Find research datasets worth reusing

The .xlsx file contains individual data from all figures / tables of the associated manuscript and each .csv file contains information from one figure / table.   Dataset Fig 2 Table 1. Maximal voluntary contraction force (Newton) from the knee extensor muscles measured before (pre) and after (post) control / hypnosis suggestion Table 2. Maximal voluntary activation level (%) from the knee extensor muscles measured before (pre) and after (post) control / hypnosis suggestion Table 3. Peak doublet force (Newton) evoked from 100 Hz paired stimuli at the knee extensor level measured before (pre) and after (post) control / hypnosis suggestion   Dataset Fig 4 Table 1. Time to task failure (s) of a submaximal isometric contraction performed at 20% maximal voluntary contraction force with the knee extensors for the control session and the hypnosis session   Dataset Fig 5 Table 1. Maximal voluntary contraction force (Newton) from the knee extensor muscles measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session Table 2. Maximal voluntary activation level (%) from the knee extensor muscles measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session Table 3. Peak doublet force (Newton) evoked from 100 Hz paired stimuli at the knee extensor level measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session   Dataset Fig 6 Table 1. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the vastus lateralis muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session Table 2. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the vastus medialis muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session Table 3. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the rectus femoris muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session   Dataset Fig 7 Table 1. Motor evoked potential peak to peak amplitude from the vastus lateralis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 2) and the peak-to-peak M-wave amplitude expressed in mV (Table 3). Table 4. Motor evoked potential peak to peak amplitude from the vastus medialis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 5) and the peak-to-peak M-wave amplitude expressed in mV (Table 6). Table 7. Motor evoked potential peak to peak amplitude from the rectus femoris muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 8) and the peak-to-peak M-wave amplitude expressed in mV (Table 9). Table 10. Short intracortical inhibition peak to peak amplitude from the vastus lateralis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 11) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 12). Table 13. Short intracortical inhibition peak to peak amplitude from the vastus medialis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 14) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 15). Table 16. Short intracortical inhibition peak to peak amplitude from the rectus femoris muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 17) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 18). Dataset Fig 8 Table 1. Rate of perceived exertion (6-20 Borg scale) measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session   Dataset Table 1 Table 1. Motor evoked potential peak to peak amplitude from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 2) and the peak-to-peak M-wave amplitude expressed in mV (Table 3). Table 4. Motor evoked potential peak to peak amplitude from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 5) and the peak-to-peak M-wave amplitude expressed in mV (Table 6). Table 7. Motor evoked potential peak to peak amplitude from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 8) and the peak-to-peak M-wave amplitude expressed in mV (Table 9). Table 10. Short intracortical inhibition peak to peak amplitude from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 11) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 12). Table 13. Short intracortical inhibition peak to peak amplitude from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 14) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 15). Table 16. Short intracortical inhibition peak to peak amplitude from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 17) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 18).   Dataset table 2 Table 1. M-wave peak to peak amplitude (mV) from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion Table 2. M-wave peak to peak amplitude (mV) from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion Table 3. M-wave peak to peak amplitude (mV) from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion

opencc-by-4.0Apr 2018View details →

zenodo44/100

Challenges in Replay Detection by TDLM in Post-Encoding Resting State

Data for the paper "Challenges in Replay Detection by TDLM in Post-Encoding Resting State". This extension of the previous dataset contains the resting state data. For each participant, a 8 minutes resting state was recorded before and after the main experiment (localizer plus learning), and before the final retrieval session. Two files are uploaded per participant, the pre-experiment resting state (RS1) and the post-learning resting state (RS2). All files are MaxFiltered and the head positioning has been realigned using MaxFilter movement correction to the head position during the initial localizer. The localizer data has been previously published and can be downloaded in v1 of this dataset at https://doi.org/10.5281/zenodo.8001755 All relevant information can be found in the related publication. Behavioural data necessary to reproduce the results will be uploaded to GitHub at https://github.com/CIMH-Clinical-Psychology/DeSMRRest-TDLM-Simulation There are markers in the files as follows: ############################################################### ## Port Trigger Table ## Port Code | Meaning ## --------------------------------------------------- ## 0      | don't send trigger ## 10         | start RS session ## 11         | end RS session ## 127    | button press has happened ## 255    | start and end of session ###############################################################

opencc-by-4.0Jun 2023View details →

OpenNeuro40/100

Resting State Perfusion in Healthy Aging

Open the record for dataset details and reuse information.

