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69 results for “TEC”
Mother as child TEC Dataset for ZEPT 2008152
<p>Dataset to go with the (submitted) paper on Mother as child test of emotion comprehension and interpersonal violence related post traumatic stress disorder. Variable names are partially in French partially in English.</p>
Dataset: ADS-TEC Energy PLC (ADSEW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: ADS-TEC Energy PLC (ADSE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: ADS-TEC Energy PLC (ADSE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: ADS-TEC Energy PLC (ADSEW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data sets for distributed ionospheric L-band scintillation and TEC observations made in the American sector during the March 23-24, 2023 geomagnetic storm
<p>These data sets contain the scintillation measurements presented in the manuscript titled, "On the extraordinary L-band scintillation event observed in the American sector during the March 23-24, 2023 geomagnetic storm".</p> <p><br>The HDF5 files are organized by constellations and satellites. Each satellite includes the following parameters: Azimuth (AZIM), Elevation (ELEV), Number of Samples (NOS), Amplitude Scintillation Index (S4), 1-minute average SNR (SNR), relative Total Electron Content (PTEC), and Time of Week in seconds (S_TW)</p>
TEC over continental US and Europe for the fall equinox and low solar flux conditions
<p>The files contain median TEC (total electron content) values for a given geographic sector for a fall equinox and low solar flux conditions.<br> The 10th, 25th, 75th, and 90th percentiles are also included.</p> <p>These median TEC values were calculated using TEC data provided by the CEDAR Madrigal database for the years 2000-2018.<br> Median values of TEC and different percentiles were calculated for the following<br> conditions: low solar activity (F10.7 daily index is 70 ± 5 SFU; 81‐day average F10.7 index is 70 ± 5 SFU),<br> low geomagnetic activity (daily Ap index < 15 for the current day and previous 24 h), average or below average<br> stratospheric planetary wave activity at 10 hPa and 60°S, and centered on 15 September with a ±15‐day window.<br> Average level of stratospheric planetary wave activity was calculated from 40+ years of MERRA data. The Madrigal<br> database contains 79 days that satisfy the aforementioned conditions, with data collected in 2008, 2009,<br> and 2018. The TEC observations for the selected 79 days were then binned in 30‐min intervals, resulting<br> in several hundred data points per each 1° longitude x ~ 1° latitude bin in areas with good data coverage.<br> Median TEC values determined from these bins were used as a baseline<br> that describes the “dynamically quiet” ionosphere in each geographic sector with high resolution in latitude<br> and longitude. Different percentiles were used to describe typical quiet-time variability. The users are advised to use median TEC only in areas with sufficient number of data points (> 50) included in the median.</p>
Monthly GPP datasets for 2000-2017 in China from the big-leaf TEC model and the two-leaf DTEC model
<p>The monthly GPP datasets were calculated from MODIS LAI/FPAR products (MOD15A2, Version 6) and meteorological data for 2000-2017 in China at a resolution of 0.05 degree ×0.05 degree. The TwoLeaf-DTEC-GPP-5km data was derived from the Two-leaf-based DTEC GPP model. The BigLeaf-TEC-GPP-5km data was calculated from the Big-leaf-based TEC GPP model. The BigLeaf-TEC-GPP-5km-2000-2017.zip includes 19 separate GPP data files and each represents one year data including 12 monthly data.</p>
IGS TEC data grid, 10-16 June 2022
<p>This dataset provides the processed IGS TEC map data that were used in the study by Stephan et al. (2024, submitted).</p> <p>Data are an IDL save set, with the following gridded arrays:</p> <p>; IGSDATE FLOAT = griday[6, 576]<br>; IGSDOY FLOAT = griday[576] ; DOY 161 to 167, corresponding to June 10-16 2022<br>; IGSLAT FLOAT = griday[71]<br>; IGSLON FLOAT = griday[73]<br>; IGSTEC FLOAT = griday[73, 71, 576]<br>;</p> <p>Elements within IGSDATE array:<br>;print, igsdate(*,0)<br>; year month dom hour min sec <br>;2022.00 6.00000 11.0000 0.00000 0.00000 0.00000</p>
Robotics and Digital Systems Engineering at the Tec de Monterrey
<p><b>Abstract</b></p><p class="dhik-abstract-content">This talk addresses the core competencies of Robotics and Digital Systems Engineers nowadays as well as their integration into undergraduate-level curriculum. Background, curricular map, and examples of international collaboration are detailed based on the experience of Tecnológico de Monterrey.</p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li><b>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (<a href="#collapseTwo">Video</a>)</b></li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Dataset from the ground-based TEC and scintillation receiver in Troll station for events on March 18th and 25th, 2018
