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1,041 results for “elections”
Gender codification of 412 national (general) and European Parliament elections in six European countries (2003-2021)
<p>This dataset has been produced by applying the Manifesto Gender Analysis (MGA) codebook to 412 national (general) and European Parliament elections in the six countries participating in the UNTWIST project (Denmark, Germany, Hungary, Spain, Switzerland, and the UK) from 2003 to 2021.</p> <p> The Manifesto Gender Analysis coding procedure, developed by WP4 of the UNTWIST consortium, aims to analyse gender-related content in party manifestos. It relies on existing manifestos collected by MARPOR and EM projects from 2003-2021 in six national contexts: Denmark, Germany, Hungary, Spain, Switzerland, and the United Kingdom. The process involves splitting manifestos into quasi-sentences, coding them based on a scheme inspired by previous projects and feminist typology, and completing an expert survey. This method ensures comprehensive analysis and potential scalability through computational methods. </p> <p>The coding procedure involves a series of essential steps, divided in two main activities: the classification of manifestos’ quasi-sentences, and the completion of a survey dedicated to more general concepts which can be gauged by evaluating the content of the entire documents. In the latter case, then, the unit of measure of each coder consists in the manifesto document, whereas in the former the units of measure are quasi-sentences - i.e., arguments denoting a verbal expression of a political idea or issue. Coders are instructed to split sentences containing multiple arguments into quasi-sentences and ensure that each quasi-sentence encapsulates a single political idea or issue. </p> <p>Once the manifestos are split into said units, coders classify the arguments following the MGA coding scheme. The coding scheme (MGA) consists of 5 domains and 25 coding categories, covering various aspects of gender-related issues. Each domain includes an "other" category for relevant statements that do not fit precisely into the defined categories. Apart from coding categories related to specific themes, the coding scheme then includes additional dimensions. The classification process consists of seven steps: (1) assessing whether the quasi-sentence addresses gender-related issues, (2) defining both the domain and coding category, (3) determining whether the quasi-sentence refers to a specific recipient or group based on gender and/or sexual orientation, (4) evaluating intersectionality, (5)<strong> </strong>assigning the sentiment or connotation, (6) determining if it's related to a goal, issue, or policy, and (7) characterising the policy if applicable.</p> <p>After completing the classification of the quasi-sentences in a given manifesto, coders fill in a survey for each manifesto document. The surveys provide information that cannot be directly inferred from the quasi-sentences, focusing on the gender ontology of a manifesto, the degree to which a manifesto entails a binary conception of sexes, the extent to which a manifesto promotes a patriarchal conception of the society, and how much a manifesto promotes heterosexuality as the only normal and socially acceptable sexual orientation of individuals. While the last four characteristics are gauged relying on quasi-interval measures (scales ranging from 0 to 10), the first one, gender ontology, consists in a categorical variable which distinguishes between manifestos with an essentialist ontology – gender and sex are the same and inseparable –, a constructivist ontology – biological sex is mediated through social construction of femininity and masculinity –, and other or undefined ontologies.</p>
Polarized and nonpolarized Twitter networks from the 2019 Finnish Parliamentary Elections
