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285 results for “rapid tests”

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zenodo44/100

Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho

<p>These are pseudo-anonymised data from the MISTRAL study: &quot;Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho&quot;. The data dictionary explains the data available in the dataset. Between December 2020 and September 2021, 2131 individuals with either COVID symptoms or contact with a COVID-positive case were included from two hospitals in Lesotho and had a valid PCR results.</p>

opencc-by-4.0Dec 2022View details →
zenodo44/100

Multipad Agarose Plate (MAP): A Rapid and High-Throughput Approach for Antibiotic Susceptibility Testing

<p>The datasets used for the Multipad Agarose Plate (MAP) paper. Each experiment was labelled with BE followed by a number.&nbsp;</p> <p>The file&nbsp;<em>BE_condition_map.json</em> describes what was placed on each pad for the experiments. Attached here are JSON files with&nbsp;Pandas data frames that contain all segmentation information, along with debug videos showing how the segmentation aligns with the images. Contact us for access to the raw data.</p> <p>Datasets used for validation experiments:</p> <ul> <li>Leakage test: BE100, BE102</li> <li>Agarose concentration: BE103</li> <li>Illumination wavelength verification: BE138</li> <li>Seeding density verification: BE162</li> </ul> <p>Datasets used for AST:</p> <ul> <li>Chloramphenicol and Rifampicin:&nbsp;BE140, BE141, BE142, BE144, BE145</li> <li>Vancomycin,&nbsp;Ampicillin,&nbsp;Kanamycin: BE148, BE149</li> <li>Ciprofloxacin,&nbsp;Tetracycline,&nbsp;Carbenicillin,&nbsp;Mecillinam: BE150, BE151</li> </ul> <p>Broth microdilution&nbsp;data used for AST validation:</p> <ul> <li>BE139, BE143, BE160</li> </ul> <p>Some datasets also include data that was discarded.&nbsp;</p>

opencc-by-4.0Jul 2023View details →
zenodo44/100

Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data

<p>Dataset with Zambia data belonging to the publication: Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data</p>

opencc-by-4.0Oct 2023View details →
zenodo40/100

Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications

<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>

opencc-by-4.0Mar 2024View details →
zenodo40/100

Fig. 4 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling

Fig. 4. | Faecal bacterial community profile of captive chimpanzees infected with Giardia duodenalis detected by rapid antigen test. (A) Relative abundance of colour coded bacterial phyla separated based on presence (+) or absence (‒) of Giardia using rapid antigen test (RAT). The sample identity is located at the bottom of the graph with two labels (C20, C3) shaded indicating samples that were found as Giardia positive by real-time PCR. (B) Alpha diversity based on observed OTU and Shannon's index plotted as box plot and evaluated using t-tests. (C) Principal coordinates analysis (PCoA) 2D plot using first two principal components from Bray-Curtis dissimilarity matrix at the genus taxonomic levels. The clustering between Giardia positive (RAT+) and negative (RAT-) samples was tested using ANOSIM. (D) Linear discriminant analysis effect size (LEfSe) used plot of significant factors discriminating G. duodenalis positive from negative sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Apr 2022View details →
zenodo40/100

Fig. 3 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling

Fig. 3. | Faecal bacterial community profile of captive chimpanzees infected with Giardia duodenalis as detected by rapid antigen test and real-time PCR combined. (A) Relative abundance of colour coded bacterial phyla separated based on presence (+) or absence (‒) of Giardia. The sample identity is located at the bottom of the graph. (B) Alpha diversity based on observed OTU and Shannon's index plotted as box plot and evaluated using t-tests. (C) Principal coordinates analysis (PCoA) 2D plot using first two principal components from Bray-Curtis dissimilarity matrix at the genus taxonomic levels. The clustering between Giardia positive (+) and negative (‒) samples was tested using ANOSIM. (D) Linear discriminant analysis effect size (LEfSe) used plot of significant factors discriminating G. duodenalis positive from negative sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Apr 2022View details →
zenodo40/100

Fig. 2 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling

Fig. 2. | Results of Giardia duodenalis rapid antigen test applied on faecal samples from chimpanzees. A positive result for the Giardia duodenalis rapid antigen test (RAT, Anigen Rapid Giardia AG Test Kit) is represented by the line in the 'T' position in the window along with the positive control line in the 'C' position.

