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zenodo36/100

Experimental data - project: adaptive strategy on part-list cueing and ego-depletion

<p>Experimental data of two experiments on adaptive strategy and its relation to&nbsp;recall performance and task persistence.</p>

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

Supplementary File S2 for the publication 'Predicting Bacterial Virulence Factors - Evaluation of Machine Learning and Negative Data Strategies' by Rentzsch, R et al.

<p>Supplementary File S2 for the publication &#39;Predicting Bacterial Virulence Factors - Evaluation of Machine Learning and Negative Data Strategies&#39; by Robert Rentzsch, Carlus Deneke, Andreas Nitsche, and Bernhard Y. Renard</p>

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

Data supplementing the article "Diatom DNA metabarcoding for biomonitoring : strategies to avoid major taxonomical and bioinformatical biases limiting molecular indices capacities" K. Tapolczai, F. Keck, A. Bouchez, F. Rimet, M. Kahlert and V. Vasselon submitted to "Frontiers in Ecology and Evolution" journal

<p>These data supplement the article &quot;Diatom DNA metabarcoding for biomonitoring : strategies to avoid major taxonomical and bioinformatical biases limiting molecular indices capacities&quot; &nbsp;K. Tapolczai, F. Keck, A. Bouchez, F. Rimet, M. Kahlert and V. Vasselon submitted to &quot;Frontiers in Ecology and Evolution&quot; journal.</p> <p>The directory contains&nbsp;the following files:</p> <p><strong>464_samples_fastq_files_(mothur).rar&nbsp;</strong>- contains the 464 fastq files proceed together during the Mothur bioinformatics treatments&nbsp;to produce the OTUs and ISUs tables. As the contig and the demultiplexing steps were performed by the sequencing platform, there is 1 fastq file per sample. From this 464 samples OTU/ISU tables, only information regarding&nbsp;76 samples were used in this study and are listed in the &quot;<strong>76_samples_list_(mothur).xlsx&quot; </strong>file<strong>.</strong></p> <p><strong>76_samples_list_(mothur).xlsx&nbsp;</strong>-&nbsp;contains the information regarding the 76 samples used to create the OTUs and ISUs tables presented in the paper.</p> <p><strong>76_samples_R1_R2_fastq_files(DADA2).rar -&nbsp;</strong>contains the raw demultiplexed fastq files (R1.fastq&nbsp;and R2.fastq)&nbsp;for each&nbsp;of the 76 samples used in this study to produce the ESVs table using the DADA2 bioinformatics pipeline.</p>

opencc-by-4.0Jun 2019View 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

Investigating Discontinuous X-ray Irradiation as a Damage Mitigation Strategy for [M(COD)Cl]2 Catalysts - Data

<div> <p>This Origin file contains the supporting processed data for all of the figures from the main paper from the publication 'Investigating Discontinuous X-ray Irradiation as a Damage Mitigation Strategy for [M(COD)Cl]2 Catalysts.&nbsp;</p> </div>

opencc-by-4.0Oct 2024View details →
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Comparing and Combining Existing and Emerging Data Collection and Modeling Strategies in Support of Signal Control Optimization and Management (Project M2)

<p>For decades, traffic signal management agencies have used signal timing optimization tools combined with fine-tuning of signal timing based on field observations in their updates of time-of-day signal timing plans.&nbsp;&nbsp;These traditional signal optimization methods and tools use very limited amount of data and depend on default values in the signal timing optimization/simulation tools to estimate network performance under different signal optimization strategies. In recent years, new data collection technologies are emerging including high resolution controller data, more advanced detection technologies such as video image detection that are based on vehicle tracking and possible integration with microwave detectors, automatic-vehicle based identification technologies, third party crowdsourcing data, connected vehicles, and connected automated vehicles data.&nbsp;&nbsp;The objective of the proposed study is to propose methods and algorithms to combine data collected from existing and emerging sources with enhanced models and optimization algorithms to optimize and manage signal operations. The results from applying the developed methods and algorithms will be compared with traditional signal timing and optimization methods currently used by transportation agencies.&nbsp;</p>

opencc-by-4.0Jul 2021View details →
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Understanding practitioners' strategies to handle test smells: a multi-method study [DATA]

<p>Test smells are poor design and implementation choices that can affect the test code&#39;s understanding and maintainability. Recent studies show the industry is not aware of the test smells concept, while software engineers commonly encounter obstacles to maintain test code. This study investigated test creation and maintenance strategies through developers&#39; perception of eight test smells types. We surveyed 87 software testers and interviewed eight to understand their view on test smells in practice. Our results show that most participants use manual strategies for creating and maintaining test cases. Based on data captured from software testers&#39; perspectives, this study contributes with possible directions and treatments to analyze test smells, seeking to understand how the test affects and potential solutions for test smells.</p>

opencc-by-4.0Jul 2021View details →
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Data from: Trophic strategies of two sympatric endemic pigeons in insular ecosystems: a framework for understanding spatiotemporal frugivory interactions

