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.
15
datasets available to search
ShareScore release 0.9.0
Dataset results
15 results for “slow oscillations”
Data set of manuscript entiteld Sigma Oscillations Protect or Reinstate Motor Memory Depending on their Temporal Coordination with Slow Waves
<p>Data set of manuscript entiteld Sigma Oscillations Protect or Reinstate Motor Memory Depending on their Temporal Coordination with Slow Waves</p> <p>EEG files : Brainvision</p> <p>*.dat; *.edf : eeg files of the experimental nap </p> <p>*.vhdr: corresponding header files</p> <p>*.vmrk: corresponding marker files time starting from recording sample</p> <p>*._Pauselog.txt: markers with condition in the stimulation computer time</p> <p>Sleep Scores: </p> <p>Scores by the sleep technician (one file per participants). </p> <p>Surveys:</p> <p>General survey (first screening) and complete survey (for inclusion). Code correspondance is provided in ID_correspondance.txt</p> <p>BehavData:</p> <p>For each participant, a text file with all the cues and responses logged for each tasks. *_raw.txt are present when an issue arised during experiment and a task needed to be re-started. </p> <p> </p>
X-ray diffraction images recorded for Aumonier et al., (2022) Slow protein dynamics probed by time-resolved oscillation crystallography at room temperature, IUCrJ
<p>The present repository contains diffraction images corresponding to 27 distinct datasets collected at room temperature on the ESRF beamline ID30A-3 using an Eiger X 4M detector.</p> <p>Datasets have been uploaded with their original names to maintain the metadata integrity. The two following tables match the original names with those attributed in the supplementary table S1 of Aumonier et al., IUCrJ (2022) (https://doi.org/10.1107/S2052252522009150).</p> <table> <tbody> <tr> <td> <p>Data set name on Zenodo</p> </td> <td> <p>X06_01</p> </td> <td> <p>X12_05</p> </td> <td> <p>X07_02_</p> </td> <td> <p>X06_08</p> </td> <td> <p>X14_06</p> </td> <td> <p>X13_03</p> </td> <td> <p>X08_06</p> </td> <td> <p>X11_05</p> </td> <td> <p>X13_05</p> </td> <td> <p>X06_02</p> </td> <td> <p>X11_01</p> </td> <td> <p>X08_01</p> </td> <td> <p>X14_01</p> </td> <td> <p>X13_01</p> </td> <td> <p>X06_03</p> </td> </tr> <tr> <td> <p>Data set in Aumonier et al. 2022</p> </td> <td> <p>Dark</p> </td> <td> <p>PS2</p> </td> <td> <p>PS2</p> </td> <td> <p>PS3</p> </td> <td> <p>PS4</p> </td> <td> <p>PS5</p> </td> <td> <p>PS6</p> </td> <td> <p>PS7</p> </td> <td> <p>R<sub>2”</sub></p> </td> <td> <p>R<sub>3”</sub></p> </td> <td> <p>R<sub>7”</sub></p> </td> <td> <p>R<sub>10”</sub></p> </td> <td> <p>R<sub>13”</sub></p> </td> <td> <p>R<sub>21”</sub></p> </td> <td> <p>R<sub>35”</sub></p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p>Data set on Zenodo</p> </td> <td> <p>X08_02</p> </td> <td> <p>X11_02</p> </td> <td> <p>X12_02</p> </td> <td> <p>X14_02</p> </td> <td> <p>X13_04</p> </td> <td> <p>X13_02</p> </td> <td> <p>X12_06</p> </td> <td> <p>X06_09</p> </td> <td> <p>X09_04</p> </td> <td> <p>X12_04</p> </td> <td> <p>X06_07</p> </td> <td> <p>X13_07</p> </td> </tr> <tr> <td> <p>Data set in Aumonier et al. 2022</p> </td> <td> <p>R<sub>51”</sub></p> </td> <td> <p>R<sub>62”</sub></p> </td> <td> <p>R<sub>62”</sub></p> </td> <td> <p>R<sub>67”</sub></p> </td> <td> <p>R<sub>72”</sub></p> </td> <td> <p>R<sub>80”</sub></p> </td> <td> <p>R<sub>90”</sub></p> </td> <td> <p>R<sub>130”</sub></p> </td> <td> <p>R<sub>166”</sub></p> </td> <td> <p>R<sub>258”</sub></p> </td> <td> <p>R<sub>630”</sub></p> </td> <td> <p>R<sub>1620”</sub></p> </td> </tr> </tbody> </table> <p>One dataset consists of a master file, four data files and two metadata files.</p>
Data associated with "Up and Down states during slow oscillations in slow wave sleep and different levels of anesthesia"
<p>Data collection uploaded is associated with the publication "Up and Down states during slow oscillations in slow wave sleep and different levels of anesthesia" by the same authors in Frontiers in Systems Neuroscience 2021 DOI: 10.3389/fnsys.2021.609645</p> <p>Raw recordings are in *.smr, readable with Spike 2 (Cambridge Electronics Design <a href="http://ced.co.uk/downloads/latestsoftware">CED Downloads | Latest software</a></p> <p> </p>
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.
