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1,855 results for “Framework”
Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs
<p>This dataset is associated with the study <em>"Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs."</em> Detailed instructions for installation, usage, and example scripts for the PACMOF2 models can be found on our GitHub <a href="https://github.com/snurr-group/pacmof2">repository</a>.</p> <div> <div> <div> <div> <p>The dataset includes the following files:</p> <ul> <li><strong>DDEC6_data.zip</strong>: Contains the crystal structures of MOFs with DDEC6 partial charges.</li> <li><strong>PACMOF2_prediction_cifs.zip</strong>: Contains the crystal structures of MOFs with DDEC6 charges as predicted by the PACMOF2 models.</li> <li><strong>PACMOF2_ionic.gz</strong>: A machine learning model designed to predict charges in ionic MOFs with a non-zero formal charge.</li> <li><strong>PACMOF2_neutral.gz</strong>: A machine learning model designed to predict charges in neutral MOFs.</li> </ul> <p>For more details about each file, please refer to the accompanying <code>README.md</code> file.<br><br><br>Updates: <br>- September 2024 (version 1.0.1): Added README.md file.</p> </div> </div> </div> </div>
Accompanying dataset for the paper "An explicit dynamics framework suited to highly non-smooth interface behaviors"
<h2>Contributions</h2> <ul> <li>Author #1 carried out most of the study, performed numerical simulations, and drafted the manuscript</li> <li>Author #2 helped with implementation and numerical issues</li> <li>All authors developed the methodology, conceived the study, and participated in its design, coordination, and critical review of the manuscript. All authors read and approved the final manuscript.</li> </ul> <h2>Funding sources</h2> <ul> <li>We gratefully acknowledge the French National Association for Research and Technology (ANRT, CIFRE grant number 2021/0957).</li> <li>This work was supported by the "Manufacture Française de Pneumatiques Michelin"</li> </ul> <h2>Data structure and information</h2> <ul> <li><code>data</code> -- folder for raw data<ul> <li><code>Peeling3Dv7.dgibi</code> -- <a href="https://www-cast3m.cea.fr">CAST3M</a> input file to produce the mesh (Gibiane language)</li> <li><code>Peeling3Dv7.inp</code> -- input FE data file (ASCII AVS UCD format)</li> <li><code>Peeling3Dv7.m</code> -- main matlab/Octave source file uses the open-source library matlabEF for reading input data and producing the FE required operators, which is available at <a href="https://github.com/dureisse/matlabEF.git">https://github.com/dureisse/matlabEF.git</a> and <a href="https://hal.science/hal-04647638">https://hal.science/hal-04647638</a>.</li> </ul> </li> <li><code>workflows</code><ul> <li><code>install.sh</code> -- script to reinstall the dependencies</li> <li><code>reproduce.sh</code> -- script for re-running the study</li> </ul> </li> </ul> <h2>Paper Description</h2> <p>Dynamic systems, and in particular mechanical structures, may be subjected to non-smooth loadings such as impacts or shocks. Moreover, their behavior itself may exhibit more or less non-smooth evolutions, as when fracture occurs. Therefore, robust simulation models are of interest to capture such behaviors. A particular focus is made herein on time-stepping explicit dynamics schemes to allow efficient simulations, and non-smoothness is embedded within the discrete resolution model, so that robust simulations can be obtained, with a minimum number of numerical parameters. The original contributions of this article lie in the way the non-smooth behavior is formulated to be embedded in an explicit dynamics framework. This study focuses on the solver for dynamics with non-smooth interface behavior, rather than on the behavior models themselves. The applications concern non-smooth interface behaviors at macroscopic scale, between displacement jump on the 2D interface surface with no thickness, and interfacial force distributions acting on the bodies apart the interface. The proposed test cases which can serve as benchmarks for simulation codes, concern in a first step contact and perfectly plastic interface behavior (for illustrative purpose, on a 0D example). The last numerical test deals with contact, friction, fracture and adhesion for an extrinsic perfectly brittle interface behavior, to exemplify the feasibility on a full 3D finite element model.</p>
