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FIGURE 3. A–C. Synoeca septentrionalis. D–F. S in SyNoeca ilheeNsis sp. nov., a new social wasp (Hymenoptera, Vespidae, Polistinae) from Brazilian lowland Atlantic Forest
FIGURE 3. A–C. Synoeca septentrionalis. D–F. S. ilheensis sp. nov. Male genitalia. A, D. Aedeagus, ventral view. B, E. Aedeagus, lateral view. C, F. Gonocoxite and volsella, inner view. Triangle indicates dorsal curvature. Arrowhead indicates apex of digitus. Scale bar = 1.0 mm.
PyTAIL Benchmark of Active Learning on Social Media Text Classification
<p>PyTAIL Benchmark of Active Learning on Social Media Text Classification</p><p>Read our paper for details: https://arxiv.org/abs/2211.13786</p><ul><li>ArXiv: https://arxiv.org/abs/2211.13786</li><li>Dataset: https://doi.org/10.5281/zenodo.7236430</li><li>Code: https://github.com/socialmediaie/pytail</li><li>Video: https://www.youtube.com/watch?v=AwDu64gN8t4 </li></ul>
Dataset Vibrations emitted in presence of reproductives affect social organization in termites
<p>These data were collected from video recordings analyzed with an automated individual tracking system. Variables related to the spatial distribution of individuals and their activity were extracted from the identity and coordinates of each individual for each image.</p>
ON THE ROLE OF NATIONAL-MENTAL AND SOCIAL REPRESENTATIONS IN THE PROCESS OF FIGURE-UP INTERPRETATION OF THE MEANING OF WORDS
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FIGURE 1–2. 1 in Phylogenetic analysis of the Neotropical social wasps of the genus Angiopolybia Araujo, 1946 (Hymenoptera, Vespidae, Epiponini)
FIGURE 1–2. 1, Head in frontal view: A= Angiopolybia pallens. B= A. paraensis. Scale bar = 1.0 mm.; 2, Metasoma in lateral view: A= Angiopolybia pallens. B= A. obidensis. Scale bar = 1.0 mm.
Graphed data in the manuscript "Visuo-frontal interactions during social learning in freely moving macaques" by Franch et al., Nature 2024
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Social Marketing Scimat
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Word of mouth: How upward social comparisons influence the sharing of consumption experiences. Journal of Consumer Behaviour. 20 - 3, pp. 736 - 747. Wiley, 2021.
<p>Data file. </p>
Is comparison the thief of joy? Students' emotions after socially comparing their task grades, influence on their motivation. International Journal of Management Education. 21, pp. 1 - 10. Elsevier, 2023.
<p>Data file</p>
SOCIAL NETWORK TEXT CLASSIFICATION ALGORITHM AND SOFTWARE TOOL
<p><em><span>In this article, today's information in social networks, global information flows and algorithms for their analysis, research in this regard and their effects are studied. Recent effective algorithms are presented and the procedure for their use is shown.</span></em></p>
Figure 7 in The first known riodinid 'cuckoo' butterfly reveals deep-time convergence and parallelism in ant social parasites
Figure 7. Map of South America (top left) and detail of topographic map of Río de la Plata basin (red rectangle) showing the geographical distribution of Aricoris arenarum (red circles), type locality in Uruguay (yellow star) and overview of studied vegetation habitats (black circles). A, mountain Chaco in Volcán, Jujuy, Argentina. B, mountain Chaco in Capilla del Monte, Córdoba, Argentina. C, dry Espinal in El Carancho, La Pampa, Argentina. D, Pampean grassland in Castillos, Uruguay. E, coastal sand grasslands in Parque Estadual de Itapuã, Viamão, Rio Grande do Sul (RS), Brazil. F, grassland–Atlantic forest mosaic in Vacaria, RS, Brazil. G, natural grasslands in Mbopicua, Paraguari, Paraguay. Dashed white line delimits the Peripampasic Orogenic Arc (modified from Ferretti et al., 2012), and black dashed line indicates the life cycle study sites (A, B, D, respectively).
