Skip to main content
ConductScienceScienceDex
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

2,885

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

2,885 results for “transferability”

Learn how ShareScore rates datasets ↗
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database E

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;20.0 and contains 409,076 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database D

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;5.0 and contains 265,603&nbsp;trajectories with 128 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database C

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;0.4&nbsp;and contains 764,479 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database B

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;0.4&nbsp;and contains 382,193&nbsp;trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database G

<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus&#39; orbit&nbsp;starting on the date 7th of May 2005 and arriving at Venus&#39; orbit.&nbsp;</p> <p>This database was generated with a perturbation size of&nbsp;(5.0, 1.0, 1.0, 0.0, 0.0, 0.01)&nbsp;and contains 999,985 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the &#39;nominal&#39;, &#39;train&#39;, &#39;val&#39; and &#39;test&#39; dataframes each of which contains rows of entries in the following format:</p> <pre>[&#39;t&#39;, &#39;p&#39;, &#39;f&#39;, &#39;g&#39;, &#39;h&#39;, &#39;k&#39;, &#39;L&#39;, &#39;m&#39;, &#39;lp&#39;, &#39;lf&#39;, &#39;lg&#39;, &#39;lh&#39;, &#39;lk&#39;, &#39;lL&#39;, &#39;lm&#39;, &#39;T&#39;, &#39;ux&#39;, &#39;uy&#39;, &#39;uz&#39;, &#39;traj_id&#39;, &#39;sampl_id&#39;, &#39;vf&#39;]</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled &quot;Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks&quot; that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) &quot;Backward Generation of Optimal Samples&quot; method.</p>

opencc-by-4.0Jan 2020View details →
zenodo44/100

Energy transfers and reflexion of infragravity waves at a dissipative beach under storm waves.

<p>%%% Author: &nbsp;&nbsp; &nbsp;Xavier Bertin (xbertin@univ-lr.fr)&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; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %%%<br> %%% Date: &nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;15/04/2020&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; &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;<br> %%% Purpose:&nbsp;&nbsp; &nbsp;This repository provides the field observations and XBeach model input&nbsp; %%%<br> %%%&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;required to reproduce the results presented in paper referred below.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;%%%<br> %%%&nbsp;Reference:&nbsp;&nbsp; &nbsp;Bertin, X., Martins, K., de Bakker, A., Gu&eacute;rin, T., Chataigner, T.,&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; %%%<br> %%%&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Coulombier, T. et de Viron, O., 2020. Energy transfers and reflexion of&nbsp; &nbsp; &nbsp; &nbsp; %%%<br> %%%&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;infragravity waves at a dissipative beach under storm waves. In press&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;%%%<br> %%%&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;to Journal of Geophysical Research-Ocean.&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;%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>*The directory Obs includes:<br> &nbsp;&nbsp; &nbsp;-The wave bulk parameters computed as explained in the paper for the 10 sensores used in this<br> &nbsp;&nbsp; &nbsp;study: the offshore ADCP1, the intertidal PT1, PT2, ADCP2/PT3, PT4, PT5, ADV/PT6, PT7/Altus, PT8<br> &nbsp;&nbsp; &nbsp;and PT9. Each file has the same format and includes: the date (YYYY MM DD), the time (HH MM SS),&nbsp;<br> &nbsp;&nbsp; &nbsp;the mean water depth, the spectral significant wave height Hm0, mean wave periods Tm01 and Tm02,&nbsp;<br> &nbsp;&nbsp; &nbsp;the discrete and continuous peak periods, the energetic wave period Tm0,-2 and the spectral<br> &nbsp;&nbsp; &nbsp;significant height of IG waves Hm0,IG.&nbsp;<br> &nbsp;&nbsp; &nbsp;-The spectral significant height Hm0,IG+ and mean wave period Tm02,IG+ of incoming IG waves<br> &nbsp;&nbsp; &nbsp;separated at the ADCP2 and ADV using the method of Guza et al. (1984). The two files have the same&nbsp;<br> &nbsp;&nbsp; &nbsp;format and includes the date (YYYY MM DD), the time (HH MM SS), Hm0,IG+ and Tm02,IG+.<br> &nbsp;&nbsp; &nbsp;-The position of each sensore measured with a geodetic GNSS and provided in the same datum as the&nbsp;<br> &nbsp;&nbsp; &nbsp;bathymetry used in the model (Lambert93 and mean sea level). &nbsp;&nbsp;&nbsp; &nbsp;</p> <p>*The directory XBeach includes all the necessary files required to reproduce the simulations presented&nbsp;<br> in this study<br> &nbsp;&nbsp; &nbsp;-The bathymetry interpolated over a rectilinear grid, with X and Y given in Lambert93 coordinates (files<br> &nbsp;&nbsp; &nbsp;X_L93.grd and Y_L93.grd) and Z referred with respect to mean sea level (Z_L93.grd).<br> &nbsp;&nbsp; &nbsp;-The water level fluctuations measured at ADCP1 (WLevel_ADCP_201702.dat).<br> &nbsp;&nbsp; &nbsp;-The XBeach input file (params.txt) and a file providing the list of directional wave spectra<br> &nbsp;&nbsp; &nbsp;provided in the directory &quot;Spectra_WWIII&quot;. These spectra were computed from a regional application of&nbsp;<br> &nbsp;&nbsp; &nbsp;WaveWatchIII over the North Atlantic Ocean and forced with CFSR wind fields but they were converted<br> &nbsp;&nbsp; &nbsp;in the format of SWAN, readable by XBeach.</p>

