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510 results for “STORM”
Persistent viral activity, cytokine storm and lung fibrosis in severe COVID-19
GEO Series GSE149878. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Molecular predictors of prevention of recurrence in hepatocellular carcinoma with sorafenib as adjuvant treatment in the phase 3 STORM trial
GEO Series GSE109211. Homo sapiens. 140 samples. Type: Expression profiling by array.
Self-reactive B cells traverse a perfect storm of somatic mutagenesis to cause a virus-induced autoimmune disease
GEO Series GSE269333. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Prolonged sleep deprivation induces cytokine storm-like syndrome in mammals [RNA-Seq]
GEO Series GSE213494. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
The GICs measurement at the Huangmeishan 220 kV substation during three storms
<p>This data set includes the GICs measurements at the Huangmeishan 220 kV substation during three stroms. The storms took place during 17-18 March 2015, 14-15 December 2015 and 6-8 March 2016, respectively. The time resolution of the data is 1 minute.</p>
Analysis of a gigantic jet in southern China: morphology, meteorology, storm evolution, lightning and narrow bipolar events
<p>The dataset contains radar, Cloud top brightness temperature, lightning, meteorological and sounding data.</p>
Video S.M. 1 from Coastal Impacts of Storm Gloria (January 2020) over the Northwestern Mediterranean
<p>Animation of the simulated period. The first panel shows the atmospheric forcing (wind speed in colours and black arrows; surface atmospheric pressure in black isolines), the second the total simulated surge, and the third panel shows the wind waves.</p>
Data for the paper "Impacts of soft electron precipitations on the neutral density and satellite drag during the 28-29 May 2010 geomagnetic storm"
<p>Data for the paper "Impacts of soft electron precipitations on the neutral density and satellite drag during the 28-29 May 2010 geomagnetic storm" by Qingyu Zhu, Yue Deng, Cheng Sheng, Phil Andersen and Aaron Bukowski</p> <p>GITM_3D.zip: GITM outputs at 12 UT, 05/29/2010 (Figure 3)</p> <p>HPs.zip: NOAA and Z2019 HP (Figure 1)</p> <p>rhos.zip: Neutral density along the GOCE orbit from observation, run1, run2, run3 and JB2008 model (Figures 2 and 4)</p>
Typhoon storm surge in the southeast Chinese mainland modulated by ENSO
<p>AMTSS.xlsx<br> Description:The observed annual maximum typhoon storm surge (AMTSS) at 23 tide-gauge stations located at the typhoon-prone coastal areas of Chinese mainland<br> Correlation coefficients.xlsx<br> Description:Correlation coefficients between the annual maximum storm surges (AMTSSs) and Niño-3.4 index at 23 tide-gauge stations located at the typhoon-prone coastal areas of Chinese mainland<br> Typhoon storm surge in 2015.xlsx<br> Description:Observed maximum storm surge induced by the typhoons of (a) Kujira, (b) Chan-hom, (c) Linfa, (d) Soudelor, (e) Dujuan and (f) Mujigae in 2015 at 23 tide-gauge stations located at the typhoon-prone coastal areas of Chinese mainland</p>
The role of idealized storms in sand wave formation: A numerical modeling study
<p>The typical Delft3D cases for "The role of idealized storms in sand wave formation: A numerical modeling study".</p>
STORM tropical cyclone wind speed return periods as global GeoTIFFs
<p>Global tropical cyclone wind speed return period maps.</p> <p>This dataset is derived with minimal processing from the following datasets created by Bloemendaal et al, which are released with a CC0 license:</p> <p>[1] Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2023): STORM tropical cyclone wind speed return periods. Version 4. 4TU.ResearchData. Dataset. <a href="https://doi.org/10.4121/12705164.v4">https://doi.org/10.4121/12705164.v4</a></p> <p>[2] Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan) et. al. (2023): STORM climate change tropical cyclone wind speed return periods. Version 4. 4TU.ResearchData. Dataset. <a href="https://doi.org/10.4121/14510817.v4">https://doi.org/10.4121/14510817.v4</a></p> <p>Datasets containing tropical cyclone maximum wind speed (in m/s) return periods, generated using the STORM datasets (see <a href="https://www.nature.com/articles/s41597-020-0381-2">https://www.nature.com/articles/s41597-020-0381-2</a>) and STORM climate change datasets (see <a href="https://figshare.com/s/397aff8631a7da2843fc">https://figshare.com/s/397aff8631a7da2843fc</a>). Return periods were empirically calculated using Weibull's plotting formula. The STORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a fixed set of return periods at 10 km resolution in every basin and for every climate model used here (see below).</p> <p>The GeoTIFFs provided in the datasets linked above have been mosaicked into single files with global extent for each climate model/return period using the following code: </p> <p><a href="https://github.com/nismod/open-gira/blob/88dc522dd267020b16927e1b51ba46c4df4da277/workflow/tropical-cyclone/STORM.smk">https://github.com/nismod/open-gira/blob/88dc522dd267020b16927e1b51ba46c4df4da277/workflow/tropical-cyclone/STORM.smk</a></p> <p>Files are named on the pattern: <code>STORM_FIXED_RETURN_PERIODS_{STORM_MODEL}_{STORM_RP}_YR_RP.tif</code></p> <p>STORM_MODEL is be one of constant, CMCC-CM2-VHR4, CNRM-CM6-1-HR, EC-Earth3P-HR or HadGEM3-GC31-HM. The "constant" files are for the present day, baseline climate scenario as explained in dataset [1]. The other files are for 2050, RCP8.5 under different models as explained in the paper linked from dataset [2].</p> <p>STORM_RP is one of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000 or 10000.</p> <h3>Updates</h3> <p>2024-04-05: recreated GeoTIFFs to align precisely with source pixels, updated references to v4, uploaded GeoTIFFs within ZIP to meet Zenodo file limits.</p>
TIEGCM 2019_11 Storm
Open the record for dataset details and reuse information.
