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475 results for “words”
Voice data in Dioula language relating to the words Yes, No, Call, Next, Previous, Dial, Stop, True, False, Enter and Exit
<p><span>For this project entitled “Development of a speech recognition model for under-resourced languages”, the collected dataset includes audio recordings of native speakers of the Dioula language pronouncing a series of specific keywords such as “Yes”, “No”, “Call”, “Next”, “Previous”, “Dial”, “Stop”, “True”, “False”, “Enter” and “Exit”. These recordings were obtained in diverse environments, with a wide variety of speakers to ensure that the different tones, accents, and phonetic variations specific to Dioula are taken into account. Each audio file was assigned to a category corresponding to the spoken word, creating a class-structured dataset. This corpus will be used to train a speech recognition model using deep learning techniques, with the objective of improving recognition accuracy in real contexts. This database is essential for the design and evaluation of a model capable of facilitating access and voice interaction for the Dioula community.</span></p>
DWUG SV Resampled: Diachronic Word Usage Graphs for Swedish
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for Swedish. Uses were sampled for the target words from the <a href="https://zenodo.org/doi/10.5281/zenodo.5090647">DWUG SV</a> dataset and from the same source corpora. DWUG SV Resampled can thus be seen as a small-scale replication of DWUG SV. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. <a href="https://aclanthology.org/2024.emnlp-main.796/">More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages</a>. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p> <p>Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. <a href="https://aclanthology.org/2021.emnlp-main.567/">DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages</a>. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.</p>
Data from: Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types
<p>Concerns about gender bias in word embedding models have captured substantial attention in the algorithmic bias research literature. Other bias types however have received lesser amounts of scrutiny. This work describes a large-scale analysis of sentiment associations in popular word embedding models along the lines of gender and ethnicity but also along the less frequently studied dimensions of socioeconomic status, age, physical appearance, sexual orientation, religious sentiment and political leanings. Consistent with previous scholarly literature, this work has found systemic bias against given names popular among African-Americans in most embedding models examined. Gender bias in embedding models however appears to be multifaceted and often reversed in polarity to what has been regularly reported. Interestingly, using the common operationalization of the term bias in the fairness literature, novel types of so far unreported bias types in word embedding models have also been identified. Specifically, the popular embedding models analyzed here display negative biases against middle and working-class socioeconomic status, male children, senior citizens, plain physical appearance and intellectual phenomena such as Islamic religious faith, non-religiosity and conservative political orientation. Reasons for the paradoxical underreporting of these bias types in the relevant literature are probably manifold but widely held blind spots when searching for algorithmic bias and a lack of widespread technical jargon to unambiguously describe a variety of algorithmic associations could conceivably be playing a role. The causal origins for the multiplicity of loaded associations attached to distinct demographic groups within embedding models are often unclear but the heterogeneity of said associations and their potential multifactorial roots raises doubts about the validity of grouping them all under the umbrella term bias. Richer and more fine-grained terminology as well as a more comprehensive exploration of the bias landscape could help the fairness epistemic community to characterize and neutralize algorithmic discrimination more efficiently.</p>
Data for manuscript: "Using Word Embeddings to Probe Sentiment Associations of Politically Loaded Terms in News and Opinion Articles from News Media Outlets"
<p>This data set contains material for the purpose of scientific reproducibility of the accompanying manuscript "Using Word Embeddings to Probe Sentiment Associations of Politically Loaded Terms in News and Opinion Articles from News Media Outlets".</p> <p>Note that this data set is distributed with an Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. NonCommercial means you may not use the material for commercial purposes. NoDerivatives means if you remix, transform, or build upon the material, you may not distribute the modified material. Attribution means you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. See attached license terms for details.</p> <p>The work "Using Word Embeddings to Probe Sentiment Associations of Politically Loaded Terms in News and Opinion Articles from News Media Outlets" describes an analysis of political associations in 27 million diachronic (1975-2019) news and opinion articles from 47 news media outlets popular in the United States. We use embedding models trained on individual outlets content to quantify outlet-specific latent associations between positive/negative sentiment words and terms loaded with political connotations such as those describing political orientation, party affiliation, names of influential politicians and ideologically aligned public figures. </p> <p>News and opinion articles from the outlets listed in Figure 3 are available in the outlet's online domains and/or public cache repositories such as Google cache, The Internet Wayback Machine [31] and Common Crawl [32]. This work has not analyzed video or audio content of news media organizations, except when the outlet explicitly provides a transcript of such content in article form.