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444 results for “social networks”
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
Dataset of homophobic messages on social networks in Colombia
<p>The messages were collected using the API that Twitter provides upon invitation. During an observation window of one month and five days, we made around 38 requests of 100 messages. From these requests, 440 tweets segmented between negative and positive messages were selected, from which we captured ID, tweet (body's message), and date fields. To discern whether a message promoted homophobia, we chose those that contained colloquial terms of the country used to refer to the LGBTQ community or its members. We also considered those that included a word related to the subject and used offensive language. We should note that the double meaning is an aspect that makes classification difficult, since a word may or may not be offensive depending on the context. Due to it, we used terms similar to the negative ones for non-homophobic messages, but that expressed an opinion or support towards the community in question.</p>
Harry Potter's social network
<p>Literary works can be analysed in the framework of network theories, as proposed in 2011 by the Stanford Literary Laboratory in some of its experiments. In fact, the plot of a play or a novel can be displayed as a network of interacting characters, where the timeline of the plot is projected on a planar graph. This approach can help in highlighting some features of the literary work. Here in the image, it is shown the Harry Potter’s social network, as we can obtain from “Harry Potter and the Philosopher’s Stone” (or Sorcerer’s Stone), the J.K. Rowling's debut novel, published on 26 June 1997. A small part of the network was already proposed in [1]. In the same article, we detailed in the appendix the edges between vertices, that is the links among characters. [1] Amelia Carolina Sparavigna, On Social Networks in Plays and Novels, International Journal of Sciences 10(2013):20-25 DOI: 10.18483/ijSci.312</p>
Data from: Hierarchical social networks shape gut microbial composition in wild Verreaux's sifaka
<p>In wild primates, social behaviour influences exposure to environmentally acquired and directly transmitted microorganisms. Prior studies indicate that gut microbiota reflect pairwise social interactions among chimpanzee and baboon hosts. Here, we demonstrate that higher-order social network structure—beyond just pairwise interactions—drives gut bacterial composition in wild lemurs, which live in smaller and more cohesive groups than previously studied anthropoid species. Using 16S rRNA gene sequencing and social network analysis of grooming contacts, we estimate the relative impacts of hierarchical (i.e. multilevel) social structure, individual demographic traits, diet, scent-marking, and habitat overlap on bacteria acquisition in a wild population of Verreaux's sifaka (<em>Propithecus</em> <em>verreauxi</em>) consisting of seven social groups. We show that social group membership is clearly reflected in the microbiomes of individual sifaka, and that social groups with denser grooming networks have more homogeneous gut microbial compositions. Within social groups, adults, more gregarious individuals, and individuals that scent-mark frequently harbour the greatest microbial diversity. Thus, the community structure of wild lemurs governs symbiotic relationships by constraining transmission between hosts and partitioning environmental exposure to microorganisms. This social cultivation of mutualistic gut flora may be an evolutionary benefit of tight-knit group living.</p>
facebook social network graph
<p>This dataset is also contained in http://snap.stanford.edu/data/egonets-Facebook.html, but it is converted to one by one single graphs in this dataset.</p> <p>Graph Example:</p> <p># graph_id, graph_activity, number_of_vertices, number_of_edges</p> <p>vertex_1， vertex_2, ... , vertex_n</p> <p>vertex_1 vertex_k edge_label vertex_2 vertex_m edge_label, ..., vertex_i, vertex_j, edge_label</p>
Evaluation Data of a Trust-Aware Decentralized Social Network
<p>This dataset includes the evaluation data for the Paper "Trusting Decentralized Web Data in a Solid-based Social Network".<br>Within the ZIP File the following files are included in the dataset:</p> <ul> <li><strong>calculations.csv:</strong> All calculated numbers based on the raw data of the conducted empircal user study.</li> <li><strong>questions_translation.csv:</strong> A translation of all German questions asked in the survey to English, including a mapping of the question codes to the questions.</li> <li><strong>raw_data.csv:</strong> The raw data exported from the used survey tool of the conducted empircal user study.</li> <li><strong>survey.pdf:</strong> The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> </ul>
Hierarchically embedded scales of movement shape the social networks of vampire bats
<p>Social structure can emerge from <em>hierarchically embedded scales of movement</em>, where movement at one scale is constrained within a larger scale (e.g., among branches, trees, forests). In most studies of animal social networks, some scales of movement are unobserved, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (<em>Desmodus rotundus</em>), we created an agent-based model of how three nested scales of movement—among roosts, clusters, and grooming partners—each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically-derived rates that were either fixed or individually variable and either independent or correlated across scales. We found the number of partners groomed per bat was driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks.</p>
Extended Evaluation Data of TrADS: a Trust-Aware Decentralized Social Network
