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10,016 results for “Observation”
Mu-rhythm modulation associated with the observation and execution of a goal-directed action in healthy toddlers estimated using two different baseline conditions
<p>The database includes mu-rhythm ERD/ERS values elicited by the observation and execution of a goal-directed action in healthy toddlers (N=19). ERD/ERS values has been computed using to different baseline conditions: 1) observation of a black and white static image (BL1) and 2) short period of stillness (BL2).</p> <p>The first sheet of the database refers to action execution (AE) data, whereas the second sheet refers to action observation (AO) data. The database table in each sheet includes for each subject single trial ERD/ERS values (table rows) registered in 7 different scalp sites (table columns) for both BL1 and BL2.</p> <p>The database was used for the statistical analysis of the following paper: Piazza, C.; Visintin, E.; Reni, G.; Montirosso, R. The Effect of Baseline on Toddler Event-Related Mu-Rhythm Modulation. <em>Brain Sci.</em> <strong>2021</strong>, <em>11</em>, 1159. https://doi.org/10.3390/brainsci11091159 (<a href="https://www.mdpi.com/2076-3425/11/9/1159/htm">https://www.mdpi.com/2076-3425/11/9/1159/htm</a>)</p> <p>All the details about data acquisition and preprocessing are reported in the above mentioned publication.</p>
Observational Data for: Polarized information ecosystems can reorganize social networks via information cascades
<p><strong>General Information</strong></p> <p>This contains the observational data for the publication:</p> <blockquote> <p>Tokita, Guess, and Tarnita (2021). Polarized information ecosystems can reorganize social networks via information cascades. <em>Proceedings of the National Academies of Science of the United States.</em></p> </blockquote> <p>Please see the above peer-reviewed article that resulted from this data for more details.</p> <p>Please see the <em>o</em><em>bservational/ </em>directory in the Github repository <a href="http://%28https//github.com/christokita/information-cascades)">https://github.com/christokita/information-cascades</a> for the code that analyzes the Twitter data and generates derived data. Data directly pertaining to the four news sources of interest are in the <em>data/</em> directory, while data that is related to the 4,000 monitored news followers are in the <em>data_derived/</em> directory.</p> <p>The main data file is in a zipped file. We also included a read me with a description of each directory and subdirectory of the data.</p> <p><strong>Methods</strong></p> <p>For each of our four news sources of interest (CBS News, USA Today, Vox, and the Washington Examiner), we used the Twitter API to sample 3,000 random Twitter users that followed that news source's account. We then used the <em>tweetscores</em> R package to estimate the ideology of each of the 12,000 sampled users.</p> <p>From the original pool of approximately 12,000 sampled users who follow a news outlet of interest, we monitored the follower networks of 1,000 liberal followers of CBS News, 1,000 conservative followers of USA Today, 1,000 liberal followers of Vox, and 1,000 conservative followers of the Washington Examiner. When selecting the 1,000 random users to monitor for a given news outlet, we first conducted filters based on self-reported geolocation and other Twitter profile attributes to reasonably filter down to US-based users who primarily tweet in English. We then pulled the complete follower network of each of our 4,000 monitored users at the beginning and end of a 6-week period from August to September 2020, allowing us to assess who unfollowed these users over this period of time. Finally, using the initial follower networks of each monitored user, we estimated the ideology of up to 50 random followers to create a baseline for the ideological composition of each user’s follower network. </p> <p>Using the initial and final follower networks of each individual, we calculated the rate of unfollows by opposite-ideology users in their follower network. To determine whether an unfollow event was breaking a cross-ideology social tie, users and their unfollowers were classified simply as either liberal (ideology score < 0) or conservative (ideology score > 0). We excluded from analysis unfollowers for whom we could not estimate ideology, either because the account had been suspended, deleted, or made private, or because they did not follow any of the political, news, or cultural accounts needed to do the estimation. We then compared the proportion of unfollowers that were of the opposite ideology (i.e., conservative unfollowers if the focal user is liberal) against the estimated proportion of opposite-ideology followers in the focal user’s initial follower network. This allowed us to account for the fact that many user’s follower networks were not ideologically balanced and set the baseline expectation that if unfollows were random then the proportion of unfollows by opposite-ideology users should match the proportion of followers that were of the opposite ideology.</p> <p> </p> <p><strong>Abstract (for main paper)</strong></p> <p>The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively “unimportant” story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others’ political identities. Observational follow network data collected on Twitter support this prediction:We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be “unimportant” news at the expense of missing out on subjectively “important” news far more frequently. This suggests that “echo chambers”—to the extent that they exist—may not echo so much as silence.</p>
Fig. 6 in Observations On Breeding And Moulting Of The Grey-Eyed Bulbul, Iole Propinqua, In Thailand
Fig. 6. Primary moult score of Grey-eyed Bulbul plotted against rectrice moult score.
