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1,216 results for “real-time”

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zenodo36/100

Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor

<p>Dataset and code to replicate the paper:</p> <p>Orlandi et al., "Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor"</p>

openapache2.0Apr 2024View details →
zenodo36/100

Data set for the figures in the manuscript "Real-Time Identification of Aerosol-Phase Carboxylic Acid Production Using Extractive Electrospray Ionization Mass Spectrometry"

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2024View details →
zenodo36/100

Simulations dataset and pre-trained models of "Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory" Ph.D. project

<p>Ph.D. project datasets and models release, <br><em>Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory.</em></p>

opencc-by-4.0May 2024View details →
zenodo36/100

Experimental datasets on "Real-time Observation of sub-100-fs Charge and Energy Transfer Processes in DNA Dinucleotides"

<p>Experimental datasets referring to the manuscript entitled "Real-time Observation of sub-100-fs Charge and Energy Transfer Processes in DNA Dinucleotides"</p>

opencc-by-4.0Jun 2024View details →
zenodo36/100

Analysis of thermal and dielectric loss features of lunar regolith considering real-time effect solar irradiance

<p>ESI data for "Analysis of thermal and dielectric loss features of lunar regolith considering real-time effect solar irradiance".</p>

opencc-by-4.0Jun 2024View details →
zenodo36/100

Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"

<p>Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv.</p> <p>Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.</p>

opencc-by-4.0Sep 2023View details →
zenodo36/100

Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)

<p>To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.</p>

openafl-3.0Dec 2014View details →
zenodo36/100

Real-time dynamics of nanoplasmonic dimer, distance d = 0.5 nm

<p>In Ref. <a href="http://doi.org/10.5281/zenodo.1476721">http://doi.org/10.5281/zenodo.1476721 </a>we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.5 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 8.33 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>

opencc-by-sa-4.0Oct 2018View details →
zenodo36/100

Real-time dynamics of nanoplasmonic dimer, distance d = 0.1 nm

<p>In Ref. <a href="http://dx.doi.org/10.5281/zenodo.1476719">http://doi.org/10.5281/zenodo.1476719 </a>we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>

opencc-by-sa-4.0Oct 2018View details →
zenodo36/100

Real-time dynamics of nanoplasmonic dimer, distance d = 0.5 nm

<p>In Ref. we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.5 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 8.33 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>

opencc-by-sa-4.0Oct 2018View details →
zenodo36/100

Real-time dynamics of nanoplasmonic dimer, distance d = 0.1 nm

<p>In http://dx.doi.org/10.5281/zenodo.1482739 we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>

opencc-by-sa-4.0Oct 2018View details →
zenodo36/100

Real-Time High-Rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes

<p>Post-processed and real-time GNSS displacement waveforms for the 2019 Ridgecrest earthquakes. Waveforms are for the M6.4 and M7.1 events recorded at 1 and 5Hz sample rates.&nbsp;Data are in miniSEED format, channel codes LYE, LYN, LYZ correspond to east, north, and up respectively. Displacement units are meters and time is UTC. The data are trimmed 60s before the USGS origin times for<br> the earthquakes. No filtering has been applied.</p> <p>A paper describing the data has been submitted to SRL. In the meantime if you use the data please cite our <a href="https://eartharxiv.org/pdxqw/">preprint on the EarthArXiv</a> as:</p> <p>Melgar, D., TI&nbsp;Melbourne, BW&nbsp;Crowell, J Geng, W Szeliga, C Scrivner, M Santillan, DER Goldberg. 2019. Real-time High-rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes.&nbsp;EarthArXiv, doi:10.31223/osf.io/pdxqw.</p>

opencc-by-4.0Aug 2019View details →
zenodo36/100

Yeast solutions and hyperpolarization enable real-time observation of metabolized substrates even at natural abundance

