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Ecology and conservation of socially learned foraging tactics in odontocetes
<h3>Overview</h3> <p>This package contains the data and R code to replicate the analyses and figures of the review article, "Ecology and conservation of socially learned foraging tactics in odontocetes", submitted to the special issue of Philosophical Transactions B, "Animal Culture: conservation in a changing world". </p> <p>Metadata for CSV files used for analyses are provided below, and detailed instructions on running code are available at: https://github.com/JoaoVallePereira/Toothed_Whales_Forag_Tactics. For full description of variables, see supplemental material associated with publication. </p> <div> <h3>Main data table - dataTable_Forag_Tactics.csv</h3> </div> <table> <tbody> <tr> <th>Variable</th> <th>Class</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>common_name</td> <td>Character</td> <td>The common name of the species exhibiting the foraging tactic</td> </tr> <tr> <td>latin_name</td> <td>Character</td> <td>The Latin name of the species exhibiting the foraging tactic</td> </tr> <tr> <td>country</td> <td>Character</td> <td>The country that has jurisdiction over the region where the foraging tactic occurs</td> </tr> <tr> <td>region</td> <td>Character</td> <td>The region where the foraging tactic occurs</td> </tr> <tr> <td>animal_identity_data</td> <td>Character</td> <td>Whether identity information for the individual(s) exhibiting the foraging tactic is available</td> </tr> <tr> <td>number_of_animals</td> <td>Character</td> <td>The number of different individuals exhibiting the foraging tactic</td> </tr> <tr> <td>foraging_category</td> <td>Character</td> <td>The broad foraging category that the specific foraging tactic most closely aligns with</td> </tr> <tr> <td>foraging_tactic</td> <td>Character</td> <td>The specific foraging tactic</td> </tr> <tr> <td>tactic_driver</td> <td>Character</td> <td>The key factor influencing or determining the observed foraging tactic</td> </tr> <tr> <td>human_induced</td> <td>Character</td> <td>Whether the foraging tactic is human-induced or not</td> </tr> <tr> <td>prey_category</td> <td>Character</td> <td>The type of prey being targeted during the foraging tactic</td> </tr> <tr> <td>habitat</td> <td>Character</td> <td>The type of habitat in which the foraging tactic is exhibited</td> </tr> <tr> <td>prey_category</td> <td>Character</td> <td>The type of prey being targeted during the foraging tactic</td> </tr> <tr> <td>putative_specialised_foraging_tactic</td> <td>Character</td> <td>Foraging tactics having both individual identity information and evidence of being shared among conspecifics</td> </tr> <tr> <td>putative_cultural_foraging_tactic</td> <td>Character</td> <td>Foraging tactics with positive evidence of social learning</td> </tr> <tr> <td>transmission_direction</td> <td>Character</td> <td>How the foraging tactic is transmitted, given positive evidence of social learning</td> </tr> <tr> <td>nature_of_social_learning_evidence</td> <td>Character</td> <td>The type of evidence for social learning</td> </tr> <tr> <td>culture_acknowledgement</td> <td>Character</td> <td>The type of evidence for social learning</td> </tr> <tr> <td>evidence_for_discreteness_significance</td> <td>Character</td> <td>Evidence for differences in diet or foraging techniques that are stable</td> </tr> <tr> <td>threat_acknowledgement</td> <td>Character</td> <td>Whether the reviewed studies acknowledge anthropogenic threats</td> </tr> <tr> <td>threat_category</td> <td>Character</td> <td>For studies that acknowledge anthropogenic threats and impacts, the type of IUCN-CMP first-level threat classification</td> </tr> <tr> <td>threat_subcategory</td> <td>Character</td> <td>For studies that acknowledge anthropogenic threats and impacts, the type of IUCN-CMP second-level threat classification</td> </tr> <tr> <td>threat_direction</td> <td>Character</td> <td>Whether the acknowledged threats were considered a threat to or a consequence of the foraging tactic</td> </tr> <tr> <td>conservation_actions_acknowledgement</td> <td>Character</td> <td>Whether the reviewed studies acknowledge existing or proposed conservation actions related to the foraging tactic</td> </tr> <tr> <td>existing_conservation_actions_category</td> <td>Character</td> <td>Existing conservation actions related to the foraging tactic, the type of IUCN-CMP first-level action classification</td> </tr> <tr> <td>existing_conservation_actions_subcategory</td> <td>Character</td> <td>Existing conservation actions related to the foraging tactic, the type of IUCN-CMP second-level action classification</td> </tr> <tr> <td>proposed_conservation_actions_category</td> <td>Character</td> <td>Proposed conservation actions