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11,177 results for “identify”

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zenodo36/100

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Forrest, Laura, <a href="https://orcid.org/0000-0002-0235-9506">https://orcid.org/0000-0002-0235-9506</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-zeroSep 2019View details →
zenodo36/100

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Seltmann, Katja, <a href="https://orcid.org/0000-0001-5354-6048">https://orcid.org/0000-0001-5354-6048</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-zeroOct 2019View details →
zenodo36/100

Supplementary Data for "Identifying Promising Metal−Organic Frameworks for Heterogeneous Catalysis via High-Throughput Periodic Density Functional Theory"

<p>Supplementary data to accompany:</p> <p>A.S. Rosen, J.M. Notestein, R.Q. Snurr. &quot;Identifying Promising Metal-Organic Frameworks for Heterogeneous Catalysis via High-Throughput Periodic Density Functional Theory&quot;, J. Comput. Chem (2019). DOI: 10.1002/jcc.25787</p>

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

Comparing the Use of Research Resource Identifiers and Natural Language Processing for Citation of Databases, Software and Other Digital Artifacts

<p><strong>The Research Resource Identifier was introduced in biomedicine in 2014 to more precisely identify the reagents and tools used in published biomedical research and to track use of tools across the breadth of the biomedical literature. The current RRID specification covers key biological and digital resources. Authors are instructed to include an RRID after the first mention of any resource used. RRIDs are designed to be easy to find using &nbsp;a full text search search engine. </strong></p> <p><strong>The published data sets were used in our comparative study where comparing the output of our RRID curation workflow with the outputs of automated text mining systems that have been used to identify mentions of resources in the text of publications. All files in tab-separated format (tsv). </strong></p> <p><strong>Scibot.tsv: Records of the RRID curation workflow using SciBot. </strong></p> <p>Each record shows that a resource RRID was identified in paper PMID with curator tags (Tag1, Tag2, both optional)</p> <p><strong>&nbsp;&nbsp;&nbsp; </strong>PMID: Pubmed ID</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; RRID: Research Resource Identifier</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; Tag1: Curator tags (optional)</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; Tag2: Additional curator tags (optional)</p> <p><strong>rdwsorted.tsv: Records of the output from RDW, a text mining software. </strong></p> <p>RDW identifies mentions of research resources in papers. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong>&nbsp;&nbsp;&nbsp; </strong>PMID: Pubmed ID</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; RRID: Research Resource Identifier</p> <p><strong>rridbyrdw05282019.tsv:&nbsp;Records of the output of the RRID-by-RDW in RDW. </strong></p> <p>RRID-by-RDW is a component in RDW that identifies mentions of research resources in papers by matching patterns of RRID specifications. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong>&nbsp;&nbsp;&nbsp; </strong>PMID: Pubmed ID</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; RRID: Research Resource Identifier</p> <p>&nbsp;&nbsp;&nbsp;&nbsp; Context: Snippet where the RRID was found</p> <p><strong>resource_metadata20190418.tsv: Metadata of RRIDs</strong></p> <p>This file contains metadata of resources and their RRIDs. See file header for column definitions.</p> <p><strong>RRIDCUR-definitions.tsv: Definitions of curator tags used in Scibot.tsv.</strong></p> <p><strong>&nbsp;&nbsp; </strong>tag: Tag name</p> <p>&nbsp;&nbsp;&nbsp; definition: Definition of the tag</p>

openbsd-3-clause-clearJun 2019View details →
zenodo36/100

Genome-wide association studies identify candidate genes for coat color and mohair traits in the Iranian Markhoz goat

<p>Illumina Caprine 53K SNPchip genotypes of 228 Iranian goats used for coat color and mohair traits GWAS</p>

opencc-by-4.0Mar 2018View details →
zenodo36/100

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Friel, John, <a href="https://orcid.org/0000-0001-7063-7849">https://orcid.org/0000-0001-7063-7849</a>. Claims were made on Bloodhound, <a href="http://bloodhound-tracker.net">https://bloodhound-tracker.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-zeroNov 2019View details →
zenodo36/100

Figure 2 in Identifying the hotspots of wildlife-vehicle collision on the Çankırı-Kırıkkale highway during summer

Figure 2. The distribution of accidentally killed mammals on the road course.

opencc-by-4.0Jan 2017View details →
zenodo36/100

Figure 1 in Identifying the hotspots of wildlife-vehicle collision on the Çankırı-Kırıkkale highway during summer

Figure 1. Study area.

opencc-by-4.0Jan 2017View details →
zenodo36/100

Dataset and Code Accompanying "Challenges and Learning from Exploring Deep Metric Learning for Identifying Seismic Stratigraphy"

<p>Dataset and code related to the work outlined in "Challenges and Learning from Exploring Deep Metric Learning for Identifying Seismic Stratigraphy".</p> <p>File Description:</p> <ul> <li>seis_seg.h5 - trained model weights.</li> <li>topseis_temp.npy - seismic survey.</li> <li>train_demo.ipynb - training demo including synthetic modelling pipeline.</li> <li>inference_and_plot - inference and reproducing main results.</li> </ul> <p>Pre-calculated phase volume and encoded features for reproducing figures:</p> <ul> <li>unwrapped_phase_volume_full.npy.</li> <li>features_full_ds222_ps32_s222.npy.</li> <li>indices_full_ds222_ps32_s222.npy.</li> </ul>