<div>This data corresponds to the following paper:</div> <div>&nbsp;</div> <div>Title: Yeast solutions and hyperpolarization enable real-time observation of metabolized substrates even at natural abundance</div> <div>Journal: Analytical Chemistry</div> <div>Authors: Josh P. Peters, Charbel Assaf, Farhad Haj Mohamad, Eric Beitz, Sanjay Tiwari, Konrad Aden, Jan-Bernd H&ouml;vener and Andrey N. Pravdivtsev</div> <div>&nbsp;</div> <div>The data is organized with respect to the subfigures in figure 2 to 6.</div> <div>A description of acquisition parameters is provided in the "Acquisition parameters.xlsx" file for each dataset.</div> <div>&nbsp;</div> <div>Figure 2:</div> <div>- Overview of pyruvate metabolism in yeast cells using hyperpolarized and 13C labeled [1-13C]pyruvate.&nbsp;</div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.&nbsp;</div> <div>&nbsp;</div> <div>Figure 3:</div> <div>- Overview of pyruvate metabolism in yeast cells using co-hyperpolarized [1-13C] and [2-13C]pyruvate at an n.a. of 13C.&nbsp;</div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div>&nbsp;</div> <div>Figure 4:</div> <div>- Overview of fumarate metabolism in yeast cells using hyperpolarized and 13C labeled [1,4-13C2]fumarate</div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div>&nbsp;</div> <div>Figure 5:</div> <div>- The fitted conversion exchange rate constants as a function of the yeast concentration.&nbsp;</div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div>&nbsp;</div> <div>Figure 6:</div> <div>- Metabolic data from pyruvate metabolism in yeast depending on the position of the yeast in the NMR tube. We&nbsp;</div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <p>&nbsp;</p>

opencc-by-4.0Jul 2024View details →
zenodo36/100

Part 2 of real-time testing data for: "Identifying data sources and physical strategies used by neural networks to predict TC rapid intensification"

