Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
112
datasets available to search
ShareScore release 0.9.0
Dataset results
112 results for “language learning”
Limitations of Audiovisual Speech on Robots for Second Language Pronunciation Learning
<p>Additional material accompanying the HRI'23 publication found at <a href="https://doi.org/10.1145/3568162.3578633">https://doi.org/10.1145/3568162.3578633</a>.</p> <p>Simulations showing the audiovisual speech performed by Furhat during the second language tutoring experiment.</p> <p>Each video consists of the Furhat saying a Dutch word, followed by saying the Japanese translation twice.</p> <p>Videos M1-M10 correspond to the Matched condition, M11-M20 to Mismatched, and M21-M30 to the Computer-generated lip movement.</p> <p>In the experiment, participants interacted with a physical Furhat, but as it is difficult to make video recordings of the Furhat, simulations are shown here to allow for high-fidelity comparison between conditions.</p>
Integrated Second Language Learning for Chronic Care
ClinicalTrials.gov study NCT02864160. IPD Sharing: YES. Countries: 1. Publications: 1.
AFQ056 for Language Learning in Children With FXS
ClinicalTrials.gov study NCT02920892. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Improving STEM Outcomes for Young Children With Language Learning Disabilities
ClinicalTrials.gov study NCT03438760. IPD Sharing: NO. Countries: 1. Publications: 2.
Predicting Language and Literacy Growth in Children With ASD Using Statistical Learning
ClinicalTrials.gov study NCT06332144. IPD Sharing: YES. Countries: 1. Publications: 4.
Do children learn from their mistakes? A registered report evaluating error-based theories of language acquisition
Open the record for dataset details and reuse information.
MLRegTest: A benchmark for the machine learning of regular languages
Open the record for dataset details and reuse information.
Development of compositionality through interactive learning of language and action of robots
Open the record for dataset details and reuse information.
MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities
<p><strong>Abstract:</strong></p> <p>We introduce the <strong>MLM (Multiple Languages and Modalities)</strong> dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. The second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single-task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalizing on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding. </p> <p><strong>Introduction:</strong><br> Multiple Languages and Modalities comprises data points on 236k human settlements for evaluating and optimizing multitask learning systems. MLM presents a dataset with a high level of diversity in terms of modality and language. For each entity, we have extracted text summaries, images, coordinates, and their respective triple classes. Text summaries are available in three languages (English, French, and German) with each entity having between one and three language entries. </p> <p>Human settlements from all continents are provided in the overall dataset (MLM) with 72% located in Europe. Two further versions of the dataset - MLM-irle and MLM-irle-gr - were generated for use in the benchmark evaluation for multitask systems described in the paper (see above). MLM-irle-gr (ie geo-representative) was generated to serve organizations that focus on the European Union by providing a geographically balanced coverage of human settlements in this region. MLM-irle-gr contains data on 24k human settlements across the EU weighted in relation to the population count for each of the 28 countries.</p> <p>MLM contains the following fields:</p> <pre><code>---------------------------------------------------------------------- # field-label description ---------------------------------------------------------------------- 1. id a unique identifier 2. label textual label 3. coordinates longitude, latitude geo-location value 4. summaries list of textual summaries related to the entity 5. images list of images related to the entity 6. classes list of associated triple class ----------------------------------------------------------------------</code></pre> <p>MLM - Details by Dataset Version:</p> <pre><code>----------------------------------------------------------- Num. of MLM MLM-irle MLM-irle-gr ----------------------------------------------------------- Entities 236496 218681 22501 Images 412422 314533 31621 Summaries 497899 462328 47508 Triple classes 1685 1655 452 -----------------------------------------------------------</code></pre> <p><strong>Availability:</strong></p> <p>All three versions of MLM listed in the table directly above are available for direct download and use. To support findability and sustainability, the MLM dataset is published as an on-line resource at<em> <a href="https://doi.org/10.5281/zenodo.3885753">https://doi.org/10.5281/zenodo.3885753</a></em>. A separate page with detailed explanations and illustrations is available at <em><a href="http://cleopatra.ijs.si/goal-mlm/">http://cleopatra.ijs.si/goal-mlm/</a> </em>to promote ease-of-use. The project GitHub repository contains the complete source code for the system and the generation script is available at <em><a href="http://github.com/GOALCLEOPATRA/MLM">https://github.com/GOALCLEOPATRA/MLM</a></em>. Documentation adheres to the standards of <em>FAIR Data principles</em> with all relevant metadata specified to the research community and users. It is freely accessible under the Creative Commons Attribution 4.0 International license, which makes it reusable for