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

Machine learning classification of archaea and bacteria identifies novel predictive genomic features

<p>Dataset used for the classification analysis of archaea and bacteria based on 77 genomic features calculated using GBRAP (GenBank Retrieving, Analyzing and Parsing) tool (Vischioni, C. et al. Gbrap: a tool to retrieve, parse and analyze genbank files of viral and bacterial species. bioRxiv 2021&ndash;09 (2021)).</p>

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

Figure 2 in Occlusal surface variations in genetically-identified specimens of the genus Apodemus (Mammalia: Rodentia) distributed in the Northern Anatolia region and three Turkish islands: Gökçeada, Marmara Island, and Bozcaada

Figure 2. Nomenclature of upper (a) and lower (b) teeth (Zykov and Izvarin 2020).

opencc-by-4.0Apr 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

Phylogenetic tree of 1819 YfaL protein sequences identified in 2053 E. coli genomes.

<p><strong><span>Phylogenetic tree of 1819 YfaL protein sequences identified in 2053 <em>E. coli</em> genomes. </span></strong><span>The purple circles on the branches represent bootstrap values &gt; 0.8. The different strain&rsquo;s phylogroups are displayed outside of the tree.</span></p>

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

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by David P. Shorthouse, <a href="https://orcid.org/0000-0001-7618-5230">https://orcid.org/0000-0001-7618-5230</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-zeroAug 2024View details →
zenodo36/100

Whole-Genome Resequencing identifies SNPs in Sucrose Synthase and Sugar Transporter Genes Associated with Sweetness in Coconut

<p><span>This vcf file constitute underlying raw data material for the manuscript</span> "<span>Whole-Genome Resequencing identifies SNPs in Sucrose Synthase and Sugar Transporter Genes Associated with Sweetness in Coconut"</span>. <span>The SNP genotype data came from a whole-genome resequencing and were called using an unpublished coconut reference genome. SNPs with a non-missing and minor allele frequency (MAF) less than 5% were removed. &nbsp;Finally, 19,149,289 SNPs were selected and used in the population study and gene mining.</span></p>

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

Inputs & results for "Identifying circular DNA using short-read mapping"

<div>* `inputs`: Contains genome assemblies, annotations, and sample sheets for the Nextflow pipeline</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; * `parasite_species`: Contains the sample sheet and input data for the parasite/related species dataset</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; * `parasitoid_wasps`: Contains the sample sheet and input data for the parasitoid wasp dataset</div> <div>&nbsp;</div> <div>* `results`: Contains filtered BAM and coverage files, figures, and `GenomeInfo`-filtered example files for the parasitoid wasp and parasite/related species datasets</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; * `parasite_species`:</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; * `bam_files`: Contains BAM files with mapped distances &gt;= 1 kb</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;* `coverage_files`: Contains coverage depth files filtered by the BAM files</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;* `insert_filtered_results`: Contains examples of filtered file outputs from the `GenomeInfo` class. The complete set of outputs can be found on Zenodo.</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;* `fig`: Figures generated for the pub.</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; * `parasitoid_wasps`:</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;* `bam_files`: Contains BAM files with mapped distances &gt;= 1 kb</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;* `coverage_files`: Contains coverage depth files filtered by the BAM files</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;* `fig`: Figures generated for the pub.</div> <div>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;* `hyposoter_didymator_blastx_results`: Contains the manual BLASTx results from the _Hyposoter didymator_ search.</div>

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

Data to "Phenylphosphonic Acid on Rutile TiO2(110): Using Theoretically Predicted O 1s Spectra to Identify the Adsorption Binding Modes"

<p>Primary data, meta data, and corresponding lists of figures and tables are included. Version 2 includes the complete DFT data.</p>

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

Natural history specimens collected and/or identified and deposited.

Natural history specimen data collected and/or identified by Tommy Andriollo, <a href="https://orcid.org/0000-0003-3922-7475">https://orcid.org/0000-0003-3922-7475</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.

opencc-zeroAug 2024View details →
zenodo36/100

The identified bow shock crossings based on Venus Express observations

<div>Bow shock locations and upstream parameters observed by Venus Express.</div> <div>&nbsp;</div> <div>The columns are structured as follows:</div> <div>&nbsp; &nbsp; 1: time of bow shock crossings dd-mmm-yyyy HH:MM:SS</div> <div>&nbsp; &nbsp; 2: X-VSE [Rv]</div> <div>&nbsp; &nbsp; 3: Y-VSE [Rv]</div> <div>&nbsp; &nbsp; 4: Z-VSE [Rv]</div> <div>5: Solar wind magnetosonic Mach number</div> <div>&nbsp; &nbsp; 6: EUV [mW/m^2]</div> <div>&nbsp; &nbsp; 7: IMF Bx in VSE [nT]</div> <div>8: IMF By in VSE [nT]</div> <div>&nbsp; &nbsp; 9: IMF intensity |B| [nT]</div> <div>&nbsp; &nbsp; 10: IMF cone angle [degree]</div> <div>11: Solar wind dynamic pressure [nPa]</div> <div>12: The angle between the IMF and the shock normal, thetaBn [degree]</div> <div>&nbsp;</div> <div>Please contact xuqi01@nssc.ac.cn if you have questions.</div>

opencc-by-4.0Aug 2024View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

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

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