opencc-by-4.0Apr 2020View details →
zenodo44/100

Full Virginia Girls' Reformatory Transfer and Dismissal Data 1910-1938

<p>Slice created from parent data set:<a href="https://zenodo.org/record/3872019#.XtVXS0GM-Uk"> Full Virginia Girls' Reformatory Admissions Database 1910-1938</a></p> <p>Data set of 2,370 individual female reformatory inmates admitted to the Virginia Home and Industrial School for Girls at Bon Air and the Industrial Home School for Colored Girls at Peake&rsquo;s Turnout between 1910 and 1938. Created out of the unpublished and archived admissions books of these institutions. Due to Virginia&rsquo;s 75-year privacy restriction, I stopped collecting at December 1938 for each institution. Data from the Home at Bon Air runs 1910 to 1938; from the Home at Peake&rsquo;s Turnout from 1915 to 1938. Each reformatory kept separate books, which were archived into separate collections. There was enough similarity between the two books to transcribe the data into one large data set.</p> <p>Reformatory administrators hand wrote basic administrative information about each girl into bound books. For incoming delinquent girls, they recorded: student number, name, birthdate or age at admittance, date of admittance, and committing jurisdiction (by county or city jurisdiction.) The books also recorded the individual&rsquo;s parole history, including first parole (and up to her third parole on an individual&rsquo;s performance) and any return dates; administrators recorded the destination of the first parole, but this was inconsistently recorded. The books note when and to whom inmates were married, usually after their official dismissal. Because transferring an inmate officially removed them from the responsibility of the reformatory, administrators recorded transfer information, including where they went and when. Lastly, the books recorded the official dismissal date and reason. Bon Air&rsquo;s books were more consistent with recording dismissals and neither institution used consistent definitions of &ldquo;transfer&rdquo; versus &ldquo;dismissal.&rdquo; &nbsp;<br>&nbsp;<br>I manually transcribed these books verbatim into a database. From this core data, I added categories of information to aid my analysis. These include: race, gender, and reformatory; calculations of either age or birthdate (Peake&rsquo;s recorded birthdates, Bon Air only ages); parole year taken from the first parole date; parole type determined by me based on the parole destination, if recorded. To help me analyze transfer and dismissal information, I determined the &ldquo;type&rdquo; and &ldquo;category&rdquo; of each transfer or dismissal and added new categories. These allowed me to &ldquo;rollup&rdquo; the varieties of recorded data into fewer descriptive types and categories. Because administrators recorded only the institution name or location when girls were transferred and dismissed elsewhere, this allowed me to organize this info into 18 &ldquo;types&rdquo;: asylum, colony, court, death, department of public welfare, escape, family, honorable discharge, illegal commitment, maternity, orphanage, other, penal, private, reorganization (only used for Bon Air in 1914), sanitorium/hospital, venereal disease, and wages. These 18 &ldquo;types&rdquo; were then further distilled into 9 &ldquo;categories&rdquo; to capture the broadest possible categorization of the reasons why girls were transferred or dismissed: administrative, death, escape, mental, penal, physical, private, unknown, and work.</p> <p>I have removed the names of the individual inmates upon publication.<strong> Researchers interested in using this data in their own work can contact me at erin@erinbush.org to request the versions that include full name fields. The Data Dictionary is also available upon request.</strong></p> <p>The contents of these data sets, as government records, I believe fall under fair use.</p> <p><strong>Full Collection</strong></p> <ul> <li>Full Virginia Girls' Reformatory Admissions Database 1910-1938: <a href="https://zenodo.org/records/3872019">https://zenodo.org/records/3872019</a></li> <li>Full Virginia Girls' Reformatory Transfer and Dismissal Data 1910-1938: <a href="https://zenodo.org/records/3872110">https://zenodo.org/records/3872110</a></li> <li>Full Virginia Girls' Reformatory Parole Data 1910-1938: <a href="https://zenodo.org/records/3872100">https://zenodo.org/records/3872100</a></li> </ul>

