James E. Andrews
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
James E. Andrews has a total of 19 co-authors. Here are the top co-authors by number of shared publications:
- J. David Johnson
University of Kentucky
2 shared publications - Rachel Lynn Richesson
University of Michigan Medical School
2 shared publications - Alice Graves
School of Information, University of South Florida, U.S.A.
1 shared publication Christina Eldredge
University of South Florida
1 shared publication- Christina Eldredge
School of Information, University of South Florida
1 shared publication - Denise Shereff
Division of Bioinformatics and Biostatistics, University of South Florida, U.S.A.
1 shared publication
Latest Publications
- 1 May 2026
- Published by Springer Nature
- p. 31-40
Information Science: The Basics
- 15 June 2023
- Published by Springer Nature
- p. 1-11
Introduction to Clinical Research Informatics
- 15 June 2023
- Published by Springer Nature
- p. 473-494
Evolving Opportunities and Challenges for Patients in Clinical Research
- 15 June 2023
- Published by Springer Nature
- p. 473-494
Evolving Opportunities and Challenges for Patients in Clinical Research
- 26 May 2023
- Published by Elsevier in Patient Education and Counseling
- Vol. 114, 107812
Cancer-related information behavior among Black and Hispanic populations in an NCI-designated comprehensive cancer center catchment
- 26 May 2023
- Published by Elsevier in Patient Education and Counseling
- Vol. 114, 107812
Cancer-related information behavior among Black and Hispanic populations in an NCI-designated comprehensive cancer center catchment
- 8 February 2019
- Published by Springer Nature
- p. 123-145
The Evolving Role of Consumers
- 1 January 2019
- Published by Wiley in Proceedings of the Association for Information Science and Technology
- Vol. 56 (1) , 536-537
Data Science Education in Library and Information Science Schools
The need for data science education has grown recently among Library and Information Science schools to better prepare information professionals for the world of big data. However, there are many challenges to providing education on data science in Library and Information Science schools. For example, developing curricula and models for managing faculty resources (full‐time teaching, buy‐out, or specialized faculty) are some initial, common challenges. Participants will present their experiences and insights regarding data science education, which may include curricula, barriers, and best practices in the panel presentations. The panel session then will open up to an active discussion session with the audience, who will be encouraged to share their experiences and insights. This panel session is part of an ongoing effort by the organizers to establish sustainable data science education in Information Science schools. Developing a framework and curricula of data science education in Information Science schools, based on best practices and informed by experience, is the optimal goal of this ongoing effort. The success of this effort depends on active participation of the participants. This panel is sponsored by SIG ED.
- 1 January 2019
- Published by Wiley in Proceedings of the Association for Information Science and Technology
- Vol. 56 (1) , 536-537
Data Science Education in Library and Information Science Schools
The need for data science education has grown recently among Library and Information Science schools to better prepare information professionals for the world of big data. However, there are many challenges to providing education on data science in Library and Information Science schools. For example, developing curricula and models for managing faculty resources (full‐time teaching, buy‐out, or specialized faculty) are some initial, common challenges. Participants will present their experiences and insights regarding data science education, which may include curricula, barriers, and best practices in the panel presentations. The panel session then will open up to an active discussion session with the audience, who will be encouraged to share their experiences and insights. This panel session is part of an ongoing effort by the organizers to establish sustainable data science education in Information Science schools. Developing a framework and curricula of data science education in Information Science schools, based on best practices and informed by experience, is the optimal goal of this ongoing effort. The success of this effort depends on active participation of the participants. This panel is sponsored by SIG ED.
- 15 November 2011
- Published by Wiley in Journal of the American Society for Information Science and Technology
- Vol. 63 (3) , 490-497
Citation characterization and impact normalization in bioinformatics journals
- 15 November 2011
- Published by Wiley in Journal of the American Society for Information Science and Technology
- Vol. 63 (3) , 490-497
Citation characterization and impact normalization in bioinformatics journals
- Open Access
- 1 January 2011
- Published by Wiley in Proceedings of the American Society for Information Science and Technology
- Vol. 48 (1) , 1-4
Promoting the use of standard questions in patient registries and research: A library of questions related to rare diseases
- 1 January 2011
- Published by Wiley in Proceedings of the American Society for Information Science and Technology
- Vol. 48 (1) , 1-4
Open AccessPromoting the use of standard questions in patient registries and research: A library of questions related to rare diseases