<p>Each file in the dataset contains machine-learning-ready data for one unique tropical cyclone (TC) from the real-time testing dataset. &nbsp;"Machine-learning-ready" means that all data-processing methods described in the journal paper have already been applied. &nbsp;This includes cropping satellite images to make them TC-centered; rotating satellite images to align them with TC motion (TC motion is always towards the +x-direction, or in the direction of increasing column number); flipping satellite images in the southern hemisphere upside-down; and normalizing data via the two-step procedure.</p> <p>The file name gives you the unique identifier of the TC -- e.g., "learning_examples_2010AL01.nc.gz" contains data for storm 2010AL01, or the first North Atlantic storm of the 2010 season. &nbsp;Each file can be read with the method `example_io.read_file` in the ml4tc Python library (https://zenodo.org/doi/10.5281/zenodo.10268620). &nbsp;However, since `example_io.read_file` is a lightweight wrapper for `xarray.open_dataset`, you can equivalently just use `xarray.open_dataset`. &nbsp;Variables in the table are listed below (the same printout produced by `print(xarray_table)`):</p> <p>Dimensions: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_valid_time_unix_sec: 289,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_grid_row: 380,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_grid_column: 540,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_predictor_name_gridded: 1,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_predictor_name_ungridded: 16,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_valid_time_unix_sec: 19,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_storm_object_index: 19,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_forecast_hour: 23,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_intensity_threshold_m_s01: 21,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_lag_time_hours: 5,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_predictor_name_lagged: 17,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_predictor_name_forecast: 129)<br>Coordinates:<br>&nbsp; * satellite_grid_row &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_grid_row) int32 2kB ...<br>&nbsp; * satellite_grid_column &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_grid_column) int32 2kB ...<br>&nbsp; * satellite_valid_time_unix_sec &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) int32 1kB ...<br>&nbsp; * ships_lag_time_hours &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (ships_lag_time_hours) float64 40B ...<br>&nbsp; * ships_intensity_threshold_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_intensity_threshold_m_s01) float64 168B ...<br>&nbsp; * ships_forecast_hour &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_forecast_hour) int32 92B ...<br>&nbsp; * satellite_predictor_name_gridded &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_predictor_name_gridded) object 8B ...<br>&nbsp; * satellite_predictor_name_ungridded &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_predictor_name_ungridded) object 128B ...<br>&nbsp; * ships_valid_time_unix_sec &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_valid_time_unix_sec) int32 76B ...<br>&nbsp; * ships_predictor_name_lagged &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_predictor_name_lagged) object 136B ...<br>&nbsp; * ships_predictor_name_forecast &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_predictor_name_forecast) object 1kB ...<br>Dimensions without coordinates: ships_storm_object_index<br>Data variables:<br>&nbsp; &nbsp; satellite_number &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) int32 1kB ...<br>&nbsp; &nbsp; satellite_band_number &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) int32 1kB ...<br>&nbsp; &nbsp; satellite_band_wavelength_micrometres &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_cyclone_id_string &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) |S8 2kB ...<br>&nbsp; &nbsp; satellite_storm_type_string &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) |S2 578B ...<br>&nbsp; &nbsp; satellite_storm_name &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) |S10 3kB ...<br>&nbsp; &nbsp; satellite_storm_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_storm_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_storm_intensity_number &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_storm_u_motion_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_storm_v_motion_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_predictors_gridded &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column, satellite_predictor_name_gridded) float64 474MB ...<br>&nbsp; &nbsp; satellite_grid_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br>&nbsp; &nbsp; satellite_grid_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br>&nbsp; &nbsp; satellite_predictors_ungridded &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec, satellite_predictor_name_ungridded) float64 37kB ...<br>&nbsp; &nbsp; ships_storm_intensity_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_valid_time_unix_sec) float64 152B ...<br>&nbsp; &nbsp; ships_storm_type_enum &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) int32 2kB ...<br>&nbsp; &nbsp; ships_forecast_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_forecast_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_v_wind_200mb_0to500km_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_vorticity_850mb_0to1000km_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_vortex_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_vortex_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_850mb_0to600km_m_s01 &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_max_tangential_wind_850mb_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_1000mb_at500km_m_s01 &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_850mb_at500km_m_s01 &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_500mb_at500km_m_s01 &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_300mb_at500km_m_s01 &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_srh_1000to700mb_200to800km_j_kg01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_srh_1000to500mb_200to800km_j_kg01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_threshold_exceedance_num_6hour_periods &nbsp; &nbsp; (ships_storm_object_index, ships_intensity_threshold_m_s01) int32 2kB ...<br>&nbsp; &nbsp; ships_v_motion_observed_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_v_motion_1000to100mb_flow_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_v_motion_optimal_flow_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_cyclone_id_string &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) object 152B ...<br>&nbsp; &nbsp; ships_storm_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_storm_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_predictors_lagged &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_valid_time_unix_sec, ships_lag_time_hours, ships_predictor_name_lagged) float64 13kB ...<br>&nbsp; &nbsp; ships_predictors_forecast &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_valid_time_unix_sec, ships_forecast_hour, ships_predictor_name_forecast) float64 451kB ...</p> <p>Variable names are meant to be as self-explanatory as possible. &nbsp;Potentially confusing ones are listed below.</p> <ul> <li>The dimension ships_storm_object_index is redundant with the dimension ships_valid_time_unix_sec and can be ignored.</li> <li>ships_forecast_hour ranges up to values that we do not actually use in the paper. &nbsp;Keep in mind that our max forecast hour used in machine learning is 24.</li> <li>The dimension ships_intensity_threshold_m_s01 (and any variable including this dimension) can be ignored.</li> <li>ships_lag_time_hours corresponds to lag times for the SHIPS satellite-based predictors. &nbsp;The only lag time we use in machine learning is "NaN", which is a stand-in for the best available of all lag times. &nbsp;See the discussion of the "priority list" in the paper for more details.</li> <li>Most of the data variables can be ignored, unless you're doing a deep dive into storm properties. &nbsp;The important variables are satellite_predictors_gridded (full satellite images), ships_predictors_lagged (satellite-based SHIPS predictors), and ships_predictors_forecast (environmental and storm-history-based SHIPS predictors). &nbsp;These variables are all discussed in the paper.</li> <li>Every variable name (including elements of the coordinate lists ships_predictor_name_lagged and ships_predictor_name_forecast) includes units at the end. &nbsp;For example, "m_s01" = metres per second; "deg_n" = degrees north; "deg_e" = degrees east; "j_kg01" = Joules per kilogram; ...; etc.</li> </ul>

opencc-by-4.0Aug 2024View details →
zenodo36/100

Real-time testing data for: "Identifying data sources and physical strategies used by neural networks to predict TC rapid intensification"

