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429 results for “December”
December 2021 raw audio data at 9999 kHz using Grape 1 for radio station JJ1BDX
<p>This dataset contains all audio data (WAV, 8kHz S16_LE monaural) for the analysis of 10MHz standard frequency stations (mostly BPM, including WWVH and WWV) for HamSCI December 2021 Eclipse Festival recorded at radio station JJ1BDX in Setagaya City, Tokyo, Japan. </p> <p>Other references:</p> <ul> <li>See <a href="https://github.com/jj1bdx/hamsci-202112-freqdata">https://github.com/jj1bdx/hamsci-202112-freqdata</a> for the analysis results.</li> <li>See <a href="https://github.com/jj1bdx/dcrx-10MHz-design/">https://github.com/jj1bdx/dcrx-10MHz-design/</a> for the technical specification of the direct conversion receiver HamSCI Grape 1.</li> </ul> <p> </p>
LPL4 - December 2021
<p>Measurements of LPL4 (Isla de Corvo) in December 2021</p> <p>Azores</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL4">info</a></p>
SG - December 2021
<p>Measurements of SG (Global) in December 2021</p> <p> </p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/SG">info</a></p>
Conjugate observation data between DMSP satellites and all-sky imager of Chinese Yellow River station at Ny-Ålesund, Svalbard from January 2005 to December 2009
<p>Chinese Arctic Yellow River Station (YRS) locates at Ny-Ålesund, Svalbard with the geographic coordinates (78.92°N, 11.93°E) and the corrected geomagnetic latitude 76.24°. The relation between local time and universal time is MLT≈UT+3hr. In November 2003, a set of monochromatic auroral observation system was installed at YRS, which is consisted of three identical all-sky imageries (ASIs) with the filters at 427.8nm, 557.7nm and 630.0nm, respectively. DMSP (Defense Meteorological Satellite Program) satellites are a collection of polar-orbit weather satellites launched by the U.S. department of defense. This series of satellites is sun synchronous satellite, which takes about 101 minutes to orbit the earth, has an altitude of about 835-850km, an inclination of about 96°, and crosses the equatorial plane daily from south to north (ascending segment) and from north to south (descending segment) at a fixed time (DMSP F13 is at 05:45LT and 17:45LT, and F15 is at 09:30LT and 21:30LT). DMSP satellite is equipped with Special Sensor for Particle Flux (SSJ/4), which can measure the fluxes of downgoing electrons and ions with energies between from 30eV to 30keV in 19 energy steps (34, 49, 71, 101, 150, 218, 320, 460, 670, 960 eV, and 1.4, 2.1, 3.0, 4.4, 6.5, 9.5, 14.0, 20.5, 29.5 keV), with a time resolution of 1 second. According to the orbit of the DMSP satellites and the observation of the ASIs of YRS, we obtained the conjugate observation periods of the satellite flying over the ASI.</p> <p>During the period from January 2005 to December 2009, a total of 136 conjugate observation events were obtained. Moreover, according to the morphological characteristics of discrete aurora in the all-sky image, 136 events are classified according to four typical forms of dayside discrete auroras, namely 27 events of drapery dayside corona (DDC), 24 events of radial dayside corona (RDC), 37 events of hot-spot aurora (HSA), and 48 events of arc aurora (ARC). The event list of 136 events is recorded in the “asi&dmsp@YRS03-09-v2.xlsx” file. The EPS file gives the trajectory of the DMSP satellite crossing the ASI in each conjugate observation event, while the JPG files are the corresponding all-sky images.</p>
HARMONIE-AROME model data (cy40REF and cy40NEW) and observations for December 2018
<p>File kmds_202812 contains the observations at weather stations (following international code) that are used for Figs. 19 and 20 in: Model development in practice: a comprehensive update to the boundary layer schemes in HARMONIE-AROME cycle 40, Wim C. de Rooy et al. 2022, Geosc. Mod. Dev.</p> <p>Similarly, files vlfd* contain the model data (cy40REF and cy40NEW in the file name refer to the corresponding model version) used for Figs 19 and 20, where the relevant parameters are:<br> FI 0: Geopotential station height<br> NN 0: Total cloud cover<br> DD 0: Wind direction (°)<br> FF 0: Wind speed<br> TT 0: 2-Meter Temperature<br> RH 0: 2-Meter Relative Humidity<br> PS 0: Mean Sea Level Pressure<br> PE 15: Total precipitation?<br> QQ 0: 2-m Specific Humidity<br> VI 0: Visibility<br> N75 0: Cloud cover below 7500 m<br> CH 0: Cloud base height<br> LC 0: Low cloud cover</p>