openCC0Jan 2021View details →

zenodo40/100

Resting-State High-Density EEG using EGI GES 300 with 256 Channels of Healthy Elders, People with Subjective and Mild Cognitive Impairment and Alzheimer's Disease

This repository contains Matlab files including 4 samples of resting-state EEG recording for Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Subjective Cognitive Decline (SCD), and Healthy Controls (HC) using the HD-EEG EGI GES 300. [AD: i108, MCI: i100, SCD: i090, HC: s055]   Participants & Settings In total 230 participants have been recruited from the memory and dementia clinic of the Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD) and the 1st Department of Neurology, U.H. AHEPA, Aristotle University of Thessaloniki, Greece. The full dataset includes: Healthy Controls Elders (60+ years old): 33 participants Subjective Cognitive Decline: 34 participants Mild Cognitive Impairment: 79 participants Alzheimer's Disease: 48 participants Healthy Young (25-40 years old): 36 participants The study was carried out in accordance with the Declaration of Helsinki and received approval by the Scientific and Ethics Committee of GAADRD (No56_27/11/2016), and written informed consent was obtained from all participants prior to their participation in the study. The diagnosis of AD was conducted by a neuropsychiatrist according to their medical history, neuropsychological performance, structural magnetic resonance imaging (MRI), and clinical and neurological examinations. Participants with AD fulfilled the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD, as well as the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria for dementia of Alzheimer’s type (American Psychological Association, 1994). On the other hand, the MCI participants fulfilled the Petersen criteria, while the SCD group met International Working Group-2 guidelines and the recent National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (NI-AA), as well as the SCD-I Working Group instructions.  Resting-State EEG Recording Fifteen-minute resting EEG activity was recorded for all the participants. For the whole duration of the resting state EEG recording, participants were advised to keep themselves relaxed as much as possible, close their eyes and open them after the researcher’s demand, sit still, minimize blinking or mouth movements and let their mind wander. The experimental procedure was monitored by a research assistant aiming to identify cases of horizontal eye movements, continued blinking, or excessive movement by visually inspecting the EEG traces during the experiment. More specifically, an EEG was registered for both resting conditions (eyes open, EO and eyes closed, EC) for at least 2–3 min for each period. EEG Data Acquisition The EEG data were collected by using the EGI 300 Geodesic EEG system (GES 300, CERTH-ITI, Thessaloniki, Greece) with a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz (EGI Eugene, OR). Moreover, the researcher placed the electrodes in accordance with the 256 HCGSN adult 1.0 montage system, while the signals were recorded relative to a vertex reference electrode (Cz), with AFz as the ground electrode with the electrodes’ impedance below 50 kΩ throughout the experimental procedure, as recommended for the high-input impedance amplifier. In detail, the HD-EEG data were analyzed offline in order to detect any artifact, as well as to conduct pre-processing (filtering, segmentation, bad channel replacement) using Net Station 4.3 software (EGI). HD-EEG data were initially filtered with a 5th-order bandpass Butterworth IIR filter of 0.3–30 Hz. Once the segmentation was completed, the detection of artifacts was performed by using the Net Station artifact detection tool for the automatic detection of excessive eye blinking and movement. Afterward, the signals were baseline corrected using 200 msec before the start of the experiment period and average re-referenced to transform them into reference-independent values.   Full Dataset Access More information about the sample dataset and access to the full dataset can be available after request via e-mail: Ioulietta Lazarou BSc, MSc, PhD candidate Neuropsychologist - Clinical Research Associate  Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI) 6th km Charilaou-Thermi Road, P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece E-mail: <a href="mailto:iouliettalaz@iti.gr">iouliettalaz@iti.gr</a>

