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60 results for “Computational Design”
Computational Design of Multimodal Combinatorial Mechanical Metamaterials
<p>This dataset contains the data used to design multimodal mechanical metamaterials as described in the paper 'Prospecting for Pluripotency in Metamaterial Design', as published in Phys. Rev. Research 7(2), 023299.</p> <p>In this paper, the data is used to design 5×5 unit cells with desired deformation (zero) modes. The dataset contains the data used to train neural networks (CNN_data.zip), the designs generated by genetic algorithm (step_i.zip) and their mode structures (step_ii.zip), and the designs obtained through our design approach as described in the paper (step_ii.zip). Additionally, there is data comparing the efficiency of using a genetic algorithm or a hill climbing method to generate designs with a large number of intensive modes (step_i.zip).</p>
Data base of the complexity Indexes to compute MFA and MCI from the paper "Unleashing The Potential Of Artificial Reefs Design"
<p>This data base compiles the Complexity indexes used to compute the MFA and extract the MCI from the paper "Unleashing The Potential Of Artificial Reefs Design: A Purpose-Driven Evaluation Of Structural Complexity" (https://doi.org/10.32942/X2G300)</p>
Optimizing the design of a bioabsorbable metal stent using computer simulation methods: Supporting Data
<p>Data including UMATs and Abaqus input files related to the paper 'Optimizing the design of a bioabsorbable metal stent using computer simulation methods' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.biomaterials.2013.07.010" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.biomaterials.2013.07.010</span></a></p> <p> </p>
Design and in vitro realization of carbon-conserving photorespiration - Computational Analysis
<p>Our aim is to develop a framework for modeling C3 photosynthesis in mesophyll cells that allows us to compare native photorespiration with engineered photosynthetic shunts. In particular, we want to model conditions that are most relevant to agricultural crops, i.e. a range of light intensities and both ambient and low CO2 intercellular airspace concentrations. Here we presented our computational analysis based on pathSeekR, the stoichiometric-kinetic model, kinetic models of photorespiration shunts and pathSeekR pathway architectures. </p>
Data for "Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations"
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The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes
<p>The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.</p> <p>This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).</p>
Supporting data for: "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides"
<p>This repository contains supporting data and code for the paper titled "Hybrid Computational-Experimental Data-Driven Design of Self-Assembling π-Conjugated Peptides" by Kirill Shmilovich, Sayak Subhra Panda, Anna Stouffer, John D. Tovar, and Andrew L. Ferguson.</p>
Computer Aided Design (CAD) files for capillaric circuit with 8 retention burst valves
<p>AutoCAD design file and STL file for capillaric circuit with 8 retention burst valves.</p>
Global Sensitivity Analysis is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design
<p>This Zenodo repository contains all the results generated for the book chapter "Global Sensitivity Analysis is Not Always Beneficial for Evolutionary Computation: A Study in Engineering Design".</p>
Efficient Implementation of a Novel Decomposition Approach for the Hazmat Network Design Problem with Capacity Constraints in Java Including Computational Results
<p>Supplementary material for the Publication "Solving Multi-Follower Mixed-Integer Bilevel Problems with Binary Linking Variables"</p>
Computational Design Dataset
<p><strong>Computational Design Dataset</strong><br> This dataset contains data and scripts for the book <em>Computational Design for Landscape Architects</em>. Code includes Python scripts and Grasshopper definitions. This dataset includes laser scanned plants, lidar and raster data for Governor's Island, New York City, USA, and lidar and raster data for White Sands National Monument, New Mexico, USA. The CRS for the Governor's Island data is NAD83 / New York Long Island (ftUS) with the EPSG code 2263. The CRS for the White Sands data is NAD83 / UTM Zone 13N with EPSG code 26913. </p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://orthos.dhses.ny.gov">https://orthos.dhses.ny.gov</a></li> <li><a href="https://data.cityofnewyork.us">https://data.cityofnewyork.us</a></li> <li><a href="https://opentopography.org">https://opentopography.org</a></li> <li><a href="https://xyz.cct.lsu.edu">https://xyz.cct.lsu.edu</a></li> </ul> <p><strong>License</strong><br> The data in this dataset is licensed under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 Universal Public Domain Dedication</a>, while the code in this dataset is licensed under <a href="https://opensource.org/license/mit/">The MIT License</a> by Brendan Harmon.<br> </p>
Target-focused library design by pocket-applied computer vision and fragment deep generative linking
<p>Data inputs and outputs used in</p> <pre>Target-focused library design by pocket-applied computer vision and fragment deep generative linking</pre> <p>Code: https://github.com/kimeguida/POEM</p> <p> </p>
Dataset, Model Statistics, and 3D designs for "From Eyes to Cameras: Computer Vision for High-Throughput Liquid-Liquid Separation"
