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opencc-by-4.0May 2021View details →

Projetos habitacionais de interesse social são tema de pesquisa na UFSCar

Érico Masiero, docente do Departamento de Engenharia Civil da Universidade Federal de São Carlos (DECiv - UFSCar), fala de sua pesquisa sobre projetos públicos habitacionais de interesse social, especialmente no que diz respeito aos procedimentos nos órgãos públicos, tendo como objetivo auxiliar o trabalho dos gestores e facilitar a avaliação de impactos ambientais dos projetos. Lattes: http://lattes.cnpq.br/2691827106518167 CLICK CIÊNCIA Projeto de divulgação e popularização da Ciência produzido pelo Laboratório Aberto de Interatividade para Disseminação do Conhecimento Científico e Tecnológico da Universidade Federal de São Carlos (LAbI - UFSCar). Projetos habitacionais de interesse social são tema de pesquisa na UFSCar de <a href="https://youtu.be/pxBv3WqDypo">https://youtu.be/pxBv3WqDypo</a> está licenciado com uma Licença <a href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons - Atribuição-NãoComercial-SemDerivações 4.0 Internacional</a>. Podem estar disponíveis autorizações adicionais às concedidas no âmbito desta licença em <a href="https://www.labi.ufscar.br/">https://www.labi.ufscar.br/</a>.

opencc-by-4.0Sep 2017View details →

Metadata and index on social science research data

The dataset contains metadata on social science research data. It is a snapshot of the metadata used for the creation of the search index for http://datasearch.gesis.org. Metadata-DC.20170713.tar.gz contains the raw XML Dublin Core metadata harvested via OAI-PMH.  elasticsearch-2.4.4.-snapshot-20170713.tar.gz contains a corresponding elasticsearch v.2.4.4 index.

opencc-zeroApr 2024View details →

Data from: Response to food restriction, but not social information use, varies seasonally in captive cardueline finches

Temperate winters can impose severe conditions on songbirds that threaten survival, including shorter days and often lower temperature and food availability. One well-studied mechanism by which songbirds cope with such conditions is seasonal acclimatization of thermal metabolic traits, with strong evidence for both preparative and responsive changes in thermogenic capacity (i.e., the ability to generate heat) to low winter temperature. However, a bird's ability to cope with seasonal extremes or unpredictable events is likely dependent on a combination of behavioral and physiological traits that function to maintain allostatic balance. The ability to cope with reduced food availability may be an important component of organismal response to temperate winters in songbirds. Here we compare responses to experimentally reduced food availability at different times of year in captive red crossbills (Loxia curvirostra) and pine siskins (Spinus pinus) – two species that cope with variable food resources and live in cold places – to investigate seasonal changes in the organismal response to food availability. Further, red crossbills are known to use social information to improve response to reduced food availability, so we also examine whether use of social information in this context varies seasonally in this species. We find that pine siskins and red crossbills lose less body mass during time-restricted feedings in late winter compared to summer, and that red crossbills further benefit from social information gathered from observing other food-restricted red crossbills in both seasons. Observed changes in body mass were only partially explained by seasonal differences in food intake. Our results demonstrate seasonal acclimation to food stress and social information use across seasons in a controlled captive environment and highlight the importance of considering diverse physiological systems (e.g., thermogenic, metabolic, digestive, etc) to understand organismal responses to environmental challenges.

opencc-zeroApr 2024View details →

Data for: Social-ecological predictors of spotted hyena navigation through a shared landscape

