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5,573 results for “optimization”

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zenodo44/100

Earth - Venus Low-Thrust Optimal Transfers / Database A

<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;0.2 and contains 429,316 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 F

<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 557,395 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 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

Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: EOS library

<p>This repository includes a&nbsp;library of equations of state&nbsp;(EOS) and stellar models presented in the publications Weih et al. (2019) (see also the related identifier) and Most et al. (2018). The library&nbsp;includes ~ 3&nbsp;Million physically plausible EOSs that fulfill a number of astrophysical and nuclear constraints. See the README for more information.&nbsp;</p>

opencc-by-4.0Jun 2019View details →
zenodo44/100

User Experience Optimization Experiment Simulations

<p># The `uxo_sim` Package</p> <p>A package for simulations of data matching industry UX optimization experiments, as discussed in:</p> <p>```<br> @article{van_adelsberg_modeling_2019,<br> &nbsp;&nbsp; &nbsp;title = {Modeling {A}/{B} {Test} {Data} is {Hard}: {Effects} of {Overdispersion}, {RandomWalks}, and {Cointegration}},<br> &nbsp;&nbsp; &nbsp;language = {en},<br> &nbsp;&nbsp; &nbsp;journal = {NeurIPS 2019 Workshop on Robust &nbsp;AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy},<br> &nbsp;&nbsp; &nbsp;author = {van Adelsberg, Matthew and Sweeney, Mackenzie},<br> &nbsp;&nbsp; &nbsp;month = dec,<br> &nbsp;&nbsp; &nbsp;year = {2019}<br> }<br> ```</p> <p>The code for running the simulations is included, along with figures and CSV files for each of three specific simulation runs that are used in a publication currently under review for ICML 2020.</p> <p>## Packages:</p> <p>1. `data`: code for running the simulations to produce datasets<br> 2. `viz`: code for visualizing the simulation outputs</p> <p>## Scripts:</p> <p>1. `save_datasets`: save CSV for each simulated dataset in the `inventory`<br> 2. `save_figs`: save PNG figure for each simulated dataset in `plots`</p> <p>## Simulation Datasets:</p> <p>### `fixed_effects_od_20_21_seed27`</p> <p>Data is simulated from a beta-binomial distribution with overdispersion parameter `gamma=0.01` for each of the two treatments and rates `theta=0.20` and `theta=0.21`. This corresponds to beta distribution parameters `alpha, beta = 19.8, 79.2` and `alpha, beta = 20.79, 78.21`.</p> <p>### `drift_down_then_up`</p> <p>Data is simulated from a beta-binomial distribution with overdispersion parameter `gamma=0.01` for each of the two treatments. The rates start at `theta=0.20` and `theta=0.21` and then:</p> <p>1. decrease by 0.005 each day for 20 days<br> 2. increase by 0.005 each day for 30 days<br> 3. stay constant for 10 days</p> <p>The corresponding beta distribution parameters on each day are a function of `theta, gamma` and can be obtained via this function (implemented in `uxo_sims.data.simulations`:<br> ```python<br> def alpha_beta_from_gamma_theta(gamma, theta):<br> &nbsp; &nbsp; virtual_sample_size = 1 / gamma - 1<br> &nbsp; &nbsp; alpha = theta * virtual_sample_size<br> &nbsp; &nbsp; beta = virtual_sample_size - alpha<br> &nbsp; &nbsp; return alpha, beta<br> ```</p> <p><br> ### `arm_addition`</p> <p>Data is simulated from a beta-binomial distribution with overdispersion parameter `gamma=0.01` for each of the two treatments. The rates start at `theta=0.10` and `theta=0.11` and increase by 0.005 each day for 40 days. The corresponding beta distribution parameters can be obtained with the same function as indicated in `drift_down_then_up`.</p>

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

An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization

<p>This is the data and source code used in the paper below:</p> <p>Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas B&auml;ck, &ldquo;An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization&rdquo;, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi:&nbsp;10.1109/SSCI44817.2019.9002805</p> <p>This research investigates the potential of using meta-modeling techniques in the context of robust optimization namely optimization under uncertainty/noise. A systematic empirical comparison is performed for evaluating and comparing different meta-modeling techniques for robust optimization. The experimental setup includes three noise levels, six meta-modeling algorithms, and six benchmark problems from the continuous optimization domain, each for three different dimensionalities. Two robustness definitions: robust regularization and robust composition, are used in the experiments. The meta-modeling techniques are evaluated and compared with respect to the modeling accuracy and the optimal function values. The results clearly show that Kriging, Support Vector Machine and Polynomial regression perform excellently as they achieve high accuracy and the optimal point on the model landscape is close to the true optimum of test functions in most cases.</p>

