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In April 2022, Matthias Scheffler and colleagues argued in Nature that FAIR principles are "a must" so that data mining and artificial intelligence can extract useful scientific information from the data. [21] However, making data (and research outcomes) FAIR is a challenging task, and it is challenging to assess the FAIRness. [22]
FAIR's main document is "An Introduction to Factor Analysis of Information Risk (FAIR)", Risk Management Insight LLC, November 2006; [4] The contents of this white paper and the FAIR framework itself are released under the Creative Commons Attribution-Noncommercial-Share Alike 2.5 license. The document first defines what risk is.
Appropriate application of the four principles requires that Stakeholder analysis must first be performed. Thorough stakeholder analysis is important to identify: the correct entity(s) from whom to seek informed consent; the party(s) who bear the burdens or face risks of research; the party(s) who will benefit from research activity; and, the party(s) who are critical to mitigation in the ...
The Five Safes is a framework for helping make decisions about making effective use of data which is confidential or sensitive. It is mainly used to describe or design research access to statistical data held by government and health agencies, and by data archives such as the UK Data Service.
Fair Information Practice was initially proposed and named [5] by the US Secretary's Advisory Committee on Automated Personal Data Systems in a 1973 report, Records, Computers and the Rights of Citizens, [6] issued in response to the growing use of automated data systems containing information about individuals. The central contribution of the ...
Data management has recently become a primary focus of the policy and research debate on open scientific data. The influential FAIR principles are voluntarily centered on the key features of "good data management" in a scientific context. [44] In a research context, data management is frequently associated to data lifecycles. Various models of ...
In Denmark, scientific misconduct is defined as "intention[al] negligence leading to fabrication of the scientific message or a false credit or emphasis given to a scientist", and in Sweden as "intention[al] distortion of the research process by fabrication of data, text, hypothesis, or methods from another researcher's manuscript form or ...
The European Code of Conduct for Research Integrity 2023 states, for example, the principles that, "Researchers, research institutions, and organisations ensure that access to data is as open as possible, as closed as necessary, and where appropriate in line with the FAIR Principles (Findable, Accessible, Interoperable and Reusable) for data ...