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256 results for “Cyclone”
Recent Increases in Tropical Cyclone Rapid Intensification Events in Global Offshore Regions
<p>The data and scripts in this repository can be used to support the main conclusion in the manuscript "Recent Increases in Tropical Cyclone Rapid Intensification Events in Global Offshore Regions" by Li et al., submitted to Nature Communications. The global distribution and annual variability of rapid intensification (RI) events of tropical cyclones (TCs) dervied from the open-source International Best Track Archive for Climate Stewardship (IBTrACS, https://www.ncei.noaa.gov/products/international-best-track-archive) are provided. The enviromental variables, including mid-level (600 hPa), vertical wind shear (200-850 hPa), and maximum potential intensity (MPI), were also calculated using the fifth generation of ECMWF reanalysis (ERA5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) forced in different scenarios. The python script (coastal_RI_submit ipynb) can be used to reproduce figures in the article.</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>
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Dataset
<p>This dataset provides output of RAMEFI for the wind feature climatology presented in Eisenstein et al. (2023; 10.5194/wcd-2023-10) for the winter months October to March 2000-2019 using COSMO-REA6 (https://reanalysis.meteo.uni-bonn.de/?COSMO-REA6).</p> <p><strong>rf_crea_<yyyymm>.nc</strong> include the unfiltered probabilities for 'no feature' (p0), warm jet (p1), cold-frontal convection (p2), cold jet (p3) and cold-sector winds (p5) for each month.</p> <p>To filter for cyclone tracks, use <strong>cyclone_tracks.csv</strong>. The<strong> </strong>file includes interpolated ERA5 cyclone tracks for the investigated area and time period (see Section 2.4 of the paper).</p> <p><strong>mask.nc</strong> includes a land sea mask, height of surface level and a mask to exclude certain grid points as discussed in the manuscript (e.g., grid points with an altitude over 800m and the Balkans) for further filtering.</p>
High-frequency water temperature and dissolved oxygen data and derived stability and metabolism metrics for nine lakes in northeastern North America for months before and after Tropical Cyclone Irene, Fall 2011
This dataset is used in the analysis published in the following manuscript: Klug, J.L., D.C. Richardson, H.A. Ewing, B.R.Hargreaves, N. R. Samal, D. Vachon, D.C. Pierson, A. E. Lindsey, D. O'Donnell, S.W. Effler, and K.C. Weathers. 2012. Ecosystem effects of a tropical cyclone on a network of lakes in northeastern North America. Environmental Science and Technology 46(21): 11693–11701. We include Quality Assurance Quality Controlled (QAQC) high-frequency dissolved oxygen, wind speed, and water temperature data from nine lakes and reservoirs in northeastern North America which were near the track of Tropical Cyclone Irene in August 2011. These data were collected using a set of in situ, automated monitoring systems associated with the Global Lake Ecological Observatory Network (GLEON) that record data at high frequency (10 min to 6 h). These sensor data were the basis for the derived measures of Schmidt stability, net ecosystem production, respiration, and gross primary production included in the dataset. We also include daily rainfall data collected at on-site or nearby weather stations. All data cover the period from 01 August through 15 October 2011.
