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124 results for “social information”

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zenodo44/100

Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"

<pre>These files contain the time series and the associated hashtags we obtained by sampling Twitter for our paper &quot;Universality, criticality and complexity of information propagation on social media&quot;. The analysis is reported in <a href="https://arxiv.org/abs/2109.00116">https://www.nature.com/articles/s41467-022-28964-8</a> Please acknowledge the use of these data by citing the paper above. ################################# ################################# DATA ORGANIZATION We created a single zip file with all the time series and a single zip file with all the hashtags. There is a one-to-one correspondence between lines in the two files. ################################# ################################# FILES CONTENT As stated, here is a one-to-one correspondence between lines in the time series file and lines in the hashtags file, i.e., the hashtag stored in line X is the hashtag of the time series stored in line X. Time series are stored as follows: Ka t1 t2 t3 \n Kb t1 t2 t3 t4 t5 \n . . . Kn t1 t2 \n where: Ka, Kb,..., Kn is an integer specifying the number of events that compose the time series a, b,..., n respectively. In the example above we would have Ka=3, Kb=5, Kn=2. t1 t2 ... is the time series, i.e., a sequence of chronologically ordered interevent times. The last interevent time, in our implementation, represents the distance between the end of the temporal window and the last event time. It thus does not represent an event. As stated in the Supplemental Material of our paper, the temporal window ranges from 2019, October 1st to 2019, November 30th. </pre>

opencc-by-4.0Dec 2021View details →
zenodo44/100

TwitCID: a Collection of Data Sets for Studies on Information Diffusion on Social Networks

<p>The TwitCID collection consists of five Twitter datasets which were extracted&nbsp;from the 1 percent of tweets from Twitter API.&nbsp;</p> <p>The Firstweek and Secondweek data set were collected during the first week and second&nbsp;week of January 2017 while the Iphone, Gucci and Galaxy data sets were collected from 21 September 2015 to 31 May 2017 using the keywords &ldquo;iphone&rdquo;,&nbsp;&ldquo;gucci&rdquo; and &ldquo;galaxys&rdquo; respectively.&nbsp;&nbsp;</p> <p>We publish these datasets on behalf of our academic institution &ndash; IRIT, France&nbsp;and for the sole purpose of non-commercial research under the license&nbsp;CC BY-NC-SA (Attribution-NonCommercial-ShareAlike).&nbsp;In accordance with Twitter&#39;s Terms of Service, we only provide identifiers of tweets. In order to collect the actual tweets in JSON, you could use the script Collect_JSONtweets.py attached.</p> <p>If you would like to use this collection, please cite our paper:&nbsp;</p> <p>Hoang, T. B. N., Mothe, J., &amp; Baillon, M. (2019, September). TwitCID: a collection of data sets for studies on information diffusion on social networks. In&nbsp;<em>International Conference of the Cross-Language Evaluation Forum for European Languages</em>&nbsp;(pp. 88-100). Springer, Cham.</p>

