Data Literacy and Academic Library Pedagogy

Back in my first year of PhD, I became curious about how academic libraries support their patrons' evolving information needs. While information literacy has been studied extensively, data literacy remains an area where many people continue to struggle, especially as generative AI and data-intensive tools become more integrated into everyday academic work. To me, data competency felt like an important and timely area to explore.

I began applying for IRB approval for a study with Heather Charlotte Owen, the Data Librarian at the University of Rochester (UR) Libraries and a close friend of mine. We both completed our MLIS degrees at Syracuse University and share a strong passion for coding and data processing. Heather contributed significantly in organizing UR Libraries’ data events, trainings, and workshops that serve students, faculty, staff, and the broader Rochester community. Together, we designed a follow-up survey for participants who attended these workshops, aiming to measure their progress in data access, retrieval, interpretation, and reuse.

Over the course of a year, we were fortunate to receive more than 100 valid responses from students, faculty and staff, and members of the Rochester community. Their honest and anonymous reflections allowed us to examine how data literacy training affects people’s confidence and emotional experience with data. Our analysis showed a clear pattern: participants who attended these workshops not only improved their data literacy, but also experienced lower cognitive overload, the mental strain of processing too much information at once. In turn, this reduction in cognitive overload led to a meaningful decrease in information anxiety, the stress people feel when they cannot access, understand, or manage the information they need. The signs were clear: learning how to navigate data made library patrons feel more capable and less overwhelmed.

We also found that different groups benefited in different ways. Students and participants in health or social professions tended to show the strongest gains in confidence after training, while STEM professionals and some faculty/staff sometimes became more aware of their own limitations—an important reminder that data literacy support must be tailored to diverse needs. Still, across all groups, the evidence was clear: academic libraries play a vital role in helping people build data skills and reduce the anxieties that often accompany data-intensive environments.

As academic libraries continue to expand their data services, there is a growing need to explore how creative and user-centered pedagogy can make data literacy training even more effective. Not all learners engage with data in the same way, and not all disciplines face the same challenges. Understanding these needs will be essential for helping patrons navigate an increasingly data-driven and AI-augmented world with confidence rather than anxiety.

Some of our findings were accepted to the Association for Information Science and Technology (ASIS&T) 2025 annual conference in Washington D.C. Here is a glimpse to our poster and submission:

https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/pra2.1467

Using Gene Ontology and ML Models for Gene-Disease Association Identification

Genomic data in large genomic knowledgebases (KB) such as Gene Ontology (GO) are being integrated into clinical diagnosis and disease prediction. This type of integration is particularly useful in predicting complex diseases with highly heterogeneous genotypes that make biological marker identifications difficult. Several types of machine learning (ML) models have been applied to identify relatively small number of disease-associated genetic sequence amongst the large number of common variants carried by an individual [1]. Biomedical knowledge organization systems (KOSs) containing relationships between variants, genes, and diseases promise higher precision and performance of ML models.

I use GO as a case study of biomedical KOS that provides rich annotation data in directed acyclic graph (DAG) structure as train dataset for ML algorithms to identify potential disease-triggering gene products. Different from approaches in bioinformatics, my research focus is not on designing models and packages. Rather, I discuss the data quality, ontology structure, crosslink with external resources e.g. Disease Ontology, to evaluate the current design of KOS for biological research, which applies theories in knowledge organization and library science. Past findings on this area were contributed by either bioinformatics or computer science scholars. My role is to reveal the importance of knowledge work in bridging these two communities, and discuss the usage of ontology data in LLM to achieve trustworthiness and precision.

Currently, I test collecting GO annotation data to identify potential gene products that may be associated with Autism disease using ML algorithms - Random Forest, Support-Vector Machine, and Gradient Boosting. A demo of this preliminary step will be presented at the DCMI 2024 NKOS workshop (https://www.dublincore.org/conferences/2024/sessions/nkos-workshop/).

A Social Network Analysis on Climate Change Polarization

In 2023 I began to learn and use social network analysis (SNA) for science of science (SoS) research in the Metadata Lab. I grew some interest in this method and to complete my research practicum, I conducted a project on climate change skeptics and believers on YouTube. I used SNA to plot the users who commented on videos and others on whether to believe or deny anthropogenic climate change.

I collected data on YouTube comments using the free YouTube API key of videos that agree or deny climate change. This polarized topic has gained popularity among scientists, non-scientists, influencers, politicians, etc. even though there is evidence of human-caused global warming. It is an interesting issue because people are skeptical of climate change for one or multiple reasons:

(1) Knowledge

(2) Critical reasoning

(3) Political/Socio- Identity

And possibly (4) The value of good science inquiry, which denies some scientific evidence as valid and trustworthy due to it not meeting standards of objective, rigorous scientific research process. Currently, I am trying to study the 4th reason on a group of scientists who claim to be climate change deniers, or at least skeptics.