Dataset: Bacterioplankton metabolism of phytoplankton lysates across a cyclone-anticyclone eddy dipole impacts the cycling of semi-labile organic matter in the photic zone
<p>This dataset contains both field data and the results of dilution batch-culture bioassay experiments characterizing bacterioplankton usage of ambient and added dissolved organic matter across a cyclone to anticyclone spatial transect in the North Pacific Subtropical Gyre. Field data include total organic carbon, total nitrogen, bacterioplankton cell abundances, ammonia monooxygenase subunit A gene concentrations, and 16S rRNA gene amplicons. Experimental data include time-resolved changes in total organic carbon, nitrogen species, and bacterioplankton cell abundances.</p>
Cyclone tracks from 1901 to 2010 in dynamically downscaled ERA-20C reanalysis (COSMO-CLM+NEMO)
<p>The database contains two files: one with all cyclone trajectories from 1901 to 2010, and another one only with the so-called Vb-cyclones that propagate from the Mediterranean Sea north-eastward to Central Europe.</p> <p>We detected the cyclone trajectories with the method of Wernli and Schwierz (2006) and Sprenger et al. (2017) and classified all cyclone trajectories that crossed the 47°N latitude between 12°E and 22°E as Vb-cyclones following Hofstätter and Blöschl (2019). The cyclone tracking was based on mean sea level pressure data of dynamically downscaled ERA-20C reanalysis. The downscaling was performed over Europe [including MED-CORDEX (Somot et al. 2018) and EURO-CORDEX (Giorgi et al. 2009)] from 1901 to 2010 with an interactively coupled high-resolution atmosphere-ocean model (COSMO-CLM+NEMO) by Cristina Primo. More details on the data basis can be found in Primo et al. (2019) and Krug et al. (2020).</p> <p> </p> <p>Giorgi, F., Jones, C. & Asrar, G. Addressing climate information needs at the regional level: the CORDEX framework.<em> WMO Bulletin</em> <strong>58</strong>, 175–183 (2009).</p> <p>Hofstätter, M. & Blöschl, G. Vb Cyclones Synchronized With the Arctic-/North Atlantic Oscillation. <em>J. Geophys. Res. Atmos.</em> <strong>124</strong>, 3259–3278 (2019).</p> <p>Krug, A., Primo, C., Fischer, S., Schumann, A. & Ahrens, B. On the temporal variability of widespread rain-on-snow floods. <em>Meteorol. Zeitschrift</em> <strong>29</strong>, 147–163 (2020).</p> <p>Primo, C., Kelemen, F. D., Feldmann, H., Akhtar, N. & Ahrens, B. A regional atmosphere-ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance. <em>Geosci. Model Dev.</em> <strong>12</strong>, 5077–5095 (2019).</p> <p>Somot, S. <em>et al.</em> Editorial for the Med-CORDEX special issue. <em>Clim. Dyn.</em> <strong>51</strong>, 771–777 (2018). doi: 10.1007/s00382-018-4325-x</p> <p>Sprenger, M. <em>et al.</em> Global climatologies of Eulerian and Lagrangian flow features based on ERA-Interim. <em>Bull. Am. Meteorol. Soc.</em> (2017). doi:10.1175/BAMS-D-15-00299.1</p> <p>Wernli, H. & Schwierz, C. Surface Cyclones in the ERA-40 Dataset (1958–2001). Part I: Novel Identification Method and Global Climatology. <em>J. Atmos. Sci.</em> <strong>63</strong>, 2486–2507 (2006).</p>
A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset
<p>Tropical cyclone precipitation (TCP) is a key diagnostic in the context of atmospheric science, hazard, risk and flood research. This dataset provides estimates of TCP from global datasets. The various TCP metrics reported were estimated through the analysis of the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation product and the International Best Track Archive for Climate Stewardship (IBTrACS) version 4. There are two main files that comprise the dataset. The main dataset file includes information on the mean and maximum TCP found within 500 km of each storm centre as well as the rainfall area and radius of maximum rain. The second file includes the estimates of azimuthally averaged precipitation using a 10 km bin spacing which is useful for analyses of the storm-scale structure of precipitation.</p>
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF
<p>This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"</p>
Trait-based sensitivity of large mammals to a catastrophic tropical cyclone: DNA metabarcoding data