<p><strong>Polarized and nonpolarized Twitter networks from the 2019 Finnish Parliamentary Elections</strong></p> <p>This dataset includes 183 Twitter retweet networks collected during the 2019 Finnish Parliamentary Elections.</p> <p>The first 150 networks are built around single hashtags, such as #police, #nature, and #immigration. The remaining 33 networks are constructed using a combination of hashtags focused on specific topics like climate change and economic policy.</p> <p>Each filename consists of two parts: the first part indicates whether the network is based on a single hashtag (in lowercase) or a set of hashtags (in uppercase). The second part represents the tweet period.</p> <ul> <li> <p>"p1" corresponds to the pre-election period (March 1 to April 14).</p> </li> <li> <p>"p2" corresponds to the inter-election period (April 15 to May 26).</p> </li> <li> <p>"p3" corresponds to the post-election period (May 27 to July 31).</p> </li> </ul> <p>The nodes in the networks represent anonymized Twitter accounts, and directed ties indicate retweet endorsements on specific topics. Each file contains three columns: retweeter, retweeted, and weight.</p> <p>Please see the references for more details.</p> <p>Network labels, whether they are labeled as controversial, and whether they are based on single or multiple hashtags, can be found in the "networks_info.csv" file.</p> <p>Importantly, the dataset does not contain any identifying information or original raw data from the Twitter platform. Anonymization was achieved by shuffling the order of unique nodes across all networks and assigning each node a new identifier (ID). These new IDs were then applied to the edgelists to obtain the anonymized version.</p> <p>Kindly ensure to reference the original article(s) when utilizing this dataset.</p> <p>Chen, T. H. Y., Salloum, A., Gronow, A., Ylä-Anttila, T., & Kivelä, M. (2021). Polarization of climate politics results from partisan sorting: Evidence from Finnish Twittersphere. <em>Global Environmental Change</em>, <em>71</em>, 102348. <a href="https://doi.org/10.1016/j.gloenvcha.2021.102348">https://doi.org/10.1016/j.gloenvcha.2021.102348</a></p> <p>Salloum, A., Chen, T. H. Y., & Kivelä, M. (2022). Separating polarization from noise: comparison and normalization of structural polarization measures. <em>Proceedings of the ACM on human-computer interaction</em>, <em>6</em>(CSCW1), 1-33. <a href="https://doi.org/10.1145/3512962">https://doi.org/10.1145/3512962</a></p>
Campaign content analysis on X for the Castilla y León and Andalusia 2022 regional elections
<p>This dataset refers to the replication data of the article titled: "Could you teach new tricks to old dogs? An analysis of online communication of political leaders in Spanish regional elections", to be published in Frontiers in Political Sciece, after reviewing process.</p>
POLCAND_ELEC (Allocation of Seats on Electoral Lists: A Dynamic Analysis of Candidate Positioning in Parliamentary Elections from 1991 to 2023)
<p>The aim of the study was to supplement and expand the data contained in the EAST PaC database (Joshua Kjerulf Dubrow: East European Parliamentarian and Candidate Data (EAST PaC), 1985 - 2015 [dane]. Institute of Philosophy and Sociology, Polish Academy of Sciences [producent], Warsaw, 2016. PADS21320. Polish Social Data Archive [dystrybutor], Repozytorium Danych Społecznych [wydawca], 2021. <a href="https://doi.org/10.18150/LSBNLO" target="_blank" rel="noopener">https://doi.org/10.18150/LSBNLO</a>, V1).</p> <p>Currently, an open-access database called EAST PaC (with a data structure similar to panel data, which allows for identifying candidates each time they participate in subsequent elections) contains information on all candidates who have ever run in elections to the Sejm in Poland, from the last elections in the People's Republic of Poland in 1985 to the elections in 2015.</p> <p>This study allowed for the expansion of previous analyses regarding the ways of forming political representation and the associated quality of political elites. This is essential for conducting a dynamic analysis of candidate positioning on electoral lists, which enables tracing the electoral activity of all candidates in elections from 1991 to 2023. It allows for illustrating the occurrence of events over time by tracking candidates' electoral activities. Many previous empirical studies on the course and consequences of parliamentary elections have significant gaps, as they are limited to analyzing the situation of parliamentarians (often focusing on only selected categories). They lack the history of candidacies in elections, even though voters' final decisions are based on the assessment of previous results of candidates and the political entities they represent.These analyses are significant for explaining the dynamics of the political system and assessing the quality of democracy. They also enable empirical verification of hypotheses concerning the periodization of the institutionalization of the electoral system in Poland from 1991 to 2023.</p>
Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"
