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53 results for “Physiological Signals”

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zenodo44/100

Physiological Signals During Motor Imagery Brain-Computer Interface Training Using Virtual Reality and Haptics

<p><strong>Participant demographics:</strong></p> <p>The sample is consisted by 20 healthy volunteers with a mean age of 24.79 years (SD = 3.54 years).&nbsp; The cohort was 68% male and 32% female.&nbsp; In terms of education, 16% had attended only high school, while 32% had a bachelor&#39;s degree, 42% a master&#39;s degree, and 11% a doctorate. 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>The experiment consisted in having the subjects perform motor imagery of a bimanual rowing task with two individual paddles, one in each hand, under five experimental conditions. Four of these conditions used NeuRow (<a href="https://link.springer.com/chapter/10.1007/978-3-030-27950-9_1"><strong>Vourvopoulos et al. (2016-2019</strong>))</a>&mdash;a VR environment that renders virtual arms from a first-person perspective&mdash;while the other conditions used abstract feedback based on the BCI-Graz paradigm<a href="https://ieeexplore.ieee.org/abstract/document/1214714"> (<strong>Pfurtscheller et al. (2003))</strong></a>. All six conditions and their acronyms are described below:</p> <ol> <li><strong>Motor Imagery(MI)</strong>: The standard motor imagery training, with a fixation cross and directional arrows on a black background guiding the subjects through the experiment.</li> <li><strong>Motor Imagery/Motor Observation (MIMO):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor.</li> <li><strong>Motor Imagery/Motor Observation with Haptics (MIMOHP): </strong>A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a monitor. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD (MIMOVR):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD.</li> <li><strong>Motor Imagery/Motor Observation with VR HMD and Haptics (MIMOVRHP):</strong> A motor imagery training paradigm using NeuRow, with a fixation cross and directional arrows overlaid on the VR environment, which was displayed through a VR HMD. Hand controllers also provided haptic feedback through vibrotactile stimulation.</li> <li><strong>Motor Execution (ME):</strong> A fixation cross and directional arrows were displayed on a black background through a monitor (same as in MI), and guided the subjects through the experiment by having them tap their fingers accordingly. Data from this condition was available only after S07, so only 10 subjects<br> have performed ME.</li> </ol> <p>Finally, this experiment followed a within-subject design, in a randomized order of the conditions to minimize any order effects, while MI and ME conditions acted as control.</p> <p><strong>Equipment:</strong></p> <p>A wireless EEG amplifier (LiveAmp; Brain Products GmbH, Gilching, Germany) was used, with 32 active electrodes(+3 ACC) with a sampling rate of 500Hz. In addition, <strong>ECG, PPG</strong> and <strong>Respiration</strong> signals have been recorded synchronously in a bipolar montage, and connected to the EEG amplifier&rsquo;s AUX input through the Brain Products BIP2AUX adapter.</p> <p>Visual feedback was provided through a monitor in all conditions except in MIMOVR and MIMOVRHP, in which an Oculus Rift CV1 headset (Reality Labs, formerly Facebook, Inc., CA, USA) was used instead. Haptic feedback was provided through the Oculus Rift hand controllers.<br> &nbsp;</p> <p><strong>Channel Indices:</strong></p> <p><strong>EEG</strong>: 1-32<br> <strong>PPG</strong> (AUX1): 33<br> <strong>Resp</strong>. (AUX2): 34<br> <strong>ECG</strong> (AUX3): 35<br> <strong>ACC</strong>: 36-38</p> <p>&nbsp;</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&nbsp;</td> </tr> <tr> <td>S08</td> <td>class2, Right hand&nbsp;</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>&nbsp;</p> <p><strong>Directory tree:</strong></p> <p>ROOT<br> |<br> +--- USER #<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---SESSION #<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---TASK #<br> |&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; +---MI<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---MIMO<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---MIMOHP<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---MIMOVR<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;+---MIMOHPVR<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk<br> |&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; +---ME<br> |&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;|&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; | &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; .eeg<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vhdr<br> |&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;|&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;|&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .vmrk</p> <p>&nbsp;</p> <p><strong>Note: </strong>The first three datasets are from pilot sessions: sub-p01 to p03. From sub-01 to 19, subjects 10 and 11 have been removed due to the lack of markers. Subject sub-13, task MIMOVRHP is missing.</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2022View details →
zenodo40/100

