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1,855 results for “Framework”
A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems
<p>Sequence alignment is a fundamentally memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory architectures alleviate this bottleneck by providing the memory with computing competencies. We propose Alignment-in-Memory (AIM), a framework for high-throughput sequence alignment using processing-in-memory, and evaluate it on UPMEM, the first publicly-available general-purpose programmable processing-in-memory system.</p>
On-Surface Synthesis of Disilabenzene-Bridged Covalent Organic Frameworks
<p>Here you will find the list of folders and their contents used to generate the data and the figures published on "On-Surface Synthesis of Disilabenzene-Bridged Covalent Organic Frameworks" paper</p> <p><strong>On SiCOF_data.zip you will find the following folders:</strong><br> <strong>2D_network_on_au:</strong><br> outcar_k3.fin: VASP output file. Contains the DFT parameters used to relax the Si-COF network geometry on gold<br> poscar_k3.fin: VASP geometry file. This is the relaxed geometry of the Si-COF network on gold obtained in the previous calculation<br> output_aims.txt: FHI-aims output file. Contains the DFT parameters used to print the eigenvectors of the Si-COF network on gold<br> <strong>C4Si2_ribb_on_au:</strong><br> outcar.fin: VASP output file. Contains the DFT parameters used to relax the Si ribbon 1 geometry on gold<br> poscar.fin: VASP geometry file. This is the relaxed geometry of the Si ribbon 1 on gold obtained in the previous calculation<br> output_aims.txt: FHI-aims output file. Contains the DFT parameters used to print the eigenvectors of the Si ribbon 1 network on gold<br> <strong>C4Si_ribb_on_au:</strong><br> outcar.fin: VASP output file. Contains the DFT parameters used to relax the Si ribbon 2 network geometry on gold<br> poscar.fin: VASP geometry file. This is the relaxed geometry of the Si ribbon 2 network on gold obtained in the previous calculation<br> output_aims.txt: FHI-aims output file. Contains the DFT parameters used to print the eigenvectors of the Si ribbon 2 network on gold<br> <strong>critic2</strong>:<br> - Example of input files to simulate constant current (cc) and constant height (ch) stm using the Tersoff-Hamann approximation through the critic2 code:<br> stm_cc.inp stm_ch.inp<br> <strong>NICs</strong>:<br> - ORCA output files. Contains the DFT parameters and geometries used to calculate the nucleus independent nuclear shift (NICs) of several molecules:<br> 2D-buckled.out<br> benzene.out<br> disilahexa.out<br> mol2Br.out<br> 2D-flat.out<br> Brsila.out<br> mol2Br+2Au.out<br> silaben.out<br> <strong>PPSTM</strong>:<br> params.ini: control file for the PPM (PP-AFM) code, that is used to calculate the position of oxygen for the relaxed STM scan calculated by PP-STM<br> <strong>PPSTM_simple.py</strong>: Script running PP-STM simulations with 13% of s and 87% of pxy orbitals (one possibility for simulating CO tip)</p> <p><strong>On Source_data.zip you will find all the unprocessed images used in the paper. Additionally, it is included in CHGCAR_files.zip the charge densities used to generate the supplementary figure 5. </strong></p> <p><strong>Version of the softwares and workflow on the PP-STM:</strong><br> FHI-aims version ( https://aimsclub.fhi-berlin.mpg.de ) was aims.191119.mpi.scalapack.x .<br> cirtic2: https://aoterodelaroza.github.io/critic2/examples/example_14_01_stmqe/<br> PPM (PP-AFM) version was a master version from Nov 4, 2021: https://github.com/ProkopHapala/ProbeParticleModel/commit/327c61cdbd348307c5255c4618f12d28f4ababd5<br> PP-STM version was a master version from Nov 16, 2021: https://github.com/Probe-Particle/PPSTM/commit/4434739bd737e58a2fc556dff24e8e7d6eab084e</p> <p>The workflow for the PP-STM (CO-tip STM) images was as follows:<br> Using the poscar*.fin for creating the geometry.<br> Run a single point (no optimization) calculation with FHI-aims for creating the hartree potential ("cube_001_hartree_potential.cube") and <br> then with control.in and PPSTM_simple.py file in the folder and with properly set way to<br> PP-AFM folder and PP-STM path (in the top of the PPSTM_simple.py file) running following commands in command line:</p> <p>python3 PPAFM_PATH/generate_LJFF.py -i cube_001_hartree_potential.cube<br> python3 PPAFM_PATH/generate_ElFF.py -i cube_001_hartree_potential.cube # these will create force-field for PP-AFM calculations #<br> python3 PPAFM_PATH/relaxed_scan.py --pos # this will create position of Probe Particle (simulating oxygen postions ) for the STM scan #<br> python3 PPSTM_simple.py # will create the PPSTM images #</p>
Data for: An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean
<p><span>Soybean [<em>Glycine max (L.) Merr.</em>] </span><span>serves as one of the most economically valuable crops globally, but it is sensitive to low temperatures during the crop growing season. Currently, agriculture around the world has faced more serious abiotic stresses due to climate change, so there is an urgent need to breed cold-tolerant cultivars to resist the changing environment. The cold-tolerant trait is a complex and quantitative trait controlled by multiple genes, environmental factors, and their interaction. A total of 56 soybean samples were used, including 28 resistant varieties and 28 susceptible varieties, in the field experiments. We selected 55 SNPs (which were mapped to 39 CTgenes) from the CTgenes for distinguishing cold-tolerant lines from cold-susceptible lines. The SNP data can be applied for soybean's cold-tolerant experiment, such as soybean marker-assisted selection, soybean varieties clustering, the systems biology analysis, and further validation. </span></p>
Data of "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework"
<p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework",<br> journal = "Engineering Fracture Mechanics",<br> year = "2022",<br> volume = "275",<br> pages = "108844 ",<br> doi = "https://doi.org/10.1016/j.engfracmech.2022.108844",<br> author = "Antoine Hilhorst, Julien Leclerc, Thomas Pardoen, Pascal J. Jacques, Ludovic Noels, Van-Dung Nguyen"</p> <p>New version following review.</p> <p> </p> <p> </p>
Graph Neural Network for Metal Organic Framework Potential Energy Approximation
<p>Data set consists of 50,000 different configurations for the Metal Organic Framework (MOF) FIGXAU. Was generated by randomly modifying the positions of the atoms and doing an SCF relaxation on each configuration.</p>
Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
<p>This is the dataset for the work titled and authored by:</p> <p><strong>Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution</strong></p> <p>Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig</p> <p>We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.</p> <p>Software can be found under this DOI:</p> <p>10.5281/zenodo.846448</p>
Global Habitat Suitability for Framework-Forming Cold-Water Corals
<p>Global habitat suitability model outputs for several species of Scleractinian corals from the publication:</p> <p><strong>Davies, A.J. & Guinotte, J.M. (2011) Global Habitat Suitability for Framework-Forming Cold-Water Corals. PLoS ONE 6(4): e18483. doi:10.1371/journal.pone.0018483</strong></p> <p>Files are ArcGIS compatible LZW compressed TIFF image, the values are:</p> <ul> <li>0-100 = habitat suitability value (was 0-1 but to save space, data has been converted to 8 bit unsigned integer type)</li> <li>255 = no data</li> </ul> <p>The 5 point categorical colour scale used in the publication is available in each species zip file <em>"5_point_categorical_scale.lyr". </em>Which is compatible with ArcGIS.</p>
Simultaneous Control of Aluminum Atoms and Defects in MOR Zeolite Framework by Post-Synthetic Treatments
<p><span>Input and output files used to calculate 27Al NMR chemical shifts in Quantum Espresso 6.5 for modernities with various defects and Al substitutions.</span></p>
Data for "Tetramine Aspect Ratio and Flexibility Determine Framework Symmetry for Zn8L6 Self-Assembled Structures"
<p>In the following subdirectories are the input and outputs of cage and face analysis for:</p> <p>Published DOI: <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202217987">10.1002/anie.202217987 </a></p> <p>Code: <a href="https://github.com/andrewtarzia/sca_cage_assembler/tree/cubism-production">sca_cage_assembler</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>NOTES:</p> <ul> <li>the naming convention differs from manuscript:</li> </ul> <table> <tbody> <tr> <th>manuscript tetra-aniline</th> <th>computational label</th> <th>xtal-label</th> </tr> <tr> <td>A</td> <td>5</td> <td>370</td> </tr> <tr> <td>B</td> <td>16</td> <td>326</td> </tr> <tr> <td>C</td> <td>12</td> <td>235</td> </tr> <tr> <td>D</td> <td>3</td> <td>301</td> </tr> <tr> <td>E</td> <td>8</td> <td>257</td> </tr> <tr> <td>F</td> <td>2</td> <td>354</td> </tr> </tbody> </table> <ul> <li>computational labels are often preceded by `quad2_` or `cl1_quad2_`</li> <li>much of the analysis was not used in the manuscript but remains part of the accumulated data</li> </ul> <p> </p> <p>cage_library directory:</p> <ul> <li>_CS.json: information on all cages in the set of diastereomers - properties and whether they optimized successfully.</li> <li>_ligand_measures.json: information on the ligand associated with a set of cage diastereomers.