opencc-by-4.0Apr 2022View details →
zenodo40/100

Fig. 1 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling

Fig. 1. Captive chimpanzees and their enclosure in Sydney, Australia. (A) Main chimpanzee open air exhibit with multiple climbing structures. (B) View from the other direction showing entry to the indoor area at the end of the exhibit. (C) smaller exhibit with mesh covering and more climbing and sleeping structures. (D) Members of the chimpanzee troop at the Taronga Zoo.

opencc-by-4.0Apr 2022View details →
dryad40/100

Data from: Testing for a role of postzygotic incompatibilities in rapidly speciated Lake Victoria cichlids

Open the record for dataset details and reuse information.

publicJan 2024View details →
zenodo36/100

Rapid Scan Test - Distressed Buddha Head

This is a test scan using our new EinScan Pro HD This should not be used in a proffesional or commercial context - it is just a proof-of-concept. https://culture-factory.io/ Culture Factory is a leading concierge enabling creative ownership in the web 3.0 metaverse. Source: Objaverse 1.0 / Sketchfab

opencc-bySep 2021View details →
zenodo36/100

Rapid Scan Test - Balenciaga Triple S Mens 44

This is a test scan using our new EinScan Pro HD This should not be used in a proffesional or commercial context - it is just a proof-of-concept. https://culture-factory.io/ Culture Factory is a leading concierge enabling creative ownership in the web 3.0 metaverse. Source: Objaverse 1.0 / Sketchfab

opencc-bySep 2021View details →
zenodo36/100

Rapid Scan Test - Distressed Mayan Figure

This is a test scan using our new EinScan Pro HD This should not be used in a proffesional or commercial context - it is just a proof-of-concept. https://culture-factory.io/ Culture Factory is a leading concierge enabling creative ownership in the web 3.0 metaverse. Source: Objaverse 1.0 / Sketchfab

opencc-bySep 2021View 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

Test dataset for "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks"

<p>Data corresponding to test dataset used in Aberra AS, Lopez A, Grill WM, Peterchev AV. (2022). &quot;Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks&quot;. bioRxiv. Dataset includes:</p> <ul> <li><em>simnibs/ -</em>&nbsp;SimNIBS mesh and E-field solution file used in test dataset (posterior-anterior TMS of M1 in <em>ernie</em> example mesh, meshed with mri2mesh pipeline)</li> <li><em>layer_data/ - </em>surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li> <li><em>nrn_sim_data/&nbsp;-&nbsp;</em>Thresholds from NEURON simulations for all 25 model neurons included in the study,&nbsp;each at&nbsp;4,999-5,000 positions and 12 azimuthal orientations&nbsp;(&quot;ground truth&quot; for CNN)&nbsp;</li> <li><em>cell_data/</em> - Coordinates and morphology information&nbsp;for all&nbsp;model neurons</li> <li><em>weights/</em>&nbsp; -&nbsp;Trained 3D convolutional neural networks for estimating neuron model-specific TMS thresholds given input&nbsp;E-field distributions on a&nbsp;3D grid (see code/manuscript for dimensions)</li> <li><em>est_data/&nbsp;</em>- Output of trained CNNs on all E-field data for test dataset&nbsp;<em>&nbsp;</em></li> </ul> <p>&nbsp;</p>

opencc-by-4.0Nov 2022View details →
zenodo36/100

Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Lesotho data

<p>Dataset with Lesotho data belonging to the publication: Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia</p>

opencc-by-4.0Oct 2023View details →
ClinicalTrials.gov36/100

Pilot Study Evaluating the Use of Simultaneous HBV, HCV, and HIV Rapid Tests

ClinicalTrials.gov study NCT01790633. IPD Sharing: YES. Countries: 1. Publications: 2.

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

Developing and Testing the Opioid Rapid Response System

ClinicalTrials.gov study NCT04589676. IPD Sharing: NO. Countries: 1. Publications: 14.

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

ImmunoCARE: Rapid, Accurate COVID Testing to Reduce Hospitalization of Immunocompromised Individuals

ClinicalTrials.gov study NCT05655546. IPD Sharing: NO. Countries: 1. Publications: 1.

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

Rapid Flu Tests in Travelers With Fever

ClinicalTrials.gov study NCT00821626. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View 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