Characterization Data for the Manuscript: "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments"
<p>This entry contains characterization data for the manuscript "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments".</p>
Companion data artifacts: Technical framework demonstration for deep learning-based wood species classification with advanced sub-μ-CT imaging
<p>This is the companion data artifact collection for the IWAWA paper manuscript by Jannik Stebani, Tim Lewandrowski, Kilian Dremel, Simon Zabler and Volker Haag.It is generally to be used with the visualization and prediction showcases implemented in the Binder notebooks launched from this <a href="https://github.com/stebix/woodnet-showcase" target="_blank" rel="noopener">woodnet-showcase</a> GitHub repository.</p> <p>The artifacts amount to the following:</p> <ol> <li><code>acer-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Acer pseudoplatanus</em> sub-μ-CT scan</li> <li><code>pinus-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Pinus sylvestris </em>sub-μ-CT scan</li> <li><code>weights-artifact.pth</code> : Exemplary PyTorch trained weights for a woodnet/deep neural network to demonstrate classification of the above samples</li> </ol>
Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework
<h1><strong><span>Data Description and File Structure:</span></strong></h1> <p>This data repository contains the raw data collected across all the experiments describe from the paper entitled “Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework”. These are available as CSV files under the appropriate directories.</p> <p>Three main folders are found in this repository:</p> <ul> <li><code><strong>1FA-single-factor-auth/</strong></code> <ul> <li>Contains raw data from experiments using the Single-Factor Authentication (1FA) framework, where only on-chain consensus validation (OCV) is applied without the off-chain peer verification (OPV) phase.</li> </ul> </li> <li><code><strong>2FBC_two-factor-blockchain/</strong></code> <ul> <li>Includes data from experiments employing the Two-Factor Blockchain Consensus (2FBC) framework, which integrates both off-chain peer verification (OPV) and on-chain consensus validation (OCV) phases for enhanced security. This also contains the baseline results.</li> </ul> </li> <li><code><strong>BB_blockchain-base/</strong></code> <ul> <li>Stores the experimental data from the Blockchain Base (BB) framework, where a basic blockchain model was used without the multi-factor authentication features of 1FA or 2FBC. Most data points here are obtained from the work of Strobel et al. (2023) in their work, <u>doi/10.1126/scirobotics.abm4636</u></li> </ul> </li> </ul> <p>Under each directory, we have the following folders:</p> <ul> <li><code><strong>exp_1/</strong></code> <ul> <li>Contains data from scalability experiments, where swarm size was increased within a fixed 3.6 m² arena to evaluate the framework’s performance as the number of robots grows.</li> </ul> </li> <li><code><strong>exp_2/</strong></code> <ul> <li>Includes data from accuracy tests that varied the percentage of white tiles in the environment to assess the framework's ability to reach accurate consensus under different conditions.</li> </ul> </li> <li><code><strong>exp_3a/</strong></code> <ul> <li>Stores data from robustness experiments focused on testing the swarm's resilience to different numbers of Byzantine robots within the network.</li> </ul> </li> <li><code><strong>exp_3b/</strong></code> <ul> <li>Contains data from experiments evaluating the robustness of the swarm when subjected to various Byzantine attack types, testing the framework’s ability to handle adversarial behaviors.</li> </ul> </li> <li><code><strong>exp_4/</strong></code> <ul> <li>Holds data from the resource efficiency experiments, which measured the computational resource usage (CPU, RAM, and blockchain size) during a prolonged 10-hour swarm operation.</li> </ul> </li> </ul> <p>Each experiment configuration is carried out in 20 repetitions.</p> <h3><strong><em>Experiment 1 (exp_1):</em></strong></h3> <ul> <li><code><strong>8rob-2byz/</strong></code> Data for scalability experiments with 8 robots, 2 of which are Byzantine.</li> <li><code><strong>16rob-4byz/</strong></code> Data for scalability experiments with 16 robots, 4 of which are Byzantine.