Brazilian Butterflies Collected December 2020 to January 2021
<p>Wing Photographs Acraeini, Heliconiini, Danaini, and Ithomiini. Butterflies Collected in the Brazilian Atlantic Forest between December 2020 to January 2021</p> <p>Author List: Érika C Pinheiro de Castro, Karina Lucas da Silva-Brandão, André Victor Lucci Freitas, Marcio Zikan Cardoso, Eva van der Heijden, Joana Meier, Ian Warren, and Chris Jiggins</p> <p> </p> <p>EN:</p> <p>This upload contains photographs taken by Eva Van der Heijden, in the Insect Evolution and Genomics Group at the University of Cambridge. This batch contains wild caught specimens collected in the Brazilian Atlantic Forest from December 2020 to January 2021 Butterflies from the following tribes can be found here: Acraeini, Heliconiini, Danaini, Ithomiini. Information on the individual IDs can additionally be found in the csv.</p> <p>Nomenclature</p> <p>CAMXXXXXX : unit ID corresponding to individual samples _v _d: ventral or dorsal</p> <p>Both JPGs and raw image files (CR2) have been uploaded.</p> <p>Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (click <a href="http://heliconius.ecdb.io">here</a> for the database, and here for <a href="https://heliconius.zoo.cam.ac.uk/databases/">FAQ</a>)</p> <p>Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) and/or Erika Pinheiro de Castro (ecp38[at]cam.ac.uk)</p> <p> </p> <p>------------------------------------------------------</p> <p>PT:</p> <p> </p> <p>Este banco de dados contém fotos tiradas por Eva van der Heijden do grupo de pesquisa Insect Evolution and Genomics da Universidade de Cambridge.</p> <p>As fotos correspondem à asas de borboletas coletadas na Mata Atlântica brasileira entre dezembro de 2020 e janeiro de 2021.</p> <p> </p> <p>As fotos estão organizadas em pastas nomeadas de acordo com as tribos as quais as mesmas fazem parte: Acraeini, Heliconiini, Danaini e Ithomiini. As informações sobre a identificação da espécie de cada amostra estão no arquivo .csv</p> <p>Nomenclatura</p> <p>CAMXXXXXX : número de queda/ID de cada indivíduo _v _d: ventral ou dorsal</p> <p>Este banco de dados contém imagens no formato JPG e CR2. Mais informações sobre cada amostra (data de coleta, local de coleta, coordenadas geográficas, etc.) podem ser encontradas banco de dados de domínio público Earthcape (clique <a href="http://heliconius.ecdb.io/">aqui</a> para acessar o domínio,e <a href="https://heliconius.zoo.cam.ac.uk/databases/">aqui</a> para FAQ). Para mais informações, favor entre em contato com Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) e/ou Erika Pinheiro de Castro (ecp38[at]cam.ac.uk).</p>
Data complementing the Avian influenza overview December 2021 – March 2022
<p>The Annexes contain data and information on highly pathogenic avian influenza outbreaks in poultry and detections in wild birds in Europe</p> <p>Annex A – Characteristics of the HPAI A(H5Nx)-positive poultry establishments</p> <p>Annex B – Applied prevention and control measures on avian influenza</p> <p>Annex C – Data on wild birds</p>
Environmental effects of the December 2020 earthquake sequence near Petrinja, Croatia
<p>Those files (Spreadsheet of observations of coseismic deformation features, Surface rupture shapefile) are in support of the paper submitted to Geophysical Journal International. The data have been collected by the coauthors of this paper, implemented in the files by them, and the database has been processed and verified by SB, AT, SP.