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4,325 results for “real”
FalleDesinfo_ES: Dataset de noticias en español reales y falsas sobre fallecimientos de celebridades
<p>Este conjunto de datos contiene 33 noticias en español relacionadas con fallecimientos de celebridades, clasificadas en tres tipos, cada uno de los cuales contiene 11 noticias distintas:</p> <p>Tipo 1 (T1): Noticias erróneas (misinformation) que se creyeron la noticia falsa (fake news, disinformation) de la muerte de Noam Chomsky, el 18 de junio de 2024.<br>Tipo 2 (T2): Noticias reales que desmintieron el hecho noticioso de las noticias de T1, el mismo día.<br>Tipo 3 (T3): Noticias reales sobre la muerte de Stephen Hawking, el 14 de marzo de 2018.</p> <p>Cada noticia contiene su tipo, un identificador único, la fecha de su publicación, titular, bajada y cuerpo de la noticia. Note que la mayoría de las noticias del grupo T1 han sido eliminadas, una vez que se verificó que se trataba de un falso rumor.</p> <p>Conjunto de datos creado con propósitos de investigación, para el análisis y clasificación de noticias falsas no intencionadas mediante métodos de inteligencia artificial y procesamiento de lenguaje natural.</p>
Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)
<p>To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.5 nm
<p>In Ref. <a href="http://doi.org/10.5281/zenodo.1476721">http://doi.org/10.5281/zenodo.1476721 </a>we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.5 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 8.33 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.1 nm
<p>In Ref. <a href="http://dx.doi.org/10.5281/zenodo.1476719">http://doi.org/10.5281/zenodo.1476719 </a>we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.5 nm
<p>In Ref. we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.5 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 8.33 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Real-time dynamics of nanoplasmonic dimer, distance d = 0.1 nm
<p>In http://dx.doi.org/10.5281/zenodo.1482739 we provide a movie that shows the real-time dynamics of the nanoplasmonic dimer with distance $ d_1=0.1 $ nm. The time-evolution in the movie corresponds to the runs that we discuss in section VI. In the figure, we show a frame of the movie at time 6.89 fs. The upper two panels show contour plots of matter variables, the absolute value of the current density and the electron localized function (ELF). The most relevant Maxwell field variables, the electric field along the laser polarization direction z and the total Maxwell energy are presented in the lower panels. In the top of the figure, we show the incident laser pulse and at the center the geometry of the nanoplasmonic dimer.</p>
Visual stimuli used in fMRI experiment on processing of real and illusory surfaces
<p>These videos contain samples of visual stimuli used in an fMRI experiment on the processing of real and illusory surfaces in human early visual cortex [the experiments are part of my PhD thesis, in preparation]. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the duration of rest blocks was much longer.</p>
DroidLeaks: A Large Collection of Resource Leak Bugs in Real-World Android Apps
<p>DroidLeaks features 292 diverse resource leak bugs in popular and large-scale open-source Android apps. For each bug, DroidLeaks provides links to:<br> 1. the code repository of the app subject<br> 2. the concerned resource class<br> 3. the buggy code revision (and buggy file and method names)<br> 4. the bug-fixing code revision (i.e., link to the patch)<br> 5. the bug report or the corresponding pull request for patches (if located)</p>
Fully synthetic longitudinal real-world data from hearing aid wearers for public health policy modeling
<p>Real-world data from hearing aids and Bluetooth connected smartphones. The associated data report can be found here: <a href="https://doi.org/10.3389/fnins.2019.00850">https://doi.org/10.3389/fnins.2019.00850</a></p>
InCLosure Code for Real Automatic Differentiation: Supplementary Material for Article "A Consistent and Categorical Axiomatization of Differentiation Arithmetic Applicable to First and Higher Order Derivatives"
<p>InCLosure Code for Real Automatic Differentiation: Supplementary Material for Article "A Consistent and Categorical Axiomatization of Differentiation Arithmetic Applicable to First and Higher Order Derivatives", Punjab University Journal of Mathematics, October 2019. Download latest release of InCLosure via <a href="https://doi.org/10.5281/zenodo.2702404">https://doi.org/10.5281/zenodo.2702404</a></p>
Real-Time High-Rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes
<p>Post-processed and real-time GNSS displacement waveforms for the 2019 Ridgecrest earthquakes. Waveforms are for the M6.4 and M7.1 events recorded at 1 and 5Hz sample rates. Data are in miniSEED format, channel codes LYE, LYN, LYZ correspond to east, north, and up respectively. Displacement units are meters and time is UTC. The data are trimmed 60s before the USGS origin times for<br> the earthquakes. No filtering has been applied.</p> <p>A paper describing the data has been submitted to SRL. In the meantime if you use the data please cite our <a href="https://eartharxiv.org/pdxqw/">preprint on the EarthArXiv</a> as:</p> <p>Melgar, D., TI Melbourne, BW Crowell, J Geng, W Szeliga, C Scrivner, M Santillan, DER Goldberg. 2019. Real-time High-rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes. EarthArXiv, doi:10.31223/osf.io/pdxqw.</p>
Simulation output of Lirung glacier (REAL EXP)
<p>This is the data used for <strong><em>Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier, </em></strong>by PNJ Bonekamp, CC van Heerwaarden, JF Steiner and WW Immerzeel. In this netcdf file only the data of the REAL experiment can be found. See for data of the other experiments 10.5281/zenodo.3375325. <br> <br> These variables are present;<br> u_xz, v_xz, qt_xz, thl_xz: which are the wind vectors, total specific humidity and liquid potential temperature for the xz cross section respectively<br> u_yz, v_yz, qt_yz, thl_yz: which are the wind vectors, total specific humidity and liquid potential temperature for the yz cross section respectively<br> qtfluxbot_ib, thlfluxbot_ib: the total specific moisture and temperature flux at the surface respectively. </p> <p>See for more specific information:<br> <strong><em>Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier, </em></strong>PNJ Bonekamp, CC van Heerwaarden, JF Steiner and WW Immerzeel, The Cryosphere, submitted</p> <p> </p>
