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.
300
datasets available to search
ShareScore release 0.7.1
Dataset results
300 results for “Echo”
International survey of the ECHOES project
<p>International survey of the ECHOES project is a large survey dataset than contains responses from 31 countries: EU-28 (2018) + 3 countries.</p> <p>The dataset contains raw data in four different formats: dta, rdata, sav and xlsx. Codebook and survey questionnaire are included.</p> <p> </p>
Standardized psychological experiments of the ECHOES project
<p>Standardized psychological experiments of the ECHOES project were conducted in six European countries: Bulgaria, Finland, Germany, Italy, Norway and Turkey. </p> <p>The dataset contains raw data in four different formats: dta, rdata, sav and xlsx. Survey questionnaires and data preparation syntax are included.</p>
Psychological experiment in Spain of the ECHOES project (psychological determinants of investment in renewable energy)
<p>Psychological experiment in Spain of the ECHOES project (psychological determinants of investment in renewable energy). </p> <p>Data file is available in four formats: dta, rdata, sav and xlsx. </p> <p> </p>
Energy company survey of the ECHOES project
<p>Survey on psychological factors at the basis of energy choices at work among employees of a large scale European energy company.</p> <p>Data file is available in four formats: dta, rdata, sav and xls. </p>
Data sets for "MELISSA: System description and spectral features of pre‐ and post‐midnight F‐region echoes. Journal of Geophysical Research: Space Physics" by Rodrigues et al.
<p>Observations used in the study "Rodrigues, F. S., Zhan, W., Milla, M. A., Fejer, B. G., de Paula, E. R., Neto, A. C., et al ( 2019). MELISSA: System description and spectral features of pre‐ and post‐midnight <em>F</em>‐region echoes. <em>Journal of Geophysical Research: Space Physics</em>, 124. <a href="https://doi.org/10.1029/2019JA027445">https://doi.org/10.1029/2019JA027445</a>."</p> <p>The uploaded files include the RTI maps measured by the MELISSA radar system between 2014 and 2018 (.tif files). They also include values of SNR versus local time and height and the spectra presented in the manuscript (.mat files).</p> <p>Please, see README.txt for additional details.</p>
Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research
<p><strong>This is the dataset of the report: Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research</strong></p> <p>It contains the following information of all the papers from ASE, FSE, and ICSE in 2023:</p> <ul> <li>Paper title</li> <li>Keyword</li> <li>Is the source data available and accessible in the paper?</li> <li>If the source data is not available, do the authors explain why?</li> <li>Hosting platforms</li> <li>Access mode</li> <li>License</li> <li>Is their experiment data reused from previous work, or newly generated specifically for this study, or combination of both? </li> <li>Do the authors change/modify their experiment data before experiment?</li> <li>What modifications do they perform?</li> <li>Does the link provide detailed instructions about how to replicate their paper?</li> <li>Does the link contains their complete experiment data, their source code or other materials that are necessary to replicate their experiments?</li> <li>What's the data format inside the link?</li> <li>What's the content of the link?</li> </ul> <p> </p> <p>We collect the data in a rush.</p> <p>If you want to use this dataset and find any errors, please contact us ;-)</p> <p> </p> <p>Our emails:</p> <ul> <li>echo.xiangchen@gmail.com</li> <li>zhifengyao731@gmail.com</li> </ul>
Ice Anatomy: A Benchmark Dataset and Methodology for Automatic Ice Boundary Extraction from Radio-Echo Sounding Data
<p>The measurement of ice thickness is of great importance for the accurate estimation of glacier volume and the delineation of their bedrock topography. In particular, this is a crucial factor in forecasting the future evolution of glaciers in the context of a changing climate. In order to derive the ice thickness, the travel time of electromagnetic waves in radargrams acquired by radio-echo sounding (RES) systems is analyzed. This can only be achieved by identifying the ice surface and underlying ice bottom in corresponding radargrams. Manually identifying these two reflection horizons in RES data is a laborious and time-consuming process. Consequently, scientists are attempting