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304 results for “lightning”
West Texas Lightning Mapping Array - KTaL 2015-2016
<p>West Texas Lightning Mapping Array data collected near Lubbock, Texas as part of the Kinematic Texture and Lightning (KTaL) field campaign in 2015-2016. Includes VHF source locations and clustered flashes and their physical properties. Data are a subset of the full record, corresponding to the storms studied in a forthcoming publication.</p>
Dataset of lightning flashovers on medium voltage distribution lines
<p>This <strong>synthetic dataset</strong> was generated from <strong>Monte Carlo</strong> simulations of <strong>lightning flashovers</strong> on medium voltage (MV) <strong>distribution lines</strong>. It is suitable for training <strong>machine learning</strong> models for classifying lightning flashovers on distribution lines. The dataset is <strong>hierarchical</strong> in nature (see below for more information) and <strong>class imbalanced</strong>.</p> <p>Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Three different methods of indirect strike analysis have been implemented, as follows: Rusck's model, Chowdhuri-Gross model and Liew-Mar model. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.</p> <p><strong>Dataset</strong> consists of two independent distribution lines, with heights of 12 m and 15 m, each with a flat configuration of phase conductors. Twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart [2]. CFO level of the 12 m distribution line is 150 kV and that of the 15 m distribution line is 160 kV. Dataset consists of <strong>10,000 simulations</strong> for each of the distribution lines.</p> <p>Dataset contains following variables (features):</p> <ul> <li>'<em>dist</em>': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,</li> <li>'<em>ampl</em>': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),</li> <li>'<em>front</em>': lightning current wave-front time (us), generated from the Log-Normal distribution; it needs to be emphasized that amplitudes (ampl) and wave-front times (front), as random variables, have been generated from the appropriate bivariate probability distribution which includes statistical correlation between these variates,</li> <li>'<em>veloc</em>': velocity of the lightning return-stroke current defined indirectly through the parameter "w" that is generated from the Uniform distribution [50, 500] m/us, which is then used for computing the velocity from the following relation: v = c/sqrt(1+w/I), where "c" is the speed of light in free space (300 m/us) and "I" is the lightning-current amplitude,</li> <li>'<em>shield</em>': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,</li> <li>'<em>Ri</em>': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,</li> <li>'<em>EGM</em>': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],</li> <li>'<em>ind</em>': indirect stroke model used for analyzing near-by indirect lightning strikes; following options were implemented: 'rusk' for the Rusck's model, 'chow' for the Chowdhuri-Gross model (with Jakubowski modification) and 'liew' for the Liew-Mar model; statistical distribution of these three models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.6, 0.2, 0.2],</li> <li>'<em>CFO</em>': critical flashover voltage level of the distribution line's insulation (kV),</li> <li>'<em>height</em>': height of the phase conductors of the distribution line (m),</li> <li>'<em>flash</em>': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome/label (i.e. binary class).</li> </ul> <p>Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references cited below.</p> <p><strong>References</strong>:</p> <ol> <li>A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.</li> <li>J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005.</li> <li>A. Borghetti, C. A. Nucci and M. Paolone, An Improved Procedure for the Assessment of Overhead Line Indirect Lightning Performance and Its Comparison with the IEEE Std. 1410 Method, IEEE Transactions on Power Delivery, Vol. 22, No. 1, 2007, pp. 684-692.</li> </ol>
Data files for: Comparison of high-speed optical observations of a lightning flash from space and the ground
<p>This dataset accompanies the paper in the AGU open acces journal <em>Earth and Space Science</em>, special section "A New Era of Lightning Observations From Space"<em> </em>and contains data of the lightning flash in Colombia detected by:</p> <ul> <li>Geostationary Lightning Mapper (GLM) </li> <li>Lightning Imaging Sensor on the International Space Station (ISS-LIS) </li> <li>Atmosphere-Space Interactions Monitor - Modular Multi-spectral Imaging Array (ASIM MMIA)</li> <li>High-speed intensified Phantom V7.3 camera fielded in Cartagena, Colombia.</li> </ul> <p>The high-speed video .cine files can be read by (free) CineViewer and PCC software of Vision Research Inc. which can convert to avi files. For any questions, contact the first author.</p>
