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5,666 results for “Social”
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 two disaster events and two long-term natural disasters across Europe (floods and wildfires) that received substantial news coverage internationally.</p> <p>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 hundreds of messages for each event in other five languages, including Arabic (ar), German (de), Japanese (ja), Indonesian (id), Italian (it) and Portuguese (pt). </p> <p>After collecting the data, we labelled tweets 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 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>
Phoenix Area Social Survey (PASS): 2006
The first Phoenix Area Social Survey (PASS) was conducted as a pilot study in 2001. Our main objective was to examine the reciprocal relationships, or the interplay, between the social and natural environments in an urban ecosystem. In order to begin to understand this complex process, social scientists affiliated with the Central Arizona-Phoenix LTER conducted a pilot survey of 302 residents in 8 neighborhoods in the city of Phoenix. Our central research questions asked how neighborhood social ties, values, and behaviors are connected with one another in ways that reflect willingness to act socially and politically with respect to the environment, and how changing environmental conditions, in turn, affect the quality of human life. The survey measured the social ties of individuals to their communities, values and sentiments regarding communities, behaviors that affect the natural environment, and satisfaction with the quality of life in the area. The second wave of PASS was conducted in 2006 with an expanded sample size in 40 neighborhoods across the metropolitan region. Many of the questions about community were repeated from the pilot study and the new survey added items for perceptions, values, and behavior concerning water supply and conservation; land use, preservation and growth management; air quality and transportation; and climate change and the urban heat island. This data package previously contained data relating to a 2001 iteration of the Phoenix Area Social Survey. Those data are now available at the following location: "Harlan, Sharon; Kirby, Andrew; Nelson, Amy; Hope, Diane; Pijawka, K. David; Bolin, Robert; Rex, Tom R; Larsen, Larissa; Wolf, Shaphard; Hackett, Edward (2016-07-05): Phoenix Area Social Survey (PASS): 2001. Long Term Ecological Research Network. http://dx.doi.org/10.6073/pasta/6849e6e3ecc196de0f3c8491eb375cc3." Datasets in the Phoenix Area Social Survey (PASS) series of long-term studies are discoverable in the LTER data system with
Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona
This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset. Locations and areas of PASS study neighborhood boundaries and source imagery used to calculate these summaries are available through the Environmental Data Initiative - see resouce listing in the methods of this data set.
German ZIP codes, Kreisschlüssel (Administration Unit), Kreis, Inhabitant per ZIP, City Names, responsible Arbeitsagentur (Social Agency
<p>This dataset from 2019 contains all German ZIP codes, city names associated with it, Kreisschlüssel (Administration Unit ID) Kreis, (Administration Unit), Bundesland (State), Inhabitants, responsible Arbeitsagentur (Social Agency). Note that especially the PLZ ZIP Codes and the responsible Arbeitsagentur change from time to time due to administrative reasons.</p>
Undirected Node Attributed Social Network Graph of Twitter Users interested in plastic pollution - created in the framework of the PlasticTwist project
<p>This dataset has been created in the framework of the Plastic Twist project (<a href="https://ptwist.eu/">Ptwist</a>) and more specifically using the Ptwist crowdsourcing application (<a href="https://crowdsourcing.plastictwist.com/">crowdsourcing.plastictwist.com/</a>). We are sharing the edge list and specific node attributes (hashtags) of Twitter users posting about plastic pollution. The dataset can be used for community detection,clustering, node importance, influence maximization tasks, etc. Each user is represented by a unique integer which has nothing to do with the official Twitter user ID. The dataset contains three (3) files: </p> <ul> <li>ptwist.edgelist: A list containing all the 1,362,863 edges between the users. When loaded they create an undirected graph of 800K+ users.</li> <li>node_attributes.txt: This file contains information about the hashtags used by each user. (e.g. "652003": ["SingleUsePlastic"] -> user 6529003 has used the hashtag SingleUsePlastic) </li> <li>annotated_graph: A pickle file which, when loaded, returns a <a href="https://networkx.github.io/">NetworkX</a> node attributed undirected graph.</li> </ul> <p> </p> <p> </p>
Internet use: participating in social networks [percentage of individuals] processed Eurostat data [CEEMID indicator]