Figs. 1, 2 in Observations On Breeding And Moulting Of The Grey-Eyed Bulbul, Iole Propinqua, In Thailand
Figs. 1, 2. The nest of Grey-eyed Bulbul Iola propinqua.
Fig. 5 in Observations On Breeding And Moulting Of The Grey-Eyed Bulbul, Iole Propinqua, In Thailand
Fig. 5. Primary moult score of Grey-eyed Bulbul plotted against tertial moult score.
Fig. 4 in Observations On Breeding And Moulting Of The Grey-Eyed Bulbul, Iole Propinqua, In Thailand
Fig. 4. Primary moult score of Grey-eyed Bulbul plotted against secondary moult score.
Fig. 19 in The Perinate Skull of Byronosaurus (Troodontidae) with Observations on the Cranial Ontogeny of Paravian Theropods
Fig. 19. Lateral views of the left (A) and right (B) mandibles of IGM 100/972.
Fig. 17 in The Perinate Skull of Byronosaurus (Troodontidae) with Observations on the Cranial Ontogeny of Paravian Theropods
Fig. 17. Left prootic of IGM 100/974 in caudal (A, A9), rostral (B, B9) and lateral (C, C9) views.
Fig. 10 in The Perinate Skull of Byronosaurus (Troodontidae) with Observations on the Cranial Ontogeny of Paravian Theropods
Fig. 10. Dorsal view of right frontal of IGM 100/974.
Fig. 4 in The Perinate Skull of Byronosaurus (Troodontidae) with Observations on the Cranial Ontogeny of Paravian Theropods
Fig. 4. Dorsal (A, A9) and ventral (B, B9) views of IGM 100/974.
Fig. 8 in The Perinate Skull of Byronosaurus (Troodontidae) with Observations on the Cranial Ontogeny of Paravian Theropods
Fig. 8. Dorsal (A) and ventral (B) views of the rostrum in IGM 100/974.
FIGURE 6 in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 6. Various ventral views of Deepstaria enigmatica.
FIGURE 3 in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 3. Deepstaria enigmatica (below) with a blue-emitting bioluminescent Tomopteris sp. (above).
FIGURE 4 in In situ Observations of the Meso-Bathypelagic Scyphozoan, Deepstaria enigmatica (Semaeostomeae: Ulmaridae)
FIGURE 4. Time-lapse series of Deepstaria enigmatica closing, illustrating peristaltic motion.
Fig. 26 in Braincase Of The Upper Devonian Shark Cladodoides Wildungensis (Chondrichthyes, Elasmobranchii With Observations On The Braincase In Early Chondrichthyans
Fig. 26. Anterior (A) and posterior (B) views of the Cladodoides endocast.
Fig. 1 in Braincase Of The Upper Devonian Shark Cladodoides Wildungensis (Chondrichthyes, Elasmobranchii With Observations On The Braincase In Early Chondrichthyans
Fig. 1. Views of the Cladodoides braincase. (A) Dorsal view; (B) ventral view; (C) lateral view;
Fig. 23 in Braincase Of The Upper Devonian Shark Cladodoides Wildungensis (Chondrichthyes, Elasmobranchii With Observations On The Braincase In Early Chondrichthyans
Fig. 23. Lateral view (left side) of the Cladodoides endocast (surface rendering).
Fig. 4 in Braincase Of The Upper Devonian Shark Cladodoides Wildungensis (Chondrichthyes, Elasmobranchii With Observations On The Braincase In Early Chondrichthyans
Fig. 4. Dorsal view of the Cladodoides braincase (surface rendering).
Fig. 6 in Braincase Of The Upper Devonian Shark Cladodoides Wildungensis (Chondrichthyes, Elasmobranchii With Observations On The Braincase In Early Chondrichthyans
Fig. 6. Lateral view of the Cladodoides braincase (surface rendering).
Fig. 24 in Braincase Of The Upper Devonian Shark Cladodoides Wildungensis (Chondrichthyes, Elasmobranchii With Observations On The Braincase In Early Chondrichthyans
Fig. 24. Dorsal view of the Cladodoides endocast. Anterior to top.
ScienceDex guides
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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.