opencc-by-4.0May 2020View details →
zenodo44/100

Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al.

<p>Radiance data for &quot;Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry&quot; by Zawada et al. which is to be submitted to Atmospheric Measurement Techniques.&nbsp;</p> <p>A comprehensive inter-comparison of seven radiative transfer models in the limb scattering geometry has been<br> performed. Every model is capable of accounting for polarisation within a fully spherical atmosphere. Three models (GSLS, SASKTRAN-HR, and SCIATRAN) are deterministic, and four models (MYSTIC, SASKTRAN-MC, Siro, and SMART-G)<br> are statistical using the Monte Carlo technique.&nbsp; This dataset consists of the raw radiance data used to perform the intercomparisons, atmospheric input data for the optical properties of the atmosphere, and data specifying the geometry of the test cases.</p> <p>Data is provided in NetCDF4 format with documentation present inside the variable attributes.</p> <p>More detail on the comparison scenarios can be found within the published article.&nbsp; (Link to be added when available).</p>

opencc-by-4.0Nov 2020View details →
zenodo44/100

The Hearpiece database of individual transfer functions of an openly available in-the-ear earpiece for hearing device research

<p>We present a database of acoustic transfer functions of the Hearpiece, an openly available multi-microphone multi-driver in-the-ear earpiece for hearing device research. The database includes HRTFs for 87 incidence directions as well as responses of the drivers, all measured at the four microphones of the Hearpiece as well as the eardrum in the occluded and open ear. The transfer functions were measured in both ears of 25 human subjects and a KEMAR with anthropometric ears for five reinsertions of the device. We describe the measurements of the database and analyse derived acoustic parameters of the device. All regarded transfer functions are subject to differences between subjects as well as variations due to reinsertion into the same ear. Also, the results show that KEMAR measurements represent a median human ear well for all assessed transfer functions. The database is a rich basis for development, evaluation and robustness analysis of multiple hearing device algorithms and applications.</p>

opencc-by-sa-4.0Apr 2020View details →
zenodo44/100

Radiative transfer modeling in structurally-complex stands: what aspects matter most?: Dataset

<p>This repository is linked to the paper &quot;Radiative transfer modeling in structurally-complex stands: what aspects matter most?&quot; submitted to Annals of Forest Science and written by Fr&eacute;d&eacute;ric ANDR&Eacute; (corresponding author), Louis DE WERGIFOSSE, Fran&ccedil;ois DE COLIGNY, Nicolas BEUDEZ, Gauthier LIGOT, Vincent&nbsp;GAUTHRAY-GUY&Eacute;NET, Benoit COURBAUD&nbsp;and Mathieu JONARD.</p> <p>The repository contains the three following files :</p> <ul> <li>CalibrationResults.csv: Bayes factors and summary statistics of parameter estimates for each calibration run</li> <li>ParameterPosteriorDistributions.csv: median values and 90% credible intervals for the parameter posterior distributions</li> <li>StatisticalComparison.csv: statistics (Fractional bias, Root mean square&nbsp;error, Paired Student test, Pearson correlation coefficient, Parameters of the Deming regression between observed and predicted values) used to compare the &#39;Best model configurations&#39;</li> </ul> <p>For more information concerning this repository or the study, please do not hesitate to contact Fr&eacute;d&eacute;ric ANDR&Eacute; (frederic.andre@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