<p>The indicator '<strong>Internet use: participating in social networks (creating user profile, posting messages or other contributions to facebook, twitter, etc.) [percentage of individuals]</strong>' from the Eurostat statistical product <em>Individuals who used the internet, frequency of use and activities.</em></p> <p>- NUTS2013 regional codes are recoded to NUTS2016<br> - missing data is handled with last observation carry forward, next observation carry back, linear interpolation<br> -NUTS2 areas are imputed when only NUTS1 level data is available. <br> <br> The original dataset is available here:<br> <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en">https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en</a></p> <p>More about CEEMID: <a href="http://ceemid.eu">www.ceemid.eu</a><br> Get in touch: <a href="http://danielantal.eu/#contact">danielantal.eu/#contact</a></p>
Detecting East Asian Prejudice on Social Media
<p>This repository contains:</p> <ul> <li>A deep learning model which distinguishes between Hostililty against East Asia, Criticism of East Asia, Discussion of East Asian prejudice and Neutral content. The F1 score is 0.83.</li> <li>A detailed annotation codebook used for marking up the tweets.</li> <li>A labelled dataset with 20,000 entries.</li> <li>A dataset with all 40,000 annotations, which can be used to investigate annotation processes for abusive content moderation.</li> <li>A list of thematic hashtag replacements.</li> <li>Three sets of annotations for the 1,000 most used hashtags in the original database of COVID-19 related tweets. Hashtags were annotated for COVID-19 relevance, East Asian relevance and stance.</li> </ul> <p>The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. This data repository is for a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic.</p> <ul> </ul> <p>This work is a collaboration between The Alan Turing Institute and the Oxford Internet Institute. It was funded by the Criminal JusticeTheme of the Alan Turing Institute under Wave 1 of The UKRI Strategic Priorities Fund, EPSRC Grant EP/T001569/1</p>
Albero study: a longitudinal database of the social network and personal networks of a cohort of students at the end of high school
<p><strong>ABSTRACT</strong></p> <p>The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.</p> <p><strong>INTRODUCTION</strong></p> <p>Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.</p> <p>The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).</p> <p>Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).</p> <p>These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.</p> <p><strong>PARTICIPANTS</strong></p> <p>The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).</p> <p><strong>DATE STRUCTURE AND ARCHIVES FORMAT</strong></p> <p>The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.</p> <p><strong>Social network</strong></p> <p>The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.</p> <p>The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.</p> <p>In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.</p> <p><strong>Personal networks</strong></p> <p>Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).</p> <p>Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.</p> <p><strong>Sense of community and metropolitan displacements</strong></p> <p>The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:</p> <p> • Socio-economic data.</p> <p> • Data on habitual residence.</p> <p> • Information on intercity journeys.</p> <p> • Identity and sense of community.</p> <p> • Personal network indicators.</p> <p> • Social network indicators.</p> <p><strong>DATA ACCESS</strong></p> <p>Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.</p> <p>The visual representation of the personal networks of the respondents in both waves is available in the following album of the <em>Graphic Gallery of Personal Networks</em> on Flickr: <<a href="https://www.flickr.com/photos/25906481@N07/albums/72157667029974755">https://www.flickr.com/photos/25906481@N07/albums/72157667029974755</a>>.</p> <p>In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:</p> <ul> <li>Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. <em>Universitas Psychologica, 17</em>(1), 86-98. <a href="https://doi.org/10.11144/Javeriana.upsy17-1.eerp">https://doi.org/10.11144/Javeriana.upsy17-1.eerp</a> </li> </ul> <p>The English version of this article can be downloaded from: <a href="https://tinyurl.com/yy9s2byl">https://tinyurl.com/yy9s2byl</a></p> <p><strong>CONCLUSION</strong></p> <p>The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.</p> <p>The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.</p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford.</p> <p><strong>REFERENCES</strong></p> <p><strong>Brandes, U., & Wagner, D. (2004). </strong>Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), <em>Graph Drawing Software</em> (pp. 321-340). New York: Springer-Verlag. </p> <p><strong>Maya-Jariego, I. (2018).</strong> Why name generators with a fixed number of alters may be a pragmatic option for personal network analysis. <em>American Journal of Community Psychology, 62</em>(1-2), 233-238. DOI 10.1002/ajcp.12271</p> <p><strong>Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018).</strong> Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. <em>Universitas Psychologica, 17</em>(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp</p> <p><strong>Maya-Jariego, I., Lubbers, M. J. & Molina, J. L. (2019).</strong> A friendship network in decay: The dynamics of social relationships of a secondary school cohort over the transition to university. <em>Remitido</em>.</p> <p><strong>Seidman, E., & French, S. E. (2004).</strong> Developmental trajectories and ecological transitions: A two-step procedure to aid in the choice of prevention and promotion interventions. <em>Development and Psychopathology, 16</em>(4), 1141-1159. https://doi.org/10.1017/s0954579404040179</p>