related to the foraging tactic, the type of IUCN-CMP first-level action classification</td> </tr> <tr> <td>proposed_conservation_actions_subcategory</td> <td>Character</td> <td>Proposed conservation actions related to the foraging tactic, the type of IUCN-CMP second-level action classification</td> </tr> <tr> <td>references</td> <td>Character</td> <td>Reviewed primary and secondary literature used to fill out metrics for the foraging tactic</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Maps data table - dataTable_Forag_Tactics_map.csv</h3> </div> <table> <tbody> <tr> <th>Variable</th> <th>Class</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>common_name</td> <td>Character</td> <td>The common name of the species exhibiting the foraging tactic</td> </tr> <tr> <td>latin_name</td> <td>Character</td> <td>The Latin name of the species exhibiting the foraging tactic</td> </tr> <tr> <td>orca_ecotype</td> <td>Character</td> <td>The orca ecotypes exhibiting the foraging tactic</td> </tr> <tr> <td>country</td> <td>Character</td> <td>The country that has jurisdiction over the region where the foraging tactic occurs</td> </tr> <tr> <td>region</td> <td>Character</td> <td>The region where the foraging tactic occurs</td> </tr> <tr> <td>latitude</td> <td>Numeric</td> <td>The latitude where the foraging tactic occurs</td> </tr> <tr> <td>longitude</td> <td>Numeric</td> <td>The longitude where the foraging tactic occurs</td> </tr> <tr> <td>putative_specialised_foraging_tactic</td> <td>Character</td> <td>Foraging tactics having both individual identity information and evidence of being shared among conspecifics</td> </tr> <tr> <td>foraging_category</td> <td>Character</td> <td>The broad foraging category that the specific foraging tactic most closely aligns with</td> </tr> <tr> <td>tactic_cat_fact</td> <td>Factor (10 levels)</td> <td>The broad foraging category that the specific foraging tactic most closely aligns with</td> </tr> <tr> <td>evidence_for_discreteness_significance</td> <td>Character</td> <td>Evidence for differences in diet or foraging techniques that are stable</td> </tr> </tbody> </table>
Learning to embed lifetime social behavior from interaction dynamics - Data
<p><strong>Interaction matrices and metadata used in "Learning to embed lifetime social behavior from interaction dynamics"</strong></p> <p>The following files are included:</p> <ul> <li>interactions_bn16_sparse.npz and interactions_bn19_sparse.npz: These are the interaction affinity matrices for the BN16 and BN19 datasets as described in the publication. The data is stored as compressed sparse tensors with time on the first, and the individuals on the second and third dimensions. The data was stored using the <a href="http://sparse.pydata.org">pydata/sparse</a> library 0.9.1</li> <li> <p>alive_bn16.csv and alive_bn19.csv: These files contain the dates of emergence (also corresponding to the dates they were introduced into the colonies) and heuristically determined number days alive for all individuals in the interaction matrices. Death dates were determined using a bayesian changepoint model and the number of daily detections of each individual</p> </li> <li> <p>rhythmicity_bn16.csv and rhythmicity_bn19.csv: These files contain the circadian rhythmicity values used in the evaluation of the method. The circadian rhythmicity is the <span class="math-tex">\(R^2\)</span> value of a sine with a 24 hour period fitted to the individuals' movement velocities over a three day window</p> </li> <li> <p>indices_bn16.csv and indices_bn19.csv: These files contain the mapping between the original marker IDs used during the recording of the data (which has gaps, because not all markers were used) and the sequential indices used in the interaction matrices. These files can therefore be used to look up the original ID of an individual based on it's index in the interaction matrix and vice versa</p> </li> <li> <p>time_spent_on_substrates.csv: This data was used for the mapping from factors to the proportion of time spent on various cell substrates (Figure 5). The positions of the individuals were accumulated by minute, and the column "location_descriptor_count" contains the total number of minutes on the respective day that the individual was detected</p> </li> </ul> <p>See <a href="https://doi.org/10.1101/2020.05.06.076943">10.1101/2020.05.06.076943</a> for more details about the bayesian changepoint model, circadian rhythmicity calculation, and location mapping.</p>
Social learning data in a foraging setting for Heliconius erato
