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5,666 results for “Social”
Masked or not, I smile to you: Exploring full-term and preterm infants' social smiles to adults wearing a protective facemask
<p>This database includes the raw data linked with the paper “Exploring full-term and preterm infants’ social smiles to adults wearing a protective facemask”, published on Infant Behavior and Development Journal, doi: 10.1016/j.infbeh.2024.101947. This publication explores early emergence of social smiles in preterm (PT) and full-term (FT) infants at 3 months (corrected age for prematurity) and how the use of protective facemasks by adults affects the display of social smiles.</p> <p>Procedures</p> <p>We enrolled 30 FT and 30 PT infants (gestational age ≤ 32 weeks). Infants' social smiles displays were assessed at 2–3-month-age (corrected) across a three-episode (masked mother; unmasked mother; masked adult female stranger) videotaped interactive task. During each episode, the adult was instructed to maintain specific facial expressions (happy-smiling, sad-frowning, neutral-unresponsive) for 15 second windows and then instructed to interact spontaneously for 45 s (of which the first 15 s were coded). Social smiles were coded in each 15 s sequence as 0, absent or 1, present at least once.</p> <p>Analytical plan</p> <p>A 2-block hierarchical logistic regression was used, firstly testing for differences in probability of social smiles display in the three mask conditions (masked mother – MM, masked stranger – MS, and unmasked mother – UM) (Model 1) and subsequently exploring possible additional effects of group (full term – FT, preterm – PT) and facial expression (smiling, neutral, frown, and interactive) (Model 2).</p> <p>Findings in brief</p> <p>FT and PT infants did not differ in the display of social smiles. In both groups, social smiles were mostly exhibited in response to happy/smiling and spontaneously interacting partners. Overall, no effect of wearing a protective facemask emerged. The use of protective facemasks did not result in a lower display of social smiles. The findings suggest that FT and PT might be equally sensitive to their adult interactive partners in terms of social smiles displays at 2 – 3-month-age.<span> </span></p>
THE CAUCASUS ECONOMIC & SOCIAL ANALYSIS JOURNAL OF SOUTHERN CAUCASUS
<p>THE CAUCASUS ECONOMIC & SOCIAL ANALYSIS JOURNAL OF SOUTHERN CAUCASUS <br><span> </span></p>
Gaze orienting in the social world: An exploration of the role played by caregiving vocal and tactile behaviors in infants with visual impairment and in sighted controls
<p>This database includes the raw data linked with the paper “Gaze orienting in the social world: An exploration of the role played by caregiving vocal and tactile behaviors in infants with visual impairment and in sighted controls”, published on Brain Sciences Journal, doi: 10.5281/zenodo.11121406. In this paper we report on data on the association between maternal vocal and tactile behaviors and the gaze orientation of visually impaired (VI) infants compared to sighted controls (SI).</p> <p>Procedures</p> <p>45 infants (24 VI and 21 SI) and their mothers participated in a videorecorded 6-minute observational procedure including the well-acknowledged Face-to-Face Still-Face (FFSF) paradigm. For the purposes of this study, only the play and still episodes were analyzed. Videos were micro-analytically coded (in 2 seconds bins) for infant’s gaze orientation (unfocused GO, object GO or mother GO) and for mother’s vocal production (socio-cognitive or nurturing vocalizations) and touch behavior (pragmatic or nurturing touch).</p> <p>Analytical plan</p> <p>Correlation statistics (Spearman's rho) were used to test the associations between maternal behaviors and infants' gaze orientation in the play episode for the two groups. Two separate analyses of variance (ANOVA) were performed to compare the gaze orientation of the two groups during the play and still episodes. Two separate analyses of variance (ANOVA) were performed to compare the gaze orientation among three groups of children with different vision levels (severe, moderate, and sighted) during the play and still episodes.