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141 results for “Sentiment”
BRI Sentiment Analysis
<p>This dataset contains news articles related to the Belt and Road Initiative (BRI) from various sources, collected between 2015 and 2023 in English and from 2019 to 2023 in Chinese. The articles are labeled with sentiment scores for sentiment analysis, using a three-point scale: negative, neutral, positive The dataset aims to provide insights into the public perception of the BRI and its impact on various countries and regions. The data can be used for sentiment analysis, natural language processing, and machine learning research related to the BRI.</p> <p>Data source: media outlets in Chinese and in English (the name of the outlet is indicated in the column source)</p> <p>Sentiment analysis algorithms:</p> <p>- SnowNLP for Chinese (https://github.com/isnowfy/snownlp) - Scale 0-1<br> - Own algorithm based on FinABSA approach for English (https://github.com/guijinSON/FinABSA) - Scale -11</p> <p>The files contain the following columns</p> <ul> <li>title - title of the article</li> <li>date - date of the article</li> <li>source - name of the source</li> <li>month,</li> <li>year,</li> <li>quarter,</li> <li>sentiment - sentiment score calculated by the algorithms</li> <li>sentiment_label - sentiment label based on optimized thresholds (positive, neutral, negative)</li> </ul>
Italian Verb Lexicon for Sentiment Inference
<p><strong>Italian Verb Lexicon for Sentiment Inference</strong></p> <p><strong>Theory:</strong></p> <p>For a description of the theory behind the specifications of the corpus, please read the attached paper. </p> <p><br> <strong>Example of json entry:</strong></p> <p>{"verb": "soddisfare", "frames": [{"fillers": ["Subj", "DirObj/IndObj"], "polarity": "POS", "effects": [["DirObj/IndObj", "pos"]], "expectations": [], "examples": ["L'offerta ha soddisfatto i clienti.", "Soddisfare al pubblico."], "remarks": [], "relations": [["Subj", "DirObj/IndObj", "pro"]]}]}</p> <p><strong>Description: </strong><br> The verb "soddisfare" has 2 frames, a subject followed by a direct or indirect object. The verb polarity is positive. There is an positive effect on the direct (indirect) object. No expectations. There is a in favour (pro) relation from the subject to the direct (indirect) object. Two example sentenes are given.</p> <p><strong>Synonyms:</strong><br> Some entries are references to synonym verbs with identical frames:</p> <p>{"verb": "consacrare", "germanTranslation": "widmen", "frameReference": "dedicare", "examples": ["Consacrare tempo alle sue passioni"]}</p> <p>Here, "cosacrare" and "dedicare" are assumed synonyms with the same syntactic frames.</p> <p><strong>Used Tags:</strong></p> <p>A few explanations on the tags used in the verb specifications sheets.</p> <p>Subj = subject</p> <p>DirObj = direct object, as in "Il professore legge __il giornale__".</p> <p>IndObj = indirect object, as in "Permettere qualcosa __a qualcuno__".</p> <p>RefObj = reflexive object (pronoun), as in "La squadra avversaria __si__ è arrabbiata moltissimo". </p> <p>PrepObj[prep] = prepositional phrase; the preposition is specified in the square brackets. If more than one preposition can occur,<br> no specification is given.</p> <p>SubCl = a subordinate clause, usually introduced by "che" or "di" such as in "Ha detto __di andarsene__", <br> "Ha detto __che tutto è andato bene__".</p> <p>mod = any type of modifier, mostly adverbs, e.g. "Se ne è andato __subito__".</p> <p><br> *, e.g. mod* = indicates optionality</p> <p><br> </p> <p> </p>
Data and code for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter"
<p>This is the code and data for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter" by Müller-Hansen et al. (Global Environmental Change, 2023).</p><p>This archive contains the following materials:</p><ul><li>Data set of tweets</li><li>Table of subqueries for searching Twitter</li><li>Code for figure generation</li></ul><p>Please see the Readme for further details.</p><p> </p>
Broad-Coverage German Sentiment Classification Model and Dataset for Dialog Systems
<p><a href="http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf"><strong>Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems</strong></a></p> <p>This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.</p> <p>You can find the code for training testing the models, that was published along with the paper in this <a href="https://github.com/oliverguhr/german-sentiment">repository</a>.</p> <p>The <a href="https://github.com/oliverguhr/german-sentiment-lib"><em>germansentiment</em></a> Python package contains a easy to use interface for the model that was published with this paper.</p> <p> </p> <p> </p>
Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-12.2019)
<p>Sentiment analysis of tech media articles using VADER package and co-occurrence analysis</p> <p>Sources with weights:</p> <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> <p>Methodology</p> <p>The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.</p> <p>As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.</p> <p>The process included the following main steps:</p> <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> <p>Therefore, the procedure has been repeated for 100 words for all identified social issues.</p> <p>The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.</p> <p> </p> <p>*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</p>