DailySense: a Daily Self-reports and Physiological Signals Sensing Dataset for Subjective Health Research in the Wild

<p><strong>Description:<br></strong>This is a daily self-reports and physiological signals sensing dataset for subjective health research in the wild (<strong><em>DailySense</em></strong>). This is a dataset from a consecutive 14-day experiment in real-life settings composed of smartphone-based subjective psychological evaluation and physiological sensing. A Total of 36 healthy Japanese adult remote workers (mean&plusmn;SD, 35.8&plusmn;7.5; range, 27&ndash;58 years; 21 male and 15 female participants) participated in the experiment through two terms (1st term [1], 18 participants from a Japanese company without rewards; 2nd term, 18 participants from a participant's pool with rewards).</p> <p>The study protocol was approved by the internal review board of Research &amp; Development Group, Hitachi, Ltd., and was conducted in accordance with the Declaration of Helsinki. All participants provided informed consent prior to enrollment in this study. The permission to share the raw data with participants' anonymization was included in this approval and explicitly obtained in that informed consent.</p> <p>This dataset contains the following data:</p> <p>&nbsp;- <strong>pre- and post-term data of<br></strong>&nbsp; &nbsp; - responses to self-reporting questionnaires (i.e., the Japanese versions of NEO-FFI, STAI, CES-D, CFS, PSQI, WHO-QOL, and SF-36v2<sup>&copy;</sup>).<br>&nbsp; &nbsp; - demographics<br>&nbsp; &nbsp; - survey regarding this experiment</p> <p>- <strong>mid-term data of<br></strong>&nbsp; &nbsp; - responses to emotional self-reports (i.e., Affective Slider and I-PANAS-SF) and their behavior in ESM 6 times/day at maximum<br>&nbsp; &nbsp; - responses to subjective health (i.e., degree of fatigue, stress, anxiety, depression, and sleeplessness), wake-up/in-bed times, and work style 1time/day<br>&nbsp; &nbsp; - continuously monitored physiological data (i.e., EDA, PPG, Acc) and event tags obtained by a wristband sensor (E4 wristband, Empatica Inc.) during their waking hours<br>&nbsp; &nbsp; - response profile data estimated using the proposed method<br>&nbsp; &nbsp; - log data of an experience sampling support system (exkuma, Japan Experience Sampling Method Association)</p> <p>Details of data are mentioned in an xlsx file of the root directory. Due to the limitation of questionnaires, descriptions of original instructions of items in each questionnaire are omitted.</p> <p>The details of the experiment are described in [1][2]. Note that, in [1], participant #29 was excluded due to insufficient physiological data quality. In addition, in [2], participant #47 was excluded since he did not complete a personality questionnaire, but #29 was included since he completed responses to all the questionnaires.</p> <p>&nbsp;</p> <p><strong>License:<br></strong>This dataset is made available by <strong>Hitachi, Ltd.</strong> under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p>&nbsp;</p> <p><strong>References:<br></strong>If you use this dataset, please cite the following papers:</p> <p>[1] Shunsuke Minusa, Chihiro Yoshimura, and Hiroyuki Mizuno "Emodiversity evaluation of remote workers through health monitoring based on intra-day emotion sampling," Front. Public Heal., vol. 11, no. August, pp. 1&ndash;12, 2023, doi: <a href="https://doi.org/10.3389/fpubh.2023.1196539" target="_blank" rel="noopener">10.3389/fpubh.2023.1196539</a></p> <p>[2] Shunsuke Minusa, Tadayuki Matsumura, Kanako Esaki, Yang Shao, Chihiro Yoshimura, and Hiroyuki Mizuno, "Response Style Characterization for Repeated Measures Using the Visual Analogue Scale," arXiv preprint <a href="https://arxiv.org/abs/2403.10136" target="_blank" rel="noopener">arXiv:2403.10136</a>, 2024.</p> <p>&nbsp;</p> <p><strong>Contact:<br></strong>If there is any problem, please contact us:</p> <ul> <li>Shunsuke Minusa, <a href="mailto:shunsuke.minusa.hd@hitachi.com" target="_blank" rel="noopener">shunsuke.minusa.hd@hitachi.com</a></li> </ul>

opencc-by-4.0Mar 2024View details →
zenodo40/100

FIG. 3 in Structural, functional, and physiological signals in ichthyosaur vertebral centrum microanatomy and histology

