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1,956 results for “test data”
Binary data file used for analysis_common_envelope unit testing in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** this file is automatically downloaded as part of the Phantom github actions tests **</p> <p>This is an example snapshot from a Phantom simulation of a common envelope interaction, taken from the paper by <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.3181G">González-Bolívar et al. (2022)</a>. It is posted here primarily in order to perform unit and regression testing on the <a href="https://github.com/danieljprice/phantom/blob/master/src/utils/analysis_common_envelope.f90">analysis_common_envelope</a> module in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>).</p>
Search-Based Test Data Generation for SQL Queries: Appendix
<p>The appendix of our ICSE 2018 paper "Search-Based Test Data Generation for SQL Queries: Appendix".</p> <p>The appendix contains:</p> <ul> <li>The queries from the three open source systems we used in the evaluation of our tool (the industry software system is not part of this appendix, due to privacy reasons)</li> <li>The results of our evaluation.</li> <li>The source code of the tool. Most recent version can be found at https://github.com/SERG-Delft/evosql.</li> <li>The results of the tuning procedure we conducted before running the final evaluation.</li> </ul>
eMERLIN test data of 1407+284 at C-band
<p>eMERLIN test data in measurement set (.ms) format of the bandpass calibrator source 1407+284 at C-band for the RadioNet RINGS project. Data has been flagged (including a few minutes at the start of the scan and the end channels of each spectral window) and averaged to 128 channels. The data are in a state ready to test fringe fitting, no initial calibration needs to be done.</p> <p>See the file DD6001_001_20171220_avg_1407+284.listobs.txt (output from the CASA task listobs) for further details of the observation.</p>
Test data for C-QTL analysis of barley Recombinant Chromosome Substitution Lines
<p>This data shows the testing of C-QTL approach to visualise the influence of ensembles of groups of genetic markers on plant traits from a selection of Recombinant Chromosome Substitution Lines. The analysis was performed on of 29 genotypes and two traits: Heading Date and plant height. For heading date, the two major QTLs on 2H and 7H associated with heading date using the REML approach (de la Fuente Canto 2016) are also detected using the CQTL analysis, getting the highest 'rank' or score with this approach. Similarly, marker main effect for plant height at the region of the sdw1 seems to be detected with the CQTL analysis.</p>
Survey Data for "Software Testing: Survey of the Industry Practices"
<p>Dataset for the surveys presented in our article "Software Testing: Survey of the Industry Practices."</p>
Test data for BSReconFramework
<p>Test data for the Bloch Siegert reconstrution framework for highly subsampled data available at: https://github.com/IMTtugraz/BSReconFramework.git</p> <p>Please save all files under './data' in the root path of 'BSReconFramework'</p> <p>1) gre_BlochSiegert_3D.dat<br> fully sampled dataset used for retrospective subsampling for algorithm testing</p> <p>2) gre_BlochSiegert_acc_12x4.dat<br> prospectively subsampled dataset with a block pattern of size 12x4 in k-space center</p> <p>3) smaps_walsh3d_slice.mat<br> externally provided coil sensitivities for prospectively subsampled dataset</p>
Training and testing data, associated code and estimators for emulating a convection scheme
<p>Data and code for a random-forest convection scheme associated with the paper:</p> <p>"Using machine learning to parameterize moist convection: potential for modeling of climate, climate change and extreme events"</p> <p>by Paul A. O'Gorman and John G. Dwyer (to appear in JAMES)</p>
Tomographic data for testing, demonstrating, and developing methods of removing ring artifacts
