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.
64
datasets available to search
ShareScore release 0.9.0
Dataset results
64 results for “ripples”
Data for: Inhibition is a prevalent mode of activity in the neocortex around awake hippocampal ripples in mice
<p>Coordinated peri-ripple activity in the hippocampal-neocortical network is essential for mnemonic information processing in the brain. Hippocampal ripples likely serve different functions in sleep and awake states. Thus, the corresponding neocortical activity patterns may differ in important ways. We addressed this possibility by conducting voltage and glutamate wide-field imaging of the neocortex with concurrent hippocampal electrophysiology in awake mice. Contrary to our previously published sleep results, deactivation and activation were dominant in post-ripple neocortical voltage and glutamate activity, respectively, especially in the agranular retrosplenial cortex (aRSC). Additionally, the spiking activity of aRSC neurons, estimated by two-photon calcium imaging, revealed the existence of two subpopulations of excitatory neurons with opposite peri-ripple modulation patterns: one increases and the other decreases firing rate. These differences in peri-ripple spatiotemporal patterns of neocortical activity in sleep versus awake states might underlie the reported differences in the function of sleep versus awake ripples.</p>
Movies of the ripple phase in DPPC bilayers
<p>This was a cover article in the Biophysical Journal:</p> <ul> <li><strong>Cover article:</strong> <a href="https://doi.org/10.1016/j.bpj.2022.11.024"><em>Elucidating Lipid Conformations in the Ripple Phase: Machine Learning Reveals Four Lipid Populations</em></a></li> <li><strong>Biophys J Blog:</strong> <a href="https://www.biophysics.org/blog/ripple-me-this-elucidating-lipid-conformations-in-the-ripple-phase"><em>Ripple Me This: Elucidating Lipid Conformations in the Ripple Phase</em></a></li> </ul> <p><strong>Reference:</strong></p> <ul> <li><em>Elucidating Lipid Conformations in the Ripple Phase: Machine Learning Reveals Four Lipid Populations</em>. Davies, Matthew; Reyes-Figueroa, A. D.; Gurtovenko, Andrey A.; Frankel, Daniel; <a href="https://www.researchgate.net/profile/Mikko-Karttunen-2">Karttunen, Mikko</a>. <em>Biophys. J.</em> 122, P442-450 (2023). DOI link: <a href="https://doi.org/10.1016/j.bpj.2022.11.024">https://doi.org/10.1016/j.bpj.2022.11.024</a></li> </ul>
Effectiveness of Ripple Mapping in Atrial Tachycardia Ablation
ClinicalTrials.gov study NCT02451995. IPD Sharing: NO. Countries: 1. Publications: 3.
Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe
Open the record for dataset details and reuse information.
Data for: Inhibition is a prevalent mode of activity in the neocortex around awake hippocampal ripples in mice
Open the record for dataset details and reuse information.
LES-DPM simulation dataset for oscillatory flow over a mobile, rippled bed
Open the record for dataset details and reuse information.
Data from: Spatiotemporal patterns of neocortical activity around hippocampal sharp-wave ripples
Open the record for dataset details and reuse information.
Raw traces of HC ripple
<p>For the PDF: Randomly selected 10 learning ripple traces, 10 sleep ripple traces and 10 spindle traces for each subject. The red line indicates the occurrence of the ripple/spindle.<br>For the .mat file: Two tables (contains information of ripple) and the raw LFP of hippocampus. The data was exported by Matlab 2019b.</p>
dataset and code for "Awake ripples enhance emotional memory encoding in the human brain"
Open the record for dataset details and reuse information.