<p>Each file in the dataset contains machine-learning-ready data for one unique tropical cyclone (TC) from the real-time testing dataset.&nbsp; "Machine-learning-ready" means that all data-processing methods described in the journal paper have already been applied.&nbsp; This includes cropping satellite images to make them TC-centered; rotating satellite images to align them with TC motion (TC motion is always towards the +x-direction, or in the direction of increasing column number); flipping satellite images in the southern hemisphere upside-down; and normalizing data via the two-step procedure.</p> <p>The file name gives you the unique identifier of the TC -- e.g., "learning_examples_2010AL01.nc.gz" contains data for storm 2010AL01, or the first North Atlantic storm of the 2010 season.&nbsp; Each file can be read with the method `example_io.read_file` in the ml4tc Python library (https://zenodo.org/doi/10.5281/zenodo.10268620).&nbsp; However, since `example_io.read_file` is a lightweight wrapper for `xarray.open_dataset`, you can equivalently just use `xarray.open_dataset`.&nbsp; Variables in the table are listed below (the same printout produced by `print(xarray_table)`):</p> <p>Dimensions: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_valid_time_unix_sec: 289,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_grid_row: 380,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_grid_column: 540,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_predictor_name_gridded: 1,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; satellite_predictor_name_ungridded: 16,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_valid_time_unix_sec: 19,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_storm_object_index: 19,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_forecast_hour: 23,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_intensity_threshold_m_s01: 21,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_lag_time_hours: 5,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_predictor_name_lagged: 17,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ships_predictor_name_forecast: 129)<br>Coordinates:<br>&nbsp; * satellite_grid_row &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_grid_row) int32 2kB ...<br>&nbsp; * satellite_grid_column &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_grid_column) int32 2kB ...<br>&nbsp; * satellite_valid_time_unix_sec &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) int32 1kB ...<br>&nbsp; * ships_lag_time_hours &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (ships_lag_time_hours) float64 40B ...<br>&nbsp; * ships_intensity_threshold_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_intensity_threshold_m_s01) float64 168B ...<br>&nbsp; * ships_forecast_hour &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_forecast_hour) int32 92B ...<br>&nbsp; * satellite_predictor_name_gridded &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_predictor_name_gridded) object 8B ...<br>&nbsp; * satellite_predictor_name_ungridded &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_predictor_name_ungridded) object 128B ...<br>&nbsp; * ships_valid_time_unix_sec &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_valid_time_unix_sec) int32 76B ...<br>&nbsp; * ships_predictor_name_lagged &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_predictor_name_lagged) object 136B ...<br>&nbsp; * ships_predictor_name_forecast &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_predictor_name_forecast) object 1kB ...<br>Dimensions without coordinates: ships_storm_object_index<br>Data variables:<br>&nbsp; &nbsp; satellite_number &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) int32 1kB ...<br>&nbsp; &nbsp; satellite_band_number &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) int32 1kB ...<br>&nbsp; &nbsp; satellite_band_wavelength_micrometres &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_cyclone_id_string &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) |S8 2kB ...<br>&nbsp; &nbsp; satellite_storm_type_string &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) |S2 578B ...<br>&nbsp; &nbsp; satellite_storm_name &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) |S10 3kB ...<br>&nbsp; &nbsp; satellite_storm_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_storm_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_storm_intensity_number &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_storm_u_motion_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_storm_v_motion_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec) float64 2kB ...