Data from: Slow oscillation-spindle coupling predicts enhanced memory formation from childhood to adolescence
Precise temporal coordination of slow oscillations (SO) and sleep spindles is a fundamental mechanism of sleep-dependent memory consolidation. SO and spindle morphology changes considerably throughout development. Critically, it remains unknown how the precise temporal coordination of these two sleep oscillations develops during brain maturation and whether their synchronization indexes the development of memory networks. Here, we use a longitudinal study design spanning from childhood to adolescence, where participants underwent polysomnography and performed a declarative word-pair learning task. Performance on the memory task was better during adolescence. After disentangling oscillatory components from 1/f activity, we found frequency shifts within SO and spindle frequency bands. Consequently, we devised an individualized cross-frequency coupling approach, which demonstrates that SO-spindle coupling strength increases during maturation. Critically, this increase indicated enhanced memory formation from childhood to adolescence. Our results provide evidence that improved coordination between SOs and spindles indexes the development of sleep-dependent memory networks.
Data from: Slow oscillation-spindle coupling predicts enhanced memory formation from childhood to adolescence
Open the record for dataset details and reuse information.
Data from: Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance
Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha–gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions.
Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults
<p>Previously, we demonstrated that precise temporal coordination between slow oscillations (SO) and sleep spindles indexes declarative memory network development (Hahn et al., 2020). However, it is unclear whether these findings in the declarative memory domain also apply in the motor memory domain. Here, we compared adolescents and adults learning juggling, a real-life gross-motor task. We found that improved task proficiency after sleep lead to an attenuation of the learning curve, suggesting a dynamic juggling learning process. We employed individualized cross-frequency coupling analyses to reduce inter and intra-group variability of oscillatory features. Advancing our previous findings, we identified a more precise SO-spindle coupling in adults compared to adolescents. Importantly, coupling precision over motor areas predicted overnight changes in task proficiency and learning curve, indicating that SO-spindle coupling is sensitive to the dynamic motor learning process. Our results provide first evidence that regionally specific precisely coupled sleep oscillations support gross-motor learning.</p>
Data from: Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance
Open the record for dataset details and reuse information.
Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults
Open the record for dataset details and reuse information.
Effects of Synchronised Auditory Stimulations of the Sleep Slow Oscillation on Deep Sleep Quality
ClinicalTrials.gov study NCT02956161. IPD Sharing: YES. Countries: 1. Publications: 0.
Electrical Synchronization of Slow Oscillations to Enhance Deep Sleep
ClinicalTrials.gov study NCT04461769. IPD Sharing: NO. Countries: 1. Publications: 0.
Transcranial Electrical Stimulation (TES) at Slow Oscillation (SO) Frequency During NREM Sleep
ClinicalTrials.gov study NCT02945501. IPD Sharing: NO. Countries: 1. Publications: 0.
Sleep Infra-slow Oscillations III
ClinicalTrials.gov study NCT03681327. IPD Sharing: YES. Countries: 1. Publications: 0.
Infra-slow Oscillations During Sleep
ClinicalTrials.gov study NCT02532608. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.