JeSLIB is an open access, peer-reviewed journal that advances the theory and practice of librarianship focusing on services related to data-driven research in science, technology, engineering, math, social sciences, medicine, and public health.
Qin was noted for her work as a guest editor for the journal’s special edition covering data visualization, published earlier this year.
The journal’s January issue, Visualizing the (Data) Future, focused on the evolution of libraries and academic institutions as data-driven organizations, as they look for meaningful ways to convey their data to funding and regulatory agencies, licensing and accreditation boards, and institutional students, faculty and staff. This data-driven culture, Qin explains, is integrated into all facets of library operations, and data visualization services are emerging as a distinct library research and service development area.
JeSLIB also recognized Qin for her contributions to the journal through her two submitted papers, Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics, and Mentoring for Emerging Careers in eScience Librarianship: An iSchool – Academic Library Partnership, which have received a combined 1,300 downloads since their publication.
Qin also shared her multiple experiences at the University of Massachusetts and New England Area Librarian eScience Symposium, as well as her current efforts in library science and data science education in a special video reflection for the journal.
At the iSchool, Qin’s areas of research interest include metadata; knowledge and data modeling; scientific communication; research networks; and research data management. She has received funding from the Institute of Museum and Library Services to develop an eScience librarianship curriculum and from the National Science Foundation (NSF) for the Science Data Literacy project.
Her recent research projects include metadata modeling for gravitational wave research data management and big metadata analytics using GenBank metadata records for DNA sequences, both with funding from NSF.