<br> The temporal coverage of articles from different news outlets is not uniform. For most media organizations, news articles availability in their online domains or Internet cache backups becomes sparse as a function of articles’ age. This is not the case for some news outlets, where availability of news articles goes back to the 1970s. The Supplementary Material (SM) illustrates the time ranges of article data analyzed based on news outlets articles online availability.</p> <p>Textual content included in our analysis is circumscribed to the articles’ headlines and main text and does not include other article elements such as figure captions. Targeted textual content was located in HTML raw data using outlet specific XPath expressions. Tokens were lowercased prior to estimating embedding models. Markup language tags, URLs, nonalphanumeric characters, punctuation, digits, 330 common stop words and multiple spaces were removed prior to estimating word embeddings models.<br> All the analysis scripts and the diachronic word embedding models built from each of the 47 news media outlets analyzed in this work are available in this repository.</p> <p>For the purpose of reproducibility, we also provide in the above repository the articles’ text used to train the news outlets embedding models with the caveat that outlets articles not accessible without a subscription have been excluded. Also, for the included articles, stop words have been removed and the remaining words have been randomly scrambled within a sliding window of size 10 to render the articles incomprehensible to a human reader. These steps have been taken to not infringe articles copyright. These preprocessing steps have only minor impact on Continuous Bag of Words (CBOW) word2vec and the results reported in this work are similar when using the scrambled articles text to train outlet-specific embedding models.</p> <p>We derived outlet-specific word embedding models at every five-year time intervals within the 1975-2019 time range. The gensim [33] implementation of word2vec was used to train the embedding models. The continuous bag of words (CBOW) architecture performed slightly better than the Skip-Gram architecture in commonly used validation metrics so it was used for all subsequent analysis. </p> <p>For training the word embedding models, the following parameters were used: vector dimensions=300, window size=10, negative sampling=10, down sampling frequent words = 0.0001, minimum frequency count of 5 (only terms that appear more than 5 times in the corpus were included into the word embedding model vocabulary), number of training iterations (epochs) through the corpus=5. The exponent used to shape the negative sampling distribution was the default 0.75. </p> <p>Outlet-specific embedding models performance across a range of commonly used semantic, syntactic and analogy tasks was similar to popular pre-trained embedding models trained on corpora such as Twitter or Google books on similarity, association and word analogy tasks, see Supplemeentary Material of the manuscript for detailed validation tests results.</p> <p> </p>
Data for manuscript: "Prevalence of prejudice denoting words in news media discourse: a chronological analysis"
<p>This data set contains frequency counts of target words in 27 million news and opinion articles from 47 popular news media outlets in the United States. The target words are listed in the associated manuscript and are mostly words that denote some type of prejudice. A few additional words not denoting prejudice are also available since they are used in the manuscript for illustration purposes.</p> <p>The textual content of news and opinion articles from the outlets listed in Figure 4 of the main manuscript is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We used derived word frequency counts from these sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.</p> <p>Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1.25 million words of article content from an outlet. This threshold was chosen to maximize inclusion in our analysis of outlets with sparse amounts of articles text per year such as Reason, Alternet or The American Spectator. </p> <p>Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in all articles of a given year by the number of all words in all articles of that year. This method of estimating frequency accounts for variable volume of total article output over time.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript </p> <p>-articlesContainingTargetWords.rar contains counts of target words in outlets articles as well as total counts of words in articles</p> <p>-cableNews.rar contains prevalence of target words in TV cable news. Data is from Stanford Cable TV News Analyzer (https://tvnews.stanford.edu/)</p> <p>-surveyData.rar contains longitudinal survey data used in the manuscript and links to original sources</p> <p>Usage Notes</p> <p>In a small percentage of articles, outlet specific XPath expressions failed to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles are not precise. In a random sample of articles and outlets, manual estimation of target words counts overlapped with the automatically derived counts for over 90% of the articles.