Find research datasets worth reusing

opencc-by-nc-nd-4.0Jul 2024View details →

Compliance Graph Network Files and Exploit Models

Description provided from the abstract of the corresponding manuscript. Compliance graphs provide the ability to analyze an environment in terms of its standing to a regulation, mandate, or standard. These graphs are directed acyclic graphs, and share commonalities with attack graphs. Though generator tools and example graph sets are available for attack graphs, the novelty of compliance graphs presents its own set of challenges with a lack of publicly available example graphs. In order to develop analysis techniques for compliance graphs, example networks are required for an examination and testing process. This work presents the generation of compliance graphs and releases their affiliated data for use in furthering the analysis process of this research area.

opencc-by-4.0Jul 2024View details →

Supplementary Material to the Manuscript "On How to Transform Refueling Station Networks: Planning Framework, Model, and Exact Solution Approach"

This dataset consists of two .xlsx files containing the supplementary material to the manuscript "On How to Transform Refueling Station Networks: Planning Framework, Model, and Exact Solution Approach". The file "supplementary_parameter.xlsx" contains the parameters used in the experiments.  The file "supplementary_random_networks.xlsx" contains the random networks used in the numerical experiments.

opencc-by-4.0Aug 2024View details →

Dataset for efficient modelling of ionic and electronic interactions by resistive memory- based reservoir graph neural network

Dataset for training the resistive memory-based reservoir graph neural network. In the atomic force calculation experiment, a Li3PO4 dataset is derived from the melting and quenching trajectory via AIMD simulations. The training, validation, and testing datasets consist of 40,000, 5,000, and 5,000 samples, respectively. In the Hamiltonian calculation, a dataset of various graphene (72 atoms) configurations are generated by AIMD simulations at room temperature, with Hamiltonian data calculated via the OpenMX code. The training, validation, and testing datasets consist of 270, 90, and 90 samples (including atomic structure and Hamiltonian matrix), respectively. Code:   https://github.com/hustmeng/RGNN.git 1-Atomic_force_dataset.zip and 2-Hamiltonian_dataset.zip are original data. 3-Graph_atomic_force.zip and  4-Graph_training_Hamiltonian.zip are graphs.    References:   1. C.W. Park, M. Kornbluth, J. Vandermause, C. Wolverton, B. Kozinsky, J.P. Mailoa, Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture, npj Comput. Mater. 7(1) (2021) 73. https://github.com/ken2403/gnnff.git 2. H. Li, Z. Wang, N. Zou, M. Ye, R. Xu, X. Gong, W. Duan, Y. Xu, Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation, Nat. Comput. Sci. 2(6) (2022) 367-377. https://github.com/mzjb/DeepH-pack.git 3. D. Pfau, J.S. Spencer, A.G.D.G. Matthews, W.M.C. Foulkes, Ab initio solution of the many-electron Schrödinger equation with deep neural networks, Phys. Rev. Res. 2(3) (2020) 033429. https://github.com/google-deepmind/ferminet.git

opencc-by-4.0Dec 2018View details →

Simulation results of an agent-based model of civil violence with the effect of introducing a small world network

The data set contains the simulation results of an agent-based model of civil violence with the effect of introducing a small world network. Some details on the agent-based model (without small world network) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921]. Files in folder "Appropriate_Rate_of_Violence" are related to the case with the appropriate rate of violence. Subfolder NoSWN_CitVis_14 is for the case with no small world network and the citizen vision of 14. Subfolder SWN_CitVis_13.16 is for the case with small world network and the citizen vision of 13.16. Subfolder SWN_CitVis_14 is for the case with small world network and the citizen vision of 14. Each file with name starting with Act has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds. Files in folder "High_Rate_of_Violence" are related to the case with the high rate of violence. Subfolder Beta0.2 is for the case with small world network and \beta=0.2. Subfolder Beta0.8 is for the case with small world network and \beta=0.8. Subfolder NoSWN is for the case with no small world network. Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.

opencc-by-4.0Nov 2022View details →

Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks

Some data and codes for Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks.

