 170ms stimuli is <strong>4600x2x3000</strong>.</li> </ul> </li> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> </ul> <p><strong>Data is recorded in Volts and will need to be converted to Micro Volts</strong></p> <p><strong>Date of data collection</strong>: May to November 2016</p>
Raw data belonging to paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters"
<p>The Zip file contains the raw data used for drawing conclusions in the paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters" accepted for publication in Energy Informatics 2018.</p> <p>The raw data is the parameters of a sample of 50 domestic electric water heaters (DEWHs)<br> used for evaluating the algorithm in the paper. It is explained in the readme.txt.</p>
CODEX: Oscillatory brain activity during acute exercise: Tonic and transient neural response to an Oddball task.
<p><strong>2. Method</strong></p> <p><em>2.1.</em> <em>Participants</em></p> <p>We recruited 20 young males with a high level of aerobic fitness (age between 18-31 years old, average age 23.9 years old) from the University of Granada (Spain). All participants met the inclusion criteria of reporting at least 8 hours of cycling or triathlon training per week, normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders and were taking no medication. Note that high-fit cyclists and triathletes were selected because they are capable of maintaining a pedalling cadence at moderate-to-high intensity during long periods of time. Furthermore, they are able to keep a fixed posture over time, which reduces EEG movement artifacts considerably. Their fitness level was verified by an incremental effort test (see below). Participants were required to maintain a regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense physical activity 24 hours before each session. All subjects gave written informed consent before the study. The protocol was in accordance with both, the ethical guidelines of the University of Granada, and the Declaration of Helsinki.</p> <p> </p> <p><em>2.2. Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Oddball stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 100 cm between the head of participants and the centre of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p> </p> <p><em>2.3. Fitness Assessments</em></p> <p>Participants came to the laboratory at least one week before the first experimental session to provide the informed consent, complete an anthropometric evaluation (height, weight and body mass index [BMI]) and to familiarize with the oddball task. Subsequently, they performed an incremental cycle-ergometer test to obtain their VO<sub>2max</sub> that was used in the following experimental sessions to adjust the exercise intensity individually. The incremental effort test started with a 3 minutes warm-up at 30 Watts (W), with the power output increasing 10 W every minute. Each participant set his preferred cadence (between 60-90 rpm · min<sup>-1</sup>) during the warm-up period and was asked to maintain this cadence during the entire protocol. The test began at 60 W and was followed by an incremental protocol of 30 W every 3 minutes. Each step of the incremental protocol consisted of 2 minutes of stabilized load and 1 minute of progressive load increase (5 W every 10 seconds). The oxygen uptake (VO<sub>2</sub> ml • min<sup>-1 </sup>• kg<sup>-1</sup>), respiratory exchange ratio (RER; i.e., CO<sub>2</sub> production • O<sub>2</sub> consumption<sup>-1</sup>), relative power output (W • Kg<sup>-1</sup>) and heart rate (bpm) were continuously recorded throughout the test.</p> <p> </p> <p><em>2.4. Experimental sessions</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 100 min each. To avoid possible fatigue and/or training effects, visits to the laboratory were scheduled on different days allowing 48–72 hours between sessions. On each experimental session, after 10’ warm-up on a cycle-ergometer at a power load of 30% of their individual VO<sub>2max</sub>, participants performed an oddball task for 20’ while pedalling either at 30% (Light intensity exercise session) or 80% (Moderate-intensity exercise session) of their VO<sub>2max</sub>. Upon completion of the oddball task, a 10’ cool down period at 30% of intensity followed (see Table 1). Each participant set his preferred cadence (between 60-90 rpm · min<sup>-1</sup>) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions, as much as possible, in terms of dual-task demands.