Natalie Meyers serves as CNI/ARL AI researcher in residence. She focuses on the strategic implications of artificial intelligence (AI) for research libraries, furthering the Association’s research, advocacy, and tracking of machine learning (ML), deep learning, AI, and generative AI. Reporting to the ARL senior director for Scholarship, Policy, and Engagement Strategy and working closely with CNI leadership, Natalie plays a key role in the ongoing collaboration between CNI and ARL around AI/ML technologies and applications.
In addition to her role at ARL/CNI, Meyers is a research specialist at SDSC and previously was professor of the practice at the Lucy Family Institute for Data & Society , and prior to that as e-research librarian at the Navari Family Center for Digital Scholarship at the University of Notre Dame. She has co-chaired the Artificial Intelligence/Data Visitation Working Group of the Research Data Alliance (RDA) and is co-author of the group’s AI Bill of Rights Recommendation.
Natalie holds an MLIS from the University of California, Berkeley with a concentration in database design and systems analysis; an MA in English from the University of Wisconsin–Milwaukee; and a BA in philosophy and English from DePauw University.
The context of increasing volumes of data being created by researchers and the strengthening of requirements for research data management and data sharing has created demand for a new and evolving set...
Enabling FAIR Data in the Earth, Space, and Environmental Sciences
Open, accessible, and high-quality data and related data products and software are critical to the integrity of published research. ...
The Earth, space (planetary), and environmental science communities are developing, through multiple international efforts, both general and domain-specific leading practices for data, software, a...
Ethical and social issues with respect to data archiving, sharing, and reuse cut across many of the technical and policy work of the rest of the RDA. Such issues are complementary to but separate fr...
FAIR Data Maturity Model: core criteria to assess the implementation level of the FAIR data principles
Webinar – The RDA FAIR Data Maturity Model WG: Aligning International Initiatives for Promo...
The idea of FAIR (findable, accessible, interoperable, and reusable) in the context of scientific data management and stewardship was developed in 2014 and turned into specific principles in 2016[1]. ...
News:
May 24th, 2022. The RDA Council have endorsed the FAIR4RS Principles as an official output!
Citation and download: Chue Hong, N. P., Katz, D. S., Barker, M., Lamprecht, A-L, Martinez, C., Psomop...
How to get involved with this interest Group?
Join the RDA group to get updates.
The design and production of research hardware i.e., physical artefacts, including mechanical, electrical and softw...
Preparing for the 16th Plenary
The IG on Surveying Open Data Practices draws attention to the growth of global and national-level surveys as a lens to characterize how researchers’ practices and per...
Endorsed Outputs
23 Things: Libraries For Research Data – overview of practical, free, online resources and tools that you can begin using today to incorporate research data management into y...