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18 results for “language comprehension”
The Alice Dataset: fMRI Dataset to Study Natural Language Comprehension in the Brain
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Data associated with the article 'Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis'
<p>The efficacy of oral language comprehension interventions varies, but the reasons for this variation have received little attention. A meta-analysis was conducted to examine intervention factors associated with the efficacy (as expressed with effect sizes) of oral language comprehension interventions in children under the age of 18 with or at risk for (Developmental) Language Disorder, (D)LD.</p> <p>The meta-analysis article together with this additional material comprise the content needed for a thorough understanding and replication of the results.</p> <p>This dataset is based on two systematic scoping reviews on oral language comprehension interventions (Tarvainen et al., 2020, 2021). Further information from the sourced articles was extracted for this study titled ‘Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis’. </p> <p>In the future, we hope that this data is used with a growing body of oral language comprehension interventions to conduct further and more detailed examinations of intervention factors associated with efficacy.</p> <p>References:</p> <p>Tarvainen, S., Launonen, K., & Stolt, S. (2021). Oral language comprehension interventions in school-age children and adolescents with developmental language disorder: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>6</em>, 1–24. https://doi.org/10.1177/23969415211010423</p> <p>Tarvainen, S., Stolt, S., & Launonen, K. (2020). Oral language comprehension interventions in 1–8-year-old children with language disorders or difficulties: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>5</em>, 1–24. https://doi.org/10.1177/2396941520946</p> <p> </p>
CausalBench A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models
<p>CausalBench is a comprehensive benchmark dataset designed to evaluate the causal reasoning capabilities of large language models. The primary uses of this dataset include, but are not limited to:</p> <p>- Testing the performance of large language models on causal reasoning tasks</p> <p>- Serving as a benchmark dataset for causal reasoning research</p> <p>- Improving and developing new causal reasoning algorithms and models</p>
Metrics of Collaboration Across Programming Paradigms and Languages: Comprehensive Dataset
<h3>[New version] : </h3> <p>Following a recent revision of our taxonomy of programming paradigms, we have observed slight changes in the data of our dataset. Consequently, to ensure accuracy and relevance, the dataset has been updated to reflect these changes. We encourage you to download the latest version to benefit from the updated data.</p> <h3>Description :</h3> <p>This dataset provides a comprehensive analysis of collaboration metrics across various programming paradigms and languages, aimed at researchers and practitioners interested in the interoperability and integration challenges within multi-language development environments. The data encapsulates both raw and cleaned metrics detailing potential collaboration avenues and interoperability between different programming paradigms, as well as between distinct programming languages, with and without redundancy.</p> <h3>Contents:</h3> <p>CollaborationMetric_Paradigms_Raw.csv - Contains the raw metrics of collaboration potential between programming paradigms based on a novel collaboration metric developed in our research. This dataset encompasses various paradigms' compatibility and integration potential, offering a wide-angle view on the landscape of programming paradigm interoperability.<br>CollaborationMetric_Paradigms_Cleaned.csv - A cleaned and refined version of the raw paradigm collaboration metrics, with redundant data removed for clarity and ease of analysis. This file is optimized for researchers seeking to delve directly into the significant findings of paradigm compatibility without the need for further data cleaning.<br>CollaborationMetric_Languages_Raw.csv - Offers raw collaboration metrics between different programming languages, providing insights into how languages may synergize or face integration challenges within multi-language projects. The data reflects an extensive analysis of language interoperability, grounded in the theoretical framework outlined in our associated research paper.<br>CollaborationMetric_Languages_Cleaned.csv - A streamlined version of the language collaboration metrics, free from redundancies, and focused on the core insights into language interoperability. This dataset is intended for immediate application in research and practical scenarios, facilitating quick identification of potential language synergies or conflicts.</p> <h3><br>Methodology:</h3> <p>The datasets were generated through a rigorous quantitative analysis, employing a collaboration metric that considers structural characteristics, kinship relationships, and fundamental computational and behavioural properties of programming paradigms and languages. Detailed methodology and theoretical underpinnings are described in our associated research paper, "[Towards a quantitative evaluation of paradigmatic collaboration: insights from Prolog and beyond]," intended for submission to the International Conference on Logic Programming (ICLP).</p>
SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models
<p>This repository contains the documentation, test datasets and scripts used in the following study:</p> <p>"SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models"<br><br>- `sonic-manuscript-master.zip` contains all the scripts to reproduce the study, including those for generating the figures and tables included in the manuscript.</p> <p>- <a href="../api/records/11361985/draft/files/sonicparanoid2.wiki.tar.xz/content" target="_blank" rel="noopener noreferrer">sonicparanoid2.wiki.tar.xz</a> contains a snapshot fo the wiki for SonicParanoid2 as of May 30, 2024</p>
Results and log of LLM-KG-Bench runs described in article "Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023
<p>Results and log of LLM-KG-Bench runs described in article ""Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023, to appear in proceedings for workshop DL4KG@ISWC 2023.</p> <p>For data on task FactExtractStatic please contact authors.</p>
Neurophysiological Mechanisms of Language Comprehension
ClinicalTrials.gov study NCT05283265. IPD Sharing: YES. Countries: 1. Publications: 12.