to automate this task through the use of techniques such as deep learning. Such automation can significantly reduce the time between a field campaign and the calculation of the glacier's ice thickness distribution. In this paper, we present the first benchmark dataset for delineating the ice surface and bottom boundaries in RES data, to facilitate straightforward comparisons of deep learning models in the future. The ``IceAnatomy'' dataset comprises radargrams and the corresponding manual picks, amounting to a total of over 45,000km of observations. The RES data originates from three sources: FAU, CReSIS, and AWI. The dataset comprises different RES systems as well as different pre-processing methods. In addition, the data was acquired over a large range of geographical and glaciological settings, featuring different thermal regimes present in Antarctica and the Southern Patagonian Icefield. This diversity ensures that the models' behaviors can be analyzed in different scenarios. We define a standardized train-test split for each source in the dataset. This allows us to introduce not only a baseline model trained on the entire training set (the ``omni'' model), but also three source-specific baseline models. The source-specific models are trained exclusively on the subset of the training data acquired by the specified source. The baseline models provide an initial benchmark against which subsequent models can be compared. The source-specific models demonstrate more accurate results than the omni model. For the FAU, CReSIS, and AWI test sets, the source-specific models achieve Mean Meter Errors of 2.1m, 23.1m, and 4.9m for the ice surface and 9.1m, 78.2m, and 29.3m for the ice bottom. In relation to the mean measured ice thickness, these errors equate to 1.2%, 3.1%, and 0.3% for the ice surface and 4.9%, 10.4%, and 1.5% for the ice bottom.</p> <p> </p> <p> For more information, please read the following paper:</p> <p>[Coming soon. Currently under review.]</p> <p>Please also cite this paper if you plan on using the dataset.</p> <p> </p> <p>For the implementation of a baseline model please visit:</p> <p>[Coming soon]</p> <p> </p>
Dataset for mammatus-like radar echoes along the bases of upper-tropospheric outflow-layer clouds of typhoons.
<p>PPI and RHI data of radar observations of mammatus-like echoes at the cloud bases of upper-level clouds of typhoons observed by the Nagoya-University cloud radar. The observations were carried out at Okinawa, Japan in 2016 and 2019, and Kobe, Japan in 2018 This dataset includes raw data at polar coordinates and grid data interpolated to Cartesian coordinates. This dataset also includes upper-level sounding observation data during the appearance of mammatus-like echoes at Okinawa, Japan in 2016.</p> <p>In this version 2, all RHI data obtained in 3 October 2016 were added.</p>
ECHO_dataset
<p>This is the ECHO dataset presented in 'Cross-time registration of 3D point clouds' (https://www.sciencedirect.com/science/article/pii/S0097849321001357). </p> <p>The dataset consists of eroded cultura heritage objects, transformed randomly in 3D space. It is seperated into two datasets, the datasetCulture and the datasetShape. Each dataset contains, the Source object, the Target objects (the transformed and the eroded objects) and the ground truth transformation.</p>
Echolocating toothed whales use ultra-fast echo-kinetic responses to track evasive prey
<p>Visual predators rely on fast-acting optokinetic responses to track and capture agile prey. Most toothed whales, however, rely on echolocation for hunting and have converged on biosonar clicking rates reaching 500/s during prey pu rsuits. If echoes are processed on a click by click basis, as assumed, neural responses 100x faster than those in vision are required to keep pace with this information flow. Using high resolution bio-logging of wild predator prey interactions we show that toothed whales adjust clicking rates to track prey movement within 50 200 ms of prey escape responses. Hypothesising that these stereotyped biosonar adjustments are elicited by sudden prey accelerations, we measured echo kinetic responses from trained harb our porpoises to a moving target and found similar latencies. High biosonar sampling rates are, therefore, not supported by extreme speeds of neural processing and muscular responses. Instead, the neuro kinetic response times in echolocation are similar to those of tracking responses in vision, suggesting a common neural underpinning.</p>