opencc-by-4.0Dec 2022View details →

Raw data of the heterogeneous Hegselmann-Krause model on network ensembles

# Raw data of the heterogeneous Hegselmann-Krause model on network ensembles This is the raw data underlying the results of the article *«On the effects of over-compromising: heterogeneity and network effects on a bounded confidence opinion dynamics model.»*. For each measured combination of the parameters, there is one gzipped file. The parameters are:  - Lower and upper bounds of the confidence interval, [ε_l, ε_u].  - Topology: the different types of networks and the average degree with which the networks are generated.  - System size  - Number of realizations for the parameter combination   The single files follow a naming scheme of `data_HK_uni[{eps_l},{eps_u}]_topo={topology}_N={N}_trajrecord=0_{m}real.dat.gz`,  where:  - `{eps_l},{eps_u}` are the values of the lower and upper bounds of the confidence interval.  - `{topology}` contains the type of network and the average degree. The possibilities are `BA_k=10`, `ER_c=10`, `sl1`, `sl2`, and `sl3`.  - `N` is the system size. The sizes are powers of two.  - `trajrecord=0` signals the fact that file contains only the final state.  - `{m}` is the number of realizations. # Data format Each file contains the final state of each realization back to back. Each final state is encoded as three lines:  - The convergence time is a single integer with a line prefix '\# iterations:'  - The positions of all clusters in opinion space with a line prefix '\# ' (unsorted)  - The number of agents in each of the clusters without a line prefix # Folders structure The files are organized as follows:  - **`phase_plots.tar`**: contains the data for the different phase plots (full exploration of the [ε_l, ε_u] space) with `N=16384` and `m=100` realizations.      - **`ER`** contains the data for Erdos Renyi with mean degree of 10 (c=10)      - **`BA`** contains the data for Barabasi Albert with a mean degree of 10 (k=10)      - **`SL`** contains the data for Square lattice with first, second and third nearest neighbors (k=4, 8, 12)  - **`swipes.tar`** contains the data for the finite size effects study at fixed ε_l with `m=1000` realizations.      - **`ER`** contains the data for Erdos Renyi with mean degree of 10 (c=10) with ε_l = 0.05      - **`BA`** contains the data for Barabasi Albert with a mean degree of 10 (k=10) with ε_l = 0.05      - **`SL`** contains the data for Square lattice with third nearest neighbors (k=12) with ε_l =0.03  - the different videos referenced in the main text and the SM follow various naming schemes:      - **`scatter3D_el_eu_Smax_uni_{topology}_N=16384.mp4`**: 360° rotation of the 3D visualisation of the data leading the average phase plots.      - **`scatter2D_el={eps_l}_eu_Smax_extremism_{topology}_SizeEffect.mp4`**: evolution of the scatter plot leading the finite size study as a function of N.      - **`scatter3D_el={eps_l}_eu_Smax_extremism_{topology}_SizeEffect.mp4`**: same as before, but in 3D where the Z-axis is the extremism.      - **`scatter_x0_xt_{topology}_N={N}_{realization_type}.mp4`**: time evolution of the scatter plot of the opinion at time `t` versus initial opinion, color-coded with the extremism. {realization_type} can be mild, skewed or U-turn.      - **`traj_2D_SL_k=12_N=16384_{realization_type}.mp4`**: because of the spatial embedding, the time evolution of those realizations on the Square Lattice can be visualized in 2D. # Python example for reading the format An example script, which visualizes <S\> vs ε_u graph for the largest size of the ER case, with a function to read this format is given in `example.py`.

An Observational Clinical Study on the Construction of an Artificial Neural Network Model for ICU Pneumonia

ClinicalTrials.gov study NCT06661499. IPD Sharing: NO. Countries: 1. Publications: 0.

The Quantitative Study of the Habenula Based on Multi-channel Cascaded Neural Network and the Establishment of the Prediction Model of the Curative Effect in Patients With Depression

ClinicalTrials.gov study NCT05872607. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Multidisciplinary Network OSA Code (Obstructive Apnea Syndrome): Digital Operative Model in Public Health for an Early Diagnosis and Therapy Monitoring

ClinicalTrials.gov study NCT06677580. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Pharmacological and Mechanical Support Approaches in the Management of Acute Heart Failure in the Regional Network Model for the Management of Cardiogenic Shock

ClinicalTrials.gov study NCT06827314. IPD Sharing: Not stated. Countries: 1. Publications: 0.

The Value of a Convolutional Neural Network-Based Renal Artery Perfusion Model in Predicting Renal Function After Partial Nephrectomy: A Prospective Study

ClinicalTrials.gov study NCT06751498. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Research on the Risk Warning Model and Prevention Strategies for Acute Kidney Injury Associated With Cyclosporine Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring

ClinicalTrials.gov study NCT06596811. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Three-level Network Rehabilitation Model

ClinicalTrials.gov study NCT05807230. IPD Sharing: NO. Countries: 1. Publications: 0.

A Retrospective Study of Neural Network Model to Dynamically Quantificate the Severity in COVID-19 Disease

ClinicalTrials.gov study NCT04347369. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Invasive Approach to Model Human Cortex-Basal Ganglia Action-Regulating Networks

ClinicalTrials.gov study NCT03608228. IPD Sharing: NO. Countries: 1. Publications: 0.

Convolutional Neural Network Model to Detect Coronavirus Disease 2019 (COVID-19) Pneumonia in Chest Radiographs

ClinicalTrials.gov study NCT05722665. IPD Sharing: NO. Countries: 1. Publications: 0.

Brain Network Models of Motor Recovery After Stroke

ClinicalTrials.gov study NCT03784534. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.