<br>&nbsp; &nbsp; satellite_predictors_gridded &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column, satellite_predictor_name_gridded) float64 474MB ...<br>&nbsp; &nbsp; satellite_grid_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br>&nbsp; &nbsp; satellite_grid_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br>&nbsp; &nbsp; satellite_predictors_ungridded &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (satellite_valid_time_unix_sec, satellite_predictor_name_ungridded) float64 37kB ...<br>&nbsp; &nbsp; ships_storm_intensity_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_valid_time_unix_sec) float64 152B ...<br>&nbsp; &nbsp; ships_storm_type_enum &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) int32 2kB ...<br>&nbsp; &nbsp; ships_forecast_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_forecast_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_v_wind_200mb_0to500km_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_vorticity_850mb_0to1000km_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_vortex_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_vortex_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_850mb_0to600km_m_s01 &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_max_tangential_wind_850mb_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_1000mb_at500km_m_s01 &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_850mb_at500km_m_s01 &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_500mb_at500km_m_s01 &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_mean_tangential_wind_300mb_at500km_m_s01 &nbsp; (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_srh_1000to700mb_200to800km_j_kg01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_srh_1000to500mb_200to800km_j_kg01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br>&nbsp; &nbsp; ships_threshold_exceedance_num_6hour_periods &nbsp; &nbsp; (ships_storm_object_index, ships_intensity_threshold_m_s01) int32 2kB ...<br>&nbsp; &nbsp; ships_v_motion_observed_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_v_motion_1000to100mb_flow_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_v_motion_optimal_flow_m_s01 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_cyclone_id_string &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) object 152B ...<br>&nbsp; &nbsp; ships_storm_latitude_deg_n &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; (ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_storm_longitude_deg_e &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_storm_object_index) float64 152B ...<br>&nbsp; &nbsp; ships_predictors_lagged &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_valid_time_unix_sec, ships_lag_time_hours, ships_predictor_name_lagged) float64 13kB ...<br>&nbsp; &nbsp; ships_predictors_forecast &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;(ships_valid_time_unix_sec, ships_forecast_hour, ships_predictor_name_forecast) float64 451kB ...</p> <p>Variable names are meant to be as self-explanatory as possible.&nbsp; Potentially confusing ones are listed below.</p> <ul> <li>The dimension ships_storm_object_index is redundant with the dimension ships_valid_time_unix_sec and can be ignored.</li> <li>ships_forecast_hour ranges up to values that we do not actually use in the paper.&nbsp; Keep in mind that our max forecast hour used in machine learning is 24.</li> <li>The dimension ships_intensity_threshold_m_s01 (and any variable including this dimension) can be ignored.</li> <li>ships_lag_time_hours corresponds to lag times for the SHIPS satellite-based predictors.&nbsp; The only lag time we use in machine learning is "NaN", which is a stand-in for the best available of all lag times.&nbsp; See the discussion of the "priority list" in the paper for more details.</li> <li>Most of the data variables can be ignored, unless you're doing a deep dive into storm properties.&nbsp; The important variables are satellite_predictors_gridded (full satellite images), ships_predictors_lagged (satellite-based SHIPS predictors), and ships_predictors_forecast (environmental and storm-history-based SHIPS predictors).&nbsp; These variables are all discussed in the paper.</li> <li>Every variable name (including elements of the coordinate lists ships_predictor_name_lagged and ships_predictor_name_forecast) includes units at the end.&nbsp; For example, "m_s01" = metres per second; "deg_n" = degrees north; "deg_e" = degrees east; "j_kg01" = Joules per kilogram; ...; etc.</li> </ul>