opencc-by-4.0Dec 2020View details →

zenodo40/100

EEGlass motor-imagery and resting-state data

Pilot acquisition of EEG data during motor-imagery and resting state (eyes-closed) from <a href="https://dl.acm.org/doi/10.1145/3341162.3348383">EEGlass eyeware prototype for ubiquitous brain-computer interaction.</a> There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files. Datasets: <ul> <li>Motor Imagery <ul> <li>EEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)</li> <li>Enobio (data: MI_Enobio.set; header: MI_Enobio.fdt)</li> </ul> </li> <li>Resting State (eyes-closed) <ul> <li>EEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)</li> <li>Enobio (data: EC_Enobio.set; header: EC_Enobio.fdt)</li> </ul> </li> <li>EOG <ul> <li>EEGlass <ul> <li> EOG Up (EOG_U_EEGlass.set, .fdt) </li> <li> EOG Down (EOG_U_EEGlass.set, .fdt) </li> <li> EOG Left (EOG_U_EEGlass.set, .fdt) </li> <li> EOG Right (EOG_U_EEGlass.set, .fdt) </li> </ul> </li> </ul> </li> </ul> Pre-processing: <ol> <li>Bandpass filtering: FIR 1-40 Hz</li> <li>Re-referencing: Common average reference (CAR)</li> <li>Channel locations <ul> <li>EEGlass [1:Nz; 2:TP9; 3:TP10]</li> <li>Enobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]</li> </ul> </li> </ol>   Details from the pilot study can be found below: <blockquote> A. Vourvopoulos, E. Niforatos, M. Giannakos, 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers(UbiComp/ISWC '19 Adjunct). Association for Computing Machinery, New York, NY, USA, 647–652. DOI: https://doi.org/10.1145/3341162.3348383 </blockquote>

opencc-by-4.0Aug 2019View details →

zenodo40/100

The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity

This upload comprises supplementary material and data for the paper "The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity" Albrecht et al. (2015), Human Brain Mapping DOI: 10.1002/hbm.23052 1) The cleaned and group ICA resting state data in EEGLAB format. 2) Basic demographics for the participants. Drug order 1 = placebo first, then dexamphetamine second. Drug order 2 = dexamphetamine first, then placebo second. Gender 1 = Female, Gender 2 = Male. 3) Bayesian hierarchical modelling functions for R and Stan (through rstan). See paper for more details.

opencc-by-nc-sa-4.0Nov 2015View details →

zenodo40/100

Resting-state EEG simulations

Cortical-level activity was generated using a flexible neural mass model framework, named COALIA. This multi-population neural mass model enables the simulation of brain-scale electrophysiological activity while accounting for the macro- (between regions) and micro-circuitry (within a single region) of the brain, with one neural mass representing the local field potential of one Desikan-Killiany atlas region [for details, readers may refer to <a href="https://paperpile.com/c/vi4R1W/FXx0">(Bensaid et al. 2019)</a>]. The simulated cortical networks (DMN and DAN) each included six regions based on the Desikan-Killiany atlas <a href="https://paperpile.com/c/vi4R1W/Rmev">(Desikan et al. 2006)</a> in terms of region parcellation. The DMN consisted of the right and left posterior cingulate cortex (PCC), medial orbitofrontal (MOF) gyrus, and inferior parietal lobe (IPL). Regarding the DAN, this network consisted of the right and left inferior parietal lobe (IPL), caudal middle frontal gyrus (cMFG), and superior parietal lobe (SPL). Activity in the alpha band ([8-12] Hz) was attributed to the regions belonging to reference RSNs, while background activity was assigned to remaining cortical regions. A variability between simulated data segments was introduced at the subject level, as well as at the level of epochs per subject. Each “virtual subject” had different connectivity matrices provided to the model, while each epoch for the same subject had a different input noise (mean =90, standard deviation = 30)  set within the model. More specifically, for each subject, a different fractional anisotropy matrix of the HCP dataset was used <a href="https://paperpile.com/c/vi4R1W/XVSq">(Van Essen et al. 2013)</a>, and the weights corresponding to a RSN-connection were modified and set to a value of (1 ± 20%). A corresponding scaling of the matrices followed in accordance with COALIA’s requisites and the type of each input matrix (inhibitory/excitatory). A total of 50 “virtual subjects”, 4 epochs per subject (i.e., 200 data segments) were simulated; with a duration of 40 seconds each and a sampling rate of 2048 Hz. The time delay between NMMs was determined by the euclidean distance between the centroids of Desikan-Killainy’s regions divided by the velocity of action potentials propagation, which was set as 100 cm/s. Scalp EEG signals can be estimated from simulated cortical activity by solving the forward problem.   <a href="http://paperpile.com/b/vi4R1W/FXx0">Bensaid, Siouar, Julien Modolo, Isabelle Merlet, Fabrice Wendling, and Pascal Benquet. 2019. “COALIA: A Computational Model of Human EEG for Consciousness Research.” Frontiers in Systems Neuroscience 13: 1–18.</a> <a href="http://paperpile.com/b/vi4R1W/Rmev">Desikan, Rahul S., Florent Ségonne, Bruce Fischl, Brian T. Quinn, Bradford C. Dickerson, Deborah Blacker, Randy L. Buckner, et al. 2006. “An Automated Labeling System for Subdividing the Human Cerebral Cortex on MRI Scans into Gyral Based Regions of Interest.” NeuroImage 31: 968–80.</a> <a href="http://paperpile.com/b/vi4R1W/XVSq">Van Essen, David C., Stephen M. Smith, Deanna M. Barch, Timothy E. J. Behrens, Essa Yacoub, Kamil Ugurbil, and WU-Minn HCP Consortium. 2013. “The WU-Minn Human Connectome Project: An Overview.” NeuroImage 80 (October): 62–79.</a>

