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5,666 results for “Social”
Hormone Replacement Therapy Social Media Claims
<p>The claims made in social media posts about hormone replacement therapy, the tpye of person/organisation posting these claims, whether the claims agree with NICE/BNF guidance and whether the person/organisation posting these claims has a conflict of interest. Full unredacted dataset available on request to: <a href="mailto:mm494@st-andrews.ac.uk">mm494@st-andrews.ac.uk</a></p>
Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework
<p>This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play. </p> <p>In this data set, <strong>team level multi-modal behavioral data</strong> is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below: </p> <ul> <li><em>condition:</em> This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li><em>Error: </em>This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li><em>Learning Gain: </em>It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li><em>Usefulness Score: </em>The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li><em>PE Score: </em>It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li><em>Right_Suggestions: </em>This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li><em>Right_Time: </em>This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li><em>Exploration:</em> This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li><em>Reflection: </em>This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li><em>Communication: </em>This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li><em>LG_status: </em>This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> <p>This dataset corresponds to the publication <em><strong>"Social robots as skilled ignorant peers for supporting learning"</strong></em>: <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a></p> <p> </p>
Dataset of Social buffering switches fear to safety encoding by oxytocin recruitment of central amygdala buffer neurons
<p>Dataset of <span>Hegoburu et al., Social buffering switches fear to safety encoding by oxytocin recruitment of central amygdala buffer neurons, Nature Communications.</span></p> <p><span>ABSTRACT</span></p> <p><span>The presence of a companion can reduce fear, but the precise neural mechanisms underlying this social buffering of fear (SBF) are incompletely known. We studied SBF in male and female rats, and its encoding in the amygdala of males, that were fear-conditioned (FC) to auditory conditioned stimuli (CS). Pharmacological, opto,- and/or chemogenetic interventions showed that oxytocin (OT) signaling from hypothalamus-to-central amygdala (CeA) projections was required for acute fear reduction in the presence, and SBF retention 24h later without the companion. Single-unit recordings with optetrodes revealed fear-encoding CeA neurons (characterized by increased CS-responses after FC) were inhibited by SBF and blue light (BL) stimulation of OTergic projections. Other CeA neurons increased CS responses only after SBF exposure. Their baseline activity was enhanced by BL and exposure to the companion. SBF thus switches the CS from encoding "fear" to "safety" by OT-mediated recruitment of a distinct group of CeA "buffer neurons".</span></p> <p> </p>
CoMix social contact data (Belgium )
<p>CoMix social contact data for Belgium, collected within the EpiPose project.<br> <br> Change log (V2):<br> - Validation of participants</p> <p>Change log (V3):<br> - Added data up to wave 43<br> <br> Change log (V5):<br> - Removal of duplicated file</p>
Research data for "Hutters in the Zamoyski Family Entail - history of an environmentally conditioned social group"
<p>Research data for "Hutters in the Zamoyski Family Entail - history of an environmentally conditioned social group" (v1_2024)</p>
Facial and body colouration is linked to social rank in the African cichlid Astatotilapia burtoni
<p>These are raw data files for our publication studying animal colouration and behaviour in an African cichlid, <em>Astatotilapia burtoni</em>. </p> <p> </p> <p>Abstract<br>Animal colouration is important for social communication within conspecifics to signal threats to competitors or fitness to possible mates. Social status and animal colouration are covarying traits that are plastic in response to dynamic environments. In the African cichlid, Astatotilapia burtoni, body colouration and behaviour have been reported to vary with social rank. However, the nature of the interaction between these two traits is poorly understood. We hypothesise that colouration patterns could be linked to the behavioural repertoires underlying social status and situated across regions of interest on the cichlid body plan. To test this hypothesis, we generated Territorial and Non-territorial males and employed computer vision tools to quantify and visualise patterns/colour enrichment associated with stereotyped Territorial/Non-Territorial male behaviour. We report colour-behaviour interactions localised in specific areas of the body and face for two colour morphs, illustrating a more nuanced view of social behaviour and colouration. Since behavioural and morphological variation are key drivers of selection in the East African Great Rift Lakes, we surmise our data may be translatable to other cichlid lineages and underline the importance of trait covariance in sexual selection and male competition.</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1945 (Maternal and Developmental Risks from Environmental and Social Stressors (MADRES))
