This dataset contains a range of directed signed networks (signed digraphs) from social domain. The data come from 9 different sources and in total there are 29 network files. There are two temporal networks and one multilayer network in this dataset. Each network is provided in two formats: edgelist (.csv) and .gml format.This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as they provide citations to this data repository (https://doi.org/10.6084/m9.figshare.12152628) and the reference article listed below (https://doi.org/10.1038/s41598-020-71838-6), and license the new creations under the identical terms.For more information about the data, one may refer to the article below:Samin Aref, Ly Dinh, Rezvaneh Rezapour, and Jana Diesner. "Multilevel Structural Evaluation of Signed Directed Social Networks based on Balance Theory" Scientific Reports (2020) https://doi.org/10.1038/s41598-020-71838-6

The Intelligent Road Network dataset provided by the Transport Department includes traffic directions, turning restrictions at road junctions, stopping restrictions, on-street parking spaces and other road traffic data for supporting the development of intelligent transport system, fleet management system and car navigation etc. by the public.

Esri China (HK) has prepared this File Geodatabase containing a Network Dataset for the Intelligent Road Network to support Esri GIS users to use the dataset in ArcGIS Pro without going through long configuration steps. Please refer to this guideline to use the Road Network Dataset in ArcGIS Pro for routing analysis. This network dataset has been configured and deployed the following restrictions:

Speed LimitTurnIntersectionTraffic FeaturesPedestrian ZoneTraffic Sign of ProhibitionVehicle RestrictionThe coordinate system of this dataset is Hong Kong 1980 Grid.The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Transport Department at https://data.gov.hk/en-data/dataset/hk-td-tis_15-road-network-v2.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.Dataset last updated on: 2021 July

Collecting network information on political elites using conventional methods such as surveys and text records is challenging in authoritarian and/or conflict-ridden states. I introduce a data collection method for elite networks using scraping algorithms to capture public co-appearances at political and social events. Validity checks using existing data show the method effectively replicates interaction-based networks but not networks based on behavioral similarities; in both cases, measurement error remains a concern. Applying the method to Nigeria illustrates that patronage---measured in terms of public connectivity---does not drive national-oil-company appointments. Given that theories of elite behavior aim to understand individual-level interactions, the applicability of data using this technique is well-suited to situations where intrusive data collection is costly or prohibitive.

Datasets containing measured environmental functions, environmental characteristics and species traits data divided into functional clusters. Data collected from an intertidal sandflat.

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.

In a world where complex networks are an increasingly important part of science, it is interesting to question how the new reading of social realities they provide applies to our cultural background and in particular, popular culture. Are authors of successful novels able to reproduce social networks faithful to the ones found in reality? Is there any common trend connecting an author's oeuvre, or a genre of fiction? Such an analysis could provide new insight on how we, as a culture, perceive human interactions and consume media. The purpose of the work presented in this paper is to define the signature of a novel's story based on the topological analysis of its social network of characters. For this purpose, an automated tool was built that analyses the dialogs in novels, identifies characters and computes their relationships in a time-dependent manner in order to assess the network's evolution over the course of the story.

The repository contains two datset files. An edge list neede to replicate the network multimorbidity modules. The edge list is a data structure used to represent a graph as a list of its edges. And a dataset for multimorbidity patterns (modules) with modules' diseases description. The datasets serve as a supplementary material for the publication "Exploring multimorbidity patterns in older hospitalized Norwegian patients using network analysis modularity". The aim of this study is to demonstrate the use of Network Analysis (NA) in describing the Multimorbidity Patterns (Mps) in the Norwegian hospitalized older patient population based on national register data. The source of the original data used in the project is the Norwegian Patient Register (NPR) which contains administrative, demographic, and medical data of patients who have been in contact with specialized health services [16]. Our data cohort comprises all admissions of the patients who were admitted to a Norwegian hospital in a span of three years (2017-2019).

This dataset is from a longitudinal social network analysis research project that collected survey data on three separate occasions over a 6-month period from residents in a single assisted living facility. It includes pyschosocial survey data and social network survey data on acquaintance, companionship, social support, and negative interaction ties among residents of the assisted living facility. === We recommend reading the README.txt and Data Overview - SEAL.pdf files for an orientation to the dataset. ===

