Ixchel M. Faniel, Ph.D.
Senior Research Scientist
Ixchel M. Faniel's research interests include improving how people discover, access and use/reuse content. She is currently examining how academics manage, share and reuse research data and librarians' experiences designing and delivering supportive research data management programs. She also is investigating how science, technology, engineering and mathematics (STEM) students from grade school to grad school identify and judge the credibility of digital resources. Ixchel's research has been funded by the National Science Foundation (NSF), Institute of Museum and Library Services (IMLS) and National Endowment for the Humanities (NEH). Prior to joining OCLC Ixchel worked at the University of Michigan, School of Information, IBM and Andersen Consulting (now Accenture). She graduated from Tufts University with a BS in Computer Science and earned an MBA and Ph.D. in Business Administration at the University of Southern California, Marshall School of Business.
Curriculum vitae (.pdf)
26 September 2019
Ixchel M. Faniel, Rebecca D. Frank, Elizabeth Yakel
Context is critical for data reuse, and digital curation should include both context and content preservation. Both data producers and curators benefit from expanding context categories to better determine what information is vital to capture and manage during data collection to support data reuse.
Exposing Standardization and Consistency Issues in Repository Metadata Requirements for Data Deposition
1 September 2019
Jihyun Kim, Elizabeth Yakel, Ixchel M. Faniel
In this article in College & Research Libraries Journal, the authors examine common and unique metadata requirements and their levels of description, determined by the data deposit forms of 20 repositories in three disciplines—archaeology, quantitative social science, and zoology.
2 June 2019
Elizabeth Yakel, Ixchel M. Faniel, Zachary J. Maiorana
A data life cycle model illustrates how factors in one data life cycle phase impacts other phases, forming virtuous (positive) and vicious (negative) circles. This method comprehensively studies how data producers, sharers, curators, and reusers can better collaborate across data life cycle phases.