Title: Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) <br>Species: Homo sapiens <br>Number of samples: 421 <br>Number of named analytes: 21 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=32 <br>
Citation network data sets for 'Oxytocin – a social peptide? Deconstructing the evidence'
<p><strong>Introduction</strong></p> <p>This note describes the data sets used for all analyses contained in the manuscript 'Oxytocin - a social peptide?’<a href="#_ftn1">[1]</a> </p> <p><strong>Data Collection</strong></p> <p>The datasets described here were originally retrieved from Web of Science (WoS) Core Collection via the University of Edinburgh’s library subscription <a href="#_ftn2">[2]</a>. The aim of the original study for which these data were gathered was to survey peer-reviewed primary studies on oxytocin and social behaviour. To capture relevant papers, we used the following query:</p> <p><em>TI = (“oxytocin” OR “pitocin” OR “syntocinon”) AND TS = (“social*” OR “pro$social” OR “anti$social”)</em></p> <p>The final search was performed on the 13 September 2021. This returned a total of 2,747 records, of which 2,049 were classified by WoS as ‘articles’. Given our interest in primary studies <em>only</em> – articles reporting original data – we excluded all other document types. We further excluded all articles sub-classified as ‘book chapters’ or as ‘proceeding papers’ in order to limit our analysis to primary studies published in peer-reviewed academic journals. This reduced the set to 1,977 articles. All of these were published in the English language, and no further language refinements were unnecessary.</p> <p>All available metadata on these 1,977 articles was exported as plain text ‘flat’ format files in four batches, which we later merged together via Notepad++. Upon manually examination, we discovered examples of papers classified as ‘articles’ by WoS that were, in fact, reviews. To further filter our results, we searched all available PMIDs in PubMed (1,903 had associated PMIDs - ~96% of set). We then filtered results to identify all records classified as ‘review’, ‘systematic review’, or ‘meta-analysis’, identifying 75 records <a href="#_ftn3">[3]</a> (thus, ~4% of records classified by WoS were classified as reviews in PubMed). After examining a sample and agreeing with the PubMed classification, these were removed these from our dataset - leaving a total of 1,902 articles.</p> <p>From these data, we constructed two datasets via parsing out relevant reference data via the Sci2 Tool <a href="#_ftn4">[4]</a>. First, we constructed a ‘node-attribute-list’ by first linking unique reference strings (‘Cite Me As’ column in WoS data files) to unique identifiers, we then parsed into this dataset information on the identify of a paper, including the title of the article, all authors, journal publication, year of publication, total citations as recorded from WoS, and WoS accession number. Second, we constructed an ‘edge-list’ that records the citations from a <em>citing paper</em> in the ‘Source’ column and identifies the <em>cited paper</em> in the ‘Target’ column, using the unique identifies as described previously to link these data to the node-attribute-list.</p> <p>We then constructed a network in which papers are nodes, and citation links between nodes are directed edges between nodes. We used Gephi Version 0.9.2 <a href="#_ftn5">[5]</a> to manually clean these data by merging duplicate references that are caused by different reference formats or by referencing errors. To do this, we needed to retain both all retrieved records (1,902) as well as including <em>all</em> of their references to papers whether these were included in our original search or not. In total, this produced a network of 46,633 nodes (unique reference strings) and 112,520 edges (citation links). Thus, the average reference list size of these articles is ~59 references. The mean indegree (within network citations) is 2.4 (median is 1) for the entire network reflecting a great diversity in referencing choices among our 1,902 articles.</p> <p>After merging duplicates, we then restricted the network to include <em>only</em> articles fully retrieved (1,902), and retrained <em>only</em> those that were connected together by citations links in a large interconnected network (i.e. the largest component). In total, 1,892 (99.5%) of our initial set were connected together via citation links, meaning a total of ten papers were removed from the following analysis – and these were neither connected to the largest component, nor did they form connections with one another (i.e. these were ‘isolates’).