Contemporary data (2017/2018): An open area on the north side of the ESGR (GPS coordinates: 42.461808, -84.011128) was the primary site for this study as it corresponds to the location of “Evans’ Old Field”, one of the areas historically sampled for bees. The field was described by Evans as a 7.7 ha abandoned field with a mid-successional community of plants surrounded by oak-hickory woods. It is now 1.3 ha of semi-open habitat with significant encroachment of the surrounding oak-hickory woods and invasive autumn olive (Elaeagnus umbellata Thunb.). The site was visited every other week during the summers of 2017 and 2018 to sample bees. In 2017, the first sampling day was June 1 and the final sampling day was September 25. In 2018, the first sampling day was May 8 and the final day was October 3. We expanded sampling in 2018 to include a wider diversity of bees with narrower phenological periods.During each visit we sampled bees using three methods. First, we walked to the center of the open field and randomly selected a direction to start the first 25 meter transect. Three other 25 m transects were then established based on the first one, each at a 90-degree angle from the neighboring transect for a total of 100m sampled, with each transect segment moving away from a central location. Each transect was walked for 10 minutes each, a total of 40 minutes of sampling. We used aerial insect nets to collect bees found within 1.5m of the transect, and time was stopped for specimen processing. The host plant was recorded for all specimens captured from flowers. Flowering plants were identified to the lowest taxonomic level in the field using Newcomb’s guide and the PlantNet app, usually to species. Second, we spent 20 minutes collecting bees from plants of any species in the general vicinity of the open field. Third, to most closely match the methods used by Evans (see below), we spent 30 minutes sampling bees at each of the primary blooming plant species located in the field. Total time spent conducting this final sampling method varied based on the number of primary blooming plants at each visit, with a minimum of 30-minutes if there was only one primary plant. This sampling method was always done last, and included any plants that we collected more than one bee from that day. All bees were identified to species (or lowest possible taxonomic level) using relevant keys. All specimens collected in 2017 and 2018 are currently held in the Isaacs Lab at Michigan State University (as of 2024), and will eventually be deposited at the A.J. Cook Arthropod Collection at Michigan State University for long-term inclusion in that collection.Historical data (1921-1999): The University of Michigan Museum of Zoology Insect Collection (UMMZI), Ann Arbor, MI, holds over 4,000 bee specimens from the historical collections at the ESGR, and specimens were databased as part of this study. Historical data were checked for entry errors and outdated taxonomies. Specimens with questionable species determinations were re-examined and re-identified using relevant keys (see above) where possible. Bees that could not be confidently identified to the species level were excluded from the dataset, and entries that were missing the date of collection were also removed. Excluded entries accounted for less than 1% of the specimens. There were notable gaps in records at the ESGR, as there were no focused survey efforts since Evans’ last efforts in 1989, and only occasional specimen records from 1990-1999. There were no surveys and no records for the ESGR after 1999 and prior to this study in 2017/2018. All specimens from the ESGR were included in this dataset, not only those specifically collected at the Evans’ Old Field.In addition to the 4,000 plus records from the ESGR since 1921, we also include Evans’ dataset from his 1972 and 1973 collection effort. Evans’ original dataset from 1972/1973 was available through UM records. The dataset is unique compared to the records from the museum, because Evans did not always collect observed bees if he was confident in their identification (especially Bombus spp. and oligolectic species, e.g., Andrena rudbeckiae Robertson, 1891 and Dufourea monardae (Viereck, 1924)), and these records come only from the site now called Evans’ Old Field, whereas the exact sampling locations within the ESGR of many other specimens in the collection are not known. Therefore, his original dataset provides a more complete representation of the community he encountered at the Evans’ Old Field location.Evans describes his sampling as: “records of the dates and duration of flowering were made at frequent intervals (2-3 days every week) throughout the flowering season…Observation of visitation by bees was usually made between 9:00 am and 4:00 pm and on any given day was limited to a maximum of 30-40 minutes per flower species…no orderly system of monitoring was developed. More attention was given to abundant resources when they were being heavily visited than was paid to them near the beginning or end of their flower periods or to less frequently encountered species".Please open the README file first, which has descriptions of each included data file.

Group descriptive statistics and network parameters.

Network analysis and troubleshooting is a book. It was written by J. Scott Haugdahl and published by Addison-Wesley in 2000.

The study of the role of collaboration networks in the production of knowledge is important and has attracted the attention of a substantial number of researchers and policy makers around the world. This paper aims to analyze the structural properties of relationship networks among Finance researchers in Brazil. By applying Social Network Analysis to data from 532 articles produced by 806 researchers between 2003 and 2012, this article's results suggest that: (a) the Brazilian environment has structural features that indicate the existence of Small Worlds; (b) a small fraction (~3%) of researchers has regular production; (c) the higher the centrality of researchers in the network, the greater the number of articles published by them.

This data contains the data file of mosquito-pathogen relationships and literature-based evidence for wild infection, lab infection, lab dissemination, lab transmission, or known vector, and the associated output from network analysis. This dataset is associated with the following publication: Yee, D., and S. Yee. Robust network stability of mosquitoes and human pathogens of medical importance. Parasites & Vectors. BioMed Central Ltd, London, UK, 9, (2022).

