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5,573 results for “optimality”
An optimized buffer for repeatable Multicolor STORM Raw Data
<p>Raw microcopy data used to generate the figures in the paper "An optimized buffer for repeatable Multicolor STORM"</p> <p>Camera: Orca Fusion binning 2x2 readout speed 1</p> <p>Objective 100x/1.3 (Olympus) -> Effective pixel size 130x130 nm</p> <p>Expected Conversion factor according to spec sheet: 0.21 electrons/count</p> <p> </p> <p> </p> <p> </p>
Reference Reflectance Transformation Imaging acquisitions for RTI stitching and acquisition optimization
<p>This dataset contains 1. RTI acquisitions a canvas painting and a metal print plate in parts, for development of RTI-stitching methods. 2. Dense RTI acquisitions of brushed metal and ruse coarse metal surfaces for development of methods for determining ideal light positions in a RTI acquisitions. </p>
Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
<p>Raw experimental data associated with the manuscript "Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches", by Sosa-Carrillo and colleagues, bioRxiv, 10.1101/2022.11.02.514931.<br> The code to analyze this raw data and generate the figures for the manuscript is available on GitLab at https://gitlab.inria.fr/InBio/Public/yeastcybersecretion.</p>
NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH
<p>This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009).</p> <p>The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm<sup>-1</sup> and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances.</p> <p>In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior.</p> <p>The file format is netcdf and the file naming conventions are</p> <p>NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p> </p> <p>The time stamp of all data is in UTC.</p> <p>Selected basic variables are (many more provided):</p> <p> </p> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Dimension</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>base_time</p> </td> <td> <p>Single value</p> </td> <td> <p>Seconds (since 00 UTC 1 Jan 1970)</p> </td> </tr> <tr> <td> <p>time_offset</p> </td> <td> <p>Time</p> </td> <td> <p>Second (since base_time)</p> </td> </tr> <tr> <td> <p>hour</p> </td> <td> <p>Time</p> </td> <td> <p>Hours since 00UTC this day</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p>Height</p> </td> <td> <p>km AGL</p> </td> </tr> <tr> <td> <p><strong>temperature </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, temperature</p> </td> </tr> <tr> <td> <p><strong>waterVapor </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, water vapor mixing ratio</p> </td> </tr> <tr> <td> <p>theta</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, potential temperature</p> </td> </tr> <tr> <td> <p>pressure</p> </td> <td> <p>Time, Height</p> </td> <td> <p>hPa, pressure</p> </td> </tr> <tr> <td> <p>rh</p> </td> <td> <p>Time, Height</p> </td> <td> <p>%, relative humidity</p> </td> </tr> <tr> <td> <p>dewpt</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, dew point temperature</p> </td> </tr> <tr> <td> <p>thetae</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, equivalent potential temperature</p> </td> </tr> <tr> <td> <p>sigma_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, 1-sigma uncertainty temperature</p> </td> </tr> <tr> <td> <p>sigma_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, 1-sigma uncertainty water vapor</p> </td> </tr> <tr> <td> <p>cdfs_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for temperature</p> </td> </tr> <tr> <td> <p>cdfs_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for water vapor</p> </td> </tr> </tbody> </table> <p>Bold variables are the main retrieved profiles, from which the other variables are derived.</p> <p>Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model.</p> <p>Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. </p> <p><strong>References: </strong></p> <p>Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17.</p> <p>Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1.</p> <p>Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.</p> <p>Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.</p>
Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting
<p>Dataset for 'Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting' article published at <em>Cell Reports Physical Science, </em><a href="https://doi.org/10.1016/j.xcrp.2023.101252">https://doi.org/10.1016/j.xcrp.2023.101252</a>.</p> <p>Code used for data analysis, visualization and kinetics modelling can be found at <a href="https://github.com/AndreyBezrukov/Water_Sorption_Kinetics">AndreyBezrukov/Water_Sorption_Kinetics (github.com)</a></p>
Reference dataset of multi-objective and multi-fidelity optimization in laser-plasma acceleration
