Meet CADRE’s first class of fellows

July 18, 2019
The Collaborative Archive & Data Research Environment (CADRE) accepted its first class of CADRE Fellows.

These seven fellowship teams span across disciplines and offer compelling research that incorporates big data and bibliometrics. Each fellow team will access CADRE’s Web of Science (WoS) and Microsoft Academic Graph (MAG) datasets to achieve their research goals.

Our fellows will present their research at the International Society for Scientometrics and Informetrics (ISSI) 2019 Conference in Rome at either the workshop or tutorial that CADRE is hosting on Sept. 2.

Not only will these fellows show how CADRE helped advance their work, they will serve as integral use cases for how we develop our platform to suit the needs of every type of academic researcher.

Now, let’s meet the research teams!

Utilizing Data Citation for Aggregating, Contextualizing, and Engaging with Research Data in STEM Education Research from Purdue University

Collection of three headshots in the order of researchers listed below.

Researchers:

Researchers will characterize citation of data from the literature in the field of STEM education research. A sample of relevant publication venues in the field will be identified from WoS and MAG. Digital Object Identifiers (DOIs) of datasets registered with DataCite will be used to query and associate datasets with publications. The team will assess rates of citation for datasets that are cited using DataCite DOIs for each publication venue and analyze a sample of data citations and publications to determine suitability for providing an initial context to help a researcher who is unfamiliar with the data determine whether to use the dataset.

Understanding citation impact of scientific publications through ego-centered citation networks from Indiana University Bloomington, Nanjing University

Collection of three headshots in the order of researchers listed below.

Researchers:

The research team seeks to find the “deeper” and “broader” impact of network-based citation measurements in the scientific community. This project will determine the citation impact of scientific publications using an ego-centered citation network, which contains the citing relationships between a publication and its citing publications, as well as the relationships within its citing publications. Researchers will use the entirety of the WoS and MAG data to establish empirical evidence in this project.

MCAP: Mapping Collaborations and Partnerships in SDG Research from Michigan State University

Collection of four headshots in the order of researchers listed below.

Researchers:

This project will build on the WoS report “Navigating the Structure of Research on Sustainable Development Goals (SDG),” as the researchers search for patterns of global collaboration and support the United Nations’ SDG call for action. Researchers will design a prototype to analyze and visualize the input-output of partnerships over time in SDG-supportive research. They also plan to create a scoring measure or partnership index that defines and conducts partnership analytics for SDGs by using data sourced from WoS and MAG.

The global network of air links and scientific collaboration – a quasi-experimental analysis from Indiana University Bloomington and University of Warsaw

Collection of four headshots in the order of researchers listed below.

Researchers:

Researchers plan to determine the impact of the introduction and availability of long-distance flights on international scientific collaboration. The team will measure collaboration through co-authorship and co-affiliation. They will also geocode publication affiliations from WoS and MAG from 1998 through 2017. This quasi-experimental research will apply state-of-the-art causal modeling techniques and explore how data-driven causality can enhance science of science policy relevance.

Measuring and Modeling the Dynamics of Science Using the CADRE Platform from University of Minnesota, New York University, Boston University, University of Pennsylvania, University of Arizona

Collection of five headshots in the order of the first five researchers listed below. Collection of four headshots in the order of the last four researchers listed below.

Researchers:

This research team wants to better characterize scientific influence of papers, typically measured by how many times papers are cited, by distinguishing between papers that destabilize existing knowledge with novel concepts and papers that consolidate existing knowledge. In a separate but closely related aim, the researchers also plan to create a novel unsupervised machine learning technique for author-name disambiguation by pulling abstract, title, and citation data from WoS and MAG. For both aims, the CADRE platform will provide essential infrastructure in terms of large-scale data storage and high performance computational resources.

Comparative analysis of legacy and emerging journals in mathematical biology from University of Michigan and University of Michigan Medical School

Collection of four headshots in the order of the researchers listed below.

Researchers:

Researchers will perform a comparative analysis on papers published in four mathematical biology legacy journals and on newer journals with different publication models and disciplinary scope. The team will use the CADRE datasets to develop methodologies for comparative bibliometrics and content analyses; provide insight into publication trends in theoretical and applied domains; give authors new factors to consider when trying to publish; and help editors in similar disciplines use informatics to distinguish their journals.

Systematic over-time study of the similarities and differences in research across mathematics and the sciences from University of Michigan

Headshot of the researcher listed below.

Researcher:

Samuel’s project uses reference and citation aging, bibliographic coupling, and network breadth and depth to find similarities and differences between research fields in mathematics and the sciences. Specifically, they will find how information ages differently across disciplines, generate data about changes in the development of these research fields, and study how actively collaborative the disciplines are. Samuel will use WoS data from 1900 to 2017 to perform these analyses, which have typically only been done on a smaller scale in a single discipline.