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64 results for “ripples”
R-CAUSTIC: Rippling CAUSTICs underwater Image dataset
<p><strong>Description</strong></p><p>Rippling caustics seem to be the main factor degrading the underwater RGB image quality and affecting the image- based 3D reconstruction process in very shallow waters. These effects are adversely affecting image matching algorithms by throwing off most of them, leading to less accurate matches and causing issues in the Simultaneous Localization and Mapping (SLAM) based navigation of the Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles (AUV) on shallow waters. Also, they are the main cause for dissimilarities in the generated textures and orthoimages. In order to fill the gap in the literature regading underwater rippling caustics imagery with real ground truth and reference images, the first real-world underwater caustics benchmark dataset which contains 1465 underwater images is presented. Together with the RGB imagery, the corresponding generated ground truth images are delivered for facilitating the training and testing of machine learning and deep learning methods for image classification. R-CAUSTIC dataset also provides the necessary data to evaluate, at least to some extent, the performance of 3D reconstruction approaches. Data were acquired using a GoPro Hero 4 Black action camera with image dimensions of 4000 x 3000 pixels, focal length of 2.77mm and pixel size of 1.55μm and a tripod. Action cameras are widely used for underwater image acquisition. The dataset was captured in near-shore underwater sites at depths varying from 0.5 to 2m. No artificial light sources were used. Due to the wind, the turbulent surface of the water created dynamic rippling caustics on the seabed. In total 1465 RGB images were collected, separated in 7 different datasets; five of them containing stereo images, one of them tri-stereo images and one consists of multi-stereo imagery acquired in 7 different camera poses.</p><p> </p><p><strong>Publication</strong></p><p>The paper is availbale in Open Access here: https://ieeexplore.ieee.org/document/10172291</p><p><strong>If you use this dataset please cite it as R-CAUSTIC</strong> [Reference].<br>[Reference]: <strong>P. Agrafiotis, K. Karantzalos and A. Georgopoulos, "Seafloor-Invariant Caustics Removal From Underwater Imagery," in </strong><i><strong>IEEE Journal of Oceanic Engineering</strong></i><strong>, vol. 48, no. 4, pp. 1300-1321, Oct. 2023, doi: 10.1109/JOE.2023.3277168.</strong></p><p>BibTeX:</p><p>@ARTICLE{10172291, author={Agrafiotis, Panagiotis and Karantzalos, Konstantinos and Georgopoulos, Andreas}, journal={IEEE Journal of Oceanic Engineering}, title={Seafloor-Invariant Caustics Removal From Underwater Imagery}, year={2023}, volume={48}, number={4}, pages={1300-1321}, doi={10.1109/JOE.2023.3277168}}</p><p> </p>
Multiple Evolution Modes of Megaripples in the Qaidam Basin and Implications for Ripple-Like Aeolian Landforms on Mars
<p>The dataset includes wind regime data for Golmud, Sebei, and the west bank of the Narin Gol River in the Qaidam Basin, as well as sediment grain size and morphological parameters of the megaripples. In addition, we provide R language source code for data processing and visualization.</p><p>The primary directory contains the data and the source code in the R language. The data includes sediment grain size, morphological parameters and wind regime analysis data of megaripples. Modifying the working path and installation package is necessary to call the R source code for data loading.</p>
Graph Theoretical Measures of Fast Ripple Networks Support the Epileptic Network Hypothesis
<p>MongoDB JSON files of the (high-frequency oscillation) HFO and electrode databases used for this study and others.</p>
Data related to: Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection, Norman et al. (2021)
