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187 results for “Language Data”
Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year.
<p>Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year out of a significant sample of 1638 tweets.</p>
Plomley (1976) Data on the Languages of Tasmania
<p>Dataset used for research on the languages of Tasmania, representing keyboarding and categorization of data originally collated and published by NJB Plomley. Distributed with permission.</p> <p>The following files are the complete extant records for the languages of Tasmania, from Plomley (1976). There are five files:</p> <ul> <li>Band names: a list of the band names recorded in Robinson</li> <li>Data: The lexical data, from Plomley, organized by Plomley's headword.</li> <li>Recorders: The people who recorded the original wordlists.</li> <li>Regions: The regions (defined by Plomley, from the original sources) from which vocabulary items originate.</li> <li>Speakers: The speakers of Tasmanian languages identified in the original sources.</li> </ul>
Raw data on the validation of the Polish language version of THE BRIEF HEPATITIS C KNOWLEDGE SCALE
<p>Raw data on the validation of the Polish language version of THE BRIEF HEPATITIS C KNOWLEDGE SCALE.</p>
Language as social and cultural data
<p>Closing lecture</p>
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
<p>As the current existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles.</p> <p>We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006--2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. This document is the data companion for a paper published elsewhere and presents a detailed list of the selected papers. Besides, the document also presents the form of our questionnaire-based survey. </p> <p>We also include the filled in questionnaires and raw data of the referred survey. To validate the SMS results we conducted a survey (in November 2018) with 28 HPC experts involved in the cHiPSet COST action to which we added, in October 2019, 29 HPC experts which were not involved in that COST action. Participants were recruited through convenience sampling, and contacted directly by the authors. In total, we received 57 filled survey forms.</p>
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
<p>As the current existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles.</p> <p>We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006--2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. This document is the data companion for a paper published elsewhere and presents a detailed list of the selected papers. Besides, the document also presents the form of our questionnaire-based survey. </p> <p>We also include the filled in questionnaires and raw data of the referred survey. To validate the SMS results we conducted a survey (in November 2018) with 28 HPC experts involved in the cHiPSet COST action to which we added, in October 2019, 29 HPC experts which were not involved in that COST action. Participants were recruited through convenience sampling, and contacted directly by the authors. In total, we received 57 filled survey forms.</p>
Dataset from TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]
<p>TableLabler: Scalable Labeling of Data Tables with Language Models for Tabular Dataset Creation [Scalable Data Science]</p> <p>Pre-publicatoin upload for VLDB review.</p> <p>Code is at https://github.com/RelationalAI/annotated-tables/. The Github repository name is from a previous draft version and cannot be changed. It is code for TableLabler.</p>
DATA: Language experience predicts music processing in a half-million speakers of fifty-four languages
<p>This repository contains the data for the paper 'Language experience predicts music processing in a half-million speakers of fifty-four languages'<br> <br> The code that accompanies this data can be found on GitHub - https://github.com/themusiclab/language-experience-music</p>
Supplementary code and data for the paper `From stage to page: language independent bootstrap measures of distinctiveness in fictional speech`
<p>The repository provides full data and processing / analysis pipeline for the paper <strong>'From stage to page: language independent bootstrap measures of distinctiveness in fictional speech</strong>'<br> <br> Rendered notebooks are also available through Github:</p> <p>1) <a href="https://github.com/perechen/difs-character-voices/blob/master/data/all_stars_clean.ipynb">Preparation, energy distance and exploration</a> (main)</p> <p>2) <a href="https://github.com/perechen/difs-character-voices/blob/master/03_analysis.md">Keyword curves & formal modeling</a></p> <p> </p> <p>- `00_dracor_get_data.R`. Script uses <a href="https://dracor.org/">DraCor</a> dedicated API to get texts spoken by characters</p> <p>- `01_distinctiveness_energy.ipynb` does the heavy lifting of data wrangling, cleaning and preprocessing, plus implements energy distance bootstrapping and does exploratory analysis</p> <p>- `02_logodds_curves.R` calculates keyword curves for characters<br> <br> - `03_analysis_and_models.R` explores keyword curves and does Bayesian models</p>
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments - Supplementary Data
<p><strong>Introduction</strong></p> <p>In this zip, we release the auxiliary data that is beneficial to execute the implementation of AVLEN described in our paper AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments by Sudipta Paul, Amit K Roy-Chowdhury, and Anoop Cherian, NeurIPS, 2022.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped data is 4.6G</li> <li>The unzipped folder contains: (i) a README.md file and (ii) ./AVLEN-data folder. The latter contains the following zip files. Please see the AVLEN code to see how to unzip these files into their respective folders. <ul> <li>ckpt.119.pth -- 61M </li> <li>connectivity.zip -- 1.4M </li> <li>pretrained_weights.zip -- 1.7G</li> <li>ResNet-152-imagenet.zip -- 2.9G</li> <li>semantic_audionav_dialog_approx.zip -- 2.7M</li> <li>soundspaces.zip -- 479K</li> <li>speaker_model_weights.zip -- 51M</li> </ul> </li> </ul> <p><strong>Other Resources</strong></p> <p>For the implementation of AVLEN that uses the data shared here, please visit <a href="https://www.merl.com/publications/TR2022-131">MERL TR2022-131</a>.</p> <p><strong>Citation</strong></p> <p>If you use AVLEN in your research, please cite our paper:</p> <pre><code>@InProceedings{paul2022avlen, title={AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments}, booktitle={Advances in Neural Information Processing Systems}, author={Paul, Sudipta and Roy-Chowdhury, Amit and Cherian, Anoop}, volume={35}, pages={6236--6249}, year={2022} }</code></pre> <p><strong>Copyright and License</strong></p> <p>The AVLEN dataset is released under CC-BY-SA-4.0 license.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