Datasets of ASONAM-2015 paper "Tweet sentiment: From classification to quantification"
<p>Datasets used for the following ASONAM 2015 paper:<br> ---------------------------------------------------------------------------------------------------<br> Title: Tweet Sentiment: From Classification to Quantification<br> Authors: Wei Gao and Fabrizio Sebastiani<br> Organization: Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar<br> ---------------------------------------------------------------------------------------------------</p> <p>[Content]</p> <p>* SemEval2013, SemEval2014, SemEval2015 datasets:<br> - semeval.train.feature.txt: Training set for learning sentiment models at development stage<br> - semeval.dev.feature.txt: Held-out set for tuning parameters<br> - semeval.train+dev.feature.txt: Training set for learning the final sentiment model<br> - semeval13.test.feature.txt: SemEval2013 test set<br> - semeval14.test.feature.txt: SemEval2014 test set<br> - semeval15.test.feature.txt: SemEval2015 test set<br> <br> * Other datasets: sanders, sst, omd, hcr, gasp<br> - X.train.feature.txt: Training set for learning sentiment models at development stage<br> - X.dev.feature.txt: Held-out set for tuning parameters<br> - X.train+dev.feature.txt: Traing set for learning the final sentiment model<br> - X.test.feature.txt: Test set<br> where X is one of sanders, sst, omd, hcr and gasp.</p> <p>For more details, please refer to the paper.</p> <p><br> [Citation]<br> You can cite the folowing paper when referring to the dataset:</p> <p>@inproceedings{gao2015tweet,<br> title={Tweet sentiment: From classification to quantification},<br> author={Gao, Wei and Sebastiani, Fabrizio},<br> booktitle={2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},<br> pages={97--104},<br> year={2015},<br> organization={IEEE}<br> }</p>
Tunizi: Tunisian Arabizi Sentiment Analysis Dataset
<p>Tunizi is the first 100% Tunisian Arabizi sentiment analysis dataset. Tunisian Arabizi is the representation of the tunisian dialect written in Latin characters and numbers rather than Arabic letters.We gathered comments from social media platforms that express sentiment about popular topics. For this purpose, we extracted 100k comments using public streaming APIs. Tunizi was preprocessed by removing links, emoji symbols, and punctuations.</p> <p>The collected comments were manually annotated using an overall polarity: positive (1), negative (-1) and neutral (0) class. We divided the dataset into separate training, validation and test sets, with a ratio of 7:1:2 with a balanced split where the number of comments from positive class and negative class are almost the same.</p>
Research Artifact: How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem
<p><strong>Research Artifact: How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem</strong></p> <p><a href="https://github.com/yusufsn/EclipseForumData">https://github.com/yusufsn/EclipseForumData</a></p> <p>This is a research artefact for the paper: <strong>How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem</strong>. This artifact is a repository consisting of collected dataset including (i) 289,061 threads, (ii) 216,864 extracted links from threads, (iii) 2,170 contributors, and the results of our qualitative analysis, (i) 1,142 manually annotated type of discussion and (ii) 1,142 manually annotated sentiment analysis. This artefact aims to enable researchers to replicate our mixed-methods quantitative results of the paper and reuse the dataset for further software engineering research.</p> <p>Contents</p> <ul> <li>dataset: <ul> <li><code>289061_threads.csv.zip</code> - 289,061 collected main threads of all users without post from webmaster (.zip format)</li> <li><code>216864_links.csv.zip</code> - 216,864 extracted links from collected threads (.zip format)</li> <li><code>2170_contributors.csv</code> - 2,170 list of contributions (.csv format)</li> </ul> </li> <li>Results of manual analysis: <ul> <li><a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vQoRpcbrV66OEB4vaCG9Njq65zW7XpLRITYG3BlUOoa_DmeOKcdQIgYJ8y2aSlmL3y9bCUhjpP3rYmT/pubhtml">Manual annotation of discussion type</a></li> <li><a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vQfyvsP1Zq3b9p_BYwkkgYXuEePJB6sIroC47jOUMcR5P8t7DCJFNpOJD565SPgqi--L3AAIQWs2kC5/pubhtml">Manual annotation of sentiment analysis</a></li> </ul> </li> </ul> <p>Authors</p> <ul> <li><a href="https://yusufsn.github.io/">Yusuf Sulistyo Nugroho</a></li> <li><a href="https://syful-is.github.io/">Syful Islam</a></li> <li>Keitaro Nakasai</li> <li><a href="https://ifrazrehman.github.io/">Ifraz Rehman</a></li> <li><a href="https://hideakihata.github.io/">Hideaki Hata</a></li> <li><a href="https://raux.github.io/">Raula Gaikovina Kula</a></li> <li><a href="https://cs.uwaterloo.ca/~m2nagapp/">Meiyappan Nagappan</a></li> <li><a href="https://matsumotokenichi.github.io/">Kenichi Matsumoto</a></li> </ul>
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>
Hansard Speeches and Sentiment V1.0.1