</p> <p>Findings in brief</p> <p>Maternal vocalizations and touch were found to influence infants' gaze orientation differently in VI infants compared SI. In particular, for VI infants no specific caregiving behaviors emerged as associated with the social orienting of attention. The group comparisons during the play episode showed that controls were predominantly oriented to the mothers, while VI infants were less socially oriented. No differences between the two groups in GO emerged during the still face episode. The visual impairment severity did not emerge as linked with social attention.</p>
The Role of Adopting Social Media Gamification (SMG) in SMEs towards Increasing Customer Retention and Loyalty
<p>Questionnaires and meta data</p>
Mechanisms Influencing Group Teaching Learning Achieve-ment in University Students: The Mediating Role of Social Loafing
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European Social Survey Data Round 1-11 merged
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Teachers' Social Representations Regarding Grade Retention
<h3>Database of the Questionnaire on Teachers' Practices and Representations from Preschool to 12th Grade Regarding Grade Retention</h3> <p>The database is divided according to the dimensions analyzed, totaling five:</p> <ol> <li> <p><strong>First Dimension</strong>:</p> <ul> <li>Composed of 28 items where respondents answer each one using a Likert scale with four response options. In this first part, it is noted that the 28 items were grouped into four factors, which allowed for: a) obtaining teachers' views on grade retention (favorable or unfavorable view on the application of the retention measure); b) understanding the general consequences of retention for the student; c) identifying the consequences of retention when students are retained only once; d) identifying the consequences of retention when students experience repeated retentions.</li> </ul> </li> <li> <p><strong>Second Dimension</strong>:</p> <ul> <li>Contains only one open-ended question aiming for teachers to provide their thoughts on whether the retention measure is beneficial for the student or not.</li> </ul> </li> <li> <p><strong>Third Dimension</strong>:</p> <ul> <li>Indicates the degree of agreement among teachers (using a Likert scale with four response options) regarding the factors they consider in the decision to retain a student, such as academic performance, academic ability, maturity, commitment, self-esteem, family context, age relative to peers, attendance, national exams, and behavior.</li> </ul> </li> <li> <p><strong>Fourth Dimension</strong>:</p> <ul> <li>Identifies interventions to promote educational success through multiple choice, organized as follows: a) Identification of measures to promote educational success existing in the school cluster where they teach; b) Identification of five measures that seem most effective in promoting educational success for retained students or students with low performance; c) Identification of six programs/measures/interventions considered effective to prevent low-performing students from being retained.</li> </ul> </li> <li> <p><strong>Fifth Dimension</strong>:</p> <ul> <li>Pertains to parental involvement, aiming to identify the most common types of parental involvement promoted by the school through a four-option Likert scale.</li> </ul> </li> </ol>
Figure 2 Cataglyphis species show a in Social Life in Arid Environments: The Case Study of Cataglyphis Ants
Figure 2 Cataglyphis species show a great diversity of reproductive systems. (a) Classical sexual reproduction in hymenopterans: Males derive from arrhenotoky, whereas females (workers and queens) derive from sexual reproduction. (b) In some populations of C. cursor, conditional use of sex allows for the production of both males and queens by parthenogenesis, whereas workers derive from sexual reproduction. (c) In the altisquamis group, males and queens derive from parthenogenesis, whereas workers derive from interlineage fertilization.
