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Geomagnetic Storms - Classified - 1993 - 2025
<p>List of Geomagnetic Storms from 1993 to 2025 classified in main and recovery phase. The requirement for a storm to be identified is that it reaches an SMR index of -50 [nT]</p> <p>More information on the SMR ring current index can be found here : http://supermag.jhuapl.edu </p> <p>This version is based on the work published on Geophysical Research letters (GRL) "Plasma sheet Magnetic Flux Transport During Geomagnetic Storms" : https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2024GL110839</p> <p>Columns are in order: index, storm number, minimum SMR, start time, end time, phase characterization, and duration.</p> <p>Comapred to version V2 the latest storms require manual verification</p>
Emilia-Romagna coastal area NBS (OAL ITALY) for storm surge mitigation
<p>Within the framework of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project, the seagrass NBS is presented within a simulation design methodology consisting of the comparison between validated wave numerical simulations for the present/ future climate, and modified wave simulations with marine seagrass. Ten years of WWIII simulations have been executed to generate the wave climatology, particularly over the Emilia-Romagna coastal strip for the present (2010-19) and future climate (2040-49) using MedCordex winds (based on RCP8.5). The WWIII model was modified to include a modified bottom dissipation stress due to submerged vegetation, thereby incorporating the NBS4 as a potential mechanism for wave amplitude reduction. The seagrass species <em>‘Zostera marina’</em> was chosen in this study and an along-shore seagrass belt was first inserted in WWIII and sensitivity experiments were carried out to assess the effects of different types of seagrass landscape designs in the Bellocchio beach. Simulation experiments with and without seagrass (NBS4) were carried out for the present and future climates. Based on the present and future climate simulations, it is noted that the seagrass landscaping is an important aspect in the numerical modelling of vegetation. A combination of broken vegetation stripes and clusters were seen to be effective in reduction of wave energy at the coast in comparison to other landscape designs. The wave height comparisons in the Bellocchio beach, with and without vegetation showed a considerable reduction in wave heights specifically in the higher ranges for both the present and future climates. There exists a strong seasonality in the attenuation rates along the coastal belt with higher attenuations during winter and comparatively lower in summer. In comparison to the present climate, a slightly increased rate of mean attenuation is expected in the future scenarios. Overall, the Zostera Marina seagrass applied for the Emilia-Romagna coastal belt was found to be efficient in reduction of wave energy (> 50%). The limitation being that the experiments were done with rigid seagrass and in the future, we look for advanced parameterization using flexible seagrass.</p> <p>This dataset contains wave model outputs for the OAL-ITALY, mainly:</p> <ul> <li>Bathymetry of the model domain, Spatial maps of mean significant wave height (Hs in m) for present (2010-19) and future climate (2040-49), Seagrass belt position in the Bellocchio beach, Time-series comparison of Hs, with & without vegetation, and Wave attenuation maps.</li> </ul> <ul> <li>Selected locations (station map) for the time series in the Emilia-Romagna coastal belt during the period 2010-19, and 2040-49 (8 stations), Selected locations (station map) in the Emilia-Romagna coastal belt for the time series comparison (with and without vegetation) during the period 2010-19, and 2040-49 (5 stations).</li> </ul> <ul> <li>WW3 time series of wave parameters (wave height, peak period, & direction) for 8 stations in the Emilia-Romagna coastal belt (2010-19, present climate).</li> <li>WW3 time series of significant wave height (Hs in metres) with and without vegetation for 5 stations in the Emilia-Romagna coastal belt (2010-19, present climate).</li> <li>WW3 time series of wave parameters (wave height, peak period, & direction) for 8 stations in the Emilia-Romagna coastal belt (2040-49, future climate).</li> <li>WW3 time series of significant wave height (Hs in metres) with and without vegetation for 5 stations in the Emilia-Romagna coastal belt (2040-49, future climate).</li> </ul>
Data supporting: "Trends in Europe storm surge extremes match the rate of sea-level rise"