opencc-by-4.0Jun 2019View details →
zenodo44/100

A Reputation Game Simulation: Emergent Social Phenomena from Information Theory

<p>Here, the data underlying the article &quot;A Reputation Game Simulation: Emergent Social Phenomena from Information Theory&quot; (<a href="https://doi.org/10.1002/andp.202100277">https://doi.org/10.1002/andp.202100277</a>) is&nbsp;provided.<br> <br> The data is structured&nbsp;according to the figures it has&nbsp;been used for. There are</p> <ul> <li>example simulations with basic communication strategies in the folder&nbsp;&quot;single_simulations_3_agents&quot; (Figures 4,5,8,D1)</li> <li>statistical simulations with 3 agents and special communication strategies in the folder&nbsp;&quot;statistical_simulations_3_agents&quot; (Figures 9-13, the upper panel of figure 15, figures&nbsp;16-18,&nbsp;D2 and&nbsp;the left panels of figure D3)</li> <li>statistical simulations with 4&nbsp;agents and special communication strategies in the folder&nbsp;&quot;statistical_simulations_4_agents&quot; (Figure 14, the middle panel of figure 15,&nbsp;the middle panels of figure D3 and&nbsp;the upper panels of figures D4, D5)</li> <li>statistical simulations with 5&nbsp;agents and special communication strategies in the folder&nbsp;&quot;statistical_simulations_5_agents&quot; (The lower panel of figure 15, the right panels of figure D3 and the lower panels of figures D4,D5)</li> <li>propaganda&nbsp;simulations&nbsp;in&nbsp;the&nbsp;folder&nbsp;&quot;propaganda_simulations&quot;&nbsp;(Figure&nbsp;7)</li> </ul> <p><br> Each simulation is represented by a .json file in which all events that&nbsp;happened during the simulation are collected. Generally, there are&nbsp;three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies&nbsp;the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained:</p> <ul> <li>parameters <ul> <li>decpeting:&nbsp;whether&nbsp;or&nbsp;not&nbsp;agents&nbsp;in&nbsp;generally&nbsp;make&nbsp;dishonest&nbsp;statements</li> <li>listening: whether or not agents in listen to their communication partners</li> <li>disturbing: whether or not agents are&nbsp;particularly risk-taking when making dishonest statements</li> <li>x_est:&nbsp;intrinsic&nbsp;honesties&nbsp;of&nbsp;the&nbsp;agents</li> <li>RSeed:&nbsp;the&nbsp;used&nbsp;random&nbsp;seed</li> <li>NA:&nbsp;number&nbsp;of&nbsp;agents</li> <li>NR:&nbsp;number&nbsp;of&nbsp;rounds</li> </ul> </li> <li>communication <ul> <li>a:&nbsp;speaker</li> <li>b:&nbsp;receiver</li> <li>c:&nbsp;topic</li> <li>J:&nbsp;transmitted&nbsp;message&nbsp;in&nbsp;the&nbsp;form&nbsp;of</li> </ul> </li> <li>self_update <ul> <li>id:&nbsp;number&nbsp;of&nbsp;agent&nbsp;who&nbsp;is&nbsp;updating&nbsp;knowledge&nbsp;about&nbsp;itself</li> <li>Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far</li> <li>I_&lt;id&gt;: knowledge that the agents has about itself after the update in the form of</li> </ul> </li> <li>update <ul> <li>id:&nbsp;number&nbsp;of&nbsp;agent&nbsp;who&nbsp;is&nbsp;updating&nbsp;its&nbsp;knowledge</li> <li>I_&lt;id1&gt;: knowledge that the updating agent&nbsp;has about agent &lt;id1&gt;&nbsp;in the form of</li> <li>Jothers_&lt;id1&gt;_&lt;id2&gt;:&nbsp;last statement that the updating agent heared&nbsp;agent &lt;id1&gt; make about agent &lt;id2&gt;</li> <li>Iothers_&lt;id1&gt;_&lt;id2&gt;: what the updating agent believes that agent &lt;id1&gt; thinks about agent &lt;id2&gt; after the update</li> <li>Cothers_&lt;id1&gt;_&lt;id2&gt;: what the updating agent believes&nbsp;after the update that agent &lt;id1&gt; wants it to think&nbsp;about agent &lt;id2&gt;</li> <li>new_friends/enemies: id of the agent, the updating agent after the update considers&nbsp;a friend/enemy</li> <li>new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa)</li> <li>kappa: median of the last ten normalized surprises the updating agent experienced</li> </ul> </li> <li>final_status <ul> <li>id/name:&nbsp;number&nbsp;if&nbsp;the&nbsp;described&nbsp;agent</li> <li>x:&nbsp;the&nbsp;agent&#39;s&nbsp;honesty</li> <li>I:&nbsp;the&nbsp;agent&#39;s&nbsp;knowledge&nbsp;about&nbsp;all&nbsp;others</li> <li>Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made</li> <li>K:&nbsp;the&nbsp;last&nbsp;10&nbsp;normalized&nbsp;surprises&nbsp;the&nbsp;agent&nbsp;experienced</li> <li>kappa:&nbsp;the&nbsp;median&nbsp;of&nbsp;K</li> <li>friends/enemies:&nbsp;list&nbsp;of&nbsp;the&nbsp;agent&#39;s&nbsp;friends/enemies</li> <li>Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined&nbsp;information about all others</li> <li>openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent&#39;s character traits</li> </ul> </li> </ul>

opencc-by-4.0Nov 2022View details →
OpenNeuro40/100

Agreeableness personality trait and social information encoding

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →
zenodo40/100

Supplementary material for "Playback experiments highlight the importance of nearest-neighbor distance and social information for nest site selection in the House Martin (Delichon urbicum)"