</li> <li>_measures.json: represenets a cleaned up collation of all measures the diastereomers made from a given ligand</li> <li>C_NAME_optc.mol: optimized (at xTB level) structure of each cage.</li> <li>set_dft_run directory contains the input and output of the CP2K optimisations of one set of diastereomers</li> </ul> <p>complex_library directory:</p> <ul> <li>contains the optimised structures of both complexes</li> </ul> <p>ligand_library directory:</p> <ul> <li>contains `_opt.mol` input ligand structures for cage construction</li> <li>for cap, the input was provided manually in `manual/` directory</li> <li>in `face_analysis` directory: <ul> <li>contains manual_complex directory, with necessary input for face construction</li> <li>_long_properties.json files contains the measurements for the named face (in file name)</li> <li>_long_lopt.mol files contain the optimised structure of the named face, on which analysis was performed</li> <li>`long` corresponds to the longer restricted optimization discussed in the SI.</li> </ul> </li> </ul> <p>xray_structures directory:</p> <ul> <li>analysis directory: <ul> <li>contains input .pdb files for xray structure (as single molecules) used in analysis</li> <li>contains `all_xray_csv_data.csv`, which has all data needed on xray structures.</li> </ul> </li> </ul>
Data for "Water Sorption Controls Extreme Single-Crystal-to-Single Crystal Molecular Reorganization in Hydrogen Bonded Organic Frameworks"
<p>Paper DOI: <a href="https://doi.org/10.1002/chem.202201929">10.1002/chem.202201929</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>Each .out file is generated from zeo_runs_production.py, which includes the output from Zeo++ for the probe radius and sampling value in the file name.</p> <p>zeo_runs.py tests sampling values to check for convergence, those output files are not included here.</p> <p>Each python script includes the list of CIFs to run the analysis on. Only the CIFs shown in the manuscript are included here, as testing was done on a series to see the effect of symmetry, disorder and cell size.</p>
Data from: Testing frameworks for early life effects: The developmental constraints and adaptive response hypotheses do not explain key fertility outcomes in wild female baboons
<p>In evolutionary ecology, two classes of explanations are frequently invoked to explain "early life effects" on adult outcomes. Developmental constraints (DC) explanations contend that costs of early adversity arise from limitations adversity places on optimal development. Adaptive response (AR) hypotheses propose that later life outcomes will be worse when early and adult environments are poorly "matched." Here, we use recently proposed mathematical definitions for these hypotheses and a quadratic-regression based approach to test the long-term consequences of variation in developmental environments on fertility in wild baboons. We evaluate whether low rainfall and/or dominance rank during development predict three female fertility measures in adulthood, and whether any observed relationships are consistent with DC and/or AR. Neither rainfall during development nor the difference between rainfall in development and adulthood predicted any fertility measures. Females who were low-ranking during development had an elevated risk of losing infants later in life, and greater change in rank between development and adulthood predicted greater risk of infant loss. However, both effects were statistically marginal and consistent with alternative explanations, including adult environmental quality effects. Consequently, our data do not provide compelling support for either of these common explanations for the evolution of early life effects.</p>
Assessing Pairwise Ecological Association Inference using a novel Ecological Network Inference Simulation-Validation Framework - Data Repository
<p>Accompanying data for manuscript entitled "<span>A novel Network Inference Simulation-Validation Framework for Assessment of Ecological Network Inference Performance</span>" whose submission is imminent.</p>
Supplementary Data for "Exploring the Chemical Space of Metal–Organic Frameworks with rht Topology for High Capacity Hydrogen Storage"
<p>This dataset includes optimization and simulation inputs, optimized structures, building blocks, computed hydrogen uptakes and textural properties of the metal–organic frameworks that correspond with work in "Exploring the Chemical Space of Metal–Organic Frameworks with rht Topology for High Capacity Hydrogen Storage" (DOI: 10.1021/acs.jpcc.4c00638).</p>
Data for: A linear response framework for simulating bosonic and fermionic correlation functions on quantum computers
<p>Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiments. Within the context of quantum computing, and by using a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and is ancilla-free. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material on {\emph{ibm\_auckland}}. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.</p>