</p> <p>This left us with a network of 1,892 nodes connected together by 26,019 edges. <strong><em>It is this network that is described by the ‘node-attribute-list’ and ‘edge-list’ provided here</em></strong>. This network has a mean in-degree of 13.76 (median in-degree of 4). By restricting our analysis in this way, we lose 44,741 unique references (96%) and 86,501 citations (77%) from the full network, but retain a set of articles tightly knitted together, all of which have been fully retrieved due to possessing certain terms related to oxytocin AND social behaviour in their title, abstract, or associated keywords.</p> <p>Before moving on, we calculated indegree for all nodes in this network – this counts the number of citations to a given paper from other papers within this network – and have included this in the <em>node-attribute-list</em>. We further clustered this network via modularity maximisation via the Leiden algorithm <a href="#_ftn6">[6]</a>. We set the algorithm to resolution 1, and allowed the algorithm to run over 100 iterations and 100 restarts. This gave <em>Q</em>=0.43 and identified seven clusters, which we describe in detail within the body of the paper. We have included cluster membership as an attribute in the node-attribute-list.</p> <p>For additional analysis, we also analysed the full reference list data to examine the most commonly cited references between 2016 and 2021 - the results of this are described in OTSOC_Cited_2016-2021.csv. This takes the reference lists of all retrieved papers within the network and examines their full reference lists (including references to other papers not contained within the network). These data were cleaned by matching DOIs and manual cleansing. </p> <p><strong>Data description</strong></p> <p>We include here two network datasets: (i) ‘OTSOC-node-attribute-list.csv’ consists of the attributes of 1,892 primary articles retrieved from WoS that include terms indicating a focus on oxytocin and social behaviour; (ii) ‘OTSOC-edge-list.csv’ records the citations between these papers. Together, these can be imported into a range of different software for network analysis; however, we have formatted these for ease of upload into Gephi 0.9.2. Finally, we include (iii) 'OTSOC_Cited_2016-2021' that lists all papers cited by >10 papers in the OTSOC network following any analysis of the bibliographies of retrieved papers. Below, we detail their contents:</p> <p><strong>1. ‘OTSOC-node-attribute-list.csv’</strong> is a comma-separate values file that contains all node attributes for the citation network (n=1,892) analysed in the paper. The columns refer to:</p> <p><em>Id</em>, the unique identifier</p> <p><em>Label</em>, the reference string of the paper to which the attributes in this row correspond. This is taken from the ‘Cite Me As’ column from the original WoS download. The reference string is in the following format: last name of first author, publication year, journal, volume, start page, and DOI (if available). </p> <p><em>Wos_id</em>, unique Web of Science (WoS) accession number. These can be used to query WoS to find further data on all papers via the ‘UT= ’ field tag.</p> <p><em>Title</em>, paper title.</p> <p><em>Authors</em>, all named authors.</p> <p><em>Journal, </em>journal of publication.</p> <p><em>Pub_year</em>, year of publication.</p> <p><em>Wos_citations</em>, total number of citations recorded by WoS Core Collection to a given paper as of 13 September 2021</p> <p><em>Indegree</em>, the number of within network citations to a given paper, calculated for the network shown in Figure 1 of the manuscript.</p> <p><em>Cluster</em>, provides the cluster membership number as discussed within the manuscript (Figure 1). This was established via modularity maximisation via the Leiden algorithm (Res 1; Q=0.43|7 clusters)</p> <p><strong>2. ‘OTSOC-edge -list.csv’</strong> is a comma-separated values file that contains all citation links between the 1,892 articles (n=26,019). The columns refer to:</p> <p><em>Source</em>, the unique identifier of the citing paper.</p> <p><em>Target, </em>the unique identifier of the cited paper.</p> <p><em>Type, </em>edges are ‘Directed’, and this column tells Gephi to regard all edges as such.</p> <p><em>Syr_date, </em>this contains the date of publication of the citing paper.</p> <p><em>Tyr_date, </em>this contains the date of publication of the cited paper.</p> <p><strong>3. 'OTSOC_Cited_2016-2021.csv'</strong> is a comma-separated values file that contain citations to all cited references that were cited by at least 10 of the retrieved papers within the OTSOC network published from 2016 onwards. The columns refer to: </p> <p><em>Reference, </em>the cited reference string extracted from the bibliographies of retrieved papers.