<p>Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper. </p> <p>The full text of the paper can be found <a href="https://zenodo.org/record/4699959#.YngKatNBy3K">here</a>.</p> <p>The folder "Tweet_IDs" contains the complete list of the 152,514,929 tweet IDs (together with their timestamps) which we used for the analysis in the study: "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"</p> <p>by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos</p> <p>We have split the tweets into separate .zip files based on the date listed in their timestamps.</p> <p>The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016).</p> <p>Each "tweet_day_X.zip" file contains the file "tweet_day_X.csv", where X in [1,2,...,47]. For instance, the file "tweets_day_1.zip" contains the tweets of the 1st day: 09/21/2016.</p> <p>Please cite the paper in any published work that uses any of these resources. </p> <p>@misc{nikos_salamanos_2021_4699959,<br> author = {Nikos Salamanos and<br> Michael J. Jensen and<br> Costas Iordanou and<br> Michael Sirivianos},<br> title = {{Did State-sponsored Trolls Shape the 2016 US <br> Presidential Election Discourse? Quantifying<br> Influence on Twitter}},<br> month = apr,<br> year = 2021,<br> publisher = {Zenodo},<br> version = 3,<br> doi = {10.5281/zenodo.4699959},<br> url = {https://doi.org/10.5281/zenodo.4699959}<br> }</p>
The allocation of Chinese and Indian development finance in Nepal and its influence on local election results
<p>This realease contains the used datasets and calculations for my Bachelorthesis about Indian and Chinese allocation of Overall Development Assistance in Nepal.</p>
Facebook pages for parties in 2019 EU parliamentary elections
<p>This dataset contains information about the parties from UK, Italy, Germany, Spain, and Poland that ran ads on Facebook during the 2019 EU parliamentary elections. For each party, we manually associated the corresponding Facebook page that ran the ads. We also report the PopuList tags as per https://popu-list.org.</p> <p>"2019 EU elections parties.csv" contains aggregate data:</p> <ul> <li>State: the country of the party</li> <li>Party: acronym of the party</li> <li>Name: extended name of the party</li> <li>Percentage: share of votes in the election</li> <li>Facebook page_name: name of the Facebook page that ran the ads</li> <li>Facebook page_id: id of the Facebook page that ran the ads</li> <li>populist: binary label indicating whether the party is populist according to PopuList</li> <li>farright: binary label indicating whether the party is far right according to PopuList</li> <li>farleft: binary label indicating whether the party is far left according to PopuList</li> <li>eurosceptic: binary label indicating whether the party is eurosceptic according to PopuList</li> <li>found on populist: binary label indicating whether the party is on any list on PopuList</li> <li>seat: whether the party got a seat in any previous national election (selection criterion from PopuList)</li> </ul> <p>"FB ads impressions cost populist demographic votes" contains detailed data:</p> <ul> <li>ad_id: Facebook ad id</li> <li>page_id: id of the Facebook page who published the ad</li> <li>page_name: name of the Facebook page who published the ad</li> <li>ad_creative_body: text of the ad</li> <li>ad_creative_*: other properties of the ad (caption, links, title)</li> <li>ad_delivery_start_time,ad_delivery_stop_time: time of beginning and end of the ad campaign</li> <li>funding_entity: declared party who financed the ad</li> <li>impressions_min, impressions_max: range of impressions for the ad</li> <li>spend_min, spend_max: range of expenditure for the ad</li> <li>ad_url: URL of the ad</li> <li>state: state of the party</li> <li>impression_mean, spend_mean: average number of impressions and expenditure</li> <li>imp_per_day: number of impressions per day</li> <li>populist, farright, farleft, eurosceptic, popu-list: binary tags from PopuList (refer to the description above)</li> <li>female_*, male_*, unknown_*: fraction of impressions for each demographic bucket (defined as gender_age)</li> <li>party: name of the party</li> <li>percentage: share of votes in the election</li> </ul>
The Biodata of Legislative Candidates for Indonesian General Election 2019
<p>The dataset of biodata of Legislative Candidates for General Election 2019 is crawled from the Indonesian General Election Committee. We remove privacy information, such as the birth of data and home address. We only store the year of birth and the city of home. The dataset is in CSV file.</p>