Datasets from `Discovering and analysing lexical variation in social media text'
<p>This repository contains the datasets that were used in the following three papers, which are also included within P. Shoemark's PhD dissertation `Discovering and analysing lexical variation in social media text':</p> <ul> <li>P. Shoemark, D. Sur, L. Shrimpton, I. Murray, and S. Goldwater. <a href="https://www.aclweb.org/anthology/E17-1116/"><em>Aye or naw, whit dae ye hink? Scottish independence and linguistic identity on social media.</em></a> 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL). 2017. </li> <li>P. Shoemark, J. Kirby, and S. Goldwater. <a href="https://www.aclweb.org/anthology/W17-4908/"><em>Topic and audience effects on distinctively Scottish vocabulary usage in Twitter data.</em> </a>Workshop on Stylistic Variation at EMNLP. 2017.</li> <li>P. Shoemark, J. Kirby, and S. Goldwater. <a href="https://www.aclweb.org/anthology/W18-6101/"><em>Inducing a lexicon of sociolinguistic variables from code-mixed text.</em> </a>Workshop on Noisy User Generated Text at EMNLP. 2018. </li> </ul> <p>Datasets consist of tab-separated-values files, in which rows correspond to tweets, with columns for user ID, tweet ID, and timestamp. </p> <p>The text of the tweets (and associated metadata) can be re-downloaded (in batches of 100 per request) using Twitter's <a href="https://developer.twitter.com/en/docs/tweets/post-and-engage/api-reference/get-statuses-lookup">GET Statuses/Lookup</a> API endpoint <em>(NB: Tweets which have been deleted or made private since the original datasets were collected can <strong>not </strong>be re-downloaded, so it may not be possible to reconstruct the original datasets in their entirety). </em></p> <p> </p> <p>Most of these datasets were originally drawn from <a href="https://developer.twitter.com/en/docs/tweets/sample-realtime/api-reference/get-statuses-sample">the Sample endpoint of Twitter’s Streaming API (</a>a.k.a. the ‘Spritzer’), which provides a random 1% sample of all public tweets in near real-time:</p> <p> </p> <p><strong>GU Dataset: <a href="https://zenodo.org/api/files/caf09b6a-e57a-4a01-b2f0-ce5ab15583d1/Geotagged-UK.zip?versionId=a9cc222e-1d07-4026-9b55-cf2189b58191">Geotagged-UK.zip</a></strong></p> <p><em>Tweets from Sept 2013 - Sept 2014 which are geotagged to locations within the UK.</em></p> <p>The file <strong>GU_pre-filtering.tsv </strong>contains the IDs for all tweets from the ‘Spritzer’ stream which were posted between September 1st 2013 and September 30th 2014, were classified as English by <a href="https://github.com/saffsd/langid.py">langid.py</a>, are not retweets or quotes, and are geotagged to locations within the UK.<strong> </strong><em><strong>• Tweets: </strong>1,768,334<strong> • Unique Users: </strong>455,075 <strong>• </strong></em></p> <p>The file <strong>GU.tsv </strong>contains the IDs for tweets in the <strong>final</strong> GU dataset that was used for the analyses in our <a href="http://www.aclweb.org/anthology/E17-1116/">EACL 2017</a> paper, after applying additional pre-processing heuristics to filter out tweets by bots and spammers. <em><strong>• Tweets: </strong>1,654,204<strong> • Unique Users: </strong>446,510 <strong>• </strong><sub>(the number of users in the GU dataset was slightly over-counted when reported in the paper; this is the actual number)</sub></em></p> <p> </p> <p><strong>GS Dataset: <a href="https://zenodo.org/api/files/caf09b6a-e57a-4a01-b2f0-ce5ab15583d1/Geotagged-Scotland.zip?versionId=146a0589-ddc0-4c42-86f6-7da79686ae56">Geotagged-Scotland.zip </a></strong></p> <p><em>The subset of Tweets in the GU dataset which are geo-tagged to locations within Scotland, specifically.