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.
75
datasets available to search
ShareScore release 0.9.0
Dataset results
75 results for “structure optimization”
Docked structures from "Optimizing active learning for free energy calculations"
<p>This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ailsci.2022.100050" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ailsci.2022.100050</span></span></a>). AM1-BCC charges are stored in the field "AM1Cache" in the SD file. The charges can be extracted using the code sample below. </p> <p> </p> <pre><code>from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") am1_charges = pickle.loads(base64.b64decode(mol.GetProp("AM1Cache"))) assert len(am1_charges) == mol.GetNumAtoms(), "Charge cache has different number of charges than mol atoms"</code></pre>
Optimized structures of the stationary points on the potential energy surface of the OH(2Π) + C2H4 reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> potential energy surface published in our article “OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> Reaction: A Combined Crossed Molecular Beam and Theoretical Study” (P<em>hys. Chem. A</em> 2023, 127, 21, 4609–4623), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c08662">https://doi.org/10.1021/acs.jpca.2c08662</a>.</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 structures of the stationary points on the potential energy surface of the O(3P, 1D) + HCCCN(X1Σ+) reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) potential energy surface published in our article “Reactions O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) (Cyanoacetylene): Crossed-Beam and Theoretical Studies and Implications for the Chemistry of Extraterrestrial Environments” (<em>J. Phys. Chem. A</em> 2023, 127, 3, 685–703), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c07708">https://doi.org/10.1021/acs.jpca.2c07708</a>.</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 structures of the stationary points on the potential energy surface of the dissociation of the CH3OH˙+ cation
<p>This Zip file contains the optimized stationary points structures of the potential energy surface (PES) for the dissociation of the CH3OH˙+ cation.</p> <p>The PES has been published in our paper “Fragmentation of interstellar methanol by collisions with He˙<sup>+</sup>: an experimental and computational study” (<em><strong>Phys. Chem. Chem. Phys.</strong></em>, 2022, <strong>24</strong>, 22437-22452), that can be found in https://doi.org/10.1039/D2CP02458F .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01 and the structures were optimized at ωB97X-D/aug-cc-pVTZ level of theory.</p>
Supplementary codes and datasets for "Modular-topology optimization of structures and mechanisms with free material design and clustering"
<p>This repository supports Tyburec, M., Doškář, M., Zeman, J., & Kružík, M. (2022). Modular-topology optimization of structures and mechanisms with free material design and clustering. <em>Computer Methods in Applied Mechanics and Engineering</em>, <em>395</em>, 114977. <a href="https://doi.org/10.1016/j.cma.2022.114977">https://doi.org/10.1016/j.cma.2022.114977</a> (first published as preprint <a href="http://arxiv.org/abs/2111.10439">2111.10439</a> at arXiv.org).</p> <p>This repository contains:</p> <ol> <li>MATLAB source codes for <em>(modular) free material optimisation</em> and <em>hierarchical stiffness clustering</em> (folder <code>./mFMO/</code>)</li> <li>C++ source codes for <em>modular topology optimization</em> (folder <code>./MTO/</code>)</li> <li>Input/output data of the test suite (folder <code>./data/</code>)</li> </ol> <p><strong>1. Data flow</strong></p> <p>The test suite considered in the manuscript covers 4 problems:</p> <ol> <li>Messerschmitt-Bölkow-Blohm beam (labelled as <code>mbb</code>)</li> <li>Inverter compliant mechanism (labelled as <code>inv</code>)</li> <li>Gripper compliant mechanism (labelled as <code>grip</code>)</li> <li>Reusable design of both compliant mechanisms (labelled as <code>invgrip</code>)</li> </ol> <p>Each problem in the dataset is stored within a separate subfolder named according to the labels mentioned above. The final level of subdirectories <code>{X}color</code> comprises of the results for problems with <code>X</code> denoting the number of edge codes considered for each edge direction during the clustering (<code>0color</code> stands for a non-modular design and <code>1color</code> represents the design based on Periodic Unit Cell).</p> <p>Each of the folders contains outputs of the modular free material optimisation in the following form:</p> <ul> <li><code>{label}{X}.mat</code></li> <li><code>{label}{X}.til</code></li> <li><code>{label}{X}.tset</code></li> <li><code>{label}{X}guess.mat</code></li> </ul> <p>Files <code>*.til</code>, <code>*.tset</code>, and <code>*guess.mat</code> are then converted into a JSON input file for the modular topology optimization code with generator scripts which can be found in <code>./MTO/scripts</code> folder. Note that each of the problems in the test suite has its own generator script <code>generate_modular_problem_{MBB,inverter,gripper,inverterAndGripper}.mat</code>. The generator scripts make a directory named according to the key <code>MTO_{n}_kernelSensitivity</code>, where <code>n</code> denotes the resolution of each module (i.e. the number of nodes along one direction). The directory also contains the outputs of the modular topology optimisation in the form of the initial and the final state of the optimization in <code>VTK</code> files and visualisation of the final state in <code>SVG</code> files. The log file <code>log.txt</code> stores the optimized objective and progress of the value along with stopping criteria quantities during iterations.