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5,666 results for “Social”
Social Network Online Activity of 100+ Users Over Two Years
<p>This dataset contains a precise (error margin is within 5 seconds) activity log of 138 users recorded over a period of approximately two years. It includes users' log in/log off timestamps as well as a device id which was used during the session. An activity heat map is also provided which can be used to determine the online time (in seconds) in a given hour for a given user. The dataset is completely anonymized and is not linked to real peoples' accounts. Russian social network VK was used to record the data.</p> <p>The database is provided in SQLite3 format. The data format is the following:</p> <p><strong>'sessions' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>platform</td> <td>INTEGER</td> <td>Device identifier for the session (refer to the table below).</td> </tr> <tr> <td>time_from</td> <td>DATE</td> <td>Timestamp of the session's start.</td> </tr> <tr> <td>time_to</td> <td>DATE</td> <td>Timestamp of the session's end.</td> </tr> </tbody> </table> <p><strong>'map' </strong>table:</p> <table> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Data Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>user_id</td> <td>TEXT</td> <td>Unique user's identifier.</td> </tr> <tr> <td>hour</td> <td>INTEGER</td> <td>Hour from the 1st Jan 1970 (Unix Epoch / 3600).</td> </tr> <tr> <td>time</td> <td>INTEGER</td> <td>Accumulated online time in the hour (in seconds).</td> </tr> </tbody> </table> <p>Device identifiers:</p> <table> <tbody> <tr> <td>0</td> <td>Unknown</td> </tr> <tr> <td>1</td> <td>Web on Mobile </td> </tr> <tr> <td>2</td> <td>iPhone App</td> </tr> <tr> <td>3</td> <td>iPad App</td> </tr> <tr> <td>4</td> <td>Android App</td> </tr> <tr> <td>5</td> <td>Windows Phone App</td> </tr> <tr> <td>6</td> <td>Windows App</td> </tr> <tr> <td>7</td> <td>Web on Desktop</td> </tr> </tbody> </table> <p> </p> <p>This dataset is associated with the VKWatcher independent research project. The code used to gather the information can be found <a href="https://github.com/Azarattum/VKWatcher-Backend">on GitHub</a>.</p>
Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"
<pre>These files contain the time series and the associated hashtags we obtained by sampling Twitter for our paper "Universality, criticality and complexity of information propagation on social media". The analysis is reported in <a href="https://arxiv.org/abs/2109.00116">https://www.nature.com/articles/s41467-022-28964-8</a> Please acknowledge the use of these data by citing the paper above. ################################# ################################# DATA ORGANIZATION We created a single zip file with all the time series and a single zip file with all the hashtags. There is a one-to-one correspondence between lines in the two files. ################################# ################################# FILES CONTENT As stated, here is a one-to-one correspondence between lines in the time series file and lines in the hashtags file, i.e., the hashtag stored in line X is the hashtag of the time series stored in line X. Time series are stored as follows: Ka t1 t2 t3 \n Kb t1 t2 t3 t4 t5 \n . . . Kn t1 t2 \n where: Ka, Kb,..., Kn is an integer specifying the number of events that compose the time series a, b,..., n respectively. In the example above we would have Ka=3, Kb=5, Kn=2. t1 t2 ... is the time series, i.e., a sequence of chronologically ordered interevent times. The last interevent time, in our implementation, represents the distance between the end of the temporal window and the last event time. It thus does not represent an event. As stated in the Supplemental Material of our paper, the temporal window ranges from 2019, October 1st to 2019, November 30th. </pre>
VABB-SHW: Dataset of Flemish Academic Bibliography for the Social Sciences and Humanities (edition 11)
<p>This dataset contains the eleventh 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 Media Mask Dataset
<p>The Social Media Mask Dataset is a dataset made up of Twitter Images intended for the training of Convolutional Neural Networks to detect masks in images and video. In this case, the term masks refer to a device worn on the face intended to reduce the spread of respiratory illness. Due to Twitter's TOS, we cannot directly publish Twitter images. Instead, we publish Tweet keys along with information our script uses to download the target image. The file training.json is our suggested training set while testing.json is our suggested test set. Our script download_dataset.py can be used to download the full dataset with a Twitter developer account. </p>
Are marriage-related taxes and Social Security benefits holding back female labor supply?
