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DTSTART:20260825T170000Z
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SUMMARY:Introduction to Apptainer for Reproducible Analysis and Portable Pipelines
DESCRIPTION:Bridge the "it works on my machine" gap with portable\, 
 HPC-ready research environments.\n\nIn biomedical research\, computational 
 reproducibility is often hindered by complex software dependencies and 
 restrictive high-performance computing (HPC) environments. While Docker is 
 the industry standard for containerization\, Apptainer (formerly 
 Singularity) is the gold standard for secure\, multi-user research 
 clusters.\n\nThis 2-hour\, hands-on workshop demystifies containerization 
 for the life sciences. We will move from basic concepts to building a 
 custom\, lightweight Linux container specifically for biomedical data 
 science. Using a real-world diabetes analysis pipeline\, you will learn how 
 to "freeze" your entire computational environment into a single\, portable 
 file that can be shared with collaborators or moved from your laptop to a 
 supercomputer without installation errors.\n\nWhat you will build: A 
 reproducible analysis pipeline that binds local data into a custom-built 
 container image to produce validated research outputs.\n\nLearning 
 objectives:\n\n\n\nBy the end of this workshop\, participants will be able 
 to:\n\n\n	Explain the core differences between containers and virtual 
 machines\, and why Apptainer is preferred over Docker in secure HPC 
 environments.\n	Execute fundamental Apptainer CLI commands to pull remote 
 images\, explore containers interactively\, and run non-interactive 
 commands.\n	Construct an Apptainer Definition File (.def) to build a custom 
 Linux environment containing specific biomedical libraries (e.g.\, pandas\, 
 matplotlib).\n	Analyze a biomedical dataset by binding local data into a 
 containerized pipeline and extracting reproducible\, visualized 
 results.\n	Evaluate a research workflow’s reproducibility based on 
 environment documentation\, image portability\, and data provenance.\n
LOCATION:SHM L 111\, Cushing/Whitney Medical Library\, 333 Cedar Street\, Medical School
ORGANIZER;CN="Justin DeMayo":MAILTO:justin.demayo@yale.edu
CATEGORIES:Coding & Computing, Data
CONTACT;CN="Justin DeMayo":MAILTO:justin.demayo@yale.edu
STATUS:CONFIRMED
UID:LibCal-17273140
URL:https://schedule.yale.edu/event/17273140
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