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Abstract In the rapidly evolving landscape of healthcare and drug development, the ability to efficiently collect, process, and analyze large volumes of real‐world data (RWD) is critical for advancing drug development. This article provides a blueprint for establishing an end‐to‐end data and analytics pipeline in a cloud‐based environment. The pipeline presented here includes four major components, including data ingestion, transformation, visualization, and analytics, each supported by a suite of Amazon Web Services (AWS) tools. The pipeline is exemplified through the CURE ID platform, a collaborative tool designed to capture and analyze real‐world, off‐label treatment administrations. By using services such as AWS Lambda, Amazon Relational Database Service (RDS), Amazon QuickSight, and Amazon SageMaker, the pipeline facilitates the ingestion of diverse data sources, the transformation of raw data into structured formats, the creation of interactive dashboards for data visualization, and the application of advanced machine learning models for data analytics. The described architecture not only supports the needs of the CURE ID platform, but also offers a scalable and adaptable framework that can be applied across various domains to enhance data‐driven decision making beyond drug repurposing.

A growing body of critical care research draws on real-world data from electronic health records (EHRs). The bedside clinician has myriad data sources to aid in clinical decision-making, but the lack of data sharing and harmonization standards leaves much of this data out of reach for multi-institution critical care research. The Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign convened a panel of critical care and data science experts to explore and document unique advantages and opportunities for leveraging EHR data in critical care research. This article reviews and illustrates six organizing topics (data domains and common data elements; data harmonization; data quality; data interoperability and digital infrastructure; data access, sharing, and governance; and ethics and equity) as a data science primer for critical care researchers, laying a foundation for future publications from the SCCM Discovery Data Harmonization and Sharing Guiding Principles Panel.

Abstract An individual’s spatial behavior is shaped by social and environmental factors and provides critical information about population processes to inform conservation and management actions. Heterogeneity in spatial overlap among conspecifics can be evaluated using estimates of home ranges and core areas and used to understand factors influencing space use and territoriality. To understand and test predictions about spatial behavior in an invasive large mammal, the wild pig (Sus scrofa), we examined variation in space use between sexes and seasons. We predicted that if animals were territorial that there would be a reduction in space-use overlap when comparing overlap of home ranges (HR–HR), to home ranges and core areas (HR–CA), and in-turn between core areas (CA–CA). Home ranges and core areas were estimated for 54 wild pigs at Buck Island Ranch, FL from GPS telemetry data. Overlap indices were calculated to estimate the strength (space-use overlap) and number of potential interactions within three wet seasons (June–October) and two dry seasons (December–April). Among sexes, home range size did not vary seasonally, and males exhibited larger home ranges compared to females (M = 10.36 ± 0.79 km2 (± SE), F = 3.21 ± 0.16 km2). Strength of overlap varied by season with wild pig home ranges overlapping more during the dry season. Males interacted with a greater number of individuals of both sexes, compared to females, and exhibited greater strength of overlap during the dry season. Consistent with our predictions, wild pigs appeared to exhibit territorial behavior, where strength of overlap decreased when comparing HR–HR to HR–CA and HR–CA to CA–CA. Our framework can be used to understand patterns of space use and territoriality in populations, which has important implications in understanding intraspecific interactions and population processes, such as how pathogens and parasites might spread within and among populations.