Human-wildlife interactions are increasing in severity due to climate change and proliferating urbanization. Regions where human infrastructure and activity are rapidly densifying or newly appearing constitute novel environments in which wildlife must learn to coexist with people, thereby serving as ideal case studies with which to infer future human-wildlife interactions in shared landscapes. As a widely reviled and behaviorally plastic apex predator, the spotted hyena (Crocuta crocuta) is a model species for understanding how large carnivores navigate these human-caused 'landscapes of fear' in a changing world. Using high-resolution GPS collar data, we applied resource selection functions and step selection functions to assess spotted hyena landscape navigation and fine-scale movement decisions in relation to social-ecological features in a rapidly developing region comprising two protected areas: Lake Nakuru National Park and Soysambu Conservancy, Kenya. We then used camera trap imagery and Barrier Behavior Analysis (BaBA) to further examine hyena interactions with barriers. Our results show that environmental factors, linear infrastructure, human-carnivore conflict hotspots, and human tolerance were all important predictors for landscape-scale resource selection by hyenas, while human experience elements were less important for fine-scale hyena movement decisions. Hyena selection for these characteristics also changed seasonally and across land management types. Camera traps documented an exceptionally high number of individual spotted hyenas (234) approaching the national park fence at 16 sites during the study period, and BaBA results suggested that hyenas perceive protected area boundaries' semi-permeable electric fences as risky but may cross them out of necessity. Our findings highlight that the ability of carnivores to flexibly respond within human-caused landscapes of fear may be expressed differently depending on context, scale, and climatic factors. These results also point to the need to incorporate societal factors into multiscale analyses of wildlife movement to effectively plan for human-wildlife coexistence.

opencc-by-4.0Apr 2024View details →

data and code from: Submissive behaviour is affected by group size in a social fish

This repository contains data on individual aggression and submissive behaviour, collected during laboratory observations of agonistic interactions within Neolamprologus pulcher daffodil cichlids social groups, and the R code used to analyse the data.

opencc-by-4.0Apr 2024View details →

Exploring Behavioral Tendencies on Social Media: A Perspective Through Claim Check-Worthiness

<h2>Randomly Sampled Users Dataset (RSU.csv)</h2> This dataset consists of 11,173 users collected through Twitter's APIs. We collected 10,000 random English tweets in February 2023 using Twitter's Volume Stream API.  The tweets were posted by around 3,000 users.  For each user, we collected up to 100 of its most recent followees using Twitter's Following API. Through the Timeline and Liking APIs, for each user, we collected their most recent tweets (up to 3,200 tweets due to Twitter's limit) and liked-tweets (up to 3,200 too). We then filtered out users that have insufficient tweets (less than 100 original-tweets or less than 80 retweets/liked-tweets) to ensure that the sample sizes are statistically significant in our analyses.  Finally, we have 11,173 users along with 40,405,150 tweets.  <h2>Humanities Dataset (HUM.csv)</h2> This dataset contains 341,285 tweets and 498 Twitter accounts from selected Twitter lists including Book Author, Christianity, Artists, Buddhism, Musician, and Philosophers. We use Twitter's List and Timeline APIs to collect the accounts and their most recent tweets (up to 1,000). The dataset was collected in January 2024. <h2>Politics Dataset (POL.csv)</h2> This dataset contains all tweets from selected U.S. news media and U.S. politicians including Senators, House Members, US Governors, US Secretaries of State, US Cabinet, and US Election Officials at collection time. We used Twitter's Timeline API to collect the accounts' tweets (up to 3,200 tweets). The dataset was collected in May 2023, with 8,153,745 tweets and 3,784 Twitter accounts.  <h2>Data Fields</h2> Due to Twitter's content redistribution policy, we are only allowed to publish tweet IDs and user IDs. Therefore, in each dataset, each row/datapoint represent a tweet, containing two fields --- tweet_id and user_id.

opencc-zeroMay 2024View details →

Neighborhood benthic configuration reveals hidden social diversity: classified benthic data

Ecological interactions among benthic communities are crucial for shaping marine ecosystems. Understanding these interactions is essential for predicting how ecosystems will respond to environmental changes, invasive species, and conservation management. However, determining the prevalence of species interactions at the community scale is challenging. To overcome this challenge, we employ tools from social network analysis, specifically exponential random graph modeling (ERGM). Our approach explores the relationships among animal and plant organisms within their neighborhoods. Inspired by companion planting in agriculture, we use spatiotemporal co-occurrence as a measure of mixed species interaction. In other words, the variety of community interactions based on co-occurrence defines what we call "co-occurrence social diversity." Our objective is to use ERGM to quantify the proportion of interactions at both the simple paired level and the more complex triangle level, enabling us to measure and compare co-occurrence social diversity. Applying our approach to the Spanish coastal zone across 8 sites, 5 depths, and sunlit/shaded aspects, we discover that 80% of sessile communities, consisting of over a hundred species, exhibit co-occurrence social diversity, with 5% of species consistently forming associations with other species. These organism-level interactions likely have a significant impact on the overall character of the site.