</em></p> <p>The file <strong>GS_pre-filtering.tsv </strong>contains the IDs for all tweets from the ‘Spritzer’ stream which were posted between September 1st 2013 and September 30th 2014, were classified as English by <a href="https://github.com/saffsd/langid.py">langid.py</a>, are not retweets or quotes, and are geotagged to locations within Scotland. <em><strong>• Tweets: </strong>178,401<strong> • Unique Users: </strong>41,685 <strong>• </strong></em></p> <p>The file <strong>GS.tsv </strong>contains the IDs for tweets in the <strong>final</strong> GS dataset that was used for the analyses in our <a href="http://www.aclweb.org/anthology/E17-1116/">EACL 2017</a> paper, after applying additional pre-processing heuristics to filter out tweets by bots and spammers. <em><strong>• Tweets: </strong>166,992<strong> • Unique Users: </strong>40,837 <strong>• </strong><sub>(the number of users in the GS dataset was slightly over-counted when reported in the paper; this is the actual number)</sub></em></p> <p> </p> <p><strong>IT Dataset & Controls: <a href="https://zenodo.org/api/files/caf09b6a-e57a-4a01-b2f0-ce5ab15583d1/Indyref-Tweets.zip?versionId=41042a3e-f247-4493-9abe-29fb9ae81ee1">Indyref-Tweets.zip</a></strong></p> <p><em>Tweets from Sept 2013 - Sept 2014 which contain hashtags relating to the 2014 Scottish Independence Referendum (plus 'control' tweets which are by the same users but do not contain referendum-related hashtags)</em></p> <p>The file <strong>IT_</strong><strong>pre-filtering.tsv </strong>contains the IDs for all tweets from the ‘Spritzer’ stream which were posted between September 1st 2013 and September 30th 2014, were classified as English by <a href="https://github.com/saffsd/langid.py">langid.py</a>, are not retweets or quotes, and contain at least one of 47 hashtags we identified as relating to the 2014 Scottish Independence Referendum (see paper for hashtag list). <em><strong>• Tweets: </strong>77,708<strong> • Unique Users: </strong>26,019 <strong>• </strong></em></p> <p>The file <strong>IT</strong><strong>.tsv </strong>contains the IDs for tweets in the <strong>final</strong> IT dataset that was used for the analyses in our <a href="http://www.aclweb.org/anthology/E17-1116/">EACL 2017</a> paper, after applying additional pre-processing heuristics to filter out tweets by bots and spammers, and tweets which do not contain hashtags that we judged to <em>unambiguously</em> relate to the referendum. <em><strong>• Tweets: </strong>59,664 <strong>• Unique Users: </strong>18,589 <strong>• </strong></em></p> <p>The file <strong>IT_controls_pre-filtering.tsv </strong>contains the IDs for all tweets from the ‘Spritzer’ stream which were posted between September 1st 2013 and September 30th 2014, were classified as English by <a href="https://github.com/saffsd/langid.py">langid.py</a>, are not retweets or quotes, and do <em><strong>not</strong></em> contain any of the hashtags we identified as relating to the 2014 Scottish Independence Referendum, but are authored by a user who has <em><strong>also</strong> </em>authored a tweet in <strong>IT_</strong><strong>pre-filtering.tsv</strong>. <em><strong>• Tweets: </strong>1,354,701 <strong>• Unique Users: </strong>26,019 <strong>• </strong></em></p> <p>The file <strong>IT_controls</strong><strong>.tsv </strong>contains the IDs for tweets in the <strong>final</strong> set of Control tweets that was used for the analyses in our <a href="http://www.aclweb.org/anthology/E17-1116/">EACL 2017</a> paper, i.e. tweets which do not contain referendum-related hashtags but are authored by users who also have also authored tweets in <strong>IT</strong><strong>.tsv</strong>. <em><strong>• Tweets: </strong>881,679 <strong>• Unique Users: </strong>18,589 <strong>• </strong></em></p> <p> </p> <p><strong>SG-Users’ and IH-Users' Autumn 2014 Timeline Datasets: <a href="https://zenodo.org/api/files/caf09b6a-e57a-4a01-b2f0-ce5ab15583d1/Autumn-2014_Timelines.zip?versionId=c456b530-361e-4b88-9006-5bebd4a43c92">Autumn-2014_Timelines.zip</a></strong></p> <p><em>Complete tweet histories from Aug-Oct 2014 for users from the GS and IT datasets.