<p>This dataset contains intra-cranial EEG recordings and analysis code related to the paper: "Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection" by Norman et al. (https://doi.org/10.1016/j.neuron.2021.06.020)<br> The study investigates the role of hippocampal ripples in the human brain during retrieval of recent and remote autobiographical memories and semantic facts. The intracranial recordings underwent standard preprocessing as described in the paper and were stored in EEGLAB datasets. The analysis code that accompanies the dataset implements the main analyses described in the paper.</p> <p>The dataset includes the following zip files:</p> <ul> <li>iEEG data and main analysis code: <ul> <li>“Norman_et_al_2021_iEEG_data_and_code_1.zip" </li> <li>“Norman_et_al_2021_iEEG_data_and_code_2.zip" </li> </ul> </li> <li>Patients' anatomical data: <ul> <li>“Norman_et_al_2021_Freesurfer.zip”</li> </ul> </li> <li>Additional toolboxes (developed by others): <ul> <li>“MATLAB_toolboxes.zip”</li> </ul> </li> </ul> <p>The code is written primarily in Matlab (version R2018b) and runs on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM. Matlab's Signal Processing Toolbox is required, as well as EEGLAB, Unfold toolbox, and several other open-source toolboxes.</p>
Ripple effects in a communication network: Anti-eavesdropper defence elicits elaborated sexual signals in rival males
<p>Emitting conspicuous signals into the environment to attract mates comes with the increased risk of interception by eavesdropping enemies. As a defence, a commonly described strategy is for signallers to group together in leks, diluting each individual's risk. Lekking systems are often highly social settings in which competing males dynamically alter their signalling behaviour to attract mates. Thus, signalling at the lek requires navigating fluctuations in risk, competition, and reproductive opportunities. Here, we investigate how behavioural defence strategies directed at an eavesdropping enemy have cascading effects across the communication network. We investigated these behaviours in the túngara frog (<em>Engystomops pustulosus</em>), examining how a calling male's swatting defence directed at frog-biting midges indirectly affects the calling behaviour of his rival. We found that the rival responds to swat-induced water ripples by increasing his call rate and complexity. Then, performing phonotaxis experiments, we found that eavesdropping fringe-lipped bats (<em>Trachops cirrhosus</em>) do not exhibit a preference for a swatting male compared to his rival, but females strongly prefer the rival male. Defences to minimize attacks from eavesdroppers thus shift the mate competition landscape in favour of rival males. By modulating the attractiveness of signalling prey to female receivers, we posit that eavesdropping micropredators likely have an unappreciated impact on the ecology and evolution of sexual communication systems.</p>
MODEX: Ripple Geometry Data
<p>Processed ripple geometry data from the MODEX (Morphological Diffusivity EXperiment - PI: Dr. Matthieu A. de Schipper) experiment. For more detail, refer to the README (<a href="../api/records/10864977/draft/files/MODEX_RippleGeometryData_README.pdf/content" target="_blank" rel="noopener noreferrer">MODEX_RippleGeometryData_README.pdf</a>).</p> <p>This data serves as a reference for the following paper:</p> <p>Lee, S.B., Wengrove, M.E., de Schipper, M.A., Kleinhans, M.G., Ruessink, G., and Hopkins, J. Observation and Prediction of Sand Ripple Geometry on a Sloped Bed Under Varying Combined Wave-Current Flows.</p>
Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall
<p>These are the data and code for the article 'Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall.' Please cite this article when using these data or code.</p>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
Text-fig. 7. Vertical polished sections of samples from selected layers. a: Layer No. 2, completely bioturbated, collection of the Czech Geological Survey (abbr. BK), BK 7; b: Layer No. 3, low: nearly completely bioturbated, upper: cross- to ripple bedding, weakly bioturbated, BK 6; c: Layer No. 7, incompletely bioturbated siltstone/mudstone, BK 5; d: Layer No. 8, low: totally bioturbated background with Zoophycos ichnofabric, upper: spotted, completely bioturbated siltstone, BK 4; e: Layer No. 8, low: in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 7. Vertical polished sections of samples from selected layers. a: Layer No. 2, completely bioturbated, collection of the Czech Geological Survey (abbr. BK), BK 7; b: Layer No. 3, low: nearly completely bioturbated, upper: cross- to ripple bedding, weakly bioturbated, BK 6; c: Layer No. 7, incompletely bioturbated siltstone/mudstone, BK 5; d: Layer No. 8, low: totally bioturbated background with Zoophycos ichnofabric, upper: spotted, completely bioturbated siltstone, BK 4; e: Layer No. 8, low:
Ripple Complex Experiments data set at CIEM large scale wave flume within Hydralab + project.