</p> <p>Most of the incorrect frequency counts were minor deviations from the actual counts such as for instance counting the word "Facebook" in an article footnote encouraging article readers to follow the journalist’s Facebook profile and that the XPath expression mistakenly included as the content of the article main text. Some additional outlet-specific inaccuracies that we could identify occurred in "The Hill" and "Newsmax" news outlets where XPath expressions had some shortfalls at precisely capturing articles’ content. For "The Hill", in years 2007-2009, XPath expressions failed to capture the complete text of the article in about 40% of the articles. This does not necessarily result in incorrect frequency counts for that outlet but in a sample of articles’ words that is about 40% smaller than the total population of articles words for those three years. In the case of "NewsMax", the issue was that for some articles, XPath expressions captured the entire text of the article twice. Notice that this does not result in incorrect frequency counts. If a word appears x times in an article with a total of y words, the same frequency count will still be derived when our scripts count the word 2x times in the version of the article with a total of 2y words. To conclude, in a data analysis of 27 million articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Figure 1 and Figure 2 of main manuscript for supporting evidence).</p>
Developmental Changes in Gaze Behavior and the Effects of Auditory Emotion Word Priming in Emotional Face Categorization
<p>Data used for statistical analyses in the journal article "Developmental Changes in Gaze Behavior and the Effects of Auditory Emotion Word Priming in Emotional Face Categorization" published in the journal Multisensory Research (online publication date: 16 September 2021).</p>
Perceptual Contrast Reduces the JOLs of Small-font Words and Increases the JOLs of Large-font Words Compared with the No-contrast Conditions
<p>The data and procedures in Experiment 1 & 2.</p>
Data underlying the study on 'Lability and the rigidification of word order. Evidence from Early Middle English', published in 'Linguistics'
<p>see the publication for a description of the data</p>
Arabic words sign language video dataset
<p>The dataset consists of 3000 unaltered videos captured by five volunteers, with each volunteer performing 20 repetitions of 30 signs, resulting in approximately 600 videos per volunteer. These videos were recorded without stabilization tools, reflecting the prevalent use of smartphones with built-in cameras. They encompass diverse resolutions, locations, places, and backgrounds, providing a comprehensive representation of real-life scenarios.</p>
Selection of Han dynasty transcriptions of foreign names and words
<p>This dataset contains the Chinese transcriptions of foreign words and names in Han dynasty sources that appear in the appendix of the following article:</p> <p>Schuessler, Axel (2014). "Phonological Notes on Hàn Period Transcriptions of Foreign Names and Words." Richard VanNess Simmons and Newell Ann Van Auken, eds<em>. Studies in Chinese and Sino-Tibetan Linguistics: Dialect, Phonology, Transcription and Text</em>, pp. 249- 293. Taipei: Institute of Linguistics, Academia Sinica.</p>
Predicting brain activity from word embeddings during natural language comprehension: Example Data
<p>Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.</p>
Experimental Data for "Is a Picture Worth a Thousand Words? Effects of Foregrounded Multimodal and Narrative Features"
<p>This dataset contains data of the publication "Is a Picture Worth a Thousand Words? Effects of Foregrounded Multimodal and Narrative Features". It includes measures for the Story World Absorption Scale, the Transformative Reading Scale, and the Empathy subscale of the Literary Response Questionnaire used to test the effect of multimodal features of multimodal novels and narrative perspectives and their interactive effects on reading-induced imagery and emotional reactions in the process of reading. </p>
Impact of Non-invasive Brain Stimulation on Learning Novel Spoken Words
ClinicalTrials.gov study NCT06387615. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study of the Effect of Daridorexant on Nighttime Body Posture, the Noise Level Required to Wake up, and the Ability to Remember Words Previously Presented
ClinicalTrials.gov study NCT05702177. IPD Sharing: NO. Countries: 1. Publications: 1.
Thirty Million Words Home Visiting
ClinicalTrials.gov study NCT02216032. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Verb Network Strengthening Treatment (VNeST) Via Telerehabilitation on Word Finding in Post-stroke Aphasia.
ClinicalTrials.gov study NCT06096662. IPD Sharing: NO. Countries: 1. Publications: 11.
Brain Areas Involved in Sound and Spoken Word Memory
ClinicalTrials.gov study NCT01375595. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effects of Verb Network Strengthening Treatment (VNeST) on Word Finding in Aphasia
ClinicalTrials.gov study NCT05152979. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Nurses' Negative Words and Postoperative Pain Management
ClinicalTrials.gov study NCT00495807. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Thirty Million Words- Well Baby Initiative
ClinicalTrials.gov study NCT02812017. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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
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DANDI Archive for NWB datasets
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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.