<p><span>Insects may acquire social information by active communication and through inadvertent social cues. In a foraging setting, the latter may indicate the presence and quality of resources. Although social learning in foraging contexts is prevalent in eusocial species, this behaviour has been hypothesised to also exist between conspecifics in non-social species with sophisticated behaviours, including </span><span><em>Heliconius</em> </span><span>butterflies</span><span>. </span><span><em>Heliconius</em> </span><span>are the</span><span> only butterfly genus with active pollen feeding, a dietary innovation </span><span>associated with a specialised, spatially faithful foraging behaviour known as trap-lining. Long-standing hypotheses suggest that <em>Heliconius</em> may acquire trap-line information by following experienced individuals. Indeed, <em>Heliconius</em></span> <span>often aggregate in social roosts, which could act as 'information centres', and present conspecific following behaviour, enhancing opportunities for social learning. Here, we provide a direct test of social learning ability in <em>Heliconius</em> using an associative learning task in which naïve individuals completed a colour preference test in the presence of demonstrators trained to feed randomly or with a strong colour preference. We found no evidence that </span><span><em>Heliconius</em> <em>erato</em></span><span>, which roost socially, used social information in this task</span><span>. Combined with existing field studies our results add to data which contradict the hypothesised role of social learning in <em>Heliconius</em> foraging behaviour.</span></p>
Data from: A social learning primacy trend in mate-copying; an experiment in Drosophila melanogaster
<p>Social learning is learning from the observation of how others interact with the environment. However, in nature, individuals often need to process serial social information and may either favour the most recent information (recency bias), constantly updating knowledge to match the environment, or the information that appeared first in the series (primacy bias), which may slow down adjustment to environmental change. Mate-copying is a widespread form of social learning in a mate choice context related to conformity in mate choice, and where a naïve individual develops a preference for a given mate (or mate phenotype) seen being chosen by conspecifics. Mate-copying is documented in most vertebrate taxa and in the fruit fly <em>Drosophila melanogaster</em>. Here, we tested experimentally whether female fruit flies show a primacy or a recency bias by presenting pictures of a female copulating with one of two contrastingly coloured male phenotypes. We found that after two sequential contradictory demonstrations, females show a tendency to prefer males of the phenotype preferred in the first demonstration, suggesting that mate-copying in <em>D. melanogaster</em> is not based on the most recently observed mating and may be influenced by a form of primacy bias.</p>
Assessing Student Sustainable Learning Engagement in Mobile Learning through Social Cognitive Theory and Social Learning Theory
<p><span>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381)</em>.</span><span> the data was collected as part of ODDEA WP2.</span></p>
Social signal learning of referential communication in a social insect
<p>This is the dataset for a paper showing that honey bees can use social learning to improve their waggle dancing.</p>
Repeated parallel differentiation of social learning differences in benthic and limnetic threespine stickleback fish
<p>Individuals can reduce sampling costs and increase foraging efficiency by using information provided by others. One simple form of social information use is delayed local enhancement, or increased interest in a location because of the past presence of others. We tested for delayed local enhancement in two ecomorphs of stickleback fish, benthic and limnetic, from three different lakes with putative independent evolutionary origins. Two of these lakes have reproductively isolated ecomorphs ('species-pairs'), whereas in the third a previously intact species-pair recently collapsed into a hybrid swarm. Benthic fish in both intact species-pair lakes were more likely to exhibit delayed local enhancement despite being more solitary than limnetic fish. Their behaviour and morphology suggest their current perceived risk and past evolutionary pressure from predation did not drive this difference. In the hybrid swarm lake, we found a reversal in patterns of social information use, with limnetic-looking fish showing delayed local enhancement rather than benthic-looking fish. Together, our results strongly support parallel differentiation of social learning differences in recently evolved fish species, although hybridization can apparently erode and possibly even reverse these differences.</p>
African elephant rumbles differ between populations and sympatric social groups: possible consequences of vocal learning?