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

DFT-optimized Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014

<p>There are two folders inside the zipped file:</p> <p>- 838 structures (without DDEC partial atomic charges)</p> <p>- 502 structures (with DDEC partial atomic charges)<br> &nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2016View details →
zenodo44/100

UWB-IODA project, Work package 1: IR-UWB optimized pulses

<p>The data files contain optimized UWB waveforms using B-spline functions. The spectral efficiency of each waveform is maximized under the constraint of the spectral mask defined by the FCC/ECC regulation authorities.</p>

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

The top performer: towards optimized parameters for Reduced graphene oxide uniformity by Spin coating

<p>This dataset contains the raw data used for the publication:</p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------<br> &quot;The top performer: towards optimized parameters for Reduced graphene oxide uniformity by Spin coating&quot;<br> by C. Reiner-Rozman, R. Hasler, J. Andersson, T. Rodrigues, A. Bozdogan and P. Aspermair<br> --------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><br> It consists of the SEM images (in .tif format) and the determined surface coverages (in .dat format) as well as the measured electrical data (in .dat format) of the prepared graphene field-effect transistor chips. Headers/information in the data files are in English. When using this data in any form please refer to the above-mentioned publication.</p> <p>The data is structured according to the figures of the paper. Each folder contains the data relevant to validate the results presented in the respective figure of the publication. The files are labeled according to the following description:</p> <p>&quot;measurement-type&quot;_&quot;chip-number&quot;_&quot;GO-concentration&quot;_&quot;spin-coating speed&quot;</p> <p>&quot;measurement-type&quot;:&nbsp;&nbsp; &nbsp;SEM, IDVG, baseline<br> &quot;chip-number&quot;: an increasing number of fabricated device (only used when needed)<br> &quot;GO-concentration&quot;:&nbsp;&nbsp; &nbsp;143/214/285 &micro;g/mL of graphene oxide (GO) in solution<br> &quot;spin-coating speed&quot;:&nbsp;&nbsp; &nbsp;in rpm</p>

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

Optimizing a Cantilever Measurement System towards High Speed, Nonreactive Contact-Resonance-Profilometry (Data)

<p>Raw data, scripts and figures used for the article &quot;Optimizing a Cantilever Measurement System towards High Speed, Nonreactive Contact-Resonance-Profilometry&quot;, published in <em>Proceedings </em>on 21 Nov&nbsp;2018.</p> <p>The data/scripts can be opened/executed&nbsp;by the software &quot;Matlab&quot;</p>

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

Base images for the article "Optimization of a frosting process for soda lime silicate glass based on phosphoric acid"

<p>Raw dataset of the optimization of a frosting process for soda lime silicate glass based on phosphoric acid.</p> <p><strong>Naming scheme:</strong></p> <ul> <li>Images starting with <strong>HGr</strong> are frosted using the industrial process. These files represent the reference frosting.</li> <li>Images starting with <strong>HG</strong> are frosted manually following the industrial process.</li> <li>In all other images, the solution concentrations within the preliminary bath are noted als follows: <ul> <li><strong>[c<sub>H3PO4</sub>]-[c<sub>NH4HF2</sub>]_[specimen]_[position].jpg</strong></li> <li>For example 10-2_e_1.jpg: This specimen was treated with a preliminary bath with 10 M-% H<sub>3</sub>PO<sub>4 </sub>and 20 g/L NH<sub>4</sub>HF<sub>2</sub>. It originates from the fith specimen (e) and is the first image of this series.</li> </ul> </li> </ul> <p>&nbsp;</p>

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

Optimizing Production and Storage: Carlsberg's Injection-Molding Operations

<p>The research paper investigates the optimization of production and storage for a custom molder, using a dataset that includes production times, weekly production hours, stockroom capacity, storage space per case, contribution per case, and customer limits for different types of glass produced using specific dies. The paper aims to determine the optimal production quantities for each type of glass to maximize the total contribution, taking into account production constraints and customer demand.</p>

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

Data and scripts for the publication "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations"

<p>Coils and VMEC configurations for the three stellarator configurations presented in "Coil Optimization for Quasi-helically Symmetric Stellarator Configurations", including the optimization scripts used to find the coils and plot scripts used to produce the figures in the text.</p>