<p>These tomographic data were used for demonstrating our methods of eliminating ring artifacts published in Optics Express, <em>Nghia T. Vo, Robert C. Atwood, and Michael Drakopoulos, "Superior techniques for eliminating ring artifacts in X-ray micro-tomography," <strong>26</strong>, 28396-28412 (2018)</em><em>. </em>In sinogram, the artifacts appear as straight lines or stripe artifacts. The data have many types of stripe artifacts: full stripes, partial stripes, unresponsive stripes, fluctuating stripes, and blurry stripes. They are very useful for testing and developing methods of removing ring artifacts.</p> <p>Documentation: <a href="https://sarepy.readthedocs.io/">https://sarepy.readthedocs.io/</a></p> <p>Python implementations of these methods:</p> <p><a href="https://github.com/nghia-vo/sarepy">https://github.com/nghia-vo/sarepy</a></p> <p>In Tomopy:</p> <p><a href="https://tomopy.readthedocs.io/en/latest/api/tomopy.prep.stripe.html">https://tomopy.readthedocs.io/en/latest/api/tomopy.prep.stripe.html</a></p> <p>In Savu:</p> <p><a href="http://github.com/DiamondLightSource/Savu/tree/master/savu/plugins/ring_removal">https://github.com/DiamondLightSource/Savu/tree/master/savu/plugins/ring_removal</a></p> <p>In Algotom:</p> <p><a href="https://github.com/algotom/algotom/blob/master/algotom/prep/removal.py">https://github.com/algotom/algotom/blob/master/algotom/prep/removal.py</a> </p>
Test data for use with pyXsis
<p>The tar-ball contains HEG +/-1 data from two Chandra HETG observations of Mrk 421, downloaded from TGcat (http://tgcat.mit.edu/)</p> <p>Data reduction process described in Huenemoerder et al. (2011): http://adsabs.harvard.edu/abs/2011AJ....141..129H</p> <p>This data is used to test the pyXsis (Python X-ray Spectral Interpretation System) library at https://github.com/eblur/pyxsis</p>
Test and validation data for Robbie: A Batch Processing Work-flow for the Detection of Radio Transients and Variables
<p>Robbie: a general work-flow for the detection and characterization of radio variability and transient events in the image domain.<br> Robbie is designed to work in a batch processing paradigm with a modular design so that components can be swapped out or upgraded to adapt to different input data, whilst retaining a consistent and coherent methodological approach.<br> Robbie is based on commonly used and open software, and is encapsulated in a Makefile to aid portability and reproducibility.<br> In the description paper we describe the methodology behind Robbie, and demonstrate its use on real and simulated data.</p> <p>This repository contains the observed and simulated data that was used in the description paper.</p> <p> </p>
Biotea-2-Bioschemas test data
<p>Biotea-2-Bioschemas mapps Biotea model to schema.org following the approach proposed by Bioschemas. Here we present the test data used in Biotea GitHub pages, corresponding to 2596 PubMed Open Access (PMC-OA) subset publications together with the software used to render schema.org markup.</p> <p>Date deposited includes (i) publications retrieved from PMC-OA API, i.e., full text in JATS/XML, (ii) ontology terms recognized in the abstracts and obtained from the NCBO Annotator, i.e., semantic annotations, and (iii) the same annotations following the PubAnnotation format.</p> <p>Software deposited includes (i) biotea-bioschemas-metadata which parses JATS/XML files and creates Bioschemas markup including metadata, abstract and references, (ii) biotea-bioschemas-annotations which parses PubAnnotation annotations and creates Bioschemas markup, and (iii) biotea-bioschemas-showcase which uses the other two in order to display markup in a graphical basic way and render it as a script element in the HTML following the JSON-LD format. The corresonding GitHub repositories are: (i) https://github.com/biotea/biotea-bioschemas-metadata, (ii) https://github.com/biotea/biotea-bioschemas-annotations, and (iii) https://github.com/biotea/biotea-bioschemas-showcase.</p> <p>Biotea-2-bioschemas can be seen in action at http://biotea.github.io/bioschemas/</p>
Geochemical data for protolith classification testing
<p>Global major element geochemical data for igneous and sedimentary rocks. This dataset is used to train via machine learning a classifier for igneous and sedimentary protoliths. It contains the major element chemistry, ilr transformed chemistry, rock type, protolith class, and reference to the original data. Codes to train a protolith classifier and analyze the results are found in the GitHub repository github.com/dhasterok/global_geochemistry/</p>
Tensile test data of a 9 microns thick aluminium foil