</li> <li><strong><code>24rob-6byz/</code> </strong>Data for scalability experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for scalability experiments with 48 robots, 12 of which are Byzantine.</li> </ul> <h3><strong><em>Experiment 2 (exp_2):</em></strong></h3> <ul> <li><code><strong>24rob-5floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 5% white floor tiles.</li> <li><code><strong>24rob-25floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 25% white floor tiles.</li> <li><strong><code>24rob-45floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 45% white floor tiles.</li> <li><strong><code>24rob-75floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 75% white floor tiles.</li> </ul> <h3><strong><em>Experiment 3a (exp_3a):</em></strong></h3> <ul> <li><strong><code>24rob-0byz/</code> </strong>Data for robustness experiments with 24 robots and no Byzantine robots.</li> <li><strong><code>24rob-3byz/</code> </strong>Data for robustness experiments with 24 robots and 3 Byzantine robots.</li> <li><code><strong>24rob-6byz/</strong></code> Data for robustness experiments with 24 robots and 6 Byzantine robots.</li> <li><strong><code>24rob-9byz/</code> </strong>Data for robustness experiments with 24 robots and 9 Byzantine robots.</li> </ul> <h3><strong><em>Experiment 3b (exp_3b):</em></strong></h3> <ul> <li><strong><code>24rob-6byz-1style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 1 or 0% white tile estimate</li> <li><strong><code>24rob-6byz-2style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 2 or 100% white tile estimate</li> <li><strong><code>24rob-6byz-3style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 3 or attack from a Bernoulli distribution</li> <li><code><strong>24rob-6byz-4style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 4 or attack from a Uniform distribution</li> <li><code><strong>24rob-6byz-5style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 5 or flooding</li> <li><strong><code>24rob-6byz-6style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 6 or eavesdropping</li> </ul> <h3><strong><em>Experiment 4 (exp_4):</em></strong></h3> <ul> <li><strong><code>8rob-2byz/</code> </strong>Data for resource efficiency experiments with 8 robots, 2 of which are Byzantine.</li> <li><strong><code>16rob-4byz/</code> </strong>Data for resource efficiency experiments with 16 robots, 4 of which are Byzantine.</li> <li><code><strong>24rob-6byz/</strong></code> Data for resource efficiency experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for resource efficiency experiments with 48 robots, 12 of which are Byzantine.</li> <li><strong><code>72rob-18byz/</code> </strong>Data for resource efficiency experiments with 72 robots, 18 of which are Byzantine.</li> <li><strong><code>96rob-24byz/</code> </strong>Data for resource efficiency experiments with 96 robots, 24 of which are Byzantine.</li> <li><strong><code>120rob-30byz/</code> </strong>Data for resource efficiency experiments with 120 robots, 30 of which are Byzantine.</li> </ul> <h3><strong>Relevant Files:</strong></h3> <ul> <li><code><strong>block.csv</strong></code> Contains information about each blockchain block generated during the experiment, including block number, size, timestamp, and the number of transactions. The TELAPSED column indicates the time elapsed since the previous block was generated.</li> <li><code><strong>estimate.csv</strong></code> Stores the estimates collected by each robot during the simulation. Each entry includes the time of the estimate and the estimated percentage of white tiles in the arena.</li> <li><code><strong>sc.csv</strong></code> Contains information on smart contract interactions, including the mean estimate across robots, vote counts, and whether consensus was achieved (C?).</li> <li><strong><code>extra.csv</code></strong> Records additional performance metrics during the experiments, including CPU and RAM usage, as well as the size of the blockchain data folder.</li> </ul> <h3><strong>Relevant Data Fields:</strong></h3> <ul> <li><code><strong>ID</strong></code> The identifier assigned to each robot participating in the experiment. It remains constant across all entries for a particular robot.</li> <li><code><strong>TIME</strong></code> The timestamp (in seconds) at which the data was recorded. This is relative to the start of the simulation.