<p><span><span><span><span>Pigeons are considered to play key ecological roles in frugivory and seed dispersal. They have colonised numerous oceanic islands and diversified into several species in sympatry. How these species coexist in similar niches is </span></span><span>poorly understood, although dietary separation is among the mechanisms suggested to avoid trophic </span><span><span>overlap. </span></span><span>We investigated the trophic ecology of the two endemic </span><i><span>Columba </span></i><span>species co-occurring in the laurel forest and thermosclerophyllous relicts of two of the Canary Islands. This study includes diet description in spatiotemporal terms, its relationship with fruit availability, and seed treatment in ten study areas established on La Palma and La Gomera. We used non-invasive DNA analysis to identify the faeces of the two congeneric species and microhistological methods to examine their diets. The degree of trophic overlap was evaluated by niche similarity and breadth indices. Molecular faecal sampling determined the spatiotemporal distribution of both pigeons to identify their areas of coexistence. These frugivorous pigeons' diets did not differ concerning the main plant species, but they diverged quantitatively in the proportions and parts of plants consumed. Lauraceae fruits were their staple foods, although Rhamnaceae and some Fabaceae and Solanaceae were also important. Both pigeons showed selective preferences for some fruits. Significant spatiotemporal variations in their diets were observed along with a general tendency to increase fruit intake at its ripening times. Our results suggest that different trophic strategies facilitate the coexistence of these frugivorous columbids. These pigeons act as seed dispersers and/or predators depending on seed features (size and hardness), and this may have valuable implications for their conservation.</span></span></span></p>

opencc-zeroAug 2021View details →
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Data - Sow what you sell: strategies for integrating organic breeding and seed production into value chain partnerships

<p>Online survey targeting organic farmers on organic seed use; 25 questions in the survey. The survey was conducted between November 2018 and June 2019 and distributed through the networks of partners involved in the Horizon2020 project LIVESEED, including 23 breeding &amp; research institutes, seven breeding companies, eight seed companies, and 11 organic associations. 752 complete entries by farmers from 20 countries from Central, Northern, Southern and Eastern Europe could be used from the 1,475 total accesses to the survey.</p>

opencc-by-4.0Jun 2021View details →
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Illumina next generation ddRAD sequencing SNP data from: Contrasting genetic diversity and structure between endemic and widespread damselfishes are related to differing adaptive strategies

<p class="MsoNormal"><strong><u><span>Aim:</span></u></strong><span> Discerning when, where, and how processes of isolation lead to differing biogeography is especially complex for marine species with similar ecological niches and within the same geographic location. We assessed population genetics of congeneric and ecologically similar damselfishes within their overlapping distributions and across potential barriers to geneflow.</span></p> <p class="MsoNormal"> </p> <p class="MsoNormal"><strong><u><span>Taxon:</span></u></strong><span> <em>Dascyllus marginatus </em>(endemic) and <em>Dascyllus abudafur </em>(widespread)<em>.</em></span></p> <p class="MsoNormal"> </p> <p class="MsoNormal"><strong><u><span>Location:</span></u></strong><span> Coral reefs from the Red Sea, Djibouti, Yemen, Oman, and Madagascar. </span></p> <p class="MsoNormal"> </p> <p class="MsoNormal"><strong><u><span>Methods:</span></u></strong><span> We used RADseq derived SNPs to investigate key differences in population genetics between both species and discuss barriers shaping genetic differentiation (neutral vs. selective) and biogeography. </span></p> <p class="MsoNormal"> </p> <p class="MsoNormal"><strong><u><span>Results:</span></u></strong><strong><span> </span></strong><em><span>Dascyllus marginatus </span></em><span>inhabited the Red Sea, the coasts of Yemen (including Socotra), and the Gulf of Oman. <em>Dascyllus abudafur</em> species was present from the Red Sea to Madagascar but was absent from Yemen and Oman. Populations of <em>D. marginatus </em>had an order of magnitude higher genetic differentiation compared to <em>D. abudafur</em>, as well as several outlier loci (suggesting selective pressure), which were absent in <em>D. abudafur</em> despite equal sampling locations. In both species, specimens from the Red Sea and Djibouti formed one genetic cluster separated from all other locations.  </span></p> <p class="MsoNormal"> </p> <p class="MsoNormal"><strong><u><span>Main conclusions:</span></u></strong><span> The stronger genetic structure at smaller geographic scale of the endemic species seems associated to faster adaptation to environmental differences; whereas the widespread species only experienced reduced geneflow and neutral differentiation at much larger geographic scales. Restrictive transitions (between the Gulf of Aqaba and the Red Sea or the Red Sea and the Gulf of Aden) did not affect the genetic architecture of either species, while the environmental shift within the Red Sea (at 22°N/20°N) affected the endemic but not the widespread species. Samples from continental Yemen revealed that a genetic break in the Gulf of Aden likely reflects historical colonization processes and not contemporary environmental regimes.</span></p>

