OBJECTIVES: Establishing a robust and secure framework allowing creation and sharing of custom features within the REDCap electronic data capture platform. MATERIALS AND METHODS: In partnership with REDCap Consortium members, we developed a framework for creating external modules enabling project-specific REDCap custom functionality (EM Framework). The EM Framework includes guidance and standard processes for developers to ensure basic functionality, compatibility, and security across REDCap instances. The EM Framework also includes an optional dissemination mechanism, the REDCap Repository of External Modules (Repo), for developers to easily share their work with other institutions in the REDCap Consortium. RESULTS: From the EM Framework's launch in 2017 through 2024, 356 external modules have been published to the Repo by software developers at 59 institutions. These modules have been used on 29 485 projects at 2107 institutions in 67 countries. Over time, features from 22 of these external modules have been integrated into the core REDCap code serving 7700+ REDCap Consortium members in 160 countries. DISCUSSION: The EM Framework permits developers to create, test, and deploy custom features to their local REDCap platform. It further enables a process to distribute these features to other REDCap administrators across the Consortium. CONCLUSION: The EM Framework has enhanced innovation in electronic data capture and dissemination of those innovations to a global research community.

We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and fluent text. Based on this approach, we introduce, for the first time, an AI commentary system, which was deployed to produce automated narrations for highlight packages at the 2023 US Open, Wimbledon, and Masters tournaments. In the same vein, our solution was extended to create personalized content for ESPN Fantasy Football and stories about music artists for the GRAMMY awards. These applications were built using a common software architecture achieved a 15x speed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Our work was successfully deployed at the aforementioned events, supporting 90 million fans around the world with 8 billion page views, continuously pushing the bounds on what is possible at the intersection of sports, entertainment, and AI.

Acoustic signals contain rich information of the environment. They can be used for detecting anomalous events such as in automated machine monitoring. In this demonstration, we present our acoustic anomaly detection system that captures acoustic signals and classifies them using machine learning techniques. Our system includes a server for sound management and model training, a mobile client for sound capturing and real-time classification, and a workbench that acts as a user interface. We will show the full operational pipeline of our system in this demonstration.