almost any purpose. </p> <p><strong>Updating and Reusability:</strong><br> MLM is supported by a team of researchers from the University of Bonn, the Leibniz Information Center for Science and Technology, and Jožef Stefan Institute. The resource is already in use for individual projects and as a contribution to the project deliverables of the Marie Skłodowska-Curie CLEOPATRA Innovative Training Network. In addition to the steps above that make the resource available to the wider community, the usage of MLM will be promoted to the network of researchers in this project. Use among researchers and practitioners in digital humanities will be promoted by demonstrations and presentations at domain-related events. Activities are planned for the Digital Methods Summer School run by the University of Amsterdam. The range of modalities and languages present in the dataset also extend its application to research on multimodal representation learning, multilingual machine learning, information retrieval, location estimation, and the Semantic Web. MLM will be supported and maintained for three years in the first instance. A second release of the dataset is already scheduled and the generation process outlined above is designed to enable rapid scaling.</p>
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
<p>This repository contains the raw results (by word information-theoretic measures for the experimental stimuli) and the LSTM models analyzed in <a href="https://www.aclweb.org/anthology/2020.acl-main.179/">Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment</a>. The models from the synthetic experiments are given in the synthetic archive, as well as the training data generation script. There is a README included that gives more details for recreating/evaluating results from those experiments.</p> <p>The naming convention for each model in the models directory is:<br> [Language]_hidden[Hidden Units]_batch[Batch Size]_dropout[Dropout Rate]_lr[Learning Rate]_[Model Number].pt</p> <p>Language: en for English and es for Spanish<br> Hidden Units: All models had two layers with 650 hidden units per layer<br> Batch Size: The size of the batch (128 for English, 64 for Spanish)<br> Dropout Rate: All models used a dropout rate of 0.2<br> Learning Rate: All models has a learning rate of 20<br> Model Number: Identifier of the model (English model 0 is the best model from <a href="https://github.com/facebookresearch/colorlessgreenRNNs">Gulordava et al. (2018)</a>) </p> <p> </p> <p> </p>
PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach
<p>This data set for the publication "The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach" in PlosONE (2021) comprises:</p> <p>EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged) pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.</p> <p>fNIRS: oxy-Hb and deoxy-Hb data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged) pseudoword-picture pairings (PPP) for learned as well as new PPPs</p> <p>Behavioral data: Task Performance during the recognition experiment (reaction times for and percentage of correctly identified learned and new PPPs)</p> <p> </p>
Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning"
<p>Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning"</p> <p>File A: Datasets (.txt)</p> <p> A1: Webex</p> <p> A2: Zoom</p> <p> A3: Teams</p> <p> A4: Word</p> <p> A5: PowerPoint</p> <p> A6: Excel</p> <p> </p> <p>File B: Python Code (Both ours and baseline)</p> <p> B1: adversarial_samples.ipynb</p> <p> B2: Baseline.ipynb</p> <p> </p> <p>FIle C: Result Tables (.xlsx)</p> <p> C1: Table of perturbed outputs in Webex </p> <p> C2: Table of perturbed outputs in Zoom</p> <p> C3: Table of perturbed outputs in Teams</p> <p> C4: Table of perturbed outputs in Word</p> <p> C5: Table of perturbed outputs in PowerPoint</p> <p> C6: Table of perturbed outputs in Excel (Excel)</p> <p> </p> <p>File D: Trend of Adversarial Shifts (Graphs)</p> <p> D1: Adversarial shifts of office suit (LSTM)</p> <p> D2: Adversarial shifts of video conferencing suit (LSTM)</p> <p> D3: Non-Adversarial shifts of office suit (LSTM)</p> <p> D4: Non-Adversarial shifts of video conferencing suit(LSTM)</p> <p> D5: Adversarial shifts of office suit (GRU)</p> <p> D6: Adversarial shifts of video conferencing suit (GRU)</p> <p> D7: Non-Adversarial shifts of office suit (GRU)</p> <p> D8: Non-Adversarial shifts of video conferencing suit(GRU)</p> <p> D9: Adversarial shifts of office suit (Bi-LSTM)</p> <p> D10: Adversarial shifts of video conferencing suit (Bi-LSTM)</p> <p> D11: Non-Adversarial shifts of office suit (Bi-LSTM)</p> <p> D12: Non-Adversarial shifts of video conferencing suit(Bi-LSTM)</p> <p> </p> <p>File E: Adversarial Examples (.pdf)</p> <p> E1: Adversarial vs original in Webex</p> <p> E2: Adversarial vs original in Zoom</p> <p> E3: Adversarial vs original in Teams</p> <p> E4: Adversarial vs original in Word</p> <p> E5: Adversarial vs original in PowerPoint</p> <p> E6: Adversarial vs original in Excel</p> <p> </p> <p>File F: Questionnaire</p> <p> </p>
How Wikimedia projects can assist in language learning