<p><strong>Abstract</strong></p> <p>Understanding nest site selection is crucial for species conservation. Bird conservation often involves installing nesting aids to increase nest site availability and induce colonization of unoccupied sites. However, prospecting individuals must find nesting aids, which may be facilitated by social information. Here, we investigated the effectiveness of artificial nests and playback in the declining, migratory House Martin <em>Delichon urbicum</em>. We selected unoccupied sites with artificial nests along a distance gradient to occupied sites and broadcasted conspecific vocalizations during prospection times of House Martins in both the post- and the following pre-breeding periods. Visitation and colonization rates increased considerably in proximity to occupied sites. Playback during the post-breeding and pre-breeding periods enhanced visitation rates, while pre-breeding-only and post-breeding-only playback had smaller positive effects. Colonization rate increased exclusively with pre-breeding-only playback. Colonized playback and non-playback sites had similar breeding success, indicating that playback did not create ecological traps by attracting House Martins to suboptimal sites. Hence, broadcasting conspecific vocalizations informs prospecting birds of nest site availability, thereby increasing visitation, and to some degree, colonization of unoccupied House Martin sites. To boost colonization, we recommend installing artificial House Martin nests within approximately 500 meters of occupied sites and using playback of conspecific vocalizations.</p>

opencc-by-4.0Jul 2024View details →
zenodo40/100

EduQuick: A Dataset for Assessing Summarization of Informal Educational Content for Social Media

<p>The presented dataset is a curated collection of model-generated text for educational TikTok content, abbreviated as EduQuick. This dataset is the result of evaluating and selecting high-quality content generated by the GPT-4 model following an empirical study. It aims to provide engaging and informative summaries suitable for TikTok's educational audience.</p>

opencc-by-4.0Nov 2023View details →
dryad40/100

Social information use about novel aposematic prey depends on the intensity of the observed cue

<p>Animals gather social information by observing the behavior of others, but how the intensity of observed cues influences decision-making is rarely investigated. This is crucial for understanding how social information influences ecological and evolutionary dynamics. For example, observing a predator's distaste of unpalatable prey can reduce predation by naïve birds, and help explain the evolution and maintenance of aposematic warning signals. However, previous studies have only used demonstrators that responded vigorously, showing intense beak-wiping after tasting prey. Therefore, here we conducted an experiment with blue tits (<em>Cyanistes caeruleus</em>) informed by variation in predator responses. First, we found that the response to unpalatable food varies greatly, with only few individuals performing intensive beak-wiping. We then tested how the intensity of beak-wiping influences observers' foraging choices using video-playback of a conspecific tasting a novel conspicuous prey item. Observers were provided social information from: (1) no distaste response, (2) a weak distaste response, or (3) a strong distaste response, and were then allowed to forage on evolutionarily novel (artificial) prey. Consistent with previous studies, we found that birds consumed fewer aposematic prey after seeing a strong distaste response, however a weak response did not influence foraging choices. Our results suggest that while beak-wiping is a salient cue, its information content may vary with cue intensity. Furthermore, the number of potential demonstrators in the predator population might be lower than previously thought, although determining how this influences social transmission of avoidance in the wild will require uncovering the effects of intermediate cue salience.</p>

opencc-zeroMay 2022View details →
dryad40/100

Dataset for paper titled: Conceptual preferences can be transmitted via selective social information use between competing wild bird species

<p><a name="_Hlk66629591"></a><span>Concept learning is considered a high-level adaptive ability. Thus far, it has been studied in laboratory via asocial trial and error learning. Yet, social information use is common among animals but it remains unknown whether concept learning by observing others occurs. We tested whether pied flycatchers (</span><em><span>Ficedula hypoleuca</span></em><span>) form conceptual relationships from the apparent choices of nest-site characteristics (geometric symbol attached to the nest box) of great tits (</span><em><span>Parus major</span></em><span>). Each wild flycatcher female (n = 124) observed one tit pair that exhibited an apparent preference for either a large or a small symbol and was then allowed to choose between two nest boxes with a large and a small symbol, but the symbol shape was different to that on the tit nest. Older flycatcher females were more likely to copy the symbol size preference of tits than yearling flycatcher females when there was a high number of visible eggs or a few partially visible eggs in the tit nest. However, this depended on the phenotype; copying switched to rejection as a function of increasing body size. Possibly the quality of and overlap in resource use with the tits affected flycatchers' decisions. Hence, our results suggest that conceptual preferences can be horizontally transmitted across co-existing animals, which may increase the performance of individuals that utilize concept learning abilities in their decision-making.</span></p>

opencc-zeroMay 2022View details →
zenodo40/100

Disclosure Privacy Information Social Media on TikTok

<p><em><span>With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy</span></em></p>

opencc-by-4.0Jul 2024View details →
zenodo40/100

BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 3. Integration of Social Media elements on tourism organization's websites

