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187 results for “Language Data”
Data for: Speech naturalness detection and language representation in the dog brain
<p>Abstract<br> Family dogs are exposed to a continuous flow of human speech throughout their lives. However, the extent of their abilities in speech perception is unknown. Here, we used functional magnetic resonance imaging (fMRI) to test speech detection and language representation in the dog brain. Dogs (n = 18) listened to natural speech and scrambled speech in a familiar and an unfamiliar language. Speech scrambling distorts auditory regularities specific to speech and to a given language, but keeps spectral voice cues intact. We hypothesized that if dogs can extract auditory regularities of speech, and of a familiar language, then there will be distinct patterns of brain activity for natural speech vs. scrambled speech, and also for familiar vs. unfamiliar language. Using multivoxel pattern analysis (MVPA) we found that bilateral auditory cortical regions represented natural speech and scrambled speech differently; with a better classifier performance in longer-headed dogs in a right auditory region. This neural capacity for speech detection was not based on preferential processing for speech but rather on sensitivity to sound naturalness.<br> Furthermore, in case of natural speech, distinct activity patterns were found for the two languages in the secondary auditory cortex and in the precruciate gyrus; with a greater difference in responses to the familiar and unfamiliar languages in older dogs, indicating a role for the amount of language exposure. No regions represented differently the scrambled versions of the two languages, suggesting that the activity difference between languages in natural speech reflected sensitivity to language-specific regularities rather than to spectral voice cues. These findings suggest that separate cortical regions support speech naturalness detection and language representation in the dog brain.<br> <br> This dataset contains</p> <ul> <li>Raw data (four functional runs and Matlab logs n = 18)</li> <li>Dog brain template</li> <li>Stimuli (natural and scrambled speech in Hungarian and Spanish) </li> <li>MVPA main results maps (Speech detection and Language discrimination, n = 18)</li> <li>GLM All sounds > Silence contrast (n = 18) </li> </ul>
Polifonia Corpus - Encyclopedic Module Data - Italian Language
<p>We release the data of the Encyclopedic Module of the Polifonia Textual Corpus (Wikipedia pages), collected by selecting from <a href="http://lcl.uniroma1.it/babeldomains/">BabelNet domains</a> all the <a href="https://www.wikipedia.org">Wikipedia</a> musical pages.</p> <p>Full description at <a href="/github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Encyclopedic Module Data - German Language
<p>We release the data of the Encyclopedic Module of the Polifonia Textual Corpus (Wikipedia pages), collected by selecting from <a href="http://lcl.uniroma1.it/babeldomains/">BabelNet domains</a> all the <a href="https://www.wikipedia.org">Wikipedia</a> musical pages.</p> <p>Full description at <a href="/github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Encyclopedic Module Data - Dutch Language
<p>We release the data of the Encyclopedic Module of the Polifonia Textual Corpus (Wikipedia pages), collected by selecting from <a href="http://lcl.uniroma1.it/babeldomains/">BabelNet domains</a> all the <a href="https://www.wikipedia.org">Wikipedia</a> musical pages.</p> <p>Full description at <a href="/github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Encyclopedic Module Data - French Language
<p>We release the data of the Encyclopedic Module of the Polifonia Textual Corpus (Wikipedia pages), collected by selecting from <a href="http://lcl.uniroma1.it/babeldomains/">BabelNet domains</a> all the <a href="https://www.wikipedia.org">Wikipedia</a> musical pages.</p> <p>Full description at <a href="/github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Encyclopedic Module Data - English Language
<p>We release the data of the Encyclopedic Module of the Polifonia Textual Corpus (Wikipedia pages), collected by selecting from <a href="http://lcl.uniroma1.it/babeldomains/">BabelNet domains</a> all the <a href="https://www.wikipedia.org">Wikipedia</a> musical pages.</p> <p>Full description at <a href="/github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Polifonia Corpus - Encyclopedic Module Data - Spanish Language
<p>We release the data of the Encyclopedic Module of the Polifonia Textual Corpus (Wikipedia pages), collected by selecting from <a href="http://lcl.uniroma1.it/babeldomains/">BabelNet domains</a> all the <a href="https://www.wikipedia.org">Wikipedia</a> musical pages.</p> <p>Full description at <a href="/github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Data belonging to the article "Syllabification in Language Contact between French and Vietnamese"
<p>This dataset belongs to an article which will appear in the ICAAL 8 conference proceedings and which is dedicated to the syllabification in two language contact settings between Vietnamese and French. The paper discusses gemination and related phenomena. First, it contributes to the theoretical discussion on borrowing processes in loanwords and presents a model that includes all extant views and also accounts for other language contact situations. In our data analysis, we compare loan data to experimental data, and find similar processes in both scenarios. Syllable boundaries are shifted, consonants are geminated and consonant slots are doubled in certain syllabic environments. The emergence of gemination is especially noteworthy as it is only a marginal phenomenon in French and has not been found to exist in Vietnamese. Results provide evidence for our hypothesis that optimal Vietnamese syllables are closed, as these syllables have the highest frequency in the native lexicon. This goes beyond, but also corresponds to the idea that Vietnamese has a dimoraic minimum, and this paper provides more evidence for it.</p>
Data for manuscript: "Longitudinal Analysis of Sentiment and Emotion in News Media Headlines Using Automated Labelling with Transformer Language Models"