Albero study: a longitudinal database of the social network and personal networks of a cohort of students at the end of high school
<p>The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.</p> <p><strong>INTRODUCTION</strong></p> <p>Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context.</p>
Social networks predict the life and death of honey bees - Data
<p><strong>Interaction matrices and metadata used in "Social networks predict the life and death of honey bees"</strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.05.06.076943v2">Preprint: Social networks predict the life and death of honey bees</a></p> <p>See the README file in <a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition</a> for example code.</p> <p><strong>The following files are included:</strong></p> <p><strong>interaction_networks_20160729to20160827.h5</strong></p> <p>The social interaction networks as a dense tensor and metadata.</p> <p>Keys:</p> <ul> <li>interactions: Tensor of shape (29, 2010, 2010, 9) (days x individuals x individuals x interaction_types). I_{d,i,j,t} = log(1 + x), where x is the number of interactions of type t between individuals i and j at recording day d. See the methods section of paper of the interaction types.</li> <li>labels: Names of the 9 interaction types in the order they are stored in the interactions tensor.</li> <li>bee_ids: List of length 2010, mapping from sequential index used in the interaction tensor to the original BeesBook tag ID of the individual</li> </ul> <p><strong>alive_bees_bayesian.csv </strong></p> <p>This file contains the results of the bayesian lifetime model with one row for each bee.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>days_alive: Number of bees the bees was determined to be alive. If the individual was still alive at the end of the recording, the number of days from the day she hatched until the end of the recording.</li> <li>death_observed: Boolean indicator whether the death occurred during the recording period.</li> <li>annotated_tagged_date: Hatch date of the individual, i.e. the date she was tagged.</li> <li>inferred_death_date: The death date as determined by the model.</li> </ul> <p><strong>bee_daily_data.csv</strong></p> <p>This file contains one row per bee per day that she was alive for the focal period.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>date: Date in year-month-day format.</li> <li>age: Age in days. Can be NaN if the bee has no associated death_date.</li> <li>network_age, network_age_1, network_age_2: The first three dimensions of network age.</li> <li>dance_floor, honey_storage, near_exit, brood_area_total: Normalized (sum to 1). Can be NaN if a bee had no high confidence detections (>0.9) for a given day. Can be 0 if a bee was only seen outside of the annotated areas.</li> <li>location_descriptor_count: The number of minutes the bee was seen in one of the location labels during that day. I.e., dance_floor * location_descriptor_count calculates the number of minutes, the bee was seen on the dance floor on the given day.</li> <li>death_date: Date the bee was last seen in the colony in year-month-day format. Can be NaN for individuals that did not die until the end of the recording period.</li> <li>circadian_rhythm: R² value of a sine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</li> <li>velocity_peak_time: Phase of the circadian sine fit in hours as an offset to 12:00 UTC. Can be NaN if circadian_rhythm is NaN.</li> <li>velocity_day, velocity_night: Mean velocity of the individual between 09:00-18:00 UTC and 21:00-06:00 UTC, respectively. Can be NaN if no velocity data was available for that interval.</li> <li>days_left: Difference in days between date and death_date. Can be NaN if death_date is NaN.</li> </ul> <p><strong>location_data.csv</strong></p> <p>This file contains subsampled position information for all bees during the focal period. The data contains one row for every individual for every minute of the recording if that individual was seen at least once during that minute with a tag confidence of at least 0.9. The first matching detection for each individual is used.</p> <p>Columns:</p> <p>In addition to the bee_id and date columns as in the bee_daily_data.csv, the file contains these additional columns:</p> <ul> <li>cam_id, cams: The cam_id is a numerical identifier from {0, 1, 2, 3}. Each side of the hive is filmed by two cameras where {0, 1} and {2, 3} record the same side respectively. The cams column contains values either “(0, 1)” or “(2, 3)” and indicates to which sides of the hive this detection belongs.