Quantifying hail and lightning risk factors using long-term observations around Australia - Hail and Lightning Datasets
<p>These data accompany a paper referenced as doi: 10.1029/2020JD033101. Two spreadsheets are provided as used to derive the hourly and monthly occurrence of hail or lightning events within the domains of the ten radars sites (Melbourne, Wollongong, Gympie, Grafton, Canberra, Marburg, Adelaide, Namoi, Perth, Hobart). Lightning events as defined in this paper (derived from post-processed lightning information) were provided for 2005-2018 and hail data from 1997-2018 (noting that the start date of hail is dependent on when the respective radar site was established). A value of 1 indicates a lightning/hail event occurred during that hour (in UTC +0). Information on processing methods and data are provided in the published paper for the doi listed above.</p>
Transkribus - Handwritten Text Recognition for Premodern Documents (SIMS 2020 Lightning Talk)
<p>Transkribus is a platform for text recognition and can be used via the Transkribus Expert Software (available after registration: transkribus.eu). Through Transkribus different tools for document analysis and text recognition can be directly applied. The intro demonstrates very briefly how Transkribus can help with regards to premodern documents especially since a variety of pre-trained models are already available: for Latin (prints and handwriting), for early modern vernaculars in French, Dutch, English, and German. For more information go to transkribus.eu.</p> <p>Presented as a Schoenberg Symposium 2020 Lightning Talk</p>
Lightning Potential Index Using ICON Simulation at the km-scale over the Third Pole Region: ISS-LIS events and ICON-CLM simulated LPI
<p>This dataset contains records of lightning events recorded by the International Space Station (ISS) Lightning Imaging Sensor (LIS) from October 2019 to September 2022 in the Third Pole region. Furthermore, the Icosahedral Nonhydrostatic Weather and Climate Model in Climate Limited-Area Mode (ICON-CLM) was utilized to simulate the hourly Lightning Potential Index (LPI) over the Third Pole region for the same duration. The aforementioned dataset was utilized in the creation of the research article titled "Modeling Lightning Activity in the Third Pole Region: Performance of a km-scale ICON-CLM Simulation" authored by Prashant Singh and Bodo Ahrens. The paper has been submitted to the journal Atmosphere. In CORDEX-FPS-CPTP contribution no. 17 (GUF), you can find more data from ICON-CLM, such as precipitation, CAPE, wind vectors, and more.</p>
Distribution and Characteristics of Lightning-Ignited Wildfires in Boreal Forests - the BoLtFire database
<p>This repository holds a dataset of lightning-ignited wildfires across the boreal biome. The BoLtFire dataset covers the period 2012 to 2022 and encompasses 6,902 fires - 4,201 in Eurasia and 2,701 in North America.</p> <p>The layers included in this dataset are: FireID, StartDate, EndDate, FireYdear, AreaHa (burned area), ClassSize, BiomeName, EcoBiome, EcoName, EcoID, Realm, LCDN (Land cover number), LCName (land cover name), Country, Continent, HoldoverD (days), HoldoverRD (holdover rounded), IgnLat (Ignition location Latitude), IgnLong (Ignition Location Longitude), DisPol (Distance of the ignition location to the fire perimeter if it is located outside the polygon), and PerCheck (designates if the ignition location is within the fire perimeter or oustide the perimeter).</p> <p> </p> <p>The datasets are available per continent (North America, Europe, and Asia) as shapefiles. The spatial reference system is Global LANd Cover mapping and Estimation (GLANCE) Grids - Version 01 CRS.</p> <p> </p> <p>*Please note: Versions 1 and 2 are missing LIW from Canada between 2021-2022. </p>
The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series
<p><strong>The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series</strong></p> <p>This repository contains global lightning stroke density and stroke power calculated from georeferenced stroke count data from the World Wide Lightning Location Network <a href="http://wwlln.net">WWLLN</a>. The real-time raw stroke count data were reprocessed by WWLLN to remove artifacts and improve geolocation, which resulted in the "AE" georeferenced and timestamped stroke count data. These data were then gridded at 0.5 degree 5 arc-minute and hourly resolution, converted into density, and corrected for detection efficiency using the WWLLN global gridded detection efficiency maps. Mean, median, and standard deviation of stroke power are also provided at 30-minute resolution. The corrected hourly rasters were then aggregated into daily and monthly totals and into a multi-year monthly mean climatology. The data cover the period 2010-2024 and will be updated in the coming years.