Princeton Research Data Service (PRDS) is a joint initiative between the Office of the Dean for Research, the Office of the University Librarian, and Office of Information Technology with support from the Office of the Provost. Hosted by the Princeton University Library, the initiative was launched in Spring 2019 to provide the Princeton research community with the expert services and infrastructure needed to store, manage, retain, and curate digital research data, and to make research data available to the broader research community and to the public.
We provide researchers with crucial resources throughout the lifecycle of their research projects, working with them to make the process of data management and storage as seamless as possible with their current research projects.
Meet the PRDS Team
Director of Data, Research, and Teaching Services
Head, Princeton Research Data Service
Research Data Services Manager
Matt is a dedicated proponent of open research practices across disciplines, with expertise in the social sciences and experience with data management in a variety of settings. He has a PhD in Sociology and Peace Studies from the University of Notre Dame, where his research focused on contentious political transitions and social network dynamics. He also has a background in the humanities, having concentrated on philosophy and religious studies as an undergraduate. Before joining the Princeton Research Data Service in November 2019, Matt completed a postdoctoral fellowship through the Interdisciplinary Center for Network Science and Applications at Notre Dame, where he was a core researcher and data manager for the NetHealth Project, a large study funded by the National Institutes of Health (NIH).
Research Data Management Specialist
Neggin Keshavarzian is a passionate advocate of open science. Before joining the Princeton Research Data Service Neggin worked as a research specialist and data curator at the Princeton Neuroscience Institute. She curated large neuroimaging datasets and worked on multiple research projects in Dr. Ken Norman's Computational Memory Lab. Neggin received a MSc in Psychology from Drexel University with a focus on statistics and neuroimaging data analysis and received a BA in psychology from CSU Channel Islands in sunny southern California.
Research Data Management Specialist
Prior to joining the Princeton Research Data Service, Halle worked as the Data Librarian at the University of Nevada, Las Vegas, focusing on establishing and building out programming related to data management, open science, and data literacy. Halle received an MSLIS from University of Illinois at Urbana-Champaign with a focus on data management and curation and received BAs from the University of Rochester in Psychology and English (with a concentration in Theatre). She is also a Certified Carpentries Instructor. Halle is very excited to support data management and open research practices across the Princeton University community.
Sarah Reiff Conell
Research Data Management Specialist
Sarah earned her Ph.D. in the History of Art and Architecture from the University of Pittsburgh in 2022. Her research involved collecting and exploring data from stories about miracles that were attributed to the Virgin Mary in 16th Century Bavaria and Austria. While in Pittsburgh, she managed a variety of digital projects run out of the Visual Media Workshop, including the long-term digital project, Itinera. She has worked with collections data while at the National Gallery in Washington, DC - both as a curatorial intern in the Old Masters Prints and Drawings Department and as a participant in the 2019 “Coding Our Collection” Datathon.
Research Data Management and Storage Engineer
Rishi's focus is in enabling research by applying supercomputing architecture to new areas of need. Before joining Princeton’s Advanced Systems and Storage team, Rishi was fortunate to work on the largest private and public clusters as a specialty platform engineer, where he found a passion in bringing HPC infrastructure and tools to various new AI, research, and media platforms previously facing bottlenecks. Now at Princeton, his goal is to strategically architect storage clusters to handle the next generations of diverse research workloads. Rishi received his MS in Information Assurance and Cybersecurity.
Princeton Institute for Computational Science and Engineering (PICSciE)
Princeton Research Computing is a consortium of campus groups dedicated to providing computing resources to the Princeton University community led by the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology (OIT) Research Computing. Together we provide a central hub on campus for cutting-edge computational and data science infrastructure and support, including HPC hardware, software, system administration, cloud computing, training and education, research software engineering, programming, and visualization.
Center For Digital Humanities
The Center for Digital Humanities (CDH) is an interdisciplinary research center devoted to building knowledge infrastructures for the use of digital evidence, fostering communities of practice across disciplines and professions, and modeling critical approaches to the role of data and technology in research and daily life. Our team consists of developers, designers, scholars, writers, and teachers.
The CDH conducts consultations on digital research methods and offers various funding opportunities throughout the academic year. We are especially interested in hearing from researchers interested in machine learning in the humanities, library collections as data, or public humanities and community partnerships.
Center For Statistics and Machine Learning
The Center for Statistics and Machine Learning (CSML) fosters and supports a community of scholars addressing the challenges of modern algorithmic data-driven research, the development of innovative methodologies for extracting information from data across different domains, and the education of students in the foundations of data science.
The center fulfills this via support of research and teaching that harnesses insights from computation, machine learning, and statistics, to advance both theoretical foundations and scientific discovery. The center offers courses, graduate and undergraduate certificate programs, and special initiatives for students. The center also co-sponsors seminars that enhance researchers’ knowledge base and workshops that deepen the use and reach of data science on campus. The center supports a research software engineer to help multiple research projects across all divisions of the university. With a generous gift, CSML also supports research seed grants, workshops, and educational support under the DataX initiative. Undergirding all these efforts is the center’s open collaborative, interdisciplinary nature. We work with students, faculty, researchers, departments, centers, and others on and off-campus.
Initiative for Data-Driven Social Science
The Initiative for Data-Driven Social Science (DDSS) was launched in 2019 to enhance support of data-, computation-, and IT-intensive social science research at Princeton. Our goal is to build a multidisciplinary community of faculty, graduate and postdoctoral fellows to foster both resource sharing and collective problem solving. Our initiative works with partners across the University to encourage innovation and better position Princeton as a leader in data-driven social science inquiry.
DDSS offers funding opportunities for research, infrastructure building, and data collection/wrangling, and our team provides technical, analytic, and administrative support to help facilitate the use of ‘big data’ in academic research, with a particular focus on the social sciences.
Research Software Engineering Group
The Research Software Engineering (RSE) group sits within the Research Computing Department in OIT. Our mission is to help researchers associated with our partner organizations create the most efficient, scalable, and sustainable research codes possible in order to enable new scientific advances. We do this by working as an integral part of traditional academic research groups, providing leadership in the design and construction of complex and highly customized software systems. We can support sophisticated data science and computational research projects in a variety of domains.
We provide our partner groups with domain-specific algorithms and solution techniques; optimization and performance tuning; and insights and guidance with current and future software development tools, programming languages, and high-performance computing hardware.
Our group is committed to creating a collaborative environment in which best software engineering practices are valued, and to sharing and applying cross-disciplinary computational techniques to new and emerging areas.
Center for Information Technology Policy
The Center for Information Technology Policy (CITP) is an interdisciplinary center at Princeton University. The Center is a nexus of expertise in technology, engineering, public policy, and the social sciences on campus. The Center’s research, teaching and events address digital technologies as they interact with society. It produces both leading research and practical demonstrations of issues at the crossroads of technology and policy.
CITP brings in a diverse community of researchers, visiting faculty and practitioners through its fellows program each year. CITP also serves students on campus in many ways, including offering an undergraduate certificate program, funding for internship programs and other student oriented events and funding opportunities. In addition to this, CITP runs a technology policy clinic that aims to stimulate cutting-edge research at CITP and engage students and scholars in pragmatic policy discussions concerning emerging digital technologies.