It’s So UnFAIR!

EOS - Earth & Space Science News

Ankur Rashmikant Desai 02-05-18

Fluxnet is a global network of eddy covariance (EC) flux measurements on carbon and water exchanges with the land surface. Dai et al. [2018] conducted a meta-analysis of EC publications, authors, citation metrics, and accessibility of EC data.

The more likely one shared data, the more likely one was a more influential researcher.

The endgame is science that is reproducible and extensible.

The exercise demonstrated that the benefits of sharing data do outweigh the costs for both the providers and the users.

https://eos.org/editors-vox/its-so-unfair

FAIR Data At The Right Time: What Researchers Are Saying About Data Stewardship

By Allison Proffitt - Bio-IT World

April 23, 2018

In a survey about data stewardship, Bio-IT World found that while data standards and sharing are widely considered important, most people still struggle with both.

http://www.bio-itworld.com/2018/04/23/fair-data-at-the-right-time-what-researchers-are-saying-about-data-stewardship.aspx

Seems they haven't provided a link to the actual survey.

NIH awards to test ways to store, access, share, and compute on biomedical data in the cloud

NIH Data Commons Pilot Phase to seek best practices for developing and managing a data commons.

https://www.nih.gov/news-events/news-releases/nih-awards-test-ways-store-access-share-compute-biomedical-data-cloud 

Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud

Abstract

The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from con-temporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. As support for the Principles has spread, so has the breadth of these interpretations. In observing this creeping spread of interpretation, several of the original authors felt it was now appropriate to revisit the Principles, to clarify both what FAIRness is, and is not.

DOI: 10.3233/ISU-170824 

Mons, B., Neylon, C., Velterop, J., Dumontier, M., Da Silva Santos, L. O. B., & Wilkinson, M. D. (2017). Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services and Use, 37(1), 49–56. https://doi.org/10.3233/ISU-170824 

New article: Publishing FAIR Data: An Exemplar Methodology Utilizing PHI-Base

Alejandro Rodríguez-Iglesias^1†, Alejandro Rodríguez-González^2†, Alistair G. Irvine³, Ane Sesma¹, Martin Urban⁴, Kim E. Hammond-Kosack⁴ and Mark D. Wilkinson^1*

Front. Plant Sci., 12 May 2016 | http://dx.doi.org/10.3389/fpls.2016.00641




Pathogen-Host interaction data is core to our understanding of disease processes and their molecular/genetic bases. Facile access to such core data is particularly important for the plant sciences, where individual genetic and phenotypic observations have the added complexity of being dispersed over a wide diversity of plant species vs. the relatively fewer host species of interest to biomedical researchers.


Recently, an international initiative interested in scholarly data publishing proposed that all scientific data should be “FAIR”—Findable, Accessible, Interoperable, and Reusable. In this work, we describe the process of migrating a database of notable relevance to the plant sciences—the Pathogen-Host Interaction Database (PHI-base)—to a form that conforms to each of the FAIR Principles.


We discuss the technical and architectural decisions, and the migration pathway, including observations of the difficulty and/or fidelity of each step. We examine how multiple FAIR principles can be addressed simultaneously through careful design decisions, including making data FAIR for both humans and machines with minimal duplication of effort. We note how FAIR data publishing involves more than data reformatting, requiring features beyond those exhibited by most life science Semantic Web or Linked Data resources. We explore the value-added by completing this FAIR data transformation, and then test the result through integrative questions that could not easily be asked over traditional Web-based data resources.


Finally, we demonstrate the utility of providing explicit and reliable access to provenance information, which we argue enhances citation rates by encouraging and facilitating transparent scholarly reuse of these valuable data holdings.


http://journal.frontiersin.org/article/10.3389/fpls.2016.00641/full