</p> <p>The complete of authors of the paper is as follows:</p> <p>S. Baize<sup>1</sup>, S. Amoroso<sup>2,3</sup>, N. Belić<sup>4</sup>, L. Benedetti<sup>5</sup>, P. Boncio<sup>2</sup>, M. Budić<sup>4</sup>, F.R. Cinti<sup>3</sup>, M. Henriquet<sup>5</sup>, P. Jamšek Rupnik<sup>6</sup>, B. Kordić<sup>4</sup>, S. Markušić<sup>7</sup>, L. Minarelli<sup>3</sup>, D. Pantosti<sup>3</sup>, S. Pucci<sup>3</sup>, M. Špelić<sup>4</sup>, A. Testa<sup>2</sup>, S. Valkaniotis<sup>8</sup>, M. Vukovski<sup>4</sup>, J. Atanackov<sup>6</sup>, J. Barbača<sup>4</sup>, M. Bavec<sup>6</sup>, R. Brajkovič<sup>6</sup>, V. Brčić<sup>4</sup>, M. Caciagli<sup>3</sup>, B. Celarc<sup>6</sup>, R. Civico<sup>3</sup>, P.M. De Martini<sup>3</sup>, R. Filjak<sup>4</sup>, F. Iezzi<sup>2</sup>, A. Moulin<sup>5</sup>, T. Kurečić<sup>4</sup>, M. Métois<sup>9</sup>, R. Nappi<sup>3</sup>, A. Novak<sup>6,10</sup>, M. Novak<sup>6</sup>, B. Pace<sup>2</sup>, D. Palenik<sup>4</sup>, T. Ricci<sup>3</sup></p> <p><sup>1</sup> Institut de Radioprotection et de Sûreté Nucléaire, IRSN/PRP-ENV/SCAN/BERSSIN, 92262 Fontenay-Aux-Roses, France</p> <p><sup>2</sup> Università “G. d'Annunzio” Chieti - Pescara, Via dei Vestini 31, 66100, Chieti, Italy</p> <p><sup>3</sup> Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143 Rome, Italy</p> <p><sup>4</sup> Croatian Geological Survey, Department of Geology, Sachsova 2, Zagreb, 10000, Croatia</p> <p><sup>5</sup> Aix Marseille Univ, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, France</p> <p><sup>6</sup> Geological Survey of Slovenia, GeoZS, Dimičeva ulica 14, 1000 Ljubljana, Slovenia</p> <p><sup>7</sup> University of Zagreb, Faculty of Science, Department of Geophysics, Zagreb, Croatia</p> <p><sup>8</sup> Department of Civil Engineering, Polytechnic School, Democritus University of Thrace, 67100 Xanthi, Greece</p> <p><sup>9 </sup>Laboratoire de Géologie de Lyon (LGLTPE), Université Claude Bernard Lyon 1, campus de La Doua, 69100 Villeurbane, France</p> <p><sup>10 </sup>University of Ljubljana, Faculty of Natural Sciences and Engineering, Department of Geology, Croatia</p>
GPR-BMP-SPI: High spatial resolution multi-scale SPI datasets over China from January 1984 to December 2020
<p>The datasets include standard precipitation index (SPI) at 1-month, 3-month, 6-month, 9-month and 12-month scales over the main terrestrial lands of China from January 1984 to December 2020. The SPI datasets were produced by blending the information from meteorological stations, and precipitation products, as well as topographical and geographical variables based on Gaussian process regression (GPR) models.</p> <p>The meteorological station data are from the China Meteorological Data Service Centre. Five precipitation products are used: (1) CHIRPS Daily: Climate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final); (3) ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis; (3) FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System; (4) PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record; (5) TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces.</p> <p>The maps of the difference of the confidence intervals (the upper prediction limit minus the lower prediction limit) at a significance level of 95% are also provided to show the spatial uncertainty of every single SPI map.</p> <p>The drought events were counted during 1984-2020 at annual and seasonal scales. The variables related to the drought events are presented in “Drought_Event.zip”.</p> <p>Reference: He, Q., Wang, M., Liu, K., Li, B., & Jiang, Z. (2023). Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning. <em>Weather and Climate Extremes</em>, <em>40</em>, 100567.</p> <p> </p>
EXTREMA: Ballistic capture sets at Mars with initial epoch December 9, 2023
<p>EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture, the focus of this work. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019.</p> <p>In Pillar 3 it is investigated how a spacecraft can attain ballistic capture in autonomy. Ballistic capture is an event that occurs in extremely-rare occasions, and requires acquiring a proper state (position, velocity) far away from the target planet [1]. Massive numerical simulations are required to find the specific conditions that support capture [2]. On average, 1 out of 10,000 conditions explored by the algorithm grants capture [3]. The union of these points defines the capture set, which in turn is used to find the capture corridors: these are streams of orbits that can be targeted far away from the planet and that guarantee ballistic capture.