<p>This dataset includes the evaluation data for the Paper "TrADS: a Trust-Aware Decentralized Social Network".<br>Within the ZIP File the following files are included in the dataset:</p> <ul> <li><strong>survey.pdf:</strong> The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> <li><strong>all.xlsx:</strong> An Excelfile containing all the following .CSV files as worksheets.</li> <li><strong>raw_data.csv:</strong> The raw data exported from the used survey tool of the conducted empircal user study.</li> <li><strong>group1_unfiltered.csv:</strong> All participants' data of group 1.</li> <li><strong>group2_unfiltered.csv:</strong> All participants' data of group 2.</li> <li><strong>group1.csv:</strong> All filtered participants' data of group 1, who correctly answered the control questions.</li> <li><strong>group1_ueq_data.csv:</strong> All filtered participants's ueq+ question responses of group 1. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li><strong>group1_ueq_importance.csv:</strong> All filtered participants's ueq+ question responses about personal importance of group 1. Only including the question 5 about dimension importance.</li> <li><strong>group1_ueq_kpis:</strong> Including the ueq+ KPI values of all participants in group 1.</li> <li><strong>group2.csv:</strong> All filtered participants' data of group 2, who correctly answered the control questions.</li> <li><strong>group2_ueq_data.csv:</strong> All filtered participants's ueq+ question responses of group 2. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li><strong>group2_ueq_importance.csv:</strong> All filtered participants's ueq+ question responses about personal importance of group 2. Only including the question 5 about dimension importance.</li> <li><strong>group2_ueq_kpis:</strong> Including the ueq+ KPI values of all participants in group 2.</li> <li><strong>participants.csv:</strong> General information about participants grouped by both groups and joint.</li> <li><strong>likert_questions.csv:</strong> Mean Values and Standard Deviations (Std) of all statements rated on a 5-point Likert scale. It includes Means and Stds for Group 1, Group 2, Group 1 + Group 2 concatinated, and the values of the first user study published in the previous paper on TrADS <a href="https://zenodo.org/records/10641724" target="_blank" rel="noopener">(also available in previous dataset on Zenodo)</a>.</li> </ul>
The gut microbiota affects the social network of honey bees
<p>This dataset contains input files needed to reproduce the automated behavioral tracking data analyses of the research article "The gut microbiota affects the social network of honey bees”. Codes using these data and additional datasets are available at: https://github.com/JoanitoLiberti/The-gut-microbiota-affects-the-social-network-of-honey-bees/</p> <p> </p>
Group composition of individual personalities alters social network structure in experimental populations of forked fungus beetles
<p><span>Social network structure is a critical group character that mediates the flow of information, pathogens, and resources among individuals in a population, yet little is known about what shapes social structures. In this study, we experimentally tested whether social network structure depends on the personalities of group members. Replicate groups of forked fungus beetles (<i>Bolitotherus cornutus</i>) were engineered to include only members previously assessed as either more social or less social. We found that individuals behaved consistently across social contexts, exhibiting repeatable numbers of interactions and numbers of partners. At the group level, networks composed of more social individuals had higher interaction rates, higher tie density, higher global clustering, and shorter average shortest paths than those composed of less social individuals. We highlight group composition of personalities as a source of variance in group traits and a potential mechanism by which networks could evolve.</span></p>
A Social Network of the Prosopography of the Neo-Assyrian Empire
<p>The data used for creating "A Social Network of the Prosopography of the Neo-Assyrian Empire"</p> <p>The networks were created from the text and pdf file versions of the Prosopography of the Neo-Assyrian Empire (PNA). There are two different networks created from the same data. In the two-mode network, persons are connected to the texts in which they are attested. The one-mode network connects two persons if they are attested in the same document. </p> <p>In order to study and visualise the networks, it is sufficient to download the folder Networks.zip. SupplementaryData.zip contain more in-depth explanations of the different stages of creating the networks, the normalization lists used for that, and the output of different stages of the procedure.</p> <p>The dataset has also been described in Jauhiainen, H., & Alstola, T. (2022). A Social Network of the Prosopography of the Neo-Assyrian Empire. <em>Journal of Open Humanities Data</em>, <em>8</em>, 8. DOI: <a href="http://doi.org/10.5334/johd.74">http://doi.org/10.5334/johd.74</a>.</p> <p>For the reasons of copyright, we are not able to publish the data from which the networks have been extracted. But we hope that by publishing all the source codes, concordance lists, and outputs with explanations, we give the user a picture of how the networks were created.</p>
Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles
<p>Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioral ecology is the expectation that the distribution of resources shapes patterns of social interactions.</p> <p>We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions.</p> <p>We experimentally manipulated the distribution of an essential food and reproductive resource in semi-natural populations of forked fungus beetles (Bolitotherus cornutus). We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half-way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioral differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments.</p> <p>We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length, and global clustering coefficient) did not differ between environments with dramatically different distributions of resources.</p> <p>Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggests that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.</p>
Opinion dynamics in social network under competition: the role of influencing factors in consensus reaching
<p>The profitability of opinion and the finiteness of individual attention have already spawned the extensive competition for individual preferences on social networks. It's quite necessary to investigate the opinion dynamics over social networks in a competitive environment. To this point, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize the opinion evolution in a competitive opinion dynamics. Based on the DGCG model, we obtain equilibrium results in the stable state of opinion evolution. Consecutively, we analyze what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions and network structure. Theoretical analyses and simulation experiments show that these factors can significantly sway the consensus and even reverse competition outcomes.</p>
Influence of social networks as a distribution channel in volatile markets of non-fungible tokens..