</li> <li><code><strong>TELAPSED</strong></code> Indicates the time elapsed between blocks or events, recorded in seconds.</li> <li><code><strong>TIMESTAMP </strong></code>Represents the Unix timestamp when a blockchain block was generated, denoting the actual system time.</li> <li><code><strong>BLOCK </strong></code>The blockchain block number created by the system during the simulation. This value increments as new blocks are added.</li> <li><code><strong>SIZE</strong></code> The size of each block in bytes, indicating the data storage requirement of each blockchain entry.</li> <li><strong><code>ESTIMATE</code> </strong>The estimate provided by the robot, representing the percentage of white tiles detected in the arena.</li> <li><strong><code>MEAN</code> </strong>The mean estimate across the swarm, as calculated on-chain via the smart contract.</li> <li><code><strong>VOTECOUNT</strong></code> Total number of estimates submitted to the smart contract for consensus validation.</li> <li><code><strong>VOTEOKCOUNT</strong></code> The number of valid votes that passed the validation process (e.g., not flagged as outliers).</li> <li><strong><code>C?</code> </strong> A Boolean value indicating whether consensus has been achieved for a given block of estimates.</li> <li><code><strong>CPU</strong></code> Percentage of CPU utilization, showing the computational load on the robot during the simulation.</li> <li><code><strong>RAM</strong></code> The amount of RAM used by each robot during the experiment, measured in percent or bytes.</li> <li><code><strong>KB</strong></code> The size of the blockchain data folder, measured in kilobytes (KB). This indicates how much data was stored by the blockchain system during the experiment.</li> </ul>
Linked collectors and determiners for: Data mobilized by the National Herbarium of Benin in the framework of the JRS Biodiversity project of Benin.
Natural history specimen data linked to collectors and determiners held within, "Data mobilized by the National Herbarium of Benin in the framework of the JRS Biodiversity project of Benin". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9d850f67-e358-4dfb-8748-57cd668031e8">https://bionomia.net/dataset/9d850f67-e358-4dfb-8748-57cd668031e8</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9d850f67-e358-4dfb-8748-57cd668031e8">https://gbif.org/dataset/9d850f67-e358-4dfb-8748-57cd668031e8</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: The South American spider genera Mesabolivar and Carapoia (Araneae, Pholcidae): new species and a framework for redrawing generic limits.
Natural history specimen data linked to collectors and determiners held within, "The South American spider genera Mesabolivar and Carapoia (Araneae, Pholcidae): new species and a framework for redrawing generic limits". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/8bee84d8-4a01-4e53-8eb3-af176ce1feb9">https://bionomia.net/dataset/8bee84d8-4a01-4e53-8eb3-af176ce1feb9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8bee84d8-4a01-4e53-8eb3-af176ce1feb9">https://gbif.org/dataset/8bee84d8-4a01-4e53-8eb3-af176ce1feb9</a>. Formatted as a Frictionless Data package.
iDTw framework deployment
<p>iDTw framework deployment dataset of the journal paper entitled "Incremental Digital Twin Framework: a Design Science Research Approach for a Practical Deployment Flow".</p> <p><br><em>Further information is to be added after academic publication.</em></p>
Matrix Framework for Analysis: Building a Matrix from Advanced Projects
<p>This raw dataset is part of a Framework Global - Matrix that enables a comprehensive and holistic assessment, simplifying comparisons and guiding decision-makers in selecting the alternatives that offer the greatest value in terms of financial return, social and environmental impact, and regulatory compliance. To streamline the analysis and comparison of indicators, the author utilized categories linked to Sustainable Competitiveness and the World Business Council for Sustainable Development (WBCSD). As a result, the indicators contribute to the functionality of the matrix, facilitating the identification of key projects related to the desired area and enabling future application of the data in sustainability-focused initiatives.</p>
Data from an experiment on the effects of using an experimental framework on the quality of research designs
Open the record for dataset details and reuse information.