<p>Vocal production learning, the ability to modify vocalizations in response to sounds made by others, was a critical prerequisite for the evolution of human speech but is rare among mammals. Elephants have exhibited this ability in captivity, yet its function in wild elephants remains unknown. Female African savannah elephants (<em>Loxodonta</em> <em>africana</em>) live in large societies with nested tiers of association in which vocal signatures of group identity could facilitate recognition of distant social affiliates. Vocal production learning allows the formation of such group signatures in many species and can also cause vocal differentiation between populations. However, the existence of vocal signatures of social group or population in elephants was unexplored. We recorded multiple social groups of wild elephants in two Kenyan populations (Samburu and Amboseli) and used random forest models to determine if calls could be assigned to individual callers, family groups, bond groups (collections of family groups), or populations based on acoustic structure. Calls were assigned by a random forest model to individual callers and populations with better-than-chance accuracy, demonstrating population-level divergence in vocalization structure. While random forest models failed to accurately assign calls to family or bond group, calls from the same family or bond group were significantly more similar (higher proximity scores) than calls from different groups, suggesting the existence of group signatures as well. We discuss possible drivers of this differentiation and argue that vocal learning is the most likely explanation for population- and group-level variation in elephants. The existence of group signatures suggests recognition of large numbers of individuals as a possible adaptive function for vocal production learning in elephants.</p>
Autistic traits relate to reduced reward sensitivity in learning from social point-light displays (PLDs)
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Nestling birds learn socially to eavesdrop on heterospecific alarm calls through acoustic association
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Social learning data in a foraging setting for Heliconius erato
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Data from: Repeated parallel differentiation of social learning differences in benthic and limnetic threespine stickleback fish
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Data from: A social learning primacy trend in mate-copying; an experiment in Drosophila melanogaster
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Social learning by mate-choice copying increases dispersal and reduces local adaptation
<p class="MsoPlainText">1. In heterogeneous environments, dispersal may be hampered not only by direct costs, but also because immigrants may be locally maladapted. While maladaptation affects both sexes, this cost may be modulated in females if they express mate preferences that are either adaptive or maladaptive in the new local population.</p> <p class="MsoPlainText">2. Dispersal costs under local adaptation may be mitigated if it is possible to switch to expressing traits of locally adapted residents. In a sexual selection context, immigrant females may learn to mate with locally favoured males. Mate-choice copying is a type of social learning, where individuals, usually females, update their mating preferences after observing others mate. If it allows immigrant females to switch from maladapted to locally adapted preferences, their dispersal costs are mitigated as mate choice helps them create locally adapted offspring.</p> <p class="MsoPlainText">3. To study if copying can promote the evolution of dispersal, we created an individual-based model to simulate the coevolution of four traits: copying, dispersal, a trait relevant for local adaptation, and female preference. We contrast two scenarios with copying — either unconditional, or conditional such that only dispersers copy — with a control scenario that lacks any copying.</p> <p class="MsoPlainText">4. We show copying to lead to higher dispersal, especially if copying is conditionally expressed. This leads to an increase in gene flow between patches and, consequently, a decrease in local adaptation and trait-preference correlations.</p> <p class="MsoPlainText">5. While our study is phrased with female preference as the learned trait, one may generally expect social learning to mitigate dispersal costs, with consequent feedback effects on the spatial dynamics of adaptation.</p>
Competition for resources can promote the divergence of social learning phenotypes
<p>Social learning occurs when animals acquire knowledge or skills by observing or interacting with others, and is the fundamental building block of culture. Within populations, some individuals use social learning more frequently than others, but why social learning phenotypes differ among individuals is poorly understood. We modelled the evolution of social learning frequency in a system where foragers compete for resources and there are many different foraging options to learn about. Social learning phenotypes diverged when some options offered much better rewards than others and expected rewards changed moderately quickly over time. When options offered similar rewards or when rewards changed slowly, a single social learning phenotype evolved. This held for fixed and simple conditional social learning rules. Sufficiently complex conditional social learning rules prevented the divergence of social learning phenotypes under all conditions. Our results explain how competition can promote the divergence of social learning phenotypes.</p>