btw17 query auto completion - query suggestions for German politicians and parties before the federal election 2017
<p>The dataset contains the query suggestions for 5 major German parties (terms: "afd", "csu", "dielinke", "fdp", "grüne", "spd") and ten popular politicians and party leaders (terms: "Alexander Gauland", "Alice Weidel", "Angela Merkel", "Cem Özdemir", "Christian Lindner", "Dietmar Bartsch", "Katrin Göring-Eckardt", "Martin Schulz", "Sahra Wagenknecht").</p> <p>The data was crawled on (mostly) two times per day from Tue Aug 04, 2017 to Tue Oct 31, 2017. The dataset contains 20001 suggestions from Bing search (http://api.bing.net/osjson.aspx), 11935 suggestions from Duck-Duck-Go (https://duckduckgo.com/ac/) and 33521 suggestions from Google search (http://clients1.google.de/complete/search). Note, that for some terms and dates no suggestions were returned by some of the APIs.</p> <p>German language settings were used for Google and Bing, English language setting was used for Duck-Duck-Go. The API requests were sent with an IP address from Cologne, Germany. </p> <p>The UTF-8 encoded comma separated text file contains the following columns:</p> <p><source>: google, bing or ddg</p> <p><queryterm>: the query term</p> <p><date>: the date and time of the API call formatted as ISO8601</p> <p><suggestterm>: the suggested query completion (the query term was removed from the suggestion)</p> <p><position>: the position of the query suggestion within the list returned by the API (ranges from 0 to 19)</p> <p> </p> <p> </p> <p><br> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Evidence of a coordinated network amplifying inauthentic narratives in the 2020 election
<p>On 15 September 2020, the Washington Post published an article by Isaac Stanley-Becker titled “<a href="https://www.washingtonpost.com/politics/turning-point-teens-disinformation-trump/2020/09/15/c84091ae-f20a-11ea-b796-2dd09962649c_story.html">Pro-Trump youth group enlists teens in secretive campaign likened to a ‘troll farm,’ prompting rebuke by Facebook and Twitter</a>.” The article reported on a preliminary analysis we conducted at the request of The Post. Here we would like to share the dataset used in our analysis with the research community.</p> <p>Our Observatory on Social Media at Indiana University has been studying <a href="https://theconversation.com/misinformation-on-social-media-can-technology-save-us-69264">social media manipulation</a> and <a href="https://theconversation.com/misinformation-and-biases-infect-social-media-both-intentionally-and-accidentally-97148">online misinformation</a> for over ten years. We uncovered the first known instances of <a href="http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2850">astroturf campaigns</a>, <a href="https://cacm.acm.org/magazines/2016/7/204021-the-rise-of-social-bots/fulltext">social bots</a>, and <a href="http://doi.org/10.1126/science.aao2998">fake news</a> websites during the 2010 US midterm election, long before these phenomena became widely known in 2016. We develop public, state-of-the art network and data science methods and <a href="https://osome.iu.edu/tools/">tools</a>, such as <a href="https://botometer.osome.iu.edu/">Botometer</a>, <a href="https://hoaxy.iuni.iu.edu/">Hoaxy</a>, and <a href="https://osome.iu.edu/tools/botslayer/">BotSlayer</a>, to help researchers, journalists, and civil society organizations study coordinated inauthentic campaigns. So when Stanley-Becker contacted us about accounts posting identical political content on Twitter, we were happy to apply our <a href="https://arxiv.org/abs/2001.05658">analytical framework</a> to map out what was going on. </p>
#elxn42 tweets (42nd Canadian Federal Election)
<p>Tweet ids for #elxn42 tweets. Tweets can be "hydrated" with Ed Summers' twarc (https://github.com/edsu/twarc). twarc.py --hydrate elxn42-tweet-ids.txt > elxn42-tweets.json. Hydrating will recreate the original tweet(s) in json format, provided the content is still available on Twitter. This dataset is the combination of hydrated http://hdl.handle.net/10864/11310 tweet ids, and htttp://hdl.handle.net/10864/11270.</p>
Datasets for paper: J. A. Cerón-Guzmán and E. León-Guzmán (2016), A Sentiment Analysis System of Spanish Tweets and Its Application in Colombia 2014 Presidential Election. SocialCom 2016. DOI: 10.1109/BDCloud-SocialCom-SustainCom.2016.47