<p>Dataset with tweets, prices (ETH & USD), volume (USD) and sentiment from BAYC, WOW and CoolCats NFTs. </p> <p>Obtained from Twitter and from the Ethereum blockchain using Dune Analytics (Dune.xyz)</p>
TrueFace: a Dataset for the Detection of Synthetic Face Images from Social Networks
<p>TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram.</p> <p>Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.</p>
Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles
<p>Both individual and group behavior can influence individual fitness, but multilevel selection is rarely quantified on social behaviors. Social networks provide a unique opportunity to study multilevel selection on social behaviors, as they describe complex social traits and patterns of interaction at both the individual and group levels. In this study, we used contextual analysis to measure the consequences of both individual network position and group network structure on individual fitness in experimental populations of forked fungus beetles (<em>Bolitotherus</em> <em>cornutus</em>) with two different resource distributions. We found that males with high individual connectivity (strength) and centrality (betweenness) had higher mating success. However, group network structure did not influence their mating success. Conversely, we found that individual network position had no effect on female reproductive success but that females in populations with many social interactions experienced lower reproductive success. The strength of individual-level selection in males and group-level selection in females intensified when resources were clumped together, showing that habitat structure influences multilevel selection. Individual and emergent group social behavior both influence variation in components of individual fitness but impact male mating success and female reproductive success differently, setting up intersexual conflicts over patterns of social interactions at multiple levels. </p>
Social Network analysis on European countries involved in agroecology research
<p>All the 124 (68 European and 56 Transnational) agroecology research projects identified in the mapping activities carried out by the task 1.3 of the AE4EU project were used to perform a weighted social network analysis (SNA) having the participating countries as nodes and collaborations in projects as edges.</p> <p>This dataset contains data related to this SNA and consists of two sheets:</p> <ul> <li><strong>Indexes</strong> where values of some measures for each identified country in the social network analysis are reported (number of European agroecological research projects coordinated by the country; number of transnational agroecological research projects coordinated by the country; Degree Centrality; Closeness Centrality)</li> <li><strong>Edge_weights</strong> where the weights for each edge between two countries are provided according to the times two countries cooperated together for a European or a transnational project.</li> </ul>
Figure 2. Workflow in Collaborative Portals-Questions Regarding Alterity in Social Collaborative Networks
<p>Despite the divergence of each members individual interests and structure, the media as well<br> as the Internet become new forms of social binding, and we believe they do not exclude the<br> traditional ways of communication and interaction, but help people in their duties, in addition to the<br> traditional, formal ways. In Figure 2 [7]. there is a clear image about how each of the benefficiaries<br> interact with the educational portal.</p>
Figure 1. 2nd year section in the Educational Portal - Questions Regarding Alterity in Social Collaborative Networks
<p>Portlets proved to be the most intelligent way of presenting information on an educational<br> portal, due to the richness of the user interface tools. The portal may be found at http://cursfs.ub.ro<br> URL. In Figure 1 there is a screenshot of the 2nd year students section in educational portal, which is<br> very easy to use and helpful.</p>
Growing up with nutritional stress leads to peripheral social network positions, independent of 'personality'
<p>Variables:</p> <p>BirdID: unique identifier of individuals</p> <p>Day: numerical, day of testing</p> <p>Room: physical Location of aviary, arbitrary numbering</p> <p>Group: numerical. Each group consists of one sex. </p> <p>Degree, Strength, EC, CV: social network variables. When precluded by a "D"; calculated using duration-based networks. When precluded by "F"; calculated using frequency-based networks. </p> <p>Sex: 1 = female, 2 = male</p> <p>DT: developmental treatment. Easy or Hard </p> <p>GenDad & GenMom: unique identifier. Genetic father or mother. </p> <p>DS, GP, PC1NO, PC1exp, Immobility: personality scores from Gerritsma et al., 2023. When precluded by "s", they are standardized ((x - mean) / sd). </p> <p>Broodsize: numerical, natal broodsize. </p> <p>nestID: unique identifier for nest, arbitrary numbering. </p> <p>Aviary: aviary in which individual was born. Arbitrary numbering. </p> <p>Groupday: combination of day and group. First day of first group would be 11. Tenth day of first group would be 101. </p> <p>Sexmc: mean-centered sex (sex-avg(sex)). </p> <p>Broodsizemc: mean-centered brood size. </p> <p>logTI: log10(immobility)</p> <p>DT3: numerical representation of DT. 1 = hard, 0 = easy. </p>
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.