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444 results for “social networks”
Dynamic shifts in social network structure and composition within a breeding hybrid population
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Data from: Heat stress conditions affect the social network structure of free‐ranging sheep
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Social network shrinking is explained by active and passive effects but not increasing selectivity with age in wild macaques
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Little brown myotis social networks
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Code and data from: Familiarity, dominance, sex and season shape common waxbill social networks.
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Data from: Sexual conflict and social networks in bed bugs: Effects of social experience
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Data from: Jeans and language: social networks and reproductive success are associated with the adoption of outgroup norms
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Social network structure is robust to parasite induced changes in contact behavior of domestic sheep
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Data from: Parasites alter interaction patterns in fish social networks
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Data from: Influence of periaqueductal gray on other salience network nodes predicts social sensitivity
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The "Last.fm" data set used in the article "Cumulative effects of triadic closure and homophily in social networks"
<p>This is the "Last.fm" network used in the article:</p> <p>A. Asikainen, G. Iñiguez, J. Ureña-Carrión, K. Kaski, M. Kivelä. Cumulative effects of triadic closure and homophily in social networks. Science Advances (in press)</p> <p>https://doi.org/10.1126/sciadv.aax7310</p> <p>The data set is described in the article. Please cite the original article when using this data set.</p> <p>The original data in which this network is based on was donwloaded from audioscrobbler.net where it was licensed under the "Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales" licese, and accordinly this data set uses the same license.</p> <p>The data contains two files:</p> <p><strong>lastfm.edg</strong><br> This is the network formatted as an edge list, where each row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website.</p> <p><strong>lastfm_genders.txt</strong><br> This is the list of genders of the nodes. Each row corresponds to one node. The first number is the node id (matching the one in the edge list) and the second number indicates the gender such that 0=male and 1=female.</p>
Data from: Co-prescription network reveals social dynamics of opioid doctor shopping
This paper examines network prominence in a co-prescription network as an indicator of opioid doctor shopping (i.e., fraudulent solicitation of opioids from multiple prescribers). Using longitudinal data from a large commercially insured population, we construct a network where a tie between patients is weighted by the number of shared opioid prescribers. Given prior research suggesting that doctor shopping may be a social process, we hypothesize that active doctor shoppers will occupy central structural positions in this network. We show that network prominence, operationalized using PageRank, is associated with more opioid prescriptions, higher predicted risk for dangerous morphine dosage, opioid overdose, and opioid use disorder, controlling for number of prescribers and other variables. Moreover, as a patient's prominence increases over time, so does their risk for these outcomes, compared to their own average level of risk. Results highlight the importance of co-prescription networks in characterizing high-risk social dynamics.
Data from: Findings from an exploration of a social network intervention to promote diet quality and health behaviours in older adults with COPD: a feasibility study
<p><span><b>Background: </b>Diet quality in older people with Chronic Obstructive Pulmonary Disease (COPD) is associated with better health and lung function. Social factors, such as social support, social networks and participation in activities, have been linked with diet quality in older age. A social network tool – GENIE (Generating Engagement in Network Involvement) – was implemented in a COPD community care context. The study aimed to assess the feasibility of the GENIE intervention to promote diet quality and other health behaviours in COPD. </span></p> <p><span><b>Methods:</b> Twenty-two community-dwelling older adults with COPD were recruited from a local COPD Service. Participants were offered usual care or the GENIE intervention. Process evaluation methods were used to assess intervention implementation, context and mechanisms of impact; <a name="_Hlk11247522">these included observations of patient interactions with the intervention, documented in observational field notes and in films of a patient group discussion.</a> Diet quality was assessed by food frequency questionnaire; 'prudent' diet scores were used to describe diet quality at baseline and at 3-month follow-up. Change in diet quality was expressed per month, from baseline to follow-up.</span></p> <p><b>Results: </b>Feasibility data showed that the GENIE intervention could be implemented in this sample of community-living older people. The intervention was acceptable to clinicians and older people with COPD, especially for those with less severe disease, when facilitated appropriately and considering the levels of literacy of participants. There was no significant change in diet quality in the intervention group over the follow-up period (median change in prudent diet score per month, (interquartile range (IQR)): 0.03, (-0.24 – 0.07)); whereas an overall fall in diet quality was observed in the control group (-0.15, (-0.24 – 0.03)).</p> <p><span><b>Conclusion: </b>The process evaluation findings suggest that this intervention is feasible and acceptable to both patients and clinicians. Although the sample size achieved in this study was small, findings suggest that the intervention may have a protective effect against declines in diet quality, and other health behaviours, in an older COPD population. Findings from this feasibility study indicate that further evaluation of the GENIE intervention is warranted in a larger study, with a longer follow-up.</span></p> <p> </p> <p> </p>