<p>A public dataset of speeches in the Hansard, the record of the speeches, votes and legislation in the UK Parliament. The dataset provides information on each speech of ten words or longer, made in the House of Commons between 1980 and 2016, with information on the speaking MP, their party, gender and age at the time of the speech. The dataset also includes all speeches of ten words made from 1936 to 1979, without identifying information on the speaker.</p> <p>The speeches have been classified for sentiment using a total of five libraries from the R packages `sentimentr`, `syuzhet` and `lexicon`.</p> <p>The integrity of the public Hansard record is questionable at times, and while I have improved it, the data is presented 'as is'. More details on the dataset are available at: http://evanodell.com/datasets/hansard-data/</p>
VnEmoLex: A Vietnamese emotion lexicon for sentiment intensity analysis
<p>VnEmoLex is the moderate-sized data set annotated for eight basic emotions: joy, sadness, anger, fear, trust, disgust, surprise and anticipation for Vietnamese. It is built on the NRC Word-Emotion Association Lexicon (EmoLex)<sup>1 </sup>and the Viet Wordnet<sup>2</sup> . VnEmoLex has total 12,795 words of which 4431 words are from the EmoLex dictionary, 8364 words are taken from the Viet Wordnet.</p> <p><sup>1 </sup>http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm</p> <p><sup>2 </sup>http://http://viet.wordnet.vn/wnms/</p>
Gujarati Movie Reviews with Tagged Sentiments (Positive/ Negative/ Neutral)
<p>This dataset encompasses around 500 entries of movie review descriptions written in the Gujarati language. Each entry is paired with a sentiment classification tag. The first column contains the actual movie review descriptions, while the second column contains sentiment tags with the following meanings:</p><ul><li>"0" denotes that the corresponding review expresses a negative sentiment.</li><li>"1" signifies a neutral sentiment.</li><li>"2" conveys a positive sentiment.</li></ul><p>To sum it up succinctly, this dataset provides a valuable collection of <strong>Gujarati movie reviews</strong>, each thoughtfully categorized as either <strong>negative</strong>, <strong>neutral</strong>, or <strong>positive</strong> in tone, offering rich insights for sentiment analysis tasks and research.</p><p>The dataset is manually tagged by native speakers with more than 20 years of experience in using the language.</p>
Sentiment dataset of Algerian dialect
<p>* This sentiment dataset of Algerian dialect consists of 11760 comments (6111 positive/ 5649 negative comments)) collected from (Facebook, YouTube and Twitter) during Hirak 2019.<br>* Comments concern the Algerian spoken language, written in Arabic and/or Latin characters and/or Arabizi, which could be either Modern Standard Arabic, French or local dialect.<br>* Value ‘1’ is attributed for Positive review / value ‘0’ attributed for Negative review.<br>* Due to the nature of this Dataset, some comments contain offensive language. This does not reflect author values, however the aim is to providing a resource to help in analysing positive and negative sentiments (that probably containing harmful content).<br>* For more information please contact (@Ahmed Cherif Mazari) : <a href="mailto:mazari.ac@gmail.com" target="_blank" rel="noopener">mazari.ac@gmail.com</a></p>
Synthetic Product Desirability Datasets for Sentiment Analysis Testing
<p><strong>Overview:</strong><br>This collection contains three synthetic datasets produced by gpt-4o-mini for sentiment analysis and PDT (Product Desirability Toolkit) testing. Each dataset contains 1000 hypothetical software product reviews with the aim to produce a diversity of sentiment and text. The datasets were created as part of the research described in:</p> <p>Hastings, J.D., Weitl-Harms, S., Doty, J., Myers, Z. L., and Thompson, W., “Utilizing Large Language Models to Synthesize Product Desirability Datasets,” in Proceedings of the 2024 IEEE International Conference<br>on Big Data (BigData-24), Workshop on Large Language and Foundation Models (WLLFM-24), Dec. 2024.<br><a href="https://arxiv.org/abs/2411.13485">https://arxiv.org/abs/2411.13485</a>.</p> <p>Briefly, each row in the datasets was produced as follows:<br>1) Word+Review: The LLM selected a word and synthesized a review that would align with a random target sentiment.<br>2) Review+Word: The LLM produced a review to align with the target sentiment score, and then selected a word appropriate for the review.<br>3) Supply-Word: A word was supplied to the LLM which was then scored, and a review was produced to align with that score.</p> <p>For sentiment analysis and PDT testing, the two columns of main interest across the datasets are likely 'Selected Word' and 'Hypothetical Review'.</p> <p><strong>License:</strong><br>This data is licensed under the CC Attribution 4.0 international license, and may be taken and used freely with credit given. Cite as:</p> <p>Hastings, J., Weitl-Harms, S., Doty, J., Myers, Z., & Thompson, W. (2024). Synthetic Product Desirability Datasets for Sentiment Analysis Testing (1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.14188456" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14188456</a></p>
Sentiment Analysis of Kampus Merdeka Policy
A project analyzing public sentiment on Kampus Merdeka through data collection, text preprocessing, sentiment classification, and performance evaluation.
Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-04.2021)
<p>Sentiment analysis of tech media articles using VADER package and co-occurrence analysis</p> <p>Sources with weights:</p> <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> <p>Methodology</p> <p>The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.</p> <p>As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.</p> <p>The process included the following main steps:</p> <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> <p>Therefore, the procedure has been repeated for 100 words for all identified social issues.</p> <p>The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.</p> <p> </p> <p>*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</p>
Dataset: Sentiment Analysis annotation of News headlines covering the Olympic legacy of Rio 2016 and London 2012 published by the Brazilian and British online media
<p>Dataset of 464 news headlines with sentiment manually annotated by a domain expert using the labels positive, negative and neutral. Data contains URLs for news articles published between 2004-2020 by the British and Brazilian media in English and Brazilian Portuguese covering the Olympic legacies of London 2012 and Rio 2016. Articles were collected from the news outlets’ websites using Google search engine.</p> <p>News outlets:</p> <ul> <li>The Guardian</li> <li>Daily Mail</li> <li>Globo</li> <li>Estadao</li> </ul>
Sentiment Analysis outputs based on the combination of three classifiers for news headlines and body text
<p>Sentiment Analysis outputs based on the combination of three classifiers for news headlines and body text covering the Olympic legacy of Rio 2016 and London 2012. Data was searched via Google search engine. It is composed of sentiment labels assigned to 1271 news articles in total.</p> <p><strong>News outlets:</strong></p> <ul> <li>BBC</li> <li>Daily Mail</li> <li>The Telegraph</li> <li>The Guardian</li> <li>Globo</li> <li>Estadao</li> <li>Folha de S. Paulo</li> </ul> <p><strong>Events covered by the articles:</strong></p> <ul> <li>London 2012 Olympic legacy</li> <li>Rio 2016 Olympic legacy</li> </ul> <p>All classifiers were used in texts in English. Text originally published in Portuguese by the Brazilian media were automatically translated.</p> <p><strong>Sentiment classifiers used:</strong></p> <ul> <li>Vader</li> <li>BERT (Trained on Amazon data)</li> <li>BERT (Trained on twitter data - 140)</li> </ul> <p>Each document (spreadsheet - xlsx) refers to one outlet and one event (London 2012 or Rio 2016).</p> <p><strong>How were labels assigned to the texts?</strong></p> <p>These labels are a combination of the three sentiment classifiers listed above. If two of them agree with the same label, then this label would be considered as right. Otherwise, the label ‘other’ was assigned.</p> <p>For news article body text: the proportion of sentences of each sentiment type was used to assign labels to the whole article instead of averaging the sentence scores. For example, if the proportion of sentences with negative labels is greater than 50%, then the article is assigned a negative label.</p> <p><strong>The documents are composed of the following columns:</strong></p> <ul> <li>Rank: the position of the article on Google search ranking</li> <li>Date: date of article's publication (DD/MM/YYYY)</li> <li>Link: article's link</li> <li>Title: article's title</li> <li>Sentiment_Title: final sentiment for article headline</li> <li>Sentiment_Text: final sentiment for article's body text</li> </ul> <p><em>PS: Documents do not include articles' body text. </em></p> <p><strong>Sentiment is presented in labels as follows:</strong></p> <ul> <li>Pos: Positive</li> <li>Neg: Negative</li> <li>Neutral: Neutral</li> <li>other: inconclusive - if each of the 3 classifiers assigned a different label to the article, the label 'other' was used. Therefore, 'other' identifies contradictory results.</li> </ul> <p> </p>
Brazilian tweets classified for sentiment analysis
<p>Brazilian tweets classified for sentiment analysis</p>
Dataset: SentiSurvey for Sentiment Analysis in Software Projects
<p><strong>Description</strong></p> <p>In 2022, we conducted a survey about the perceptions of developers regarding sentiments in statements. We published a paper about the results. The dataset includes the survey questions and the answers of the total 180 participants.</p> <p><strong>Citation</strong></p> <p>Information on the study design and execution are presented in the paper linked below.</p> <p>Please, see also the references below for the papers to cite.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.