Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
461
datasets available to search
ShareScore release 0.9.0
Dataset results
461 results for “Electrophysiology”
Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20160622_01
<p>This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> .<br> <br> <strong>General description.</strong> These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20160622_01".</p> <p><strong>Filtering. </strong>The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate.</p> <p><strong>File format.</strong> The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification.</p> <p><strong>Datasets. </strong>A few of the relevant dataset paths are listed here for convenience. In the below, <em>n</em> refers to the number of recording channels and <em>k</em> refers to the number of samples.</p> <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> <p>Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.</p>
Figure 1 in Cardiotoxic effects of enrofloxacin on electrophysiological activity, cardiac markers, oxidative stress, and haematological findings in rabbits
Figure 1. Histological examination of rabbit heart in the negative control (A) shows normal morphology. Histological examination of rabbit heart in group 1 (B) and group 2 (C) shows normal morphology except for some minor abnormalities including hyperaemia in some areas (H&E, 400×).
MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs obtained through electrophysiological simulations
<p>Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess precisely known ground truth labels of the underlying disease (model parameterization) and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECG signals were used to enrich sparse clinical data for machine learning or even replace them completely during training leading to good performance on real-world clinical test data.<br> <br> We thus generated a large synthetic database comprising a total of 16,900 12 lead ECGs based on multi-scale electrophysiological simulations equally distributed into 1 normal healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted timing and amplitude features between the virtual cohort and a large publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The novel dataset of simulated ECG signals is split into training, validation and test data folds for development of novel machine learning algorithms and their objective assessment. </p> <p>This folder WP2_largeDataset_Noise contains the 12 lead ECGs of 10 seconds length. Each ECG is stored in a separate CSV file with one row per lead (lead order: I, II, III, aVR, aVL, aVF, V1-V6) and one sample per column (sampling rate: 500Hz). Data are split by pathologies (avblock = AV block, lbbb = left bundle branch block, rbbb = right bundle branch block, sinus = normal sinus rhythm, lae = left atrial enlargement, fam = fibrotic atrial cardiomyopathy, iab = interatrial conduction block, mi = myocardial infarction). MI data are further split into subclasses depending on the occlusion site (LAD, LCX, RCA) and transmurality (0.3 or 1.0). Each pathology subclass contains training, validation and testing data (~ 70/15/15 split). Training, validation and testing datasets were defined according to the model with which QRST complexes were simulated, i.e., ECGs calculated with the same anatomical model but different electrophysiological parameters are only present in one of the test, validation and training datasets but never in multiple. Each subfolder also contains a "siginfo.csv" file specifying the respective simulation run for the P wave and the QRST segment that was used to synthesize the 10 second ECG segment. Each signal is available in three variations:</p> <ul> <li>*_raw.csv contains the synthesized ECG without added noise and without filtering</li> <li>*_noise.csv contains the synthesized ECG (unfiltered) with superimposed noise</li> <li>*_filtered.csv contains the filtered synthesized ECG (fiter settings: highpass cutoff frequency 0.5Hz, lowpass cutoff frequency 150Hz, butterworth filters of order 3).</li> </ul> <p>The folder WP2_largeDataset_ParameterFiles contains the parameter files used to simulate the 12 lead ECGs. Parameters are split for atrial and ventricular simulations, which were run independently from one another. <br> See <a href="https://doi.org/10.48550/arXiv.2211.15997">Gillette*, Gsell*, Nagel* et al. "MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs obtained through electrophysiological simulations"</a> for a description of the model parameters.</p>
Electrophysiological Signals of Embodiment and MI-BCI Training in VR
<p><strong>DATASET DETAILS:</strong></p> <p><strong>Participant demographics:</strong></p> <p>A total of 26 participants were included, consisting of 10 males (mean age 25.4 ± 7.4) and 16 females (mean age 23 ± 3.2). All participants were right-handed, reported normal or corrected-to-normal vision, and had no motor impairments. Three participants had previous experience with BCIs, and five participants used VR more than twice. Participants were randomly assigned to either the embodied group (N=13) or the non-embodied group (N=13), which served as a control. All participants signed an informed consent before participating in the study in accordance with the 1964 Declaration of Helsinki. </p> <p><strong>Experiment Description:</strong></p> <p>A between-subject design was used to investigate the effect of virtual embodiment priming phase on the subsequent motor-imagery training phase in VR. The experiment comprised four main blocks: (1) equipment setup and instructions (45-60 minutes), (2) resting state EEG recording (4 minutes), (3) inducing or breaking the sense of embodiment in VR (5 minutes), and (4) MI training in VR (15 minutes). The entire experiment lasted approximately 90-120 minutes. Directly after block 3, participants answered a questionnaire that measured their subjective sense of embodiment and physical presence.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active <strong>EEG</strong> electrodes (+3 ACC) with a sampling rate of 500Hz. In addition, <strong>EMG, </strong>and <strong>Temperature</strong> signals (in uV) have been recorded synchronously in a bipolar montage and connected to the EEG amplifier’s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA).<br> </p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>EMG Left</strong> (AUX1): 33<br> <strong>EMG Right</strong>. (AUX2): 34<br> <strong>Temperature</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p> </p> <p><strong>Event codes:</strong></p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>S01</td> <td>Experiment Start</td> </tr> <tr> <td>S02</td> <td>Baseline Start</td> </tr> <tr> <td>S03</td> <td>Baseline Stop</td> </tr> <tr> <td>S04</td> <td>Start Of Trial</td> </tr> <tr> <td>S05</td> <td>Cross On Screen</td> </tr> <tr> <td>S07</td> <td>class1, Left hand </td> </tr> <tr> <td>S08</td> <td>class2, Right hand </td> </tr> <tr> <td>S09</td> <td>Feedback Continuous</td> </tr> <tr> <td>S10</td> <td>End of Trial</td> </tr> <tr> <td>S11</td> <td>End Of Session</td> </tr> <tr> <td>S12</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- GROUP [Control or Embodied]<br> | +---USER #<br> | | +---TASK #<br> | | | +---Resting State<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---Embodiment<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk<br> | | | +---MI<br> | | | | .eeg<br> | | | | .vhdr<br> | | | | .vmrk</p> <p> </p> <p>For demographics and questionnaire data, please contact the authors.</p>
Data deposition for Complex electrophysiological remodeling in postinfarction ischemic heart failure
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 7 of 16
Open the record for dataset details and reuse information.
Data from: Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 5 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 10 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 3 of 16
Open the record for dataset details and reuse information.
Molecular and electrophysiological features of GABAergic neurons in the dentate gyrus reveal limited homology with cortical interneurons
Open the record for dataset details and reuse information.
A selective small-molecule agonist of G protein-gated inwardly-rectifying potassium channels reduces epileptiform activity in a mouse model of tumor associated epilepsy - Thy1-GCaMP Tumor Electrophysiology
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 1 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 11 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 16 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 4 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 9 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 6 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 12 of 16
Open the record for dataset details and reuse information.
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 14 of 16
Open the record for dataset details and reuse information.
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.