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., T. Wahl, M. G. Tadesse, & S. Sparrow. Trends in Europe storm surge extremes match the rate of sea-level rise. <em>Nature</em> 603, 841-845.</strong></p> <p>Please cite the paper above when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_solutions_historical_total.nc</strong>: Bayesian estimates (posterior draws) of the GEV parameters, including trends in the GEV location parameter, at both tide gauge sites and prediction locations. This file also contains the observed surge annual maxima from tide gauge records on which these estimates are conditioned.</li> <li><strong>Bayesian_solutions_historical_contributions.nc</strong>: Bayesian estimates (posterior draws) of the contributions from external forcing and internal climate variability to the trends in the GEV location parameter.</li> <li><strong>Surge_annual_max_ensemble.nc</strong>: ensemble of surge annual maxima used to extract the pattern of response to external forcing.</li> </ul>
Supplementary data for "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise"
<p>This dataset comprises topography and bathymetric data at three coastal locations in Australia (Narrabeen), UK (Perranporth) and used for the publication "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise". Please refer to readme files for metadata</p>
Hailstorm Identification and Tracking over Brazil (HIToB): A Storm Polygons Database From GOES ABI Data from 2018 to 2023
<p>This dataset comprises a detailed record of deep convective storm events tracked across South America from 2018 to 2023, utilizing brightness temperature (BT) data from Channel 13 of the GOES-16 Advanced Baseline Imager (ABI) and the TATHU (Tracking and Analysis of Thunderstorms) toolset, that caused hail-fall over Brazil. The database includes storm identification, tracking details, and associated meteorological variables such as brightness temperature statistics inside the storm polygon at each scene and event classifications (e.g., spontaneous generation, continuity, split, merge). The storms were detected and tracked based on brightness temperature threshold of 235 K, with tracking data refined by a 10% overlap criterion between sequential scenes. The tracked convective systems were filtered for intersections in space and time with verified hail reports from Prevots group. The whole family of storm polygons that matched the reports were exported to this database with SpatiaLite enabled dtaa format, in order to make it easier for spatial data queries and analysis. Some example queries using Python library SQLAlchemy are displayed in the code repository as well as the process of creating the tables in the database.<br><br>The data is organized in three tables: "storms", "storm_events" and "intersections". In table "storms" are the records of storm families identifier. Each identifier represents a sequence of storm polygons tracked over subsequent satellite scenes. Table "storm_events" holds the evolution of the storm's geometry through its lifecycle, including BT's mean, minimum and standard deviation inside the storm polygon; as well as storm's pixel count (i.e. storm size). Intersections table stores every instance where a storm event polygon intersects with a hailstorm report's buffer at the corresponding time. In total, there are 9893 intersections belonging to 2172 unique storm families.</p>
Storm Alex Landslide Inventory
<p>The storm Alex that in 2020 hit the Mediterranean Alps represented and extreme meteorological event triggering devastating floods and landslides in both Italy and France, with severe consequences for people and anthropic settlements. After the Storm Alex, a detailed inventory of rainfall-induced hillslope instability processes was prepared by means of the visual interpretation of VHR satellite imagery in two adjacent mountain catchments of the Liguria Region (northern Italy) impacted by intense rainfall. The inventory map included a total of 302 features classified in debris slide (214), debris slides/debris flow (79) and channelized flow erosion (9). </p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with a prototype based on Arduino UNO. Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Aula I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a prototype based on Arduino UNO, have been investigated:</p> <ul> <li>Temperature and Relative Humidity of the internal environment and Temperature and Relative Humidity of contact on the masonry (with two sensors DHT22 AM2302);</li> <li>Acceleration along the three spatial axes (X, Y and Z), Pitch Index and Roll Index (with Gy 521 sensor);</li> <li>Surface pressure for the movement of a lesion (with FlexiForxe sensor).</li> </ul> <p>The data produced by the sensors were acquired and sent to the computer which automatically saved them every 10 seconds. The dataset is composed of the data obtained from 2017 to 2019 and separated by month in 11 sheets. In total about 40 million values were recorded, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Michelangelo's Cloister, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2018 - 2019
<p>This dataset for monitoring the masonry of Michelangelo's Cloister at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0 and S3);</li> <li>Temperature of masonry (sensors S1, S2 and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2018 to May 2019 and separated by month in 8 sheets. In total more than 2 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
STORM Project: monitoring environment conditions at Baths of Diocletian site (Rome, Italy). Dataset 2018 - 2019
<p>This dataset was created by the Engineering Ingegneria Informatica S.p.A. through a set of prototypes based on Libelium Waspmote for collecting the following parameters: </p> <ul> <li>Climate parameters (Temperature, Relative Humidity, Barometric Pressure, Luminosity, Wind direction/speed and Rainfull) using a Libelim PlugAndSense Agricolture Pro;</li> <li>Environmental Parameters (Monoxide Carbon, Oxigen, Air Polluction, Volatile Organic Compounds VOC, Carbon Dioxide, Nitric Dioxide , Hydrogen Sulfide, Sulfure Dioxide and Particle Matter PM 1, 2.5 and 10) using two nodes: PlugAndSense Smart Cities Pro and Waspmote with gases sensor board;</li> <li>Acoustic Noise Sensor and Vibrations with accelerometer, using a prototype based on Libelium Waspmote.</li> </ul> <p>The data produced by the sensors were acquired and sent to the Meshlium (mini-pc linux based) which automatically saved and sent to the STORM Platform. The dataset is composed of the data obtained from February 2018 to March 2019.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Project STORM: monitoring the masonry of Hall I, at the Baths of Diocletian, with Fiber Bragg Grating (FBG) sensors. RAW Dataset 2017 - 2019
<p>This dataset for monitoring the masonry of Hall I at the Baths of Diocletian (Rome) was created by the University of Tuscia.<br> The measurements are carried out with a Fiber Bragg Grating (FBG) sensors, have been investigated:</p> <ul> <li>Strain of lesions (sensors S0, S2 and S3);</li> <li>Temperature of masonry (sensors S1, and S8);</li> <li>Humidity of masonry (sensors S4, S5, S6 and S7).</li> </ul> <p>The data produced by the sensors were automatically saved them every 30 seconds. The dataset is composed of the data raw obtained from October 2017 to May 2019 and separated by month in 14 sheets. In total more than 4 million values were registered, used to understand the slow hazard phenomena present on the monitored masonry.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
Two-wave Post-Disaster Survey on Climate Change Attitudes: Texas after Hurricane Harvey and the 2021 North American Winter Storms
<p><strong>Overview</strong></p> <p>This repository contains data needed to reproduce the analysis results from Chen et al. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas," <em>Global Environmental Change</em>. It is a study about climate change attitudes and experience with climate disasters across U.S. partisan groups. For details about the data, please see the published paper. Results reproduction code is available at <a href="https://github.com/tedhchen/floodStorm" target="_blank" rel="noopener">https://github.com/tedhchen/floodStorm</a>.</p> <p> </p> <p><strong>Data Set Details</strong></p> <p>`texas_climate_attitudes.csv` contains data from two waves of surveys of Democrats and Republicans living in Texas, with the following groups of variables.</p> <ul> <li>climate change attitudes</li> <li>self-reported exposure to climate disasters</li> <li>scientific information treatment condition and checks</li> <li>political leaning</li> <li>sociodemographics and residential location</li> <li>survey administration details</li> </ul> <p>`outage2021_data.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021.</p> <p>`outage2021_data_multithreshold.RData` contains power outage data for counties and cities in Texas during Feb. 2020 and Feb. 2021, aggregated to the county level based on different thresholds of uncertainty about which cities people live in.</p> <p>`gtrends_archive.RData` contains Google Trends data for "hurricane", "astros", and "power", in Texas between 2017 and 2021.</p> <p> </p> <p><strong>References</strong></p> <p>Please reference the original study when using this data set.</p> <p>Ted Hsuan Yun Chen, Christopher J. Fariss, Hwayong Shin, Xu Xu. 2024. "Disaster Experience Mitigates the Partisan Divide on Climate Change: Evidence from Texas." <em>Global Environmental Change</em>. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102918" target="_blank" rel="noopener">doi:10.1016/j.gloenvcha.2024.102918</a>.</p>
The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region
<p>The file contains the data set and the software for the generation the plots used in the manuscript " The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region".</p>
An optimized buffer for repeatable Multicolor STORM Raw Data
<p>Raw microcopy data used to generate the figures in the paper "An optimized buffer for repeatable Multicolor STORM"</p> <p>Camera: Orca Fusion binning 2x2 readout speed 1</p> <p>Objective 100x/1.3 (Olympus) -> Effective pixel size 130x130 nm</p> <p>Expected Conversion factor according to spec sheet: 0.21 electrons/count</p> <p> </p> <p> </p> <p> </p>
Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon
<p>The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022).</p> <p>All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.)</p> <p>The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times. </p> <p>Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.</p> <p><br> The resize command used was:</p> <p><br> #to resize and then quarter<br> #Dir structure is:<br> # --Desktop<br> # |- originals<br> # |- resized<br> # |- quarters</p> <p>`cd originals`<br> `mogrify -resize 2000x2000 -path ../resized *.jpg`</p> <p>#then quarter them<br> `cd ..`<br> `cd resized`</p> <p>`mogrify -crop 2x2@ +repage -path ../quarters *.jpg`</p> <p>For full size images, please download the jpegs directly from NOAA.</p>
STORM imaging of Bacillus subtilis labeled by fluorescent d-amino acids
<p>Bacillus subtilus cells were labeled by fluorescent d-amino acids, followed by STORM super-resolution imaging.</p> <p>The wide-field image and STORM imaging stack are uploaded.</p>
Storm nutrient dynamics at Andrews Experimental Forest stream gages, 2001 to 2003
This study examines headwater stream nutrients at stream gaging sites during selected storms in the Andrews Experimental Forest. High frequency storm sampling was conducted to examine flow path dynamics, basin characteristics, seasonal trends, and differing responses and mobility among analytes on streamwater nutrient concentrations and fluxes.
Stream sampling for total suspended solids (TSS), volatile suspended solids (VSS), and chemistry during storm events at the Coweeta LTER intensive and hillslope sites in Macon County, NC.
Stream storm samples were collected at 21 streams and rivers in Macon County, NC. Nine intensive sites were monitored in 2010-2011, nine hillslope sites were monitored in 2012-2013, and three river sites were monitored from 2010-2013. An ISCO water sampler was used to collect stream water samples during storm events. Water samples were analyzed at the Coweeta Analytical Lab.
Lysimeter chemistry from the Ice Storm Experiment (ISE) at the Hubbard Brook Experimental Forest
An ice storm simulation was performed at the Hubbard Brook Experimental Forest to evaluate impacts of these extreme weather events on northern hardwood forests. Water was pumped from the main branch of Hubbard Brook and sprayed above the forest canopy in subfreezing conditions so that it rained down and froze on contact with trees. The experiment included five ice storm intensities (0, 6.4, 12.7 and 19.1 mm radial ice accretion) applied in a single year, and one ice storm intensity (12.7 mm) applied in two consecutive years. Samples of soil solution chemistry were collected with lysimeters throughout the year before and after the ice was applied. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Ice Storm Experiment (ISE) Canopy Hemispherical Photos
Abstract An ice storm simulation was performed at the Hubbard Brook Experimental Forest to evaluate impacts of these extreme weather events on northern hardwood forests. Water was pumped from the main branch of Hubbard Brook and sprayed above the forest canopy in subfreezing conditions so that it rained down and froze on contact with trees. The experiment consisted of five treatments, including a control (no ice) and three target levels of radial ice accretion: low (6.4 mm), mid (12.7 mm), and high (19.0 mm). Two of the mid-level treatment plots (midx2) were iced in back-to-back years to evaluate impacts of consecutive storms. This dataset consists of hemispherical photographs of the forest canopy with leaves on and off the trees before and after the various ice treatments. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Compound flood potential from river discharge and storm surge extremes at the global scale
<p>This dataset presents the results presented in <a href="https://doi.org/10.5194/nhess-20-489-2020">Couasnon et al. (2019) - Measuring compound flood potential from river discharge and storm surge extremes at the global scale</a>. For more information about the methods, please refer to the paper. This dataset was created using as input <a href="https://zenodo.org/record/3552820#.XmIdoVxKhaQ">time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014</a>.</p> <p>If using this data, please cite: </p> <p>Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.</p>
ScienceDex guides
Understand access before you commit
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.