Reproducible big data science: A case study in continuous FAIRness

Ravi Madduri, Kyle Chard, Mike D'arcy, Segun C. Jung, Alexis Rodriguez, Dinanath Sulakhe, Eric Deutsch, Cory Funk, Ben Heavner, Matthew Richards, Paul Shannon, Gustavo Glusman, Nathan Price, Carl Kesselman, Ian Foster

Research output: Contribution to journalArticlepeer-review

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. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes.

Original languageEnglish (US)
Article numbere0213013
JournalPloS one
Volume14
Issue number4
DOIs
StatePublished - Apr 2019
Externally publishedYes

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Reproducible big data science: A case study in continuous FAIRness'. Together they form a unique fingerprint.

Cite this