Neural dynamics differentially encode phrases and sentences in spoken language comprehension
<p>Data used to plot the summarized figures in the research article, <em>Neural dynamics differentially encode phrases and sentences in spoken language comprehension. </em></p>
Replication data for the paper "Leveraging Large Language Models for Comprehensive Psychological Analysis: Insights from Four Theoretical Frameworks"
<p>This is a replication data for the paper titled "Leveraging Large Language Models for Comprehensive Psychological Analysis: Insights from Four Theoretical Frameworks" submitted for a blind review.</p> <p>Abstract</p> <p>The rapid advancement of generative Artificial Intelligence (AI) has significantly transformed various research domains. This paper introduces a novel, fully automated methodology for applying Large Language Models (LLMs) to psychological text analysis. The approach includes prompt design for zero-shot and few-shot learning, model internal consistency analysis, autonomous machine evaluation, and additional human validation. Applied to four psychological theories—Self-Determination Theory, the Big Five Personality Traits, Psychological Well-being, and Cognitive Behavioral Therapy—this methodology is tested on a dataset of 25,780 emails written by a senior executive (called Person X) over 16 years. The analysis involves extracting psychological characteristics from the emails and regressing these characteristics against personal, professional, and environmental factors. The results demonstrate that the methodology provides unique insights into the examined psychological theories, offering a detailed understanding of how various factors influence psychological states and traits over time. This research highlights the potential of LLMs in capturing and analyzing complex psychological patterns in large text corpora, contributing a robust framework for future studies and practical applications in psychological assessment and intervention. The findings underscore the transformative impact of generative AI in psychological research, opening new avenues for understanding human behavior through advanced language models.</p> <p>The zipped file contains five csv files:</p> <ol> <li>Email_classification-csv: LLM (GPT-3.5 Turbo) classification of 25,780 emails for four psychological theories: SDT, Big Five, PWB and CBT.</li> <li>SDT_regression_data.csv</li> <li>Big_Five_regression_data.csv</li> <li>PWB_regression_data.csv</li> <li>CBT_regression_data.csv</li> </ol> <p>For 2-5 files the dependent variable is monthy percentage share of emails the were assigned a given value for categories of one of the four psychological theories analyzed. </p> <p>Linear regression model has been applied, where dependent variable is the percentage of emails in a specified category that assigned a specific value in this category. For example in Big Five Traits Model, for the Openness category, for each month we calculated percentage of emails that exhibit <em>High</em> or <em>Low</em> openness, or <em>None</em> if the content of the email does not provide enough information to assess whether the specific need is relevant. Two dependent variables were created: <em>Openness-high</em> and <em>Openness-low</em> and regressed on all independent variables. Regressions were not run for the <em>None</em> values.</p> <p>Descriptions of independent variables:</p> <p>- <em>income_index</em>: Person X salary income and consulting fees in a given month, normalized to [0,1].</p> <p>- <em>card_spending</em>: Person X credit card expenditures in a given month, normalized to [0,1].</p> <p>- <em>abroad_far</em>: dummy variable set to 1 for months when Person X worked in Central Asia</p> <p>- <em>abroad_near</em>: dummy variable set to 1 when Person X worked in other EU country</p> <p>- <em>death_1_war</em>: variable set to 1 in a month when Person X’ farther in law passed away. In the same month Russia invaded Ukraine. The variable was set to .75 in the following month, and to .5 in the month after that.</p> <p>- <em>death_2</em>: variable set to 1 in a month when Person X’ mother passed away. The variable was set to .75 in the following month, and to .5 in the month after that.</p> <p>- <em>court_case</em>: dummy variable set to 1 for months with the emotionally engaging inheritance court case involving other family members.</p> <p>- <em>BIG4_partner</em>: dummy variable set to 1 for months when Person X worked as a partner in BIG4 accounting firm, which resulted in adopting a professional activity sharply different from the usual Person X habits.</p> <p>- <em>AI_company</em>: dummy variable set to 1 for months when Person X worked as C-level executive at a company specializing in artificial intelligence.</p> <p>- <em>elections</em>: dummy variable set to 1 for months when Person X unsuccessfully run in parliamentary elections</p> <p>- <em>covid_lockdown</em>: dummy variable set to 1 for month where Polish government imposed tough measures during two covid lockdowns.</p> <p>- <em>no_receive</em>: number of different email recipients each month, normalized to [0,1].</p> <p>- <em>avg_length</em>: average number of words in emails sent each month, normalized to [0,1].</p> <p> While the email data was collected for January 2008 – March 2014 period, financial data was available from October 2009. There were some months where no emails with more than 10 words were sent, yielding 166 monthly observations used for regressions, before removing outliers.</p> <p> Independent variables were tested for multicollinearity, outlier months were removed, regressions were estimated with robust standard errors, and a range of standard tests were conducted for normality and autocorrelation of residuals, confirming good statistical properties of estimated models.</p> <p>Due to privacy concerns, the email texts cannot be publicly shared. However, the classifications of psychological categories derived from the email texts, along with all other relevant data, are made publicly available in this open access repository, with the consent of email author.</p> <p> </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>
Let's Know!2: Language-focused Intervention for Children at Risk of Comprehension Difficulties
ClinicalTrials.gov study NCT05133479. IPD Sharing: UNDECIDED. Countries: 1. Publications: 28.
GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench
<p>This is the full dataset of GPTCloneBench (version 2)</p>
How Simplified Language Affects Comprehension and Learning in Young Autistic Children
ClinicalTrials.gov study NCT05707923. IPD Sharing: UNDECIDED. 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.
Evaluating AI-Generated Plain Language Summaries on Patient Comprehension of Ophthalmology Notes Among English-Speaking Patients
ClinicalTrials.gov study NCT06859216. IPD Sharing: YES. Countries: 1. Publications: 0.
Language and Lewy Body Diseases: Sentence Comprehension Problems and Modifying Noninvasive Brain Stimulation Treatment
ClinicalTrials.gov study NCT05847985. IPD Sharing: Not stated. 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.
Assessment of Language Comprehension and Learning in Children Attending Child Psychiatry Services (TICOALA-SOINS)
ClinicalTrials.gov study NCT07283458. IPD Sharing: YES. Countries: 0. Publications: 0.
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.