This repository contains replication materials for the paper, "Inferential Approaches for Network Analysis: AMEN for Latent Factor Models" in Political Analysis. See the README.html for instructions. Replication materials are also available at https://github.com/s7minhas/netModels.

How does individuals’ influence in a large social network change? Social scientists have difficulty answering this question because measuring influence requires frequent observations of a population of individuals’ connections to each other, while sampling that social network removes information in a way that can bias inferences. This paper introduces a method to measure influence over time accurately from sampled network data. Ranking individuals by the sum of their connections’ connections — neighbor cumulative indegree centrality — preserves the rank influence ordering that would be achieved in the presence of complete network data, lowering the barrier to measuring influence accurately. The paper then shows how to measure that variable changes each day, making it possible to analyze when and why an individual’s influence in a network changes. This method is demonstrated and validated on 21 Twitter accounts in Bahrain and Egypt from early 2011. The paper then discusses how to use the method in domains such as voter mobilization and marketing.

The Multiple Myeloma Research Foundation (MMRF) runs a multi-site longitudinal clinical registry study of patients newly diagnosed with MM. This project is called CoMMpass, and collects both clinical and genomic information periodically. Researchers used interim analysis 19 (IA19) for their conclusions in "Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival." Includes clinical information, RNA sequencing (RNA-Seq) information, and copy number aberration (CNA), among others.

1. In social networks analysis, two different approaches have predominated in creating null models for hypothesis testing, namely pre-network and node network permutation approaches. Although the pre-network permutation approach appears more advantageous, its use has mainly been restricted to data on associations and sampling methods such as 'group follows.'
2. The pre-network permutation approach has recently been adapted to data on interactions and the focal sampling method, but its performance in different scenarios has not been thoroughly explored. Here, we assessed the performance of the pre- and node network permutation approach in several simulated scenarios based on proneness to false positive or false negatives and with or without observation bias.
3. Our results showed that the pre-network permutation was sensitive to false positives in scenarios with or without observation bias. The node network permutation approach produced fewer false positives and negatives than the pre-network approach, but only in scenarios without observation bias. In scenarios with observation bias, the node network permutation approach was outperformed by pre-network permutation.
4. Caution should be taken when using the pre- and node network permutations to create null models with data collected via focal sampling. This study provides future methodological research perspectives for social network analyses.

Viral Networks: Connecting Digital Humanities and Medical History is a collection of original essays examining networks as an object of study, a tool for analysis, a framework for collaboration, and a means of scholarly communication. The chapters began as papers for the Viral Networks Workshop, hosted by the History of Medicine Division of the National Library of Medicine (NIH), funded by the Office of Digital Humanities of the National Endowment for the Humanities, and organized by Virginia Tech. The scholars involved in this project examined networks in medical history even as they became participants in a network of scholars engaged in collaborative learning. Inspired by models of networked pedagogy, these chapters developed through a connected series of activities that began with reading proposals, included one face-to-face and two virtual conferences, and ended with final edits on revised chapters. The papers should therefore be understood and read as a fully networked project, not as chapters written individually and placed together. The chapters in this collection demonstrate what a network analysis can reveal, but also how a network analysis can help a humanities scholar approach a problem a different way, or understand what is missing in their sources or interpretations. The dataset contains data and data visualization related to the workshop and book. The original dataset can be found at the following DOI, doi:10.7294/w4tar0jd88.

Existing studies of intergovernmental organizations (IGOs) and militarized conflict focus on dyadic counts of shared IGO membership. However, dyadic approaches are inconsistent with the basic properties of IGOs. Because IGOs are multilateral organizations, shared membership necessarily involves ties to third parties. This article employs network analytics to develop a novel explanation of how third-party IGO ties reduce militarized conflict. The analysis first examines the "structural similarity" of states, defined by the extent to which states share similar patterns of IGO membership with relevant third parties. High levels of structural similarity indicate that states interact with a common set of IGO collaborators. The analysis then shows that micro-level changes in IGO membership effect changes in structural similarity, leading to the macro-level phenomenon of "network convergence," wherein states increasingly collaborate with the same third parties over time. Substantively, convergence results in increased overlap and integration between states' respective local networks of IGO partners. Because network convergence is costly, involving a combination of IGO-based accession, sovereignty, and alignment costs, it is unlikely to be pursued by purely exploitative state types. Consequently, convergence provides cooperative types with a mechanism for signaling a preference for cooperation over conflict. These credible signals in turn establish mutual trust among cooperators and effectively reduce the risk of militarized conflict. Extensive empirical analysis shows that, in fact, network convergence strongly correlates with a decline in militarized dispute initiations. The more that states collaborate with one another's IGO partners, the less likely they are to fight.

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between water years 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient ...