ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

ABSTRACT Objective: the literature on board interlocks has increased in recent years, focusing on understanding board composition and its relationships with other companies’ boards. Such studies usually require multiple procedures of data extraction, handling, and analysis to create and analyze social networks. However, these procedures are not standardized, and there is a lack of methodological instructions available to make this process easier for researchers. This tutorial intends to describe the logical steps taken to collect data, treat them, and map and measure the network properties to provide researchers with the sources to replicate it in their own research. We contribute to the literature in the management field by proposing an empirical methodological approach to conduct board interlocks’ research. Proposal: our tutorial describes and provides examples of data collection, directors’ data treatment, and the use of these data to map and measure network structural properties using an open-source tool - R statistical software. Conclusions: our main contribution is a tutorial detailing the steps required to map and analyze board interlocks, making this process easier, standardized, and more accessible for all researchers who wish to develop social network analysis studies.

This dataset describes and provides examples of data collection, directors’ data treatment, and the use of these data to map and measure network structural properties using an open-source tool -– R statistical software.

This is a tutorial on how to use GIP data for the ESRI ArcGIS Network Analyst.

This includes several tutorial datasets for Dictys' context specific and dynamic gene regulatory network inference and analysis. See Dictys and its tutorial instructions at https://github.com/pinellolab/dictys.

Here are the materials for the workshop that I did with Fred Trotter on using Gephi to analyze the DocGraph data set at the 2013 StrataRX conference: http://strataconf.com/rx2013/public/schedule/detail/29840 Details on how the GraphML and Gephi files were produced are detailed. The edge data set is based on the DocGraph data set available from here: http://notonlydev.com/docgraph-data/ as the V1.0 open source dataset. The NPPES or NPI node information is from the file: npidata_20050523-20130113.csv The NPPES data was processed with the following script: https://github.com/jhajagos/DocGraph/blob/master/nppes/npi_schema.sql The subsets of the larger graph are generated by the following criteria: jamestown_core_provider_graph.graphml -Providers selected with practice addresses in Jamestown, NY -179 nodes with 5,560 edges jamestown_core_and_leaf_provider_graph.graphml Includes providers above and those who are linked to them 1,322 nodes with 12,457 edges albany_core_provider_graph.graphml Providers selected with practice addresses in Albany, NY 1,368 nodes with 44,711 edges Subsets were produced using this script https://github.com/jhajagos/DocGraph/blob/master/extract_providers_to_graphml.py To these files I have added latitude and longitude from geocoding the practice address generated with this script: https://github.com/jhajagos/DocGraph/blob/master/nppes/geocode_nppes_using_arcgis.py The locators were based on ArcLogistics 2012 release 1 data.

The data provided here are part of a Galaxy Training Network tutorial that demonstrates mapping-by-sequencing analysis and represent a subsample of the data used in Sun & Schneeberger, 2015 (DOI:10.1007/978-1-4939-2444-8_19).

Networks have emerged as a popular method for studying mental disorders. Psychopathology networks consist of aspects (e.g., symptoms) of mental disorders (nodes) and the connections between those aspects (edges). Unfortunately, the visual presentation of networks can occasionally be misleading. For instance, researchers may be tempted to conclude that nodes that appear close together are highly related, and that nodes that are far apart are less related. Yet this is not always the case. In networks plotted with force-directed algorithms, the most popular approach, the spatial arrangement of nodes is not easily interpretable. However, other plotting approaches can render node positioning interpretable. We provide a brief tutorial on several methods including multidimensional scaling, principal components plotting, and eigenmodel networks. We compare the strengths and weaknesses of each method, noting how to properly interpret each type of plotting approach.

Information for data and supplementary materials of :

Briganti G., Kempenaers C. , Braun S., Fried E.I., Linkowski P. (2018). Network Analysis of empathy items from the Interpersonal Reactivity Index in 1973 young adults. Psychiatry Research

Corresponding author : giovanni.briganti@hotmail.com

The supplementary materials zip file contains 3 files.

1)Supplementary Figures

Figure 2bis: three centrality estimates for the 28-item IRI, including closeness and betweenness centrality.

Figure 3: bootstrapped confidence intervals of all edge weights of the estimated network; details in Epskamp, Borsboom & Fried, 2017.

Figure 4: edge weight difference test for the estimated network; details in Epskamp, Borsboom & Fried, 2017.

Figure 5 : centrality stability for the estimated network ; details in Epskamp, Borsboom & Fried, 2017.

Figure 6: centrality-difference test for the estimated network; details in Epskamp, Borsboom & Fried, 2017.

Figure Description : brief description of the figures.

2) Data data: the dataset from our 1973 young adults.

3) Supplementary R-Files

boot1davis.RData, boot2davis.Rdata: results of bootnet stability analysis.

Briganti2018_Network_empathy_Syntax.R: full code.

Details_R_packages: details and specs of the packages used for the analysis.