<p>Datasets for paper: J. A. Cerón-Guzmán and E. León-Guzmán (2016), A Sentiment Analysis System of Spanish Tweets and Its Application in Colombia 2014 Presidential Election. The 9th IEEE International Conference on Social Computing and Networking (SocialCom). DOI: 10.1109/BDCloud-SocialCom-SustainCom.2016.47</p>
Dataset for paper: J. A. Cerón-Guzmán and E. León (2015), Detecting Social Spammers in Colombia 2014 Presidential Election. MICAI 2015. DOI: 10.1007/978-3-319-27101-9_9
<p>Dataset for paper: J. A. Cerón-Guzmán and E. León (2015), Detecting Social Spammers in Colombia 2014 Presidential Election. The 14th Mexican International Conference on Artificial Intelligence. DOI: 10.1007/978-3-319-27101-9_9</p>
The #BTW17 Twitter Dataset - Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag
<p>The German Bundestag elections are the most important democratic elections of Germany. This dataset comprises Twitter interactions related with German politicians of the most important political parties over several months in the (pre-)phase of the German election campaigns in 2017. The Twitter accounts of 364 politicians (that is approximately half of the German parliament, the German Bundestag) were followed for almost half a year. The collected data comprise of about 10 GB of Twitter raw data generated by more than 120.000 active Twitter users generating more than 1.200.000 tweets during the pre- and hot-phase of the election campaigns for the 19th German Bundestag. <br> The dataset can be used to study how political parties, their followers and supporters make use of social media channels like Twitter in the context of political election campaigns and what kind of content is shared.</p> <p>The following files contain relevant context information:</p> <ul> <li><strong>crawled-pages.json</strong> contains the URLs of the official party faction websites of the 18th German Bundestag that were crawled to identify the Twitter screennames of German politicians of all Bundestag factions. Because the <em>Alternative für Deutschland (AfD)</em> and the <em>Freie Demokratische Partei (FDP)</em> were not part of the 18th German Bundestag (but it was likely that they will enter the 19th German Bundestag) other official websites were selected to crawl for relevant and representative politicians for these both parties (in case of the <em>AfD</em> this was the website of the directorate of the <em>AfD</em> federal party and the list of members of the European Parliament, in case of the <em>FDP</em> this was the website of the executive committee of the <em>FDP</em> federal party of Germany).</li> <li><strong>followed-accounts.json</strong> contains the (manually checked and edited) crawling result of 327 Twitter screennames of politicians that have been observed via the Twitter streaming API to collect this dataset.</li> </ul>
MERICS China Podcast: China and the European Parliament election, with Ivana Karásková and Grzegorz Stec
<p>Ahead of the European Parliament election on June 6-9, 2024, this episode looks at the role of the European Parliament in EU-China relations and the possible impact of the election results on the European “de-risking” agenda among other topics. </p> <p><strong>Johannes Heller-John</strong> talks to <strong>Ivana Karásková</strong> and <strong>Grzegorz Stec</strong>. Ivana is a European China Policy Fellow at MERICS and the founder of MapInfluenCE and China Observers in Central and Eastern Europe (CHOICE) at the Association for International Affairs (AMO) in Prague. Grzegorz is the Head of the MERICS Brussels Office.</p> <p>Recently, Ivana co-authored two reports, one on <a href="https://www.amo.cz/en/foreign-electoral-interference-affecting-eu-democratic-processes/" target="_blank" rel="noopener noreferrer">foreign electoral interference in the EU</a> and one on the <a href="https://www.amo.cz/en/from-the-fringes-to-the-forefront-how-extreme-parties-in-the-european-parliament-can-shape-eu-china-relations/" target="_blank" rel="noopener noreferrer">rise of fringe parties in the EP and their impact on EU-China relations</a>. Grzegorz has published articles on <a href="https://www.merics.org/en/merics-briefs/how-ep-parties-see-china-ev-exports-trade-and-technology-council" target="_blank" rel="noopener">how EP parties see China</a> and on <a href="https://www.merics.org/en/comment/meps-key-lessons-eu-china-policy-during-last-mandate" target="_blank" rel="noopener">key lessons learned by Members of the EP during the last mandate</a>.</p>