</p> <p><strong>2. Running codes</strong></p> <p><strong>2.1 Modular free material optimisation</strong></p> <p>MATLAB scripts and functions for (modular) Free Material Optimization (FMO) are contained in the <code>mFMO</code> data folder. The codes have been tested with MATLAB R2019b. To run the codes the user is required to install the <a href="http://www.penopt.com">PENNON optimizer</a>. A free academic license is provided by its authors on request.</p> <p>Input files for individual problems are defined in the <code>mFMO/problems</code> folder and are launched with the <code>runproblem(problemName, numClusters)</code>, where <code>problemName</code> refers to the file in the <code>mFMO/problems</code> folder without the file extension and <code>numClusters</code> denotes the maximum number of color codes in Wang tiling formalism.</p> <p>If successful, the optimization produces output files in <code>mFMO/fmo_fig/{label}/{X}colors/{T}/</code>:</p> <ul> <li><code>{label}{X}.mat</code> (contains clustering and tiling information)</li> <li><code>{label}{X}_tmp.mat</code> (contains results of non-modular FMO)</li> <li><code>{label}{X}.til</code> (the assembly plan)</li> <li><code>{label}{X}.tset</code> (Wang tile set)</li> <li><code>{label}{X}guess.mat</code> (guess for TO)</li> </ul> <p>where <code>T</code> is the optimization time stamp.</p> <p><strong>2.2 Modular topology optimisation</strong></p> <p>All results were obtained with version <code>v1.1.2</code>, which is also provided in the folder <code>MTO</code>, and linked Intel® oneAPI Math Kernel Library and the incorporated PARDISO sparse solver. For the recent development of the code see the open git repository at <a href="https://gitlab.com/MartinDoskar/modular-topology-optimization">https://gitlab.com/MartinDoskar/modular-topology-optimization</a>. The repository also contains a detailed description of input parameters and code design.</p> <p>Modular topology optimisation code uses CMake for the cross-platform build automation. For instance, under Linux, the whole code can be compiled in the standard five steps:</p> <pre><code>cd ./MTO mkdir build cd ./build cmake -DCMAKE_BUILD_TYPE=Release .. make </code></pre> <p>All executables are automatically stored in <code>./MTO/bin/</code> folder. Individual problems can be optimized by parsing the JSON files obtained from the generator scripts as an argument to the MTO.Application binary, e.g.,</p> <pre><code>./MTO/bin/MTO.Application.exe path_to_data/mbb/2color/MTO_100_kernelSensitivity/input_modular_mbb_2colours_100.json </code></pre> <p><strong>Acknowledgement</strong></p> <p>The related research and code development was supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</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>
Optimized structures of selected stationary points on the potential energy surface of the HC3N + CN reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the HC<sub>3</sub>N + CN potential energy surface published in our article “Semiempirical Potential in Kinetics Calculations on the HC<sub>3</sub>N + CN Reaction” (<em>Molecules</em> <strong>2022</strong>, <em>27(7)</em>, 2297), that can be found in <a href="https://doi.org/10.3390/molecules27072297">https://doi.org/10.3390/molecules27072297</a> .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at M06-2X/6-311+G(d,p) level of theory.</p>
Fig. 8 in Optimal fishing samplers to reveal the morphological structure of a fish assemblage in a subtropical tidal flat
Fig. 8. Redundancy analysis (RDA): individual species-gear relationships explained by morphological shape variability, using incidence data. In blue, fishing gear acronyms: beach seine (BS), cast net (CN), fish trap (FT), hook and line (HL), inner encircling gillnet (EG1), large gillnet (LG), marginal encircling gillnet (EG2), otter trawl (OT), small gillnet (SG).
Fig. 4 in Optimal fishing samplers to reveal the morphological structure of a fish assemblage in a subtropical tidal flat
Fig. 4. Morphospace represented by shape variation of all species in the nine fishing gears grouped, using the relative warps: a. 1 and 2; b. 1 and 3. Acronyms: Anc tri, Anchoa tricolor; Bat sop, Bathygobius soporator; Bot oce, Bothus ocellatus; Cen par, Centropomus parallelus; Chi spi, Chilomycterus spinosus; Dia rho, Diapterus rhombeus; Elo sau, Elops saurus; Epi mar, Epinephelus marginatus; Hip rei, Hippocampus reidi; Lut ana, Lutjanus analis; Lut cya, Lutjanus cyanopterus; Mal del, Malacoctenus delalandii; Myr pun, Myrophis punctatus; Oph gom, Ophichthus gomesii; Pol vir, Polydactylus virginicus; Sca cri, Scartella cristata; Sco bra, Scomberomorus brasiliensis; Sel set, Selene setapinnis; Sel vom, Selene vomer; Sph tes, Sphoeroides testudineus; Str mar, Strongylura marina; Sym tes, Symphurus tesselatus; Tra myo, Trachinocephalus myops. Threatened species (Hip rei, Lut ana, Lut cya, Epi mar) are highlighted in red.