Adjacency matrices and nodal attributes for prestige and homophily predict network structure for social learning of medicinal plant knowledge
<p>Human subsistence societies have thrived in environmental extremes while maintaining biodiversity through social learning of ecological knowledge, such as techniques to prepare food and medicine from local resources. However, there is limited understanding of which processes shape social learning patterns and configuration in ecological knowledge networks, or how these processes apply to resource management and biological conservation. In this study, we test the hypothesis that the prestige (rarity or exclusivity) of knowledge shapes social learning networks. In addition, we test whether people tend to select who to learn from based on prestige (knowledge or reputation), and homophily (e.g., people of the same age or gender). We used interviews to assess five types of medicinal plant knowledge and how 303 people share this knowledge across four villages in Solomon Islands. We developed exponential random graph models (ERGMs) to test whether hypothesized patterns of knowledge sharing based on prestige and homophily are more common in the observed network than in randomly simulated networks of the same size. We found that prestige predicts five hypothesized network configurations and all three hypothesized learning patterns, while homophily predicts one of three hypothesized network configurations and five of the seven hypothesized learning patterns. These results compare the strength of different prestige and homophily effects on social learning and show how cultural practices such as intermarriage can affect certain aspects of prestige and homophily. By advancing our understanding of how prestige and homophily affect ecological knowledge networks, we identify which social learning patterns have the largest effects on biocultural conservation of ecological knowledge.</p>
Joint Autoregressive and Graph Models for Software and Developer Social Networks
<p>This zip contains three CSV files and one folder. This dataset contains information for the recent ten distributions.</p> <ul> <li><strong>developer_attributes.csv</strong>: There are seven columns in this file. "distro" (str) represents distribution name. "source" (str) denotes source package name. "person_id" (str) indicates developer identity. "closes" (int), "high" (int), "medium" (int), "low" (int) are the features.</li> <li><strong>source_bugs.csv</strong>: In this file, three columns are present. "distro" (str) represents the distribution name. "source" (str) represents the source package name. "bug_count" (int) denotes the number of bugs that source package has at a particular distribution.</li> <li><strong>source_sizes.csv</strong>: In this file, three columns are present. "distro" (str) represents the distribution name. "source" (str) represents source package name. "size" (int) denotes the size of the package.</li> <li><strong>Dependency folder:</strong> Within this folder, ten dependency lists are present. Each file contains two columns i.e "start" (str) and "target" (str). Both of them represent source packages. So, we read as the "start" source package depends on "target" source package. </li> </ul> <p>Here is the arxiv version of our paper: <a href="https://arxiv.org/abs/2101.08729">https://arxiv.org/abs/2101.08729</a>. </p> <p>Here is the portal link: <a href="https://sites.google.com/view/rima-hazra/swnet">https://sites.google.com/view/rima-hazra/swnet</a></p>
Familiist (pro-natalist) communities in the social network VKontakte
<p>The database contains an upload of text comments in Russian from the social networkVkontakte in <strong>.csv format (UTF-8 encoding). </strong>Comments are collected from communities, which discuss pregnancy, childhood, motherhood, paternity, etc. The unloading contains comments under the posts with which the interaction took place. The absolute amount of likes was used as a criterion, (comments were collected where the number of likes is greater than or equal to 5). The text data was processed (stemmization and lemmatization). The data are suitable for thematic analysis (e.g. LDA - Latent Dirichlet Allocation), for modelling the graph structure of communities (the link_comment variable contains a unique identifier of the post, link_author contains a unique user identifier), for analysis of the tonalities of statements and forming a dictionary of demographic connotation. </p> <p>Sample Information:</p> <p>- Number of communities 38 </p> <p>- Content type of communities: communities in which users are mainly positive about the birth of children, motherhood, parenthood and own family are selected. But users (communities) with anti-familistic biases may be encountered. </p> <p>- Only comments with the number of likes >= 5 are collected </p> <p>- Comments are collected only from communities (the list of communities below) discussing issues related to childhood, motherhood, pregnancy, etc. </p> <p>- A sample of communities on average contains 309 thousand subscribers (maximum value - 1,482,303, minimum value - 72,570, total number of subscribers excluding intersections - 11,743 295) </p> <p>- The sample of comments contains 112,900 user comments </p> <p>Sample Structure: </p> <p>link_author - link to the author of the comment in the form of https://vk.com/*author identificator* </p> <p>gender of author (F - female, M - male, NaN - no data) </p> <p>link_comment - link to comment in the form of https://vk.com/* post identificatior on a *community wall*?reply=*comment id * </p> <p>date_time - date and time of publication (format “YYYY-MM-DD HH:MM:SS”) </p> <p>text - raw comment text </p> <p>likes - number of likes the comment has </p> <p>text_prep - processed text (punctuation marks removed, words brought down to lowercase) </p> <p>text_stem - processed text (based on the text_prep column stemmization using SnowBallstemmer (“Russian”) of the nltk library) is performed </p> <p>text_sw - processed text (based on the text_prep column stop words are deleted