<p>Extreme weather events perturb ecosystems and increasingly threaten biodiversity<sup>1</sup>. Ecologists emphasize the need to forecast and mitigate the impacts of these incidents, which requires knowledge of how risk is distributed among species and environments, but the scale and unpredictability of extreme events complicates assessment<sup>1</sup><sup>–4</sup>. These challenges are compounded for large animals ('megafauna'), which play crucial ecological roles but are hard to study<sup>5</sup>. Traits such as body size, dispersal ability, and habitat affiliation are among the hypothesized determinants of animals' vulnerability to natural hazards<sup>1,6,7</sup>. However, it has rarely been possible to test these propositions or, more generally, to link short- and longer-term effects of weather-related disturbance<sup>8,9</sup>. Here, we show how large herbivores and carnivores in Mozambique responded to Intense Tropical Cyclone Idai, the deadliest storm on record in Africa, across scales ranging from individual decisions in the hours after landfall to community-level responses nearly 20 months later. Animals occupying low-elevation habitats exhibited strong spatial responses to rising floodwaters. Body size predicted species' subsequent numerical responses: small-bodied species exhibited the greatest population declines. We trace this sensitivity to limited mobility, which increased likelihood of death during the flood and constrained animals' capacity to withstand food shortages afterward. Our results identify potentially general trait-based mechanisms underlying animal responses to severe weather and may help to inform strategies for wildlife conservation in a volatile climate.</p> <ol> <li><span><em><span>Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change</span></em><span> [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, (2022).</span></span></li> <li><span><span>Smith, M. An ecological perspective on extreme climatic events: A synthetic definition and framework to guide future research. <em>J. Ecol.</em> <strong>99</strong>, 656-663 (2011).</span></span></li> <li><span><span>Ummenhofer, C. C., & Meehl, G. A. Extreme weather and climate events with ecological relevance: a review, <em>Phil. Trans. R. Soc. B. </em><strong>372</strong>, 20160135 (2017).</span></span></li> <li><span><span>Jentsch, A., Kreyling, J., & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. <em>Front. Ecol. Environ. </em><strong>5</strong>, 365-374 (2007).</span></span></li> <li><span><span>Pringle, R. M., et. al. Impacts of large herbivores on terrestrial ecosystems. <em>Current Biology</em> <strong>33</strong>, R584-R610 (2023).</span></span></li> <li><span><span>Spiller, D. A., Losos, J. B., & Schoener, T. W. Impact of a catastrophic hurricane on island populations. <em>Science </em><strong>281</strong>, 695-697 (1998). </span></span></li> <li><span><span>Schoener, T. W., & Spiller, D. A. Nonsynchronous recovery of community characteristics in island spiders after a catastrophic hurricane. <em>PNAS </em><strong>103</strong>, 2220-2225 (2006).</span></span></li> <li><span><span>Pruitt, N., Little, A. G., Majumdar, S. J., Schoener, T. W., & Fisher, D. N. Call-to-Action: A global consortium for tropical cyclone ecology. <em>TREE </em><strong>34</strong>, 588-590 (2019).</span></span></li> <li><span><span>Lin, T. C., Hogan, J. A., & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. <em>TREE </em><strong>35</strong>, 594-604 (2020).</span></span></li> </ol>
North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset from RAFT
<p>The Risk Analysis Framework for Tropical Cyclones (RAFT)'s comprehensive and unified simulation of 40,000 synthetic North Atlantic tropical cyclone (TC) events are presented in this dataset. RAFT meticulously models these events based on large-scale environmental conditions, providing a valuable tool for in-depth TC impact analysis. The dataset encompasses detailed 6-hourly track information, along-track intensity metrics (including maximum wind speed and minimum pressure), the radius of maximum winds, and cumulative precipitation for each event.</p> <p>The primary dataset is encapsulated in a NetCDF4 file, "<a href="../records/10392725/files/RAFT.NA.v20231016.nc?download=1">RAFT.NA.v20231016.nc</a>", which contains a complete array of variables pertinent to the 40,000 synthetic TCs. These variables, detailed in Table 1 of the accompanying paper and summarized below, offer a comprehensive view of each TC event:</p> <ul> <li><strong>Basin ID</strong>: Identifies the basin (1 for North Atlantic)</li> <li><strong>Storm ID</strong>: Unique identification number for each TC, starting from 0</li> <li><strong>Year</strong>: Year of the environmental conditions used for modeling</li> <li><strong>Jday</strong>: Julian day of the year, ranging from 0 to 365</li> <li><strong>Longitude (lon)</strong>: Geographical longitude in degrees</li> <li><strong>Latitude (lat)</strong>: Geographical latitude in degrees</li> <li><strong>Maximum Wind Speed (vmax)</strong>: Measured in knots</li> <li><strong>Minimum Pressure (mslp)</strong>: Measured in hectopascals (hPa)</li> <li><strong>Radius of Maximum Wind (rmax)</strong>: Measured in nautical miles (nmi)</li> </ul> <p>Additionally, the dataset offers individualized accumulated rainfall data for each TC event, stored in NetCDF4 files named according to the convention "modeled_rainfall_ERA5_syn_{i}.h5", where "{i}" is the synthetic storm's ID. "ERA5" signifies the reanalysis input source, and "syn" indicates a synthetic track. This component of the dataset includes the following variables, all measured in total millimeters of precipitation:</p> <ul> <li><strong>Total