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300 results for “Echo”
European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks
<p><strong>European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks</strong></p> <p>ECHO indicators rationale and code definition mapped out in ICD-9 and ICD-10 (for diagnoses) and ICD-9, NOMESCO, OPCS-4, ACHI and Leustungkatalog. </p> <p> </p>
Valence processing differs across stimulus modalities (Multi-echo)
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Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
<p>This dataset provides various acquisitions for T2 mapping of the MnCl2 array of the NIST phantom at 1.5T. Data were acquired on a MAGNETOM Sola (Siemens Healthcare, Erlangen, Germany), with an 18-channel body coil and a 32-channel spine coil (12 elements used). It gathers original acquisitions from Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12.</p> <p>The dataset is composed of DICOM images from:</p> <p>i) Gold-standard single-echo spin echo (SE) sequences acquired at variable TE;</p> <p>ii) Alternative reference multi-echo spin echo (MESE) acquisitions;</p> <p>iii) Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images at variable TE in three orthogonal orientations.</p> <p>The acquisition parameters are further detailed in the ReadMe.txt file provided along with the images.</p> <p>These acquisitions were repeated independently on three different days during the month of January 2020.</p> <p>These data are made publicly available as a support for further reproducibility studies as well as for the validation of new T2 relaxometry strategies.</p> <p>Works using any of these data should cite the following two references:</p> <p>- Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12</p> <p>- Lajous, Hélène, Ledoux, Jean-Baptiste, Hilbert, Tom, van Heeswijk, Ruud B., & Bach Cuadra, Meritxell. (2020). Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3931812</p>
Twitter Dataset for "Will You Take the Knee? Italian Twitter Echo Chambers' Genesis During EURO 2020"
<p>Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, distancing from such a trend, we study, at multiple topological levels, echo chambers genesis related to the social discussions that took place in Italy during the EURO 2020 Championship. Our analysis focuses on a well-defined topic (i.e., BLM/racism) discussed on Twitter during a perfect temporally bound (sporting) event. Such characteristics allow us to track the rise and evolution of echo chambers in time, thus relating their existence to specific episodes.</p>
Inter-Chemical Correlation results for the study: HHEARx2016-1461 (ECHO ReCHARGE Study - Environmental Exposures)
Title: ECHO ReCHARGE Study - Environmental Exposures <br>Species: Homo sapiens <br>Number of samples: 1231 <br>Number of named analytes: 75 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=17 <br>
Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks
<p>The publication titled "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks" is supported by the STRIDE K3 project. The dataset used in the publication is uploaded here.</p>
Multi-echo masking test dataset
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Neutron spin echo and intramolecular FRET and DEER-EPR measurements on hGBP1 (human guanylate binding protein 1)
<p>Neutron spin echo (NSE), double electron–electron resonance (<em>DEER</em>) <em>EPR</em>, ensemble time-correlated single photon counting (eTCSPC) fluorescence, and single-molecule detection (SMD) fluorescence spectroscopy data of the human guanylate binding protein 1 (hGBP1).</p> <p>CSH prepared samples for smFRET and performed protein activity assays. TV prepared sampled for EPR measurements. TOP, CSH, and AV performed the smFRET measurements under the supervision of CAMS. TOP analyzed the smFRET measurements. JPK performed and analyzed the EPR measurements.</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>This is a course project and I collect the data in a rush.</p> <p>If you want to use this dataset and find any error, please contact me ;-)</p> <p>My email: echo.xiangchen@gmail.com</p>
MengxiaoZhao_Using sky-wave echoes information to extend HFSWR's maximum detection range