FIG. 3. — Classical sections of ichthyosaur vertebral centra illustrating the intraspecific size variation (interpolated as possible ontogenetic variation): A-D, Stenopterygius sp.; A, B, juvenile vertebra SMNS uncat.; C, D, adult vertebra SMNS uncat.; E-H, Ichthyosauria new taxon A, Middle Triassic, Nevada, LACM 8031; E, F, fetal vertebra LACM DI 158109; G, H, adult vertebra DI 158109; A, C, E, G, transverse (and half-transverse) sections; B, D, F, H, sagittal sections. Scale bars: 5 mm.

opencc-zeroApr 2018View details →
zenodo40/100

FIG. 2 in Structural, functional, and physiological signals in ichthyosaur vertebral centrum microanatomy and histology

FIG. 2. — Virtual (A, B) and classical (C-G) sections of ichthyosaur vertebral centra illustrating the microanatomical types 1 (A-E) and 2 (F, G): A, B, Grippioidea indet., Lower Triassic, Russia, NSM PV 23854; C, D, Grippioidea indet., Middle Triassic, Nevada, LACM uncat. Nevada; D, E, Mixosaurus sp. PIMUZ T 2004; F, Temnodontosaurus sp. half transverse section; G, Eurhinosaurus sp. SMNS 50913 sagittal section; A, C, F, transverse sections; B, D, E, G, sagittal (and half mid-sagittal) sections. Abbreviations: GC, growth center; ET, endochondral territory; PT, periosteal territory. Scale bars: 5 mm.

opencc-zeroApr 2018View details →
zenodo40/100

FIG. 5 in Structural, functional, and physiological signals in ichthyosaur vertebral centrum microanatomy and histology

FIG. 5. — Histological features of ichthyosaur vertebrae: A, Stenopterygius sp. SMNS uncat. Longitudinal section. Numerous Sharpey's fibers at the limit between the periosteal (left) and endochondral (right) territories in PL with gypsum filter; pointed by arrows; B, C, Temnodontosaurus sp. SMNS uncat. parasagittal section showing the compact deposits of parallel-fibered bone (blue) in the outer core of the vertebra and secondary bone (red) in its inner core in PL with gypsum filter (B) and NL (C); D, Temnodontosaurus sp. SMNS uncat. Transverse section showing on the right the layer of rather compact parallel-fibered bone lining the centrum core (NC, notochordal canal), whereas the rest of the centrum is spongious (left) in NL. Scale bars: A, 200 µm; B, D, 1 mm; C, 500 µm.

opencc-zeroApr 2018View details →
zenodo40/100

FIG. 1 in Structural, functional, and physiological signals in ichthyosaur vertebral centrum microanatomy and histology

FIG. 1. — Consensus phylogenetic tree of Ichthyopterygia including (in bold) the taxa sampled for this study; modified from Ji et al. (2016); with associated silhouettes (from McGowan &amp; Motani 2003) and label of the microanatomical type encountered.

