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389 results for “dance”
CoHERE Work Package 3 Survey of Inhabitants of Baltic Countries on Song and Dance Celebrations
<p>Part of Work Package 3 for the 'Critical Heritages' research project ( https://research.ncl.ac.uk/cohere/researchstrands/#WP3%20Cultural%20forms%20and%20expressions%20of%20identity%20in%20Europe ).</p> <p>One of the key case studies in CoHERE Work Package 3 has been the Song and Dance Celebration tradition in the Baltic states (included in the UNESCO list as a masterpiece of the oral and intangible heritage of humanity in 2003). The case study reveals several aspects of this festival: cultural, economic, social dimensions and governance. Through examining different aspects of this festival tradition and everyday practices it responds to several objectives of the WP3. Being a key social and cultural event in three Baltic countries, it provides a ground for debates on how performative practices and festivals can contribute to identity construction and transformation, developing sense of belonging, serve as platform for where heritage practices of different social groups can meet.</p>
Measuring individual and group flow in collaborative improvisational dance.
<p>Flow is a state of being fully absorbed and experiencing feelings of energised focus, deep involvement, and success in the process of doing things. Flow plays a vital role in innovation and creativity, as all such processes require high intrinsic motivation to break through to a new level of complexity of thoughts and ideas, while the social environment rarely provides sufficient extrinsic rewards to motivate people to extensive creative work. Meanwhile, the vast majority of creative activities have a primarily social character: e.g. theatre making, music, and dancing. Thus, group flow became central in group creativity research.</p> <p>Group flow shares many aspects with individual flow, but inevitably has differences, due to its collaborative nature. In this study, we compare individual and group flow in dance improvisation, to explore the cognitive processes and strategies underlying group improvisation and their relation to flow experience; in particular, those that might support the aspects of group flow that are dependent upon understanding the other group members’ states and intentions.</p> <p>To assess flow experience, we used a video-stimulated recall method, <em>Flow </em>(Łucznik, Loesche, 2017), which allowed participants to mark on the video-recording of the activity those moments when they remembered experiencing flow. We identified group flow as the moments when then the majority of a group declared themselves as being in flow.</p> <p>This dataset consists of the data and analysis used in the 'Measuring individual and group flow in collaborative improvisational dance.' article (in press).</p>
Creative Flow in Dance: Consensual Assessment of Creative Outcomes of Dance Group Improvisation
<p>Group flow refers to peak experience when a group is performing at its highest level of abilities. This study examined the relationship between the group flow experience of an improvising group of contemporary dancers and their creative outcomes evaluated by the consensual assessment method (CAT). The dataset consists of the data and analysis used in the "Creative Flow in Dance: Consensual Assessment of Creative Outcomes of Dance Group Improvisation (in press)."</p> <p>First, eight groups of four dancers were recruited to perform improvised dance scores for video recordings. The low and high-flow improvisations videoclips were selected based on dancers’ reports in the video-stimulated recall method, Flow (Łucznik, May, 2021). Subsequently, these videoclips were rated by 77 experts and 126 nonexperts using CAT on five dimensions: aesthetic appeal, technique demands, meaningfulness, coherence /collaboration of the group, and creativity.</p> <p>Experts rated high-flow improvisations as more creative than the low-flow ones. In comparison, nonexperts’ ratings of high and low-flow improvisation did not differ, although the reliability measures of nonexperts creativity judgments were noticeably lower than in the expert group. Regardless of expertise, creativity judgments were significantly related to all other CAT factors: aesthetic appeal, technique, meaningfulness and coherence of improvisation. The results provide evidence that group flow leads to higher creativity. Moreover, they suggest that subjective judgments of creativity of performance arts cannot be easily separated from judgments of their technical level, aesthetic appeal, meaningfulness or coherence of the group.</p>