</em></p> <p>The file <strong>SG-Users_Autumn_2014_timelines_pre-filtering.tsv </strong>contains the IDs for tweets which were posted in August, September, or October 2014 by users from the GS dataset, i.e. users we know to have used Scottish geotags. This dataset is not restricted to tweets which appear in the ‘Spritzer’ sample; instead it consists of complete User Timelines for the months concerned, retrieved using the <a href="https://developer.twitter.com/en/docs/tweets/timelines/api-reference/get-statuses-user_timeline">statuses/user timeline</a> endpoint of Twitter’s REST API in March 2017. Because there are limits on the number of tweets that can be retrieved using this endpoint, we were not able to retrieve complete Autumn 2014 tweet histories for <em>all</em> of the users in the GS dataset. <em><strong>• Tweets: </strong>3,014,029 </em> <em><strong>• Unique Users: </strong>18,274 <strong>• </strong></em></p> <p>The file <strong>SG-Users_Autumn_2014_timelines.tsv </strong>contains the IDs for tweets in the <strong>final</strong> SG-Users dataset that was used for the analyses in our <a href="http://www.aclweb.org/anthology/W17-4908/">StyleVar 2017</a> paper. This dataset consists only of tweets which contain at least one instance of one of 50 lexical variables that were the focus of the study, and has also undergone various other filtering steps; see the paper for full details. <em><strong>• Tweets: </strong>1,112,931</em> <em><strong>• Unique Users: </strong>10,103 <strong>• </strong></em></p> <p>The file <strong>IH-Users_Autumn_2014_timelines_pre-filtering.tsv </strong>contains the IDs for tweets which were posted in August, September, or October 2014 by users from the IT dataset, i.e. users we know to have used Indyref-related hashtags. This dataset was collected in the same manner as SG-Users_Autumn_2014_timelines_pre-filtering.tsv; however, due to an error in this process, <strong>the IDs of most of the tweets in this dataset were not recorded</strong>. For such tweets the tweet ID column instead contains a placeholder tweet ID of the form _<user_ID>_<month>_<integer>, where the integer denotes the tweet's position in the reverse-chronological list of tweets that were retrieved for that user from that month (e.g. _147527441_09_286 is the placeholder tweet ID we assigned to the 286th September tweet we retrieved from the user whose Account ID is 147527441). Unfortunately, therefore, the tweets in this file whose 'IDs' begin with an underscore cannot be straightforwardly re-downloaded using Twitter's free <a href="https://developer.twitter.com/en/docs/tweets/post-and-engage/api-reference/get-statuses-lookup">GET Statuses/Lookup</a> API endpoint; but since their user IDs and timestamps are intact, it would still be possible to retrieve them using the (paid-for) <a href="http:// https://developer.twitter.com/en/docs/tutorials/choosing-historical-api">Historical APIs</a>. <em><strong>• Tweets: </strong>6,997,858 <strong>• Tweets whose IDs were recorded: </strong>288,394</em><em><strong> </strong> <strong>• Unique Users: </strong>14,645</em><em> <strong>• </strong></em></p> <p>The file <strong>IH-Users_Autumn_2014_timelines.tsv </strong>contains the IDs for tweets in the <strong>final</strong> IH-Users dataset that was used for the analyses in our <a href="http://www.aclweb.org/anthology/W17-4908/">StyleVar 2017</a> paper. Like the SG-Users dataset, this dataset consists only of tweets which contain at least one instance of one of 50 lexical variables that were the focus of the study, and has also undergone various other filtering steps; see the paper for full details. As with the pre-filtered version, most of the tweet IDs are unfortunately missing in this dataset. <em><strong>• Tweets: </strong>2,165,320 <strong>• Tweets whose IDs were recorded: </strong>115,366</em><em><strong> </strong> <strong>• Unique Users: </strong>10,784 <strong>• </strong></em></p> <p> </p> <p> </p> <p><strong>US Geotags: <a href="https://zenodo.org/api/files/caf09b6a-e57a-4a01-b2f0-ce5ab15583d1/Geotagged-USA.zip">Geotagged-USA.zip</a></strong></p> <p><em>Tweets from June 2013 - July 2016 which are geotagged to locations within the USA.</em></p> <p>The file <strong>GUSA.tsv</strong> contains all tweets from the ‘Spritzer’ sample which were posted between June 30th 2013 to July 1st 2016, are classified as English by <a href="https://github.com/saffsd/langid.py">langid.py</a>, are not retweets, do not contain urls or embedded media, are not by users with more than 1000 friends or followers, and are geotagged to locations within the USA. This dataset (along with the GU Dataset) was used in our <a href="http://www.aclweb.org/anthology/W18-6101/">WNUT 2018</a> paper. <em><strong>• Tweets: </strong></em> <em>8,375,573 </em> <em><strong>• Unique Users: </strong>1</em>,<em>826,260</em><em> <strong>• </strong></em></p> <p> </p>