opencc-zeroOct 2022View details →
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EPA particulate matter data – Analyses using Local Control Strategy

<p><span class="fontstyle0">Analyses of large observational datasets tend to be complicated and prone to fault depending upon the variable selection, data cleaning and analytic methods employed. Here, we discuss the analysis of 2016 US environmental epidemiology data and outline a new "benchmark" Non-parametric and Unsupervised analysis of these data. Readers are invited to download our CSV file from the archive and apply whatever analytic approach they think appropriate. We hope to encourage the development of a widely-held "consensus view" on the effects of Secondary Organic Aerosols (Volatile Organic Compounds that have predominantly Biogenic or Anthropogenic origin) within PM</span><span class="fontstyle2">2</span><span class="fontstyle3">.</span><span class="fontstyle2">5 </span><span class="fontstyle0">particulate matter on Circulatory and/or Respiratory mortality. For example, the reanalyses described here focus on the question: "Can life in a region with an abundance of trees be relatively dangerous?"</span></p>

opencc-zeroOct 2022View details →
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Data for MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications

<p>This repository contains the results of simulations exposed in the article&nbsp;<strong>MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications.</strong></p> <p>Notebooks for easy plotting of the results are contained in this repository. The README.txt contains light explainations regarding how to use those&nbsp;notebooks.</p> <p>This article is yet to be published at the time of writing.</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2022View details →
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Data for the article: Coupling of leaf elemental traits with root fungal community composition reveals a plant resource acquisition strategy in a desert ecosystem

<p><em>Purpose</em>: Plant-associated microbes enhance nutrient access and stress tolerance of the host species, and therefore, are crucial for plant traits and resource strategies. However, the links between aboveground plant traits and belowground microbes related to plant resource strategies under stressful conditions remain poorly understood.</p> <p><em>Methods</em>: We tested the relationships between leaf traits linked to water (carbon isotopic composition, δ<sup>13</sup>C) and nutrient use (elemental concentrations and stoichiometry) with microbial compositions in roots and rhizospheres of two dominant species (<em>Artemisia ordosica</em> and <em>Leymus secalinus</em>) in the Mu Us Desert, northern China.</p> <p><em>Results</em>: <em>L. secalinus</em> exhibited higher Mg and Mn concentrations, N:P ratios, stoichiometric flexibility, and root fungi:bacteria ratios, but lower foliar K and Ca concentrations and δ<sup>13</sup>C values than <em>A. ordosica</em>. The leaf N:P of <em>L. secalinus</em> increased with the root fungi:bacteria ratios, whereas the leaf N:P of <em>A. ordosica</em> decreased with the root fungi:bacteria ratios. The plant elemental levels (P, N, K, Ca, Mn, and δ<sup>13</sup>C) of <em>L. secalinus</em> but not <em>A. ordosica</em> were significantly related to their root fungal composition. Additionally, the random forest model identified four key fungal families in predicting leaf elemental traits for both plant species.</p> <p><em>Conclusion</em>: The results suggested tight coupling and coordination between leaf elemental traits and root microbial compositions (especially fungal communities) related to plant resource acquisition strategies. By regulating aboveground and belowground feedback loops through trait flexibility and root microbial compositions, the studied plant species can sustain their resource strategies under stressful environmental conditions.</p>

opencc-zeroNov 2022View details →
zenodo36/100

Data from an eye-tracking based study of solution strategies in similar mathematical and physical tasks

