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5,666 results for “Social”
Social contact data before and during COVID-19 in China
<p>Social contact data for Wuhan and Shanghai, China before and during the COVID-19 outbreak.<br> Changelog for Version 2:<br> - Contact data from outbreak and baseline merged. A variable to distinguish the two ("collection_period") is added to "contact_extra".<br> <br> For problems with the dataset, please contact:</p> <table> <tbody> <tr> <td> </td> <td>socialcontactdata@gmail.com</td> </tr> </tbody> </table>
A Reputation Game Simulation: Emergent Social Phenomena from Information Theory
<p>Here, the data underlying the article "A Reputation Game Simulation: Emergent Social Phenomena from Information Theory" (<a href="https://doi.org/10.1002/andp.202100277">https://doi.org/10.1002/andp.202100277</a>) is provided.<br> <br> The data is structured according to the figures it has been used for. There are</p> <ul> <li>example simulations with basic communication strategies in the folder "single_simulations_3_agents" (Figures 4,5,8,D1)</li> <li>statistical simulations with 3 agents and special communication strategies in the folder "statistical_simulations_3_agents" (Figures 9-13, the upper panel of figure 15, figures 16-18, D2 and the left panels of figure D3)</li> <li>statistical simulations with 4 agents and special communication strategies in the folder "statistical_simulations_4_agents" (Figure 14, the middle panel of figure 15, the middle panels of figure D3 and the upper panels of figures D4, D5)</li> <li>statistical simulations with 5 agents and special communication strategies in the folder "statistical_simulations_5_agents" (The lower panel of figure 15, the right panels of figure D3 and the lower panels of figures D4,D5)</li> <li>propaganda simulations in the folder "propaganda_simulations" (Figure 7)</li> </ul> <p><br> Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>parameters <ul> <li>decpeting: whether or not agents in generally make dishonest statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are particularly risk-taking when making dishonest statements</li> <li>x_est: intrinsic honesties of the agents</li> <li>RSeed: the used random seed</li> <li>NA: number of agents</li> <li>NR: number of rounds</li> </ul> </li> <li>communication <ul> <li>a: speaker</li> <li>b: receiver</li> <li>c: topic</li> <li>J: transmitted message in the form of</li> </ul> </li> <li>self_update <ul> <li>id: number of agent who is updating knowledge about itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_<id>: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id: number of agent who is updating its knowledge</li> <li>I_<id1>: knowledge that the updating agent has about agent <id1> in the form of</li> <li>Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2></li> <li>Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update</li> <li>Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2></li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name: number if the described agent</li> <li>x: the agent's honesty</li> <li>I: the agent's knowledge about all others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K: the last 10 normalized surprises the agent experienced</li> <li>kappa: the median of K</li> <li>friends/enemies: list of the agent's friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits</li> </ul> </li> </ul>
Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program
<p>This repository contains several data products associated with the New York Sea Grant project R/CHD-15 entitled <em>Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program.</em><strong><em> </em></strong>These products include:</p> <p>1. Estimates of the 25-year, 50-year, and 100-year flood across the New York coastline of Lake Ontario. These design events (reported in feet) are for still water levels that take into account both average water levels across the lake as well as local variations in water level due to storm surge. Wave setup and wave run-up are not considered in these design events. The design events incorporate the effects of water level regulation and the potential impacts of climate change on water supplies to Lake Ontario, and they are tailored for 79 unique locations along the shoreline (identified based on longitude and latitude). These flood levels are presented in an online flood risk assessment tool at: https://kts48.users.earthengine.app/view/lake-ontario-water-level-scenarios</p> <p>2. Protocols and summary of results for a series of focus groups and structured telephone interviews with local officials from communities along the Lake Ontario shoreline to assess barriers to participation in the New York State Climate Smart Communities Program.</p> <p>3. A Crosswalk between activities and administrative requirements of the New York State Climate Smart Communities Program and other federal and state flood resiliency programs. </p> <p>4. A final report summarizing the products above. </p>
VABB-SHW: Dataset of Flemish Academic Bibliography for the Social Sciences and Humanities (edition 12)
<p>This dataset contains the twelfth edition of the <em>Flemish Academic Bibliography for the Social Sciences and Humanities (VABB-SHW)</em>, a database of academic publications from the social sciences and humanities authored by researchers affiliated to Flemish universities (<a href="https://www.ecoom.be/en/data-collections/vabb-shw">more information</a>). Publications in the database are used as one of the parameters of the Flemish performance-based research funding system. Only approved publications are included in this dataset.</p>
Social knowledge and irony understanding
<p>Dataset accompanying the publication titled "Impact of social knowledge about the speaker on irony understanding: Evidence from neural oscillations." It contains EEG recording (.set and .fdt files) of 22 participants reading stories during a task of irony undertsanding. Stimuli were manipulated according to a context condition (ironic, literal) and a speaker occupation stereotype (no occupation, non-sarcastic, sarcastic).</p> <p>Matlab scripts used for preprocessing and time-frequency analysis are available on github: <a href="https://github.com/deebeebolger/project_Ironie2022.git">https://github.com/deebeebolger/project_Ironie2022.git</a></p>