Data used in Machine learning reveals the waggle drift's role in the honey bee dance communication system
<p><strong>Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system" </strong></p> <p>All timestamps are given in ISO 8601 format.</p> <p><strong>The following files are included:</strong></p> <p><strong>Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv</strong></p> <p>Automatic individual detections of waggle phases during our recording periods in 2019 and 2021.</p> <ul> <li> <p>timestamp: Date and time of the detection.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>x_median, y_median: Median position of the bee during the waggle phase (for 2019 given in millimeters after applying a homography, for 2021 in the original image coordinates).</p> </li> <li> <p>waggle_angle: Body orientation of the bee during the waggle phase in radians (0: oriented to the right, PI / 4: oriented upwards).</p> </li> </ul> <p><strong>Berlin2019_dances.csv</strong></p> <p>Automatic detections of dance behavior during our recording period in 2019.</p> <ul> <li> <p>dancer_id: Unique ID of the individual bee.</p> </li> <li> <p>dance_id: Unique ID of the dance.</p> </li> <li> <p>ts_from, ts_to: Date and time of the beginning and end of the dance.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>median_x, median_y: Median position of the individual during the dance.</p> </li> <li> <p>feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.</p> </li> </ul> <p><strong>Berlin2019_followers.csv</strong></p> <p>Automatic detections of attendance and following behavior, corresponding to the dances in Berlin2019_dances.csv.</p> <ul> <li> <p>dance_id: Unique ID of the dance being attended or followed.</p> </li> <li> <p>follower_id: Unique ID of the individual attending or following the dance.</p> </li> <li> <p>ts_from, ts_to: Date and time of the beginning and end of the interaction.</p> </li> <li> <p>label: “attendance” or “follower”</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> </ul> <p><strong>Berlin2019_dances_with_manually_verified_times.csv</strong></p> <p>A sample of dances from Berlin2019_dances.csv where the exact timestamps have been manually verified to correspond to the beginning of the first and last waggle phase down to a precision of ca. 166 ms (video material was recorded at 6 FPS).</p> <ul> <li> <p>dance_id: Unique ID of the dance.</p> </li> <li> <p>dancer_id: Unique ID of the dancing individual.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.</p> </li> <li> <p>dance_start, dance_end: Manually verified date and times of the beginning and end of the dance.</p> </li> </ul> <p><strong>Berlin2019_dance_classifier_labels.csv</strong></p> <p>Manually annotated waggle phases or following behavior for our recording season in 2019 that was used to train the dancing and following classifier. Can be merged with the supplied individual detections.</p> <ul> <li> <p>timestamp: Timestamp of the individual frame the behavior was observed in.</p> </li> <li> <p>frame_id: Unique ID of the video frame the behavior was observed in.</p> </li> <li> <p>bee_id: Unique ID of the individual bee.</p> </li> <li> <p>label: One of “nothing”, “waggle”, “follower”</p> </li> </ul> <p><strong>Berlin2019_dance_classifier_unlabeled.csv</strong></p> <p>Additional unlabeled samples of timestamp and individual ID with the same format as Berlin2019_dance_classifier_labels.csv, but without a label. The data points have been sampled close to detections of our waggle phase classifier, so behaviors related to the waggle dance are likely overrepresented in that sample.</p> <p><strong>Berlin2021_waggle_phase_classifier_labels.csv</strong></p> <p>Manually annotated detections of our waggle phase detector (bb_wdd2) that were used to train the neural network filter (bb_wdd_filter) for the 2021 data.</p> <ul> <li> <p>detection_id: Unique ID of the waggle phase.</p> </li> <li> <p>label: One of “waggle”, “activating”, “ventilating”, “trembling”, “other”. Where “waggle” denoted a waggle phase, “activating” is the shaking signal, “ventilating” is a bee fanning her wings. “trembling” denotes a tremble dance, but the distinction from the “other” class was often not clear, so “trembling” was merged into “other” for training.