Closed-loop microstimulations of the orbitofrontal cortex during real-life gaze interaction enhance dynamic social attention

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Agile Social Software Engineer: An Empirical Approach to Mitigate and Manage Social Debts in Agile Environments

Replication package description:     "DataCollection.xlsx": Survey responses from Agile teams in weeks 1, 2, 3, 4 and 5.          "DataAnalysis.xlsx": Analysis of the answers from the survey to answer the research questions proposed in this work.     Team-working issues that could lead to community smells can be found in TeamworkingIssues Tab.     The root causes of community smells in agile teams over weeks can be found in Rootcauses Tab.     Requests for Agile social guardian intervention can be found in Requestagileteams Tab.     Recurring needs and concerns in agile teams can be found in Needs_Concerns Tab.

opencc-zeroMay 2024View details →

Measuring social dimensions of sustainability at the community level: An illustrative but cautionary tale

Many communities are working to enhance the sustainability of their physical, economic, and social systems. While economic and physical systems are routinely measured (e.g., money, energy, greenhouse gas emissions), important psychological and behavioral elements of social systems (norms, attitudes, individual behavior) are seldom tracked. This research evaluated a potentially scalable approach to measuring the impact of local sustainability initiatives on these variables in a community engaged in a holistic effort to promote sustainability. Survey data were collected at two timepoints measuring pro-environmental thought and behavior in two small towns in Ohio: Oberlin, a community engaged in holistic efforts to enhance environmentally sustainable behavior; and Berea, a similar community used as a control. Despite verifiable changes in Oberlin due to sustainability programs and awareness of these programs, our survey results did not provide strong evidence that program efforts resulted in the desired changes in attitudes, norms and behaviors. Pro-environmental attitudes about recycling and installing LED bulbs were two exceptions. Conclusions: Assessing the psychological and behavioral dimensions of sustainability poses particular challenges. In our study, we encountered ceiling effects and inadequate statistical power. Possibly norms and attitudes are not easily influenced even by a holistic community-wide effort.

opencc-zeroMay 2024View details →

Data from: The evolution of sex roles: The importance of ecology and social environment

Males and females often have different roles in reproduction, although the origin of these differences have remained controversial. Explaining the enigmatic reversed sex roles where males sacrifice their mating potential and provide full parental care is a particularly long-standing challenge in evolutionary biology. While most studies focused on ecological factors as the drivers of sex roles, recent research highlights the significance of social factors such as the adult sex ratio. To disentangle these propositions, here we investigate the additive and interactive effects of several ecological and social factors on sex role variation using shorebirds (sandpipers, plovers and allies) as model organisms that provide the full spectrum of sex role variation including some of the best known examples of sex role reversal. Our results consistently show that social factors play a prominent role in driving sex roles. Importantly, we show for the first time that reversed sex roles are associated with both male-skewed adult sex ratios and high breeding densities. Furthermore, phylogenetic path analyses provide the first general support for sex ratios driving sex role variations rather than being a consequence of sex roles. Together, these novel results open a new research direction by showing that different mating opportunities of males and females play a major role in generating the evolutionary diversity of sex roles, mating system and parental care.

opencc-by-4.0Apr 2024View details →

Synergistic olfactory processing for social plasticity in desert locusts

Desert locust plagues threaten the food security of millions. Central to their formation is crowding-induced plasticity, with social phenotypes changing from cryptic (solitarious) to swarming (gregarious). Here, we elucidate the implications of this transition on foraging decisions and corresponding neural circuits. We use behavioral experiments and Bayesian modeling to decompose the multi-modal facets of foraging, revealing olfactory social cues as critical. To this end, we investigate how corresponding odors are encoded in the locust olfactory system using in-vivo calcium imaging. We discover crowding-dependent synergistic interactions between food-related and social odors distributed across stable combinatorial response maps. The observed synergy was specific to the gregarious phase and manifested in distinct odor response motifs. Our results suggest a crowding-induced modulation of the locust olfactory system that enhances food detection in swarms. Overall, we demonstrate how linking sensory adaptations to behaviorally relevant tasks can improve our understanding of social modulation in non-model organisms.