<p>Currently, in Romania there are about 8 million Facebook users (Facebrands.ro). In the recent years there has been a spectacular increase of this phenomenon, which shows how important is the use of social networks for an economic and even for a non-profit entity in order to make the brand known or to promote an activity (DailyBusiness.ro).&nbsp;&nbsp;</p>

opencc-by-4.0Jun 2016View details →
zenodo40/100

The "social brain" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study

<p><strong>Abstract</strong></p> <p>How the human brain process social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies.&nbsp; Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates.&nbsp; It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by &nbsp;general social information.&nbsp; In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images.&nbsp; Results of 1,000 published fMRI studies containing the keyword of &ldquo;social&rdquo; were subject to an automated meta-analysis (neurosynth.org). &nbsp;To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study.&nbsp; The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element.&nbsp; The social &ndash; non-social contrast in the independent study showed a strong resemblance of the NeuroSynth map.&nbsp; ROI analyses revealed that a social effect was credible in 8 out of the 11 NeuroSynth regions in the independent dataset.&nbsp; The findings support that the &ldquo;social brain&rdquo; is highly sensitive to the mere presence of social information.&nbsp;</p>

opencc-by-4.0Dec 2017View details →
zenodo40/100

Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information and Social Tags on Information Search and Evaluation

<p>ABSTRACT</p> <p>Background: The public typically believes psychotherapy to be more effective than pharmacotherapy for depression treatments. This is not consistent with current scientific evidence, which shows that both types of treatment are about equally effective.</p> <p>Objective: The study investigates whether this bias towards psychotherapy guides online information search and whether the bias can be reduced by explicitly providing expert information (in a blog entry) and by providing tag clouds that implicitly reveal experts&rsquo; evaluations.</p> <p>Methods: A total of 174 participants completed a fully automated Web-based study after we invited them via mailing lists. First, participants read two blog posts by experts that either challenged or supported the bias towards psychotherapy. Subsequently, participants searched for information about depression treatment in an online environment that provided more experts&rsquo; blog posts about the effectiveness of treatments based on alleged research findings. These blogs were organized in a tag cloud; both psychotherapy tags and pharmacotherapy tags were popular. We measured tag and blog post selection, efficacy ratings of the presented treatments, and participants&rsquo; treatment recommendation after information search.</p> <p>Results: Participants demonstrated a clear bias towards psychotherapy (mean 4.53, SD 1.99) compared to pharmacotherapy (mean 2.73, SD 2.41; <em>t</em><sub>173</sub>=7.67, <em>P</em>&lt;.001, <em>d</em>=0.81) when rating treatment efficacy prior to the experiment. Accordingly, participants exhibited biased information search and evaluation. This bias was significantly reduced, however, when participants were exposed to tag clouds with challenging popular tags. Participants facing popular tags challenging their bias (n=61) showed significantly less biased tag selection (<em>F</em><sub>2,168</sub>=10.61, <em>P</em>&lt;.001, partial eta squared=0.112), blog post selection (<em>F</em><sub>2,168</sub>=6.55, <em>P</em>=.002, partial eta squared=0.072), and treatment efficacy ratings (<em>F</em><sub>2,168</sub>=8.48, <em>P</em>&lt;.001, partial eta squared=0.092), compared to bias-supporting tag clouds (n=56) and balanced tag clouds (n=57). Challenging (n=93) explicit expert information as presented in blog posts, compared to supporting expert information (n=81), decreased the bias in information search with regard to blog post selection (<em>F</em><sub>1,168</sub>=4.32, <em>P</em>=.04, partial eta squared=0.025). No significant effects were found for treatment recommendation (<em>P</em>s&gt;.33).</p> <p>Conclusions: We conclude that the psychotherapy bias is most effectively attenuated&mdash;and even eliminated&mdash;when popular tags implicitly point to blog posts that challenge the widespread view. Explicit expert information (in a blog entry) was less successful in reducing biased information search and evaluation. Since tag clouds have the potential to counter biased information processing, we recommend their insertion.</p>

opencc-by-4.0Mar 2014View details →
dryad40/100

Data from: Changes in movement characteristics in response to private and social information acquisition of socially foraging fish