</p> </li> <li> <p>orientation: The body orientation of the bee that triggered the detection in radians (0: facing to the right, PI /4: facing up).</p> </li> <li> <p>metadata_path: Path to the individual detection in the same directory structure as created by the waggle dance detector.</p> </li> </ul> <p><strong>Berlin2021_waggle_phase_classifier_ground_truth.zip</strong></p> <p>The output of the waggle dance detector (bb_wdd2) that corresponds to Berlin2021_waggle_phase_classifier_labels.csv and is used for training. The archive includes a directory structure as output by the bb_wdd2 and each directory includes the original image sequence that triggered the detection in an archive and the corresponding metadata. The training code supplied in bb_wdd_filter directly works with this directory structure.</p> <p><strong>Berlin2019_tracks.zip</strong></p> <p>Detections and tracks from the recording season in 2019 as produced by our tracking system. As the full data is several terabytes in size, we include the subset of our data here that is relevant for our publication which comprises over 46 million detections. We included tracks for all detected behaviors (dancing, following, attending) including one minute before and after the behavior. We also included all tracks that correspond to the labeled and unlabeled data that was used to train the dance classifier including 30 seconds before and after the data used for training.<br> We grouped the exported data by date to make the handling easier, but to efficiently work with the data, we recommend importing it into an indexable database.</p> <p>The individual files contain the following columns:</p> <ul> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>timestamp: Date and time of the detection.</p> </li> <li> <p>frame_id: Unique ID of the video frame of the recording from which the detection was extracted.</p> </li> <li> <p>track_id: Unique ID of an individual track (short motion path from one individual). For longer tracks, the detections can be linked based on the bee_id.</p> </li> <li> <p>bee_id: Unique ID of the individual bee.</p> </li> <li> <p>bee_id_confidence: Confidence between 0 and 1 that the bee_id is correct as output by our tracking system.</p> </li> <li> <p>x_pos_hive, y_pos_hive: Spatial position of the bee in the hive on the side indicated by cam_id. Given in millimeters after applying a homography on the video material.</p> </li> <li> <p>orientation_hive: Orientation of the bees’ thorax in the hive in radians (0: oriented to the right, PI / 4: oriented upwards).</p> </li> </ul> <p><strong>Berlin2019_feeder_experiment_log.csv</strong></p> <p>Experiment log for our feeder experiments in 2019.</p> <ul> <li> <p>date: Date given in the format year-month-day.</p> </li> <li> <p>feeder_cam_id: Numeric ID of the feeder.</p> </li> <li> <p>coordinates: Longitude and latitude of the feeder. For feeders 1 and 2 this is only given once and held constant. Feeder 3 had varying locations.</p> </li> <li> <p>time_opened, time_closed: Date and time when the feeder was set up or closed again.<br> sucrose_solution: Concentration of the sucrose solution given as sugar:water (in terms of weight). On days where feeder 3 was open, the other two feeders offered water without sugar.</p> </li> </ul> <p> </p> <ul> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li> <p><a href="https://github.com/BioroboticsLab/bb_pipeline">bb_pipeline: Tag localization and decoding pipeline</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_pipeline_models">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_binary">bb_binary: Raw detection data storage format</a></p> </li> <li> <p><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_imgacquisition">bb_imgacquisition: Recording and network storage </a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, feature extraction</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_tracking">bb_tracking: Tracking of bee detections over time</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_wdd2">bb_wdd2: Automatic detection and decoding of honey bee waggle dances</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_wdd_filter/">bb_wdd_filter: Machine learning model to improve the accuracy of the waggle dance detector</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_dance_networks/tree/master/bb_dance_networks">bb_dance_networks: Detection of dancing and following behavior from trajectories</a></p> </li> </ul> <p> </p>
