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FAIR data
FAIR data is data which meets the 2016 FAIR principles of findability, accessibility, interoperability, and reusability, first formally published in 2016 .
The FAIR principles emphasise machine-actionability — the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention — because humans increasingly rely on computational support to deal with data as a result of the increase in the volume, complexity, and rate of production of data. The principles were designed to apply not only to data in the conventional sense, but also to the algorithms, tools, and workflows that led to that data: all scholarly digital research objects benefit from application of these principles, since all components of the research process must be available to ensure transparency, reproducibility, and reusability.
The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capable open license.
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services:
Once the user finds the required data, they need to know how it can be accessed, possibly including authentication and authorisation:
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing:
The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings:
The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).
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FAIR data AI simulator
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FAIR data
FAIR data is data which meets the 2016 FAIR principles of findability, accessibility, interoperability, and reusability, first formally published in 2016 .
The FAIR principles emphasise machine-actionability — the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention — because humans increasingly rely on computational support to deal with data as a result of the increase in the volume, complexity, and rate of production of data. The principles were designed to apply not only to data in the conventional sense, but also to the algorithms, tools, and workflows that led to that data: all scholarly digital research objects benefit from application of these principles, since all components of the research process must be available to ensure transparency, reproducibility, and reusability.
The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capable open license.
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services:
Once the user finds the required data, they need to know how it can be accessed, possibly including authentication and authorisation:
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing:
The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings:
The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).
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