Social experiences shape song preference learning independent of developmental exposure to song
<p>Communication governs the formation and maintenance of social relationships. The interpretation of communication signals depends not only on the signal's content, but also on a receiver's individual experience. Experiences throughout life may interact to affect behavioral plasticity, such that a lack of developmental sensory exposure could constrain adult learning, while salient adult social experiences could remedy developmental deficits. We investigated how experiences impact the formation and direction of female auditory preferences in the zebra finch. Zebra finches form long-lasting pair bonds and females learn preferences for their mate's vocalizations. We found that after two weeks of cohabitation with a male, females formed pair bonds and learned to prefer their partner's song regardless of whether they were reared with ("normally-reared") or without ("song-naïve") developmental exposure to song. In contrast, females that heard but did not physically interact with a male did not prefer his song. In addition, previous work has found that song-naive females do not show species-typical preferences for courtship song. We found that cohabitation with a male ameliorated this difference in preference. Thus, courtship and pair bonding, but not acoustic-only interactions, strongly influence preference learning regardless of rearing experience, and may dynamically drive auditory plasticity for recognition and preference.</p>
Data from: The importance of population heterogeneities in detecting social learning as the foundation of animal cultural transmission
<p class="MsoNormal"><span>High levels of within-population behavioural variation can have drastic demographic consequences, thus changing the evolutionary fate of populations. A major source of within-population heterogeneity is personality. Nonetheless, it is still relatively rarely accounted for in social learning studies that constitute the most basic process of cultural transmission. Here, we perform in female mosquitofish (<em>Gambusia holbrooki</em>) a social learning experiment in the context of mate choice, a situation called mate copying, and for which there is strong evidence that it can lead to the emergence of persistent traditions of preferring a given male phenotype. </span><span class="TexteCourant1Car"><span>When accounting for the </span></span><span>global</span><span class="TexteCourant1Car"> <span>tendency of females to prefer lager males </span></span><span>but ignoring differences in personality we detected no evidence for mate copying. However, when accounting for the bold-shy dichotomy, we found that bold females did not show any evidence for mate copying, while shy females showed significant amounts of mate copying. This illustrates how the presence of variation in personality can hamper our capacity to detect mate copying. We conclude that mate copying may be more widespread than we thought because many studies ignored the presence of within-population heterogeneities.</span></p>
Alarm cues and alarmed conspecifics: Neural activity during social learning from different cues in Trinidadian guppies
<p>Learning to respond appropriately to novel dangers is often essential to survival and success, but carries risks. Learning about novel threats from others (social learning) can reduce these risks. Many species, including the Trinidadian guppy (<em>Poecilia reticulata</em>), respond defensively to both conspecific chemical alarm cues and conspecifix anti-predator behaviours, and in other fish such social information can lead to a learned aversion to novel threats. However, relatively little is known about the neural substrates underlying social learning and the degree to which different forms of learning share similar neural mechanisms. Here, we explored the neural substrates mediating social learning of novel threats from two different conspecific cues (i.e. social cue-based threat learning). We first demonstrated that guppies rapidly learn about threats paired with either alarm cues or with conspecific threat responses (demonstration). Then, focusing on acquisition rather than recall, we discovered that phospho-S6 expression, a marker of neural activity, was elevated in guppies during learning from alarm cues in the putative homologue of the mammalian lateral septum and the preoptic area. Surprisingly, these changes in neural activity were not observed in fish learning from conspecific demonstration. Together, these results implicate forebrain areas in social learning about threat but raise the possibility that circuits contribute to such learning in a stimulus-specific manner.</p>
Mental Health in Social Networks with Machine Learning Algorihtms