</p> <p>For a complete description of the data see:</p> <p>Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. <em>Earth System Science Data, 13</em>(7), 3219-3237. <a href="dx.doi.org/10.5194/essd-13-3219-2021">doi:10.5194/essd-13-3219-2021</a></p> <p>Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. <em>Earth System Science Data, 14</em>(12), 5665-5670. <a href="dx.doi.org/10.5194/essd-14-5665-2022">doi:10.5194/essd-14-5665-2022</a></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) files and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: half-degree and 5 arc-minute</li> <li>Temporal extent: 2010-2024</li> <li>Temporal resolution: daily and monthly*1,2</li> </ul> <p><strong>Variables included in this release</strong></p> <ul> <li>Lightning density (strokes km-2 day-1)</li> <li>Lightning mean, median, and standard deviation of stroke power (MW, 30 arc-minute version only)</li> </ul> <p>For further details, see <a href="https://github.com/ARVE-Research/WGLC">https://github.com/ARVE-Research/WGLC</a></p> <p>1*5479 elements in the time dimension for daily data; 180 for monthly data; 12 for the climatology.</p> <p>2*Daily fields currently available at 30-minute resolution only.</p> <p><a href="../doi/10.5281/zenodo.4774528">The WWLLN Global Lightning Climatology and timeseries (WGLC) </a>© 2025 by Jed O. Kaplan is licensed under <a href="http://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1">CC BY-SA 4.0</a></p>
Lightning flashover simulations on medium voltage distribution lines
<p>[Version 1.2] This version of the dataset fixes a bug found in the previous versions (see below for more information).</p> <p>Dataset has been generated from the <strong>Monte Carlo</strong> simulations of <strong>lightning flashovers</strong> on medium voltage (MV) <strong>distribution lines</strong>. It is suitable for training <strong>machine learning</strong> models for classifying lightning flashovers on distribution lines, as well as for line insulation coordination studies. The dataset is hierarchical in nature (see below for more information) and class imbalanced.</p> <p>Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.</p> <p><strong>Dataset</strong> consists of the following variables:</p> <ul> <li>'dist': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,</li> <li>'ampl': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),</li> <li>'veloc': velocity of the lightning return stroke current (m/us), generated from the Uniform distribution [50, 500] m/us,</li> <li>'shield': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,</li> <li>'Ri': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,</li> <li>'EGM': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],</li> <li>'CFO': critical flashover voltage level of the distribution line's insulation (kV); following three levels have been used: 150, 150, and 160 kV, respectively, for three different distribution lines of height 10, 12, and 14 m,</li> <li>'height': height of the phase conductors of the distribution line (m); distribution line has flat configuration of phase conductors with following heights: 10, 12, and 14 m; twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart; data set consists of 10000 simulations for each line height,</li> <li>'flash': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome (binary class).</li> </ul> <p><strong>Note</strong>: It should be mentioned that the critical flashover voltage (CFO) level of the line is taken at 150 kV for the first two lines (10 m and 12 m) and 160 kV for the third line (14 m), and that the diameters of the phase conductors and shield wires for all treated lines are, respectively, 10 mm and 5 mm. Also, average grounding resistance of the shield wire is assumed at 10 Ohm for all treated cases (it has no discernible influence on the flashover rate). Dataset is class imbalanced and consists in total of 30000 simulations, with 10000 simulations for each of the three different MV distribution line heights (geometry) and CFO levels.</p> <p><strong>Important</strong>: Version 1.2 of the dataset fixes an important bug found in the previous data sets, where the column 'Ri' contained duplicate data from the column 'veloc'. This issue is now resolved.</p> <p>Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references below.</p> <p><strong>References</strong>:</p> <p>J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005, doi: 10.1109/TPWRD.2005.848734.</p> <p>A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.</p>
Data for "Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes" by Shao et al.