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>
Cross-Lingual Dataset of Crisis-Related Social Media
<p>The cross-lingual natural disaster dataset includes public tweets collected using Twitter’s public API, filtering by location-related keywords and date, without using any additional filtering (e.g., we did not restrict the query to specific languages). We considered five disaster events between January 2020 and February 2021 that received substantial news coverage internationally.</p> <p>All messages include a “language” field computed by Twitter us ing a language detection model developed specifically for tweets. We counted the number of messages per language in each event. Three of the top languages were common to all the studied events: English (ISO 639-1 code: en), Spanish (es), and French (fr). Additionally, we found several hundred messages for each event in other languages, including Catalan (ca), Tagalog (tl), Croatian (hr), German (de), Japanese (ja), Indonesian (id), and Portuguese (pt). </p> <p>After collecting the data, we labelled tweets or their translation to English that contained potentially informative factual information. We name this group of tweets “informative messages.” Next, we used crowdsourcing to further categorize the messages into various informational categories. We asked three different workers to label each of the approximately 5,700 informative messages across languages. The target categories were based on an ontology from TREC-IS 2018, where we grouped some low level ontology categories into higher-level ones.</p>
Dataset for Evaluating Abstractive Summaries of Crisis-Related Social Media
<p>The dataset created for evaluation of summaries generated from social media posted during five natural disasters.</p> <p>The dataset contains:</p> <ul> <li> <p>ground truth reports created by human assessor based on ERCC Echo Flash reports (5 events);</p> </li> <li> <p>summaries generated by extractive and abstractive state-of-the-art with manual annotation of category-relevant and crisis-relevant claim.</p> </li> </ul> <p>The dataset with annotated social media postings — <a href="https://doi.org/10.5281/zenodo.7714014">link</a></p> <p>The full description of used summarization methods, sources of data and evaluation metrics available in the paper — <a href="https://dl.acm.org/doi/abs/10.1145/3511095.3531279">link</a></p>
Social contact patterns relevant for infectious disease transmission in Cambodia
<p>Social contact data from a community-based survey conducted in Cambodia in 2012. </p>
Changing social contact patterns among US workers during the COVID-19 pandemic: April 2020 to December 2021
<p>These are data from the CorporateMix US study rounds 1-4. Below is a description of the files:</p> <p>1. participants: this contains a list of all the study participants for each round. EaA unique participant identified by the participant_id and round.</p> <p>2. contacts: this contains the individuals with whom a participant had a contact.</p> <p>3.df_all: this is generated by merging the participant and contacts dataframes using the participant_id and round as primary keys.</p> <p>4. day_rd1: this is data for round 1 day of survey. The survey design for round 1 was different, so these data are important to distinguish between day 1 and day 2 contacts.</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>
Quantifying Hierarchy and Prestige in US Ballet Academies as Social Predictors of Career Success
<p>This data contains aggregated competition outcomes from 6,363 ballet students affiliated with 1,603 schools in the United States, who participated in the Youth America Grand Prix (YAGP) between 2000 and 2021.</p> <p>We adopt a network science and <em>science of science</em> approach that empowers logistic regression models and matching experiments to quantify social prestige and its influence on dancers' careers. The analysis of career success in the performing arts, like ballet, in the context of a competition setting offers a unique opportunity to investigate the social influences on success while controlling for competition performance.</p> <p>Our work reveals the importance of institutional prestige on career success in ballet and showcases the potential of network science approaches to provide quantitative viewpoints for the professional development of careers beyond science.</p>
EU27 social accounting matrix for 2020
<p>The dataset contains the European Union's social accounting matrix for 2020. The FIGARO database’s 2022 edition (Eurostat (2022). ESA supply, use and input-output tables) is used to create product-by-product input-output table for the EU, while Eurostat’s data on non-financial transactions (a dataset called nasa_10_nf_tr , Eurostat (2022). Non-financial transactions - annual data) is used to cover the remaining parts of the social accounting matrix.</p> <p> </p> <p>This research was funded by the grant S-MIP-20-53 from the Research Council of Lithuania.</p>
Making Social Science Research Transparent [Webinar recording]
<p>High-quality data have the potential to be reused in many ways. Archiving and publishing your data properly is at the core of making your data FAIR and will enable both your future self as well as others to get the most out of your data. Recently, more and more scientific journals are implementing open data policies, leading to researchers' dilemmas about where, when and how to publish the data. Consequently, the way that social science research is conducted and disseminated is gradually changing. A crucial element of that change is research transparency. Introduction to the topic took place in the first part of the event.</p> <p>In the second part, panellists presented in-depth the processes, policies and tools implemented for facilitating transparent research in the social sciences. They discussed the processes that need to be in place for an open research cycle, the role of data archives and repositories in sharing research data and materials, tools for reproducing research findings in practice, collaborations between archives and social science journals, and implementing Transparency and Openness Promotion Guidelines in different social science disciplines.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=B-phrIMETGk"> the CESSDA Training YouTube channel</a>.</p>