MERICS China Podcast: Taiwan and cross-Strait relations in a year of elections, with Bonnie Glaser and Abigaël Vasselier
<p>As the status quo in the Taiwan Strait is increasingly unstable, what can we expect from Taiwan’s new president Lai Ching-te when it comes to navigating relations with China? How will the results of the EU elections affect Europe’s policies vis-à-vis Taiwan? And how might a change of government in the United States impact the situation? These are some of the questions <strong>Claudia Wessling</strong>, Director of Communications and Publications at MERICS discussed with <strong>Bonnie Glaser</strong>, Managing Director of GMF's Indo-Pacific program, and MERICS’ Director Policy & European Affairs <strong>Abigaël Vasselier</strong> in this episode of the MERICS China Podcast.</p>
Chatbots: (S)elected Moderation. Measuring the Moderation of Election-Related Content Across Chatbots, Languages and Electoral Contexts
<p>AI Forensics had <a href="https://aiforensics.org/work/bing-chat-elections">previously exposed</a> that Microsoft Copilot's answers to simple election-related questions contained factual errors 30% of the time. In collaboration with Nieuwsuur, we uncovered how chatbots can recommend and support the dissemination of disinformation as a campaign strategy. Following those investigations as well as a request for information from the European Commission, Microsoft and Google introduced “moderation layers" to their chatbots so that they refuse to answer election-related prompts.</p> <p><strong>This dataset was produced during our investigation aimed at evaluating and comparing the effectiveness of these safeguards in different scenarios.</strong> In particular, we investigated the consistency with which electoral moderation was triggered, depending the language of the prompt and the electoral context.</p>
LLMs Languages Least Moderated: Testing Cross-National Moderation in the context of the EU and the US Elections on Chatbots
<p>AI Forensics had <a href="https://aiforensics.org/work/bing-chat-elections">previously exposed</a> that Microsoft Copilot's answers to simple election-related questions contained factual errors 30% of the time. In collaboration with Nieuwsuur, we uncovered how chatbots can recommend and support the dissemination of disinformation as a campaign strategy. Following those investigations as well as a request for information from the European Commission, Microsoft and Google introduced “moderation layers" to their chatbots so that they refuse to answer election-related prompts.</p> <p><strong>This dataset was produced as part<span> of project "LLMs: Languages Least Moderated" at the 2024 Digital Methods Summer School and Data Sprint, which AI Forensics facilitated</span> to allow participants to evaluate and compare the effectiveness of these safeguards in different scenarios.</strong> In particular, we investigated the consistency with which electoral moderation was triggered, depending the language of the prompt and the electoral context.</p>
Belief, Affect, and Cognitive Dissonance in a Simulated Election - Main Study Data Set
<p>Data gathered using Amazon's Mechanical Turk (MTurk) task platform and the Qualtrics survey platform for a simulated election experiment focusing on belief change and affect in response to repeated counterattitudinal information exposure. </p>
Multifaceted Online Coordinated Behavior in the 2020 US Presidential Election
<p>This dataset contains ~140M tweets related to the 2020 United States Presidential Election, published and collected between October 2, 2020, and December 2, 2020. In addition, we provide nodes and edges of the superspreader user similarity network, as described in the paper below.</p> <p><strong>Tardelli, S., Nizzoli, L., Avvenuti, M., Cresci, S., & Tesconi, M. Multifaceted Online Coordinated Behavior in the 2020 US Presidential Election.</strong></p> <p>In detail, the dataset consists of:</p> <ul> <li><em>tweet-ids.csv.zip</em></li> <li><em>user_similarity_nework_nodes.csv</em>: a CSV file with the columns "id" and "cluster," relating to the nodes of the superspreader user similarity network mentioned in the paper.</li> <li><em>user_similarity_nework_edges.csv</em>: a CSV file with the columns "source," "target," "weight," and "alpha" relating to the edges of the superspreader user similarity network mentioned in the paper.</li> </ul>
ScienceDex guides
Understand access before you commit
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.