Joel Herndon – författare

The Data Curation Network (DCN) is a membership organization of institutional and non-profit data repositories whose vision is to advance open research by making data more ethical, reusable, and understandable. Although initially conceived of and established through grant funding, the DCN transitioned to a sustainable, member-funded organization in July 2021, and is now composed of almost 50 data curators from 17 institutions.The Art, Science, and Magic of the Data Curation Network: A Retrospective on Cross Institutional Collaboration captures the results of a project retrospective meeting and describes the necessary components of the DCN’s sustained collaboration in the hopes that the insights will be of use to other collaborative efforts. In particular, the authors describe the successes of the community and challenges of launching a cross-institutional network. Additionally, this publication details the administrative, tool-based, and trust-based structures necessary for establishing this community, the “radical collaboration” that is the cornerstone of the DCN, and potential future collaborations to address shared challenges in libraries and research data management. This in-depth case study provides an overview of the critical work of launching a collaborative network and transitioning to sustainability. This publication will be of special interest to research librarians, data curators, and anyone interested in academic community building.

In the last decade, data science has generated new fields of study and transformed existing disciplines. As data science reshapes academia, how can libraries and librarians engage with this rapidly evolving, dynamic form of research? Can libraries leverage their existing strengths in information management, instruction, and research support to advance data science?Data Science in the Library: Tools and Strategies for Supporting Data-Driven Research and Instruction brings together an international group of librarians and faculty to consider the opportunities afforded by data science for research libraries. Using practical examples, each chapter focuses on data science instruction, reproducible research, establishing data science services and key data science partnerships.This book will be invaluable to library and information professionals interested in building or expanding data science services. It is a practical, useful tool for researchers, students, and instructors interested in implementing models for data science service that build community and advance the discipline.