Real‐time fiber‐based fluorescence lifetime imaging with synchronous external illumination: A new path for clinical translation
<p>Time-correlated single photon counting is the “gold-standard” method for fluorescence lifetime measurements and has demonstrated potential for clinical deployment. Its clinical adoption is hindered by the use of high gain detectors, which make the fluorescence acquisition impractical with bright lighting conditions such as in clinical settings. We address this limitation by interleaving periodic fluorescence detection with synchronous out-of-phase externally modulated light source, thus guaranteeing specimen illumination and a fluorescence signal free from bright background light upon temporal separation. Fluorescence lifetime maps are generated in real-time from single-point measurements by tracking a reference beam and using the phasor approach. We demonstrate the feasibility and practicality of this technique in a number of biological specimens, including real-time mapping of degraded articular cartilage. This method is compatible and can be integrated with existing clinical microscopic, endoscopic and robotic modalities, thus offering a new pathway towards label-free diagnostics and surgical guidance in a number of clinical applications.</p>
5-bus system and a real 221-bus low voltage network in the UK for analysing phase unbalance impacts on aggregated flexibility
<p>Two case studies for modelling flexibility services in low voltage distribution networks and quantifying the impacts of voltage unbalance limits and phase coordination constraints. The cases are in the OpenDSS format (.dss).<br>More details in the GitHub repository: https://github.com/AndreyChurkin/3FlexAnalyser.jl</p>
The Real-World Benchmark of Android App Vulnerability from CVE
<p>The Real-World Benchmark of Android App Vulnerability from CVE named CVE-based benchmark.</p> <p>For more information about this benchmark, please refer to <a href="https://github.com/android-app-sast/VulsTotal">android-app-sast/VulsTotal: A Unified Platform for Evaluating SAST Tools for Android (github.com)</a></p>
Yeast solutions and hyperpolarization enable real-time observation of metabolized substrates even at natural abundance
<div>This data corresponds to the following paper:</div> <div> </div> <div>Title: Yeast solutions and hyperpolarization enable real-time observation of metabolized substrates even at natural abundance</div> <div>Journal: Analytical Chemistry</div> <div>Authors: Josh P. Peters, Charbel Assaf, Farhad Haj Mohamad, Eric Beitz, Sanjay Tiwari, Konrad Aden, Jan-Bernd Hövener and Andrey N. Pravdivtsev</div> <div> </div> <div>The data is organized with respect to the subfigures in figure 2 to 6.</div> <div>A description of acquisition parameters is provided in the "Acquisition parameters.xlsx" file for each dataset.</div> <div> </div> <div>Figure 2:</div> <div>- Overview of pyruvate metabolism in yeast cells using hyperpolarized and 13C labeled [1-13C]pyruvate. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included. </div> <div> </div> <div>Figure 3:</div> <div>- Overview of pyruvate metabolism in yeast cells using co-hyperpolarized [1-13C] and [2-13C]pyruvate at an n.a. of 13C. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 4:</div> <div>- Overview of fumarate metabolism in yeast cells using hyperpolarized and 13C labeled [1,4-13C2]fumarate</div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 5:</div> <div>- The fitted conversion exchange rate constants as a function of the yeast concentration. </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <div> </div> <div>Figure 6:</div> <div>- Metabolic data from pyruvate metabolism in yeast depending on the position of the yeast in the NMR tube. We </div> <div>Hyperpolarized and thermally polarized spectra, if applicable, are included.</div> <p> </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>
Neutron activation analysis data of pottery from La Viña, Tambo Real, and other sites in Lambeyeque, Peru
<p>Neutron activation analysis data of pottery from La Viña, Tambo Real, Túcume, Jotoro, Julupe, Cerro Cementerios, 257X01, and 2839X05, in Lambeyeque Department, Peru.</p>
Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules
<p>The content of this repository accompanies the publication "Robust and efficient reranking in crystal structure prediction: a data driven method for real-life molecules" and contains the complete dataset produced for the fentanyl CSP. </p> <p>Three types of datasets are present : Generation, ML-Reranker and GRACE. <br>Generation refers to all the molecular crystal structures that GRACE has generated using the tailor made force field. It's the starting pool of the reranking exercise, and it contains all the structures that will be selected by the reranking processes.<br>ML-Reranker refers to the data generated by the algorithm proposed in our manuscript. The configurations and energies are obtained by selecting from structures from the generation pool and relaxing their coordinates.<br>GRACE dataset contains the configurations which a user obtains at the end of a standard GRACE reranking procedure. Since GRACE follows differet convergence and minimization critera, the structures obtained in this dataset can differ (non-substantially) from the equivalents found in the ML-Reranker. </p> <p>*.data : contains the indices, energy of the crystal structure (kcal/mol) and, in case of the ml-reranker dataset, the indices mapping the obtained landscape to their generating pool.</p> <p>*.xyz : contains ASE formatted, extended-xyz list of structures corresponding to each exercise.</p> <p>Authors:</p> <p>Andrea Anelli, Hanno Dietrich, Philipp Ectors, Frank Stowasser, Tristan Bereau, Marcus Neumann, Joost van den Ende</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.