From the hot carrier solar cell to the intermediate band solar cell, passing through the multiple-exciton generation solar cell and then back to the hot carrier solar cell: the Dance of the Electro-chemical Potentials
<p>Presentation titled "From the hot carrier solar cell to the intermediate band solar cell, passing through the multiple-exciton generation solar cell and then back to the hot carrier solar cell: the Dance of the Electro-chemical Potentials" given by Antonio Marti at the 36th European PV Solar Energy Conference and Exhibition in Marseille, in September 2019.</p>
DeepDance: Motion capture data of improvised dance (2019)
<p><em>When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. DeepDance: Motion capture data of improvised dance (2019) (version 2.0). Zenodo 10.5281/zenodo.5838178</em></p> <p><strong>Abstract</strong></p> <p>This dataset comprises full-body motion capture of improvised dance as well as corresponding audio files. 30 dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 240Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 540 1-minute motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files.</p> <p> </p> <p><strong>Music</strong></p> <ul> <li>Skarphedinsson, M. Wallace, B. (2019). “Song a”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song b”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song c”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song d”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song f”</li> <li>LaClair, J. Bounce. Jesse LaClair, (2018) <em>Referenced here as “Song e”</em></li> </ul> <p> </p> <p><strong>Data Description</strong></p> <p>The following data types are provided:</p> <ul> <li>Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files.</li> <li>Stimuli: audio .wav files containing 1 minute of the tracks described above.</li> <li>MATLAB script for animating the tsv files. (requires the MoCap Toolbox)</li> </ul> <p>Note: Recordings which contained errors such as missing markers have been replaced by subject 001. </p> <p><strong>Acknowledgements</strong></p> <p>This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762.</p> <p> </p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p> <p> </p>
ERC Locus Ludi. Play and Games in Antiquity. Play, Dance, Sport, War
<p>Mark Golden, Winnipeg, a widely known specialist of ancient childhood and sport. His talk: Play, Dance, Sport, War: Ancient Greek Bodies in Motion, was given at the International Conference, Play and Games in Antiquity. Definition, Transmission, Reception, September 17-19, 2018, Swiss Museum of Games.</p> <p>Movie/Music: <a href="https://www.edwanmusic.com/">https://www.edwanmusic.com/</a></p> <p>More about: www.locusludi.ch</p>
ERC Locus Ludi. Play and Games in Antiquity. Choral Dance as Play
<p>Anton Bierl, classical philologist, is a specialist of Greek lyric poetry. His paper explores the meanings of paizein ('to play') for dancing, happiness and seduction.</p> <p>Movie/Music: <a href="https://www.edwanmusic.com/">https://www.edwanmusic.com/</a></p> <p>More about: www.locusludi.ch</p>
A Bloody Difficult Woman: Mayalee Dancing Girl Vs. the East India Company
<p>The images that accompany this podcast may be found here: <a href="https://gate.sc?url=https%3A%2F%2Fblogs.bl.uk%2Fasian-and-african%2F2019%2F03%2Fmusicians-and-dancers-in-the-india-office-records.html&token=2f0bcb-1-1609324650767">blogs.bl.uk/asian-and-african/2…office-records.html</a></p> <p>In 1818, the East India Company signed a treaty with the autonomous Rajput states of Jaipur and Jodhpur, offering British political and military protection in exchange for heavy cash tribute. By the early 1830s, these states were swimming in debt and increasingly resisting the Company's influence. So in 1835 the Company took direct control over the revenue of the salt lake at Sambhar, still one of India’s largest sources of that most precious of commodities, salt. Sambhar Lake was returned to Jaipur's and Jodhpur’s control in 1842 when, having been brought to the brink of ruin by the Company’s protection racket, their arrears were written off by the Government in Calcutta. Short-lived and little-studied, the Sambhar Lake affair left behind a set of financial accounts in the East India Company records that are alive with details of musicians and dancers, the cycle of Sambhar's festival year, and the economics of such cultural production.