Data for: Analysis of Conformational Exchange Processes using Methyl-TROSY-Based Hahn Echo Measurements of Quadruple-Quantum Relaxation
<p>Raw experimental data used in associated publication. A full list of experiments is provided in the README.md file.</p>
IoT Forensic Analysis: a Family of Experiments with Amazon Echo Devices (ISP diagrams and Teardown videos)
<p>The two zip files (i.e., ISP Diagrams.zip and Teardown videos.zip) contain the ISP diagrams and teardown videos of the Amazon Echo Show IoT devices used in the experiment that we report in our research paper titled "IoT Forensic Analysis: a Family of Experiments with Amazon Echo Devices."</p>
chen-echo/FGNER-corpus: Geological fine-grained corpus
<ul> <li>A corpus for the identification of CHINESE geologically named entities based on three-part geological reports.</li> <li>Contains twenty-one labels, the corresponding entities for the labels are listed in the below.</li> <li>ROC.SEDI 沉积岩 ROC.META 变质岩 ROC.IG 岩浆岩</li> <li>SMG.ROC 岩性地层 SMG.chrono 年代地层</li> <li>MIN.native 自然元素矿物 MIN.sulASIM 硫化物及其类似化合物 MIN.halide 卤化物矿物 MIN.oxihydro 氧化物及其氢氧化物矿物 MIN.oxis 含氧盐矿物 MIN.ROC 岩石类非金属矿物</li> <li>GCH.AR 太古宙 GCH.PT 元古宙 CGH.PH 显生宙</li> <li>GST.fold 褶皱 GST.fault 断裂 GST.joint 节理 GST.contact 接触关系</li> <li>GAC.ENDO 内力地质作用 GAC.EXO 外力地质作用</li> <li>OT 其他</li> </ul>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
A Study of Epacadostat in Combination With Pembrolizumab and Chemotherapy in Participants With Advanced or Metastatic Solid Tumors (ECHO-207/KEYNOTE-723)
ClinicalTrials.gov study NCT03085914. IPD Sharing: YES. Countries: 1. Publications: 1.
Echolocating toothed whales use ultra-fast echo-kinetic responses to track evasive prey
Open the record for dataset details and reuse information.
Dataset for NMR quadrature echo and T1 saturation recovery pulse sequences underlying the publication 'On the quantification of solid phases in hydrated cement paste by 1H nuclear magnetic resonance relaxometry'
<p>This record comprises the datasets of combined 1H NMR quadrature echo and T1 saturation recovery pulse sequences underlying the publication “On the quantification of solid phases in hydrated cement paste by 1H nuclear magnetic resonance relaxometry” by Robert Schulte Holthausen & Peter J. McDonald, Cement and Concrete Research, https://doi.org/10.1016/j.cemconres.2020.106095.</p> <p><br> In this work different solid phases, important to cement paste hydration, are investigated with low-field bench top 1H nuclear magnetic resonance with a view to developing an alternate characterisation methodology that requires minimal invasive or destructive sample preparation.</p> <p><br> A combination of the well-established quadrature echo pulse sequence with variable pulse gap together with a T1 saturation recovery quadrature echo pulse sequence is used.</p>
Data for: Free-Breathing Water, Fat, R2∗ and B0 Field Mapping of the Liver Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (MERLOT)
<p>Data for our manuscript with title "Free-Breathing Water, Fat, R2∗ and B0 Field Mapping of the Liver Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (MERLOT)"</p>
BRAMS Radio Spectrograms and Spectrogram Samples for Automatic Detection of Meteor Echoes
<p>The files in this dataset are based of radio recordings taped by BRAMS (Belgian RAdio Meteor Stations), the Belgian meteor detection network.</p> <p>Included in the dataset are the original BRAMS radio recordings (stored as .wav audio files), the spectrogram data for each radio recording (stored as .csv files) and the meteor and non-meteor samples extracted from the radio spectrograms (stored as .csv files).</p> <p>It should be noted that the the spectrogram data was sampled using a sliding window of size 30x20 pixels and the samples extracted in this manner were further processed by calculating the vertical average of each column in the 30x20 matrixes. The result of this sampling procedure is a set of data vectors containing the average power of the signal found in the original 30x20 spectrogram sample.</p>
Whole Body Golden Angle Radial Gradient Echo MRI Data
<p>Raw k-space data from a continuously moving table, whole-body, golden angle radial MRI protocol. This data set was used to generate the images for the TRON (TRajectory Optimized Nufft) publication.</p> <p>Data is in RA format (http://github.com/davidssmith/ra).</p>
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.