Thursday, August 02, 2018

FAIR Data Principles for Scientific Data Management

All efforts being done in the information world revolve around the one aim of making information Findable, Accessible, Interoperable and Reusable. FAIR Data principles are also the result of one of them. Research data is being produced at the very fast rate. Every research data is used in further research as a basis. To make this scientific data easily findable and accessible is important to support the research and other activities. A framework to manage Research scientific data is designed collectively by the efforts of research data producers, (academicians, industry, laboratories), Research funders, government agencies and publishers.

FAIR Data (Findable, Accessible, Interoperable and Reusable)

FAIR  data is a set of guiding principles to provide the framework to manage the scholarly scientific data in order to ensure the data producers, publishers and users that their data is well managed and shared without any restrictions. Foundation for FAIR data guiding principles was laid in a workshop held in Leiden, Netherlands, in 2014, named "Jointly Designing a Data Fairport". Then FAIR working group established by the FORCE11 community Improved the principles and FAIR principles published in 2016.

To make data easily findable it is necessary to describe the data in the more structured way. Rich metadata with persistent unique identifier makes data findable.
Metadata should be described with those technologies which make it understandable by both humans and machines. Data should be deposited in some trusted repositories for its long-term preservation.
A formal and broadly accepted language should be used to describe metadata to make it interoperable between different systems and platforms.
To make data reusable it should be free from all copyright issues so that it can be used freely without any restrictions. Data should have a usage license.

Examples of FAIR Data principles

1. Harvard Dataverse based on Open source data repository software "Dataverse" is a largest Dataverse repository open to all researchers.

2. FAIRDOM open source software platform for data management service for Systems Biology.

Many more FAIR principles based field specific and general data repositories are in existence.

FAIR principles in Libraries

As our libraries are already engaged in describing data collection, managing the collection, digital preservation, thereby helping users in information access and retrieval. By incorporating FAIR principles into their data management plan and digital preservation techniques libraries can also make their Scholarly scientific data FAIR. Libraries by collecting research data, enriching metadata, and preserving the data following FAIR principles can make their scientific collection FAIR, thus helping researchers and users in accessing, retrieving, sharing and reusing information in better ways without any restrictions.

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