Complete results of Procedure section in manuscript "PathML: A unified framework for whole-slide image analysis with deep learning"
<p>The results from a complete run of the Procedure section of the paper "PathML: A unified framework for whole-slide image analysis with deep learning".</p>
A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI : an application to the ventriclar junctions
<p>Data and materials regarding the submission of the paper. See Data Avaibility section and https://github.com/valeryozenne/Cardiac-Structure-Database</p> <p> </p>
FIGURE 25 in Diverse new scale insects (Hemiptera: Coccoidea) in amber from the Cretaceous and Eocene with a phylogenetic framework for fossil Coccoidea
FIGURE 25. Diagram summarizing known fossil scale insects for each fossil deposit. Symbols indicates: grey circle = previously described taxa; dark star = herein described; white circle = undescribed.
FIGURE 21 in Diverse new scale insects (Hemiptera: Coccoidea) in amber from the Cretaceous and Eocene with a phylogenetic framework for fossil Coccoidea
FIGURE 21. Photomicrographs of (A) dorsal and (B) ventral surfaces of Rosahendersonia prisca, n. sp., holotype AMNH Bu-835. (C) Dorsal surface of Normarkicoccus cambayae, n. sp., holotype AMNH Tad-135.
FIGURE 23 in Diverse new scale insects (Hemiptera: Coccoidea) in amber from the Cretaceous and Eocene with a phylogenetic framework for fossil Coccoidea
FIGURE 23. Details of Normarkicoccus cambayae, n. sp. (A) Dorsal view of head. (B) Ventral view of head. (C) Leg. (D) Ventral view of penial sheath.
FIGURE 20 in Diverse new scale insects (Hemiptera: Coccoidea) in amber from the Cretaceous and Eocene with a phylogenetic framework for fossil Coccoidea
FIGURE 20. Details of Gilderius eukrinops, n. sp. (A) ventral view of head, (B) antenna, (C) leg, (D) dorsal view of penial sheath.
FIGURE 19 in Diverse new scale insects (Hemiptera: Coccoidea) in amber from the Cretaceous and Eocene with a phylogenetic framework for fossil Coccoidea
FIGURE 19. Details of Williamsicoccus megalops, n. sp. (A) Dorsal view of head. (B) Ventral view of head. (C) Dorsal mesothorax. (D) Wing. (E) Connection of the forewing and hamulohaltere. (F) Leg
FIGURE 18 in Diverse new scale insects (Hemiptera: Coccoidea) in amber from the Cretaceous and Eocene with a phylogenetic framework for fossil Coccoidea
FIGURE 18. Photomicrographs of (A) dorsal and (B) ventral surfaces of Williamsicoccus megalops, n. sp., holotype 1582. (C) Dorsal and (D) ventral surfaces of Gilderius eukrinops, n. sp., holotype AMNH Bu-1594.
FIGURE 22 in Diverse new scale insects (Hemiptera: Coccoidea) in amber from the Cretaceous and Eocene with a phylogenetic framework for fossil Coccoidea
FIGURE 22. Details of Rosahendersonia prisca, n. sp. (A) Ventral view of head. (B) Dorsal view of mesothorax. (C) Ventral pro- and mesothorax. (D) Antenna. (E) Leg from femur. (F) Ventral view of abdominal segments VII and VIII and penial sheath.
FIGURE 16 in Diverse new scale insects (Hemiptera: Coccoidea) in amber from the Cretaceous and Eocene with a phylogenetic framework for fossil Coccoidea
FIGURE 16. Details of Magnilens glaesaria, n. sp. (A) Head from lateral side. (B) Antenna. (C) Leg. (D) Penial sheath from lateral side.
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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.
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.