</p> <p>One musician in particular stands forth from Jaipur's accounts as exceptional, Mayalee “dancing girl”. As well as being paid a monthly cash stipend, she received 25 maunds of salt annually, and was clearly one of Sambhar’s chief courtesans. Little exculpatory notes in the margins of successive Company accounts reveal that Mayalee successfully resisted the Company’s attempt to force her to give up her salt stipend in exchange for cash. Was she merely protecting a nice little sideline selling salt? Or did the more lofty ideal of “faithfulness to the salt” (namak-halali) underpin her resistance? In this podcast I consider why Indian musicians and especially courtesans appear at all in the official records of the East India Company, and what this tells us about relations between the British colonial state and the Indian peoples whose worlds it was increasingly encroaching upon during the 1830s and 40s.</p> <p>This podcast is part of the series <a href="https://soundcloud.com/user-513302522">Histories of the Ephemeral: Writing on Music in Late Mughal India</a>, sponsored by the British Academy in association with the British Library; additional research was funded by the European Research Council.</p> <p>Mayalee Dancing Girl vs the East india Company was written by me, Katherine Butler Schofield (King's College London), and is based on my original research. It was produced by Chris Elcombe. Additional voices were Michael Bywater, Chris Elcombe, and Kanav Gupta. It is published under a Creative Commons Attribution Non-Commercial No Derivatives (CC–BY-NC–ND) license.</p> <p>The recording of Rag Jaunpuri by Jaipur gharana doyenne Kesarbai Kerkar is courtesy of the Archive of Indian Music and Vikram Sampath. <a href="https://soundcloud.com/archive-of-indian-music/kesarbai-kerkar">Archive-of-indian-music – Kesarbai-kerkar</a></p> <p>The sarangi recording of Rag Bhairavi is by Nicolas Magriel and reproduced with thanks.</p> <p>Information on the Jaipur gunijan-khana is taken from the work of Joan Erdman, and material on Amber/Jaipur’s political life from the work of Giles Tillotson and Monika Horstmann.</p> <p>With thanks to: the British Academy, the British Library, the National Archives of India, the European Research Council, Norbert Peabody, Paul Schofield, and Mrinalini Venkateswaran.</p> <p>Flute and Drum, Rishikesh by Samuel Corwin CC BY 4.0</p> <p>Prayer Temple Jaipur by Xserra CC BY 4.0</p> <p>20160922_summers.end.marshes by dobroide CC BY 4.0</p> <p>Waves on the Lake by Vlatko Blazek CC BY 4.0</p> <p>Kirtana_in_Hindi by psubhashish CC BY 4.0</p> <p>Water Music From the Handel Show by The United States Army Old Guard Fife and Drum Corps Public Domain Mark 1.0 Licence</p> <p>Ganga Aarti Ceremony V, Haridwar by Samuel Corwin CC BY 4.0</p> <p>Shiva Worship Ceremony, Varanasi by Samuel Corwin CC BY 4.0</p> <p>A Man Approaches with Bowed Sitar, Rishikesh by Samuel Corwin CC BY 4.0</p> <p>Track 1 by Deep Singh and Ikhlaq Hussain Khan<br> Originally broadcast live on Rob Weisberg's show, Transpacific Sound Paradise on WFMU. CC BY NC SA 3.0</p>
Comparative data for dance fly eye morphology and female ornamentation
<p class="western">These data were collected as part of a comparative study of the relationship between female ornamentation and sexual dimorphism in eye morphology. Data come from specimens collected in the field in Scotland near Loch Lomond in the summers of 2009, 2010, and 2011 as well as the summer of 2012 near Glen Williams in Ontario, Canada. The repository contains raw image files including information on magnifications at which these were taken, excel spreadsheets of morphological measurements taken from these images, a dataset from search of Collin's (<span>1961</span>) key to the Empidinae for reports of sexual dimorphism and exaggerations of male eye morphology, and an Rnotebook file detailing the analytical steps taken.</p>
The challenge of being slow: Effects of tempo, laterality, and experience on dance movement consistency
<p>Data set for the study published in Journal of Motor Behavior.</p>
FIGURE 5 in Dancing with the devil: courtship behaviour, mating evidences and population structure of the Mobula tarapacana (Myliobatiformes: Mobulidae) in a remote archipelago in the Equatorial Mid-Atlantic Ocean
FIGURE 5 | Distinct courtship behaviors of sicklefin devil rays Mobula tarapacana observed in the Saint Peter and Saint Paul Archipelago. A. Female being chased by two males. B. Male overlapping female. C. Male trying to overlap on female. D. Male overlaps the female with two more males chasing. E–F. Sequence of male following female.