Automated MESSENGER Plasma Region Classifications via Unsupervised Transfer Learning

<p>This file contains the 1-minute resolution dataset (&ldquo;labeled_sunside_data_3labels.csv&rdquo;) for Toy-Edens et al.&rsquo;s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, features that go into clustering and post-cleaning methods, spacecraft positions (in MSO), total magnetic field, raw and cleaned clustering labels, and raw and cleaned transition name.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.&rsquo;s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning (DOI: 10.3389/fspas.2025.1608091).</p> <p>This work was supported by NASA grants 80NSSC19K0789 and 80NSSC22K0993.</p> <p>&nbsp;</p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data_3labels.csv description</strong></p> <table style="width: 100.063%; height: 851.2px;"> <tbody> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p><strong>Column Name</strong></p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p><strong>Description</strong></p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p>&nbsp;Epoch</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Epoch in datetime (YYYY-MM-DD HH:MM:SS)</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p>&nbsp;x_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>x position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p>&nbsp;y_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>y position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p>&nbsp;z_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>z position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p>&nbsp;btot_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Total magnetic field [nT]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p>&nbsp;norm_Btot</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Magnitude of the total magnetic field normalized to 150nT. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p>&nbsp;ratio_max_width</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p>&nbsp;ratio_high_low</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p>&nbsp;high_intensity</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Boolean if there is a peak with a higher minimum intensity threshold. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p>&nbsp;spectra_counts</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>A ratio of spectra bins with non-zero counts to all possible spectra bins (i.e. way to determine if too much missing spectra data). See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p>&nbsp;raw_named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind)</p> </td> </tr> <tr> <td style="width: 17.3792%;"> <p>intermediate_named_label</p> </td> <td style="width: 78.9512%;"> <p>Cleaned cluster assigned plasma region label with only relabeling rules applied. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p>&nbsp;named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned cluster assigned plasma region label with relabeling rules and post-processing applied (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p>&nbsp;raw_transition_name</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Raw transition names (e.g. bow shock, magnetopause) based on "raw_named_label" cluster labels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p>&nbsp;transition_name</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned transition names (e.g. bow shock, magnetopause) after removing likely transient transitions based on "named_label" cluster labels. See paper for more information</p> </td> </tr> </tbody> </table> <p>&nbsp;</p>

opencc-by-4.0Oct 2024View details →
zenodo44/100

Transfer function measurements of a Moroccan rabāb

<p>Instrument: <i>rabāb&nbsp;</i><br>Country of origin: Morocco&nbsp;<br>Place of origin: Fès&nbsp;<br>Instrument maker: Abdessalam Chiki&nbsp;<br>Year of manufacture: 2015&nbsp;<br>Location: Basel, private property of Thilo Hirsch</p><p>Dimensions:&nbsp;<br>Total length: 513.2 mm&nbsp;<br>Max. Body width: 114.8 mm&nbsp;<br>Width at the upper end of the skin: 96.2 mm&nbsp;<br>Width at top nut: 31.6 mm&nbsp;<br>Body depth at the upper end of the skin: approx. 80 mm</p><p>Vibrating string lengths:&nbsp;<br>d-string: 410 mm&nbsp;<br>G-string: 403 mm</p><p>Materials:&nbsp;<br>Body: walnut&nbsp;<br>Pegbox: walnut&nbsp;<br>Fingerboard: acajou (mahogany)&nbsp;<br>Decoration: mother-of-pearl&nbsp;<br>Bars: spruce&nbsp;<br>Top nut, tailpiece button: bone&nbsp;<br>Bridge: bamboo&nbsp;<br>Top: goatskin</p><p>Transfer function measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 18.2.2020</p><p>Transfer function from shaker to microphone 1 meter in front of the instrument.&nbsp;<br>Frequency range specified in the Filename.&nbsp;<br>Shaker exciting with a frequency sweep on the bass side of the bridge (shaker type: Minishaker by BNK).&nbsp;<br>Pressure measurement with a ROGA RG50 microphone.</p><p>Photos of the setup: Thilo Hirsch 18.2.2020</p><p>________________________</p><p>How to read VIA-Files:&nbsp;<br>Line 1 to 9: Header, Line 8 holds the number of values&nbsp;<br>Data is organized as followed: 1st col: Frequency [Hz]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2nd col: Magnitude [as Factor not dB!]&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3rd col: Phase [rad]&nbsp;&nbsp;&nbsp; 4th col:&nbsp; Real part [as Factor not dB!] 5th col: Imaginary part [as Factor not dB!] (so only first 3 columns are needed)</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -&gt; 3.3075 * 10 -&gt; 33.075</p>