</li> <li>x_pos_hive, y_pos_hive: The spatial positions in millimeters on the hive. The two cameras from one side share a common coordinate system.</li> <li>location: The label that was assigned to the comb at (x_pos_hive, y_pos_hive) on the given date. The label “other” indicates detections that were outside of any annotated region. The label “not_comb” indicates the wooden frame or empty space around the comb.</li> <li>timestamp, date: The timestamp indicates the beginning of each one-minute sampling interval and is given in UTC, as indicated (example: “2016-08-13 00:00:00+00:00”). The date part of the timestamp is repeated in the “date” column. Both are given in year-month-day format.</li> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition: Network age calculation and regression analyses</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline/releases/tag/2016">bb_pipeline: Tag localization and decoding pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline_models/releases/tag/2016">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_binary/releases/tag/2016">bb_binary: Raw detection data storage format</a></li> <li><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></li> <li><a href="https://github.com/BioroboticsLab/bb_imgacquisition/releases/tag/2016">bb_imgacquisition: Recording and network storage </a></li> <li><a href="https://github.com/BioroboticsLab/bb_behavior/releases/tag/2016">bb_behavior: Database interaction and data (pre)processing, velocity calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_circadian/releases/tag/2016">bb_circadian: Circadian rhythm calculations</a></li> <li><a href="https://github.com/BioroboticsLab/bb_tracking_2016/releases/tag/2016">bb_tracking: Tracking of bee detections over time</a></li> <li><a href="https://github.com/BioroboticsLab/bb_wdd/releases/tag/2016">bb_wdd: Automatic detection and decoding of honey bee waggle dances</a></li> <li><a href="https://github.com/BioroboticsLab/bb_interval_determination/releases/tag/2016">bb_interval_determination: Homography calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_stitcher/releases/tag/2016">bb_stitcher: Image stitching</a></li> </ul> <p> </p>
Social sensing of urban land use based on analysis of Twitter users' mobility patterns
<p>A companion dataset for the paper "Social sensing of urban land use based on analysis of Twitter users' mobility patterns". This dataset contains five files and one dictionary depicting the preferential return of Twitter users to their key locations and the urban land use types at these locations. More details can be found in the README file. </p>
Base de datos, una mirada textil a la movilización social, Colombia, 2021
<p>Base de datos que reúne 118 fotografías resultado de la convocatoria pública "Una mirada textil a la movilización social”, realizada entre marzo y junio del 2023 en el marco de la investigación en curso: "Desigualdades, resiliencia comunitaria y nuevas modalidades de gobernanza en un mundo postpandemia". Proyecto liderado por Artesanal Tecnológica desde la Escuela de Estudios de Género de la Universidad Nacional de Colombia y financiado por el Ministerio de Ciencia, Tecnología e Innovación. En el marco de la convocatoria de La Plataforma Transatlántica para las Ciencias Sociales y las Humanidades (T-AP).</p> <p>Esta base de datos nos permite identificar las diferentes formas en las que lo textil vistió la movilización social del 2021 en Colombia en el marco de la pandemia de Covid-19. En ella se reúnen fotografías tomadas ciudades como Bogotá, Medellín, Pasto, Popayán, Cúcuta, Bucaramanga, Mosquera y Funza por 28 personas que a través de una diversidad de dispositivos como cámaras profesionales y celulares, atestiguaron las diferentes maneras en la que lo textil durante las movilizaciones ocupó la calle y vistió los cuerpos. La base de datos revela la presencia de lo textil como indumentaria, materializada en capuchas, tapabocas, camisetas, pañoletas, piezas de denuncia como pancartas y banderas, y como acción colectiva de intervención del espacio público, como plantones y grafitis textiles, se despliegan y sitúan en diferentes escenarios que dan cuenta de la diversidad de las movilizaciones que constituyeron el estallido social en Colombia. La base de datos ofrece información sobre el contexto de las capturas, el acceso a las imágenes y la información autoral y de contacto de quienes participaron en la convocatoria. También se puede filtrar los datos por tipo de indumentaria o repertorio de acción textil desplegada en la movilización, así como por la técnica principal de las piezas.</p>
Toxic Content Detection in online social networks: a new dataset from Brazilian Reddit Communities