<p>This data set contains automated sentiment and emotionality annotations of 23 million headlines from 47 popular news media outlets popular in the United States. </p> <p>The set of 47 news media outlets analysed (listed in Figure 1 of the main manuscript) was derived from the AllSides organization <a href="https://www.allsides.com/blog/updated-allsides-media-bias-chart-version-11">2019 Media Bias Chart v1.1</a>. The human ratings of outlets’ ideological leanings were also taken from this chart and are listed in Figure 2 of the main manuscript. </p> <p>News articles headlines from the set of outlets analyzed in the manuscript are available in the outlets’ online domains and/or public cache repositories such as The Internet Wayback Machine, Google cache and Common Crawl. Articles headlines were located in articles’ HTML raw data using outlet-specific XPath expressions. </p> <p>The temporal coverage of headlines across news outlets is not uniform. For some media organizations, news articles availability in online domains or Internet cache repositories becomes sparse for earlier years. Furthermore, some news outlets popular in 2019, such as <em>The Huffington Post</em> or <em>Breitbart</em>, did not exist in the early 2000’s. Hence, our data set is sparser in headlines sample size and representativeness for earlier years in the 2000-2019 timeline. Nevertheless, 18 outlets in our data set have chronologically continuous partial or full headline data availability fulfilling our inclusive criteria (see manuscript Methods) since the year 2000. Figure S 1 in the SI reports the number of headlines per outlet and per year in our analysis.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the headline due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. After manual testing, we determined that the percentage of headlines following in this category is very small. Additionally, our method might miss detecting some articles in the online domains of news outlets. To conclude, in a data analysis of over 23 million headlines, we cannot manually check the correctness of every single data instance and hundred percent accuracy at capturing headlines’ 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 headlines set is representative of headlines in print news media content for the studied time period and outlets analyzed.</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 as well as aggregated data of sentiment and emotionality automated annotations of the headlines and human annotations of a subset of headlines sentiment and emotionality used as ground truth. </p> <p>-models.rar contains the Transformer sentiment and emotion annotation models used in the analysis. Namely: </p> <p>Siebert/sentiment-roberta-large-english from https://huggingface.co/siebert/sentiment-roberta-large-english. This model is a fine-tuned checkpoint of <a href="https://huggingface.co/roberta-large">RoBERTa-large</a> (<a href="https://arxiv.org/pdf/1907.11692.pdf">Liu et al. 2019</a>). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). See more information from the original authors at https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>DistilbertSST2.rar is the default sentiment classification model of the HuggingFace Transformer library https://huggingface.co/ This model is only used to replicate the results of the sentiment analysis with sentiment-roberta-large-english </p> <p>DistilRoberta j-hartmann/emotion-english-distilroberta-base from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base. The model is a fine-tuned checkpoint of <a href="https://huggingface.co/distilroberta-base">DistilRoBERTa-base</a>. The model allows annotation of English text with Ekman's 6 basic emotions, plus a neutral class. The model was trained on 6 diverse datasets. Please refer to the original author at https://huggingface.co/j-hartmann/emotion-english-distilroberta-base for an overview of the data sets used for fine tuning. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromSentimentRobertaLargeModel.rar URLs of headlines analyzed and the sentiment annotations of the siebert/sentiment-roberta-large-english Transformer model. https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromDistilbertSST2.rar URLs of headlines analyzed and the sentiment annotations of the default HuggingFace sentiment analysis model fine-tuned on the SST-2 dataset. https://huggingface.co/</p> <p>-headlinesDataWithEmotionLabelsAnnotationsFromDistilRoberta.rar URLs of headlines analyzed and the emotion categories annotations of the j-hartmann/emotion-english-distilroberta-base Transformer model. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 9 April 2010
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 9 April 2010</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 14 January 2016
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 14 January 2016</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 2 August 2017
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 2 August 2017.</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 26 August 2014
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 26 August 2014</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 30 January 2018.
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 30 January 2018.</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 31 August 2016
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 31 August 2016</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 15 July 2016
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 15 July 2016</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 11 August 2011
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 11 August 2011</p>
Bangru Language Data: photographs
<p>These files form the empirical basis for the following article:</p> <p>Bodt, Timotheus Adrianus and Ismael Lieberherr. 2015. First notes on the phonology and classification of the Bangru language of India. <em>Linguistics of the Tibeto-Burman Area 38:1</em> (2015), 66–123.</p> <p>doi 10.1075/ltba.38.1.03bod</p> <p>issn 0731–3500 / e-issn 2214–5907 © John Benjamins Publishing Company</p> <p>These data were collected in Sarli circle, Kurung Kumey district, Arunachal Pradesh, India.</p> <p>The data collectors were the following faculty, students and associated researchers of the Department of English and Foreign Languages, Tezpur University, Assam, India:</p> <p>Nupur Sinha (Faculty), Ismael Lieberherr (Affiliated PhD scholar), Timotheus A. Bodt (Affiliated PhD scholar), Diksha Konwar, Eshani Baishya, Nawaf Helmi, Pinaz Mirza, Ratul Mahela, Sansuma Brahma (students).</p>
Data and model weights for a series of antibody language models
<p>This repo contains the sequence dataset for finetuning and model weights including pre-trained meta model and a series of finetuned antibody language models.</p> <p>The antibody language models were used in the paper "<em>Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy"</em> for antibody sequence embedding.</p>
Supplementary Material for "Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs)"
<h3>ABSTRACT<strong>: </strong></h3> <p><strong>Context</strong>: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. <br><strong>Goal</strong>: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. <br><strong>Method</strong>: We conducted a survey with 78 ICT practitioners from five regions of the country. <br><strong>Results</strong>: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. <br><strong>Conclusions</strong>: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.</p>
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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.