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

SONAR -- experimental redox potentials for organic compounds undergoing 2-electron/2-proton transfer reactions

<p>reference data for the demo-compounds used as input for predicting redox potentials by a trained model&nbsp;</p><p>The file</p><ul><li>lists redox potentials and oxidized/reduced form for organic molecules undergoing a two-electron/two-proton reduction reaction (M + 2 e- + 2 H+ --&gt; MH2)</li><li>contains data for 25 organic compounds compiled from various sources in literature</li><li>uses "|" as a separator</li><li>column names and explanations<ol><li><strong>ID</strong>: abbreviated trivial names e.g. for labelling</li><li><strong>orig redox potential [V]:</strong> original values reported in respective reference</li><li><strong>solvent</strong>: total formula, water (H2O) throughout</li><li><strong>pH</strong>: pH value of electrolyte solution. If not reported, inferred from the concentration of supporting electrolyte</li><li><strong>supporting_electrolyte</strong>: if spefified: total formula, if available; concentration</li><li><strong>SMILES_ox</strong>: molecular structures encoded as (manually assigned) SMILES strings for the oxidized species (M)</li><li><strong>SMILES_red</strong>: molecular structures encoded as (manually assigned) SMILES strings for the reduced species (MH2)</li><li><strong>ref_electrode:</strong> reference electrode the originally reported half cell potential refers to. If not specified, RHE was used as default</li><li><strong>redox potential vs SHE [V]</strong>:<ul><li>In case of missing information, reversible hydrogen electrode (RHE at pH = 0) was assumed, which corresponds to SHE</li><li>In case of conflicting entries (SHE and pH != 0), we assumed the pH should be accounted for and replaced "RHE" as reference electrode instead of "SHE". "NHE" was treated like "RHE".</li><li>In case the reference electrode was other than SHE, NHE or RHE, a respective offset was added. This was the case once for Ag/AgCl (assuming saturated solution, offset = 0.210, see respective reference)</li><li>Finally, the potential values were transferred to SHE according to: E(SHE) = E(RHE) + 0.05913 * pH</li><li>CAVEAT: Lacking information about individual pKa values, no other correction was made.</li></ul></li><li><strong>reference</strong>: orginal source</li></ol></li></ul>

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

Data: Steering photoinduced electron transfer in intramolecular photocatalysts by peripheral ligand control

<p>The data presented herein is analysed and showcased within the <i>ChemRxiv</i> article titled "<i>Steering photoinduced electron transfer in intramolecular photocatalysts by peripheral ligand control</i>" (<a href="10.26434/chemrxiv-2023-vspb5"><strong>DOI </strong></a><a href="https://doi.org/10.26434/chemrxiv-2023-vspb5"><strong>10.26434/chemrxiv-2023-vspb5</strong></a>). Kindly acknowledge and cite this article when referencing or utilizing the provided data.</p>

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

Data and configuration files for "Expansion of accreting main-sequence stars during rapid mass transfer"