References

Epskamp S, Borsboom D, Fried EI. Estimating Psychological Networks and their Accuracy: A Tutorial Paper. Behavior Research Methods. Behavior Research Methods; 2017; 1–34. doi:10.3758/s13428-017-0862-1

The data provided here are part of a Galaxy Training Network tutorial that analyzes small RNA-seq (sRNA-seq) data from a study published by Harrington et al. (DOI:10.1186/s12864-017-3692-8) to detect differential abundance of various classes of endogenous short interfering RNAs (esiRNAs). The goal of this study was to investigate "connections between differential retroTn and hp-derived esiRNA processing and cellular location, and to investigate the potential link between mRNA 3’ end cleavage and esiRNA biogenesis." To this end, sRNA-seq libraries were constructed from triplicate Drosophila tissue culture samples under conditions of either control RNAi or RNAi knockdown of a factor involved in mRNA 3’ end processing, Symplekin. This dataset (GEO Accession: GSE82128) consists of single-end, size-selected, non-rRNA-depleted sRNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to a subset of interesting transcript features including: (1) transposable elements, (2) Drosophila piRNA clusters, (3) Symplekin, and (4) genes encoding mass spectrometry-defined protein binding partners of Symplekin from Additional File 2 in the indicated paper by Harrington et al. More details on features 1 and 2 can be found here: https://github.com/bowhan/piPipes/blob/master/common/dm3/genomic_features (piRNA_Cluster, Trn). All features are from the Drosophila genome Apr. 2006 (BDGP R5/dm3) release.

The data provided here are part of a Galaxy Training Network tutorial that demonstrates identification of somatic and germline variants from tumor and normal sample pairs.

This collection includes MATLAB-based code, microelectrode array (MEA) data, and other resources to assist users with our computational tool, the MEA network analysis pipeline (MEA-NAP). ** Dataverse contents: ** 1. MEA-NAP.zip - The MATLAB-based code for MEA-NAP version 1.6.0 that was used for the analysis in Sit et al. (2024) Cell Reports Methods. MEA-NAP version 1.10.0 is also included, which was the latest version at the time of publication of Sit et al. (2024). For the most up-to-date version, please see https://github.com/SAND-Lab/MEA-NAP/ 2. MEA-NAP GUI Tutorial.mp4 - This tutorial related to MEA-NAP versions 1.6 and 1.7. A detailed video guide to using MEA-NAP for new users. Please see our Github for links to video updates on MEA-NAP on our Synaptic and Network Development (SAND) YouTube channel. 3. NGN2_20230208_Data - MEA recordings from NGN2 neuronal cultures and batch analysis file. This data relates to Figure 3 in Sit et al. (2024). These are example inputs to MEA-NAP. 4. NGN2_20230208_OutputData - The full output folder including Steps 1-5 is zipped to preserve the folder organization. Outputs from Steps 1 and 2 are available for download as individual files. ** When viewing "Files" below, we recommend you "Change View" to "Tree".

Road networks serve as a fundamental layer in various applications ranging from basemap preparation, which is needed for navigation, to humanitarian aid, disaster management, and transportation planning. As a result, accurate and updated road network maps are not just beneficial, but often critical. Traditional methods of road mapping and updating are time-consuming and often involve manual fieldwork. However, with deep learning solutions, the process has become both more efficient and accurate. In this tutorial, you are a GIS analyst working at the City of Mississauga in Ontario and you have been tasked with developing an automated road extraction to update the city road network. You will start with generating training samples to identify and marking out roads within the imagery, which then serve as examples for the deep learning model to learn from. Then, you’ll use the ArcGIS API for Python to train a deep learning model and use the developed model in ArcGIS Pro for inferencing and extracting the city road network. Following the road extraction, you will apply a post-processing steps to connect segmented roads and create a smooth road network and then compare it to a reference road network layer.

The data provided here are part of a Galaxy Training Network tutorial that analyzes RNA-Seq data from a study published by Brooks et al. 2011 to identify genes and exons that are regulated by Pasilla gene.

The data provided here are part of a Galaxy Training Network tutorial that analyzes EWAS data from a study published by Hugo, Willy, et al., 2015 (DOI: 10.1016/j.cell.2015.07.061) to identify differentially methylated regions and positions associated with melanoma MAPKi resistance.

Source/Credit: Michael Grogan https://github.com/MGCodesandStats https://github.com/MGCodesandStats/datasets/blob/master/cars.csv

Sample dataset for regression analysis. Given 5 attributes (age, gender, miles driven per day, debt, and income) predict how much someone will spend on purchasing a car. All 5 of the input attributes have been scaled to be in 0 to 1 range. Training set has 723 training examples. Test set has 242 test examples.

This dataset will be used in an upcoming Galaxy Training Network tutorial (https://training.galaxyproject.org/training-material/topics/statistics/) on use of feedforward neural networks for regression analysis.

This dataset provides the inputs used in the Galaxy Training Network (GTN) training 'End-to-End Tissue Microarray Image Analysis with Galaxy-ME'. The tutorial demonstrates how to use the Galaxy-ME tool suite for primary image processing, data analysis, and interactive visualization of multiple tissue imaging datasets. Original data was published by Schapiro et al.

The data provided here are part of a Galaxy Training Network tutorial that analyzes ChIP-seq data from a study published by Ross-Inness et al., 2012 (DOI:10.1038/nature10730) to identify the binding sites of the Estrogen receptor, a transcription factor known to be associated with different types of breast cancer.