using word_tokenize (text) of the nltk library) </p> <p>text_lemm - processed text (lemmatization using mystem.lemmatize (text) of pymystem3 library is performed based on the text_prep column) </p> <p>List of communities (38 communities):</p> <p>https://vk.com/club52388302</p> <p>https://vk.com/club34677924</p> <p>https://vk.com/club99834596</p> <p>https://vk.com/club170234932</p> <p>https://vk.com/club20199180</p> <p>https://vk.com/club118030893</p> <p>https://vk.com/club14395935</p> <p>https://vk.com/club100104267</p> <p>https://vk.com/club181526404</p> <p>https://vk.com/club35095382</p> <p>https://vk.com/club58530763</p> <p>https://vk.com/club69716165</p> <p>https://vk.com/club29746763</p> <p>https://vk.com/club78865067</p> <p>https://vk.com/club20709572</p> <p>https://vk.com/club93466205</p> <p>https://vk.com/club61700163</p> <p>https://vk.com/club91423062</p> <p>https://vk.com/club69285929</p> <p>https://vk.com/club104012302</p> <p>https://vk.com/club20622108</p> <p>https://vk.com/club86333616</p> <p>https://vk.com/club24765</p> <p>https://vk.com/club87169444</p> <p>https://vk.com/club86688308</p> <p>https://vk.com/club93776129</p> <p>https://vk.com/club47207301</p> <p>https://vk.com/club39873171</p> <p>https://vk.com/club59224150</p> <p>https://vk.com/club7430494</p> <p>https://vk.com/club37739956</p> <p>https://vk.com/club59701255</p> <p>https://vk.com/club27427277</p> <p>https://vk.com/club126238531</p> <p>https://vk.com/club127678644</p> <p>https://vk.com/club57782234</p> <p>https://vk.com/club51314884</p> <p>https://vk.com/club134261249</p>
Data for: Infectious disease and sickness behaviour: tumour progression affects interaction patterns and social network structure in wild Tasmanian devils
<p>Infectious diseases, including transmissible cancers, can have a broad range of impacts on host behaviour, particularly in the latter stages of disease progression. However, the difficulty of early diagnoses makes the study of behavioural influences of disease in wild animals a challenging task. Tasmanian devils (<i>Sarcophilus harrisii</i>) are affected by a transmissible cancer, devil facial tumour disease (DFTD), in which tumours are externally visible as they progress. Using telemetry and mark-recapture data sets, we quantify the impacts of cancer progression on the behaviour of wild devils by assessing how interaction patterns within the social network of a population change with increasing tumour load. DFTD negatively influences devils' likelihood of interaction within their network, an effect which increases with increasing tumour load. Infected devils were more active within their network late in the mating season, a pattern with repercussions for DFTD transmission. Our study provides a rare opportunity to quantify and understand the behavioural feedbacks of disease in wildlife and how they may affect transmission and population dynamics in general.</p>
Data from: How to make methodological decisions when inferring social networks.
<p>Social network analyses allow studying the processes underlying the associations between individuals and the consequences of those associations. Constructing and analysing social networks can be challenging, especially when designing new studies as researchers are confronted with decisions about how to collect data and construct networks, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. Here, we suggest an approach for making decisions when starting social network research in a new study system that avoids the pitfall of multiple hypotheses testing. We argue that best edge definition for a network is a decision that can be made using <i>a priori</i> knowledge about the species, and that is independent from the hypotheses that the network will ultimately be used to evaluate. We illustrate this approach with a study conducted on a colonial cooperatively breeding bird, the sociable weaver. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then evaluated which combination of data collection and association definition maximised (i) the assortment of individuals into previously known 'breeding groups' (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). This evaluation of different methods based on <i>a priori</i> knowledge of the study species can be implemented in a diverse array of study systems and makes the case for using existing, biologically meaningful knowledge about a system to help navigate the myriad of methodological decisions about data collection and network inference.</p>
Data and Code for Inequality is rising where social network segregation interacts with urban topology
<p>This folder contains data and code to reproduce results of the paper "Inequality is rising where social network segregation interacts with urban topology". arXiv version: https://arxiv.org/abs/1909.11414.</p>
Data from: Key players and hierarchical organization of prairie dog social networks
The use of social network theory in evaluating animal social groups has gained traction in recent years. Despite the utility of social network analysis in describing attributes of social groups, it remains unclear how comparable this approach is to traditional behavioral observational studies. Using data on Gunnison's prairie dog (Cynomys gunnisoni) social interactions we describe social networks from three populations. We then compare those social networks to groups identified by traditional behavioral approaches and explore whether individuals group together based on similarities. The social network social groups identified by social network analysis were consistent with those identified by more traditional behavioral approaches. However, fine-grained social sub-structuring was revealed only with social network analysis. We found variation in the patterns of interactions among prairie dog social groups that was largely independent of the behavioral attributes or genetics of the individuals within those groups. We detected that some social groups include disproportionately well-connected individuals acting as hubs or bridges. This study contributes to a growing body of evidence that social networks analysis is a robust and efficient tool for examining social dynamics.
ScienceDex guides
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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.