STORM_dataset_Prouteau_et_al_TOROIDs
<p>This repository contains two STORM datasets.</p> <p>1. Yeast_ddKog1_GFP_A647_extractedParticles.mat<br> 2. Yeast_Kog1_Wt_GFP_A647_extractedParticles.mat</p>
Fig 5 from: Gardiner T, Seago B (2019) The tide is high, but it's holding on: response of the grey bush-cricket, Platycleis albopunctata, to a storm surge. Journal of Orthoptera Research 28(2): 125-128. https://doi.org/10.3897/jor.28.34092
Fig 5 Mean ground cover of shingle and Ammophila arenaria on the fore and back dunes post-surge.
Fig 2 from: Gardiner T, Seago B (2019) The tide is high, but it's holding on: response of the grey bush-cricket, Platycleis albopunctata, to a storm surge. Journal of Orthoptera Research 28(2): 125-128. https://doi.org/10.3897/jor.28.34092
Fig 2 Shingle deposition on the back dunes. Photo credit Tim Gardiner.
Fig 1 from: Gardiner T, Seago B (2019) The tide is high, but it's holding on: response of the grey bush-cricket, Platycleis albopunctata, to a storm surge. Journal of Orthoptera Research 28(2): 125-128. https://doi.org/10.3897/jor.28.34092
Fig 1 Shingle deposition on the fore dunes. Photo credit Tim Gardiner.
Modeling files for storm surge induced salinization at sparse marshes
Open the record for dataset details and reuse information.
Data for "Interhemispheric asymmetry in the high-latitude neutral density variations during the 13-14 March 2022 storm"
<p>DMSP data and GITM outputs used for "Interhemispheric asymmetry in the high-latitude neutral density variations during the 13-14 March 2022 storm".</p> <p>Descriptions of data can be found in ReadMe.txt.</p>
Advancing Storm Surge Modelling: A High-performance Local-Inertial Approach on Unstructured Triangular Grids
<p>The powerful tropical cyclones generate various destructive forces, including storm surges, heavy winds, and inland flooding, especially in coastal areas. The low-lying coastal areas are particularly susceptible to the devastating effects of coastal flooding caused by storm surges. The mathematical models are often employed to forecast cyclonic wind speed, surge height and coastal inundation to facilitate effective planning and evacuation measures, especially for real-time events. These models provide valuable insights into the potential impacts and help authorities make informed decisions to mitigate the risks posed by storm surges. Most of the existing models mainly suffer from computational cost and numerical instability. In this context, a mathematical model called IROMS-iS2D (Integrated River Ocean Modelling System – inertial Surge two-Dimensional) is developed based on the solution of the local-inertial shallow water equations to significantly improve computational time without encountering numerical instability problems. The performance of the developed model is thoroughly investigated by comparing wind speed and storm surges with observed data and reported results from three historical cyclones along the Bay of Bengal region. Then, the application of the model is demonstrated by simulating the Sidr cyclone and probable storm surges along the Tamil Nadu coast. The IROPMS-iS2D model is found to be significantly faster than the 2D models. Overall, it is found that by leveraging mathematical models like IROMS-iS2D, authorities can enhance their preparedness and response strategies, ultimately minimizing the potential damage and loss of life caused by these hazardous weather phenomena.</p>
Storm petrel
A species of a small seabird in the Hydrobatidae family. The storm petrel Hydrobates pelagicus (Linnaeus, 1758) inhabits the European shores of the Atlantic and the western shores of the Mediterranean Sea. It is often found on rocky islands with high cliffs, far from inhabited places, in West Ireland, North West Scotland and the Faroe Islands. In winter, it reaches the western and southern coasts of Africa. ID no.: Z/155/MT Time of creation: 1885 Museum: The Dr. Tytus Chałubiński Tatra Museum in Zakopane https://muzea.malopolska.pl/en/objects-list/1824 Digitalisation: Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
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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.