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112
datasets available to search
ShareScore release 0.9.0
Dataset results
112 results for “language learning”
Retrieval-based Word Learning in Developmental Language Disorder: Adaptive Retrieval Schedule
ClinicalTrials.gov study NCT06995014. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Category Learning Retention in Adults With and Without Developmental Language Disorder
ClinicalTrials.gov study NCT05901493. IPD Sharing: YES. Countries: 1. Publications: 0.
Using a New Calibrated Tool in Specific Language and Learning Disorders : the BMTi
ClinicalTrials.gov study NCT03919357. IPD Sharing: NO. Countries: 2. Publications: 0.
Study of the Value of Trio Exome Sequencing in the Etiological Assessment of Specific Non-syndromic Language and Learning Disorders
ClinicalTrials.gov study NCT05939739. IPD Sharing: NO. Countries: 1. Publications: 0.
Contextual Word Learning in Children With Developmental Language Disorder
ClinicalTrials.gov study NCT05988190. IPD Sharing: YES. Countries: 1. Publications: 0.
Optimizing Feedback-based Learning in Children With Developmental Language Disorder
ClinicalTrials.gov study NCT05678634. IPD Sharing: NO. Countries: 1. Publications: 0.
Machine Learning to Analyze Facial Imaging, Voice and Spoken Language for the Capture and Classification of Cancer/Tumor Pain
ClinicalTrials.gov study NCT04442425. IPD Sharing: YES. Countries: 1. Publications: 0.
LAnguage-Motor Imagery Circuits to Improve Motor Learning and Language Comprehension
ClinicalTrials.gov study NCT06478303. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Motor Language Learning
ClinicalTrials.gov study NCT03223090. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Retrieval-Based Word Learning in Developmental Language Disorder During Book Reading II
ClinicalTrials.gov study NCT07048392. IPD Sharing: YES. Countries: 1. Publications: 0.
Language Learning and Language Experience
ClinicalTrials.gov study NCT06645145. IPD Sharing: YES. Countries: 1. Publications: 0.
How Simplified Language Affects Comprehension and Learning in Young Children With Down Syndrome
ClinicalTrials.gov study NCT06951516. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Exploring Context-Free Languages via Planning: The Case for Automating Machine Learning -- Additional Plots
<p>A comparison to SOTA AutoML approach on various datasets</p>
Generated Learning Content for 200 Online Language Learning Units employing GPT3.5
<p>This dataset includes 200 units of learning content in German generated utilizing GPT3.5, employed in an evaluation study to examine correctness and appropriateness to be used in an online language learning course.</p> <p>The dataset contains:</p> <ul> <li>Nouns (A-H),</li> <li>Verbs (a-d),</li> <li>Sentences (Aa-Da, ABb, CDc, ACa, BDa)</li> <li>A text containing nouns, and verbs</li> </ul> <p>of random topics. Topics were selected using a random surfer model. <br>Hence, the language proficiency level varies.</p> <p>Here is an example:</p> <table> <tbody> <tr> <td> <p>ID</p> </td> <td> <p><strong>Food (“Essen”)</strong></p> </td> <td> <p><strong>Fruits (“Obst”)</strong></p> </td> </tr> <tr> <td> <p>A</p> </td> <td> <p>Brot</p> </td> <td> <p>Banane</p> </td> </tr> <tr> <td> <p>B</p> </td> <td> <p>Apfel</p> </td> <td> <p>Mango</p> </td> </tr> <tr> <td> <p>C</p> </td> <td> <p>Fleisch</p> </td> <td> <p>Zitrone</p> </td> </tr> <tr> <td> <p>D</p> </td> <td> <p>Wurst</p> </td> <td> <p>Grapefruit</p> </td> </tr> <tr> <td> <p>E</p> </td> <td> <p>Ei</p> </td> <td> <p>Wassermelone</p> </td> </tr> <tr> <td> <p>F</p> </td> <td> <p>Kartoffel</p> </td> <td> <p>Erdbeere</p> </td> </tr> <tr> <td> <p>G</p> </td> <td> <p>Toast</p> </td> <td> <p>Melone</p> </td> </tr> <tr> <td> <p>H</p> </td> <td> <p>Gurke</p> </td> <td> <p>Limette</p> </td> </tr> <tr> <td> <p>a</p> </td> <td> <p>schneiden</p> </td> <td> <p>schälen</p> </td> </tr> <tr> <td> <p>b</p> </td> <td> <p>backen</p> </td> <td> <p>einfrieren</p> </td> </tr> <tr> <td> <p>c</p> </td> <td> <p>grillen</p> </td> <td> <p>pressen</p> </td> </tr> <tr> <td> <p>d</p> </td> <td> <p>würzen</p> </td> <td> <p>mixen</p> </td> </tr> <tr> <td> <p>Aa</p> </td> <td> <p>Ich schneide das Brot.</p> </td> <td> <p>Ich schäle die Banane.</p> </td> </tr> <tr> <td> <p>Ba</p> </td> <td> <p>Ich schneide einen Apfel.</p> </td> <td> <p>Ich schäle eine Mango.</p> </td> </tr> <tr> <td> <p>Ca</p> </td> <td> <p>Ich schneide das Fleisch.</p> </td> <td> <p>Ich schäle die Zitrone.</p> </td> </tr> <tr> <td> <p>Da</p> </td> <td> <p>Die Wurst schneiden.</p> </td> <td> <p>Ich schäle die Grapefruit.</p> </td> </tr> <tr> <td> <p>ABb</p> </td> <td> <p>Ich backe Brot und esse dazu einen Apfel.</p> </td> <td> <p>Ich kann Banane und Mango einfrieren, wenn ich sie nicht gleich esse.</p> </td> </tr> <tr> <td> <p>CDc</p> </td> <td> <p>Ich will heute Abend Fleisch und Wurst auf dem Grill grillen.</p> </td> <td> <p>Ich presse die Zitrone und die Grapefruit für den Saft.</p> </td> </tr> <tr> <td> <p>ACa</p> </td> <td> <p>Ich schneide Brot und Fleisch für das Sandwich.</p> </td> <td> <p>Ich schäle Bananen und Zitronen zum Frühstück.</p> </td> </tr> <tr> <td> <p>BDa</p> </td> <td> <p>Ich schneide den Apfel und die Wurst.</p> </td> <td> <p>Ich muss die Mango und Grapefruit schälen.</p> </td> </tr> <tr> <td> <p>Text</p> </td> <td> <p>Ich backe gerne frisches Brot.</p> <p>Dazu schneide ich eine Gurke.</p> <p>Auf dem Grill brate ich Fleisch.</p> <p>Das Wurstbrot schmeckt auch lecker.</p> <p>Ein Ei gehört auf jedes Toast.</p> <p>Mit Kartoffeln mache ich Pommes.</p> <p>Ein Apfel rundet das Frühstück ab.</p> <p>Ich würze das Steak mit Salz.</p> <p>Eine Wurst kann man auch grillen.</p> </td> <td> <p>Ich liebe Obst.</p> <p>Heute kaufe ich Bananen, Mangos und Zitronen.</p> <p>Auch Grapefruits und Wassermelonen sind im Angebot.</p> <p>Ich schäle die Bananen und friere sie ein.</p> <p>Die Mango presse ich zu Saft.</p> <p>Die Zitronen verwende ich für Limonade.</p> <p>In den Mixer kommen Erdbeeren und Melone.</p> <p>Eine Limette verleiht dem Smoothie den letzten Kick.</p> </td> </tr> </tbody> </table> <p>The study conducted can be found here: Rüdian, Sylvio; Pinkwart, Niels (2023): Auto-generated language learning online courses using generative AI models like ChatGPT. 21. Fachtagung Bildungstechnologien (DELFI). DOI: 10.18420/delfi2023-14. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-732-6. pp. 65-76. Best-Paper-Kandidaten. Aachen. 11.-13. September 2023</p>
USING VIRTUAL REALITY TO BOOST AUDITORY CAPABILITIES IN LANGUAGE LEARNING
Open the record for dataset details and reuse information.
Figure 4 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 4 Egger's regression intercept.
Figure 3 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 3 Funnel plot of oral language outcomes with missing studies imputed
Figure 11 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 11 Egger's Regression for Other Academic Outcomes
Figure 2 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 2 Forest plot of oral language outcomes.
Figure 7 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 7 Funnel plot of written language outcomes with missing studies imputed.
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.