Multi-physic analysis of power electronic control parameters in a simulation framework
<p>Dataset for conference paper "Multi-physic analysis of power electronic control parameters in a simulation framework"</p>
The basic scheme of the multi-target evaluation (MTE) framework
<p>The following supporting information can be downloaded at: https://www.mdpi.com/article/... /supplementary_MTE.mp4. Video S1: MTE. <span>The basic scheme of the multi-target evaluation (MTE) framework is shown in this video for a specific TR<sub>3</sub> size, using the HPA for mapping SAR<sub>10g,max</sub>. For MTE, hypothetical TRs (borderlines marked in black and green) are moved across the entire brain slice for the evaluation of the quality of targeted RF heating instead of using the conventional approach of defining a limited number of specific but arbitrary TRs. EMF simulations were performed for each TR position across a 26x27 rectangular grid with 702 points (step size = 7mm). For evaluation, the metric success score was used, which represents the percentage of the number of locations with </span>SAR<sub>10g<span>,max</span></sub><span>>40 w/kg over the total number of locations. </span>Video S2-S9: APA/HPA_TR1-TR4_MTE</p>
A case study of the Chesapeake Bay using the CAFE framework
<p>This file includes nutrient budget data for the Chesapeake Bay watershed, organized by county, year, and system.</p>
Precision-Engineered Metal-Organic Frameworks (PE-MOFs)
<p>This Zenodo record hosts the computationally predicted structures of 94,823 Precision-Engineered Metal-Organic Frameworks (PE-MOFs), designed using a fine-tuned Reverse Topological Approach (RTA). The structures are provided as part of a large-scale effort to systematically explore the vast combinatorial design space of metal and organic building units (BUs), pairing them based on geometric signatures and topological compatibility.</p> <p>These structures are optimized and curated for applications such as post-combustion CO2 capture.</p> <p>In this repository, you will find:</p> <p>Fully optimized structures of PE-MOFs: cif files provided in standard formats compatible with molecular simulation tools for further analysis and exploration.<br>Note: This Zenodo record only provides the computational structures. For the accompanying code, tools, and data used to generate these structures, please visit the GitHub repository here. https://github.com/xiaoyu961031/Fine-tuned-RTA</p> <p>If you use this dataset in your work, please cite our related publication:<br>Wu, X., Jiang, J. (2024). Precision-engineered metal-organic frameworks: Fine-tuning reverse topological structure prediction and design. Chemical Science, 2024, DOI: 10.1039/D4SC05616G </p>
Data from: A trait-based root acquisition-defence-decomposition framework in angiosperm tree species
<p>Plants make trade-offs between root resource acquisition and defence ability, for adapting to the complex belowground environment. This includes forming partnerships with different types of root associating microorganisms, such as arbuscular mycorrhizal and ectomycorrhizal fungi. These trade-offs, by mediating root chemistry, exert legacy effects on nutrient release during decomposition, which may, in turn, affect the ability of new roots to re-acquire resources, thereby generating a feedback loop. However, the linkages at the basis of this potential feedback loop remain largely unquantified. Here, we propose a trait-based root 'acquisition-defence-decomposition' conceptual framework and test the strength of relevant linkages across 90 angiosperm tree species. We show that, at the plant species level, the root-fungal symbiosis gradient within the root economics space, root chemical defence (condensed tannins), and root decomposition rate are closely linked, providing support to this framework. Beyond the dichotomy between arbuscular mycorrhizal-dominated versus ectomycorrhizal-dominated systems, we suggest a continuous shift in feedback loops, from "high arbuscular mycorrhizal symbiosis-low defence-fast decomposition-inorganic nutrition" by evolutionarily ancient taxa to "high ectomycorrhizal symbiosis-high defence-slow decomposition-organic nutrition" by more modern taxa. This 'acquisition-defence-decomposition' framework provides solid foundation for testable hypotheses on the multidimensional linkages between species' belowground strategies and ecosystem nutrient cycling in evolutionary context.</p>
Metadata - Exploring the Luminescence, Redox, and Magnetic Properties in a Multivariate Metal–Organic Radical Framework
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