</p> <p>The data set is made of two items containing initial conditions of weakly-stable, unstable, crash, moon-crash, and capture sets at Mars. The first is a text file containing initial conditions used to compute time regressions. The second item is a text file containing 100,000 initial conditions generated randomly in the search space and used to compute the quality criterion of the differential algebra mapping. All initial conditions have initial epoch 09 DEC 2023 00:45:18.363 (UTC). The grid of initial conditions is built to maximize the capture ratio for Mars (see Figure 10 in [3]). Initial conditions are propagated in high-fidelity. The equations of motion of the restricted n-body problem including solar radiation pressure are considered.</p> <p>For additional information about the EXTREMA project visit the page <a href="http://extrema.polimi.it">extrema.polimi.it</a>.</p> <p>The data set is associated to the following journal article:</p> <ul> <li> Article: Stable sets mapping with Taylor differential algebra with application to ballistic capture orbits around Mars</li> <li> Journal: Celestial Mechanics and Dynamical Astronomy</li> <li> Authors: Thomas Caleb (corresponding author: thomas.caleb@student.isae-supaero.fr), Gianmario Merisio, Pierluigi di Lizia, and Francesco Topputo</li> <li> DOI: <a href="https://www.doi.org/10.1007/s10569-022-10090-8">10.1007/s10569-022-10090-8</a></li> </ul> <p><strong>References</strong><br> [1] F. Topputo and E. Belbruno,'Earth–Mars transfers with ballistic capture', Celestial Mechanics and Dynamical Astronomy, Vol. 121, No. 4, 2015, pp. 329–346. DOI: <a href="http://doi.org/10.1007/s10569-015-9605-8">10.1007/s10569-015-9605-8</a>.<br> [2] F. Topputo and E. Belbruno, 'Computation of weak stability boundaries: Sun–Jupiter system', Celestial Mechanics and Dynamical Astronomy, Vol. 105, No. 1-3, 2009, pp. 3–17. DOI: <a href="http://doi.org/10.1007/s10569-009-9222-5">10.1007/s10569-009-9222-5</a>.<br> [3] Z.-F. Luo and F. Topputo, 'Analysis of ballistic capture in Sun–planet models', Advances in Space Research, Vol. 56, No. 6, 2015, pp. 1030–1041. DOI: <a href="http://doi.org/10.1007/s10569-009-9222-5">10.1016/j.asr.2015.05.042</a>.</p>
SPEAR_SST_data(August-December_initialization)
<p>Data for supporting the finding in the manuscript "Skillful forecasts of springtime CONUS tornado activity up to a year in advance" by Tseng et al. (2022)</p>
Survey on the Effects of COVID-19 on the Wellbeing of Mexico City Households (ENCOVID- 19 CDMX – DECEMBER 2020)
<p>Amid the COVID-19 outbreak, the ENCOVID-19 CDMX provides information on the well-being of Mexico City households in four main domains: labor, income, mental health, and food insecurity. It offers timely information to understand the social consequences of the pandemic and the lockdown measures. It is a cross-sectional telephone survey that, in addition to the four main domains and a set of COVID19-related questions, includes key indicators to capture the impact of the pandemic on issues like education, social programs, and crime. This is the second dataset of the project, corresponding to December 2020, collected eight months after the lockdown began in Mexico. Data collection was performed from November 29 to December 10, 2020.</p>
Multi-instrument observations of the ionospheric response to the 26 December 2019 solar eclipse over Indian and Southeast Asian longitudes
<p>Here, the impact of the 26 December 2019 solar eclipse on the equatorial and low latitude ionosphere has been investigated using ground and space-based observations over Indian and Southeast Asian longitudes. The high-resolution Ionosonde observations at Tirunelveli, GPS TEC observations from a chain of GPS receivers along and across the eclipse path, TIMED-SABER, and Ionospheric Connection Explorer (ICON) satellites were utilized to investigate the eclipse-induced variations in electron density and thermospheric cooling. We noticed a tremendous increase and decrease in the base height of the F-layer, resembling the nighttime Pre-Reversal Enhancement (PRE). Near the eclipse maximum, a strong blanketing sporadic E layer was observed at Tirunelveli with a top frequency of ~18 MHz for 1 hour and 26 minutes. Satellite traces (STs) and ‘U’ shaped ionograms were noticed for the first time over Tirunelveli during eclipse maximum and end phases. The ‘STs’ and ‘U’ shaped traces indicate the presence of short-period gravity waves or TID type of wave perturbations over the Indian region. A maximum of ~5-7 TECU (30-40%) decrease in TEC is observed on the eclipse day for iisc, hyde, and tiru stations. Periodogram analyses of TEC data showed the presence of wavelike structures with periodicities of 18-24 minutes for different stations. Simultaneous observations from the ICON satellite showed an increase and decrease in hmF2 and NmF2 which matches well with the ionosonde observations from Tirunelveli. The temperature profiles from TIMED-SABER and ICON satellites showed a reduction and enhancement in the lower and upper E regions respectively.</p>
