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19 results for “network theory”
Network data and script accompanying the paper "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"
<p>This repository accompanies the paper<a href="https://rdcu.be/dbuhi"> "Operationalizing anthropological theory: four techniques to simplify networks of co-occurring ethnographic codes"</a>, by Cottica et al. It contains:</p> <ol> <li>A data file, containing networks of co-occurrence of ethnographic codes from three ethnographies. Data are pseudonymized (see the paper for details).</li> <li>A script that, when run on the data, produces simplified versions of each network. Simplifications follow four different techniques, described in the paper. Each technique relies on a tuning parameter, so that, for each network and each techniques, the script produces several simplified networks, each one associated with a unique value of the tuning parameter.</li> </ol> <p>The data file format is that of a Tulip perspective. To open, download Tulip (https://tulip.labri.fr), launch it and open the file from within the Tulip GUI.</p> <p>The script file is in Python. To run, open it from within the Tulip IDE first.</p> <p> </p>
Database used in : Analysis of intermunicipal journeys for cardiac surgery in Brazilian Unified Health System (SUS): an approach based on network theory
<p>Data and scripts referring to the results generated in the article entitled: <strong>Analysis of intermunicipal journeys for cardiac surgery in Brazilian Unified Health System (SUS): an approach based on network theory.</strong></p> <p> </p> <p>To obtain the results of the work the following sequence of database treatment was performed:</p> <p> </p> <p>DATASUS --> BASE_PER_YEAR --> EDGES_BASE --> EDGES_VC_BASE</p> <p>The bases were downloaded from the DATASUS site (link: https://datasus.saude.gov.br/transferencia-de-arquivos/#) in .dbc format separated by month and year; using Tabwin we joined the bases generating a base for each year in csv format. The reformatted bases are gathered together in the file PER_YEAR_BASE.ZIP; from the bases for each year we manually built the files in csv format with the list of the edges with the following fields: "Source", "Target", "Type", "Id", "Label" and "Weight". The "Source" column was filled with data from MUNIC_RES and the "Target" column with data from MUNIC_MOV. The "ID" and "Weight" fields were filled in automatically using Gephi, where the "Weight" column represents the sum of the edge, defined by the pair of Source and Target columns, were repeated throughout the year. This generated the bases containing the list of edges that are grouped in the file EDGES_BASE.ZIP. Each base was filtered to contain only edges related to the city "Vitória da Conquista" and grouped in the file EDGES_VC_BASE.zip</p> <p>INDE BASE --> NODES_BASE</p> <p>To build the list of nodes containing the list of municipalities with their respective geographical locations (in UTM), we used the database of the INDE (available on the link: https://visualizador.inde.gov.br/). The file in shape format was treated in the ArqGis program and the database with the network nodes was created (file NODES_BASE.csv).</p> <p>EDGES_VC_BASE and NODES_BASE --> NETWORK</p> <p>Using the program Gephi we joined the bases referring to the edges (EDGES_VC_BASE) and those referring to the nodes of the network (NODES_BASE) and built the networks for each year studied for the city of "Vitória da Conquista". All networks are in gephi format and compressed in the NETWORKS.zip file.</p> <p>EDGES_VC_BASE and NODES_BASE --> INDICES</p> <p>Using the R script "distance.R" and using as input the files of edges (EDGES_VC_BASE) and nodes (NODES_BASE) we generate files in csv format with the columns: dist_med_in , dist_med_out, Flow_in and flow_out. The indexes dist_med_in and dist_med_out represent the average distance traveled in meters to enter and leave the municipality, respectively; the indexes flow_in and flow_out estimate the quantity of people that entered and left the municipality. All index files are grouped in the compressed file INDICES.zip.</p> <p>The last two digits at the end of all file names represent the year of analysis. </p>
Neuroimaging evidence for network sampling theory of human intelligence
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Dataset for publication "Spectral graph theory efficiently characterises ventilation heterogeneity in airway networks".