<p>This is new dataset of 2,500 manually annotated examples of comments extracted from the top 10 largest Brazilian subreddits on Reddit. The dataset has been annotated by crowd-sourcing efforts with contributions from the departments of computer science (DCC) and the linguistic group @ UFMG. As part of our contribution to the toxicity automatic detection and moderation of online social networks, we're making the dataset public for research.</p> <h3>Dataset</h3> <p>The dataset contains 2,500 manually annotated comments from the most popular brazilian communities on Reddit. The data sampling proccess was a stratified sampling by the number of generated publications by subreddit and the month of publication. The list of communities collected is presented below. The collected data period ranges from January 2022 to December 2022.</p> <p> </p> <table> <tbody> <tr> <td><strong>Subreddit</strong></td> <td><strong>Posts</strong></td> <td><strong>Comments</strong></td> </tr> <tr> <td>r/brasil</td> <td>110,829 </td> <td>2,136,866</td> </tr> <tr> <td>r/desabafos</td> <td>115,876</td> <td>1,211,643</td> </tr> <tr> <td>r/futebol</td> <td>35,826</td> <td>1,214,412</td> </tr> <tr> <td>r/saopaulo</td> <td>7,308</td> <td>81,969</td> </tr> <tr> <td>r/eu_nvr</td> <td>12,631</td> <td>188,620</td> </tr> <tr> <td>r/botecodoreddit</td> <td>7,059</td> <td>57,298</td> </tr> <tr> <td>r/conversas</td> <td>21,967</td> <td>326,061</td> </tr> <tr> <td>r/investimentos</td> <td>9,756</td> <td>141,823</td> </tr> <tr> <td>r/tiodopave</td> <td>2,371</td> <td>11,584</td> </tr> <tr> <td>r/brasilivre</td> <td>67,301</td> <td>1,219265</td> </tr> <tr> <td>Total</td> <td>390,924</td> <td>6,589,541</td> </tr> </tbody> </table> <p> </p> <h3><strong>Annotation proccess</strong></h3> <p>The annotators were divided into groups of raters and each group was assigned a batch of comments to label. The raters were then asked to label a comment as <strong>Toxic</strong>, <strong>Non-toxic</strong>, <strong>I do not know</strong> and <strong>Missing info</strong>. During the annotation process, the raters were encouraged to assign one of the uncertain labels when they're not sure about the toxicity of a comment or the context is missing. </p> <h3>Available data</h3> <p>The dataset is available as csv file and the label was assigned as a majority vote among the raters. The available data are the original collected comment id and body. The label was created from the original classification from the annotators. No data processing has been done on this version of the dataset. The overall schema of the dataset if presented below.</p> <p>- <strong>id</strong>: The unique identifier of the comment on the Reddit platform<br>- <strong>body</strong>: The original comment text publication<br>- <strong>is_toxic</strong>: The final label of a given comment. The label is <strong>0</strong> for non-toxic comments, <strong>1</strong> for toxic comments and <strong>-1</strong> for comments where the raters disagreed about the toxicity.</p>
Estimated life-cycle-based environmental indicators and social indicators for companies and investment funds
<p>The data files represent the 26 estimated life-cycle-based indicators for a sample of companies and funds, obtained using the methodology described in the linked journal article. The files SD1 and SD2 contain the individual values estimated for the fund and company samples. These estimates are based on the methodology described in the linked article. The data herein is the source for producing all figures of the paper. All companies and funds have been anonymized, as the data is sourced from proprietary databases. At the same link, supplementary file SD3 contains the summary statistics and comparison of sustainable funds versus conventional funds sample. The file SD4 contains the data used to create Figure 4. The file SD5 contains sample data to create Figure 5. The file SD6 contains sample data to create Figure 6. Additional more detailed data can be provided upon reasonable request, but cannot be publicly disclosed as it contains data from licenced databases. </p>
Data from: Selective social interactions and speed-induced leadership in schooling fish
<p>Experimental datasets for the manuscript:</p> <div>Puy, A., Gimeno, E., Torrents, J., Bartashevich, P., Miguel, M. C., Pastor-Satorras, R., & Romanczuk, P. (2024). Selective social interactions and speed-induced leadership in schooling fish. <em>Proceedings of the National Academy of Sciences</em>, <em>121</em>(18), e2309733121.</div> <div> </div> <p>The datasets provide trajectories of fish. There are 2 recordings with N=39 fish (60 minutes duration) and 6 recordings with N=8 fish (30 minutes duration). The columns are as follows:</p> <ul> <li>Time [frame]: Time of the trajectory in frames.</li> <li>X_0 [px]: Position in the x-coordinate in pixels of the trajectory of individual 0.</li> <li>Y_0 [px]: Position in the y-coordinate in pixels of the trajectory of individual 0.</li> <li>X_1 [px]: Position in the x-coordinate in pixels of the trajectory of individual 1.</li> <li>Y_1 [px]: Position in the y-coordinate in pixels of the trajectory of individual 1.</li> <li>...</li> </ul> <p>Conversion to international units:</p> <ul> <li>50 frames = 1 s.</li> <li>2745 px= 100 cm.</li> </ul>
Plant metabolites modulate animal social networks and lifespan
