Rapid Reviews\ Infectious Diseases (RR\ID) Data Science Project
Stefano M. Bertozzi, Professor
Public Health
Applications for Spring 2024 are closed for this project.
Rapid Reviews\Infectious Diseases (RR\ID) [rrid.mitpress.mit.edu], is an initiative of the MIT Press and the University of California, Berkeley. It is an open access, rapid-review overlay journal for the accelerated curation and peer review of COVID-19 and emerging infections disease-related research. RR\ID takes a transdisciplinary approach to discuss, curate, and communicate seminal research of public interest.
The RR Editorial Office is composed of senior editorial leadership and three core teams: (1) Biological & Chemical Sciences and Physical Sciences & Engineering; (2) Medical Sciences; and (3) Public Health and Social Sciences. Each core team is led by an assistant editor with domain-specific expertise and is supported by volunteer scholars from across the globe. Each week, the core teams meet individually to discuss the most impactful preprints in their domain, and every Friday, all teams meet together to pitch their top choices to the senior editorial leadership.
RR has collaborated with data scientists at Berkeley and at LBNL since it started. We seek URAP students to work with our faculty and staff and with data science graduate students to increasingly automate and improve the quality and efficiency of our work – and by extension serve as a model for all innovative open science efforts.
Role: Creating interactive, user-friendly databases to support our internal functioning. We are currently using Sheets and Slides to support our operations, but our scale has exceeded their capacity to support multiple simultaneous users and we need to develop higher-capacity applications (probably using Microsoft Access)
Developing real-time dashboards for performance management that support all phases of our operation and that synthesize data from our publishing platform (Janeway) and our internal operations data (see previous bullet). This would include creative content visualization.
Conducting retrospective analyses on publishing metrics to better understand predictors of success (peer review acceptance and performance) that consider both characteristics of the manuscript being reviewed and the characteristics of the peer reviewers identified. Propose operational changes and/or decision-support tools to improve workflow efficiency and quality.
Work on development of AI-enabled tools with internal and external (Allen AI) collaborators to improve AI tools that support scientific review. These include tools to:
Disambiguate authors/reviewers (the need to distinguish the C Wang who is a coauthor of the paper we are interested in from other C Wangs)
Improve the identification of “nearest neighbor” papers to the manuscript under review that consider methodological proximity more than topical proximity
Rank authors of nearest neighbor papers by their proximity to the manuscript under review considering the totality of their published works (requires effective disambiguation) and their proximity to the authors (potential conflict of interest).
Explore other methods of identifying peer reviewers (e.g. network analysis exploring who publishes with whom).
If you are interested, please reach out as soon as possible to Stefano Bertozzi, Editor-in-Chief (sbertozzi@berkeley.edu), Hildy Fong Baker, Managing Director (hildy.fong@berkeley.edu), and Boma Levy-Braide, Operations Manager (b.levy-braide@berkeley.edu)
Day-to-day supervisor for this project: Stefano Bertozzi, Staff Researcher
Hours: 6-8 hrs
Off-Campus Research Site: The work will be largely remote but there will be some workshops in-person.
Biological & Health Sciences Digital Humanities and Data Science