Fig. 1 in Controversial patterns of Wolbachia infestation in the social parasitic Maculinea butterflies (Lepidoptera: Lycaenidae)
Fig. 1 Sampling sites. See the abbreviations in Online Resource 1. a M. alcon and M. arion. The infection rate is 100 % in both species. b M. nausithous and M. teleius. The infection rate is indicated with the shade of symbols representing populations. Darker symbols show higher infection rate
Content and social validity of APERS-PE in Poland
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Sociality shapes the adaptive response of breeders to harsh thermal environments? An experiment in a burying beetle
<p><span>1. <span> </span>The rapid pace of environmental change seems to outstrip the evolutionary response rates of many species. Physiological, morphological, and behavioral plasticity alone may not suffice to cope with environments’ drastic changes, potentially hindering individuals’ ability to adapt. Therefore, it has been proposed that social flexibility may be a powerful mechanism for fast adaptation, but empirical evidence is lacking.</span></p> <p><span>2. <span> </span>We investigated the combined effects of social conditions and ambient temperature during the reproductive and developmental phases on parental care, reproduction and offspring fitness, in the burying beetles <em>Nicrophorus vespilloides</em>, which utilize carcasses for breeding and facultatively exhibit varied social forms of breeding and parental care.</span></p> <p><span>3. <span> </span>We conducted a mixed factorial design experiment, establishing four categories of social conditions during breeding (i.e., no-care, unifemale-care, biparenta-care, and multiple-care) alongside two thermal conditions (benign: 20°C and harsh: 23°C). Additionally, dispersed larvae produced from each of these breeding conditions were allocated to either 20°C or 23°C for pupation. Measures for reproductive success (e.g., brood size and mass), offspring performance (e.g., larval weight, body size and lifespan of newly-eclosed beetles), and larval developmental duration were collected, and the levels of individual and total parental care were investigated.</span></p> <p><span>4. <span> </span>Our results showed that (1) harsh temperature led to a significant reduction in reproduction and offspring fitness traits (brood size and mass, larval weight and size of newly-eclosed beetles), and the diminished fitness traits were found in no-care; (2) when comparing the harsh to benign thermal conditions, larval development was shortened in no-care, and lighter larvae and a shorter lifespan were observed in biparental care; (3) while harsh temperature led to a decrease in both individual and total parental care, whereas social conditions did not adjust the levels of care impacted by thermal temperature.</span></p> <p><span>5. <span> </span>Our findings shed light on the critical role of sociality in modulating the adaptive response of individuals to harsh thermal environments, one of the ecological problems exacerbated by ongoing global warming.</span></p>
Figure 1 in A comparison of social behaviour in two rodent species: implications for the mating system of mound-building mice, Mus spicilegus
Figure 1. Median number of amicable, offensive, and defensive behaviours displayed by males (black and grey bars), and females (dotted and hatched bars) during male–female encounters. Sex-specific significant statistics, Mann-Whitney U test: * p,0.05.