Dataset of structural and energetic descriptors for optimized geometries of the pyrene dimer in the S1 state and Jupyter Notebbok used for the unsupervised clustering, analysis and visualization
<p>Geometrical and energy data extracted from a set of 188 optimized geometries for the pyrene dimer in the first excited singlet state at the TD-CAMB3LYP + D3BJ / 6-31G* / C-CPCM(Cyclohexane) level. </p> <p>Coordinates (in xyz format) of the 188 optimized geometries considered.</p> <p>Jupyter notebook used to perform unsupervised clustering and analysis of the available structures.</p>
cis/trans isomeric 1,6-Diphenylhexatrienes (DPH) optimized structures.
<p>Optimized structures and frequencies in the ground and first excited state for the six possible isomers of 1,6-Diphenylhexatriene (DPH) optimized at PBE0/def2TZVP level of theory with Gaussian16. </p> <p>all-trans - ttt</p> <p>B - tct</p> <p>C - ctt</p> <p>D - cct</p> <p>E - ctc</p> <p>F - ccc</p>
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
Optimized atomic structures of thin Ag films on Pt(111) and Pd(111) surfaces
<p>VASP coordinate files of thin Ag films on Pt(111) and Pd(111) surfaces and the summary spreadsheet of corresponding energies. </p>
Electronic Structure Data for "Design of Covalent Organic Frameworks through on-the-fly Batch-based Bayesian Optimization"
<p>This is a dataset of 1736 potential building blocks for the construction of covalent organic frameworks (COF). Electronic structures were calculated with the GFN1-xTB tight binding DFT approach as implemented in the xTB package (v6.2.3). The dataset contains all necessary inputs and outputs from these calculations. Structures were optimised with xTB's internal normal coordinate rational function optimizer (ANCopt) at the default geometry convergence criterion.</p> <p>The dataset contains calculations for two major parameters determining the suitability of the resulting COFs as an organic semiconductor, specifically, the approximate energy alignment of the homo level and the reorganization free energy.</p>
Supplementary material for publication "Multi-Echelon Inventory Optimization in Supply Chain Networks: Exploring Network Structures and Predictive Modeling"
<div> <div> <div> <p>This dataset collects different supply chain network structures generated artificially. We present four types of networks: Serial, Convergent, Divergent, and General, each type consisting of 20,000 individual instances. All 80,000 network instances generated are available to researchers and practitioners in Excel. The repository consists of separate files for each network instance consisting of each network inventory data, node connections, and a visual representation.</p> </div> </div> </div>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 4. Example of a chromosome structure with permutation coding
<p>Each chromosome found in the population formed in the GA is structurally an equal-length coded series. The chromosomes are made of genes. For coding purposes, binary, permutation, and value coding methods are widely used. In the travelling salesman or other similar VRPs, permutation coding technique is preferred over the other techniques. Using the permutation coding technique, each chromosome found in the population is expressed in terms of the numbers of each stop to be followed in the route, as shown in Figure 4.</p>
Maximized response by structural optimization of soft elastic composite systems
<p>This dataset contains the underlying data and numerical optimization codes for the paper</p> <p><em>L. Fischer and Andreas M. Menzel</em><br>Maximized response by structural optimization of soft elastic composite systems<br>PNAS Nexus <strong>3</strong>, <span>pgae353 </span>(2024) (DOI: <a href="https://doi.org/10.1093/pnasnexus/pgae353" target="_blank" rel="noopener">10.1093/pnasnexus/pgae353</a>).</p> <p>For more information, please see the included "Readme.txt" in the dataset "Zenodo.zip".</p>
Case study of a rapid prototyping method for optimizing soft gripper structures with integrated piezoresistive sensors
<p>Closed-loop control systems and monitoring the activities of soft robots in the natural environment require sensing elements in soft actuator modules. In this study, additive manufacturing is used for sensorized soft actuator modules to investigate the influence of the Shore hardness and design aspects of an open-source tendon-based gripper structure, in a time-efficient way. Additionally, the placement of the piezoresistive sensing element (tension or compression side on the bending soft gripper) was investigated. A user-friendly method, based on thermoplastic material extrusion, has been explored to improve the future design optimization in of active soft robotic structures successfully. A higher Shore hardness resulted in a higher total deflection and a higher force to bend the gripper structure. By increasing the geometrical stiffness of the gripper printed with low Shore hardness, the total deflection was increased, but the force needed to activate the movement was higher in comparison to high Shore hardness and low geometrical stiffness. Moreover, the sensing element on the substrate of higher Shore hardness, leads to low drift, monotonic response, with good sensitivity, independent of the sampling rate. The gripper of higher Shore hardness had a larger functional range, being capable of gripping small and larger objects.</p>
Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023
<p>Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023</p> <p> </p> <p> </p>
Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science
<p>Files concerning the publication "Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science"(arxiv:2205.13308 )</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.