Data for continuous and discrete measurements of carbonate parameters in a productive coastal region in the Northwestern Pacific (36°09'13.5''N, 129°24'04.9''E) from January to September in 2019 and from March to December in 2020
<p><span>Photosynthetic organisms shift the dynamics of surface pCO<sub>2</sub> driven by the sea surface temperature change (thermodynamic driver) by assimilating C from seawater. Here we measured net C uptake </span><span>in a macroalgal habitat</span> <span>of coastal Korea for two years (2019–2020) and found that the macroalgal habitat</span> <span>contributed </span><span>5.8 g</span><span> C m</span><sup><span>-</span><span>2</span></sup><span> month</span><sup><span>-</span><span>1</span></sup><span> of </span><span>the net C uptake during the growing period (the cooling period, September</span><span>-</span><span>May). This massive C uptake changed the thermodynamics-driven seasonal dynamics such that the air</span><span>-</span><span>sea equilibrium of </span><span>pCO<sub>2</sub></span><span> was pushed into disequilibrium. T</span><span>he </span><span>surface </span><span>pCO<sub>2</sub></span><span> dynamics during the cooling period were </span><span>mostly influenced by the seasonal decrease in temperature and the proliferation of macroalgae, while the dynamics </span><span>during the warming period </span><span>(the stagnant period, </span><span>June</span><span>-</span><span>August) </span><span>closely followed that predicted based solely on the change in sea surface temperature only </span><span>(thermodynamic driver)</span><span>.</span><span> In contrast to the phytoplankton-dominated offshore waters (where phytoplankton populations are large in spring and summer), the impact of coastal water macroalgae on surface </span><span>pCO<sub>2</sub></span><span> dynamics was most pronounced during the cooling period, when the magnitude of </span><span>pCO<sub>2</sub></span><span> change was as much as twice that resulting from temperature change. Our study shows that</span><span> t</span><span>he distinctive features of the macroalgal habitat—in particular the seasonal temperature extremes (~18°C difference), the </span><span>active macroalgal metabolism,</span><span> and anthropogenic </span><span>nutrient</span><span> inputs—collectively influenced</span><span> the seasonal decoupling of seawater and air </span><span>pCO<sub>2</sub></span><span> dynamics</span><span>.</span></p>
TESS-December-2017
<p>Monthly dataset generated with the measurements of the TESS network (December 2017)</p>
Animation of 24h-metereologic radar data (maximum reflectivity and CFAD) for the Liebenbergvlei, Freestate, South Africa on December 30 2001
<p>The visualisation of metereologic radar data for the Liebenbergvlei, RSA, covers the time between December 30 2001 00:00:00 hours and 23:59:00 hours in five minute steps. The data war recorded by the X-band metereological radar station operated by the South African Weather Service in the Liebenbergvlei in the Freestate, South Africa. For each time step, the maximum reflectitivity is depicted on the left, while the contoured frequency by altitude (CFAD) diagram is shown on the right (derived from plan position indicator (PPI) volume data from 1 to 18km height above ground). The processing of the data based on MDV-files was done in GRASS GIS v5.0.</p>
Table S1: Records of bramble shark sightings on 16 December, 2023, offshore of Fujairah, UAE, in the Gulf of Oman. Time is provided in Zulu time (GMT), not in local time. Vessels where sightings occurred are labeled as remotely operated vessels (ROV: Chimera) or human-driven submersibles (Sub: Neptune and Nadir).; Video S1: Video footage of a bramble shark (Echinorhinus brucus) encounter with a submersible at 780 m depth offshore from Fujairah, United Arab Emirates.
