https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc#Head https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc http://www.nanopub.org/nschema#hasAssertion https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc#assertion https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc http://www.nanopub.org/nschema#hasProvenance https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc#provenance https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc#pubinfo https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/fair/icc/np/I1-Explanation/RATIi7pPk_d5IOgxCstKpSN-t-7E1p3xrIj72JoFB5xUc#assertion https://doi.org/10.1162/dint_a_00040 http://www.w3.org/2000/01/rdf-schema#label Guizzardi 2019 https://w3id.org/fair/icc/terms/I1-Explanation http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fair/icc/terms/Explanation https://w3id.org/fair/icc/terms/I1-Explanation http://www.w3.org/2000/01/rdf-schema#comment Consumers spend a disproportionate amount of time trying to make sense of the digital resources they need and designing accurate ways to combine them. This is most often due to a lack of suitably unambiguous content descriptors, or a lack of such descriptors entirely with respect to non-machine-interpretable data formats such as tables or “generic” XML. Community-defined data exchange formats work reasonably well within their original scope of a few types of data and a relatively homogeneous community, but not well beyond that. This makes interoperation and integration an expensive, often impossible task (even for humans), but also means that machines cannot easily make use of digital resources, which is the primary goal of FAIR. For example, when a machine visits two data files in which a field “temperature” is present, then it will need more contextual descriptions to distinguish between weather data in one file and body temperature measurements in another. Achieving a ‘common understanding’ of digital resources through a globally understood ‘language’ for machines is the purpose of principle I1, with emphasis on ‘knowledge’ and ‘knowledge representation’. This becomes critical when many differently formatted resources need to be visited or combined across organizations and countries and is especially challenging for interdisciplinary studies or for meta-analyses, where results from independent organizations, pertaining to the same topic, must be combined.  In this context, the principle says that producers of digital resources are required to use a language (i.e., a representation of data/knowledge) that has a defined mechanism for mechanized interpretation - a machine-readable “grammar” - where, for example, the difference between an entity, as well as any relevant relationship between entities, is defined in the structure of the language itself. This allows machines to consume the information with at least a basic “understanding” of its content. It is a step towards a common understanding of digital resources by machines, which is a prerequisite for a functional Internet of FAIR Data and Services. Several technologies can be chosen for principle I1. https://w3id.org/fair/icc/terms/I1-Explanation http://www.w3.org/2000/01/rdf-schema#isDefinedBy https://w3id.org/fair/icc/latest/I1-Explanation https://w3id.org/fair/icc/terms/I1-Explanation http://www.w3.org/2000/01/rdf-schema#label I1 Explanation https://w3id.org/fair/icc/terms/I1-Explanation http://www.w3.org/2000/01/rdf-schema#seeAlso https://doi.org/10.1162/dint_a_00040 https://w3id.org/fair/icc/terms/I1-Explanation http://www.w3.org/2000/01/rdf-schema#seeAlso https://www.w3.org/RDF/ https://w3id.org/fair/icc/terms/I1-Explanation https://w3id.org/fair/icc/terms/explains-principle https://w3id.org/fair/principles/terms/I1 https://w3id.org/fair/icc/terms/I1-Explanation https://w3id.org/fair/icc/terms/implementation-considerations Communities will have to choose an available technology or decide how they will otherwise deal with multiple representations and languages. In any case, they will have to make sure that each data item that is the same in multiple resources is interpreted in exactly the same way by every agent (human and computer), and that how items across resources relate to one another can be unambiguously understood by all agents (doi:10.1162/dint_a_00040). The key consideration in this regard is that FAIR speaks to the ability of data to be reused by a generic agent, rather than a community-specific agent. This is most easily accomplished by making the knowledge available in the most widely used format(s), even if this means duplication of the information in the community-specific format. https://w3id.org/fair/icc/terms/I1-Explanation https://w3id.org/fair/icc/terms/implementation-examples The most widely-accepted choice to adhere to this principle, at the present time, is the Resource Description Framework (RDF) which is the W3C’s recommendation for how to represent knowledge on the Web in a machine-accessible format (https://www.w3.org/RDF/). Other choices may also be acceptable, for instance when they are already in widespread use within a given community. 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