</p> <p><em>Publication year, </em>the publication year of the cited reference.</p> <p><em>DOI</em>, the DOI of the cited reference. </p> <p><em>indegree_2016, </em>the total number of citations to a cited reference from papers published in 2016 and contained within the OTSOC network. </p> <p><em>indegree_2017, </em>the total number of citations to a cited reference from papers published in 2017 and contained within the OTSOC network. </p> <p><em>indegree_2018, </em>the total number of citations to a cited reference from papers published in 2018 and contained within the OTSOC network. </p> <p><em>indegree_2019, </em>the total number of citations to a cited reference from papers published in 2019 and contained within the OTSOC network. </p> <p><em>indegree_2020, </em>the total number of citations to a cited reference from papers published in 2020 and contained within the OTSOC network. </p> <p><em>indegree_2021, </em>the total number of citations to a cited reference from papers published in 2021 and contained within the OTSOC network. </p> <p><em>total indegree 2016-21</em>, the total number of citation to a cited reference from papers published between 2016-2021 and contained within the OTSOC network. </p> <p><strong>Software recommended for analysis</strong></p> <p>Gephi version 0.9.2 was used for the visualisations within the manuscript, and both files can be read and into Gephi without modification.</p> <p><strong>Notes</strong></p> <p><a href="#_ftnref1">[1]</a> Leng, G., Leng, R. I., Ludwig, M. (Submitted). Oxytocin – a social peptide? Deconstructing the evidence.</p> <p><a href="#_ftnref2">[2]</a> Edinburgh University’s subscription to Web of Science covers the following databases: (i) Science Citation Index Expanded, 1900-present; (ii) Social Sciences Citation Index, 1900-present; (iii) Arts & Humanities Citation Index, 1975-present; (iv) Conference Proceedings Citation Index- Science, 1990-present; (v) Conference Proceedings Citation Index- Social Science & Humanities, 1990-present; (vi) Book Citation Index– Science, 2005-present; (vii) Book Citation Index– Social Sciences & Humanities, 2005-present; (viii) Emerging Sources Citation Index, 2015-present.</p> <p><a href="#_ftnref3">[3]</a> For those interested, the following PMIDs were identified as ‘articles’ by WoS, but as ‘reviews’ by PubMed: ‘34502097’ ‘33400920’ ‘32060678’ ‘31925983’ ‘31734142’ ‘30496762’ ‘30253045’ ‘29660735’ ‘29518698’ ‘29065361’ ‘29048602’ ‘28867943’ ‘28586471’ ‘28301323’ ‘27974283’ ‘27626613’ ‘27603523’ ‘27603327’ ‘27513442’ ‘27273834’ ‘27071789’ ‘26940141’ ‘26932552’ ‘26895254’ ‘26869847’ ‘26788924’ ‘26581735’ ‘26548910’ ‘26317636’ ‘26121678’ ‘26094200’ ‘25997760’ ‘25631363’ ‘25526824’ ‘25446893’ ‘25153535’ ‘25092245’ ‘25086828’ ‘24946432’ ‘24637261’ ‘24588761’ ‘24508579’ ‘24486356’ ‘24462936’ ‘24239932’ ‘24239931’ ‘24231551’ ‘24216134’ ‘23955310’ ‘23856187’ ‘23686025’ ‘23589638’ ‘23575742’ ‘23469841’ ‘23055480’ ‘22981649’ ‘22406388’ ‘22373652’ ‘22141469’ ‘21960250’ ‘21881219’ ‘21802859’ ‘21714746’ ‘21618004’ ‘21150165’ ‘20435805’ ‘20173685’ ‘19840865’ ‘19546570’ ‘19309413’ ‘15288368’ ‘12359512’ ‘9401603’ ‘9213136’ ‘7630585’</p> <p><a href="#_ftnref4">[4]</a> Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. Stable URL: <a href="https://sci2.cns.iu.edu">https://sci2.cns.iu.edu</a></p> <p><a href="#_ftnref5">[5]</a> Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. Gephi is available via <a href="https://gephi.org/">https://gephi.org/</a></p> <p><a href="#_ftnref6">[6]</a> Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. <a href="https://doi.org/10.1038/s41598-019-41695-z">https://doi.org/10.1038/s41598-019-41695-z</a></p>
Detection of Real-World Influence through Social Media
<p><strong>Description. </strong>This dataset corresponds to the resources produced for the following conference paper and its extended version:</p> <ol> <li>J.-V. Cossu, N. Dugué, and V. Labatut, “Detecting Real-World Influence Through Twitter,” in <em>2nd European Network Intelligence Conference (ENIC)</em>, 2015, pp. 83–90. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01164453">hal-01164453</a>⟩ DOI: <a href="http://doi.org/10.1109/ENIC.2015.20">10.1109/ENIC.2015.20</a></li> <li>J.-V. Cossu, V. Labatut, and N. Dugué, “A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence,” <em>Social Network Analysis and Mining </em>6:25, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01203171">hal-01203171</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-016-0329-x">10.1007/s13278-016-0329-x</a></li> </ol> <p>Raw data are available through the official RepLab page: <a href="http://nlp.uned.es/replab2014/">http://nlp.uned.es/replab2014/</a> (follow <a href="http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz">http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz</a>)</p> <p><strong>Source code. </strong>The source code used to generate these output is available on GitHub: <a href="https://github.com/CompNet/Influence">https://github.com/CompNet/Influence</a></p> <p><strong>Funding. </strong>This work was partly funded by the French National Research Agency (ANR), through the project <a href="https://anr.fr/Project-ANR-12-CORD-0002">ImagiWeb ANR-12-CORD-0002</a>.