Electric car subsidies in social media and parliament 2022–2023: analysis results

Electric car subsidies in social media and the Finnish parliament 2022–2023: comparative analysis results obtained using three methods for qualitative classification of textual data.

Mental Health in Social Networks with Machine Learning Algorihtms

The DatasetMH.xlsx excel corresponds to a corpus of mental health in social networks labelled with polarity and stigma. In particular, the corpus consists of 2,287 comments labelled with polarity (positive, negative, neutral) and stigma from comments on Instagram posts about celebrity mental health disclosures: <ol> <li>Polarity: It consists of giving a positive, negative or neutral/undefined value to the comments in response to the disclosure or description of the symptomatology in the post. Positive polarity reflects understanding, encouragement or even admiration of the publication. E.g., “Cheer up, we love you".”. Negative polarity is assigned when the person expresses negative opinions, usually questioning the post with ironic, sarcastic or even mocking and disparaging comments. E.g., “how you show that you don't know what depression or anxiety is, shame on you!”. Neutral or undefined polarity is assigned in cases where no clear opinion is detected or can be interpreted in both directions. E.g., “take medication, it will help you" "and your partner?”</li> <li>Stigma: stigmatising responses to comments are behaviours in which negative beliefs and emotions towards MH problems are expressed. Stigma manifests in a variety of forms including rejection and anger against the person, which may extend to contempt or mockery, belittling their problem. E.g.,"What a desire to draw attention to yourself"; "what you have is a story"; "you're so inconsistent and seeking the limelight". Because socially we know that "stigma is wrong" many rejection comments are made in an ironic or sarcastic way. E.g., and how do you write on insta?"; "better information from someone who doesn't have a current account". Additionally, anger is shown by arguing that such posts "trivialise or commercialise" MH. E.g.,"don't come and tell me your false stories of overcoming, without even knowing what it is to work...". Other times the stigma manifests itself as pity or sorrow for the person. E.g.,“It breaks my heart”; “poor thing”.</li> </ol> The file DatasetMH_Emotions.xlsx corresponds to a corpus of mental health in social networks labelled with emotions. In particular, the corpus consists of 2,287 comments labelled with five emotions plus a neutral class from comments on Instagram posts about celebrity mental health disclosures. These emotions are: <ul> <li>Love/admiration: This emotion involves messages where admiration, approval and love are closely related.</li> <li>Gratitude: the messages imply a sincere appreciation for sharing mental health-related content on social networks.</li> <li>Comprehension/empathy/identification: The messages involve interest in and understanding of the message, including self-identification with the situation or context.</li> <li>Sadness: This primary emotion is produced by events that are not pleasant and that denote heaviness. It includes many manifestations of pity for the person.</li> <li>Anger/contempt/mockery: This emotion involves responses of irritation and attacks on the person as ridiculous and superficial.</li> <li>Neutral: This category corresponds to messages without emotions.</li> </ul> The labelling process of both datasets was divided into two phases: an initial phase with a pilot corpus (N = 787 comments) and a second phase focused on the development of the corpus with all the comments of the selected posts (N = 21151). The same methodology was followed in both phases: once the comments were collected, the corpus was cleaned, and then two independent experts were responsible for labelling each category. A third expert then reviewed the comments to resolve discrepancies. In the third and final phase, a final corpus for application to the machine learning algorithms is built from the large corpus (N = 2287). Classification models are a set of machine learning algorithms developed to assess emotional response, i.e. polarity, stigma and emotions in social networks, based on previously developed datasets (DatasetMH_Emotions.xlsx, DatasetMH.xlsx).

Content analysis of Spanish Defense institutions' NATO eFP social media messages (2017-2022)

Content analysis of Spanish Defense institutions' social media messages about the NATO Enhanced Forward Forward (eFP) mission on Facebook, Twitter (X), Instagram and YouTube (2017-2022).