<p>The <strong>DatasetMH.xlsx</strong> excel corresponds to a corpus of mental health in social networks labelled with polarity and stigma. In particular, the corpus consists of 2,287 comments labelled with polarity (positive, negative, neutral) and stigma from comments on Instagram posts about celebrity mental health disclosures:</p> <ol> <li>Polarity: It consists of giving a positive, negative or neutral/undefined value to the comments in response to the disclosure or description of the symptomatology in the post. Positive polarity reflects understanding, encouragement or even admiration of the publication. E.g., “Cheer up, we love you".”. Negative polarity is assigned when the person expresses negative opinions, usually questioning the post with ironic, sarcastic or even mocking and disparaging comments. E.g., “how you show that you don't know what depression or anxiety is, shame on you!”. Neutral or undefined polarity is assigned in cases where no clear opinion is detected or can be interpreted in both directions. E.g., “take medication, it will help you" "and your partner?”</li> <li>Stigma: stigmatising responses to comments are behaviours in which negative beliefs and emotions towards MH problems are expressed. Stigma manifests in a variety of forms including rejection and anger against the person, which may extend to contempt or mockery, belittling their problem. E.g.,"What a desire to draw attention to yourself"; "what you have is a story"; "you're so inconsistent and seeking the limelight". Because socially we know that "stigma is wrong" many rejection comments are made in an ironic or sarcastic way. E.g., and how do you write on insta?"; "better information from someone who doesn't have a current account". Additionally, anger is shown by arguing that such posts "trivialise or commercialise" MH. E.g.,"don't come and tell me your false stories of overcoming, without even knowing what it is to work...". Other times the stigma manifests itself as pity or sorrow for the person. E.g.,“It breaks my heart”; “poor thing”.</li> </ol> <p>The file <strong>DatasetMH_Emotions.xlsx</strong> corresponds to a corpus of mental health in social networks labelled with emotions. In particular, the corpus consists of 2,287 comments labelled with five emotions plus a neutral class from comments on Instagram posts about celebrity mental health disclosures. These emotions are:</p> <ul> <li>Love/admiration: This emotion involves messages where admiration, approval and love are closely related.</li> <li>Gratitude: the messages imply a sincere appreciation for sharing mental health-related content on social networks.</li> <li>Comprehension/empathy/identification: The messages involve interest in and understanding of the message, including self-identification with the situation or context.</li> <li>Sadness: This primary emotion is produced by events that are not pleasant and that denote heaviness. It includes many manifestations of pity for the person.</li> <li>Anger/contempt/mockery: This emotion involves responses of irritation and attacks on the person as ridiculous and superficial.</li> <li>Neutral: This category corresponds to messages without emotions.</li> </ul> <p>The labelling process of both datasets was divided into two phases: an initial phase with a pilot corpus (N = 787 comments) and a second phase focused on the development of the corpus with all the comments of the selected posts (N = 21151). The same methodology was followed in both phases: once the comments were collected, the corpus was cleaned, and then two independent experts were responsible for labelling each category. A third expert then reviewed the comments to resolve discrepancies. In the third and final phase, a final corpus for application to the machine learning algorithms is built from the large corpus (N = 2287).</p> <p>Classification models are a set of machine learning algorithms developed to assess emotional response, i.e. polarity, stigma and emotions in social networks, based on previously developed datasets (<strong>DatasetMH_Emotions.xlsx</strong>, <strong>DatasetMH.xlsx</strong>).</p>
Data from: Translocation experiment of taiga bean geese Anser fabalis provides evidence for oblique social learning of moult migration
<p>While there is ample evidence supporting genetic control of migratory behaviour in short-lived passerines, long-lived social species have been assumed to rely solely on cultural inheritance of migratory routes. Evidence from experimental studies supporting this idea is scarce. We tested whether the moult migration in taiga bean geese <em>Anser fabalis </em>has an inherited component or whether the birds need oblique social learning (where knowledge on migration is transferred from any experienced individual to any naïve individual conspecific) to carry out this journey. In many waterfowl species, non-breeders and failed breeders migrate to remote places for wing moult while successful breeders stay at the breeding grounds and moult with their chicks. We translocated one-year-old taiga bean geese before their first moult migration to sites outside of the breeding range to examine whether they display innate moult migration behaviour without experienced conspecifics or not. The birds were equipped with GPS-transmitters and released in randomly assigned groups of two. Wild control one-year-old birds were released immediately after capture with other non-breeding geese, while a procedural control group consisting of older birds was held in captivity until released at the same time with the translocated one-year-old birds but in the place where they were captured. Most translocated birds found conspecifics and either joined locally moulting breeders or followed experienced birds to moulting sites in Russia. Two of the translocated birds did not find other bean geese and settled to moult together in SW Finland. The wild control birds moult-migrated as expected, while only one of the procedural control birds moult-migrated to Russia and the remaining three stayed with locally moulting breeders in Finland. Our results support the idea that moult migration in geese is culturally inherited, highlighting the importance of the non-relative, experienced adult individuals have in maintaining population-specific behaviours.</p>
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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.