<p>This repository contains a dataset used for the article "<em>Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration</em>" (<a href="https://arxiv.org/abs/2210.03484">arXiv:2210.03484</a>). The dataset consists of 2443 FBPIC particle-in-cell simulations of a laser wakefield accelerator that were selected using a Bayesian optimizer. The goal of the optimization was to perform multi-objective multi-fidelity optimization of electron beam parameters. The dataset contains simulations of different resolutions, accordingly with differing fidelities. The typical runtime at lowest (highest) resolution is approximately 1 (90) minutes.</p> <p>In the dataset we have <em>train_x </em>and <em>train_obj </em>numpy arrays with dimensions <em>(n,5)</em> and<em> (n,3)</em>, respectively. Here <em>n</em> is the number of FBPIC simulations. The five columns in <em>train_x </em>are [plasma density, upramp length, laser focus, downramp length, fidelity]. The fidelity parameter controls the resolution and hence the runtime of the simulation. The three columns in the <em>train_obj </em>are the [total charge, distance of median to target energy, bandwidth of electron beams]. For the distance, the target energy is fixed to 300 MeV and for the bandwidth is defined by the median absolute deviation around the median. The two columns have negative values since the optimizer assumes a maximization of all objectives while the distance and bandwidth in this study were being minimized.</p> <p>The different folders contain data of different kind of single and multi-objectives that were used to produce figures 2, 3, 5 in the associated paper. For more details please see the referred article. The folder "combined" contains the data of all simulations together and is most suitable for (5D x 3D) surrogate model generation.</p>
Data for GECCO2023 Paper "Many-objective (Combinatorial) Optimization is Easy"
<p><strong>Data for Paper "Many-objective (Combinatorial) Optimization is Easy"</strong></p> <ul> <li><strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances</li> <li><strong>metrics.csv</strong> contains the metric-values based on full enumeration</li> <li><strong>performance.csv</strong> contains the Pareto resolution and the hypervolume of the different algorithms one each instance</li> <li><strong>performance_neval.csv</strong> contains the number of evaluations performed by PLS</li> <li><strong>script.R</strong> is the R script for producing the figures</li> </ul> <p><strong>Reference</strong></p> <p>Arnaud Liefooghe and Manuel López-Ibáñez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15–19, 2023, Lisbon, Portugal. <a href="https://doi.org/10.1145/3583131.3590475">https://doi.org/10.1145/3583131.3590475</a></p> <p><strong>Abstract</strong></p> <p>It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives tends to reduce the difficulty of the landscape being optimized. Of course, increasing the number of objectives brings about other challenges, such as an increase in the computational effort of many operations, or the memory requirements for storing non-dominated solutions. More precisely, we consider a broad range of multi- and many-objective combinatorial benchmark problems, and we measure how the number of objectives impacts the dominance relation among solutions, the connectedness of the Pareto set, and the landscape multimodality in terms of local optimal solutions and sets. Our analysis shows the limit behavior of various landscape features when adding more objectives to a problem. Our conclusions do not contradict previous observations about the inability of Pareto-optimality to drive search, but we explain these observations from a different perspective. Our findings have important implications for the design and analysis of many-objective optimization algorithms.</p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
Creating Safe Environments: Optimal Acoustic Alarming of Laypeople in Fire Prevention - Online Supplement
<p>This online supplement contains datasets (raw data) and study material collected in an experimental study by the University of Münster, Germany. The study is part of a larger research project (<a href="https://www.brawa.ovgu.de/en/">https://www.brawa.ovgu.de/en/</a>) and examined the perception of acoustic fire alarm signals.</p> <p>Hazards like fires occur regularly and can cost people’s lives. Optimal auditory alarm signals enable laypeople to recognize dangers and to protect themselves. Existing fire alarm sound research focuses on alarm sounds and voice alerts presented singularly. We explored a combination of both and aimed to identify alarm signals that work optimally in everyday life. Thus, we conducted two online experiments: In Study 1 (<em>N</em> = 379), we tested eight alarm sounds regarding their typicality, their familiarity, their arousal, their valence, and their dominance. Siren-like alarm sounds were most effective. In Study 2 (<em>N</em> = 206), we combined the four most effective alarm sounds with a voice alert. The voice alert reinforced ambiguity reduction, action motivation, and action intention. Hence, we suggest using alarm sounds with siren-like patterns. They should be combined with a voice alert to foster a quick and specific (target task-oriented) reaction.</p> <p>The ethics committee of the University of Münster approved the studies (ID 2021-57-MT), and we preregistered both studies with AsPredicted.org under numbers #77031 and #81137 (see <a href="https://aspredicted.org/vx2rr.pdf">https://aspredicted.org/vx2rr.pdf</a> and <a href="https://aspredicted.org/mt9g3.pdf">https://aspredicted.org/mt9g3.pdf</a>). The studies were supported by the German Federal Ministry of Education and Research (grant numbers 13N15416 and 13N15419).</p> <p><strong>This online supplement includes: </strong></p> <ul> <li>Two codebooks describing all instructions and items in Study 1 and in Study 2</li> <li>Raw data (anonymized) and analysis scripts (Note: The raw data contains only the information of persons who were included in the analysis and who gave their informed consent. Some demographic information was deleted to ensure anonymity.)</li> <li>Study material: <ul> <li>Alarm signal example</li> <li>Hearing test implemented in both studies</li> </ul> </li> </ul>