<p>Material testing data from tensile tests performed on an aluminium AA8079 alloy. The specimens are 100mm long and have a cross section of 10x0.009mm^2. Specimens are cut tested in 0°, 10°, ...40°, 45°, 50°, 60°, ....90° clock wise to the direction of rolling during manufacturing of the foil. Unfortunately the precise meaning of "clock wise" vis á vis the rolling direction and side of the foil is not known but is consistent in this text. The naming of the files reveal the direction of the test and the specimen no. for the respective direction, e.g "45_RawDataySpecimen_2" is to the second specimen tested in a direction 45° clock wise to the rolling direction.</p> <p>The tests are described in detail in B. Käck & C. Malmberg, Master thesis: Aluminium foil at multiple length scales, mechanical tests and numerical simulations in abaqus (2015), div. of Solid Mechanics, Lund Institute of Technology, Sweden.</p> <p>The tests were evaluated in "On the stiffness tensor in AA8079 at small and intermediate strains" by E. Andreasson, W. Reheman, P. Ståhle and S. Kao-Walter, submitted for publication. The major discoveries were that 1) plastic deformation appear almost immediately, i.e., practically at zero load, 2) the compliances increase linearly with strain, from the value of the inverse elastic modulus to almost five times that 3) principal material directions are not along the rolling direction but rather 5° to 15° anti-clock wise from the rolling direction and finally 4) there is a minimum stress in the region of between 35° to 45° clock wise from the rolling direction and for symmetry reasons also at 55° to 65° in the anti-clock wise direction.</p>
Testing Data set - DiSCount - Masteling et al.
<p>Data set used to test the accuracy of the software DiSCount: computer vision for automated quantification of <em>Striga</em> seed germination.</p>
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. "Machine-learning-ready" means that all data-processing methods described in the journal paper have already been applied. 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. 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). However, since `example_io.read_file` is a lightweight wrapper for `xarray.open_dataset`, you can equivalently just use `xarray.open_dataset`. Variables in the table are listed below (the same printout produced by `print(xarray_table)`):</p> <p>Dimensions: (<br> satellite_valid_time_unix_sec: 289,<br> satellite_grid_row: 380,<br> satellite_grid_column: 540,<br> satellite_predictor_name_gridded: 1,<br> satellite_predictor_name_ungridded: 16,<br> ships_valid_time_unix_sec: 19,<br> ships_storm_object_index: 19,<br> ships_forecast_hour: 23,<br> ships_intensity_threshold_m_s01: 21,<br> ships_lag_time_hours: 5,<br> ships_predictor_name_lagged: 17,<br> ships_predictor_name_forecast: 129)<br>Coordinates:<br> * satellite_grid_row (satellite_grid_row) int32 2kB ...<br> * satellite_grid_column (satellite_grid_column) int32 2kB ...<br> * satellite_valid_time_unix_sec (satellite_valid_time_unix_sec) int32 1kB ...<br> * ships_lag_time_hours (ships_lag_time_hours) float64 40B ...<br> * ships_intensity_threshold_m_s01 (ships_intensity_threshold_m_s01) float64 168B ...<br> * ships_forecast_hour (ships_forecast_hour) int32 92B ...<br> * satellite_predictor_name_gridded (satellite_predictor_name_gridded) object 8B ...<br> * satellite_predictor_name_ungridded (satellite_predictor_name_ungridded) object 128B ...<br> * ships_valid_time_unix_sec (ships_valid_time_unix_sec) int32 76B ...<br> * ships_predictor_name_lagged (ships_predictor_name_lagged) object 136B ...<br> * ships_predictor_name_forecast (ships_predictor_name_forecast) object 1kB ...<br>Dimensions without coordinates: ships_storm_object_index<br>Data variables:<br> satellite_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_wavelength_micrometres (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_cyclone_id_string (satellite_valid_time_unix_sec) |S8 2kB ...<br> satellite_storm_type_string (satellite_valid_time_unix_sec) |S2 578B ...<br> satellite_storm_name (satellite_valid_time_unix_sec) |S10 3kB ...<br> satellite_storm_latitude_deg_n (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_intensity_number (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_u_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_v_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_predictors_gridded (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column, satellite_predictor_name_gridded) float64 474MB ...<br> satellite_grid_latitude_deg_n (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_grid_longitude_deg_e (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_predictors_ungridded (satellite_valid_time_unix_sec, satellite_predictor_name_ungridded) float64 37kB ...<br> ships_storm_intensity_m_s01 (ships_valid_time_unix_sec) float64 152B ...<br> ships_storm_type_enum (ships_storm_object_index, ships_forecast_hour) int32 2kB ...