JustConservation - global data of site-level biodiversity conservation and its social-ecological outcomes (1970-2019)
<p>This dataset contains evidence collected from 723 studies published in English-language peer-reviewed journals that describe and explore conservation efforts at a single site in 104 countries (1970-2019). The pool of studies was obtained through a keyword search on Web of Science, which returned 69,246 publications, followed by a screening of titles and abstracts using the open access machine-learning assisted software Colandr (11,100 publications screened; cut-off inclusion rate: 3%) and a data extraction phase (1,054 publications), based on the following criteria for inclusion: (1) the study is about biodiversity conservation; (2) the study describes a deliberate intervention; (3) the study provides empirical evidence; (4) the conservation intervention takes place in a specific site through a specific actor or actors; (5) the study identifies a discernible conservation aim, and; (6) the paper identifies a discernible conservation approach.</p> <p>Curated and compiled dataset with the following columns:</p> <ul> <li><strong>study_id</strong>: unique identifier of the study</li> <li><strong>title</strong>: title of the study</li> <li><strong>year</strong>: publication year of the study</li> <li><strong>authors</strong>: authors of the study</li> <li><strong>name_journal</strong>: name of the journal the study was published in</li> <li><strong>author_affiliation_type</strong>: type of institution the lead author of the study works for (University/Academic; NGO; public/state/UN; think thank/research institute; private company; independent; other). Only lists first affiliation.</li> <li><strong>author_affiliation_country: </strong>country of affiliation of the lead author. Only lists first affiliation.</li> <li><strong>author_continent</strong>: country of affiliation of the lead author. Only lists first affiliation.</li> <li><strong>author_north_south</strong>: indicates whether the lead author is affiliated with an institution in the Global North (N) or Global South (S). Only lists first affiliation.</li> <li><strong>major_funder_ngo</strong>: are any authors affiliated with a major conservation funder or organisation with an interest in the conservation intervention being researched (yes; no)</li> <li><strong>funding_source</strong>: funding source of the paper (not of the conservation intervention)</li> <li><strong>conflict_of_interest: </strong>overlap between the affiliations of authors, the funding source and the conservation intervention being researched (0 = no; 1 = yes)</li> <li><strong>study_country</strong>: country where the intervention was studied</li> <li><strong>study_locality</strong>: locality or region where the intervention was studied</li> <li><strong>study_continent</strong>: continent where the intervention was studied</li> <li><strong>study_north_south: </strong>indicates whether the conservation intervention was conducted in the Global North (N) or Global South (S)</li> <li><strong>primary_ecosystem_type</strong>: type of ecosystem of the intervention (Forest/Woodland; Savanna/Dry forest; Dryland/Semi-arid; Grassland/Shrubland; Rivers/Wetlands; Coastal/Marine; Mountain; Cultivated; Other)</li> <li><strong>name_primary_intervention</strong>: name of the conservation intervention or program</li> <li><strong>year_primary_intervention</strong>: year the conservation intervention or program started (single year; many years)</li> <li><strong>target_type_ecosystem</strong>: the conservation intervention targets ecosystems or habitats (yes; no)</li> <li><strong>target_type_species</strong>: the conservation intervention targets specific species (yes; no)</li> <li><strong>target_type_ess</strong>: the conservation intervention targets ecosystem services or processes (yes; no)</li> <li><strong>target_type_humans: </strong>the conservation intervention targets humans and livelihoods (yes; no)</li> <li><strong>description_primary_target</strong>: short description of the primary conservation target</li> <li><strong>description_secondary_target: </strong>short description of any additional conservation target (if applicable)</li> <li><strong>intervention_type_paca</strong>: the conservation intervention takes places through protected areas or conserved areas (yes; no)</li> <li><strong>intervention_type_restoration</strong>: the conservation intervention takes places through restoration (yes; no)</li> <li><strong>intervention_type_species_use</strong>: the conservation intervention takes places through species protection or sustainable use measures (yes; no)</li> <li><strong>intervention_type_livelihoods_tourism</strong>: the conservation intervention takes places through livelihoods support or tourism (yes; no)</li> <li><strong>intervention_type_incentives</strong>: the conservation intervention takes places through incentives, compensation schemes, or market instruments (yes; no)</li> <li><strong>intervention_type_education</strong>: the conservation intervention takes places through education or capacity building (yes; no)</li> <li><strong>intervention_type_stewardship</strong>: the conservation intervention takes places through