<div>This repo hosts the slides (in .pdf, .odp and .pptx formats) and video (in .webm and .mp4 formats) of a lightning talk at <a href="https://web.archive.org/web/20240501045703/https://engagedscholarship.csuohio.edu/oa/opencon2024/">OpenCon Ohio 2024</a>. The slides are based on a <a title="Engaging with language diversity via the Wikipedia ecosystem" href="https://doi.org/10.5281/zenodo.10725824">presentation</a> given at the Polyglot Gathering Online 2024 on 29 February 2024. Versions of the video are also available <a href="https://commons.wikimedia.org/wiki/File:How_Wikimedia_projects_can_assist_in_language_learning_-_OpenCon_Ohio_2024.webm">on Wikimedia Commons</a> and <a href="https://youtu.be/ANKV4ssp570">on YouTube</a>. A version of the slides is available <a href="https://commons.wikimedia.org/wiki/File:How_Wikimedia_projects_can_assist_in_language_learning_-_OpenCon_Ohio_2024.pdf">on Wikimedia Commons</a> too.</div> <div> </div> <div>Abstract</div> <div> </div> <div>The Wikimedia ecosystem provides ample opportunities to engage with languages. For instance, users can explore linguistic information and acquire, practice, refresh or contribute language skills, all in a collaborative atmosphere and with lots of possibilities to customize the experience. The focus can be on a single language, on a group of languages or on cross-linguistic matters more generally, with a wide range of options regarding the subject matter (e.g. flowers, disasters or architecture) as well as the wiki (e.g. Bengali Wikisource or Polish Wiktionary or multilingual Wikidata). This contribution will provide a set of examples from dozens of languages and materials ranging from ancient scripts to games and scientific articles, from reading and writing via grammar to listening and speaking, from etymology to world literature and comparative linguistics.</div> <div> </div> <div>Learning Outcomes:</div> <ol> <li>overview of ways in which Wikimedia projects can facilitate language learning </li> <li>incorporate Wikimedia projects more specifically in their language learning or teaching</li> <li>leverage tools to enhance the language learning experience on Wikimedia platforms</li> </ol>
Valentwin: Using Self-Supervised Contrastive Learning on Language Model for Schema Matching Datasets
<div>ValenTwin is a schema matching framework that uses self-supervised contrastive learning to train the model, uses the model to generate embeddings of table columns, then uses different similarity measures to match the column embeddings.</div> <div> </div> <div> <div>We provide two types of zip files for the datasets:<br>1. `data.zip` contains the raw data files, the ground truth files, the sampled data (n=[100, 200, 300, 400, 500] used in the experiments, as well as the contrastive data used to train the model.<br>2. `data-raw.zip` contains only the raw data files and the ground truth files. You can sample the data and generate the contrastive dataset yourself by following step 1 and 2 in the `How to Run` section. <br>Download and unzip one of the zip files to the `data` folder.</div> </div>
Integrated Image-based Deep Learning and Language Models for Primary Diabetes Care
<h2><strong>Example data for the paper "Integrated Image-based Deep Learning and Language Models for Primary Diabetes Care"</strong></h2> <p><strong>Example data for the paper "Integrated Image-based Deep Learning and Language Models for Primary Diabetes Care"</strong></p>
Enhancing Name Entity Recognition Through Hybrid Deep Learning in Natural Language Processing
Open the record for dataset details and reuse information.
ViLCo: VIdeo Language COntinual learning Benchmark
<p>We introduce the first <strong>VIdeo Language COntinual learning Benchmark (ViLCo-Bench). </strong>Video language continual learning involves continuously adapting to information from video and text inputs, enhancing a model’s ability to handle new tasks while retaining prior knowledge. This field is a relatively under-explored area, and establishing appropriate datasets is crucial for facilitating communication and research in this field. In this study, we present the first dedicated benchmark, ViLCo-Bench, designed to evaluate continual learning models across a range of video-text tasks. The dataset comprises ten-minute-long videos and corresponding language queries collected from publicly available datasets.</p> <p>Additionally, we introduce a novel memory-efficient framework that incorporates self-supervised learning and mimics long-term and short-term memory effects. This framework addresses challenges including memory complexity from long video clips, natural language complexity from open queries, and text-video misalignment. We posit that ViLCo-Bench, with greater complexity compared to existing continual learning benchmarks, would serve as a critical tool for exploring the video-language domain, extending beyond conventional class-incremental tasks, and addressing complex and limited annotation issues.</p> <p>More detailed information can also be found on our url: https://github.com/cruiseresearchgroup/ViLCo</p>
Dataset: Cultural Openness and desire to learn regarding language education: systematic review
<p>Documental Dataset belonging to the Conference presentation titled:</p> <div> <p><strong>Cultural Openness and desire to learn regarding language education: systematic review. </strong></p> <p>The complete reference is: Peña-Acuña, B. (2024). Cultural Openness and desire to learn regarding language education: systematic review The thirty first international Conference of Learning. Utrech, The Netherlands. July 2024. </p> </div>
Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries in Software Package Ecosystems
<p>Replication package for the paper "Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries in Software Package Ecosystems "</p>
Datasets and Trained Models for "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"
<p>Datasets and Trained models for ICSE 2020 "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"</p>
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.