opencc-by-4.0May 2022View details →

zenodo40/100

Yoga Asana Increases Pre-Frontal Cortex Activity and Reduces Resting State Functional Connectivity

This dataset characterizes changes in the prefrontal cortex (PFC) before, during and after Yoga Asana (physical postures) with the mobile neuroimaging technique of functional near-infrared spectroscopy (fNIRS). Measurements were conducted with twenty-seven healthy adults executing four basic Asanas for 23 minutes with each Asana maintained for 25 -30 seconds. All postures significantly increased PFC activity versus baseline and resting state functional connectivity showed a significant decrease post Yoga Asana. Files 8, 15 and 24 were removed due to poor signal quality. During the measurement process of Asana the following stim marks were used to distinguish between postures: Posture A (Tadasana): A Posture B (Uttanasana): B Posture C (Adho Mukah Svasana): C Posture D (Urdhva Muka Svasana): D Results of the repeated measures ANOVA are presented for each combination of Asana. Those showing a significant difference are highlighted in green in the second to last tab of the file (Final Table). Demographics of volunteers are outlined in the last tab of the excel file (Demographics Volunteers).

opencc-by-4.0Apr 2024View details →

zenodo40/100

EEG recordings during resting-state and the maintenance periods of a spatial working memory task in humans

Scripts used to analyze data for the manuscript submitted for publication in EJN Script_Curve_Fitting_HBM.rtf Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com  We therefore considered this continuous change in power as an extraneous variable yk(x) impacting the measured power spectrum Pow(Ek), and modeled it with a binomial equation that best fit the data, where the coefficients in pi are in descending powers, and the length of p is (n+1), k is trial number (k = 1 to 10): yk(x) = p1 . x2 + p2 . x + p3 In order to statistically compare the topographies between the trials with perfect recall and the trials with failed recall, we subtracted this variable from the mean spectral topographies of each subject and for each electrode by first producing the mean spectral curves of each maintenance trial in the theta and alpha frequency bands, taking into account the IAF, and then calculating the coefficients (p1, p2 and p3) of the binomial equation using the Matlab function polyfit.m. Once the coefficients were determined, this estimate was subtracted from each power spectrum matrix using the following formula: PowFit(Ek) = Pow (Ek) – yk(x)   Script_Perf_Fail_EEG_Power_Spec_HBM.rtf Dr. Hadj Meziane: hbmeziane@gmail.com This script calculates EEG power spectra then compares perf and fail conditions, then plots brain topographies with statical results   Script_Perf_Fail_EEG_Sources_Spec_HBM.rtf Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com This script compares EEG source spectra then compares Perf vs. Fail conditions then plot statistical results (significant voxels) on MRI volume

opencc-by-4.0Aug 2023View details →

dryad40/100

Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

Information processing and communication in neuronal circuits is enabled by dynamic networks of inter-areal coupling of neuronal oscillations in which hubs play a central role for regulation of communication. Oscillations are shaped by interactions between pyramidal cells and interneurons and are locally influenced by neuromodulatory systems. Here, we set out to investigate how sparial variability in neurotransmitter receptor and transporter density influences frequency-specific large-scale networks of phase-synchrony (PS) and amplitude-correlation (AC) in human magnetoencephalography data. We found that node centrality - indexing which individual brain regions function as hubs - covaried positively with GABA, NMDA, dopaminergic, and most serotonergic receptor and transporter densities in lower frequency bands (delta to low-alpha for PS, and delta for AC) and in the gamma band, but negatively in between. These results establish how local microarchitecture influences large-scale connectivity networks of neuronal oscillations in the human brain in frequency- and spatially-specific patterns.

opencc-zeroJul 2024View details →

ScienceDex guides

Understand access before you commit

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