<p>Data and configuration files that can be used to reproduce results from the paper&nbsp;<a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...966L...7L/abstract">Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer</a>. This directory contains MESA inlists and starting models used for calculations performed with MESA r15140, and YAML configuration files for calculations performed with COMPAS v02.41.04.</p> <p>See README.txt for a description of all files.</p> <p>&nbsp;</p> <p>Any work making use of these files should cite</p> <p>Lau, M., Hirai, R., Mandel, I., Tout, C., 2024, Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer, ApJL, 966, 1</p> <div></div>

opencc-by-4.0Jan 2024View details →
zenodo44/100

Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project

<p>This file contains the ADV data of <em>Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project</em>.</p>

opencc-by-4.0Nov 2024View details →
zenodo44/100

A Catalogue of All Known Mass-Transferring Ultracompact Binary Systems

<p>Introduction: I present a catalogue that collects all known mass-transferring ultracompact binary systems in one place. This includes AM CVn-type binaries, helium-enriched CVs and a variety of other objects.</p> <p>The goal of this catalogue is to prevent the duplicated effort of multiple researchers searching for known systems through the literature. Where applicable, the catalogue includes orbital periods, measured masses, Gaia cross-matches, as well as important references for each system. The catalogue includes 'confirmed' systems (usually means an orbital period measurement and/or a spectrum) and 'candidates' (may be selected based on other properties such as outburst shape).</p> <p>For full details and references see the associated paper (accepted to A&amp;A). Preprint at https://arxiv.org/abs/2505.10535</p> <p>See the catalogue itself in the file 'amcvn_catalogue.fits'.</p> <p>Up to date as of 2025-04-01.</p>

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

Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution; Supplementary Information"

<p>These files contains datasets from which the Figures appearing in the Supplementary Information have been calculated.&nbsp;</p> <p>Description of datasets:</p> <p>Datasets related to SupplFig1 contain two columns: Frequency in Hz and phase noise in rad^2/Hz</p> <p>Data_SupplFig1_stabilised_fringes: psd of the phase noise calculated from the interference fringes in a stabilised condition</p> <p>Data_SupplFig1_unstabilised_fringes: psd of the phase noise calculated from the interference fringes in an unstabilised condition</p> <p>Data_SupplFig1_roundtrip_sensing_laser: psd of the sensing laser signal after a round-trip in the interferometer, calculated&nbsp;from self-heterodyne beatnote</p> <p>Data_SupplFig1_differential_roundtrip_sensing_vs_reference_laser: psd of the difference between the round-trip self-heterodyne beatnotes at the sensing and reference laser wavelengths</p> <p>Datasets related to SupplFig2 contain two columns: time in seconds and normalised intensity (calculated as detailed in the main publication).</p> <p>Data_SupplFig2_High_power_PD_free_evol: normalised intensity of the interference signal&nbsp;obtained with classical power level at the source. This trace was recorded with&nbsp;a photodiode when no artificial phase drift was applied</p> <p>Data_SupplFig2_High_power_PD_phase_drift:&nbsp; normalised intensity of the interference signal&nbsp;obtained with classical power level at the source. This trace was recorded with&nbsp;a photodiode when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_High_power_SPD_free_evol:&nbsp;normalised intensity of the interference signal&nbsp;obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when no&nbsp;artificial phase drift was applied&nbsp;</p> <p>Data_SupplFig2_High_power_SPD_phase_drift:&nbsp;normalised intensity of the interference signal&nbsp;obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_Attenuated_SPD_free_evol:&nbsp;normalised intensity of the interference signal&nbsp;obtained with attenuated beams at the source. This trace was recorded on an SPD when no&nbsp;artificial phase drift was applied&nbsp;</p> <p>Data_SupplFig2_Attenuated_SPD_phase_drift:&nbsp;:&nbsp;normalised intensity of the interference signal&nbsp;obtained with attenuated beams at the source. This trace was recorded on an SPD when an artificial phase drift was applied (8pi/s)</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2021View details →
zenodo44/100

Pathogen-sugar interactions revealed by universal saturation transfer analysis

<p>Supporting data for the &quot;Pathogen-sugar interactions revealed by universal saturation transfer analysis&quot; manuscript.</p>

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

Corpus de revistas Proyecto Digitization and Analysis of Cultural Transfers in Colombian Literary Magazines (1892–1950)

<p>Coprpus de revistas Proyecto Digitization and Analysis of Cultural Transfers in Colombian Literary Magazines (1892&ndash;1950)</p>

opencc-by-4.0Aug 2022View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

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

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record