Figure 8 in The first known riodinid 'cuckoo' butterfly reveals deep-time convergence and parallelism in ant social parasites
Figure 8. Comparative phylogenetic position, geographical distribution and life cycles of two 'cuckoo' butterflies: 'Hormiguera Chopí' Aricoris arenarum (orange lines) and 'Kuro-shijimi' Niphanda fusca (blue lines), respectively. A, phylogeny of Riodinidae and Lycaenidae based on the study by Espeland et al. (2018), showing estimated dated origins of myrmecophily (black dots), social parasitic lineages (yellow dots) according to Fiedler (2012) and an unconfirmed case (yellow dot with question mark). Orange, A. arenarum; blue, N. fusca. B, world map indicating continental records of social parasitism in butterflies. C–F, A. arenarum life cycle sequence, illustrating: C, female oviposition close to ant–hemipteran association (dashed ellipse); D, first instar feeding on hemipteran honeydew (dashed ellipse); E, third instar with reduced setae on metathorax (yellow arrow) feeding on ant regurgitation (dashed ellipse); F, last instar inside ant nest. G–J, N. fusca life cycle sequence, depicting: G, sequence of female oviposition (dashed ellipse); H, first instar feeding on hemipteran honeydew (dashed ellipse); I, third instar with reduced setae on metathorax (yellow arrow) feeding on ant regurgitation (dashed ellipse); J, last instar inside ant nest.
Lexical Alignment and Conversational Partner Identity (Social Status)
<p>The csv file contains the following columns: </p> <p><strong>participant.ID</strong>: unique id per participant <br> <strong>item</strong>: word associated with each stimulus <br> <strong>lexical-choice</strong>: participants’ lexical choice (1 for each item)<br> <strong>alignment</strong>: 1 where item = lexical-choice, 0 if item ≠ lexical-choice<br> <strong>condition</strong>: HS for high status partner/LS for low status partner<br> <strong>gender</strong>: Female/Male<br> <strong>age</strong>: 18-30<br> <strong>sim</strong>: cosine similarity between participants and partner description vectors (DS) 0-1<br> <strong>item_n</strong>: order of presentation (fixed)<br> <strong>stimuli_set</strong>: 1 if items belong to set 1, 2 if items belong to set 2. Set1 and set2 have been matched for items’ acceptability and frequency of use <br> <strong>stimuli_presentation</strong>: 1 for if the item was presented in the first session, 2 if the item was presented in the second session (the order of presentation was randomised)</p>
SMDRM - Social Media for Disaster Risk Management
<p><strong>SMDRM - Social Media for Disaster Risk Management</strong></p> <p>Social media has been described as a form of distributed cognition, a mechanism for understanding a situation using information spread across many minds. The interactions among people in social media are a form of collective intelligence, as they allow people to make sense of a developing event collectively. Social media users can contribute to creating a "sensor" for citizen-generated data that modelling or monitoring systems can assimilate during a crisis. Gaining situational awareness in a disaster is critical and time-sensitive. Social media presents the possibilities of a growing data source to help improve response in the early hours and days of a crisis. However, social media platforms may not provide the functionality of summarising the information that is useful for crisis responders.SMDRM is a software platform that streamlines the processing of text and images extracted from Twitter in near real-time during a specific event. The data is collected using a combination of keywords and locations based on daily forecasts from the early warnings systems of the Copernicus Emergency Management Service such as EFAS, GloFAS and EFFIS (emergency.copernicus.eu) or triggered manually in case of earthquakes or not-forecasted events. Text is automatically "annotated" using a binary multilingual classifier trained on 12 languages and extended with multilingual embeddings. Simultaneously, a multi-class convolutional neural network labels relevant images for floods, storms, earthquakes and fires. The information that doesn't embed coordinates is geolocated in a two-step algorithm where location candidates are first selected using a multilingual named-entity recognition tool and then searched on available gazetteers. The last step of the SMDRM data processing is the aggregation of relevant information in spatial (administrative areas) and temporal (daily) units. Social media activity about an event can finally be distributed as a data map and visualised on a map server and made available to users.SMDRM could offer timely information useful for reducing the hazard models' uncertainty and providing added-value information such as reports or descriptions of the situation on the ground or in the vicinity. Other stakeholders, such as research groups could access new data to complement the ones extracted from traditional sensors or earth observation. The platform can adapt to cope with the varying workload as it uses scalable software containers. If the number of tweets is higher during an impactful event, the platform can use more containers to annotate them. SMDR code, together with the tens of thousands of annotated social media messages used for training its models, will be released as an open-source platform whose modules can be adapted to serve other research projects. We describe the platform's architecture and implementation details, and two use cases where images and text were used as a use-case to test the system's modules.</p> <p>Source https://ui.adsabs.harvard.edu/abs/2021EGUGA..2315012L/abstract</p>
Database for Bioeconomic models for terrestrial social ecological systems : a review
<p>Data for the replication of our analysis and for anyone who would want to dig into it!</p> <p> </p>
On following pages: 149. Meadow Vole (Microtus pennsyivanicus); 150. Long-tailed Vole (Microtus longicaudus); 151. Creeping Vole (Microtus oregoni); 152. Major's Pine Vole (Microtus majori); 153. Common Pine Vole (Microtus subterraneus); 154. Caucasian Pine Vole (Microtus daghestanicus); 155. Alpine Pine Vole (Microtus multiplex); 156. Liechtenstein's Pine Vole (Microtus liechtensteini); 157. Tatra Pine Vole (Microtus tatricus): 158. Mediterranean Pine Vole (Microtus duodecimcostatus); 159. Lusitanian Pine Vole (Microtus lusitanicus); 160. Pyrenean Pine Vole (Microtus gerbil); 161. Savi's Pine Vole (Microtus savii): 162. Calabria Pine Vole (Microtus brachycercus); 163. Sicilian Pine Vole (Microtus nebrodensis); 164. Thomas's Pine Vole (Microtus thomasi); 165. Balkan Pine Vole (Microtus felteni); 166. Schelkovnikov's Pine Vole (Microtus schelkovnikovi): 167. Harting's Vole (Microtus hartingi); 168. Levant Vole (Microtus guentheri); 169. Dogramaci's Vole (Microtus dogramacii); 170. Cyrenaica Vole (Microtus mustersi); 171. Social Vole (Microtus socialis): 172. Anatolian Vole (Microtus anatolicus): 173. Iranian Vole (Microtus iran); 174. Kopet Dag Pine Vole (Microtus paradoxus); 175. Common Vole (Microtus arvalis); 176. Altai Vole (Microtus obscurus); 177. East European Vole (Microtus mystacinus); 178. Kerman Vole (Microtus kermanensis); 179. Transcaspian Vole (Microtus transcaspicus); 180. Tian Shan Vole (Microtus ilaeus). in Cricetidae
On following pages: 149. Meadow Vole (Microtus pennsyivanicus); 150. Long-tailed Vole (Microtus longicaudus); 151. Creeping Vole (Microtus oregoni); 152. Major's Pine Vole (Microtus majori); 153. Common Pine Vole (Microtus subterraneus); 154. Caucasian Pine Vole (Microtus daghestanicus); 155. Alpine Pine Vole (Microtus multiplex); 156. Liechtenstein's Pine Vole (Microtus liechtensteini); 157. Tatra Pine Vole (Microtus tatricus): 158. Mediterranean Pine Vole (Microtus duodecimcostatus); 159. Lusitanian Pine Vole (Microtus lusitanicus); 160. Pyrenean Pine Vole (Microtus gerbil); 161. Savi's Pine Vole (Microtus savii): 162. Calabria Pine Vole (Microtus brachycercus); 163. Sicilian Pine Vole (Microtus nebrodensis); 164. Thomas's Pine Vole (Microtus thomasi); 165. Balkan Pine Vole (Microtus felteni); 166. Schelkovnikov's Pine Vole (Microtus schelkovnikovi): 167. Harting's Vole (Microtus hartingi); 168. Levant Vole (Microtus guentheri); 169. Dogramaci's Vole (Microtus dogramacii); 170. Cyrenaica Vole (Microtus mustersi); 171. Social Vole (Microtus socialis): 172. Anatolian Vole (Microtus anatolicus): 173. Iranian Vole (Microtus iran); 174. Kopet Dag Pine Vole (Microtus paradoxus); 175. Common Vole (Microtus arvalis); 176. Altai Vole (Microtus obscurus); 177. East European Vole (Microtus mystacinus); 178. Kerman Vole (Microtus kermanensis); 179. Transcaspian Vole (Microtus transcaspicus); 180. Tian Shan Vole (Microtus ilaeus).