<p>Margherita Borella, Mariacristina De Nardi, and Fang Yang, "Are marriage-related taxes and Social Security benefits holding back female labor supply?". The Review of Economic Studies, Forthcoming. This package contains all the code and detailed instructions on how to replicate the data analysis and numerical results in the paper.</p>
Youth definitions in governmental, philosophical and social science publications
<p>This datasets provides extracted meta data of youth definitions found in governmental, philosophical and social science publications. The definitions are classified according to different schemas and linked with publication resources and other meta data.</p>
Learning to embed lifetime social behavior from interaction dynamics - Data
<p><strong>Interaction matrices and metadata used in "Learning to embed lifetime social behavior from interaction dynamics"</strong></p> <p>The following files are included:</p> <ul> <li>interactions_bn16_sparse.npz and interactions_bn19_sparse.npz: These are the interaction affinity matrices for the BN16 and BN19 datasets as described in the publication. The data is stored as compressed sparse tensors with time on the first, and the individuals on the second and third dimensions. The data was stored using the <a href="http://sparse.pydata.org">pydata/sparse</a> library 0.9.1</li> <li> <p>alive_bn16.csv and alive_bn19.csv: These files contain the dates of emergence (also corresponding to the dates they were introduced into the colonies) and heuristically determined number days alive for all individuals in the interaction matrices. Death dates were determined using a bayesian changepoint model and the number of daily detections of each individual</p> </li> <li> <p>rhythmicity_bn16.csv and rhythmicity_bn19.csv: These files contain the circadian rhythmicity values used in the evaluation of the method. The circadian rhythmicity is the <span class="math-tex">\(R^2\)</span> value of a sine with a 24 hour period fitted to the individuals' movement velocities over a three day window</p> </li> <li> <p>indices_bn16.csv and indices_bn19.csv: These files contain the mapping between the original marker IDs used during the recording of the data (which has gaps, because not all markers were used) and the sequential indices used in the interaction matrices. These files can therefore be used to look up the original ID of an individual based on it's index in the interaction matrix and vice versa</p> </li> <li> <p>time_spent_on_substrates.csv: This data was used for the mapping from factors to the proportion of time spent on various cell substrates (Figure 5). The positions of the individuals were accumulated by minute, and the column "location_descriptor_count" contains the total number of minutes on the respective day that the individual was detected</p> </li> </ul> <p>See <a href="https://doi.org/10.1101/2020.05.06.076943">10.1101/2020.05.06.076943</a> for more details about the bayesian changepoint model, circadian rhythmicity calculation, and location mapping.</p>
CoMix social contact data (Italy)
<p>CoMix social contact data for Italy.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Daniela Paolotti at the ISI Foundation.</p>
CoMix social contact data (Denmark)
<p>CoMix social contact data for Austria.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Michael Bang Petersen at Aarhus University.</p>
CoMix social contact data (Greece)
<p>CoMix social contact data for Greece.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Elpida Pavi at the University of West Attica (UniWA).</p>
CoMix social contact data (Portugal)
<p>CoMix social contact data for Portugal.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Baltazar Nunes at the National Health Institute Doutor Ricardo Jorge (INSA).</p>
CoMix social contact data (Poland)
<p>CoMix social contact data for Poland.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Magdalena Rosinska at the National Institute of Public Health - National Institute of Hygiene.</p>
CoMix social contact data (France)
<p>CoMix social contact data for France.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Guillaume Béraud at the Centre Hospitalier Universitaire de Poitiers.</p>
Social Innovations for Circularity in the Built Environment – a Scoping Review and Classification. Supplementary Data to the Bibliometric Review.
<p>To transition the built environment (BE) towards circularity, i.e., maximizing the time resources spend in the BE, thus minimizing negative environmental impacts of resource usage, social innovations (SI) – understood as new ways of doing, organizing, framing, and knowing – are just as important as technological advancements. This article provides an overview of the state of the knowledge regarding SI that contribute to circularity in the BE, and proposes a framework for coherently classifying such SI in terms of their main categories and effects.</p> <p>To identify and understand the current knowledge regarding social innovations (SI) that contribute to circularity in the built environment (BE), a bibliometric review of scientific literature is conducted. It shows that the term social innovation is not frequently used in this contexts although the buzzwords circularity and circular economy are themselves often framed as SI. To assess the characteristics and contribution of SI to circularity in the BE, a scoping review that includes grey literature into the context was conducted and a framework developed to classify predominant SI using concepts from transition studies as well as the systems thinking approach. The framework is designed to help assess the potential of SI and to identify research and/or action gaps. Key findings are (1) There is a broad diversity of SIs that contribute to circularity in the BE already in the focus of research, although they are not always identified as such; (2) Most SI focus on either the design or the demolition phase (i.e., market related phases), whereas user-centered SI are less frequently discussed; and (3) It is crucial to also consider potential sustainability goal conflicts in order to guide policies that address SI as a solution.</p> <p>These datasets are the basis to the bibliometric literature review.</p>