opencc-by-4.0Aug 2024View details →
zenodo36/100

Real-time RT-PCR datasets for the CEO study

<p>Real-time RT-PCR results of testing citrus essential oils for viroid transmission.&nbsp;</p> <p>Ct values of each run are listed in Excel sheet according to the respective experiment.&nbsp;</p>

opencc-by-4.0Sep 2024View details →
zenodo36/100

AI-based real-time animal management system for flock monitoring in the husbandry of fattening turkeys

<p>The dataset was collected in a commercial turkey barn using a self-developed AI-based real-time animal management system to assess the behavior of turkeys. The data covers turkeys from the 6th to the 18th week of life.</p>

opencc-by-4.0Sep 2024View details →
zenodo36/100

Real-time GNSS troposphere products: ZTD and gradients - hurricane Lorenzo case study

<p>This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute for a subset of EPN stations during two test periods (21 days long each):</p> <p>1) Day of year (DoY) 201 - 221, 2019</p> <p>2) DoY 266 - 286, 2019.</p> <p>The processing strategy can be found in&nbsp; https://link.springer.com/article/10.1007/s10291-020-01014-w, under to &quot;advanced strategy&quot; configuration.</p> <p>For convenience, products are stored in 2 formats (but each contains identical data):</p> <p>1. Self-explanatory netCDF format (https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html), which follows the parameter naming convention of the troposphere SINEX v2 format: https://www.pecny.cz/WWW_FIL/TRO-SINEX/old/sinex_tro_2017-05-28-JD.pdf. Each file contains data for all stations but only for one test period.</p> <p>2. Semicolon delimited text files, with a self-explanatory header line. Each file contains daily products for one station.</p>

opencc-by-4.0Aug 2021View details →
dryad36/100

Real-time geographic settling of a hybrid zone between the invasive winter moth (Operophtera brumata L.) and the native Bruce spanworm (O. bruceata Hulst)

<p>Hybridization plays an important and underappreciated role in shaping the evolutionary trajectories of species. Following the introduction of a non-native organism to a novel habitat, hybridization with a native congener may affect the probability of establishment of the introduced species. In most documented cases of hybridization between a native and a non-native species, a mosaic hybrid zone is formed, with hybridization occurring heterogeneously across the landscape. In contrast, most naturally occurring hybrid zones are clinal in structure. Here we report on a long-term microsatellite dataset that monitored hybridization between the invasive winter moth, <i>Operophtera brumata </i>(Lepidoptera: Geometridae), and the native Bruce spanworm, <i>O. bruceata, </i>over a 12-year period. Our results document one of the first examples of the real-time formation and geographic settling of a clinal hybrid zone. In addition, by comparing one transect in Massachusetts where extreme winter cold temperatures have been hypothesized to restrict the distribution of winter moth, and one in coastal Connecticut, where winter temperatures are moderated by Long Island Sound, we<i> </i>find that the location of the hybrid zone appears to be independent of environmental variables and maintained under a tension model wherein the stability of the hybrid zone is constrained by population density, reduced hybrid fitness, and low dispersal rates. Documenting the formation of a contemporary clinal hybrid zone may provide important insights into the factors that shaped other well-established hybrid zones.</p>

opencc-zeroOct 2021View details →
dryad36/100

The inner mechanics of rhodopsin guanylyl cyclase during cGMP-formation revealed by real-time FTIR spectroscopy

<p>Enzymerhodopsins represent a recently discovered class of rhodopsins which includes histidine kinase rhodopsin, rhodopsin phosphodiesterases and rhodopsin guanylyl cyclases (RGCs). The regulatory influence of the rhodopsin domain on the enzyme activity is only partially understood and holds the key for a deeper understanding of intra-molecular signaling pathways. Here we present a UV-Vis and FTIR study about the light-induced dynamics of a RGC from the fungus <em>Catenaria anguillulae</em>, which provides insights into the catalytic process. After the spectroscopic characterization of the late rhodopsin photoproducts, we analyzed truncated variants and revealed the involvement of the cytosolic N-terminus in the structural rearrangements upon photo-activation of the protein. We tracked the catalytic reaction of RGC and the free GC domain independently by UV-light induced release of GTP from the photolabile NPE-GTP substrate. Our results show substrate binding to the dark-adapted RGC and GC alike and reveal differences between the constructs attributable to the regulatory influence of the rhodopsin on the conformation of the binding pocket. By monitoring the phosphate rearrangement during cGMP and pyrophosphate formation in light-activated RGC, we were able to confirm the M state as the active state of the protein. The described setup and experimental design enable real-time monitoring of substrate turnover in light-activated enzymes on a molecular scale, thus opening the pathway to a deeper understanding of enzyme activity and protein-protein interactions.</p>

opencc-zeroOct 2021View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record