<p>Dataset, model statistics, and 3D design of high throughput platform associated with HeinSight3.0. </p> <p> </p> <p>Pre-print: https://chemrxiv.org/engage/chemrxiv/article-details/65e5481f9138d231619c1879</p> <p> </p> <p>The code and model of HeinSight3.0 can be found at (https://doi.org/10.5281/zenodo.11053915)</p>
Data for "High-Throughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in System Design"
<p>In the following subdirectories are the input and output of Gaussian calculations + structures from screening for this publication:</p> <p>chemrxiv: <a href="https://chemrxiv.org/engage/chemrxiv/article-details/60c758e0ee301c7eadc7b7df">10.26434/chemrxiv.14604294</a></p> <p>Published: <a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202106721">10.1002/anie.202106721</a></p> <p>Previously uploaded in 10.5281/zenodo.8432296 and <a href="https://github.com/andrewtarzia/citable_data" rel="noopener noreferrer">https://github.com/andrewtarzia/citable_data</a></p> <p>Software repository: <a href="https://github.com/andrewtarzia/unsymm_match">github.com/andrewtarzia/unsymm_match</a></p> <p>screening_structures directory:</p> <ul> <li>contains the structures from xTB optimisation that were used in ranking in structures.tar.gz as `.mol` files</li> <li>all_cage_results.txt contains their properties for ranking</li> </ul> <p>single_point_dft directory:</p> <ul> <li>contains the structures and output of single point DFT calculations</li> <li>during the revision process, we confirmed (based on reviewer suggestions) our DFT validation results using ORCA 4.2.1 with PBE0 and B97-3c in the gas phase. These results were consistent with our previous ones, so were not added to the manuscript. But are useful for future work! <ul> <li>These results are in the s_orca directory.</li> </ul> </li> </ul> <p>free_energy_calculations directory:</p> <ul> <li>during the revision process, it was suggested to calculate the free energies using the xTB method (low-cost) and compare that to the total energies we use.</li> <li>the script `run_gfn2_free_energy.py` in the unsymm_match code repository does this for top candidate ligands using the stko.XTB class. <ul> <li>for each structure, the free energy is output to a .fey file.</li> </ul> </li> </ul>
Computer-aided design of optimal environmentally benign solvent-based adhesive products
<p>The files contain all the product design problems implemented in GAMS for this publication. All models were run on a single core of a dual 8 core Intel(R) Xeon(R) CPU E5-2650 machine at 3.52 GHz with 125GB of memory.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised Disjunctive Programming to formulate and solve the product design problem. Key design decisions in product design (number of ingredients, identity of compounds and their proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal product ingredients.</p>
MD preview for: Fighting Celiac Disease: Improvement of pH Stability of Cathepsin L In Vitro by Computational Design
<p>Dataset structure:<br> This is MD preview (some initial and final files, along with light versions of principal MD trajectories) for wild-type (WT) and acidophilic mutant (V277A) cathepsin L versions. Each folder contains three variants of pH calculations:</p> <ul> <li>pH 7</li> <li>pH 2 (considering all ionizable residues)</li> <li>pH 2 (considering only His 275)</li> </ul> <p>See paper text for details.</p> <p>Each subfolder contains seven MD-related files:</p> <ul> <li>md.mdp: Gromacs options file</li> <li>topol.top: system topology, including ionization states of the charged residues</li> <li>em.gro: system coordinates before MD</li> <li>md.gro: system coordinates after MD</li> <li>md.tpr: Gromacs tpr file required for MD start</li> <li>md_view.gro: system coordinates after MD without water and ions. Required for MD preview using the next trajectory file</li> <li>md_view.xtc: "light" MD trajectory file without water and ions with coordinates saved each 100 ps (gmx trjconv "-dt 100" option). Use two latter files for MD preview in software like VMD or Pymol.</li> </ul>
Halogen-Bond-Based Organocatalysis Unveiled: Computational Design and Mechanistic Insights
<p>Please find the supporting information for the "Halogen-Bond-Based Organocatalysis Unveiled: Computational Design and Mechanistic Insights into Electronically Activated Donor Systems" paper here.</p><p> </p><p>Halogen-Bond-Based Organocatalysis Unveiled: Computational Design and Mechanistic Insights</p><p>Nika Melnyk, 1 Marianne Rica Garcia 1 and Cristina Trujillo 1,2</p><p>1Trinity Biomedical Sciences Institute, School of Chemistry, The University of Dublin, Trinity College, D02 R590 Dublin 2, Ireland</p><p>2Department of Chemistry, University of Manchester, Oxford Road, Manchester, M139PL</p><p>Email: cristina.trujillodelvalle@manchester.ac.uk</p><p> </p><p> </p>
Data from: Targeted computational design of an interleukin-7 superkine with enhanced folding efficiency and immunotherapeutic efficacy
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Micro-computed tomography data for: Resolving the design principles that control postnatal vascular growth and scaling
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Computer-Aided Drug Design (CADD): To Screen Potential Antibiotics Against Klebsiella Pneumoniae Beta-Lactamase Enzyme
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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.