2875 research outputs found
Sort by
Pathological and Anatomical Repository Training Networks (PART Net)
No full textThe PART Net database is an extension of René Sigrist's Base de données sur les savants de la période 1700-1870 DOI:10.5281/zenodo.3956437 with a focus on medical museums. It includes six tables with information on savants, disciplines, liens (training links), infrastructures (museums and multi-museum holding institutions), excluded infrastructures, and museum personnel. The database includes information on 106 medical museums and 410 individuals who had 441 roles at 95 of these museums. The training history portion of the database includes information on 11,431 savants and 3,709 training links
Replication Data for: Co-Electrospinning Extracellular Matrix with Polycaprolactone Enables a Modular Approach to Balance Bioactivity and Mechanics of a Multifunctional Bone Wrap
No full textRaw data and analysis for "Co-Electrospinning Extracellular Matrix with Polycaprolactone Enables a Modular Approach to
Balance Bioactivity and Mechanics of a Multifunctional Bone Wrap
Data for Characterization of the Repeat Unit Sequences of Poly(lactide-co-glycolide)
No full textRaw data for the paper titled "Characterization of the Repeat Unit Sequences of Poly(lactide-co-glycolide
Replication Data for: Sex-specific proteomic analysis of epileptic brain tissues from Pten KO mice and human refractory epilepsy
No full textThis dataset contains mass spectrometry raw data from human brain and mouse brain tissue samples, collected to investigate differential protein expression between epileptic cases and controls
Modeled Stresses 2023 Coalson: High Perm. Models
No full textGIST models for 2023 Coalson; high perm. model parameterizatio
Data for: "Dissolution and Flow Channeling in Hydrate-Bearing Sediments: Implications for Permeability"
No full textData for: "Dissolution and Flow Channeling in Hydrate-Bearing Sediments: Implications for Permeability
Carbon dioxide simulations
No full textInput data, simulation files, and output results for simulation of CO2 droplets / bubbles using the Texas A&M Oil spill / outfall Calculato
Suicide Prevalence In The US: Identifying Risk Factors and Taking Data Driven Decisions
No full textThe Youth Risk Behavior Surveillance System (YRBSS) is a set of surveys that monitor priority health risk behaviors and experiences that contribute markedly to the leading causes of death, disability, and social problems among youth of grade 9 -12 in the United States. The surveys are administered every other year and it is maintained by the Centers for Disease Control and Prevention (CDC). A total of 107 questionnaire are asked. Some of the health-related behaviors and experiences monitored are:
* Student demographics: sex, sexual identity, race and ethnicity, and grade
* Youth health behaviors and conditions: sexual, injury and violence, bullying, diet and physical activity, obesity, and mental health, suicide attempt
* Substance use behaviors: electronic vapor product and tobacco product use, alcohol use, and other drug use
* Student experiences: parental monitoring, school connectedness, unstable housing, and exposure to community violence
The dataset is used by a group of graduate students from Texas State University for 2025 TXST Open Datathon. The main YRBSS dataset includes data of multiple years, various states, district. For analyzing demographic variations associated with suicide, the 1991–2023 combined district dataset (https://www.cdc.gov/yrbs/files/sadc_2023/HS/sadc_2023_district.dat) is used, which offers a broad historical perspective on trends across different groups. To examine the preventive measures and develop a predictive model for suicide risk, the 2023 dataset (https://www.cdc.gov/yrbs/files/2023/XXH2023_YRBS_Data.zip) was used, ensuring the inclusion of the most recent behavioral and attributes.
Please review the 2023 YRBS Data User's Guide by CDC for further information
Behind the Ratings: Investigating Biases in the Student Evaluation on Rate My Professor
No full textThis project investigates the systemic biases embedded in student evaluations on Rate My Professors (RMP) and their impact on perceptions of teaching quality. Millions of students use RMP to choose courses, but do these ratings truly reflect teaching effectiveness? Our research highlights how biases such as grading leniency, sampling bias, and review extremity bias distort professor evaluations, resulting in misleading assessments.
Using a dataset of 20,000 reviews from over 1,400 professors across 500 universities, we applied quantitative analysis and statistical methods to uncover patterns and correlations. We found that:
Professors labeled as “Tough Graders” received significantly lower ratings regardless of teaching quality (Grading Leniency Bias).
75% of professors had 41 or fewer reviews, indicating that a small percentage of highly-reviewed professors dominate student perceptions (Sampling Bias).
Professors with fewer displayed the highest variability, leading to more extreme ratings (Review Extremity Bias).
Our findings have important implications: students may be misled into avoiding effective professors, while strict or experienced educators suffer reputational harm. To address these issues, we propose solutions such as integrating university evaluations, implementing mandatory RMP reviews, introducing weighted rating systems, and launching student awareness campaigns to promote fair and constructive feedback.
Through this research, we aim to raise awareness of biases in online teaching evaluations and propose actionable solutions for a more transparent and reliable student feedback system
Endothelialized Vessel-Chips and Cell Morphological Analysis
No full textZ-stack confocal micrographs of endothelialized vessel-chips in .oir (Olympus) format. The immunohistochemistry stainings include VE-Cadherin for tight junctions between endothelial cells and DAPI for nuclei. Cell orientation and morphological measurements in xlsx file, and the calculated cell shape index plotted in GraphPad Prism file. Supplementary movies of 3D visualization of endothelialized vessels in .mp4 format