opencc-zeroApr 2018View details →
zenodo40/100

Physiological signals during activities for daily life: Dataset

<p>The dataset used in this work is composed by four participants, two men and two women. Each of them carried the wearable device Empatica E4 for a total number of 15 days. They carried the wearable during the day, and during the nights we asked participants to charge and load the data into an external memory unit. During these days, participants were asked to answer EMA questionnaires which are used to label our data. However, some participants could not complete the full experiment or some days were discarded due to data corruption. Specific demographic information, total sampling days and total number of EMA answers can be found in table I.</p> <p>&nbsp;</p> <table align="center"> <thead> <tr> <th scope="col">&nbsp;</th> <th scope="col">Participant 1</th> <th scope="col">Participant 2</th> <th scope="col">Participant 3</th> <th scope="col">Participant 4</th> </tr> </thead> <tbody> <tr> <td>Age</td> <td>67</td> <td>55</td> <td>60</td> <td>63</td> </tr> <tr> <td>Gender</td> <td>Male</td> <td>Female</td> <td>Male</td> <td>Female</td> </tr> <tr> <td> <p>Final Valid Days</p> </td> <td>9</td> <td>15</td> <td>12</td> <td>13</td> </tr> <tr> <td>Total EMAs</td> <td>42</td> <td>57</td> <td>64</td> <td>46</td> </tr> </tbody> </table> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Table I. Summary of participants&#39; collected data.</p> <p>&nbsp;</p> <p>This dataset provides three different type of labels. <em>Activeness</em>&nbsp;and <em>happiness</em>&nbsp;are two of these labels. These are the answers to EMA questionnaires that participants reported during their daily activities. These labels are numbers between <em>0</em> and <em>4</em>.<br> These labels are used to interpolate the mental well-being state according to [1] We report in our dataset a total number of eight emotional states: (1) pleasure, (2) excitement, (3) arousal, (4) distress, (5) misery, (6) depression, (7) sleepiness, and (8) contentment.</p> <p>The data we provide in this repository consist of two type of files:</p> <ul> <li><strong>CSV files</strong>: These files contain physiological signals recorded during the data collection process. The first line of each CSV file defines the timestamp by which data started being sampled. The second line defines the sampling frequency used for gathering the signal. From the third line until the end of the file, one can find sampled datapoints.&nbsp;<br> &nbsp;</li> <li><strong>Excel files</strong>: These files contain the labels obtained from EMA answers. It is indicated the timestamp at which the answer was registered. Labels for <em>pleasure</em>, <em>activeness</em>&nbsp;and <em>mood</em>&nbsp;can be found in this file.&nbsp;</li> </ul> <p><strong>NOTE:&nbsp;</strong>Files are numbered according to each specific sampling day. For example, ACC1.csv corresponds to the signal ACC for sampling day 1. The same applied to excel files.</p> <p>&nbsp;</p> <p>Code and a tutorial of how to labelled and extract features can be found in this repository:&nbsp;<a href="https://github.com/edugm94/temporal-feat-emotion-prediction">https://github.com/edugm94/temporal-feat-emotion-prediction</a></p> <p>&nbsp;</p> <p>References:</p> <p>[1]&nbsp;. A. Russell, &ldquo;A circumplex model of affect,&rdquo; Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980</p>

opencc-by-4.0Mar 2022View details →
zenodo40/100

Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings - The 4th China Physiological Signal Challenge 2021

<p>This dataset is part of the available dataset for <em>Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021</em>, available at&nbsp;https://physionet.org/static/published-projects/cpsc2021/paroxysmal-atrial-fibrillation-events-detection-from-dynamic-ecg-recordings-the-4th-china-physiological-signal-challenge-2021-1.0.0.zip (last accessed today 2022-07-22).</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International Public License:</p> <p>Permissions:</p> <ul> <li>Share &mdash; copy and redistribute the material in any medium or format</li> <li>Adapt &mdash; remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Conditions:</p> <ul> <li> <p>Attribution &mdash; You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> </li> </ul> <p>Limitations:</p> <ul> <li>No additional restrictions &mdash; You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</li> </ul> <p>For more information about the license, check:&nbsp;https://creativecommons.org/licenses/by/4.0/</p> <p>The following modifications were made:</p> <ul> <li>Only the training and test set folders are used</li> <li>Only the files *.hea,&nbsp;*.dat&nbsp;and *.atr are used, being the *.dat and *.atr converted to CSV and compressed in .bz2 format</li> <li>Added the LICENSE.txt file as required.</li> </ul>

opencc-by-4.0May 2021View details →
zenodo40/100

EmoPairCompete - Physiological Signals Dataset for Emotion and Frustration Assessment under Team and Competitive Behaviours

<p>Please refer to the documentation at: https://github.com/DTUComputeStatisticsAndDataAnalysis/EmoPairCompete</p>

opencc-by-4.0May 2024View details →
zenodo40/100

GamER: Gameplay Physiological Signal Dataset for Emotion Recognition

<p>A dataset of physiological signals (EEG, ECG, EDA, EOG and Respiration) collected from 48 subjects while they were playing simple games designed to elicit emotional responses.</p> <p>Data were collected using the Biosignals PLUX Researcher kit, which allows up to 10 hours of signal recordings at up to 3kHz sampling rate and 16-bit resolution per channel, while recording data from up to eight sensors simultaneously.</p> <p>The dataset consists of 3 files:</p> <ol> <li>data.zip: Contains the physiological signals. They are organized in 1 folder per subject, which contains the recorded data in .h5 and .txt format as provided by the Researcher kit.</li> <li>emotion_annotation.csv: Contains the emotion annotation that was produced by a psychologist based on the subjects self-reports and by examining the video of the data collection process (the videos are not included for privacy reasons)</li> <li>video_offsets.csv: Additional information for synchronizing the video-based annotations with the signals.</li> </ol> <p>For more information, refer to the accompanying publication.</p> <p><em>Disclaimer: All subjects have provided written consent for the publication of their anonymized physiological signals.</em></p>

opencc-by-4.0Jul 2023View details →
zenodo36/100

Dataset associated with "Constraints on the subsecond modulation of striatal dynamics by physiological dopamine signaling"

