Read about how to build a knowledge graph the semantic data modeling way in 10 steps, provided by our knowledge graph technology experts.
A group of scientists published in 2016 a paper in Nature magazine, discussing the need for a set of principles to govern the discovery, management and reuse of scientific data. Dozens of prominent scientists who contributed to the paper came up with the FAIR Data concept of describing the principles that make data valuable to researchers and scientists. The authors of the paper wrote:
The FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
These fundamental principles state that all research objects should be Findable, Accessible, Interoperable and Reusable (FAIR) both for machines and for people. The emphasis on making data understandable to machines or ‘machine-actionable’ data, as the paper on FAIR Data Principles says, helps data management, data sharing and data reuse by third parties.
Each of the four FAIR principles calls for data and metadata to be easily found, accessed, understood, exchanged and reused.
The FAIR Data Principles make data more valuable as it is easier to find through unique identifiers and easier to combine and integrate thanks to the formal shared knowledge representation. Such data is easier to reuse, repurpose and share because machines have the means to understand where data comes from and what it is about. It also accelerates research, boosts cooperation and facilitates reuse in scientific research. Policymakers and stakeholders have seen its value in driving innovation and many have embraced these principles.
As early as in 2016, the leaders of the G20 voiced their support to research based on open science and the FAIR principles. The European Union has also embraced them and had an expert group report on how to turn FAIR into reality.
In the United States, the Office of Science at the Department of Energy announced in April 2020 a total of US$8.5 million for new research aimed at advancing the FAIR Data Principles in Artificial Intelligence (AI) research and development.
By applying FAIR Data Principles, Ontotext helps companies in the pharmaceutical, biotech, agro-chemical, healthcare and health insurance industries gain insights from all their proprietary data in knowledge graph powered AI solutions
We help our clients quickly develop knowledge graphs by picking up relevant public datasets from our large LOD inventory, loading highly normalized and semantically interlinked data in your custom knowledge graph, ingesting their proprietary data and feeding the AI and analytical applications with high-quality data with traceable provenance. Some of the most important benefits for our clients are:
Thanks to the FAIR Data Principles, these organizations can improve their knowledge sharing to foster better collaboration and accelerate their research.