<p><span>Table S1: Records of bramble shark sightings on 16 December, 2023, offshore of Fujairah, UAE, in the Gulf of Oman. Time is provided in Zulu time (GMT), not in local time. Vessels where sightings occurred are labeled as remotely operated vessels (ROV: Chimera) or human-driven submersibles (Sub: Neptune and Nadir).</span></p> <p><span>Video S1: Video footage of a bramble shark (<em>Echinorhinus brucus</em>) encounter with a submersible at 780 m depth offshore from Fujairah, United Arab Emirates.</span></p>
Hourly foF2 Data at Zhongshan Station for March, June, September, and December 2014
<p><span>This dataset contains hourly values of the critical frequency of the F2 layer (foF2), measured at Zhongshan Station, Antarctica, during the solar maximum year of 2014. The measurements cover four key months: March, June, September, and December.<br>The ionospheric data are obtained from long-term observations of the digisonde DPS-4 measurements at Zhongshan, Antarctica. The ionosondes provide ionograms via transmitting radio pulses with frequency coverage of 1.0–30 MHZ and receiving echoes reflected by various layers of the ionosphere. The ionospheric F-region critical frequencies (foF2) are calculated after processing ionograms. The time resolution of the ionosonde data used is 1 h.<br>The raw digisonde DPS-4 data at Zhongshan Station can be requested through the Data-sharing Platform of Polar Science (http://www.chinare.org.cn), which is maintained by the Polar Research Institute of China and the Chinese National Antarctic & Arctic Data Center.<br></span></p>
December 2020 Eclipse Festival of Frequency Measurement: 10 MHz Data
<p>These data are the results of a frequency estimation algorithm applied to WAV recordings of a 10 MHz carrier. </p> <p>metadata.csv contains station records, notably decimal lat/lon coordinates for all stations. Ham radio callsigns are used as identifiers.</p> <p>Dec2020EclipseFestival-complete.csv contains the processed data. The following stations' data are included in this file: W8EDU, PY2ZX, AD0CY, DV1YKL, IU2EFA, JBRUH (*SWL), KI4MZA, SV2BBO, PA0SLT, VA3CBN, W2AAT, KB2YSI, VE3FYN, W2AGC, W3OA, N2UQS, K6FOD, W1ZOT, LU7WA, JJ1BDX, N2OA, K1FR-2, VK7KPC, VE4GLS, K9VB, ZS6YZ, VK5FPAW, W3MR, UA9LIF, VK2DLR, VE3YX, N6ITY-2, CE2MHW, N2KJD, W2MV, KI5JCN, G0LHZ, CA6NDC, I3JPA, CA6JAU, W7SUA, KN7BT, W8KTM, VA3EDB, W0EJO, KX4PR, PU3VRW, N6ITY-1, EA7QL, LU2JGP, KD7WPQ, W1CS, K1FR-1, G1KOT, HH2MK, DO1REH, N8VMX, VE6IXD, JI3XOK, VE6GRT, F4IEW, WV5L, K3JDB, KD2REU, KD8RV</p> <p>___________________________________________________________________________________________________</p> <p>*SWL: This indicates "shortwave listener." JBRUH is an abbreviation of a name, not a callsign.</p> <p>The station list was obtained using the following terminal command: <br> awk '{print $4}' Dec2020EclipseFestival-complete.csv | sed '/^$/d' | sed 's/Tone,//g' | sed 's/WWVH//g' | sed 's/WWV//g' | sed 's/,//g' | uniq</p> <p>___________________________________________________________________________________________________</p> <p><strong>Errata: </strong><br> -F4IEW's UTC shift may be incorrect<br> -VK7KPC's coordinates should be corrected to –41.56881 and 147.24237</p>
LBHL (~170 nm) UVI images of Polar satellite from December 1996 to January 1997
<p>The UVI aboard the Polar satellite can obtain the ultraviolet auroral intensity distribution in the northern hemisphere. In order to reduce the influence of dayglow on UVI image data, we only used LBHL (~170 nm) UVI images from December 1996 to January 1997, which have full aurora oval (the aurora oval region in the northern hemisphere during this period is in the polar night region, and the aurora image is weakly affected by dayglow and solar light). Each image has a pixel size of 200×228 and a spatial resolution of about 0.04° per pixel (UVI auroral images of Polar satellite taken at apogee have a pixel size of 40×40 km at 100 km above the ground), and the value of each pixel is converted to the value with auroral intensity, the unit is photons/cm<sup>2</sup>/s.</p> <p>These UVI data is save as the mat file of Matlab.</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.