Special Issue on FAIR Data, Systems and Analysis

The journal Data Science (https://datasciencehub.net) invites submissions for a special issue on FAIR Data, Systems and Analysis, to be edited by Michel Dumontier and Paul Groth. Submissions are due by June 1st, 2019 extended to 16 August 2019.

Topic of the Special Issue

The FAIR principles (https://www.go-fair.org/fair-principles/) outline key attributes to make digital resources more Findable, Accessible, Interoperable, and Reusable. Globally endorsed and widely adopted, there is now a pressing need to enable the establishment of an Internet of FAIR Data and Services, to demonstrate how these can be used to generate new insights, and to assess the overall value proposition for FAIR across different sectors (health, finance, law, etc). Realizing the value of the FAIR principles will require a combination of scientific, technical, social, legal, and ethical advances for the production, sharing, discovery, assessment, and reuse of data.

The aim of this special issue is to highlight unique contributions towards the development and assessment of FAIR data, systems, and analysis. Topics of submissions include, but are not limited to:

  • systems to automatically create FAIR data and services
  • methods to automatically capture detailed provenance and other metadata
  • development and maintenance of FAIR knowledge graphs
  • FAIR support tools, repositories and resources
  • methods, tools and systems for computing and using FAIR assessments
  • computable licenses and terms of use
  • novel analytics for FAIR data
  • distributed systems to share and mine sensitive data in a privacy preserving manner
  • legal and ethical contributions related to FAIR data and systems
  • contributions to assess the economic value and benefits of FAIR

Special Issue Editors

Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, the Founder and Director of the interfaculty Institute of Data Science, and the co-Founder of the FAIR (Findable, Accessible, Interoperable and Reusable) principles. His research aims to create tools and infrastructure that facilitate the automated discovery and reuse of digital content in a scalable and responsible manner. He is a principal investigator in the Dutch National Research Agenda, the NIH/NCATS Biomedical Data Translator initiative, the NIH Data Commons Pilots, and the pan-European Elixir interoperability platform. He is the editor-in-chief for the journal Data Science and an associate editor for the journal Semantic Web.

Dr. Paul Groth is Professor of Algorithmic Data Science at the University of Amsterdam’s Informatics Institute where he leads the Intelligent Data Engineering Lab (INDElab). His research focuses on intelligent systems for dealing with large amounts of diverse contextualized knowledge with a particular focus on web and science applications. This includes research in data provenance, data integration and knowledge sharing. Paul was co-chair of the W3C Provenance Working Group that created a standard for provenance interchange. He has also contributed to the emergence of community initiatives to build a better scholarly ecosystem including altmetrics and the FAIR data principles.

Important Dates
Submission deadline: June 1st, 2019

Author notification: July 15th, 2019

Final version: September 1st, 2019

Publication: October 15th, 2019

Submitting a Paper

Submissions should comply with the guidelines for authors as outlined at https://datasciencehub.net/content/guidelines-authors

For submitting please go to https://datasciencehub.net/content/submit-manuscript

Information About the Data Science Journal

Please note that all submitted papers to the special issue will be made openly available on the journal website as pre-prints before the reviewing starts, so reviewers and everybody else will be free to not only read but also share submitted papers. Pre-prints will remain available after reviewing, independent of whether the paper will be accepted or rejected for publication. Reviews to the papers are non-anonymous by default (but reviewers can request to stay anonymous). All reviews are made openly available under CC-BY licenses after a decision has been made on the submission (independent of whether the decision was accept or reject). In addition to solicited reviews, everybody is welcome to submit additional reviews and comments for papers that are under review. Editors and non-anonymous reviewers will be mentioned in the published articles. All accepted articles will be published in the official publisher version of the journal with Open Access. The Article Publication Fees (APC's) for this special issue are waived so there is no payment required to publish a paper in this special issue. Please consult https://datasciencehub.net for more detailed information about the journal.