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95 results for “Time domain”
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector
<p>We demonstrate a novel realization of Interstitial fiber, broadband, Time Domain Diffuse Optical Spectroscopy (TD-DOS) in Null Source-Detector separation (NSDS) approach without temporal gating, by using a Superconducting Nanowire single photon detector (SNSPD) for acquisition. As per the MEDPHOT protocol, we test experimentally, the absorption linearity of the system on tissue-equivalent liquid phantoms, and demonstrate the scattering-independent retrieval of the absorption spectrum of water using Intralipid phantoms in the wavelength range of 600-1100 nm.</p> <p>This work has been published in the Journal of Biomedical Optics - https://doi.org/10.1117/1.JBO.28.12.121202. Here, we present the dataset containing the acquired data pertaining to the aforementioned publication, including a brief overview, the tools to read it and the analysis corresponding to the figures in the article.</p>
Spectral Induced Polarization (SIP) field data sets for comparison between time and frequency domain (Martin et al. 2020)
<p>Data and scripts for the scientific journal article:</p> <p>Martin, T., Günther, T., Flores Orozco, A. & Dahlin, T. (2020): <br> Evaluation of spectral induced polarization field measurements in time and frequency domain, <br> J. Appl. Geophys., 180, 104141, doi:10.1016/j.jappgeo.2020.104141.</p> <p>The paper deals with the comparison of instruments for measuring induced polarization (IP)<br> data, both in frequency domain (FD) as well in time-domain. Four instruments have been used<br> to measure two profiles (IP1: a=1m, IP5: a=5m) over a known blackshale near the town Schleiz<br> in Thuringia, Germany.</p> <p>Preface: Our vision is not only to publish scientific results, but also openly demonstrate<br> how the results have been produced. This includes the raw data, processed data and inversion<br> results, but also all scripts that have been used for it in a reproducible way.<br> The scripts use the pyBERT (Python Boundless Electrical Resistivity Tomography) based on the<br> pyGIMLi (Python Geophysical Inversion and Modelling Library) computational framework.<br> Here, we used the pyBERT version 2.3.1 based on pygimli 1.1.1, but it should be reachable by<br> similar versions as well. All is based on the classes FDIPdata and TDIPdata.<br> For installing, we refer to the webpages www.pygimli.org and gitlab.com/resistivity-net/bert</p> <p>Folders:<br> figures figures used for production process, generated by the scripts in the folder<br> scripts Python scripts for generating the pdf files<br> IP1 data for the shallow profile using an electrode spacing of a=1m<br> IP5 data for the deeper profile using an electrode spacing of a=5m</p> <p>Each of the two data folders contains data and inversion results in separated folder named<br> according to the instrument names: <br> * SIP256C (Radic Research) - FDIP instrument with remote units (intelligent electrodes)<br> * DAS-1 (Multi-Phase Technology) - FDIP and TDIP instrument using multi-core cables<br> * Terrameter LS2 (GuidelineGeo ABEM) - TDIP instrument using multi-core cables<br> * Syscal Pro Switch72 (Iris instruments) - TDIP instrument using multi-core cables</p> <p>Please see readme files in the individual folders for specific information.</p> <p>Notice: In additon to the published results, we also added more data from the field <br> measurements which completes our results, e.g. <br> - TDIP data with 8s aquisition time<br> - all four instruments for the long profile IP5</p> <p>Complete full-waveform data for the Terrameter LS2 are available on request.</p>
Marine time domain electromagnetic data and true model for 2.5D inversion
<p>Dataset contains the description of complex 3D geoelectric model (with bathymetry, curved surfaces of geoelectric layers, target bodies simulated HC deposits, and background inhomogeneities) and marine time domain electromagnetic data calculated via finite element modeling. Noised data sets have been used for geometric 2.5D inversion.</p>
Radiation hardness of ultrabroadband spintronic terahertz emitters: En-route to a space-qualified terahertz time-domain gas spectrometer
<p>Data for the publication "Radiation hardness of ultrabroadband spintronic terahertz emitters: En-route to a space-qualified terahertz time-domain gas spectrometer" accepted for publication in Applied Physics Letters. The following datasets are provided: time-domain raw data (dry vs ambient conditions) and corresponding Fourier amplitude spectra as well as the impedance of the spintronic THz emitter vs frequency, THz transmission Fourier amplitude spectra from a spintronic THz emitter propagating through a gas cell with CO2 and N2, CO2 absorption coefficient vs frequency from THz transmission measurements through the gas cell, electro-optic THz signals and corresponding Fourier amplitude spectra for gamma and proton irradiated spintronic THz emitters as well as THz amplitudes vs irradiation dose/proton fluence.</p>
Supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks"
<p>This archive contains supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks".</p> <p>In particular, the following data is provided:</p> <ul> <li>dataset of labeled simulated TDR signals (1 million samples)</li> <li>dataset of labeled experimental signals (16 samples)</li> <li>trained convolutional neural network model</li> </ul>
Upper limb movements can be decoded from the time-domain of low-frequency EEG
<p>How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.</p>
Figure 9. Mel Cepstral Coefficients in time domain-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>In this section, Feature matching Algorithm has been discussed. The goal of feature<br> matching is to classify objects into one of a number of categories or classes. In this project, Vector<br> Quantization approach will be used and the best matching result will be the desired voice.</p>
Dataset: Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics
Open the record for dataset details and reuse information.
