Publication Portfolio Analytics
Description
The Data Strategy and Analytics Team (DSAT) within OS employs natural language processing (NLP) topic modeling techniques to help programs identify common themes within their publication data. By combining these efforts with bibliometric analysis, we standardize the reporting of media attention, as well as policy and academic citations, by theme and/or CDC organization. Automating these efforts helps the CDC library to optimize their allocation of resources and avoid duplication of effort.
Detailed example
The outputs of the OS DSAT Publication Portfolio Analysis work include a pipeline/ PowerBI dashboard workflow and several center/divisional organizational specific reports and presentations.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
OS DSAT utilizes NLP to generate organization-specific reports and maintain an agency-wide dashboard. In the case of the agency-wide publication impact dashboard, NLP topic modeling was used to identify common publishing themes for the agency using 10+ years of CDC publication data. This allows users to see trends in publishing topics have changed over time and, when connected to media attention and citation data, allows communication teams, leadership, and scientists interested in assessing the impact of their program’s publications. This dashboard has proven to be impactful, with 105 unique CDC staff across several CIOs and divisions using the dashboard between 7/1/2025 and 8/1/2025.
Controls / human review
ATO: Yes; PIA: Not published