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.

datasets available to search

ShareScore release 0.9.0

Dataset results

67 results for “Resting-state”

Learn how ShareScore rates datasets ↗

OpenNeuro56/100

Which multiband factor should you choose for your resting-state fMRI study? The Emory Multiband Dataset

Open the record for dataset details and reuse information.

openCC0Jan 2021View details →

OpenNeuro48/100

A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures

Open the record for dataset details and reuse information.

openCC0Jan 2018View details →

OpenNeuro48/100

An isotropic EPI database for rat brain resting-state fMRI

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

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

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 →

zenodo40/100

The dataset of article "Early Detection of Cognitive Impairment in End-Stage Renal Disease Patients Undergoing Hemodialysis: Insights from Resting-State Functional Connectivity Analysis"

This is a file as dataset of the article "Early Detection of Cognitive Impairment in End-Stage Renal Disease Patients Undergoing Hemodialysis: Insights from Resting-State Functional Connectivity Analysis". It includes fMRI brain imaging data of subjects included in the case group (ESRD group) and healthy control group (HC group).

opencc-by-4.0Oct 2024View details →

zenodo40/100

Dataset from "Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis"

Denoised and Preprocessed data from EEG: First Episode Psychosis vs. Control Resting Task 1 as described in Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis. Original dataset: <a href="https://doi.org/10.18112/openneuro.ds003944.v1.0.1">doi:10.18112/openneuro.ds003944.v1.0.1</a>

opencc-by-4.0Nov 2022View details →

zenodo40/100

Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity

GENERAL INFORMATION This data is described in the following publication:  Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity, Somayeh Shahsavarani1,2,5, David N. Thibodeaux1,5, Weihao Xu1, Sharon H. Kim1, Fatema Lodgher1, Chinwendu Nwokeabia1, Morgan Cambareri1, Alexis J. Yagielski1, Hanzhi T. Zhao1, Daniel A. Handwerker2, Javier Gonzalez-Castillo2, Peter A. Bandettini2,3, Elizabeth M. C. Hillman1,4,6,* Cell Reports (2023): <a href="https://doi.org/10.1016/j.celrep.2023.112527">https://doi.org/10.1016/j.celrep.2023.112527</a> 1. Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA 2. Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA 3. Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA 4. Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA 5. These authors contributed equally 6. Lead contact *Correspondence: elizabeth.hillman@columbia.edu Preprocessing and analysis code that generated / can be used with this data is posted at:  GitHub: <a href="https://doi.org/10.5281/zenodo.7860561">https://doi.org/10.5281/zenodo.7860561</a> DATA OVERVIEW This dataset comprises simultaneous neuronal and hemodynamic data collected using wide-field optical mapping (WFOM) techniques. The data were obtained from head-fixed mice that were allowed to behave spontaneously without any external stimulation. For more detail, please refer to the Readme file.

opencc-by-4.0May 2023View details →

zenodo40/100

Resting-state fMRI data for locating causal hubs of memory consolidation in spontaneous brain network

The mouse fMRI data for the paper "Locating causal hubs of memory consolidation in spontaneous brain network in male mice" published in Nature Communications (DOI: 10.1038/s41467-023-41024-z). This includes longitudinal resting-state fMRI data in mice after behavioural training for 1-Day or 5-Day Active Place Avoidance (APA) task, acquired at post-training day 1 and day 8. Due to the large datasets, each group has been packed into several 2GB zip files. They need to be downloaded into the same folder and unpacked together (e.g. 1-Day APA Post training day 1 has five zip files starting with "1DAPA_PostDay1"). The structural and EPI templates and the ROI labels in the AMBMC atlas space are provided in the AMBMC_label.zip.

opencc-by-4.0Aug 2023View details →

dryad40/100

Data from: Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

Open the record for dataset details and reuse information.

