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809 results for “Network Analysis”
Fig 4. Median-joining haplotype network for M in Evolutionary relationships of Macaca fascicularis fascicularis (Raffles 1821) (Primates: Cercopithecidae) from Singapore revealed by Bayesian analysis of mitochondrial DNA sequences
Fig 4. Median-joining haplotype network for M. fascicularis. The size of the circular nodes representing haplotypes is proportional to the number of sequences comprising the haplotype. Shading of circular nodes corresponds to general geographic groupings including Sundaic islands (white), mainland Indochina (gray), Malay Peninsula and northern Sumatra (dark gray), and Singapore (black). Haplotype identifications are presented in Table 1.
Arc fault detection and appliances classification in AC home electrical networks using Recurrence Quantification Plots and Image Analysis
<p>The data provided can be used for the development of methods for the detection of arcing faults in a domestic low-voltage electrical networks (230V - 50 Hz). The data files are current and voltage signatures experimentally measured.</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the list of household appliances and their main characteristics.</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p> <p> </p>
Local Optima Network Analysis of Multi-attribute Vehicle Routing Problem
<p>Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), there are other restrictions that model diverse real-life system attributes. Among the most common attributes studied in the literature are the vehicle capacity and the maximum route length constraints. The impact of these restrictions on the overall structure of the problem and on the performance of local search algorithms used to solve it is not well known. This paper aims to explain how constraints impact different variants of VRP by altering the structure of the underlying search space. We focus on the analysis of Local Optima Networks (LON) for multiple Traveling Salesman Problem (m-TSP), and VRP with capacity (CVRP), distance (DVRP), and both (DCVRP) constraints. We present results that indicate that metrics obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the constraints of the problem. <br> The dataset contains the data extracted from the local optima network for a set of variants belonging to the family of vehicle routing problems.</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>
Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"
<p>This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3). It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks.</p> <p>The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset.</p> <p>The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points.</p> <p>The annotations are given in two types of files:</p> <p>"TRL.csv" files: contain the image names and corresponding TRL values (mm).</p> <p>"pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.</p>
EpimiRNA network analysis
<p>Rcode and data (in R dataset) for EpimiRNA network analysis</p>
Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques
<p>The pathways through which primates acquire skills are a central focus of cultural evolution studies. The roles of social and genetic inheritance processes in skill acquisition are often confounded by environmental factors. Hybrid macaques from Koram Island, Thailand provide an opportunity to examine the roles of inheritance and social learning to skill acquisition within a single ecological setting. These hybrids are a cross between tool-using Burmese long-tailed (<em>Macaca</em> <em>fascicularis</em> <em>aurea</em>) and non-tool-using common long-tailed macaques (<em>Macaca</em> <em>fascicularis</em> <em>fascicularis</em>). This population provides an opportunity to explore the roles of social learning and inheritance processes while being able to exclude underlying ecological factors. Here, we investigate the roles of social learning and inheritance in tool use prevalence within this population using social network analysis and simulation. Agent-based modeling (ABM) is used to generate expectations for how social/asocial learning and inheritance structure the patterning in a social network. The results of the simulation show that various transmission mechanisms can be differentiated based on associations between individuals in a social network. The results provide an investigative framework for discussing tool-use transmission pathways in the Koram social network. By combining ABM, network analysis, and behavioral data from the field we can investigate the roles social learning and inheritance play in tool acquisition in wild primates. </p>
Deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT"- BN network and Bellman shortest path analysis_Module 3_Annex 6
<p>This file will introduce the BN network and Bellman shortest path analysis Module 3 Annex 6 in deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT".</p>
CePNEM model analysis data and ANTSUN and microscopy neural network weights
<p><strong>Citation and publication</strong></p> <p>To cite this work or access the publication, please use the citation information listed here: <a href="https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation">https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation</a></p> <p> </p> <p>Initially published as preprint in:</p> <p>Brain-wide representations of behavior spanning multiple timescales and states in C. elegans</p> <p><strong>Adam A. Atanas*</strong>, <strong>Jungsoo Kim*</strong>, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Cassi Estrem, Talya S. Kramer, Saba Baskoylu, Vikash K. Mansingkha, Steven W. Flavell<br> bioRxiv 2022.11.11.516186; doi: <a href="https://doi.org/10.1101/2022.11.11.516186">https://doi.org/10.1101/2022.11.11.516186</a></p> <p>* Equal Contribution</p> <p> </p> <p><strong>Contents</strong></p> <p>1. deepnet-weights.tar.bz2</p> <p>contains the trained weights of the neural networks used in this project.</p> <p>3dunet_540nm_voxels: 3D U-Net for segmenting neurons</p> <p>head_detector_unet: finding worm head landmark used in ANTSUN registration</p> <p>head_detector_unet_0622: an alternative version of the above, optimal for NeuroPAL datasets</p> <p>microscope_tracker: detecting keypoints for online tracking on the microscope</p> <p>behavior_nir: segmentation of the recorded NIR behavior images for behavior quantification</p> <p>2. data files</p> <p>ANTSUN processed datasets and CePNEM processed model fits and analysis data. Check the project packages and notebooks in the project github repository (<a href="https://github.com/flavell-lab/AtanasKim-Cell2023/">https://github.com/flavell-lab/AtanasKim-Cell2023/</a>) on using these datasets.</p>
Quality-aware Analysis and Optimisation of Virtual Network Function