<p>To overcome the cost of competition resulting from foraging socially, individuals may balance their use of private (i.e. acquired from personal sampling) and social (i.e. acquired by watching other individuals) information to adjust their foraging strategy accordingly. Reliability of private information about environmental characteristics, such as the spatial distribution of prey, is thus likely to affect individual movement and social interactions. We aimed to investigate how movement characteristics of foraging individuals changed as they acquired reliable information about the spatial occurrence of prey in a foraging context. We allowed guppies (<em>Poecilia reticulata</em>) to develop the reliability of their private knowledge about prey spatial occurrence by repeatedly testing shoals in a foraging task under three experimental distributions of prey: 1) aggregated prey forming three patches located in fixed locations, 2) scattered distribution of prey with random locations, or 3) no prey (used as control). Using individual time series of spatial coordinates, we computed a suite of movement variables reflecting search effort, social proximity and locomotion characteristics during foraging, to examine changes occurring over repeated trials. Over time, individuals foraging on either scattered or aggregated prey travelled greater distances, showed an increasing distance to their closest neighbour and became more stochastic in their acceleration profile, compared to control individuals. We found that behaviour changed as private information increased over time, with a behavioural shift and an increase of collective foraging efficiency occurring on the third testing day. Social proximity was the major predictor of foraging success in the absence of prior foraging information, while search effort became the most important predictors of foraging success as information increased. In conclusion, we show that individual movement patterns changed as they acquired private information. Contrary to our predictions, the spatial distribution of prey did not affect any of the movement variables of interest.</p>

opencc-zeroMar 2023View details →
dryad40/100

Does social information affect the settlement decisions of resident birds in their second breeding attempt? A case study of the Japanese tit (Parus minor)

<p>Individuals may gather information about environmental conditions when deciding where to breed in order to maximize their lifetime fitness. Many studies have shown that birds can rely on social information to select their nest site. The location of active nests and the reproductive success of conspecifics and heterospecifics can provide accurate predictions about the quality of the breeding habitat. Some short-lived species can facultatively reproduce two and/or more times within a breeding season. However, few studies have focused on how multiple-brooding individuals select nest sites for their second breeding attempts. In this study, we use long-term data to test whether the Japanese tit (<em>Parus</em> <em>minor</em>) can use social information from conspecifics and/or heterospecifics to select a nest site for the second breeding attempt. Our results showed that the nest boxes occupied by tits on their second breeding attempt tended to be surrounded by more breeding conspecific nests, first-breeding successful conspecific nests, and fewer first-breeding failed conspecific nests than the nest boxes that remained unoccupied (the control group). However, the numbers of breeding heterospecific nests, successful heterospecific nests, and failed heterospecific nests did not differ between the nest boxes occupied by tits on their second breeding attempt and the unoccupied nest boxes. Furthermore, the tits with local successful breeding experience tended to choose areas with more first-breeding successful conspecific nests than those without successful breeding experience. Thus, we suggest that conspecifics' but not heterospecifics' social information within the same breeding season is the major factor influencing the nest site selection of Japanese tits during second breeding attempts.</p>

opencc-zeroJul 2023View details →
dryad40/100

Dataset for paper titled: Conceptual preferences can be transmitted via selective social information use between competing wild bird species

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publicMay 2022View details →
dryad40/100

Data from: Changes in movement characteristics in response to private and social information acquisition of socially foraging fish

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publicMar 2023View details →
dryad40/100

A human working memory advantage for social network information

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publicOct 2024View details →
dryad40/100

Data from: The last of us: Social information enhances trappability in fruit flies

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publicSep 2025View details →
dryad40/100

Social information use about novel aposematic prey depends on the intensity of the observed cue

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publicMay 2022View details →
dryad40/100

Does social information affect the settlement decisions of resident birds in their second breeding attempt? A case study of the Japanese tit (Parus minor)

Open the record for dataset details and reuse information.

publicJul 2023View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record