</p> <p><strong>Contact. </strong>Jean-Valère Cossu <<a href="mailto:jean-valere.cossu@alumni.univ-avignon.fr">jean-valere.cossu@alumni.univ-avignon.fr</a>></p> <p><strong>Citation. </strong>If you use these data, please cite paper [1] above.</p> <p><br><code>@InProceedings{Cossu2015,</code><br><code> author = {Cossu, Jean-Valère and Dugué, Nicolas and Labatut, Vincent},</code><br><code> title = {Detecting Real-World Influence Through {Twitter}},</code><br><code> booktitle = {2\textsuperscript{nd} European Network Intelligence Conference},</code><br><code> year = {2015},</code><br><code> pages = {83-90},</code><br><code> address = {Karlskrona, SE},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ENIC.2015.20},</code><br><code>}</code></p> <p><strong>Details. </strong>This archive contains all ranking outputs formatted according to the TREC-EVAL tool format. These outputs consist for each domain in a ranked list of user from the most influential to the least influential. For a classification-type evaluation, just consider that users having a score higher than 0.5 are influential.</p> <p>File names correspond to the system (those starting with Cos*, indicate: the method BoT for Bag-of-Tweets, UaD for User-as-Document; the use of the Tweet-Selection strategy files denoted Artex; the learning process with Global or separated models which are noted Multi and last but not least the decision strategy for Bag-of-Tweets: Counting or Sum) or feature name. Files starting with out_* contain the results of logistic regression ranking outputs. Files matrix_auto.dat and matrix_bank.dat contain the data used to feed the PLS model (code: plspm4influence.R).</p> <p>RepLab 2014 uses Twitter data in English and Spanish. The balance between both languages depends on the availability of data for each of the profiles included in the dataset.</p> <p>The training dataset consists of 7,000 Twitter profiles (all with at least 1,000 followers) related to the automotive and banking domains, evaluation is performed separately. Each profile consists of (i) author name; (ii) profile URL and (iii) the last 600 tweets published by the author at crawling time and have been manually labelled by reputation experts either as “opinion maker” (i.e. authors with reputational influence) or “non-opinion maker”. The objective is to find out which authors have more reputational influence (who the opinion makers are) and which profiles are less influential or have no influence at all. </p> <p>Since Twitter ToS do not allow redistribution of tweets, only tweets ids and screen names are provided. Replab organizers provide details about how to download the tweets.</p>
Ecology and conservation of socially learned foraging tactics in odontocetes
<h3>Overview</h3> <p>This package contains the data and R code to replicate the analyses and figures of the review article, "Ecology and conservation of socially learned foraging tactics in odontocetes", submitted to the special issue of Philosophical Transactions B, "Animal Culture: conservation in a changing world". </p> <p>Metadata for CSV files used for analyses are provided below, and detailed instructions on running code are available at: https://github.com/JoaoVallePereira/Toothed_Whales_Forag_Tactics. For full description of variables, see supplemental material associated with publication. </p> <div> <h3>Main data table - dataTable_Forag_Tactics.csv</h3> </div> <table> <tbody> <tr> <th>Variable</th> <th>Class</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>common_name</td> <td>Character</td> <td>The common name of the species exhibiting the foraging tactic</td> </tr> <tr> <td>latin_name</td> <td>Character</td> <td>The Latin name of the species exhibiting the foraging tactic</td> </tr> <tr> <td>country</td> <td>Character</td> <td>The country that has jurisdiction over the region where the foraging tactic occurs</td> </tr> <tr> <td>region</td> <td>Character</td> <td>The region where the foraging tactic occurs</td> </tr> <tr> <td>animal_identity_data</td> <td>Character</td> <td>Whether identity information for the individual(s) exhibiting the foraging tactic is available</td> </tr> <tr> <td>number_of_animals</td> <td>Character</td> <td>The number of different individuals exhibiting the foraging tactic</td> </tr> <tr> <td>foraging_category</td> <td>Character</td> <td>The broad foraging category that the specific foraging tactic most closely aligns with</td> </tr> <tr> <td>foraging_tactic</td> <td>Character</td> <td>The specific foraging tactic</td> </tr> <tr> <td>tactic_driver</td> <td>Character</td> <td>The key factor influencing or determining the observed foraging tactic</td> </tr> <tr> <td>human_induced</td> <td>Character</td> <td>Whether