Data of the publication Optimizing ion implantation to create shallow NV centre ensembles in high-quality CVD diamond
<p>Data of the publication published by IOP under the reference: Midrel Wilfried Ngandeu Ngambou <em>et al</em> 2022 <em>Mater. Quantum. Technol.</em> <strong>2</strong> 045001 .</p>
Background optimization of powder electron diffraction to implement e-PDF technique and study the local structure of iron oxide nanocrystals
<p>The local structural characterization of iron oxide nanoparticles is explored using a total scattering analysis method known as Pair Distribution Function (PDF) (also known as Reduced Density Function) profiles derived from background corrected powder electron diffraction patterns. Due to the strong coulombic interaction between the electron beam and the sample, electron diffraction generally leads to multiple scattering, causing redistribution of intensities towards higher scattering angles and an increased background in the diffraction profile. In addition to this, the electron-specimen interaction gives rise to an undesirable inelastic scattering signal that contributes primarily to the background. The present work demonstrates the efficacy of a pre-treatment of the underlying complex background function, which is a combination of both incoherent multiple and inelastic scatterings that cannot be identical for different electron beam energies. Therefore, two different background subtraction approaches are proposed for the electron diffraction patterns acquired at 80 kV and 300 kV beam energies. From the least square refinement (small-box modelling), both approaches are found to be very promising, leading to a successful implementation of the e-PDF technique to study the local structure of the considered nanomaterial.</p>
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
Cold rolling mill: Dataset for Recommender system for process optimization
<p>The dataset (pickle formatted with version pickle=4.0) contains a collection of process values obtained from a process line. Each value represents the mean measurement within a window at specific intervals along the distance domain. These intervals were equidistant and sampled under steady-state conditions, ensuring consistent data collection.</p> <p>The process values included in the dataset cover a range of parameters and variables relevant to the process line. These values provide information about the behavior and characteristics of the process at different points along the distance domain.</p> <p> </p> <p>Associate source code is available at: <a href="https://github.com/CuAuPro/opti-rec-sys">Recommender system for process optimization (github.com)</a>.</p>
The Antarctic sea ice reconstruction (CMST-South) based on the optimized Data Assimilation System for the Southern Ocean
<p>The wealth of historical sea ice concentration (SIC) observations, coupled with their extensive spatial coverage, renders them indispensable for the reconstruction of long-term Antarctic sea ice variability. However, recent studies have pointed out the presence of significant uncertainties in certain aspects of Antarctic sea ice reanalyses obtained from assimilating SIC. Notably, while previous studies on ocean data assimilation have already demonstrated the significance of optimizing model-dependent parameters for assimilating oceanic observations, this aspect has received limited attention in current sea ice data assimilation studies. As a result, whether optimizing model-dependent parameters can enhance the effectiveness of assimilating SIC remains an open question. Thus, we address this gap by refining the model-dependent parameters of Data Assimilation System for the Southern Ocean (DASSO), including the development of a latitude-dependent localization scheme and the objective estimation of observation error variance of SIC which takes into account both measurement errors and representation errors.</p> <p>Here, the monthly anomalies in Antarctic sea ice extent and volume (1980 -2018) are uploaded which is produced by the optimized Data Assimilation System for the Southern Ocean (DASSO) with assimilating SIC. Besides, a 13-month moving mean is applied to monthly anomalies to focus on the low-frequency variability of Antarctic sea ice.</p>
Dataset for "Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects"
<p>Data and Code for 'Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects' by Graf et al. (Communications Earth and Environment)</p>
Data for "Challenges of variational quantum optimization with measurement shot noise"