Ripple marks in Sandstone
**SHCMS:G.12836** **Sandstone** Triassic sandstone slab from the Grinshill area, Shropshire. This specimen shows numerous ripple markings formed by the flowing water that the sediments were deposited in. Age: approx 225 million years. Length 45cm Width 19cm Depth 4cm. Imaged using an Artec spider scanner and processed using Artec studio 12. If you like this model or any others we produce we'd love to hear from you and how you've used them. Source: Objaverse 1.0 / Sketchfab
Granule ripples in the Kumtagh Desert, China: morphological and sedimentary characteristics, and development processes
<p>1. Observation of composition and morphology</p> <p>First, we selected typical granule ripples of different sizes and development stages at each observation site, and sampled the surface particles (to a depth of 1 cm) on the ridges of 85 granule ripples by scraping the surface with a steel ruler. The sample weight averaged 428 g (with values ranging between 249 and 697 g). We determined the grain-size distribution using a set of 15 standard sieves with a mesh size ranging from 0.0565 mm to 20 mm. We used the parameters proposed by Folk and Ward (1957) to calculate the grain size characteristics: the cumulative grain size of the 20 percentile (P20), and the average grain diameter, sorting coefficient, skewness, and kurtosis. Here, P20 represents the particle size corresponding to the cumulative distribution rate of 20% in the cumulative particle-size distribution diagram.</p> <p>Second, we defined ripple morphological characteristics (the wavelength, ripple height, ripple index, and symmetry coefficient) for 142 granule ripples by measuring their wavelength, ripple height, and ridge position. The ripple index (<em>Z</em>) equaled the ratio of the wavelength to the ripple height. The symmetry coefficient equaled the ratio of the horizontal windward slope length to the horizontal leeward slope length.</p> <p>Wavelength represents the horizontal distance between two crests, and the ripple height is the perpendicular height from the trough to the crest.</p> <p>We also excavated vertical profiles of five typical granule ripples with different sizes, and measured the stratification thickness for one ripple with a wavelength of 5 m, for a vertical profile whose depth was 60 cm. Based on the different grain size characteristics, we identified five strata and therefore obtained five sediment samples with different depths, and analyzed the vertical distribution of particle sizes in the sediment.</p> <p>We installed HOBO U30 field anemometers (Onset Computers, Bourne, MA, USA) at a height of 2 m and installed an eight-direction sand trap in the two areas in January 2011 to obtain the wind speed, wind direction, and the single-width sediment transport for a full year. The sensor type of wind speed and direction was S-WSET-A. The collection frequency for the wind speed and direction was 10 minutes, and the resolution of the wind speed and direction were 0.38 m/s and 1.4°, respectively. It is worth mentioning that Onset anemometers have a 5-degree window where they cannot measure wind direction, between 355 and 0 degrees. Unlike sand ripples, granule ripples are not particularly sensitive to wind direction, and these wind direction data only provide a regional main direction for the research, so a 5-degree window has little impact. The collection frequency of the sediment transport rate was 1 month, and the resolution was 10 g. The width and height of each collecting port of the sand trap were 2 cm and 1.2 m, respectively.</p> <p>2. Wind tunnel experiment to detect the gravel threshold velocity</p> <p>We performed a wind tunnel simulation experiment to test the impact threshold velocity of the gravels. We used the high-speed wind tunnel at the High Speed Railway Construction Technology National Engineering Laboratory of Central South University in Changsha, Hunan Province in the validation test. We used surface sediments from granule ripples collected at our study area, and screened these sediments through 12 sieves with a mesh size ranging from 1.6 to 31.5 mm. We used the sieved material to create 10 different test surfaces with an average particle size ranging from 1.8 to 25.8 mm, which we subsequently bombarded with saltating particles from an upwind source. The upwind supply rate for the impacting particles was 1.2 kg/min, and their particle size ranged from 0.8 to 1.0 mm, with an average size of 0.9 mm, in all tests. The impacting particles were also obtained from the surface sediments of the granule ripples and were obtained by screening. The gravel bed surface was 80 cm long (parallel to the long axis of the wind tunnel), 40 cm wide, and 3 cm deep with its upper surface level with the wind tunnel floor. To increase the roughness of the surface, we glued a layer of coarse sand with a mean particle size of 0.9 mm that was 4.2 m long by 40 cm wide to the floor of the wind tunnel immediately upwind of the gravel bed.</p> <p>We observed the particles with a high-speed camera in the wind tunnel to determine the type of motion (vibration, roll, obvious roll, saltation, obvious saltation). The wind velocities were measured using a pitot tube at 10 heights (6, 7.5, 13, 21, 41, 83, 123, 161, 220, and 242 mm above the gravel bed).</p> <p>3. Measurement of the granule ripple migration rate</p> <p>We chose a 25-m<sup>2</sup> area at the yardang site, and monitored the migration rate of some smaller typical granule ripples from 11 October 2011 to 15 October 2012 using six marking pins, at an east–west spacing of 5 m between pins. The average wavelength of these granule ripples was 1.6 m and the average height was 0.09 m. The mean grain size in the ridge was 2.08 mm, and the maximum grain size was 6.5 mm. We also chose 11 larger granule ripples (5 to 9 m in wavelength, 30 to 50 cm in height, granule size in the ridge of 6 to 10 mm) that were continuously distributed at the yardang site, and measured their migration rate over an 8-year period from 2011 to 2019 by using marking pins. Based on the migration rate, we applied the equation developed by Jerolmack et al. (2006) to calculate the sediment creep flux (<em>q</em><sub>c</sub>) at the ridge of the granule ripples:</p> <p><em>q</em><em><sub>c</sub></em> = (1-<em>p</em>) <em>ρ</em><em>cH </em>/ 2</p> <p>where<em> p</em> is the porosity of the ripple sediment (30%), ρ is the particle density (2650 kg m<sup>-3</sup>), <em>c</em> is the ripple migration rate (m/year), and <em>H</em> is the ripple height (m).</p>
