Search for: All records

Abstract MotivationEcological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of interactions underpinning system dynamics. Knowledge graphs (KGs) have been successfully applied to organize heterogeneous data and to predict new linkages in complex systems. Though not previously applied broadly in ecology, KGs have much to offer in an era when system dynamics are responding to rapid changes across multiple scales. ResultsWe developed a KG to demonstrate the method’s utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include data related to HPAI including pathogen–host associations, species distributions, and population demographics, using a semantic ontology that defines relationships within and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying patterns of HPAI ecology. We underscore the generalizable value of KGs to ecology including ability to reveal previously known relationships and testable hypotheses in support of a deeper mechanistic understanding of ecological systems. Availability and implementationThe data and code are available under the MIT License on GitHub at https://github.com/cghss-data-lab/uga-pipp. 

We introduce an open-source platform for annotating body-worn video (BWV) footage aimed at enhancing transparency and accountability in policing. Despite the widespread adoption of BWVs in police departments, analyzing the vast amount of footage generated has presented significant challenges. This is primarily due to resource constraints, the sensitive nature of the data, which limits widespread access, and consequently, lack of annotations for training machine learning models. Our platform, called CVAT-BWV, offers a secure, locally hosted annotation environment that integrates several AI tools to assist in annotating multimodal data. With features such as automatic speech recognition, speaker diarization, object detection, and face recognition, CVAT-BWV aims to reduce the manual annotation workload, improve annotation quality, and allow for capturing perspectives from a diverse population of annotators. This tool aims to streamline the collection of annotations and the building of models, enhancing the use of BWV data for oversight and learning purposes to uncover insights into police-civilian interactions.