the foraging tactic is human-induced or not</td> </tr> <tr> <td>prey_category</td> <td>Character</td> <td>The type of prey being targeted during the foraging tactic</td> </tr> <tr> <td>habitat</td> <td>Character</td> <td>The type of habitat in which the foraging tactic is exhibited</td> </tr> <tr> <td>prey_category</td> <td>Character</td> <td>The type of prey being targeted during the foraging tactic</td> </tr> <tr> <td>putative_specialised_foraging_tactic</td> <td>Character</td> <td>Foraging tactics having both individual identity information and evidence of being shared among conspecifics</td> </tr> <tr> <td>putative_cultural_foraging_tactic</td> <td>Character</td> <td>Foraging tactics with positive evidence of social learning</td> </tr> <tr> <td>transmission_direction</td> <td>Character</td> <td>How the foraging tactic is transmitted, given positive evidence of social learning</td> </tr> <tr> <td>nature_of_social_learning_evidence</td> <td>Character</td> <td>The type of evidence for social learning</td> </tr> <tr> <td>culture_acknowledgement</td> <td>Character</td> <td>The type of evidence for social learning</td> </tr> <tr> <td>evidence_for_discreteness_significance</td> <td>Character</td> <td>Evidence for differences in diet or foraging techniques that are stable</td> </tr> <tr> <td>threat_acknowledgement</td> <td>Character</td> <td>Whether the reviewed studies acknowledge anthropogenic threats</td> </tr> <tr> <td>threat_category</td> <td>Character</td> <td>For studies that acknowledge anthropogenic threats and impacts, the type of IUCN-CMP first-level threat classification</td> </tr> <tr> <td>threat_subcategory</td> <td>Character</td> <td>For studies that acknowledge anthropogenic threats and impacts, the type of IUCN-CMP second-level threat classification</td> </tr> <tr> <td>threat_direction</td> <td>Character</td> <td>Whether the acknowledged threats were considered a threat to or a consequence of the foraging tactic</td> </tr> <tr> <td>conservation_actions_acknowledgement</td> <td>Character</td> <td>Whether the reviewed studies acknowledge existing or proposed conservation actions related to the foraging tactic</td> </tr> <tr> <td>existing_conservation_actions_category</td> <td>Character</td> <td>Existing conservation actions related to the foraging tactic, the type of IUCN-CMP first-level action classification</td> </tr> <tr> <td>existing_conservation_actions_subcategory</td> <td>Character</td> <td>Existing conservation actions related to the foraging tactic, the type of IUCN-CMP second-level action classification</td> </tr> <tr> <td>proposed_conservation_actions_category</td> <td>Character</td> <td>Proposed conservation actions related to the foraging tactic, the type of IUCN-CMP first-level action classification</td> </tr> <tr> <td>proposed_conservation_actions_subcategory</td> <td>Character</td> <td>Proposed conservation actions related to the foraging tactic, the type of IUCN-CMP second-level action classification</td> </tr> <tr> <td>references</td> <td>Character</td> <td>Reviewed primary and secondary literature used to fill out metrics for the foraging tactic</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Maps data table - dataTable_Forag_Tactics_map.csv</h3> </div> <table> <tbody> <tr> <th>Variable</th> <th>Class</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>common_name</td> <td>Character</td> <td>The common name of the species exhibiting the foraging tactic</td> </tr> <tr> <td>latin_name</td> <td>Character</td> <td>The Latin name of the species exhibiting the foraging tactic</td> </tr> <tr> <td>orca_ecotype</td> <td>Character</td> <td>The orca ecotypes exhibiting the foraging tactic</td> </tr> <tr> <td>country</td> <td>Character</td> <td>The country that has jurisdiction over the region where the foraging tactic occurs</td> </tr> <tr> <td>region</td> <td>Character</td> <td>The region where the foraging tactic occurs</td> </tr> <tr> <td>latitude</td> <td>Numeric</td> <td>The latitude where the foraging tactic occurs</td> </tr> <tr> <td>longitude</td> <td>Numeric</td> <td>The longitude where the foraging tactic occurs</td> </tr> <tr> <td>putative_specialised_foraging_tactic</td> <td>Character</td> <td>Foraging tactics having both individual identity information and evidence of being shared among conspecifics</td> </tr> <tr> <td>foraging_category</td> <td>Character</td> <td>The broad foraging category that the specific foraging tactic most closely aligns with</td> </tr> <tr> <td>tactic_cat_fact</td> <td>Factor (10 levels)</td> <td>The broad foraging category that the specific foraging tactic most closely aligns with</td> </tr> <tr> <td>evidence_for_discreteness_significance</td> <td>Character</td> <td>Evidence for differences in diet or foraging techniques that are stable</td> </tr> </tbody> </table>
Share and spatial concentration of social housing in Dutch urban areas