<p>Data set for manuscript of “Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes” by Shao et al. submitted to Journal of Geophysical Research-atmosphere</p>
Volcanic Lightning and Continual Radio Frequency Impulses at Sakurajima Volcano: A Multiparametric Dataset
<p>This is a multiparametric data set of volcanic activity at Sakurajima volcano in Japan. The data set was collected in May and June 2015. The data set includes the following types of data: Lightning Mapping Array data, slow and fast electric field waveforms, log-RF VHF data, infrasound data, plume height, velocity, and temperature data.</p>
Lightning Data at Säntis Tower (August 2016)
<p>3 sets of lightning data (current and E-field) associated with 3 flashes to the Säntis tower occurred in August 2016.</p>
Data files for: The Urban Lightning Effect Revealed with Geostationary Lightning Mapper Observations
<p>Warm season (June, July, August; JJA) Geostationary Lightning Mapper (GLM) observations from 2018-2021. Original processing of 20-second Level 2 GLM packets into 5-min files and quality control performed by CPTEC/INPE. Complete description provided by Oda et al. (2022). Further processing conducted locally to isolate GLM flash data for the Southeast U.S., accumulate the 5-minute files into yearly and 4-year bins, and to derive total flash count ("flash"), flash days ("fday"), and average flashes per flash day ("fpfd").</p> <p>Included files:</p> <ul> <li>GOES-16 Full Disk <ul> <li>Yearly files containing all GLM data classes (flash, group, and event) with 5-minute timesteps</li> <li>Yearly files containing only GLM flash data with 5-minute timesteps</li> </ul> </li> <li>Southeast (lat-lon bounds: -96.00, -74.00, 41.00, 24.00) <ul> <li>Final 4-year aggregate file containing derived total lightning metrics ready for analysis in GIS</li> </ul> </li> </ul>
Ultra-Slow Discharges that Precede Lightning Initiation dataset
<p><strong>Description of Dataset:</strong></p> <p>These data were used to produce figures for the Geophysical Research Letters publication: <em>Ultra-Slow Discharges that Precede Lightning Initiation</em>. Data are provided in human readable ".csv" and ".dat" formats.</p> <p>Arrays within the in csv files are stored in rows, headings for each row are the array titles. The arrays t, x, y, z are the time, easting, northing, and altitude for arrays after cuts have been performed, while the cutT, cutX, cutY, and cutZ are the corresponding arrays for the t, x, y, z data that have been cut from the fit. The fitT, fitX, fitY, and fitZ are the fit curves for t, x, y, and z. The hist_bins, binwidth, hist, xPDF, and PDF are the bins, bin width, histogram counts, PDF x values, and PDF for the lateral spread from the fit. All data values are in milliseconds and meters.</p> <p>The arrays within the dat files are stored in equally spaced rows. The first row contains the header and array names for the columns. Data for TRI-D plots contains the source index, time in milliseconds, easting in kilometers, northing in kilometers, altitude in kilometers, and intensity in units of galactic background. For the impulsive image figure (FigureS5_data.dat, Supplemental figure S5), the data are the block index, time in milliseconds, easting in kilometers, northing in kilometers, altitude in kilometers, and the <span class="math-tex">\(\chi^2\)</span> for the located source.</p>
Machine-learning based lightning nowcasting data archive
<p>This data archive contains the derived data supporting the findings of article "Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset". The paper is currently in the preprint version: https://doi.org/10.21203/rs.3.rs-2616886/v1</p> <p>The prediction results in this data archive are generated by various models:</p> <p>1. Current model. The model involves data input of aerosol observations together with meteorological variables and auxiliary datasets, as well as data enrichment by Geostationary Lightning Mapper (GLM). In the demo of the dataset, the year of 2020 is trained and predicted on a cross-validation scheme. </p> <p>2. LMA model. The model acts as the baseline model considering only data label obtained from the ground-based Lightning Mapping Array (LMA), which observes accurate lightning occurrence in limited spatial range.</p> <p>3. No-AOD model. The model acts as the baseline model considering no aerosol observation is utilized during the machine learning process. </p> <p>The model results are demonstrated in a continuous value in 0-1. Trade-offs between Probability of Detection (POD) and False Alarm Ratio (FAR) can be optimized by selection of different thresholds. </p> <p>Other datasets:</p> <p>1. Dataset for training. It is for the public use of machine learning training for the current model and no-AOD model (training input features vary).