2875 research outputs found
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Drosophila pseudoobscura population genomics
No full textProcessed data and example code for analyzing population genomic data from Drosophila pseudoobscur
UvaHVC eGtACR1 somatic vs. axonal stimulation data
No full textSong data from Trusel et al., Nature 2025, UvaHVC eGtACR1 somatic vs. axonal stimulation dat
Student AI usage and analysis
No full textAnalyzing student AI tool usage and its impact on academic performance, including study patterns and AI tool preferences
Neuronal inhibition of ventral pallidum cells bidirectionally modulates heroin-seeking in mice_Raw data
No full textRaw data for manuscript "Neuronal inhibition of ventral pallidum cells bidirectionally modulates heroin-seeking in mice" by Owona et al
Degree_industry_enrollment_readiness_with_students_at_risk
No full text
Degree vs Destiny: Mapping the Gap Between Academic Training and the AI-Integrated Job Market
Project Overview
This project analyzes how well Texas bachelor’s degree programs prepare students for an AI-driven workforce.
We used public datasets and a deterministic, rules-based Python pipeline to identify students at risk of
graduating into high-exposure occupations without AI curriculum support.
Objectives:
Identify students enrolled in high-exposure programs at universities lacking AI integration.
Highlight gaps in university readiness for AI-driven labor market changes.
Provide actionable insights for universities, advisors, and policymakers.
Datasets
Source
Data Used
Purpose
Census PSEO
Bachelor’s program records, median earnings (1- and 5-year), post-graduation industry flows
Map degrees to workforce outcomes
NCES 2020 CIP–SOC Crosswalk
CIP codes linked to SOC occupations
Translate academic majors into likely careers
O*NET
Occupation-level AI Exposure Scores (0–10)
Measure automation risk for each occupation
THECB Enrollment Data
2025 enrollment counts for 10 major Texas public universities
Count students per program
Manual Readiness Audit
Review of 2025–2026 course catalogs
Label programs as READY or NOT READY for AI curriculum
Methodology
All analysis was deterministic and rules-based using Python (Pandas). No machine learning was used.
Data Standardization: Filtered bachelor’s programs and standardized CIP codes.
Degree-to-Occupation Mapping: Linked each major to its primary occupation via the NCES crosswalk.
AI Exposure Assignment: Merged O*NET AI Exposure Scores for each occupation.
Risk Classification: Occupations with AI exposure ≥ 6.5 labeled High Exposure.
Enrollment Integration: Added THECB enrollment counts per program.
Curriculum Readiness Audit: Programs labeled READY if AI coursework or certificates exist,
otherwise NOT READY.
Final Metric – Students at Risk: Students in programs where AI Exposure ≥ 6.5 and
Institutional Status = NOT READY.
Results
At Risk Students: ~33,000 students (19.4% of the sample) are in high-exposure programs at Not Ready institutions.
AI Exposure Distribution: Scores range from 2 to 9, mean ~5.7. High exposure is concentrated in Business, Communication, and Liberal Arts majors.
Key Findings
Readiness Divide: Tier 1 universities like UT Austin and Texas A&M have integrated AI literacy, while others remain unprepared.
Critical Risk at TXST and SHSU: High enrollment in high-exposure programs without AI curriculum.
Structural Misalignment: Degree programs are still oriented toward the 2020 labor market, leaving students unprepared for AI-driven 2026 workflows.
Implications
Universities should integrate AI literacy and practical AI tools across high-exposure majors.
Institutional leaders can use at-risk counts to prioritize AI coursework and curriculum updates.
Academic advisors can guide students toward complementary skills, minors, or micro-credentials.
Policymakers can target support toward institutions with the largest gaps between AI exposure and curriculum readiness.
Files
Dataset – Final merged dataset used for analysis
data_dictionary.tab – Definitions and descriptions of all variables in the dataset
Mapping the Gap Between Academic Training and the AI Integrated Job Market – Project poster
README.md – This file
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Rim4-seeded stress granules connect temperature sensing to meiotic regulation
No full textMeiosis is more vulnerable to heat than mitosis in many species including humans. In Saccharomyces cerevisiae, we discovered that stress granule formation halts meiosis at high temperatures. Meiotic stress granules appear at lower temperatures (33–42 °C) than mitotic stress granules (~46 °C), requiring the meiosis-specific RNA binding protein Rim4. Heat triggers site-specific Rim4 dephosphorylation, causing it to self-assemble into stress granule seeds. These recruit other stress granule components like Pab1 and mRNAs, pausing meiosis. Normally, 14-3-3 proteins block this assembly by binding phosphorylated Rim4. After temperature drops, Hsp104 assists to break down stress granules. Longer stress granule persistence correlates with better recovery, suggesting stress granules might provide temporal insulation for cellular repair processes prior to meiotic resumption
Biodegradable Poly(ε-caprolactone)/Poly(silyl fumarate) Shape Memory Scaffolds
No full textCompiled data files and images for Polymer. Submission (2025-11-04
rECM Spatial Sequence Dermal Wound Healing
No full textThis Spatial Sequencing data set contains information from sequencing of two-11 day mouse skin wounds for identification of healing processes
Low-income caregivers with toddlers fruit and vegetable shopping behaviors
No full textCross-sectional survey of caregivers with toddlers about their shopping behaviors regarding fruits and vegetables
Graduate program enrollment since 2022
No full textThis is to visualize the rate of enrollment within the higher academic institutions within the United States of America