<br> ships_forecast_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_forecast_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_v_wind_200mb_0to500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vorticity_850mb_0to1000km_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_0to600km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_max_tangential_wind_850mb_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_1000mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_500mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_300mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to700mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to500mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_threshold_exceedance_num_6hour_periods (ships_storm_object_index, ships_intensity_threshold_m_s01) int32 2kB ...<br> ships_v_motion_observed_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_1000to100mb_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_optimal_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_cyclone_id_string (ships_storm_object_index) object 152B ...<br> ships_storm_latitude_deg_n (ships_storm_object_index) float64 152B ...<br> ships_storm_longitude_deg_e (ships_storm_object_index) float64 152B ...<br> ships_predictors_lagged (ships_valid_time_unix_sec, ships_lag_time_hours, ships_predictor_name_lagged) float64 13kB ...<br> ships_predictors_forecast (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. 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. 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. The only lag time we use in machine learning is "NaN", which is a stand-in for the best available of all lag times. 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. 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). 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. For example, "m_s01" = metres per second; "deg_n" = degrees north; "deg_e" = degrees east; "j_kg01" = Joules per kilogram; ...; etc.</li> </ul>
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. "Machine-learning-ready" means that all data-processing methods described in the journal paper have already been applied. 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. 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). However, since `example_io.read_file` is a lightweight wrapper for `xarray.open_dataset`, you can equivalently just use `xarray.open_dataset`. Variables in the table are listed below (the same printout produced by `print(xarray_table)`):</p> <p>Dimensions: (<br> satellite_valid_time_unix_sec: 289,<br> satellite_grid_row: 380,<br> satellite_grid_column: 540,<br> satellite_predictor_name_gridded: 1,<br> satellite_predictor_name_ungridded: 16,<br> ships_valid_time_unix_sec: 19,<br> ships_storm_object_index: 19,<br> ships_forecast_hour: 23,<br> ships_intensity_threshold_m_s01: 21,<br> ships_lag_time_hours: 5,<br> ships_predictor_name_lagged: 17,<br> ships_predictor_name_forecast: 129)<br>Coordinates:<br> * satellite_grid_row (satellite_grid_row) int32 2kB ...<br> * satellite_grid_column (satellite_grid_column) int32 2kB ...<br> * satellite_valid_time_unix_sec (satellite_valid_time_unix_sec) int32 1kB ...<br> * ships_lag_time_hours (ships_lag_time_hours) float64 40B ...<br> * ships_intensity_threshold_m_s01 (ships_intensity_threshold_m_s01) float64 168B ...<br> * ships_forecast_hour (ships_forecast_hour) int32 92B ...<br> * satellite_predictor_name_gridded (satellite_predictor_name_gridded) object 8B ...<br> * satellite_predictor_name_ungridded (satellite_predictor_name_ungridded) object 128B ...<br> * ships_valid_time_unix_sec (ships_valid_time_unix_sec) int32 76B ...<br> * ships_predictor_name_lagged (ships_predictor_name_lagged) object 136B ...<br> * ships_predictor_name_forecast (ships_predictor_name_forecast) object 1kB ...<br>Dimensions without coordinates: ships_storm_object_index<br>Data variables:<br> satellite_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_wavelength_micrometres (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_cyclone_id_string (satellite_valid_time_unix_sec) |S8 2kB ...<br> satellite_storm_type_string (satellite_valid_time_unix_sec) |S2 578B ...<br> satellite_storm_name (satellite_valid_time_unix_sec) |S10 3kB ...<br> satellite_storm_latitude_deg_n (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_intensity_number (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_u_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_v_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_predictors_gridded (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column, satellite_predictor_name_gridded) float64 474MB ...<br> satellite_grid_latitude_deg_n (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_grid_longitude_deg_e (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_predictors_ungridded (satellite_valid_time_unix_sec, satellite_predictor_name_ungridded) float64 37kB ...<br> ships_storm_intensity_m_s01 (ships_valid_time_unix_sec) float64 152B ...