<p><span>Social interactions influence disease spread, information flow, and resource allocation across species, yet heterogeneity in social interaction frequency and its fitness consequences remain poorly understood. Additionally, animals can utilize plant metabolites for purposes beyond nutrition, but whether that shapes social networks is unclear. Here, we investigated how non-nutritive plant metabolites impact social interactions and the lifespan of the turnip sawfly, <em>Athalia rosae</em>. Adult sawflies acquire neo-clerodane diterpenoids ('clerodanoids') from non-food plants, showing intraspecific variation in natural populations and laboratory-reared individuals. Clerodanoids can also be transferred between conspecifics, leading to increased agonistic social interactions. Network analysis indicated increased social interactions <span>in sawfly groups where some or all individuals had prior access to clerodanoids</span>. Social interaction frequency varied with clerodanoid status, with fitness costs including reduced lifespan resulting from increased interactions. Our findings highlight the role of intraspecific variation in the acquisition of non-nutritional plant metabolites in shaping social networks, with fitness implications on individual social niches.</span></p>
Database of Participatory Practices and Social Innovations in Wind Energy Developments
<p>Inês Campos was responsible for designing the database, collecting data, and analyzing data. Flávio Oliveira also collaborated in the design of the database and data collection. </p>
SOCIAL MEDIA DATA 3 SPORTING EVENTS
<p>Comments on social networks (Facebook, Instagram, Twitter and YouTube) about the brand Spain linked to three chosen sporting events (mega, medium and<br>local): a football mega event (Qatar Football World Cup), a semi-massive tennis event (Davis Cup 2022) and a local marathon event (XLI<br>Marathon Valencia Trinidad Alfonso 2022</p>
Cyberhate that targets people who are plus-size in the news: The role of bystanders in mitigating social pathologies (CYBERPLUS)
<p>The dataset was created for the project "Cyberhate that targets people who are plus-size in the news: The role of bystanders in mitigating social pathologies (CYBERPLUS)". The data was collected between July 12 and July 26, 2024, from 1,030 young Czech people aged 16-25. The survey asked young people about their sociodemographic information, attitudes toward and perceptions of entitativity of three groups (overweight people, underweight people, people with physical disabilities), group identification, bystander appraisals and behavioural intentions, hate speech perception, and internet use. It included an experimental part in which the participants were exposed as bystanders to social media news posts about overweight people and comments under the posts. The dataset is accompanied by a data dictionary and a technical report.</p>
Social Media Tweets Pro and Anti Bolsonaro
<p><strong>Overview</strong></p> <p>Social media platforms have an important role in Brazilian society's polarization. Especially during the COVID-19 pandemic (2020-2021), these platforms have a peak of posts about President Bolsonaro's speech and behavior during this period. On the one hand, millions of people support Bolsonaro's attitudes and follow his controversial guidances. On the other hand, a vast number of social media users accuse Bolsonaro of acting against democracy and science. </p> <p>In this context, this dataset presents the collection of tweets posts and its linked news articles (including all related media) covering two Brazilians’ demonstrations events PRO and AGAINST President Bolsonaro government, during September 7th and October 2nd of 2021.<br> The dataset contains 4.7M tweets.</p> <p><strong>Data Collection</strong><br> We use the library Fake-News Crawler (https://github.com/phillipecardenuto/fakenews-crawler) to collect the tweet posts and related media. For this, we provided keywords related to both events to receive the data during events and following days. For instance, some of the keywords used were ‘<em>7deSet</em>’, ‘<em>BolsonaroAte2026</em>’, ‘<em>VemParaRua</em>’, ‘<em>EleNao</em>’, ‘<em>07EuVou</em>’, ‘<em>SupremoÉOPovo</em>’, '<em>aculpaédobolsonaro</em>’, ‘<em>2outeuvou</em>’.<br> <br> <strong>Disclaimer</strong>: We did not perform any filtering or procedure to assert that all collected data is, in fact, related to the demonstrations; therefore, some of the content of the dataset might not be related to these events.</p> <p><strong>Content</strong><br> brazilian_demonstration_events.json: It contains the tweet posts, their metadata (e.g., post time, language), and all related media content URLs (i.e., news article link and media links).<br> <br> <strong>Media Content</strong><br> Due to the terms of use from the social networks, we do not make publicly available the images and videos that were collected. However, we can provide some extra pieces of media content related to one (or more) events by contacting the authors.<br> <br> <strong>Funding</strong><br> DéjàVu thematic project, São Paulo Research Foundation (grants 2017/12646-3, 2020/02241-9 and 2020/02211-2)<br> </p>
ScienceDex guides
Understand access before you commit
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.