Big Metadata Analytics
Investigate research collaboration networks emerging around cyberinfrastructure (CI) enabled repositories, the dynamics of those networks, and their impact on research collaboration, productivity, and knowledge diffusion.
Metadata modeling and linking: Analyze and model domain knowledge and information structures for designing and implementing metadata applications.
About the 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: one is big metadata analytics and the other 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.
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.
Metadata Lab Updates
Jian Qin presented paper “Collaboration capacity: Measuring the impact of cyberinfrastructure-enabled collaboration networks” ”, co-authored with Jeff Hemsley and Sarah Bratt at the Science of Team Science (SCITS) 2018 Conference, Galveston, Texas, May 21-24, 2018.
Science Production Function Society Selection
Sarah Bratt is selected as one of the five Science Production Function Society project at the Laboratory for Innovation and Society at Harvard (LISH) : https://lish.harvard.edu/spfs
This timeline demonstrates the ratio of submission to publication between the year of 1994 and 2012. Each graph encompasses of the average ratio of the year, the number of authors in total, and the number of authors who has no submission.
Author: Ruiyang Chen - November 11th, 2014
This timeline demonstrates the authority and hub scores of submission and publication network vertices, ranging from 1994-2008.
This timeline demonstrates the network vertices degree of submissions and publications from 1994 to 2012. Each graph consists author and vertices degree values.
Author: Ruiyang Chen - November 14th, 2017
Metadata Lab Team
Past Team Members
Shrutik Ghanshyambhai Katchhi
Big Metadata Analytics
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. https://surface.syr.edu/etd/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. http://hdl.handle.net/2142/73756
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.
Metadata Modeling and Linking
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.
Interested in working with Metadata Lab?