<p>This repository contains behavioral data, dLight photometry measurements, smoothed spiking data from recorded striatal neurons, and miniscope recordings. Text files with further explanation are provided to assist with replication of the main analyses. Matlab code to perform key analyses can be found in the accompanying GitHub repository (https://github.com/sotmasman/Dopamine-constraints)</p>

opencc-by-4.0May 2024View details →
dryad36/100

Data from: Dialogues in colour and behaviour: Integration of complex signalling traits and physiology

Open the record for dataset details and reuse information.

publicMay 2025View details →
dryad36/100

Time-integrated δ²H in n-alkanes and carbohydrates from boreal needles reveal intra-annual physiological and environmental signals

Open the record for dataset details and reuse information.

publicJan 2025View details →
zenodo32/100

Bioactive Catalytic Nanocompartments Integrated into Cell Physiology and Their Amplification of a Native Signaling Cascade

<p>Data underlying the figures in the publication &ldquo;Bioactive Catalytic Nanocompartments Integrated into Cell Physiology and Their Amplification of a Native Signaling Cascade&rdquo;, published in <em>ACS Nano,</em> <strong>2020</strong>, 14, 9, 12101&ndash;12112.</p> <p><a href="https://pubs.acs.org/doi/10.1021/acsnano.0c05574">https://pubs.acs.org/doi/10.1021/acsnano.0c05574</a></p> <p>Table of contents:</p> <p><strong>1. Figure 1</strong>; Zip file containing the TEM micrographs, and numerical data for <em>Figure 1</em>.</p> <p><strong>2. Figure 2</strong>; Zip file containing the numerical data for the activity studies of <em>Figure 2</em>.</p> <p><strong>3. Figure 3</strong>; Zip file containing the numerical data for the activity studies of <em>Figure 3</em>.</p> <p><strong>4. Figure 4</strong>; Zip file containing the numerical data for <em>Figure 4</em>.</p> <p><strong>5. Figure 6</strong>; Zip file containing the numerical data for <em>Figure 6</em>.</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2021View details →
zenodo32/100

Figure 10. Reference topology with mapped dermal sculpture characters showing a phylogenetic signal, continued. A, character 10. B, character 11. C, character 12. For character 12 in Sculpture and vascularization of dermal bones, and the implications for the physiology of basal tetrapods

Figure 10. Reference topology with mapped dermal sculpture characters showing a phylogenetic signal, continued. A, character 10. B, character 11. C, character 12. For character 12, the coloration is similar to that in Figure 9, whereas for character 10 (four character states) and 11 (three character states), the lightest shading refers to character state 1, and the increasingly darker shadings refer to the ascending character states. For definition of characters, see Appendix 2.

opennotspecifiedJul 2010View details →
zenodo32/100

A multivariate physiological model of vagus nerve signalling during metabolic challenges in anaesthetised rats for diabetes treatment

<p>Raw recordings that support the findings of the paper. In the intact recordings, there may be more than one recoridng corresponding to the same animal. For ligated recordings,&nbsp;odd numbers correspond to&nbsp;efferent and even numbers to&nbsp;afferent recordings.</p>

opencc-by-4.0Jul 2023View details →
ClinicalTrials.gov32/100

Elucidating Kisspeptin Physiology by Blocking Kisspeptin Signaling

ClinicalTrials.gov study NCT01438073. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Setting up a Warehouse of Physiological Data and Biomedical Signals in Adult Intensive Care

ClinicalTrials.gov study NCT02893462. IPD Sharing: YES. Countries: 1. Publications: 3.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

Developing a Treatment Clustering System for Obstructive Sleep Apnea Using Polysomnographic Physiological Signals

ClinicalTrials.gov study NCT06512779. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Ultrasound and Respiratory Physiological Signals in Lung Diseases

ClinicalTrials.gov study NCT06068647. IPD Sharing: NO. Countries: 1. Publications: 8.

closedIPD-NOFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record