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78 results for “Physical Testing”

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zenodo40/100

Test Scene Dataset for Physically Based Rendering

<p>A test scene data set for physically based rendering.<br> The scenes include different geometries, materials and illumination setups of varying complexity to facilitate the evaluation and testing of Monte-Carlo rendering algorithms.<br> The source files are provided in the Blender file format for easy editing and additional exports to the Mitsuba XML format are included.</p>

opencc-by-4.0Aug 2020View details →
zenodo36/100

Wikidata Dump test_physics

<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/155">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>

opencc-zeroMar 2020View details →
zenodo36/100

Wikidata Dump test_physics

<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/155">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>

opencc-zeroMar 2020View details →
zenodo36/100

Wikidata Dump test_physics

<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/155">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>

opencc-zeroMar 2020View details →
zenodo36/100

Wikidata Dump test_physics

<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/155">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>

opencc-zeroMar 2020View details →
zenodo36/100

Wikidata Dump test_physics

<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> <br> <a href="https://tools.wmflabs.org/wdumps/dump/155">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>

opencc-zeroMar 2020View details →
zenodo36/100

PhiPiPi Dataset for our paper "Event Generation and Consistence Test for Physics with Sliced Wasserstein Distance"

<p>The PhiPiPi dataset is used to train and test our fast event simulation model for our paper &nbsp;"Event Generation and Consistence Test for Physics with Sliced Wasserstein Distance" in <a href="https://arxiv.org/abs/2310.17897">https://arxiv.org/abs/2310.17897</a>. &nbsp;The dataset is generated by BES III simulation framework to simulate phase-space Monte Carlo events, focusing on the production of psi(2S) in electron-positron annihilation. This intricate process entails the psi(2S) decay into a trio of particles: phi, pi+, and pi-, with the phi particle subsequently decaying into a K+ and K- pair.</p><p>We have meticulously partitioned the data into distinct sets for training, validation, and testing to facilitate a robust machine learning workflow. Each set is systematically saved in the convenient npy format for ease of access and integration.</p><p>The dataset contains the four-momentum components (px, py, pz, E) of the final-state particles: K+, K-, pi+, and pi-. This comprehensive inclusion ensures a detailed representation of the kinematic properties of the particles for accurate model training.</p><p>Researchers and practitioners are invited to use this dataset by incorporating it into our public code repository: <a href="https://github.com/caihao/SWD-EvtGen">https://github.com/caihao/SWD-EvtGen</a>. Doing so will enable the training of our fast event simulation model, enhancing the fidelity and efficiency of high energy physics simulations.</p>

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

Acoustic data and Loading Regime of Two Rock Physics Experiments carried out on INOVA-1000 test-complex, Borok, Russia

<p>Dataset linked to a manuscript under revision: <strong>"Source parameters of laboratory acoustic emission events estimated from the coda of waveforms"</strong>. Two experiments carried out on cilindrical samples (D30xH60mm) of Berea sandstone and granite of Voronezh crystal massif (Russia). Each experimental dataset contains: acoustic records from 16 sensors, the appearance of acoustic emission events during the experiment, velocities of P-waves changing in time, loading regime, i.e. confining pressure and axial loading curves, results of source characteristics analyses from coda-waves: corner frequencies, relative seismic moments and relative stress-drops. &nbsp;</p>

opencc-by-4.0Oct 2023View 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

Data presented in "An ultracold molecular beam for testing fundamental physics"

<p>Data presented in &quot;An ultracold molecular beam for testing fundamental physics&quot;. The original paper can be found at&nbsp;https://doi.org/10.1088/2058-9565/ac107e. This is the data underlying the simulations and the experimental results.</p>

opencc-by-4.0Apr 2021View details →
ClinicalTrials.gov36/100

Developing and Testing an Implementation Strategy for Active Learning to Promote Physical Activity in Children

ClinicalTrials.gov study NCT05048433. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Testing Adaptive Interventions to Improve Physical Activity for Sedentary Women

ClinicalTrials.gov study NCT03558828. IPD Sharing: NO. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Factorial Trial Testing Guided Thinking Tasks to Optimize a Physical Activity Intervention

ClinicalTrials.gov study NCT05235360. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov36/100

Testing Self-Control as a Behavior Change Mechanism to Increase Physical Activity

ClinicalTrials.gov study NCT04522141. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →
dryad36/100

Physical test and numerical simulation data and the amount of fish eggs in the study area

Open the record for dataset details and reuse information.

publicFeb 2025View details →
dryad36/100

Test data from SPCAM for machine learning in moist physics

Open the record for dataset details and reuse information.

publicMar 2021View details →
zenodo32/100

Training and Testing Data, Associated Code, and SCAM Validations Code and Data for ResNet in moist physics (ResCu)

<p>Data and codes for a deep convolutional residual neural network moist physics parameterization (ResCu).</p> <p>In this new version, the&nbsp;randomly selected training data samples and part of testing data samples (June, July&nbsp;and August) are provided. They are processed&nbsp;into a new data structure, which can be directly utilized in training and testing.&nbsp;For the entire second&nbsp;year training samples and&nbsp;the entire third year testing samples, we provide them in a repository at&nbsp;Dryad&nbsp;(<a href="https://doi.org/10.6075/J0CZ35PP">https://doi.org/10.6075/J0CZ35PP</a> and https://doi.org/10.6075/J03J3BGF).</p> <p>Please download and decompress ResCu_Han_et_al_JAMES.tar.gz.</p> <p>Follow the instructions in README.txt and download the training and testing data (The Dryad depositary is provided in the description).&nbsp;</p> <p>Here we provide 3 parts of data and codes:</p> <p>1, Training and testing data from SPCAM;</p> <p>2, Training and testing codes for ResCu and many other NN architectures;</p> <p>3, SCAM validations.</p> <p>&nbsp;</p>

opencc-by-4.0Jun 2020View details →
zenodo32/100

Simulation output of the reference setup in "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"

<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" contains the simulation output of the 20 reference runs. Additional data is available upon request from the corresponding author.</p>

opencc-by-4.0Sep 2024View details →
zenodo32/100

The Data and Codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en)

<p>Note: the monthly averaged NCAM, SPCAM, and CAM5 results are uploaded as *_h0.tar.gz!</p> <p>The data and codes for Training, Testing, and Prognostic Validation of A ResNet Ensemble for Moist Physics (ResCu-en)&nbsp; are stored in this repositary.</p> <p>This project is built on python3.7 and tensorflow-gpu2.3.0, and the scripts for analysis and plots are on jupyter-notebook.</p> <p>Please make&nbsp;sure to install all python packages used in an&nbsp;environment.</p> <p>Please read the ReadME-2.txt.</p> <p>For the entire training and testing datasets in both the&nbsp;baseline and +4K SST climates. Please download them from&nbsp;Dryad&nbsp;(<a href="https://doi.org/10.6075/J0CZ35PP">https://doi.org/10.6075/J0CZ35PP</a>&nbsp;and https://doi.org/10.6075/J03J3BGF), Onedrive (https://1drv.ms/u/s!ArKTPPs6U_9DjxPJeSReKlbsLzyh?e=PDlWYJ), and Dropbox (https://www.dropbox.com/s/yc4fx35laqwt0fu/SPCAM_ML_4K.tar.gz?dl=0 and&nbsp;https://www.dropbox.com/s/4pxahzwt9v55u2m/SPCAM_ML_RAD.tar.gz?dl=0).</p>

opencc-by-4.0Apr 2023View 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