SGCI Webinar: Reproducible Big Data Science
We are presenting in an SGCI webinar on big data FAIRness:
- Topic: Reproducible Big Data Science: A Case Study in Continuous FAIRness using Globus and Globus Genomics
- Date/Time: Wednesday, Oct.9 @ 1:00 PM ET
- Abstract: Big biomedical data create exciting opportunities for discovery but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility—thus ensuring that big data are not hard-to-(re)use data.
In this talk, we will describe the enhancements made to the Globus Genomics to support working with datasets referred to by minids, analyzing BagIt-based research objects called BDBags, and execution using software encapsulated using docker containers with unique identifiers. We will describe the tools and services developed to create end-to-end reproducible analysis pipelines while adhering to FAIR principles.
SGCI hosts a webinar series on the second Wednesday of each month at 1 pm Eastern/10 am Pacific (1 hour long). We feature a rotating selection of topics including tool and technology demos, best practices, and gateway showcases. These webinars are recorded for future viewing and are posted in our archives.