USPDATRO: Underrepresented Speech Dataset from Romanian language Open Data
<p> USPDATRO<br> ==========</p> <p>Underrepresented Speech Dataset from Open Data: Case Study on the Romanian Language (USPDATRO) is a manually created Romanian language speech corpus.<br> It was created specifically using speech types that are underrepresented in other speech datasets.<br> Sources for this dataset are represented by open data available on multimedia platforms under a Creative Commons license.<br> The data was manually transcribed and aligned at segment level.<br> In addition to the text and audio files, we offer text annotations (lemmatization, part of speech tags, dependency parsing) in CoNLL-U Plus format.</p> <p>Each datasource is mentioned by URL in the metadata.csv file with associated license (a Creative Commons variant).</p> <p>Dataset structure:<br> - audio: Folder with audio segments in WAV format<br> - text: Folder with corresponding transcriptions<br> - conllup: Folder with corresponding token-based annotations<br> - metadata.csv: Contains information about each segment</p> <p>LICENSING</p> <p>This work (transcriptions, alignment, metadata, annotations) is provided under the license CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International).<br> The license can be viewed online here: https://creativecommons.org/licenses/by-nc-sa/4.0/<br> and the full text here: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode .<br> The original works considered for audio sources are available under their respective licenses (Creative Commons variants) as described in the metadata.csv file.</p> <p><br> CONTACT</p> <p>Research Institute for Artificial Intelligence "Mihai Drăgănescu", Romanian Academy<br> Web: http://www.racai.ro<br> Contact emails: vasile@racai.ro</p>
ThoughtSource: A central hub for large language model reasoning data (dataset snapshot)
<p><strong>ThoughtSource is a meta-dataset and software library for chain-of-thought reasoning in large language models (LLMs). </strong></p> <p><strong>This repository contains a snapshot of the openly available ThoughtSource datasets.</strong></p>
Data from: Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling.
<p><strong>Background</strong>: Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large–scale data sets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in natural language processing (NLP) and protein language modelling (PLM) have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. <strong>Methodology</strong>: We trained a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluated its performance on the peptide presentation prediction task. <strong>Data</strong>: The data and models used in AEGIS are contained in the uploaded tar files. <strong>Results</strong>: The transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance (see preprint). We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by the non-obese diabetic (NOD) mice enabled the in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications.</p>
Vocal data in the Dioula language related to the numbers 1, 2, 3, and 4
<p>The dataset we have compiled for our research on "Setting up a speech recognition model for under-resourced languages" consists of audio recordings of Dioula speakers pronouncing the numbers 1, 2, 3, and 4. These recordings were collected under various conditions, featuring variability in speakers, accents, and environmental contexts. The data has been categorized into four distinct classes, each corresponding to one of the numbers (1, 2, 3, or 4), enabling the training and evaluation of a machine learning-based speech recognition model.</p>
Supporting data for "Software pipelines for RNA-Seq, ChIP-Seq and Germline Variant calling analyses in Common Workflow Language (CWL)"
<p>Datasets produced during the validation of CWL-based pipelines, designed for the analysis of data from RNA-Seq, ChIP-Seq and germline variant calling experiments. Specifically, the workflows were tested using publicly available High-throughput (HTS) data from published studies on Chronic Lymphocytic Leukemia (CLL) (accession numbers: E-MTAB-6962, GSE115772) and Genome in a Bottle (GIAB) project samples (accession numbers: SRR6794144, SRR22476789, SRR22476790, SRR22476791).</p> <p>The supporting data include:</p> <ul> <li>Differential transcript and gene expression results produced during the analysis with the CWL-based RNA-Seq pipeline</li> <li>Bigwig and narrowPeak files, differential binding results, table of consensus peaks and read counts of EZH2 and H3K27me3, produced during the analysis with the CWL-based ChIP-Seq pipeline</li> <li>VCF files containing the detected and filtered variants, along with the respective hap.py () results regarding comparisons against the GIAB golden standard truth sets for both CWL-based germline variant calling pipelines</li> </ul>
Data for paper on inductive reasoning in large language models
<p>This dataset contains all data that was used for:</p> <p>Han, S. J., Ransom, K. J., Perfors, A. & Kemp, C. (2023). Inductive reasoning in humans and large language models. <em>Cognitive Systems Research</em>.</p> <p>The code for this project can be found <a href="https://github.com/S-J-HAN/InductiveReasoningInLargeLanguageModels">here</a>.</p> <p> </p>
Data from: Parameterizing the robust design in the BUGS language: lifetime carry‐over effects of environmental conditions during growth on a long‐lived bird
Open the record for dataset details and reuse information.
Data from: Intrinsic functional connectivity delineates transmodal language functions
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Knowledge from non-English-language studies broadens contributions to conservation policy and helps to tackle bias in biodiversity data
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Data from: Jeans and language: social networks and reproductive success are associated with the adoption of outgroup norms
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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.