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An introduction to FAIR data and persistent identifiers. FAIR data is data which meets the FAIR principles of findability, accessibility, interoperability, and reusability (FAIR). [1] [2] The acronym and principles were defined in a March 2016 paper in the journal Scientific Data by a consortium of scientists and organizations. [1]
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 ...
A research design typically outlines the theories and models underlying a project; the research question(s) of a project; a strategy for gathering data and information; and a strategy for producing answers from the data. [1] A strong research design yields valid answers to research questions while weak designs yield unreliable, imprecise or ...
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 ...
Consisting of ten concise principles, along with a description for each, the Leiden Manifesto aims to reconstruct the way that research evaluations by academic publishers and scientific institutions are done. Its emphasis lies in detailed and close evaluation of research, rather than the excessive use of quantitative data in evaluations.
There are also calls to preserve and share research data sets and publication metadata as part of the publication process. The FAIR Data Principles are a framework for making research data and metadata “findable, accessible, interoperable, and reusable.” [ 83 ] [ 155 ] [ 156 ]
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. [1]
The principles of privacy by design "remain vague and leave many open questions about their application when engineering systems". The authors argue that "starting from data minimization is a necessary and foundational first step to engineer systems in line with the principles of privacy by design".