Indirect social influence helps shaping the diffusion of innovations

This dataset accompanies the paper "Indirect social influence helps shaping the diffusion of innovations". It contains the participant choices between two colors when shown different information about the choices taken in their social network. The objective of the participants was to arrive to a consensus. One of the colors, which was the minority at the beggining, was given an extra incentive. Further details on the experimental setup are specified in the article. The main file is "Unified_data", which contains all the data from the 21 sessions studied and whose fields are described below. The file "ParticipantsData" contains the gender and age distribution of the participants, while the rest of the files are the individual data for each session, in the format given by oTree. Explanation of "Unified_data.csv" fields: <ul> <li>Experiment: Session number.</li> <li>participant.id_in_session: Identifier for each participant, constant for each session.</li> <li>config_name: Contains the order in which the different Setups were played. Its structure is of the form "egonetwork_1_a_b_c", where a,b,c are the numbers 2,3,4 in different order. These represent: 1-Setup AmAz (first neighbors), 2-Setup VerMag (first+second neighbors), 3-Setup RojNar (first+third neighbors), 4-Setup LilMor (first+fourth neighbors).</li> <li>Setup: Name of the Setup played (AmAz, VerMag, RojNar, LilMor and Instrucctions).</li> <li>node: Node assigned to the participant during that Setup.</li> <li>initialcolor: Initial color prescribed at the beginning of the Setup. Names are in spanish: Azul (blue) = #0072B2, Amarillo (yellow) = #FFC300, Verde (green) = #67E96E, Magenta = #C700AC, Rojo (red) = #E53950, Naranja (orange) = #FFA460, Lila (lilac) = #A792FC, Morado (purple) = #8000FF.</li> <li>InnovationAsInitialcolor: Binary variable showing if participant had the promoted color as initial color.</li> <li>threshold: Best threshold obtained in the model fitting.</li> <li>parameter: Best long-range parameter obtained in the model fitting.</li> <li>Time: Best time simulation obtained in the model fitting.</li> <li>AdoptersConsensus: First round in which all the participants chose the promoted color at least once.</li> <li>SimulationConsensus: Round in which the simulation of the fitted model arrived to consensus.</li> <li>Round: Round number.</li> <li>preg: Only in the Instructions Setup. Answer given to the control question.</li> <li>action: Color chosen by the participant in that round.</li> <li>Adopted_color: Previous varible translated into binary.</li> <li>Adopted_color_bin: Previous variable as logic variable.</li> <li>MajorityColor: Color most seen between the information given to the participant. In case of draw, one was chosen randomly.</li> <li>InnovationAsMajorityColorSeen: Binary variable showing if MajorityColor is equal to Promoted_color.</li> <li>bot: Binary variable showing if the participant was a bot in the current round (did not make a decision on time).</li> <li>color_neighbors_shown: List of the colors of the first neighbors shown to the participant in that round.</li> <li>color_neighbors_shown_color: Previous variable translated from hexadecimal to spanish.</li> <li>perc_color_neighbors: Percentage of neighbors shown with color equal to the promoted color.</li> <li>color_friends_shown: List of the colors of the long-distance friends shown to the participant in that round.</li> <li>color_friends_shown_color: Previous variable translated from hexadecimal to spanish.</li> <li>perc_color_friends: Percentage of long-distance friends shown with color equal to the promoted color.</li> <li>Adopted_innovation: Number of round since the participant first chose the promoted color.</li> <li>Promoted_color: Color with incentive. Setup AmAz - Azul, Setup VerMag - Magenta, Setup RojNar - Naranja, Setup LilMor - Morado.</li> <li>num_adopters: Number of adopters (people that have chosen the promoted color at least once) in the current round.</li> <li>total_participants: Constant showing the size of the network used (31).</li> <li>perc_adopters: Percentage of participants that are adopters in the current round.</li> </ul>

opencc-by-4.0Jun 2024View details →

Identity Lexicon and Keyword Counts for "The Life of a Tie: Social Origins of Network Diversity"

Prototype identity lexicon for the categories of occupation (e.g., "reporter" at Boston Globe), familial roles (e.g., proud "father"), political affiliation (life-long "democrat"), and cultural and sports interests (e.g., "hiphop", "NFL"). Also includes a CSV file with counts of each identity keywords matched for all 572K users in our dataset.

opencc-by-4.0Jun 2024View details →

Dataset_Social-ecological heterogeneity drove contrasting tree cover restoration in South China Karst

This is the dataset for manuscript "Social-ecological heterogeneity drove contrasting tree cover restoration in South China Karst".

opencc-by-4.0Jun 2024View details →

Supplementary material of article Mining disaster in Brumadinho (Brazil): social vulnerability from the perspective of the fisherman community

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