Fig. 13 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 13. Map showing potential Akchagylian transgression mechanisms and possible connection points with brief outline of the requirements for each and the observed phenomena each would be able to explain. The main mechanisms discussed are climate driven base level rise and connection with an adjacent basin. Three potential adjacent water bodies are considered as the source of incoming water (Arctic Ocean, Indian Ocean and the Black Sea). For each of the three two potential gateways are suggested.
Fig. 10 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 10. Left: Correlation of late Pliocene - Pleistocene continental stages and reference faunas of the Pontocaspian domain with standard chronostratigraphy and mammal-based biochronological units. Right: Map of mammal reference localities. MN 16: Early Villafranchian/Early Villanyian 1) Diliska (N41.43, E43.46), 2) Kushkuna 1 (N41.28, E45.45), 3) Veselovka (N45.13, E36.92). MN 17: Middle Villafranchian/ Late Villanyian 4) Sablya (N44.53, E43.23¬), 5) Kryzhanovka 1,2 (N46.56, E30.8), 6) Kotsakhuri (N41.35, E46.23), 7) Kryzhanovka 3 (N46.56, E30.8). MN 17-MQ1: Late Villafranchian/Late Villanyian-Early Biharian 8) Muhkai 2 (N42.25, E47.36), 9) Psekups (N44.73, E39.23), 10) Dmanisi (N41.33, E44.34), 11) Tsalka (N41.6, E44.09). Late Villafranchian – Early Galerian / Early Biharian 12) Duzdag 1 (N40.68, E46.89), 13) Tizdar (N45.36, E37.1), 14) Nesmeyanovka (N47.29, E41.5), 15) Sarkel (N47.7, E42.2), 16) Iskra (N45.35, E36.83), 17) Akhalkalaki (N41.4, E43.48). Galerian / Late Biharian 18) Margaritovo 2 (N46.94, E38.87), 19) Haykadzor (N40.54, E43.65). Aurelian / Toringian 20) Chernyi Yar (N48.01, E46.11).
Fig. 14 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 14. The successive mollusc faunas in the Pontocaspian region provide clear insights into basin connection and isolation. Age and MIS zonation from Cohen et al. (2013). H = Holocene; LP = Late Pleistocene. Regional phases: CN = Chernomorian; Ne = Neoeuxinian; Ka = Karangatian; Uz = Uzunlarian; Ch= Chaudian; Gu = Gurian; Ku = Kujalnikian; NC = Novocaspian; Kv = Khvalynian; Hi = Hircanian; u-Kz = upper or late Khazarian; l-Kz = lower or early Khazarian; Ba = Bakunian; Ap = Apsheronian; Ak = Akshagylian. Representative bivalve species: (1) Flexopecten glaber; (2) Didacna pseudocrassa; (3) Didacnocatilus guriensis; (4) Submonodacna pleistopleura; (5) Avicardium rariiferum; (6) Avimactra subcaspia; (7) Apsheronia raricostata s.l.; (8) Apsheronia propinqua s.l.; (9) Didacna subpyramidata (1) from Büyükmeriç et al., 2016; (2) from Nevesskaya, 2007; (5-8) from Kolesnikov, 1940.
Fig. 8 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 8. Quaternary time scales for the Pontocaspian domain. GPTS with Systems and Stages is after Hilgen et al. (2012), oxygen isotope curve with numbered Marine Isotope Stages (MIS) is after Lisiecki and Raymo (2005). On the right side are the time scales for the Caspian Basin and the Black Sea Basin.
Fig. 7 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 7. Paleogeographic maps for the late Pleistocene Pontocaspian region. Arrows indicate the water flow direction in the gateway regions. All maps are based on Yanina (2014).