D2.2 Open data concerning social inclusion provided on the project homepage - Emerging findings
<p>Authors to the case posters and contributors from consortium partners are described in the deliverable. </p> <p>The H2020 YouCount project runs from February 2021 to January 2024 and the consortium consists of 11 partners from nine European countries. Multiple case studies—consisting of 10 co-creative Y-CSS projects with young citizen scientists (YCS) aged between about 13-29 years old across nine countries in Europe—will provide knowledge about the positive drivers of social inclusion in general. The cases will further produce knowledge as well as innovations in relation to social participation, social belonging, and citizenship.</p> <p>In line with YouCount’s commitment to Open Science and Data Management based on the FAIR Principles, D2.2 provides a sample of open data concerning social inclusion from the research and innovation activities during the implementation period. The open data is based on informed consent and includes the following files included in the report:</p> <p>1. File 1 Homepage 30-06-22, Case descriptions.</p> <p>2. File 2 Case posters 08-06-2022, Experiences with inclusive co-creative Y-CSS in multiple case study. </p> <p>3. File 3 Narrative text 26-06-22, Experiences with developing the YouCount app toolkit, methodology.</p> <p>4. File 4 Quotes 25-06-22, Views and experiences with social inclusion of youths, YouCount/ECSA WG EIE webinars 2021 and YouCount newsletters 2022.</p> <p>5. File 5 Links to YouCount app toolkit, 28-06-22, Youths’ views and experiences with social inclusion opportunities, observations.</p> <p>Notably, the open data are based on a co-creative and flexible research design and comes in an early phase of the case studies. They can thus only be used as emerging data and preliminary findings. Still, the data contain valuable information of the research experiences and voices from young people found in the early phase of conducting hands on co-creative Y-CSS. More systematic open social inclusion data will be provided later in the project. </p> <p>The open data can also be found at the project website <a href="https://www.youcountproject.eu/">Home - YouCount - Social Citizen Science (youcountproject.eu)</a>.</p> <p>Note! They are shared under CC-BY (text) and CC-BY-ND (images case posters) due to confidentiality issues.</p>
Dataset: Multi-level network dataset of social-ecological interdependencies in ten Swiss wetlands based on qualitative interviews and quantitative surveys
<p>The dataset originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).</p>
Social contact data for IDPs in Somaliland (2019)
<p>Social contact data for internally displaced people (IDP) living in Digaale IDP camp in Somaliland. Participants reported all their direct contacts in the 24 hours preceding the survey. This survey was conducted in 2019. See the corresponding paper for more information: <a href="https://doi.org/10.1016/j.epidem.2022.100625">https://doi.org/10.1016/j.epidem.2022.100625</a></p> <p>Data is formatted to be used in the <em>socialmixr</em> package in <em>R</em>.</p>
La planificación y la función social, Olivera
<p><strong>Coordinador del Seminario:</strong> Carlos A. Navarrete Ulloa.<br> <strong>Expositor</strong>: Luis Adolfo Ortega Granados</p> <p><strong>Comité Ejecutivo PRONACE-Vivienda</strong></p> <ul> <li>Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).</li> <li>Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.</li> <li>Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).</li> <li>Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</li> </ul> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:<br> Olivera, G. La planificiación y la función social de la propiedad como condición para atender las necesiaddes de vivienda de los pobres urbanos. En: Apuntes sobre tres casos relevantes en Latrinoamérica (2015). Repensar la metrópoli II. Dos tomos (1.ª ed.). Universidad Autónoma Metropolitana. </p>
Social housing is good for everyone
<p><strong>Coordinador del Seminario:</strong> Carlos A. Navarrete Ulloa.<br> <strong>Expositor</strong>: Ramona Esmeralda Velázquez García<br> <strong>Comité Ejecutivo PRONACE-Vivien</strong>da</p> <ul> <li>Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).</li> <li>Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.</li> <li>Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).</li> <li>Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</li> </ul> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:</p> <p>Khalid, M. (2015). Social housing is good por everyone. A benefits-Cost analysis. In J. Silver & J. Brandon (Eds.), Poor Housing: A Silent Crisis. Fernwood Publishing </p>
El posicionamiento de la producción social de vivienda en la política nacional de vivienda
<p><strong>Coordinador del Seminario:</strong> Carlos A. Navarrete Ulloa.<br> <strong>Expositor</strong>: Ramona Esmeralda Velázquez García </p> <p><strong>Comité Ejecutivo PRONACE-Vivienda </strong></p> <p>Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG).<br> Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.<br> Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH).<br> Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</p> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:<br> Villalobos, C. A. (2013). El posicionamiento de la producción social de vivienda en la política nacional de vivienda. Retos y perspectivas En Habitabilidad y Política de Vivienda en México, 103. <br> https://www.puec.unam.mx/index.php/publicaciones/145-publicaciones-digitales/pd-instituciones/415-habitabilidad-y-politica-de-vivienda-en-mexico</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.