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

Dataset and supplementary files - Behavioral response of chub (Squalius cephalus), barbel (Barbus barbus) and brown trout (Salmo trutta) to pulsed direct current electric fields and resulting optimal waveform for use at electrified bar racks

<p><strong>Behavior Library.zip: </strong>For each species and behavior observed during the experiments an exemplary video is provided.&nbsp;</p><p><strong>Behavior_all.pdf: </strong>Additional plots showing the thresholds for the first time each individual behavior was observed for all fish species and tested waveforms</p><p><strong>Species.pdf: </strong>Additional plot allowing direct comparison of observed thresholds for the tested species when subjected to different waveforms.&nbsp;</p><p><strong>data.csv:</strong> All data necessary to reevaluate the conducted experiments. The dataset consists of</p><ul><li>Experiment ID</li><li>waveform - indicating the set of electrical parameters used</li><li>fish species and fish id&nbsp;</li><li>behavior - observed behavior</li><li>time from and time to - time in s after the start of the experiment that a behavior was started and ended respectively</li><li>type - point or interval referring to whether a behavior is considered instantaneous or continuous</li><li>voltage - applied voltage at the start of the given behavior</li><li>experiment_timestamp - date and time of the start of the experiment</li><li>breathing rate start - breathing rate at the start of the experiment</li><li>water &nbsp;conductivity - water conductivity at a reference temperature of 25°C [muS/cm]</li><li>water temperature [°C]</li><li>breathing rate end - breathing rate at the end of the experiment</li><li>meta behavior - assigned category of meta behavior based on the observe behavior category</li><li>standard length, total length and height - standard length, total length and height of the tested fish in [mm]</li><li>volume - calculated fish volume based on the measured length and height and an assumed elliptical form of the fish</li><li>Fangdatum - Date of catch</li><li>t.Pulse - pulse length of the tested waveform [ms]</li><li>Frequency - Frequency of the tested waveform</li><li>N.Pulses.Group - Number of pulses per group of pulses for the waveform pattern</li><li>t.Gap - time between two pulses within a group of pulses [ms]</li><li>DutyCycle - Percentage of time current is flowing for a given waveform. Calculated based on the waveform parameters</li><li>usage - first, second or third time a fish was used in the experiments.&nbsp;</li><li>field strength - field strength at the time of this behavior calculated based on the applied voltage</li><li>c_w &nbsp;ambient water conductivity [muS/cm]</li><li>p_d - power density calculated based on the field strength and the ambient water conductivity</li><li>p_t - power transferred to the fish calculated based on the field strength, the ambient water conductivity and an assumed conductivity of the fish of 115 muS/cm</li></ul><p>&nbsp;</p><p>&nbsp;</p>

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

Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in Dec 2023.

<p>Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in NHESS journal in Dec 2023.</p> <p>For further technical details read the file READMEdata in the zipfile. The script MAKEFIG remakes all the figures from the data.</p> <p>For scientific details read the associated article preprint on the NHESS egusphere website.</p>

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

Mapping the global distribution of C4 vegetation using observations and optimality theory

<p>This dataset includes annual C4 vegetation distribution and its uncertainty from 2001 to 2019. We also provide the distribution of C4 natural grasses and C4 crops during the same period, as well as the code and interim dataset to generate the main figures. Please refer to manuscript for more details:</p> <p>Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. &amp; Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory.&nbsp;<em>Nature Communications.</em> https://doi.org/10.1038/s41467-024-45606-3.</p> <p><strong>Update (Nov 2023): </strong>we have updated the observational constraint from a linear model to a non-linear model - logistic curve, to better depict how C4 photosynthetic advantage translates into C4 grass coverage changes (C4_distribution_NUS_v2.2.nc).</p> <p><strong>Update (August&nbsp;2023):&nbsp;</strong>we corrected the issue caused by a bias in the remote sensing grassland base map, and released the version 2 of the C4 vmap (C4_distribution_NUS_v2.nc).</p> <p><strong>Update (June 2023):&nbsp;</strong>we noticed there is a critical issue in the version 1 of our C4 map, due to the quality of remote sensing grassland base map used. We are now working on providing a new version (V2) in the next few months (Jun 2023).</p>

opencc-by-4.0Jan 2024View details →

ScienceDex guides

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

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Allen Brain Atlas

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

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

Annotated Behaviour and Observability Dataset (ABODe)

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

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

DANDI Archive for NWB datasets

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

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

International Brain Laboratory public data

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

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

OpenNeuro

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

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