<p>The RIPCOM experiments (RIPple COMplex experiments) are presented in order to study the ripple growth conditions on large wave flume tests under fine unimodal, coarse unimodal and mixed sands conditions. The main objectives of the experiments is to improve and understand the protocols to perform mixed sediment experiments within the ripple regime and use/improve the equipment developed at Task 9.1 of the COMPLEX Joint Research Activity within Hydralab+. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona.</p> <p>The experimental plan is divided in three steps:</p> <p>1. Find the optimum wave conditions that ensure ripples (based on measured velocities and previous literature studies) on the study area. Test the targeted waves with unimodal fine sediment (d 50 =0.250 mm) and measure the obtained ripples under the tested conditions. From the obtained measurements, the waves to be used on the next two steps are selected in order to fix the best conditions to produce ripples within the experimental constrains.</p> <p>2. The 13 upper cm of the fine sediment is removed and replaced by the coarser sediment (d 50 =0.545 mm). Once that is done the selected waves to be tested are reproduced and the bottom bedforms are measured.</p> <p>3. Mix both sediments fine and coarser sand homogeneously in order to repeat the selected wave conditions and measure the ripples growth and evolution under mixed sediment conditions.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p>
Simulation of ripple oscillations in a large interneuron network under different levels of constant external drive
<p>Example simulation to be used with code on GitHub repository: https://github.com/NatalieSchieferstein/interneuron_ripples_with_ifa.git .</p><p>Simulation data was generated using pypet (pypet.readthedocs.io/) and Brian2.</p><p>Code and simulation data are Supplement to publication: 10.1101/2023.01.30.526209 .</p><p> </p>
Ripple effects in a communication network: Anti-eavesdropper defence elicits elaborated sexual signals in rival males
Open the record for dataset details and reuse information.
Human Ripples
<p>This dataset contains the data files and analysis code related to sharp wave/ripples recorded using intracranial electrodes from human brain.</p>
Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans
<h3>Contact:</h3><h3>Ilya Verzhbinsky</h3><h3>ilya@health.ucsd.edu</h3><p> </p><p>This is the processed data used to generate the results in the manuscript:</p><p>Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans. <i>PNAS</i> (2023).</p><p>To analyze this data, please first access the code at the following repository: <a href="https://github.com/iverzh/coripple-prediction"><strong>https://github.com/iverzh/coripple-prediction</strong></a></p><p>All downloaded zip files should be uncompressed and placed in a directory named <i>out/ </i>in the <i>CoRipplePredictionPNAS/</i> folder.</p><p> </p><p> </p><p> </p>
Wave-like global economic ripple response to Hurricane Sandy - Data supplement
<p>This data set includes the raw data for the figures of the article "Wave-like global economic ripple response to Hurricane Sandy" (https://doi.org/10.1088/1748-9326/ac39c0).</p>
Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe
<p>Direct brain recordings have provided important insights into how high-frequency activity captured through intracranial EEG (iEEG) supports human memory retrieval. The extent to which such activity is comprised of transient fluctuations that reflect the dynamic coordination of underlying neurons, however, remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that fast oscillations within the previously identified 80–120 Hz ripple band contribute to broadband high-frequency activity in the human cortex. These ripple oscillations exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human temporal lobe is organized into transient bouts of ripple oscillations that reflect underlying bursts of spiking activity.</p>
Shapefiles for Glen Torridon Bedrock Ridges, Ripples, Transverse Aeolian Ridges, Wavelength, and Topographic Profiles
<p>Zipfiles containing polyline shapefiles produced with ESRI's ArcMap 10.6 tracing the location and extent of ridges and transverse aeolian ridges (TARs) in the Glen Torridon region of Aeolis Mons (informally Mount Sharp), Gale crater, Mars. </p>
Data for Aeolian Ripple Migration and Associated Creep Transport Rates