publicMar 2020View details →

zenodo36/100

Data from: The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation

Included are raw neuroimaging and preprocessed neural recording data from "The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation" (see Related Works section; citation will be updated after publication). Please cite this paper if you use any of these data. Refer to the linked github repository for associated code. Neuroimaging data is organized in BIDS format and saved as NIfTI files. We used MION (monocrystalline iron oxide nanoparticle) as a contrast agent. Functional resting state files can be found in the 'func' folder for each imaging session. The final six runs are resting state data (the first two/three are short EPI sequences used to test that MION is present in the brain; all resting state data used in our analyses consist of 300 volumes). The first three of these six runs consist of baseline data with no drug treatment. Four through six are resting state data recorded after I.M. injection of vehicle (2% DMSO in saline), dechloroclozapine (DCZ) or clozapine-N-oxide (CNO).  Neural recording data is separated into LFP data, organized by folder, and putative single units, organized the 'Sorted neurons' folder. LFP data folders are named by subject's intial and date of recording. Single units are labeled according to this same system. All data are stored in .mat format and can be opened in MATLAB. KB2.mat files store timing information: the first event in the KBD2 file indicates the start of baseline, pre-injection data acquisition, and the second event indicates the start of post-injection treatment data. The KB3.mat files contains the timing information of the drug injection. As with the fMRI data, we treated animals with I.M. injection of vehicle, DCZ, or CNO.  Treatment information for both modalities is as follows. Neuroimaging: 2020/03/16 Animal L DCZ 1; 2020/05/27 Animal H vehicle 1; 2020/06/01 Animal L vehicle 1; 2020/06/08 Animal H DCZ 1; 2020/06/22 Animal L DCZ 2; 2020/06/24 Animal H vehicle 2; 2020/07/06 Animal L vehicle 2; 2020/07/08 Animal H DCZ 2; 2021/10/25 Animal L CNO; 2022/01/13 Animal H CNO. Neural recordings: 2022/04/14 Animal H DCZ 1; 2022/04/21 Animal H vehicle 1; 2022/05/12 Animal H DCZ 2; 2022/05/24 Animal H vehicle 2; 2022/06/03 Animal H CNO; 2022/08/18 Animal L vehicle 1; 2022/08/25 Animal L DCZ 1; 2022/09/01 Animal L DCZ 2; 2022/09/08 Animal L vehicle 2; 2022/09/22 Animal L CNO.

opencc-by-4.0Apr 2024View details →

dryad36/100

Coordinates activities of retrosplenial ensembles during resting-state encode spatial landmarks. Part 1 of 2

The brain likely uses off-line periods to consolidate recent memories. One hypothesis holds that the hippocampal output provides a unique, global linking or 'index' code for each memory, and that this code is stored in the cortex in association with locally encoded attributes of each memory. Activation of the index code is hypothesized to evoke coordinated memory trace reactivation thus facilitating consolidation. Retrosplenial cortex (RSC) is a major recipient of hippocampal outflow and we have described populations of neurons there with sparse and orthogonal coding characteristics that resemble hippocampal 'place' cells, and whose expression depends on an intact hippocampus. Using two-photon Ca2+ imaging, we recorded ensembles of neurons in the RSC during periods of immobility before and after active running on a familiar linear treadmill track. Synchronous bursting of distinct groups of neurons occurred during rest both prior to and after running. In the second rest epoch, these patterns were associated with the locations of tactile landmarks and reward. Complementing established views on the functions of the RSC, our findings indicate that the structure is involved with processing landmark information during rest.

opencc-zeroApr 2020View details →

dryad36/100

Coordinates activities of retrosplenial ensembles during resting-state encode spatial landmarks. Part 2 of 2

opencc-zeroApr 2020View details →

zenodo36/100

Multi-echo resting-state fMRI networks of healthy volunteers

The dataset contains the resting-state networks of 16 healthy volunteers following multi-echo combination methods: 1) optimal combination 2) tSNR-weighted combination 3) tCNR-weighted combination (PAID method) 4) second echo only (single-echo) After echo combination by one of the methods (or the second echo) 30 independent components were extracted using group ICA and dual regression. Image format: Gunzipped NIfTI (.nii.gz) Time-series format: text file (.txt) The structure of the uploaded folder: - Layer 1: PilmeyerEtAl_ICA_maps_and_timeseries - main folder - Layer 2 (combination method): OC - optimal combination, SE- second echo, tCNR - temporal contrast-to-noise, tSNR - temporal signal-to-noise - Layer 3: groupICA_desc-XX - contains the group ICA maps and time-series before dual regression, sub-YY - folders for each of the 16 subjects - Layer 4: sub-YY_desc_XX - contains the individual extracted ICA maps and time-series

opencc-by-4.0Aug 2021View details →

zenodo36/100

Resting-state functional connectivity matrices of various unconscious, psychedelic, and neuropsychiatric states.

Functional connectivity matrices of various unconscious, psychedelic, and neuropsychiatric states. Conditions include: N2 Sleep Subaneshesthetic ketamine Deep sedation with propofol Surgical-level propofol anesthesia LSD N2O  ADHD Bipolar disorder Schizophrenia Functional connectivity matrices with and without global signal regression are both included.

opencc-by-4.0Aug 2024View details →

ClinicalTrials.gov36/100

Resting-State Neural Connectivity in Patients With Subjective Tinnitus Without Bother

ClinicalTrials.gov study NCT01049828. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad36/100

Data from: Ultra-slow oscillations in fMRI and resting-state connectivity: Neuronal and vascular contributions and technical confounds

Open the record for dataset details and reuse information.

publicMar 2021View details →

dryad36/100

Coordinates activities of retrosplenial ensembles during resting-state encode spatial landmarks. Part 1 of 2

Open the record for dataset details and reuse information.

publicApr 2020View 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