Accumulated Rainfall (p_accum)</strong></li> <li><strong>Frictional Precipitation Component (p_accum_f)</strong></li> <li><strong>Topographic Precipitation Component (p_accum_h)</strong></li> <li><strong>Shear-related Precipitation Component (p_accum_s)</strong></li> <li><strong>Vortex Stretching Precipitation Component (p_accum_t)</strong></li> </ul> <p>The rainfall dataset is curated to focus on TC events within 600 km of the U.S. coast, reducing the number of rainfall events to 17,010 from the original 40,000, thereby enhancing its relevance and manageability. For user convenience, these events are compressed into grouped archives named "RAFT_accum_rainfall_{index}.tar.gz", where each "{index}" represents the index of the zipfile, containing up to 2,000 files for efficient data retrieval.</p> <p>The accumulated rainfall data is provided on a regular spatial grid, detailed in "<a href="../records/10392725/files/RAFT_rainfall_latlon_grid.h5?download=1">RAFT_rainfall_latlon_grid.h5</a>", which outlines the grid coordinates ('lat' and 'lon').</p> <p>For comprehensive usage guidelines and further insights into this dataset, users are encouraged to refer to the associated paper. This dataset is not only a significant resource for researchers and analysts in the field of meteorology but also serves as a pivotal tool for understanding and predicting the impacts of tropical cyclones.</p> <p> </p> <p><strong>How to cite:</strong></p> <p>Xu, W., Balaguru, K., Judi, D.R. <em>et al.</em> A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset. <em>Sci Data</em> <strong>11</strong>, 130 (2024). https://doi.org/10.1038/s41597-024-02952-7</p>
Data and code repository for the "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone"
<p><strong>Author:</strong> Behrooz Keshtgar, behrooz.keshtgar@kit.edu</p> <p>This archive contains the post-processed data used to generate the figures and the code repository for the publication "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone" by Behrooz Keshtgar, Aiko Voigt, Bernhard Mayer and Corinna Hoose.</p> <p>Description of the <strong>data</strong>:</p> <p>figure1.nc: precipitation rate, cloud cover, surface pressure, and cloud classes on day 4.5 of the ICON-NWP baroclinic life cycle simulation.</p> <p>figure2.nc: spatially and temporally averaged profiles of cloud water, ice mass content, and cloud fractions from ICON-LEM simulations.</p> <p>figure4.nc: spatially and temporally averaged cloud-radiative heating profiles from ICON-LEM simulations and offline radiation calculations for each LEM domain.</p> <p>figure5.nc: cross-section of radiative heating rates for 3D and 1D radiative transfer calculations in the shallow cumulus domain.</p> <p>figure6.nc: spatially averaged cloud-radiative heating profiles from 3D and 1D radiation calculations for each LEM domain.</p> <p>figure7.nc: cross-section of cloud-radiative heating calculated with the ice optics of Fu and Baum_ghm in the WCB ascent region.</p> <p>figure8.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with different ice optics for each LEM domain.</p> <p>figure9.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with LEM and NWP clouds for each LEM domain.</p> <p>figure10.nc: spatially and temporally averaged density and cloud-radiative heating profiles from different offline radiation calculations for each LEM domain.</p> <p>figure11.nc: profiles of the mean absolute difference of cloud-radiative heating from different offline radiation calculations at different resolutions for each LEM domain.</p> <p> </p> <p>The <strong>keshtgar-etal-2024-cyclone-crh-uncertainties-main.zip</strong> is the copy of the published git repository for the model run and analysis scripts. The repository contains</p> <p>- Scripts for the ICON model simulations</p> <p>- Scripts for the offline radiative transfer calculations with LibRadTran and the post-processing routine</p> <p>- Python scripts and Jupyter Notebooks for the analysis in the paper</p>
Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020
<p>The datasets are for the article 'Contributions of Anomalous Large-Scale Circulations to the Absence of Tropical Cyclones over the Western North Pacific in July 2020', including the WRF Initial conditions (horizontal wind at 850 hPa, geopotential height at 850 and 200 hPa) of sensitivity experiments (CTRL, W_WNPSH, W_SAH, W_TUTT, and S_SASM) in July 2020, and the formation records of the simulated TC formation in all experiments.</p>
Sowing storms: how model timestep can control tropical cyclone frequency in a GCM
<p>Supplementary dataset to JAMES article "Sowing storms: how model timestep can control tropical cyclone frequency in a GCM" DOI:10.1029/2021MS002791</p>
Arctic maritime cyclone distribution and trends in the ERA5
