Research Data Management

Dr. Xuan Zhou received her Ph.D. degree in Educational Psychology and a graduate certificate in Advanced Research Methods in Social Science from Texas A&M University, College Station. In her current role as a Data Curation Specialist at Texas State University, she supports the research and scholarly community by enhancing the accessibility, usability, and preservation of research data. She provides consultations on data management planning, data organization, and repository selection, helping faculty and students prepare their data for long-term stewardship and sharing. She also develops instructional materials and leads workshops on best practices in data management and curation, with a focus on compliance with federal and institutional policies.

Xuan has authored several publications in the field of RDM and regularly presents her research and professional activities at national and state-level conferences, including the Association of College & Research Libraries (ACRL) and the Texas Conference on Digital Libraries (TCDL). Xuan currently chairs the TXST Annual Open Datathon Committee, leading campus-wide efforts to promote open data literacy and engagement. She also serves as co-editor-in-chief of the Journal of Open Initiatives at Academic Libraries. Additionally, Xuan is the current Chair of the Texas Data Repository Steering Committee, where she helps facilitate statewide efforts to support open access and data preservation in academic institutions. Visit the TXST faculty profile page to know more.

Research Interests

Xuan’s research interests include research data management, with a focus on data management planning, data organization and curation, data analysis and visualization, data sharing and publishing, and open data science. She is also interested in STEM education, STEM teacher training, learning engagement, and educational program evaluation.

Skills

Xuan brings expertise in data management planning, the use of DMPTool, and managing data within the TXST Dataverse Repositories. She is skilled in data analysis and visualization in the social sciences and is a strong advocate for data literacy instruction. She also oversees research data needs and leads/coordinates workshops at Texas State University, supporting faculty and students in developing sustainable and effective data practices.

Sumit is currently a junior double majoring in Computer Science and Applied Mathematics, with a minor in Data Analytics. He works at Alkek Library in the Research & Data Services department as a Data Analysis & Visualization Student Assistant. In this role, he supports students, faculty, and staff with all stages of data-driven research — from cleaning and organizing data to creating clear visualizations and performing advanced statistical modeling.

Sumit also helps manage the library’s DataSpace, ensuring visitors can use the space and its tools effectively. To make resources more accessible, he maintains tutorials, FAQs, and documentation, and contributes to workshops and outreach events. He enjoys collaborating with the team to streamline workflows and improve the overall user experience.

Outside his library work, Sumit is the President of the Texas State Math Club, where he organizes events and helps build a strong math community.

Research Interests

Sumit is deeply interested in Data Science, Machine Learning, Statistical Modeling, Artificial Intelligence, and Computational Mathematics. What fascinates him most is how computers can be designed to mimic human-like thinking and decision-making. He enjoys exploring the mathematical and computational principles that power intelligent systems and is always curious about how theory translates into real-world applications.

Skills

Programming & Tools: Python (pandas, NumPy, scikit-learn, PyTorch), R, SPSS, SQL, C/C++, Git, Linux (basic HPC)

Data Visualization: Matplotlib, Seaborn, MATLAB, Tableau, Power BI, MANIM (3b1b), Excel

Statistics & Machine Learning: Hypothesis testing, regression, ANOVA, classification, Random Forest, XGBoost, CNNs, RNNs, Feature Engineering, Model evaluation