<p>This directory contains files for the tree networks used in the manuscript "Spectral graph theory efficiently characterises ventilation heterogeneity in airway networks" by C Whitfield et al. Publication details to follow.</p> <p>Each folder contains the network as labelled in the paper in two formats:<br> - .vtk format<br> - plain text format where it is split into 3 files with suffixes .branches .nodes and .termnodes<br> - The .nodes file has 4 columns, the first is the node index and the other 3 are the (x,y,z) node coordinates in mm<br> - The .branches file has 4 columns (ignoring extra info in following columns), which are the edge index, node in index, node out index and radius (mm)<br> - The .termnodes file contains a list of node indices corresponding to terminal nodes of the tree.</p> <p>Each folder also contains the CT centerline data (identified by the suffix _CT) in .vtk format.</p>
Theory and implementation of inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Theory and implementation of inelastic Constitutive Artificial Neural Networks.</em></p> <p>arXiv: <a href="https://doi.org/10.48550/arXiv.2311.06380">https://doi.org/10.48550/arXiv.2311.06380</a></p> <p>Computer Methods in Applied Mechanics and Engineering: <a href="https://doi.org/10.1016/j.cma.2024.117063">https://doi.org/10.1016/j.cma.2024.117063</a></p> <p> </p> <p><strong>01_Example01: </strong> Artificially generated data</p> <p>This example investigates whether the iCANN is able to discover a model for the data generated by a continuum mechanical model.</p> <p> </p> <p><strong>02_Example02:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p> <p> </p> <p><strong>03_Example03: </strong>Discovering a model for passive skeletal muscle subjected to relaxation</p> <p>In this example, we investigate whether the iCANN is able to discover a model for the material behavior of passive skeletal muscles. A total of five independent experiments are carried out in which the maximum applied compression stretch and the stretch rate are varied. In addition, the learning performance of the iCANN is investigated. Training is first carried out in each of the five experiments and then in each of four of the five experiments.</p> <p>The experimental data are taken from the literature:</p> <p>Van Loocke, M., Lyons, C. G., & Simms, C. K. (2008). Viscoelastic properties of passive skeletal muscle in compression: stress-relaxation behaviour and constitutive modelling. <em>Journal of biomechanics</em>, <em>41</em>(7), 1555-1566.</p> <p><a href="https://doi.org/10.1016/j.jbiomech.2008.02.007">https://doi.org/10.1016/j.jbiomech.2008.02.007</a></p> <p> </p> <p><strong>python_requirements.txt: </strong>File containing a list of installed Python modules used to implement the iCANN</p>
Connectome of memristive nanowire networks through graph theory - Dataset
<p>This is the dataset of "Connectome of memristive nanowire networks through graph theory"</p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
<p>Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty.<br><br></p> <p>Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.</p>
Dataset1 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"
<p>Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian". </p> <p>Contains atomic structures and Hamiltonian matrices of monolayer graphene, monolayer MoS<sub>2</sub>, bilayer graphene and bilayer bismuthene.</p> <p>Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.</p>
Dataset2 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"
<p>Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian". </p> <p>Contains atomic structures and Hamiltonian matrices of bilayer bismuth selenide. </p> <p>Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.</p>
Dataset3 for "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian"
<p>Supporting data for the paper "General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian". </p> <p>Contains atomic structures and Hamiltonian matrices of bilayer bismuth telluride. </p> <p>Detailed descriptions about the format of data and instructions on how to reproduce the results in the paper can be found in README.md.</p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
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Functional connectivity and graph theory of self networks in toodlers with ASD
<p>The study collected fMRI data from children with autism and typical developmental disorders at 18-24 months and 25-48 months, and analyzed the brain's self network using functional connectivity and graph theory methods</p>
Data and code from: Network theory predicts ecosystem robustness across environmental conditions
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A new structural theory of the elasticity of particle-filled networks
<p>A new structural theory of the elasticity of particle-filled networks</p>
Data for "Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory"