<p><strong>Overview:</strong></p> <p>The attached spreadsheet, "AeolianRippleMigration_ShermanEtAl2019.csv," summarizes the ripple migration and related data acquired from the wind tunnel and field experiment literature and from the field experiments at Jericoacoara, Ceará, Brazil (2008) and Oceano, California, USA (2015), associated with the article "Aeolian Ripple Migration and Associated Creep Transport Rates" by Douglas J. Sherman, Pei Zhang, Raleigh L. Martin, Jean T. Ellis, Jasper F. Kok, Eugene J. Farrell, and Bailiang Li.</p> <p><strong>Notes for data sources:</strong></p> <p>"a" - indicates that the data from a particular study were included in our final analyses</p> <p>"b" - indicates an estimate of threshold shear velocity (calculated as per Lorenz et al., 2011) with A = 0.1</p> <p>"c" - the value for ripple height in this study is the average of about 200 measurements for ripples in equilibrium or near-equilibrium with the wind field</p> <p>"d" - the data from this study were digitized as depicted in terms of ust/ust_th and u_r/(gd)^0.5 (see "Key to variables" below)</p> <p>"e" - Shear velocity (ust) values are from Martin & Kok, 2017. Median grain diameter (d) and threshold shear velocity (ust_th) values are from Martin & Kok, 2018 (see Table 2: "Date interval")</p> <p><br> <strong>Key to variables [units]:</strong></p> <p>Source - literature origin of previous studies or field location of observations for this study</p> <p>Note - annotation for additional information about study (see above "Notes for data sources")</p> <p>StudyType - classified as "field" or "wind tunnel"</p> <p>Date - date of observation for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>StartTime - start time of observation window (local time) for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>EndTime - end time of observation window (local time) for observations at Jericoacoara and Oceano ( "N/A" for other sites)</p> <p>u_r [mm/s] - calculated ripple migration speed ( "N/A" for Zhu et al, 2011, see "u_r_alt" below)</p> <p>sigma_u_r [mm/s] - uncertainty in ripple migration speed. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p>u_r_alt - dimensionless proxy values for ripple migration speed for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as u_r/(gd)^1/2, where "g" is gravitational acceleration and "d" is median surface grain diameter </p> <p>ust [m/s] - shear velocity ( "N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>d [mm] - median surface grain diameter ("N/A" if not reported for literature studies)</p> <p>ust_th [m/s] - threshold shear velocity ("N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>ust_over_ust_th - dimensionless proxy values for shear velocity for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as ust/ust_th</p> <p>length [m] - ripple wavelength ("N/A" if not reported or measured)</p> <p>height [mm] - ripple amplitude ("N/A" if not reported or measured)</p> <p>sigma_height [mm] - uncertainty in ripple amplitude. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p>
Data for Aeolian Ripple Migration and Associated Creep Transport Rates
<p><strong>Overview:</strong></p> <p>The attached spreadsheet, "AeolianRippleMigration_ShermanEtAl2019.csv," summarizes the ripple migration and related data acquired from the wind tunnel and field experiment literature and from the field experiments at Jericoacoara, Ceará, Brazil (2008) and Oceano, California, USA (2015), associated with the article "Aeolian Ripple Migration and Associated Creep Transport Rates" by Douglas J. Sherman, Pei Zhang, Raleigh L. Martin, Jean T. Ellis, Jasper F. Kok, Eugene J. Farrell, and Bailiang Li.</p> <p><br> <strong>Notes for data sources:</strong></p> <p>"a" - indicates that the data from a particular study were included in our final analyses</p> <p>"b" - indicates an estimate of threshold shear velocity (calculated as per Lorenz et al., 2011) with A = 0.1</p> <p>"c" - the value for ripple height in this study is the average of about 200 measurements for ripples in equilibrium or near-equilibrium with the wind field</p> <p>"d" - the data from this study were digitized as depicted in terms of ust/ust_th and u_r/(gd)^0.5 (see "Key to variables" below)</p> <p>"e" - Shear velocity (ust) values are from Martin & Kok, 2017. Median grain diameter (d) and threshold shear velocity (ust_th) values are from Martin & Kok, 2018 (see Table 2: "Date interval")</p> <p><br> <strong>Key to variables [units]:</strong></p> <p>Source - literature origin of previous studies or field location of observations for this study</p> <p>Note - annotation for additional information about study (see above "Notes for data sources")</p> <p>StudyType - classified as "field" or "wind tunnel"</p> <p>Date - date of observation for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>StartTime - start time of observation window (local time) for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>EndTime - end time of observation window (local time) for observations at Jericoacoara and Oceano ( "N/A" for other sites)</p> <p>u_r [mm/s] - calculated ripple migration speed ( "N/A" for Zhu et al, 2011, see "u_r_alt" below)</p> <p>sigma_u_r [mm/s] - uncertainty in ripple migration speed. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p>u_r_alt - dimensionless proxy values for ripple migration speed for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as u_r/(gd)^1/2, where "g" is gravitational acceleration and "d" is median surface grain diameter </p> <p>ust [m/s] - shear velocity ( "N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>d [mm] - median surface grain diameter ("N/A" if not reported for literature studies)</p> <p>ust_th [m/s] - threshold shear velocity ("N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>ust_over_ust_th - dimensionless proxy values for shear velocity for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as ust/ust_th</p> <p>length [m] - ripple wavelength ("N/A" if not reported or measured)</p> <p>height [mm] - ripple amplitude ("N/A" if not reported or measured)</p> <p>sigma_height [mm] - uncertainty in ripple amplitude. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p><br> <strong>References:</strong></p> <p>Andreotti, B.; Claudin, P.; Pouliquen, O. Aeolian Sand Ripples : Experimental Study of Fully Developed States. 2006, 028001, 1-4.</p> <p>Borsy, Z. A homokfodrok. Fldrajzi rtesito 1973, 22, 109-115.</p> <p>Cheng, H.; Liu, C.; Zou, X.; Li, J.; He, J.; Liu, B.; Wu, Y.; Kang, L.; Fang, Y. Aeolian creeping mass of different grain sizes over sand beds of varying length. Journal of Geophysical Research: Earth Surface 2015, 120, 1404-1417.</p> <p>Cornish, V. On the formation of sand-dunes. The Geographical Journal 1897, 9, 278-302.</p> <p>Kindle, E.M. Recent and fossil ripple-mark; Canada Department of Mines, Geological Survey: 1917; pp 9-29.</p> <p>Ling, Y.-q.; Qu, J.-j.; Li, C.-z. Study on sand ripple movement with close shoot method. Journal of Desert Research 2003, 23, 118-120.</p> <p>Lorenz, R.D. Observations of wind ripple migration on an Egyptian seif dune using an inexpensive digital timelapse camera. Aeolian Research 2011, 3, 229-234.</p> <p>Martin, R.L.; Kok, J.F. Aeolian saltation fieldwork 30-minute wind and saltation values (Dataset). Zenodo, https://doi.org/10.5281/zenodo.291798: 2017.</p> <p>Martin, R.L., Kok, J.F. Distinct Thresholds for the Initiation and Cessation of Aeolian Saltation From Field Measurements. Journal of Geophysical Research - Earth Surface 2018, 123, 1546–1565. https://doi.org/10.1029/2017JF004416</p> <p>Seppälä, M.; Lindé, K. Wind tunnel studies of ripple formation. Geografiska Annaler: Series A, Physical Geography 1978, 60, 29-42.</p> <p>Sharp, R.P. Wind ripples. The Journal of Geology 1963, 71, 617-636.</p> <p>Stone, R.O.; Summers, H.J. Study of Subaqueous and Subaerial Sand Ripples; University of Southern California: Los Angeles, 1972.</p> <p>Zhu, W. Investigations on the formation and evolution of aeolian sand ripples. Lanzhou University, 2011.</p>
Ripple resonance amplifies economic welfare loss from weather extremes
<p>Using the loss-propagation model Acclimate (doi: 10.5281/zenodo.853345) we computed the regional and sectoral economic repercussion due to single disasters categories (heat stress, floods and tropical cyclones) as well as their consecutive disaster scenarios. The name component "en-20xx" refers to the economic network baseline. Our main analysis was based on the economic network of 2015.</p> <p>These data sets contain the annual aggregated economic quantities: production, production value, production (zero), direct loss, direct loss value, total loss, total value loss, consumption (zero), consumption value, consumption, GDP, GDP value, GDP (zero). Consumption and GDP (and corresponding variables) are regional quantities and therefore have only one non-NaN sectoral dimension (dimension zero). Quantities with a “(zero)” correspond to the annual baseline of the quantity (leap years taken into account).</p> <p>The quantities of the disaster scenarios are divided as follows:<br> Heat stress: hs_observable<br> Floods: fl_observable<br> Tropical cyclones: tc_observable<br> Consecutive disasters: cp_observable</p> <p>These variables have - next to “year”, “region”, “sector”, and “quantities” - the dimensions of the corresponding representative concentration pathway, global climate model, tropical cyclone season realization and hydrological model.</p>
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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.