FAIR data presupposes their successful communication between machines and humans while preserving their meaning and reference, requiring all parties involved to share the same background knowledge. Inspired by English as a natural language, we investigate the linguistic structure that ensures reliable communication of information and draw parallels with data structures, understanding both as models of systems of interest. We conceptualize semantic interoperability as comprising terminological and propositional interoperability. The former includes ontological (i.e., same meaning) and referential (i.e., same referent/extension) interoperability and the latter schema (i.e., same data schema) and logical (i.e., same logical framework) interoperability. Since no best ontology and no best data schema exists, establishing semantic interoperability and FAIRness of data and metadata requires the provision of a comprehensive set of relevant ontological and referential entity mappings and schema crosswalks. We therefore propose appropriate additions to the FAIR Guiding Principles, leading to FAIR 2.0. Furthermore, achieving FAIRness of data requires the provision of FAIR services in addition to organizing data into FAIR Digital Objects. FAIR services include a terminology, a schema, and an operations service.
The FAIREr Guiding Principles: Organizing data and metadata into semantically meaningful types of FAIR Digital Objects to increase their human explorability and cognitive interoperability
TIB Leibniz Information Centre for Science and Technology（德国国家科技情报中心 TIB 莱布尼茨信息科学中心）
The "I" in FAIR: Translating from Interoperability in Principle to Interoperation in Practice
Renaissance Computing Institute, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校文艺复兴计算研究所）；Copperline Professional Solutions, LLC（铜线专业解决方案有限责任公司）；Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）
Rethinking OWL Expressivity: Semantic Units for FAIR and Cognitively Interoperable Knowledge Graphs Why OWLs don't have to understand everything they say
TIB Leibniz Information Centre for Science and Technology（TIB 莱布尼茨科学技术信息中心）
The Public Health and Environmental Surveillance Open Data Model (PHES-ODM) Version 3: An Open, Relational Data Model and Interoperability Framework for Wastewater Surveillance
Ottawa Hospital Research Institute, University of Ottawa（渥太华医院研究所，渥太华大学）；Ghent University Faculty of Bioscience Engineering（根特大学生物工程学院）；ModelEAU, Université Laval（ModelEAU，拉瓦尔大学）；Public Health Agency of Canada National Microbiology Laboratory（加拿大公共卫生局国家微生物实验室）；European Commission, Joint Research Centre（欧盟委员会联合研究中心）
Ontologies for increasing the FAIRness of plant research data
Forschungszentrum Jülich GmbH（于利希研究中心）；Bioeconomy Science Center (BioSC)（生物经济科学中心）；CEPLAS（植物科学卓越集群）；Bioversity International（生物多样性国际）；Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希海涅大学）；University of Cologne（科隆大学）；Leibniz Institute of Plant Biochemistry（莱布尼茨植物生物化学研究所）；German Centre for Integrative Biodiversity Research (iDiv)（德国综合生物多样性研究中心）
Electrospinning-Data.org, a platform for electrospinning data and analysis
Thomas Johann Seebeck Department of Electronics, Tallinn University of Technology（托马斯·约翰·塞贝克电子学系，塔林理工大学）；Department of Electron Devices, Budapest University of Technology and Economics（电子器件系，布达佩斯技术与经济大学）
The TripleA Principle: Making Knowledge Actionable, Applicable, and Auditable in Post-FAIR Infrastructures
Leibniz Institute for the Analysis of Biodiversity Change (LIB), Museum of Nature Hamburg（莱布尼茨生物多样性变化分析研究所（LIB），汉堡自然博物馆）；Johannes Kepler University Linz（约翰内斯·开普林茨大学）；Department of Biology, Carleton University（卡尔顿大学生物学系）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Argonne National Laboratory（阿贡国家实验室）；University of Chicago（芝加哥大学）；University of California San Diego（加利福尼亚大学圣地亚哥分校）；Halıcıoğlu Data Science Institute（哈利乔卢数据科学研究所）；Massachusetts Institute of Technology（麻省理工学院）；The University of Minnesota（明尼苏达大学）