<br> ships_storm_type_enum (ships_storm_object_index, ships_forecast_hour) int32 2kB ...<br> ships_forecast_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_forecast_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_v_wind_200mb_0to500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vorticity_850mb_0to1000km_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_0to600km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_max_tangential_wind_850mb_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_1000mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_500mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_300mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to700mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to500mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_threshold_exceedance_num_6hour_periods (ships_storm_object_index, ships_intensity_threshold_m_s01) int32 2kB ...<br> ships_v_motion_observed_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_1000to100mb_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_optimal_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_cyclone_id_string (ships_storm_object_index) object 152B ...<br> ships_storm_latitude_deg_n (ships_storm_object_index) float64 152B ...<br> ships_storm_longitude_deg_e (ships_storm_object_index) float64 152B ...<br> ships_predictors_lagged (ships_valid_time_unix_sec, ships_lag_time_hours, ships_predictor_name_lagged) float64 13kB ...<br> ships_predictors_forecast (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. 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. 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. The only lag time we use in machine learning is "NaN", which is a stand-in for the best available of all lag times. 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. 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). 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. For example, "m_s01" = metres per second; "deg_n" = degrees north; "deg_e" = degrees east; "j_kg01" = Joules per kilogram; ...; etc.</li> </ul>
All body size data (EOL v3 test): body size
All records for Body size, run Aug. 7 Query in EOL beta platform: https://beta.eol.org/terms/search_results?term_query%5Bclade_id%5D=&term_query%5Bfilters_attributes%5D%5B0%5D%5Bop%5D=is_any&term_query%5Bfilters_attributes%5D%5B0%5D%5Bpred_uri%5D=http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FOBA_VT0100005&term_query%5Bresult_type%5D=record
Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility testing and human health risks - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Hiller E., Faragó T., Kolesár M., Filová L., Mihaljevič M., Jurovič L., Demko R., Mchlica A., Štefánek J., Vítková M. (2024): Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility and human health risks. <em>Chemosphere</em> <strong>362</strong>, 142677. DOI: 10.1016/j.chemosphere.2024.142677</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Data for "A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies"
<p>Data for the manuscript: A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies (see also https://doi.org/10.1101/2022.05.09.491139)</p>
Data and Code for: An Experimental Test of Eco-evolutionary Dynamics on Rocky Shores
<p><span>Abstract: </span></p> <p><span>Despite a growing body of theoretical studies and laboratory experiments that have brought attention to the reciprocal impacts that ecological and evolutionary processes can have on one another, few studies have tested the importance of eco-evolutionary feedbacks in natural communities. We examined whether selection on natural phenotypic variation in a population of drilling dogwhelks (<em>Nucella canaliculata</em>) could impact rocky shore community dynamics. We performed a selection experiment raising newly-hatched dogwhelks on four diet treatments, reflecting natural variation in the abundance and shell thickness of prey species. Adult dogwhelks were tested in the laboratory on their ability to drill thick-shelled mussels. In addition, snails were outplanted to field cages to track the effects of dogwhelk phenotype on mussel bed succession. Despite our laboratory experiments suggesting that prey can impose selection and result in divergent consumer traits, successional patterns differed minimally based on the early-life diet of the dogwhelks.</span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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.
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.
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.
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.