<p>This dataset contains the amount of social housing units of the Netherlands per urban area, as well as the intensity of their spatial autocorrelation and its proportion compared to the total housing stock, for the year 2023. <a href="https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/kaart-van-100-meter-bij-100-meter-met-statistieken">Original data</a> comes from Statistics Netherlands (<em>Centraal Bureau voor de Statistiek</em>) released for 100 m x 100 m grid cells covering a large share of the Dutch territory. Grid cells with missing values were excluded from the analysis. The spatial autocorrelation of social housing was calculated with urban area-level and U-style computations of Global Moran's I based on the share of social housing units compared to the total housing stock of every grid cell. Limits and definition of urban areas are extracted from <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">the OECD</a>. Data show considerable variation in the levels of social housing and its spatial concentration among Dutch urban areas.</p>
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Supplementary File: Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia's most popular social network VK
<p>Supplementary file and dataset for the paper "Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK"</p>
SolarSMART Social Energy Atlas 2020 Georgia Interviews
<p>The SolarSMART Social Energy Atlas 2020 Georgia Interview dataset provides results from interviews with 2018 residents of the State of Georgia above the age of 18 on their perceptions of photovoltaic solar energy. The interview includes the following: demographic information for each respondent, their location in the state (by ZIP Code); the type of house they reside within; whether they have or have not adopted solar energy (and if so, how did they adopt solar energy); audio recordings of a semi-structured interview between the informants and representatives from the project who were often agents from the UGA Cooperative Extension Service; and the outputs from that interview that also included draw-a-map responses to where they perceived people adopted solar technologies in the United States and the State of Georgia. Respondents were from 25 of the 159 counties of the State of Georgia, with representation of both urban and rural residents. In total, the dataset includes 25 variables currently coded (with over 170 identified. Transcriptions of the interviews are also provided in the dataset alongside graphics for each informant that can be used in presentations and other derivatives involving this dataset in the future. This dataset inspired the SolarSMART 2020 Georgia Survey dataset (DOI: 10.5281/zenodo.4540819). </p>
RDA Overview for the Social Sciences - October 2022
<p>This upload is the fourth version of a dataset on the RDA groups and outputs which was generated in October 2022. It is connected to a report that described a project that assessed the RDA Groups and Outcomes and their relevance for social science researchers. This datasets gives an overview of the results of this analysis, providing a list of all RDA Groups as well as an indication of the relevance for Social Sciences researchers. The project was executed and updated by Data Archiving and Networked Services (DANS).</p> <p>This dataset contains:</p> <p>- an excel overview of the RDA Working and Interest groups</p> <p>- additional csv copies of the separate sheets of the excel overview</p>
The POPREBEL semantic social network data
<p>The <a href="https://populism-europe.com/poprebel/">POPREBEL project</a> explores the phenomenon of populism in Europe. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. It consists of coded interviews, realized between spring 2021 and spring 2022, to Internet users in Czechia, Germany and Poland, who used social media to gather information about the COVID-19 pandemic. The dataset is pseudonymized. POPREBEL is supported by the European Union's Horizon 2020 programme, grant n. 822682.</p> <ul> <li><a href="https://zenodo.org/record/7494327">Final ethnographic report.</a> Section 1.2 contains a detailed description of how and why data were collected.</li> <li><a href="https://wellbeing.edgeryders.eu">Funnel website</a> of the project.</li> <li><a href="https://hal.archives-ouvertes.fr/hal-02478720/document">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>
Social Materiality Map: MSCI
<p>The dataset was collected from <a href="https://www.msci.com/our-solutions/esg-investing/esg-industry-materiality-map">MSCI Industry Materiality Map: </a> "MSCI ESG Ratings assess the resilience of companies to long-term, financially relevant environmental, social, and governance (ESG) risks. Our ESG Industry Materiality Map is a representation of the current ESG Key Issues and their contribution to companies' ESG Ratings. This map is part of our ESG Ratings transparency initiatives, through which we have made ESG Ratings of <a href="https://www.msci.com/esg-ratings">companies</a> and <a href="https://www.msci.com/esg-fund-ratings">funds</a> accessible to the public." - MSCI</p> <p>We collected MSCI Materiality Map and structured. </p> <p>Further information regarding the data collection process and codebook will be published in the future.</p>