<p>Here is data from the GNSS Ionospheric Scintillation and TEC Monitor (GISTM) receiver NovAtel GPStation-6 that located at the Norwegian Research Station Troll in Queen Maud Land, Antarctica. The receiver records signals from the GPS, GLONASS, and Galileo satellites. Every minute, it provides extended summary messages, including satellite azimuth/elevation angles, C/NO, lock time, code-minus-carrier, calculations of amplitude (S4) and phase (σϕ) scintillation indices.</p> <p>The data is presented as tables. Each .txt file contains data and a header.</p> <p> </p> <p><span>This work was supported by European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Consolidator Grant agreement No. 866357, POLAR-4DSpace).</span></p>
TEC and SymH data
<p>The datasets used in this study are composed of 516 samples for TEC (corresponding to 6 different stations covering a wide range of geomagnetic regions from high to low latitudes in both hemispheres). The measurements were taken between november 19 of 2016 and december 31 of 2016 with a temporal resolution of 2 hours. Symh temporal resolution originally was 1-seg of sampling, we pre-processed to create a coincident 2-hs resolution dataset to be compared with the TEC dataset.<strong> </strong></p> <p>The location of each station is as it follows:</p> <ul> <li>Station 2: lat -85 long 0</li> <li>Station 5: lat -50 long 0</li> <li>Station 8: lat -20 long 0</li> <li>Station 11: lat 20 long 0</li> <li>Station 14: lat 50 long 0</li> <li>Station 17: lat 85 long 0</li> </ul> <p> </p>
Empirical Models of TEC and Tide-like Signatures In the Ionosphere
<p>This empirical model is developed from the global ionospheric maps (GIMs) from the Center for Orbit Determination of Europe (CODE) covering 2000-2021. With the tidal signatures parameterized, the climatic behaviors in the ionosphere could be consequently modeled. This dataset contains the model codes of the tidal signatures and TEC. </p>
TEC obtained from Madrigal for 'typical' Geomagnetic storms over the US (2000-2018)
<p>Contains Quiet day and storm day TEC gridded on the basis of dip and declination over the United States.</p> <p>TEC data are obtained from the Madrigal database (<a href="http://millstonehill.haystack.mit.edu/">http://millstonehill.haystack.mit.edu/</a>)</p> <p>Quiet days are in the same month as the storm days. These days are obtained from the kyoto database</p> <p>Files named as Storm/Quiet_ID_Sector.h5</p> <p>The storm ID and sector information are in a paper submitted to JGR-Space Physics.. </p>
GPS 50 Hz data set for the case study entitled "Comparison of TEC calculations based on Trimble, Javad, Leica and Septentrio GNSS receiver data". Part 1
<p>This dataset contains 50 Hz GPS data to explore JAVAD and Leica receivers quality to reconstruct slant TEC.</p> <p>GNSS receiver is to some extent a “black box” when its data is used for ionospheric studies. The results based on Javad, Septentrio, Trimble and Leica GNSS receivers (which is placed in this data set) proved that the accuracy of the slant Total Electron Content (TEC) calculation can differ significantly if data used for its calculation is from different GNSS receiver make/type. This is because TEC measurements depend on the carrier phase tracking technique applied in a receiver. Based on this data it was found that:</p> <p>1) Correlation coefficient between carrier phase noises in L1 and L2 channel outputs may be considered as an indicator showing whether the L1-aided tracking technique or independent tracking is applied.</p> <p>2) The empirical model of TEC noise component was provided to determine TEC noise value in GNSS receivers of different make/type.</p>
GNSS TEC Files CMN format
<p>Total electron content for 4 stations in Africa in .cmn format (text) 30 second sampling for 7 days.</p>
TEC Map during Halloween Storm 2003
<p>The file shows the spatiotemporal change of TEC during the Halloween Storm 2003.</p>
Airglow Imager and GNSS-TEC Data on 09 June 2021
Open the record for dataset details and reuse information.
Data for the paper "A new mapping function for spaceborne TEC conversion based on the plasmaspheric scale height"
<p>This dataset works specifically for the paper Wu et al., (2021), "A new mapping function for spaceborne TEC conversion based on the plasmaspheric scale height"</p>
Data used for global TEC forecasting for space weather application based on deep learning techniques: a comparative study and considerations for real-time implementation
<p>This dataset contains the measurements of TEC from Global Ionospheric Maps (GIMs), provided by the International GNSS Service (IGS), as the target parameter and the global geomagnetic Kp index as the external input. This data set is composed of samples from 2005 to 2017. The datasets have been curated to obtain the same resolution (2 hs) of the two parameters.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.