<p># SPLC'22 Quality-aware Analysis and Optimisation of Virtual Network Functions</p><p> </p><p>DATA: Quality-aware Analysis and Optimisation of Virtual Network Functions</p><p> </p><p>This repository contains the models, operations and results empirically used in [Quality-aware Analysis and Optimisation of Virtual Network Functions](https://doi.org/10.1145/3546932.3547007) at SPLC 2022.</p><p>Due to copyright issues, it does not contain the tools (i.e., automated reasoners), although their official sites are provided.</p><p> </p><p>It is licensed under the [MIT license](https://github.com/danieljmg/SPLC22/blob/main/LICENSE).</p><p> </p><p> </p><p>## SPLC'22 Models, Categorical Operations and Datasets</p><p> </p><p>This data-set contains:</p><p> </p><p>1. The 5 SPL categories in CQL alongside the 11 tested operations.</p><p>2. The 5 SPL Clafer models.</p><p>3. The 5 SPL XMLs (for the AAFM Python Framework).</p><p>4. The 5 SPL XMLs (for SATIBEA).</p><p>5. The previous models are enriched with quality attributes measurements at feature and configuration levels.</p><p>6. A Microsoft Excel file with the scalability results obtained.</p><p> </p><p> </p><p>## Automated Reasoners</p><p> </p><p>- CQL IDE: https://github.com/CategoricalData/CQL</p><p>- Clafermoo: http://t3-necsis.cs.uwaterloo.ca:8092/</p><p>- AAFM Python Framework: https://pypi.org/project/famapy/</p><p>- SATIBEA: https://github.com/jmguo/SMTIBEA</p><p> </p><p> </p><p>## Requirements</p><p> </p><p>The data-set has been generated using Java JDK 18.0.2 for CQL IDE, Clafermoo, and SATIBEA, and Python 3.9.13 x86_64 for AAFM Python Framework.</p><p> </p><p>## Authors</p><p> </p><p>1. **[Daniel-Jesus Munoz](https://github.com/danieljmg)**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p><p>2. **Mónica Pinto**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p><p>3. **Lidia Fuentes**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p>
Implementing social network analysis to understand the socio-ecology of wildlife co-occurrence and joint interactions with humans in anthropogenic environments
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Code for: A century of wild bee sampling: historical data and neural network analysis reveal ecological traits associated with species loss
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Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques
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Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
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Host-parasite relationship in urban environments: A network analysis of hemoparasite infections in Nasua nasua Linnaeus (South American coati)
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R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys
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Network analysis of nitrogen cycling in Hog Island Bay, VA and Sacca di Goro, IT
This data was used in a Network Analysis of Nitrogen Cycling in two shallow coastal lagoons: Hog Island Bay in Virginia, USA and Sacca di Goro in Italy. This dataset consists of data in the Excel spreadsheet formatted for use with the WAND software package (http://www.dsa.unipr.it/netanalysis/?Software).
Data from: Detecting and quantifying social transmission using network-based diffusion analysis
<p>1. Although social learning capabilities are taxonomically widespread, demonstrating that freely interacting animals (whether wild or captive) rely on social learning has proved remarkably challenging.</p> <p>2. Network-based diffusion analysis (NBDA) offers a means for detecting social learning using observational data on freely interacting groups. Its core assumption is that if a target behaviour is socially transmitted, then its spread should follow the connections in a social network that reflects social learning opportunities.</p> <p>3. Here, we provide a comprehensive guide for using NBDA. We first introduce its underlying mathematical framework and present the types of questions that NBDA can address. We then guide researchers through the process of: selecting an appropriate social network for their research question; determining which NBDA variant should be used; and incorporating other variables that may impact asocial and social learning. Finally, we discuss how to interpret an NBDA model's output and provide practical recommendations for model selection.</p> <p>4. Throughout, we highlight extensions to the basic NBDA framework, including incorporation of dynamic networks to capture changes in social relationships during a diffusion and using a multi-network NBDA to estimate information flow across multiple types of social relationship.</p> <p>5. Alongside this information, we provide worked examples and tutorials demonstrating how to perform analyses using the newly developed NBDA package written in the R programming language.</p>
Data from: A meta-analysis of plant interaction networks reveals competitive hierarchies as well as facilitation and intransitivity
The extent to which competitive interactions and niche differentiation structure communities has been highly controversial. To quantify evidence for key features of plant community structure, I recharacterized published data from interaction experiments as networks of competitive and facilitative interactions. I measured the network structure of 31 woody and herbaceous communities, including the intensity, distribution, and diversity of interactions at the species-pair and community level to determine the generality of competition, winner-loser relationships, and unequal interaction allocation. I developed novel methodology using meta-analysis to incorporate interaction uncertainty into estimates of structural metrics among independent networks. Plant communities were competitive, but intraspecific interactions were sometimes more intense than interspecific interactions. On the whole, interactions were imbalanced and communities were transitive. However, facilitation, balanced interactions, and intransitivity were common in individual communities. Synthesizing network metrics using meta-analysis is an original approach with which to generalize community structure in a systematic way.
Travelling Annotations: Network Analysis as a Tool to Study Glossing Networks in Carolingian Europe
<p>While traditionally, the transmission of medieval texts is studied by means of stemmatics, certian types of textuality are well-known as being particularly resistent to traditional methods. This is also the case with annotations. While annotations can behave text-like, it is more often the case that each individual gloss must be treated as an autonomous entity. In different manuscripts different combinations of glosses are combined so that two manuscripts may contain a very different assembly of glosses and look dissimilar, while being closely related. In such cases, network analysis proves handy as a mean to reveal connection between manuscripts and trace the patterns of transmission of particular annotations, while opening new ways of using this transmission as a proxy for studying the intellectual networks that participated in such an exchange. In this presentation, I will exemplify this approach on the corpus of early medieval annotations to the Etymologiae of Isidore of Seville, the most important medieval Latin encyclopaedia. More specifically, it can be presupposed that most of the glosses to this text came into being in the context of its use for teaching in Carolingian period (c. 750 – 900). Their transfer, thus, may be related to the circulation of schoolmasters, students, and books through the networks of Carolingian schools.</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.