<h2>1. Repository Information</h2> <p>This repository contains the data produced during the work discussed in in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad84af" target="_blank" rel="noopener">Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory</a>". Please refer to this paper for more details on how the data was produced.</p> <p> </p> <h2>2. Citing</h2> <p>In addition to citing this repository, please also cite the paper mentioned above if you use the data. The citations is:</p> <p>[1] Hanno Sahlmann and Waleed Sherif 2024 <em>Class. Quantum Grav.</em> <strong>41</strong> 225014</p> <p> </p> <h2>3. File Description</h2> <p>In this repository, you will find 4 general directories (here called parent directories):</p> <ol> <li>Tabulated Data</li> <li>Misc</li> <li>Entanglement Entropy</li> <li>Appendix Data</li> </ol> <p>Each of these directories correposnd to different data produced and discussed in the corresponding parts in the paper mentioned above (e.g. the directory "Tabulated Data" contains the data used in Table 1 and Table 2 in the paper).</p> <p>Each of these parent directories contain within them several sub-directories (child directories) corresponding to different produced data. The raw data can be found in a <code>.json</code> file inside the child directories.</p> <p> </p> <h2>4. Usage</h2> <h3>4.1 Raw Simulation Data</h3> <p>The <code>.json</code> files include the raw data produced during the study. These files can be easily accessed using a python script, as an example, by using:</p> <p><code>import json</code></p> <p><code>filePath = ...</code></p> <p><code>data = json.load(open(filePath))</code></p> <p>where <code>filePath</code> should hold the correct path to the local data once downloaded. Once loaded, the data is handled as a python <code>dict</code>. The dictionary will have a parent key called "Energy", which in itself is yet another dictionary which will always include the keys:</p> <ul> <li>iters</li> <li>Mean</li> <li>Variance</li> <li>Sigma</li> <li>R_hat</li> <li>TauCorr</li> </ul> <p>Hence, to access the "Mean" values, you use <code>data["Energy"]["Mean"]</code>. The data represents the values during a simulation of typically 500 iterations, hence, each of the keys mentioned above will correspond to an array of 500 items. The <code>iters</code> array includes merely the iteration number. The <code>Mean</code> array includes the value of the expectation value of the constraint at the corresponding iteration. The <code>Variance</code>, <code>Sigma</code>, <code>R_hat</code> and <code>TauCorr</code> includes the values of the variance and error in the expectation value at the given iteration as well as the split R-hat diagnostic and the time correlation also in the given iteration. </p> <p> </p> <h3>4.2 Variational State Data</h3> <p>Additionally, some child directories will include a <code>.npy</code> file, which holds the amplitudes of the variational state for the given simulation. These files should be loaded using numpy in python. For example:</p> <p><code>import numpy as np</code></p> <p><code>filePath = ...</code></p> <p><code>varState = np.load(filePath)</code></p> <p>This will load the amplitudes as an array into the <code>varState</code> variable.</p> <p> </p> <h3>4.3 Fluctuation results</h3> <p>In some child directories, there will be a <code>.txt</code> file which includes the output of the calculation of the expectation value of some operators and their quantum fluctuations. These are only results, and not data, as the data can only be computed during the simulation.</p> <p> </p> <h2>5. Contact</h2> <p>Shall you have any unanswered questions regarding the usage of the data, please contact the author:</p> <p>Waleed Sherif</p> <p>email: waleed.sherif@fau.de</p> <p> </p> <h2>6. References</h2> <p>The data provided in this repository was produced using the <a href="https://github.com/netket" target="_blank" rel="noopener">NetKet</a>[1] package</p> <p>[1] <a href="https://doi.org/10.21468/SciPostPhysCodeb.7" target="_blank" rel="noopener">doi: 10.21468/SciPostPhysCodeb.7</a></p> <p> </p>
Leveraging Human and Non-Human Actors to Advance Integrated Healthcare Delivery: Unpacking the Role of Actor-Network Theory, a Systematic Literature Review and Future Research Agenda
<p>This dataset includes the articles screened for inclusion/exclusion based on title, abstract and key words. It also includes the 197 articles screened for inlcusion/exclusion following a full-text screen. </p>
Tipping Point: Using Social Network Theory to Accelerate Scale and Impact
ClinicalTrials.gov study NCT05777473. IPD Sharing: NO. Countries: 1. Publications: 0.
Migrant Elderly's Social Network Use Intervention: An Activity Theory-Based Study
ClinicalTrials.gov study NCT07160803. IPD Sharing: NO. Countries: 1. Publications: 0.
Telegram and digital methods: mapping networked conspiracy theories through platform affordances
<p>This data accompanies the paper 'Telegram and digital methods: mapping networked conspiracy theories through platform affordances'. It comprises the the network files describing the found community's topology.</p> <p>We are currently <span><em><strong>not</strong></em></span> granting requests for access to this data.</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.