Data management in the social sciences in Macedonia [Webinar recording]
<p>This webinar aimed to introduce social science researchers to the basic principles of data management, including the creation of a Data management plan, which is an important tool for planning the research project.</p> <p>The webinar consisted of three parts. The first part introduced researchers with the basic principles of data management including the benefits of adopting Data management plans (DMPs). The DMP follows the research projects’ life cycle, starting with the initial phases of Planning and Organization and documentation of research data. This part also included a presentation of best practices for creation of appropriate structure of folders and data files, as well as instructions for their naming, documentation and organization.</p> <p>The second part of the webinar focused on the following three phases of the project life cycle: Data processing, Preservation and Protection. Contemporary social science presumes the respect of high level ethical standards during the handling of research data, in accordance with legal rules and best practices in this area.</p> <p>The last part of the webinar was dedicated to the phases of Publication - familiarizing the researchers with the possibilities of data preservation and publishing; and Data discovery - discussing the ways and means to acquire social science data, including the secondary use of data produced by other researchers.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=n05WTs58CMY"> the CESSDA Training YouTube channel</a>.</p>
Research Data Management and data protection in the Social Sciences [Workshop recording]
<p>This online workshop organized by The Austrian Social Science Data Archive (AUSSDA) focused on the Research Data Management basics, Data Management Plans and common data protection issues in the Social Sciences.</p> <p>The first part of the workshop was dedicated to RDM basics and Data Management Plans (DMPs). In many projects, DMPs are mandatory deliverables that need to be submitted at the beginning of a project and are updated throughout the project life cycle. During the workshop, it was explained which aspect funders expect to be part of DMPs in Social Sciences and how researchers can benefit from (writing) these documents.</p> <p>In the second part of the workshop, data protection issues that are common in Social Sciences were addressed and how they can be handled. In particular, differences in the curation of quantitative and qualitative data need in order to comply with data protection regulations in general and AUSSDA deposit guidelines in particular. Presentation on how AUSSDA scans quantitative data for potential data protection violations using STATA and gives participants the opportunity to test the code on their own data and devices.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=DhiL9J-Iwqg"> the CESSDA Training YouTube channel</a>.</p> <p> </p>
Data of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".
<p>This repository contains data files of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".</p>
Mu-opioid receptor-dependent changes in social reward across adolescence in mice
<p>Datasets used in publication "Mu-opioid receptor-dependent changes in social reward across adolescence in mice" (in preparation). The social conditioned place preference data were pre-processed before statistical analysis. Three diffrent datatables with the same dataset reflect the steps of the pre-processing:</p> <p>1. Animals that spent more than 70% of the pretest time in one of the contexts were excluded (datable "30_70%")</p> <p>2. Outliers were identified using Grubbs test, and removed (datatable "no_outlier")</p> <p>3. In cases where the final number of animals conditioned in each context was not equal (due to an unequal number of animals passing the 70% criterion or unequal number of animals in the litter), we pseudorandomly trimmed the larger group using a Python script (file number 10). The exception from completely random selection was introduced to preserve a mean 50% initial context preference during the pretest</p>
Social Matrix for Lithuania, 2020 with disaggregated CPA_C22
<p>The dataset contains the Lithuanians social accounting matrix for 2020 with disaggregated CPA_C22. The FIGARO database’s 2022 edition (Eurostat (2022). ESA supply, use and input-output tables) is used to create product-by-product input-output table for the EU, while Eurostat’s data on non-financial transactions (a dataset called nasa_10_nf_tr , Eurostat (2022). Non-financial transactions - annual data) is used to cover the remaining parts of the social accounting matrix.</p> <p>The dataset includes a baseline (Reference) scenario without the simulation of sustainability practices and three scenarios simulating the substitution of plastic bags by paper bags (PaperBags), bioplastic bags (BioPlastics) and the reduction of the use of plastic bags (ConsReduction).</p> <p> </p> <p>This research was funded by the grant S-MIP-20-53 from the Research Council of Lithuania.</p>
EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 7: Aspects of ENCI III.: Towards social sustainability
<p>This document is Part 7 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship (ENCI) between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe.</p> <p>EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.</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.