Guiding Principles for Findable, Accessible, Interoperable and Re-usable Data Publishing Version

By | December 14, 2018

During an age when data is becoming generate as an increasing rate, standard practices of using this data need to be implemented to ensure the usability of this data. This paper explains the eScience ecosystem and the challenge behind regulating such a vast field with so many players. Creating free and usable data is key behind a robust research community.

User, ‘pippinev’, points out the meaning behind this paper in the following quote. ” It is important to note that this document is a general ‘guide to FAIRness of data’, not a ‘specification'”. This paper simply points out expect practices of creating and sharing data. FAIR stands for the, “Findable, Accessible, Interoperable and Re-usable” methods of data. There remains much debate as to the best practices to share and maintain data, but there are some key underlying beliefs. But, “the methods to access and/or download [data] should be well described and preferably fully automated and using well established protocols.” When we look at dataset of small size, such as ones used in class with 100 rows or less this remains less important. Yet, when we collect and share public dataset of millions of rows that can be used for public benefit, it becomes important to create an MLA style process for sharing this data. We all look forward to discovering the incites of big data, assuming we can analyze it! #FAIR

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