FIGURE 3 in Dancing with the devil: courtship behaviour, mating evidences and population structure of the Mobula tarapacana (Myliobatiformes: Mobulidae) in a remote archipelago in the Equatorial Mid-Atlantic Ocean
FIGURE 3 | Female (grey) and male (black) Mobula tarapacana size distribution (disk width- DW, in meters) per month, in the Saint Peter and Saint Paul Archipelago (SPSPA), from December 2008 to June 2016. Red dashed line= size at maturity for males (White et al., 2006); blue dashed line= size at maturity for females (Notarbartolo di Sciara, 1988).
Live robot dance at the Orebro Feast Concert
<p>A human pianist, a virtual drummer and a dancing robot, all coordinated by AI. This unusual team entertained the audience of the "Festkonsert", an official yearly celebration of Örebro University with the participation of about 250 people. The performance was meant to show the use of AI to orchestrate the collaboration among human and artificial agents in a creative process. The music performed by the pianist was improvised, and an AI system decided the moods, patterns and motions to be performed by the virtual drummer and by the dancing robot.</p> <p>This clip is a recording of the public event.</p> <p><strong>Artists:</strong> Peter Knudsen (human pianist), Pepper (the robot), Oscar Thörn (AI programmer), Alessandro Saffiotti (AI expert)</p>
Figs 2-3 in Dancing behavior of Cosmopterix victor S , a species new to the fauna of China (Lepidoptera: Cosmopterigidae)
Figs 2-3: Depiction of body orientation during dance. 2, body orientation during spinning; 3, body orientation during swinging motion.
Jewish St Petersburg tour –Explore with Dancing Bear Tours
<p><strong>Are you planning for a <a href="https://dancing-bear-tours.com/st_activity/two-day-jewish-heritage-tour/">Jewish St Petersburg tour</a>? Judaism has played an essential role in the evolution of many cities, shaping neighbourhoods and buildings as well as historical events and current culture. Our special guides help you to see, taste and explore the influence of Jewish community on St Petersburg’s cultural and social life.</strong><br> </p>
Enjoy White Nights city events in St Petersburg | Dancing Bear Tours
<p><strong>The White Nights Festival in St Petersburg is an annual summer festival celebrating its near-midnight sun phenomena due to its position near the Arctic Circle; each year in June-July, the skies only reach twilight and never reach ample darkness. If you want to enjoy the White Nights city events (including music festivals and rising bridges), choose Dancing Bear Tour as your guide or tour operator. Book a tour with Dancing Bear Tour to enjoy <a href="https://dancing-bear-tours.com/st_activity/5-rising-bridges-night-boat-for-the-cruise-passengers/">White Night in St Petersburg</a></strong><br> </p>
Experimental data of dancing peanuts in beer
<p>In Argentina, some people add peanuts to their beer. Once immersed, the peanuts initially sink part way down into the beer before bubbles nucleate and grow on the peanut surfaces and remain attached. The peanuts move up and down within the beer glass in many repeating cycles. In this work, we propose a physical description of this dancing peanuts spectacle. We break down the problem into component physical phenomena, providing empirical constraint of each: (i) heterogeneous bubble nucleation occurs on peanut surfaces and this is energetically preferential to nucleation on the beer glass surfaces; (ii) peanuts enshrouded in attached bubbles are positively buoyant in beer above a critical attached gas volume; (iii) at the beer top surface, bubbles detach and pop, facilitated by peanut rotations and rearrangements; (iv) peanuts containing fewer bubbles are then negatively buoyant in beer and sink; and (v) the process repeats so long as the beer remains sufficiently supersaturated in the gas phase for continued nucleation. We used laboratory experiments and calculations to support this description, including constraint of the densities and wetting properties of the beer–gas–peanut system. We draw analogies between this peanut dance cyclicity and industrial and natural processes of wide interest, ultimately concluding that this bar-side phenomenon can be a vehicle for understanding more complex, applied systems of general interest and utility.</p>
Dance or disappear: Strategic sexual signalling in female Peninsular rock agama
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Comparative data for dance fly eye morphology and female ornamentation
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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.