Fig. 6 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 6. Schematic reconstruction of the Black Sea and Caspian Sea water-level curves in comparison to global oxygen isotopes records of Lisiecki and Raymo (2005) during the Pleistocene to Holocene. Associated interbasinal water exchanges marked by arrows pointing to the right for Mediterranean waters flooding into the Black Sea, arrows pointing to the left for Caspian Sea waters flooding into the Black Sea and double arrows for bidirectional water exchange between Black Sea and Caspian Sea. N.B. Two options exist in literature regarding the position of the Singilian: *Svitoch (2013b and references therein) and **Zastrozhnov et al. (2018 and references therein).
Fig. 12 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 12. Left: Paleogeographic map with the location of hominin-related sites in the study area. Numbers correspond to site description in Table 1. Arrows show migration pathways out of Africa and into Europe. Right: Stratigraphy, archaeology and paleomagnetic dating of the oldest hominin findings in Dmanisi, Georgia (from Ferring et al., 2011).
Fig. 5 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 5. Plio-Quaternary time scales for the Pontocaspian domain. GPTS with Systems and Stages are after Hilgen et al. (2012), oxygen isotope curve with numbered Marine Isotope Stages (MIS) is after Lisiecki and Raymo (2005). M=Mammoth (C2An.2r), K=Kaena (C2An.1r), R= Reunion (C2r.1n), O=Olduvai (C2n), C.M.= Cobb Mountain (C1r.2n), J=Jaramillo (C1r.1n). Ages of the Cobb Mountain Subchron are after Channell (2017). On the right side are the time scales for the Caspian Basin, Black Sea Basin and the terrestrial domain (mammal (MN/MQ) zonation and regional (MNR/MQR) biochronological units).
Fig. 2 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 2. Present-day drainage area of the Pontocaspian domain. Yellow circles denote the locations of the stratotype sections of the main Quaternary stages of the Caspian Basin and Black Sea Basin: 1) Akchaghylian on Krasnovodsk peninsula (Turkmenistan), 2) Apsheronian on Apsheron Peninsula (Azerbaijan), 3) Bakunian in Baku (Azerbaijan), 4) Kuyalnikian (Ukraine), 5) Gurian (Georgia), 6) Chaudian on Cape Chauda (Crimea) and 7) Uzunlarian (Crimea). White circles denote the locations of key sections: 1) Pyrnuar (N38.93, E56.26), 2) Malyi Balkhan (N39.27, E54.97), 3) Yuzhny Urundzhik (N39.27, E54.50), 4) Ushak (N40.45, E53.37), 5) Lokbatan (N40.33, E49.75) and Jeirankechmez (N40.24, E47.09), 6) Duzdag (N40.70, E46.92) and Bozdag (N40.80, E46.84), 7) Pantashara (N41.23, E46.36) and 8) Kvabebi (N41.48, E45.68) and Kushkuna (N41.25, E45.44).
Fig. 3. Paleogeographic maps for the Plio-Pleistocene Pontocaspian region. A in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 3. Paleogeographic maps for the Plio-Pleistocene Pontocaspian region. A) Middle Pliocene; B) Late Pliocene; C) Early Pleistocene; D) Middle Pleistocene. Based on Vinogradov, 1961, 1969 and Abdurakhmanov et al. (2002).
Fig. 4 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 4. Distribution of the main ostracod species, foraminifera and charophyta in the Caspian Basin during the Plio-Pleistocene, based on literature data. 1) Cyprideis torosa; 2) Ilyocypris gibba; 3) I. bradyi; 4) Cyprinotus salinus; 5) Eucypris sp.; 6) Pseudocandona compressa (juvenile); 7) Limnocythere aralensis; 8) Zonocypris membranae; 9) Darwinula stevensoni;10) Limnocythere alveolata; 11) L. luculenta; 12) L. tschaplyinae; 13) Typhlocypris gracilis; 14) Loxoconcha eichwaldi (different stages of evolution); 15) Candona candida; 16) C. combibo; 17) Eucythere naphtatscholana; 18a) Amnicythere andrussovi; 18b) A. palimpsesta; 18c) A. normalis; 18d) A. saljanica; 19) Amnicythere nata; 20) Leptocythere gubkini; 21) A.multituberculata; 22) Euxinocythere praebosqueti; 23) A. cymbula; 24) Loxoconcha babazananica; 25) L. petasa; 26) Camptocypria acronasuta; 27) Caspiocypris filona; 28) Tyrrhenocythere bailovi; 29) Xestoleberis chanakovi; 30) T. amnicola donetziensis; 31) T. azerbaidjanica; 32) T. papillosa; 33) Camptocypria acronasuta; 34) Bacunella dorsoarcuata; 35) Loxoconcha gibboides; 36) L. lepida; 37) Euxinocythere bosqueti; 38) Cytherissa bogatschovi; 39) Euxinocythere bacuana; 40) A. quinquetuberculata; 41) Loxoconcha endocarpus
ScienceDex guides
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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.