<p>In this repository, we save all the data used in the article <em>"Challenges of variational quantum optimization with measurement shot noise"</em> <a href="https://doi.org/10.1103/PhysRevA.109.032408" target="_blank" rel="noopener">Phys. Rev. A 109, 032408</a>.</p> <p>For reproducing the same figure as in the article, we provide an interactive notebook 'paper-figure.ipynb' and its HTML version. </p> <p><strong>Data</strong></p> <p>Unzip with</p> <pre><code>tar -xvf data.tar.gz</code></pre> <p>You will find ten data files:</p> <ul> <li> `dataferro.csv` Ferromagnetic model both for VQE and QAOA with random parameters initialization;</li> <li>`datanoise.csv`Ferromagnetic model for VQE with random parameters initialization and circuit noise;</li> <li>`data_spinglass.csv` Disordered model both for VQE and QAOA with random parameters initialization;</li> <li>`data_linopt.csv` Ferromagnetic model for QAOA with annealing-like parameters initialization;</li> <li>`data_lininit_rand.csv` Disordered model for QAOA with annealing-like parameters initialization;</li> <li>`data_spinglass_iter1.csv` Disordered model for QAOA, no optimization and random parameters initialization;</li> <li>`data_opt16shots.csv` Ferromagnetic model for QAOA, no optimization and annealing-like parameters initialization;</li> <li>`datagrad.csv` Ferromagnetic model for VQE with gradient-based optimization;</li> <li>`datagrad_eps05.csv` Ferromagnetic model for QAOA with gradient-based optimization;</li> <li>`grad_epsilon.npz` Ferromagnetic model for QAOA with gradient-based optimization, varying \(\varepsilon\) in the finite differences formula.</li> </ul> <p>Most of the data are CSV sheets, where you can find the aggregate results of the simulations described in our article. </p> <p><strong>Abstract</strong></p> <p>Quantum enhanced optimization of classical cost functions is a central theme of quantum computing due to its high potential value in science and technology.<br>The variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA) are popular variational approaches that are considered the most viable solutions in the noisy-intermediate scale quantum (NISQ) era.<br>Here, we study the scaling of the quantum resources, defined as the required number of circuit repetitions, to reach a fixed success probability as the problem size increases, focusing on the role played by measurement shot noise, which is unavoidable in realistic implementations.<br>Simple and reproducible problem instances are addressed, namely, the ferromagnetic and disordered Ising chains. <br>Our results show that: </p> <ol> <li>VQE with the standard heuristic ansatz scales comparably to direct brute-force search when energy-based optimizers are employed. The performance improves at most quadratically using a gradient-based optimizer;</li> <li>When the parameters are optimized from random guesses, also the scaling of QAOA implies problematically long absolute runtimes for large problem sizes;</li> <li>QAOA becomes practical when supplemented with a physically-inspired initialization of the parameters.</li> </ol> <p>Our results suggest that hybrid quantum-classical algorithms should possibly avoid a brute force classical outer loop, but focus on smart parameters initialization.</p>
Optimized stationary points on the potential energy surface of the reaction of atomic oxygen O(3P) with acrylonitrile
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P) + acrylonitrile potential energy surface (PES).</p> <p>The PES has been published in our article “A Computational Analysis of the Reaction of Atomic Oxygen O(<sup>3</sup>P) with Acrylonitrile”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2021</strong>, 12958, 339-350), that can be found in https://doi.org/10.1007/978-3-030-87016-4_25 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the N(2D) + CH2CHCN and CN + CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D) + CH<sub>2</sub>CHCN (acrylonitrile) and CN + CH<sub>2</sub>CHCN.</p> <p>The PES has been published in our article “A Theoretical Investigation of the Reactions of N(<sup>2</sup>D) and CN with Acrylonitrile and Implications for the Prebiotic Chemistry of Titan”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2022</strong>, 13378, 246-259), that can be found in https://doi.org/10.1007/978-3-031-10562-3_18 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the N(2D)+ C2H4 and N(2D)+ CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D)+ C<sub>2</sub>H<sub>4</sub> and N(<sup>2</sup>D)+ CH<sub>2</sub>CHCN.</p> <p>The PESs have been published in our article “Computational Investigation of the N(<sup>2</sup>D)+ C<sub>2</sub>H<sub>4</sub> and N(<sup>2</sup>D)+ CH<sub>2</sub>CHCN Reactions: Benchmark Analysis and Implications for Titan’s Atmosphere”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2023</strong>, 14105, 705-717), that can be found in https://doi.org/10.1007/978-3-031-37108-0_45 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the S+(4S) + SiH2(1A1) and HSiS+/SiSH+ + NH3 reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of three reactions: S<sup>+</sup>(<sup>4</sup>S) + SiH<sub>2</sub>(<sup>1</sup>A<sub>1</sub>), <sup>3</sup>HSiS<sup>+</sup> + NH<sub>3</sub> and <sup>3</sup>SiSH<sup>+</sup> + NH<sub>3</sub>.</p> <p>These PESs are part of our paper “The S<sup>+</sup>(<sup>4</sup>S)+SiH<sub>2</sub>(<sup>1</sup>A<sub>1</sub>) Reaction: Toward the Synthesis of Interstellar SiS”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2022</strong>, 13378, 233-245), that can be downloaded in https://doi.org/10.1007/978-3-031-10562-3_17 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pV(T+d)Z level of theory.</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.