<p>Eye-tracking data:</p> <p>The .nas file &quot;ET_data&quot; contains the eyetracking project in software tobii studio with gaze data of all 131 participants.<br> To open this project, the software tobii studio is required.</p> <p>----------------------------------------<br> Interview data:</p> <p>All .txt files contain transcribed interview data in German language.&nbsp; &nbsp;<br> The files are labeled by subject number (e.g., P01 for the first participant) and the item for which the interview was recorded (e.g., M8).<br> The files contain interview data of participants explaining their solution strategy for the given item.</p> <p>----------------------------------------<br> Descriptive data:</p> <p>The file descriptive_data.csv contains the descriptive data of all participants (N=131).<br> The column labels are as follows:</p> <p>code: code of participant, p01-p131&nbsp;&nbsp; &nbsp;<br> survey_period: survey period, summer_2021 / autumn_2020 / spring_2020<br> age: age of participant in years&nbsp;&nbsp; &nbsp;<br> grade: grade of participant&nbsp;&nbsp; &nbsp;<br> gen: gender, male (m) / female (f) / diverse (d)&nbsp;&nbsp; &nbsp;<br> ger_nat_speak: German native speaker, yes / no&nbsp;&nbsp; &nbsp;<br> repeater: Repeater of the last grade?, yes / no<br> math_course: basic course (basic) or advanced course (advanced) in mathematics<br> physics_course: basic course (basic) or advanced course (advanced) in physics<br> grade_math: last grade in mathematics, 1-6&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> grade_phy: last grade mark in physics, 1-6&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> grade_ger: last report mark in german, 1-6&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;<br> glasses: wearer of glasses?, yes / no<br> motivation: four-point lickert scale, very motivated (1) to not motivated at all (0)&nbsp;&nbsp; &nbsp;<br> test: physics tasks before math tasks (pm) or vice versa (mp)&nbsp;&nbsp; &nbsp;<br> For each test item 2 columns are created. Example Item k1:<br> &nbsp;&nbsp; &nbsp;ac_k1: answer correctness of item k1, not correct and/or guessed (0), correct (1).&nbsp;&nbsp; &nbsp;<br> &nbsp;&nbsp; &nbsp;cf_k1: confidence of item k1, four-point lickert scale, very confident (1) to guessed (0)&nbsp;&nbsp;&nbsp;</p>

opencc-by-4.0Dec 2022View details →
dryad36/100

Data from: Gustatory polymorphism mediates a new adaptive courtship strategy

<p><span>Human-imposed selection can lead to adaptive changes in sensory traits. However, rapid evolution of the sensory system can interfere with other behaviors, and animals must overcome such sensory conflicts. In response to insecticide baits that contain glucose, German cockroaches evolved glucose-aversion (GA), which confers behavioral resistance against baits. During courtship, the male offers the female a nuptial gift that contains maltose, which expediates copulation. However, the female's saliva rapidly hydrolyzes maltose into glucose, which causes GA females to dismount the courting male, which reduces mating success of GA females. Comparative analysis revealed two adaptive traits in GA males. They produce maltotriose, which is more resilient to salivary glucosidases, and they initiate copulation faster than wild-type (WT) males, before GA females interrupt their nuptial feeding and dismount the male. Recombinant lines of the two strains showed that the two emergent traits of GA males were not genetically associated with the GA trait. Results suggest that the two courtship traits emerged in response to the altered sexual behavior of GA females and independently of the male's GA trait. Although rapid adaptive evolution generates sexual mismatches that lower fitness, compensatory behavioral evolution can correct these sensory discrepancies.</span></p>

opencc-zeroDec 2022View details →
zenodo36/100

Data from: Hemispherically-Symmetric Strategies for Stratospheric Aerosol Injection

<p>Data in support of research:</p> <p>Stratospheric aerosol injection (SAI) comes with a wide range of possible design choices, such as where and when to inject aerosols. Different injection strategies can yield different climate responses; therefore, making informed future decisions on SAI requires an understanding of the range of possible climate outcomes. Yet to date, there has been no systematic exploration of a comprehensive set of SAI strategies. This limits the ability to determine what effects are robust across different strategies and what depend on specific injection choices, or to determine if there are underlying trade-offs between different climate goals.&nbsp;</p> <p><br> This study systematically explores how the choice of SAI strategy affects climate responses. Here, we introduce four hemispherically-symmetric injection strategies, all of which are designed to maintain the same global mean surface temperature: an annual injection at the equator (EQ), an annual injection of equal amounts of SO2 at 15N and 15S (15N+15S), an annual injection of equal amounts of SO2 at 30N and 30S (30N+30S), and a polar injection strategy that injects equal amounts of SO2 at 60N and 60S only during spring in each hemisphere (60N+60S). We compare these four hemispherically-symmetric SAI strategies with a more complex injection strategy that injects different quantities of SO2 at 30N, 15N, 15S, and 30S in order to maintain not only the global mean surface temperature but also its large scale horizontal gradients. We find that the choice of SAI strategies notably affects the spatial distribution of aerosol optical depth (AOD), injection efficiency, and various surface climate responses. Among other findings, injecting at mid-latitudes produces more global cooling per unit injection, with the EQ and the 60N+60S cases requiring, respectively, 59% and 50% more injection than the 30N+30S case to meet the same global mean temperature target. Injecting at higher latitudes results in larger equator-to-pole temperature gradients. While all five strategies restore September Arctic sea ice, the high-latitude injection one is more effective due to the SAI-induced cooling more focused at higher latitudes.</p>

opencc-by-4.0Jan 2023View details →
dryad36/100

Data from: Multiple introductions, polyploidy and mixed reproductive strategies are linked to genetic diversity and structure in the most widespread invasive plant across Southern Ocean archipelagos

