Metadata Lab page2023-01-12T20:51:57-05:00

Metadata Lab

Metadata Lab is a research group led by Professor Jian Qin that studies a wide range of topics related to metadata with two focus areas: big metadata analytics and metadata modeling and linking.

Big metadata analytics focuses on understanding scholarly communication processes by using metadata from data repositories and other databases as the source to investigate the structures and dynamics of collaboration and intellectual networks as well as the impact of such networks on scientific capacity and knowledge diffusion. Projects in this focus area include Discovering Collaboration Network Structures and Dynamics in Big Data and Cyberinfrastructure-Enabled Collaboration Networks.

Metadata modeling and linking is another research area that takes a different approach in examining big metadata residing in data repositories. By tracking and analyzing how research data in different repositories and/or different stages of a research lifecycle are related and linked, we develop models to represent domain knowledge networks for metadata applications. Projects in this focus area include metadata modeling for gravitational wave research data management, metadata portability, and relation typology.

Pathways for new discovery

The projects employ a wide variety of methods and tools, which are often highly computational, sometimes at a very large scale. Research in both big metadata analytics and metadata modeling and linking creates pathways for the two to dive deeper in the networks of collaboration and knowledge diffusion and benefit each other’s pursuit for new discoveries and knowledge.



Qin, J., J. Hemsley, & S. Bratt. (2018). Collaboration capacity: Measuring the impact of cyberinfrastructure-enabled collaboration networks. Science of Team Science (SCITS) 2018 Conference, Galveston, Texas, May 21-24, 2018.

Bratt, S., Hemsley, J., Qin, J. & Costa, M. R. (2017), Big data, big metadata and quantitative study of science: A workflow model for big scientometrics. Proc. Assoc. Info. Sci. Tech., 54: 36–45. doi:10.1002/pra2.2017.14505401005

Costa, M. R., Qin, J., & Bratt, S. (2016). Emergence of collaboration networks around large scale data repositories: A study of the genomics community using GenBank. Scientometrics, 108(1): 21-40. DOI: 10.1007/s11192-016-1954-x.

Costa, M. R. (2016). The interdependence of scientists in the era of team science: An exploratory study using temporal network analysis. Dissertations – ALL. 425.

Qin, J., Costa, M., & Wang, J. (2015). Methodological and technical challenges in big scientometric data analytics. iConference 2015, Newport Beach, CA, March 24-27, 2015.

Costa, M., Qin, J., & Wang, J. (2014). Research networks in data repositories. In: Joint Conference of Digital Libraries (JCDL) London, UK, September 8-10, 2014.

Bratt, S.E., Costa, M., Hemsley, J., & Qin, J. (2016). Validating science’s power players: Scientometric mixed methods for data verification in identifying influential scientists in a genetics collaboration community. iConference, Philadelphia, PA, March 2016. Poster presentation.

Qin, J., Costa, M., & Wang, J. (2014). Attributions from data authors to publications: Implications for data curation. The 9th International Digital Curation Conference, 24-27 February 2014, San Francisco. Poster presentation.

Qin, J., B. Yu, & L. Wang. (2018). Knowledge node and relation detection. Networked Knowledge Organization Systems (NKOS) Workshop at the Dublin Core International Conference DC-2018, Porto, Portugal, September 13, 2018.

Qin, J. (2018). A relation typology in knowledge organization systems: Case studies in the research data management domain. In: Proceedings of the Fifteenth International ISKO Conference, Porto, Portugal, July 9-11, 2018. (Abstract peer reviewed)

Qin, J., & Zou, N. (2017). Structures and Relations of Knowledge Nodes: Exploring a Knowledge Network of Disease from Precision Medicine Research Publications. In iConference 2017 Proceedings (pp. 56–65).

Liu, X. & Qin, J. (2014). An interactive metadata model for structural, descriptive, and referential representation of scholarly output. Journal of the American Society for Information Science and Technology, 65(5): 964-983.

Liu, X., Chen, M., & Qin, J. (2014). Interlinking cross language metadata using Heterogeneous graphs and Wikipedia. Dublin Core International Conference DC-2014, Austin, TX, October 8-10, 2014.

Qin, J. & Li, K. (2013). How portable are the metadata standards for scientific data? A proposal for a metadata infrastructure. In: Dublin Core International Conference DC-2013, Lisbon, Portugal, September 2-6, 2013.

Qin, J., Ball, A., & Greenberg, J. (2012). Functional and architectural requirements for metadata: Supporting discovery and management of scientific data. Dublin Core International Conference DC-2012, Kuching, Malaysia, September 3-7, 2012.

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