<p>1. for Figure 2,3,4</p> <p>a. 'Ground Attention.mat' —— The data of ground wave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> Attenuation: 501*4 —— the ground wave attenuation for four different frequency<br> ]</p> <p>b. 'Skywave Attention.mat' —— The data of skywave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> attenuation: 34*4 —— the skywave attenuation for four different frequency<br> ]</p> <p>c. 'Path attenuation of 5Mhz.mat' —— four paths' attenuation of 5Mhz<br> [<br> L —— Ground distance<br> path1 —— the attenuation of path 1<br> path23 —— the attenuation of path 2&3<br> path4 —— the attenuation of path4<br> ]</p> <p><br> 2. for Figure 7 - simulation result</p> <p>a. 'simulation_echoes data.mat' —— the data of simulation targets' echoes</p> <p>b. 'Figure7_data.mat' —— the simulation results<br> [<br> data —— Doppler*Range<br> Doppler —— the axis of Doppler <br> Range —— the axisof Range <br> ]</p> <p>3. for Figure 8,9,10 - actual data processing results. We give 5 batches of echoes' data and the final result of Figure 9.</p> <p>a. 'TCDat1.mat','TCDat2.mat','TCDat3.mat','TCDat4.mat','TCDat5.mat',<br> —— the echoes' data</p> <p>b. 'Figure9_data.mat' —— the final result of Figure 9. <br> [<br> data ——Doppler*Range<br> axist_Doppler —— the axisof Doppler<br> axist_Range —— the axisof Range<br> counT —— the number of detections<br> mTgt —— the parameters of detections<br> ]</p> <p>4. 'parameters.txt' —— the parameters of simulation data and actual data</p> <p><br> </p>
Phantom measurement data for 'Fast bias-corrected conductivity mapping using stimulated echoes', Iyyakkunnel et al. (2024)
<p>This dataset contains the phantom measurement data used in the article by Iyyakkunnel et al., titled "Fast Bias-Corrected Conductivity Mapping Using Stimulated Echoes," published in MAGMA, 2024 (doi: 10.1007/s10334-024-01194-3). In this study, the feasibility of using a stimulated echo sequence for electrical properties tomography (EPT) is demonstrated. The data were acquired with a 3T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br>The dataset includes magnitude and phase measurements for the proposed Double-Angle Stimulated Echo (DA-STE) sequence, as well as reference measurements, including Double Angle measurements using a Gradient Echo sequence (GRE-DAM) for the B1+ magnitude, and a Single Echo Spin Echo sequence (SE) for the transceive phase.<br>For both the DA-STE and SE sequences, each measurement was repeated with inverted readout gradient polarities, denoted as LR (left-right) and RL (right-left) in the respective measurement folders. For each measurement, magnitude and phase data are provided in separate folders (in dicom (.dcm) format). Please note that for DA-STE, the two echo acquisitions are sequentially stored in the same measurement folder.<br>For further measurement details, please refer to the mentioned original article.</p>
Moored echo and turbidity measurements in the Southern Adriatic Sea at mooring site BB and FF, March 2012-June 2020
<p>This data set includes four files (CSV format) containing observational data from two oceanographic moorings, BB and FF, located in the Southern Adriatic Sea from the period between March 2012 and June 2020. The stand-alone moorings are equipped with a 300 kHz ADCP-RDI system, which measures currents along the last 100 meters of the water column and a CTD recorder equipped with SeaPoint turbidity meter sensor located approximatively 10 m above the bottom. The turbidity sensor measures in a range of 0-25 FTU. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic Sea submarine observatory system of the EMSO-ERIC European Consortium. The data were subjected to quality control (QC) and the coding numbers used, shown in a dedicated column, follow the SeaDataNet L20 measurement qualifiers flags. QC applied on echo data consists of detecting signal anomalies due to interactions with the seafloor and identifying if the signal falls below a minimum threshold for which the value is no longer considered reliable. For turbidity data, QC is addressed to the detections of possible spikes, anomalies, and sensor saturation in the recordings.</p>
Phantom measurement data for 'Complex B1+ mapping with Carr-Purcell spin echoes and its application to electrical properties tomography', Iyyakkunnel et al. (2022)
<p>This dataset contains the phantom measurement data used in the published article Iyyakkunnel et al., 'Complex B1+ mapping with Carr-Purcell spin echoes and its application to electrical properties tomography', Magn Reson Med. 2022;87:1250–1260 (doi: 10.1002/mrm.29020). The acquisitions were made with a 3 T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using the body coil for transmission and the a 20-channel head and neck coil for reception. Phase images from multichannel coil data were reconstructed using the manufacturer’s “adaptive coil combine” method. Magnitude measurements for B1 reconstruction using actucal flip angle imaging (AFI) is also included. Phantom scans were also performed with a 1H/23Na transmit/receive birdcage coil (RAPID Biomedical, Rimpar, Germany) for the Supporting Information Figure S1. The data includes the magnitude and phase measurements for the suggested Carr Purcell spin echo sequence with 10 echoes (in dicom (.dcm) format). For further measurement details, please refer to the mentioned original article.</p>