</p> <p>2. PM2.5 dataset. The real-time spatially continuous and hourly-level PM<sub>2.5</sub> dataset is obtained following a published method by Zeng et al.. In this method, the fundamental in-situ measurements are obtained from Air Quality System (AQS) monitoring network operated by United States Environmental Protection Agency.</p> <p>Reference:</p> <p>Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1]</p> <p>Zeng, Z. et al. Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research 254, 105516 (2021).</p>
Data associated with "Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones"
<p>These data include all tropical cyclone tornado reports from 2013–2020, as part of all data from 1995–2022, included in the Storm Prediction Center (SPC) Tropical Cyclone TORnado database (TCTOR; Edwards and Mosier, 2022) used in the following publication:</p> <p>Schenkel, B., K. Calhoun, T. Sandmael, M. Ake, Z. Fruits, B. Kassel, and I. Schick, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. <em>J. Geophys. Res.: Atmospheres</em>, <strong>accepted</strong>.</p> <p><br> Each specific tropical cyclone tornado record has been extracted from the broader SPC tornado database, for all Atlantic and Gulf of Mexico tropical cyclones impacting the continental United States from 1995–2022. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. These data will not be updated regularly.</p> <p>Citation for SPC TCTOR dataset: Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Tropical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on severe local storms (p. 171). Santa Fe, NM.</p> <p> </p>
Data and Supplementary Material for "Insights into Lightning K-Leader Initiation and Development from Three Dimensional Broadband Interferometric Observations" by Jensen et al.
<p>Data and Supplementary Material for the manuscript “Insights into Lightning K-Leader Initiation and Development from Three Dimensional Broadband Interferometric Observations” by Jensen et al., submitted to Journal of Geophysical Research: Atmospheres. Further description in included PDF (01file_description.pdf)</p>
High Energy Lightning Emission Network (HELEN) Flight 7a 06/19/2023
<p>Data gathered during the flight of the High Energy Lightning Emission Network (HELEN) on June 19th, 2023. The first file, 1-Flight7a-Raw Data.zip, is the raw data taken directly from the payloads' micro SD cards. In the second file, 2-Flight7a-Data to Process.zip, the data has been cleaned and is suitable for further processing. The third file, 3-Flight7a-Processed Data.zip, contains data that has been combined and temporally synced. This data can be easily read into MATLAB and used to reproduce results.</p>
Data related to upward lightning events from the Säntis Tower (Switzerland) during the LMA summer campaign 2017
<p>This dataset is associated with the paper titled "Meteorological aspects of self-initiated upward lightning at the Säntis tower (Switzerland)" accepted for publication in Journal of Geophysical Research – Atmospheres (19 September 2019)</p> <p>The data includes lightning mapping array (LMA) data from 3 days of the summer of 2017 measurement campaign that was carried out in the surroundings of the Säntis Mountain (2,505 m ASL, Switzerland). The LMA was deployed to measure upward lightning activity at the Säntis tower. Additional data used in the analysis includes lightning currents from the instrumented Säntis Tower (EPFL and HEIG-VD); Polarimetric weather radar data from the Albis C-band radar (MeteoSwiss); Vertical temperature profiles from the COSMO model and; Wind data from the Säntis meteorological station (MeteoSwiss).</p>
datasets for "VLF Transmitters and Lightning Generated Whistlers 2: Diffusion of Radiation Belt Electrons"
<p>Supporting information for "VLF Transmitters and Lightning Generated Whistlers 2: Diffusion of Radiation Belt Electrons," submitted to Journal of Geophysical Research Space Physics. Diffusion coefficients for selected values of L and energy due to Very Low Frequency (VLF) transmitters and lightning generated whistlers (LGW), as well as Da0a0 and energy drag rates |dE/dt|/E from Coulomb collisions. Also provided are precipitation lifetimes, which include Da0a0 from plasmaspheric hiss but do not account for energy drag. Calculations are presented for high and low-density plasmasphere models, for all four combinations of ducted or nonducted VLF and LGW waves.</p>
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.