local/indigenous stewardship (yes; no)</li> <li><strong>description_primary_type</strong>: short description of the primary conservation type</li> <li><strong>description_secondary_type: </strong>short description of any additional conservation types (if applicable)</li> <li><strong>study_covers_governance: </strong>the paper describes governance processes (yes; no)</li> <li><strong>iplc_involved: </strong>indigenous people and/or local communities are involved in the conservation intervention (no; yes, partially; yes, locally-led)</li> <li><strong>description_governance</strong>: short description of the governance process and main actors involved</li> <li><strong>additional_notes: </strong>notes and/or remarks about conservation intervention/governance</li> <li><strong>ecological_outcomes_incl:</strong> study describes ecological outcomes or conservation effectiveness (yes; no)</li> <li><strong>ecological_outcomes: </strong>the reported ecological outcomes are positive, negative, or mixed (positive; negative; mixed; N/A)</li> <li><strong>social_outcomes_incl</strong>: study describes social impacts or outcomes associated with conservation (yes; no)</li> <li><strong>social_outcomes:</strong> the reported social outcomes are positive, negative, or mixed (positive; negative; mixed; N/A)</li> </ul>
2021 Social Vulnerability Assessment at the Civil Parish Level in Mainland Portugal
<p>The dataset represents Social Vulnerability (SV) and its main drivers, at the civil parish, in mainland Portugal, using the 2021 Census data. This assessment was conducted under the scope of the concluded FCT-funded project “HIGH WATERS – Assessment of scenarios of exposure and social vulnerability to sea level rise for sustainable land use” (EXPL/GES-AMB/1246/2021), whose objectives are: to identify hazardous areas in relation to SLR by applying and downscaling global models for forecasting SNM; to diagnose land use land cover (LULC) changes with focus on build-environments over the last 20 years and project them to mid- and long-term scenarios; to diagnose exposure, vulnerability (both physical & social) and coping capacity and project them to the end of the century; to build mid and long-term municipality SLR risk profiles and identify its main driving forces; to propose a roadmap for SLR adaptation regarding mid- and long-term land use planning strategies.</p> <p>As an intermediate product, an SV assessment was carried out to the entire mainland area, following the statistical approach of the SoVI<sup>©</sup> and incorporating variables descriptive of the support capability of the territory, following the theoretical principles defined by Cutter et al. (2003), Mendes et al. (2011), Mendes, Tavares and Santos (2019).</p> <p>The dataset is a polygonal vector in the ESRI shapefile format and, additionally to the shapefile, includes two documents with methodological notes, PCA results and metadata. The shapefile contains the scores of social vulnerability and the scores of the extracted 8 principal components (PCs) that represent the following dimensions: PC1 - Age and old urban context; PC2 - Overcrowded dwellings; PC3 – Unemployment; PC4 - Socioeconomic status; PC5 – Mobility; PC6 - Social risk groups; PC7 - Housing conditions; PC8 - Women and foreign residents with unqualified jobs.</p> <p>References</p> <p>Cutter, S.L., Boruff, B.J. and Shirley, W.L. (2003), “Social vulnerability to environmental hazards”, Social Science Quarterly, Vol. 84 No. 2, pp. 242-261, <a href="https://doi.org/10.1111/1540-6237.8402002">https://doi.org/10.1111/1540-6237.8402002</a> .</p> <p>Mendes, J.M., Tavares, A., Cunha, L. and Freiria, S. (2011), “Social vulnerability to natural and technological hazards: the relevance of scale”, in Briš, R., Soares, C.G. and Martorell, S. (Eds), Reliability, Risk and Safety, Theory and Applications, CRC Press, Taylor and Francis Group, Leiden, pp. 445-451.</p> <p>Mendes, J.M., Tavares, A.O. and Santos, P.P. (2019), "Social vulnerability and local level assessments: a new approach for planning", International Journal of Disaster Resilience in the Built Environment, Vol. 11 No. 1, pp. 15-43. <a href="https://doi.org/10.1108/IJDRBE-10-2019-0069">https://doi.org/10.1108/IJDRBE-10-2019-0069</a></p>
Angel de la Seguridad Social
Sculpture of the Angel de la Seguridad Social in front of the 4 Seasons in Mexico City (CDMX) Created with Polycam Source: Objaverse 1.0 / Sketchfab
Social Isolation and Loneliness in Older People
ClinicalTrials.gov study NCT03201731. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Comparing Sarcopenia , Physical, Psychological and Social Frailty in Hospitalized Older Women Congestive Heart Failure in Metropolitan and Rural Settings
ClinicalTrials.gov study NCT07100366. IPD Sharing: NO. Countries: 0. Publications: 0.
The Relationship Between Emotional Intelligence, Social Behavior, Sleep Quality and Academic Success in Students Studying in Health-Related Departments
ClinicalTrials.gov study NCT06632795. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Social Cognition and Language in Patients With Gliomas
ClinicalTrials.gov study NCT05764460. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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.