<p><span>Biological invasions in remote</span> <span>areas that experience low human activity provide unique opportunities to elucidate processes responsible for invasion success. Here we study the most widespread invasive plant species across the isolated islands of the Southern Ocean, the annual bluegrass, Poa annua. To </span><span>analyze</span><span> geographic variation in genome size, genetic diversity, and reproductive strategies, we sampled all major sub-Antarctic archipelagos in this region and generated microsatellite data for 470 individual plants representing 31 populations. We also estimated genome sizes for a subset of individuals using flow cytometry. Occasional events of island colonization are expected to result in high genetic structure among islands, overall low genetic diversity, and increased self-fertilization, but we show that this is not the case for Poa annua. Microsatellite data indicated low population genetic structure and lack of isolation-by-distance</span> <span>among the sub-Antarctic archipelagos we sampled, but high population structure within each archipelago. We identified high levels of genetic diversity, low clonality, and low selfing rates in sub-Antarctic P. annua populations (contrary to rates typical of continental populations). In turn, estimates of autogamy declined in populations as genetic diversity increased. Additionally, we found that most P. annua individuals are likely tetraploid and that only slight variation exists in genome size across the Southern Ocean. Our findings suggest multiple independent introductions of P. annua into the sub-Antarctic, which</span> <span>promoted the establishment of genetically diverse populations. Despite multiple introductions, the adoption of convergent reproductive strategies (outcrossing) happened independently in each major archipelago. The combination of polyploidy and a mixed reproductive strategy likely benefited P. annua in the Southern Ocean by increasing genetic diversity and its ability to cope with the novel environmental conditions.</span></p>

opencc-zeroFeb 2023View details →
zenodo36/100

Unified bursting strategies in ectopic and endogenous even-skipped expression patterns - Supplemental Data

<p>This dataset contains the supplemental data of the article &quot;Unified bursting strategies in ectopic and endogenous even-skipped expression patterns&quot;.</p>

opencc-by-4.0Feb 2023View details →
dryad36/100

Data from: Contrasting long-term trends in juvenile abundance of a widespread cold-water salmonid along a latitudinal gradient: Effects of climate, stream size and migration strategy

<p><span>A changing climate reshapes the range distribution of many organisms, and species with relatively low thermal optima, like many salmonids, are increasingly expected to face local population extinctions at lower latitudes. Understanding where and how fast these changes are happening is of pivotal importance for successful mitigation and conservation efforts.</span></p> <p><span>We used an extensive electrofishing database to explore temporal trends of brown trout juveniles (<em>Salmo</em> <em>trutta</em> L.) in 218 locations from 174 Swedish streams, over the last 30 years (1991–2020). We hypothesized that 1) declines in abundance have occurred predominately in the warmer, southern regions, while increases have occurred in the colder, northern regions, 2) larger stream sizes may partly offset negative effects of climate, and 3) migrating and resident populations are affected differently by a warming climate.</span></p> <p><span>We found that abundance of brown trout juveniles generally declined in warmer regions, especially in smaller streams (≤ 6 m wide), while the abundance increased in colder regions. In larger streams, negative effects of higher temperatures were seemingly buffered, as we found lower rates of decline or even positive trends. The rate of change (i.e. the slopes of the trends in abundance) was more pronounced towards the climate extremes and was on average zero in regions with a normal annual air temperature (average temperature over 30-year period) around 5–6 ºC. Warmer climate had stronger effects on migrating compared to resident populations, suggesting that climate-induced loss of stream connectivity could be an additional factor that hinders recruitment in anadromous populations in a changing climate.</span></p> <p><span>Considering predictions of increasing temperatures and frequency of summer droughts, management of cold-water salmonid populations should focus on conserving and restoring riparian vegetation, wetlands, climate and thermal refugia, and habitat integrity overall. Such measures may, however, not suffice for small streams at lower latitudes, unless hydrological connectivity is maintained.</span></p>

opencc-zeroFeb 2023View 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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