Global impact analysis and implementation strategy recommendations of the FAIR principles
In the current era of Big Data and artificial intelligence,data sharing and its governance are increasing in importance.However,guiding scientific data governance is a major challenge in multidisciplinary situations.In this context,the FAIR(Findable,Accessible,Interoperable and Reusable)principles were proposed in a paper in 2016,"The FAIR Guiding Principles for scientific data management and stewardship".Subsequently,the FAIR principles quickly spread across multiple domains.How many research areas and countries/regions have been impacted by this academic process?How did the dissemination process proceed?What are the direct effects of scientific data governance practices?To address these questions,we analyzed bibliometric methods and scientific data-sharing practices.The paper that introduced the FAIR principles(referred to as"cited paper")was used to construct a retrieval type.Academic papers citing FAIR principles research,from January 1,2016 to December 31,2022,were also retrieved from the Web of Science Database.This study identified more than 300 articles on FAIR principles and 3300 papers citing the cited paper.Furthermore,we conducted a bibliometric analysis that included citation frequency,discipline-wise distribution of citations,country/region-wise distribution of citations,keyword frequency count,and keyword theme clustering.A total of 137 countries were involved in FAIR impact studies,and the countries with the highest number of publications were the United States,Germany,and the United Kingdom.They are core research countries,who have established close cooperation with most countries involved in this field.The impact mainly involves basic and cutting-edge fields,such as biology,life,materials,and medicine,with the largest number of disciplines reflected in multiple disciplines,such as computer science,biology,medicine,and environmental science.The relevant international data governance cooperation dynamics after the proposal of the FAIR principles were further analyzed from both temporal and spatial perspectives.The FAIR principles were found to have a greater impact on a global scale,mainly in related international organizations.An overall bibliometric and activity impact analysis showed that the FAIR principles had rapidly expanded globally and across multiple disciplines with its concise and transparent governance rules.Combining the above analysis with the actual needs of scientific data governance in China,we put forward ten insights and suggestions.(1)Enriching and developing the field characteristics of FAIR data governance in practice;(2)the FAIR principles are an important means to support science paradigm shifting;(3)machine-readability is the key point of the FAIR principles;(4)forming an interconnectable global data network;(5)metadata is still the main content for FAIR principles governance;(6)enhancing the overall software architecture of the FAIR principles implementation;(7)strengthening the research and development of the FAIR principles application tools;(8)promoting a wider application of the FAIR principles in different fields;(9)improving the standard ecology of the FAIR principles;(10)innovating and developing new concepts of scientific data governance beyond FAIR principles.
NETL
NSTL
万方数据