Echo Nest Features
<p>The dataset contains csv-files with Echo Nest features used for Chapter 5 in Jesper Steen Andersen's PhD dissertation.</p>
Transformation of social relationships in COVID-19 America: Remote communication may amplify political echo chambers
<p>The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972-2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors, but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, though political homophily increased among non-kin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, though political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.</p>
Sensing Echoes: Temporal misalignment as the Earliest Marker of Neurodevelopmental Derail
<p>FROM THE PREPRINT:</p> <p>Sensory transduction and transmission delays operate and propagate along different time scales. From microseconds in the auditory domain, to hundreds of milliseconds in the visual, and kinesthetic domains, the brain must successfully align disparate delays arising from endogenously self-generated streams of motor and visceral sensorial information, with exogenous sensory inputs. To produce a cohesive response to environmental goals, constantly explore, adapt, and develop a sense of simultaneity, the brain must resolve this major feat and compensate for excessive delays in any sensory modality. Disruption in these processes may lead to altered perception of the self and others, and inadvertently affect social interactions. But how early such issues may emerge and be reliably detectable, remains a challenge. Here we assess in neonates, the transmission latencies of a sound wave that travels from the cochlear nerve to the brainstem on its way to the primary auditory cortex. Already at birth, we find systematic and cumulative delays in the propagation of this wave in neonates that later received a diagnosis of autism. Furthermore, we discover that the distributions of such temporal delays have far narrower bandwidth than those from neonates who did not receive the autism diagnosis. We identify associated codependent genes’ networks and define a reliable marker of neurodevelopment derail, detectable at birth. Under the precision autism model, we propose that the brainstem contains an endogenous clock anchoring and aligning disparate timescales critical for the emergence and maintenance of congruent percepts of the self and others.</p>
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning
<p>This is the source code and data for the publication "Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning". Preprint - https://arxiv.org/abs/2112.09182</p>
A framework for spatial map generation using acoustic echoes for robotic platforms
<div> <div> <div> <div> <div> <div> <p>In this work, we present a framework for constructing a spatial map of an indoor environment using the concept of echolocation. More specifically, we propose a non-linear least squares (NLS) estimator which is combined with a spatial filtering technique, e.g., beamforming, to estimate both the time-of-arrival (TOA) and direction-of-arrival (DOA) of the acoustic echoes. The proposed framework is complemented with an echo detector to classify a spurious estimate and an acoustic reflector, i.e., a wall. Based on these estimators, we propose two algorithms that complement existing range sensors and aid robotic platforms in acoustic reflector localization and mapping: single-channel localization and mapping (ScLAM) and a multi-channel localization and mapping (McLAM). Compared to commonly used sensors, such as lidar, cameras and ultrasonic sensors, our proposed model-based approach can detect transparent surfaces that are typically found in an office environment and could work in audible frequency ranges. A proof-of-concept robotic platform was built to test our algorithms. According to our evaluation, both qualitative and quantitative experiments reveal that the proposed methods can detect an acoustic reflector up to a distance of 1.5 m at a signal-to-diffuse-noise ratio (SDNR) of 0 dB in a simulated environment and 10 dB in a real environment with an accuracy of 80%.</p> </div> </div> </div> </div> </div> </div>
Fig. 3 in Echoes from the past¦ rediscovering Isoscelipteron fulvum Costa, 1863 (Neuroptera¦ Berothidae) in Italy.
Fig. 3.- Male of Isoscelipteron fulvum from Bulgaria; photo made by Georgi in 2015 (see Letardi, 2016¦ 76).
Fig. 1 in Echoes from the past¦ rediscovering Isoscelipteron fulvum Costa, 1863 (Neuroptera¦ Berothidae) in Italy.
Fig. 1.- Sampling trap used for monitoring moth communities in chestnut woodlands of the Catena Costiera mountains in 2015.
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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.