Fig. 13–15 in Species Paraphyly and Social Parasitism: Phylogenomics, Morphology, and Geography Clarify the Evolution of the Pseudomyrmex elongatulus Group (Hymenoptera:
Fig. 13–15. Pseudomyrmex elongatulus group, workers, full-face dorsal view of head (a), and lateral profile of body (b). 13, P. capillatus, holotype, Mexico (CASENT0863535); 14, P. exoratus, holotype worker, Mexico (CASENT0863539); 15, P. fasciatus, holotype, Costa Rica (CASENT0863540). Images from AntWeb (www.antweb.org); photographer Phil Ward.
Forlì view of modern building and tower of the health center of the fascist national Institute of social security on the outskirts of Forlì in the Vecchiazzano area
<u>Source</u>: Europeana <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1652106198.2393.jpg">https://4dcity.org/imgupload/1652106198.2393.jpg</a> <br><u>Original Image URL</u>: <a href="https://api.europeana.eu/thumbnail/v2/url.json?uri=http%3A%2F%2Fimage.archivioluce.com%2Ffoto%2Fhigh%2FATTUALITA%2FGP44BIS%2FA00087989.jpg&type=IMAGE">https://api.europeana.eu/thumbnail/v2/url.json?uri=http%3A%2F%2Fimage.archivioluce.com%2Ffoto%2Fhigh%2FATTUALITA%2FGP44BIS%2FA00087989.jpg&type=IMAGE</a> <br><br><u>Image-Metadata:</u><br>Filename: 1652106198.2393.jpg<br>Image Dimensions: 400x301<br>Megapixels: 0.12 MP<br>Filesize: 23.60 KB<br>
Forlì Moderno building of the health center of the fascist national Institute of social security on the outskirts of Forlì in the Vecchiazzano area
<u>Source</u>: Europeana <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1652106198.1551.jpg">https://4dcity.org/imgupload/1652106198.1551.jpg</a> <br><u>Original Image URL</u>: <a href="https://api.europeana.eu/thumbnail/v2/url.json?uri=http%3A%2F%2Fimage.archivioluce.com%2Ffoto%2Fhigh%2FATTUALITA%2FGP44BIS%2FA00087988.jpg&type=IMAGE">https://api.europeana.eu/thumbnail/v2/url.json?uri=http%3A%2F%2Fimage.archivioluce.com%2Ffoto%2Fhigh%2FATTUALITA%2FGP44BIS%2FA00087988.jpg&type=IMAGE</a> <br><br><u>Image-Metadata:</u><br>Filename: 1652106198.1551.jpg<br>Image Dimensions: 400x303<br>Megapixels: 0.12 MP<br>Filesize: 27.13 KB<br>
The sense of agency in social interactions: Exploring the association between sensory self-attenuation and pro-social behaviours.
<p>The dataset reffers to the investigation of the auditory sensory self-attentuation effect in healthy participants, when comparing the sounds generated in the context of cooperative and non-cooperative actions.</p> <p>The dataset includes</p> <ul> <li> demographic information of the sample</li> <li>PSE relative to a two-alternative force choice sound comparison taks</li> <li>self-reported measures of cognitive empathy and perceive interpersonal pleasure</li> </ul>
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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.