Input data to replicate "The social cost of tropical cyclones"
<p>Input data for the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a> that replicate the results of <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a>.</p> <p>To run the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>, the following files from this repository need to be placed in the <code>./data/input/</code> subdirectory of the project folder containing the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>:</p> <ul> <li><code>GMT.nc</code>: Global mean temperature time series as used by the <a href="https://gitlab.pik-potsdam.de/tovogt/tc_emulator">tropical cyclone emulator</a>.</li> <li><code>GrowthClimateDataset.dta</code>: The <a href="https://purl.stanford.edu/wb587wt4560">input data</a> of <a href="https://dx.doi.org/10.1038/nature15725">Burke et al. 2015</a>.</li> <li><code>IHME_GLOBAL_GDP_ESTIMATES_1950_2015.csv</code>: Historical GDP per capita data from <a href="https://doi.org/10.1186/1478-7954-10-12">James et al. 2012</a> (downloaded from <a href="https://ghdx.healthdata.org/record/ihme-data/gross-domestic-product-gdp-estimates-country-1950-2015">IHME</a>).</li> <li><code>mean_temperature_gswp3-w5e5.csv</code>: Population-weighted average national temperature time series for the historical period.</li> <li><code>pulse_response_ricke_caldeira_2014.csv</code>: The global mean temperature response of an additional emission pulse according to <a href="https://dx.doi.org/10.1088/1748-9326/9/12/124002">Ricke & Caldeira 2014</a>.</li> <li><code>tcdata/TCE-DAT_historic-exposure_1950-2015.csv</code> and <code>tcdata/TotalPopulation.csv</code>: Historical (national) numbers of people affected by tropical cyclones according to <a href="https://doi.org/10.5880/pik.2017.011">TCE-DAT</a> with the corresponding total population counts.</li> <li><code>tcdata/emulator/</code>: Projected (national) shares of people affected by tropical cyclones according to the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_emulator">tropical cyclone emulator</a> as computed by the scripts in the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_people_affected">corresponding repository</a>.</li> <li><code>wid_all_data.zip</code>: A bulk data set from the <a href="https://wid.world/bulk_download/wid_all_data.zip">World Inequality Database</a>.</li> </ul> <p>For more information, see <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a> and the README file provided with the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>.</p>
ProjectÔ - Social Acceptance Studies - Dataset
<p>This dataset has been collected as part of the EU-funded ProjectÔ within WP6 and has been analysed in <a href="https://doi.org/10.5281/zenodo.7577225">D6.13 Social Acceptance Studies</a>.</p> <p>The social acceptance research was designed to inform the implementation of the Project Ô water treatment innovations in four countries: Spain, Croatia, Italy, and Israel. Indeed, the data collection undertaken in each of these locations primarily focussed on attitudes toward the technology-based innovations for water treatment being implemented in respondents’ local Project Ô demosite. Further background information was collected through these studies to identify factors affecting social acceptance in each demonstration site’s region. The social acceptance research for the project was designed and implemented by the Institute for Methods Innovation, one of the Project Ô partners, and the Work Package 6 lead.</p> <p>To measure these attitudes, an explainer video of Project Ô’s implementation of innovative water treatment technologies focusing on the project’s innovation work in the respondents’ country was produced. The survey design used in these studies measured key aspects of local views about local Project Ô innovation plans for water reuse, circular water economy, and related issues. To understand their background views relevant to water reuse, respondents were asked about their local water consumption patterns, their perceptions of the local water supply, and their familiarity with water treatment technology. To understand differences in perceptions about environmental factors, respondents were asked to indicate their views related to different aspects of environmental attitudes relevant to water treatment innovations. This is important for clarifying drivers of social acceptance of new water treatment solutions. Finally, to understand the role of trust towards science and technology, scientists and engineers, and the European Commission in social acceptance of Project Ô’s innovation work.</p>
The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing
<p>The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.</p>
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