Simulations of Pulsed Over-Pressure Jets: Formation of Bellows and Ripples in Galactic Environments
<p>Movies in the form of animated gifs.</p> <p>Corresponding to figures, tables and notation of paper submitted to MNRAS with the same title.</p> <p>Movies of jets exiting a circular nozzle with an overpressure K and density E relative to the ambient medium. </p> <p>The Mach number is set to 2 and an initial ramp up inspeed over 10 time units.</p> <p>All simulations run to 200 time units.</p> <p>These are Mach 2 jets with superimposed velocity pulsations with pulse period P and amplitude V-1( so V1.4 is 40% and V2 is 100% relative amplitude) A ramp up of the velocity from 0 is applied over an initial period R. Adiabatic gas with specific heat ratio of 5/3. </p> <p>The animated graphs called PROFILES here are radial cross-cuts of the physical parameters as a function of time.</p> <p> </p> <p>The pressure movies are collated into four streams according to the over-pressure and density.</p> <p>Within each zipped folder are movies with pulse periods of 2, 10 and 40 time units.</p> <p> </p> <p>The fixed speed movies are non-pulsed and both density and pressure movies are included.</p> <p> </p> <p>The preview movie is chosem to illustrate the ripples in the environment.</p> <p> </p> <p> </p> <p> </p>
Ripple: A Long-Sighted Self-Adaptation Approach to Retrain Machine-Learning-Enabled Systems
<p>Data files required to reproduce the results of paper "Ripple: A Long-Sighted Self-Adaptation Approach to Retrain Machine-Learning-Enabled Systems" submitted to ICSME 2025</p>
Bow shock ripples and their modulation of whistler wave packets: MMS observations
<p>Dataset for the paper "Bow shock ripples and their modulation of whistler wave packets: MMS observations".</p> <p>The data are for the Figures 1 and 2 in the manuscript.</p> <p>These files can be processed by Matlab with the help of the IRFU-matlab package, which is available at https://github.com/irfu/irfu-matlab.</p> <p> </p>
Data for "Mid-air Collisions Control the Wavelength of Aeolian Sand Ripples"
<p>The results of the numerical simulations mentioned in the paper. </p> <p>The package "ripple formation" contains the bed surface data of the ripple formation simulations. These cases are simulated in a 1m x 0.01m x 0.3m cuboid domain. The initial bed is a flat granular surface of 0.02m in thickness. The density of the fluid rho_f=1.263 kg/m^3; the density of particles rho_p=2650 kg/m^3; the particle diameter d=2.5e-4 m; the kinetic viscosity of the fluid nu=1.49e-5 (m^2)/s. All the settings are based on the wind-blowing sand situation of earth environment. Different wind velocities are tested. "u3, u35, u4, u45..." represent the wind velocity u*=0.3, u*=0.35, u*=0.4, u*=0.45, etc.</p> <p>Variables in the surface.plt file:</p> <p>PX: location in the fluid direction. PY: location in the transverse direction. PZ: bed surface elevation at (PX, PY). DP: not used in this paper.</p> <p>The packages with prefix "preripple-" contain the particle information derived from the pre-rippled simulations. All the settings of them are similar to those in the ripple formation simulations. The right part of the package name declares the changed setting. For example, package "preripple-d200" contains the cases run with a particle diameter d=200 µm (or 2e-4 m), "preripple-rho_f05" contains the cases run with a fluid density rho_f=0.5 kg/m^3. For each run, the wave number of the pre-rippled surface varies every 20 seconds from k=50 to k=500.</p> <p>Variables in the particle_loc.plt file:</p> <p>XP, YP, ZP: particle location. DP: particle diameter. UP, VP, WP: particle velocity. FK: particle flight distance. FZ: particle height from the bed surface. FH: maximum particle height from the bed surface. FG: mid-air colliding counter. FT: particle flight duration time.</p>
RIPPLE MARKS (SIN TEXTURA 2)
*Ripple marks* (marcas de oleaje) sobre cuarcitas del Ordovícico en el sector del Paraje Natural de la [Cascada de la Cimbarra](https://skfb.ly/6RBXX) (provincia de Jaén, España). Se han suprimido las texturas para apreciar mejor los trenes de ondas. Centro de Estudios Avanzados de Ciencias de la Tierra de la Universidad de Jaén. CEAC-T UJA 2019 Source: Objaverse 1.0 / Sketchfab
Ripple Effect of Lifestyle Intervention During Pregnancy on Partners' Weight
ClinicalTrials.gov study NCT01770028. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Using Ripple Mapping to Guide Substrate Ablation of Scar Related Ventricular Tachycardia.
ClinicalTrials.gov study NCT02216760. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Contribution of Fast-ripples to the Improvement of the Neurosurgical Management of Drug-refractory Epilepsy
ClinicalTrials.gov study NCT06105645. IPD Sharing: NO. Countries: 1. Publications: 1.
Ripple Mapping Guided Ablation of Ischaemic Ventricular Tachycardia.
ClinicalTrials.gov study NCT03997201. IPD Sharing: NO. 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.