<p>This dataset supplements our paper published in the AMS Journal of Applied Meteorology and Climatology (JAMC) under the same title. The repository includes the cyclone tracks, the HST case study, and the delineation for different Arctic sea sections. For any questions, please contact Zihan Chen (via <a href="mailto:zihan_chen@alumni.brown.edu">zihan_chen@alumni.brown.edu</a>).</p>
Coastal extreme sea levels in the Caribbean Sea induced by tropical cyclones
<p>Previous version contained the return levels of coastal significant wave height and sea surface elevation along the Caribbean coastlines corresponding to the periods of 10, 50, 100, 200 and 500 years. The return periods have been computed fitting a Generalised Pareto Distribution to a set of hydrodynamic-wave coupled ocean simulations forced with 1000 synthetic tropical cyclones. See the paper Martin et al (under review) for details. </p> <p> The file (Return_levels.mat) contains a Matlab table (with header names) indicating latitude, longitude and the return levels described above, named as Hs (significant wave height) and SSE (sea surface elevation). </p> <p>Three other datasets have been included, with the subsample of the Tropical Cyclones selected for the study (Subsample.mat), the geographic data of the coastal grid points used for our analysis (Coastaline.mat), and finally a dataset with the results of the analysis presented in the paper (Results_4_runs.mat). All datasets are provided in a .mat file, generated using Matlab. </p> <p>First dataset contains a Matlab table (with header names) indicating latitude and longitude, radius of maximum wind speed, minimum pressure and maximum wind speed a long the track of the Tropical Cyclone, for the 1000 samples selected. </p> <p>Second dataset provides both Latitude and Longitude of the coastal grid points used for the analysis. </p> <p>The last dataset contains a Matlab table (with header names) where the first column represents the Tropical Cyclone, columns 2,3 and 4 contain the maximum of sea surface elevation (SSE) during the lifetime of the Tropical Cyclone for each coastal point, for the 3 decoupled runs, wind-forced only, pressure-forced only and wind and pressure respectively. The next 4 columns contain the values of the variables for the coupled simulations with WWM-III, maximum significant wave height (Hs), maximum SSE, median of Peak Direction (Dp) and median of Peak period (Tp). All these variables contain a description in the dataset as variable information, and are provided for all coastal points of our grid. </p>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
Data from : Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics
<p>These datasets have been used in the following paper:</p> <p>Ibanez, T., Bauman, B., Aiba, S.-i., Arsouze, T. Bellingham, P.J., Birkinshaw, C., Birnbaum, P., Curran, T.J., DeWalt, S.J., Dwyer, J., Fourcaud, T., Franklin, J., Kohyama, T.S., Menkes, C. Metcalfe, D.J., Murphy, H., Muscarella, R., Plunkett, G.M., Sam, C., Tanner, E., Taylor, B.N., Thompson, J., Ticktin, T., Tuiwawa, M.V., Uriarte, U., Webb, E.L., Zimmerman, J.K., Keppel, G. Damage to tropical forests caused by tropical cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics. Accepted for publication in <em>Global Change Biology</em>.</p> <p>Data users are invited to cite this paper and the original paper(s) corresponding to the data they use (see "Reference" column in each dataset). We also encourage potential users to contact the data owners for collaboration.</p> <p>These datasets are compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide. Damage are given at the tree (whether or not each tree has been uprooted or snapped) and at the plot level (number of uprooted or snapped trees in each plot).</p> <p>MSW: Maximum sustained wind speed (m.s-1)</p> <p>EXP: Topographical exposure to wind</p> <p>DBH: Diameter at breast height (cm)</p> <p>WD: Wood density (g.cm-3)</p>
Fig. 9 in Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea
Fig. 9: A) A projection of the variables onto the factor plane, where the x-axis is PC1 and the y-axis is PC2, and B) a projection of the cases onto the factor plane, where the x-axis is PC1 and the y-axis is PC2. MAW is Modified Atlantic Water; AMI is the Atlantic Mediterranean Interface; LIW is Levantine Intermediate Water.
Fig. 6 in Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea
Fig. 6: Pictures of F. japonica with A) and B) discharged mucous threads and the rod-shaped posterior mucocysts (indicated by the arrows) clearly visible, and C) a vegetative raspberry-like cell (top right) and two pre-cysts (indicated by the arrows). Cell diameter was generally 15-30 μm.
Fig. 5 in Fibrocapsa japonica (Raphidophyceae) occurrence and ecological features within the phytoplankton assemblage of a cyclonic eddy, offshore the Eastern Alboran Sea
Fig. 5: A) Total chlorophyll a concentration (chlorophyll a + divinyl-chlorophyll a, mg m-3) and B) Fibrocapsa japonica cell number map (x 103 cells l-1) overlapped to the isohalines (bold lines) as in Fig. 4.
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.