From Raw Data to FAIR Data: The FAIRification Workflow for Health Research Articles uri icon

authors

  • Sinaci, A. Anil
  • Nuñez-Benjumea, Francisco J.
  • Gencturk, Mert
  • Jauer, Malte Levin
  • Deserno, Thomas
  • Chronaki, Catherine
  • Cangioli, Giorgio
  • Cavero-Barca, Carlos
  • Rodriguez-Perez, Juan M.
  • Perez-Perez, Manuel M.
  • Laleci Erturkmen, Gokce B.
  • HERNANDEZ PEREZ, ANTONIO
  • MENDEZ RODRIGUEZ, EVA MARIA
  • Parra-Calderon, Carlos L.

publication date

  • June 2020

start page

  • e21

end page

  • e32

issue

  • S01

volume

  • 59

International Standard Serial Number (ISSN)

  • 0026-1270

abstract

  • Background: FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data [...].

keywords

  • interoperability; data science; data curation; data anonymization; metadata