</p> <p> </p> <p><em>2.5. Oddball task</em></p> <p>The visual oddball task was based on that reported in Sawaki and Katayama (Sawaki & Katayama, 2007). It consisted of a random presentation of three visual stimuli: a frequent small blue circle (approximately 1.15º x 1.15º), a rare big blue circle (approximately 1.30º x 1.30º), and a rare red square (approximately 2.00º x 2.00º). Small blue circles were considered as standard stimuli (non-target), while big blue circles (target 1) and red squares (target 2) were considered as target stimuli. Stimuli were displayed sequentially on the centre of the screen on a black background. Each trial started with the presentation of a blank screen in a black background for 1200 ms. Then, the stimulus was presented at a random time interval (between 0 and 800 ms) during 150 ms. Participants were instructed to respond to both targets by pressing a button connected to the cycle-ergometer handlebar with the thumb of their dominant hand and to not respond when standard stimuli were shown. Participants were encouraged to respond as accurately as possible. The target stimuli were randomly presented in 20% of trials (10% of target 1, 10% target 2) and the non-target stimulus in the remaining 80% of trials. A total of 600 stimuli were presented. The task lasted for 20 minutes approximately. No breaks were allowed.</p> <p> </p> <p><em>2.6. EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ) to optimize signal transduction. Participants were instructed to avoid postural movements as much as possible, and to keep their gaze on the centre of the screen during the task. Electrode impedances were kept below 10 kΩ.</p>
Dataset — Revealing the hierarchy of processes and time scales that control the tropic response of shoots to gravi-stimulations
<p>Dataset and python notebook used to produce the figures in the article "Revealing the hierarchy of processes and time scales that control the tropic response of shoots to gravi-stimulations" submited to JexpBot.</p> <p>You can interact directly with the data and python notebook using Binder!</p> <p><a href="https://mybinder.org/v2/git/https%3A%2F%2Fframagit.org%2Fhchauvet%2FDataset_Chauvet-et-Al19/62e33bc0963005fb1483788aa525e92563a6fd16?filepath=Description.ipynb"> Interact with data in Binder</a></p> <p> </p> <p> </p> <p><strong>Data are also in a git repository:</strong></p> <p><a href="https://framagit.org/hchauvet/Dataset_Chauvet-et-Al19">https://framagit.org/hchauvet/Dataset_Chauvet-et-Al19</a></p>
New Azo-DMOF-1 MOF as a photo-responsive low-energy CO2 adsorbent and its exceptional CO2/N2 separation performance in mixed matrix membranes
<p>Data repository for manuscript, published in <em>ACS Applied Materials & Interfaces</em>, <strong>2018</strong>, <em>10</em> (40), pp 34291–34301, <a href="https://dx.doi.org/10.1021/acsami.8b12261">http://dx.doi.org/10.1021/acsami.8b12261</a></p> <p><strong>Abstract</strong></p> <p>A new generation-2 light-responsive metal–organic framework (MOF) has been successfully synthesized using Zn as the metal source and both 2-phenyldiazenyl terephthalic acid and 1,4-diazabicyclo[2.2.2]octane (DABCO) as the ligands. It was found that Zn-azo-dabco MOF (Azo-DMOF-1) exhibited a photoresponsive CO<sub>2</sub> adsorption both in static and dynamic condition because of the presence of azobenzene functionalities from the ligand. Further application of this MOF was evaluated by incorporating it as a filler in a mixed matrix membrane for CO<sub>2</sub>/N<sub>2</sub> gas separation. Matrimid and polymer of intrinsic microporosity-1 (PIM-1) were used as the polymer matrix. It was found that Azo-DMOF-1 could enhance both the CO<sub>2</sub> permeability and selectivity of the pristine polymer. In particular, the Azo-DMOF-1–PIM-1 composite membranes have shown a promising performance that surpassed the 2008 Robeson Upper Bound.</p>
abonaca/stream_information: ApJ response
<p>The first release of the stream information project.</p>
Response of E3 stage Radix balthica embryos maintained in contrasting temperatures and salinities for 24h
<p>Sample datasets and video acquired using the EmbryoPhenomics platform. Experiment assessing influence of three (20, 25 and 30C) contrasting temperatures and two salinities (0 or 7 ppt) on the development of the aquatic gastropod <em>Radix balthica</em>. Exposure was initiated at the E3 developmental stage at which embryos are undergoing ciliary driven spinning. The EmbryoCV package was used to produce phenome-level data. Datasets and associated video for an individual embryo from each treatment (temperature and salinity combinations).</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.