For all parties involved in Data Stewardship, the facets of FAIRness, described below, provide incremental guidance regarding how they can benefit from moving toward the ultimate objective of having all concepts referred-to in Data Objects (Meta data or Data Elements themselves) unambiguously resolvable for machines, and thus also for humans. Die FAIR-Prinzipien erlauben auch eine Einschränkung des Datenzugangs, die in gewissen Fällen sinnvoll oder sogar erforderlich ist. Gemäß der FAIR-Prinzipien sollen Daten " F indable, A ccessible, I nteroperable, and R e-usable" sein. The FAIR data principles are a set of guidelines, developed primarily in the research and academic sector, to encourage and enable better sharing and reuse of data. There should be limits to the collection of personal data and any such data should be obtained by lawful and fair means and, where appropriate, with the knowledge or consent of the data subject. R1. FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. (Meta)data are registered or indexed in a searchable resource[2]. The principles emphasise machine-actionability (i.e., 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 volume, complexity, and creation speed of data. De FAIR-principles zijn geformuleerd door FORCE11 In Nederland worden de FAIR-principles in brede kring erkend. Sci Data 3, 160018 (2016) doi:10.1038/sdata.2016.18) and are now a standard framework for the storage and sharing of scientific information. The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published on Scientific Data in 2016, are a set of guiding principles proposed by a consortium of scientists and organizations to support the reusability of digital assets. The principles developed addressed four key aspects of making data Finable, Accessible, Interoperable and Reusable (FAIR). Principle 3: Fair Trading Practices Trading fairly with concern for the social, economic and environmental well-being of producers. The FAIR data principles (Wilkinson et al. At DTL we promote and advance FAIR Data Stewardship in the life sciences through our extensive partnerships and in close collaboration with our international network. The FAIR data principles in context. Die "FAIR Data Principles" formulieren Grundsätze, die nachhaltig nachnutzbare Forschungsdaten erfüllen müssen und die Forschungsdateninfrastrukturen dementsprechend im Rahmen der von ihnen angebotenen Services implementieren sollten. Eric Little, at Osthus, presented the FAIR data principles and discussed how applying them could help to build Data Catalogs, where data is much easier to find, access and integrate across large organizations. Hauptziel der FAIR Data Prinzipien ist sicherlich die optimale Aufbereitung der Forschungsdaten für Mensch und Maschine. Share by WhatsApp. In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. FAIR data support such collaborations and enable insight generation by facilitating the linking of data sources and enriching them with metadata. In 2017 Germany, Netherlands and France agreed to establish[6] an international office to support the FAIR initiative, the GO FAIR International Support and Coordination Office. Share on Twitter. The 'FAIR' Guiding Principles for scientific data management and stewardship form the focus of an article in the Nature journal Scientific Data an open-access, peer-reviewed journal for descriptions of scientifically valuable datasets. En wanneer u zelf gebruik maakt van andermans data, hoe weet u dan dat alles klopt? FAIR data principles: use cases. Für … The principles help data and metadata to be ‘machine readable’, supporting new discoveries through the harvest and analysis of multiple datasets. FAIR data are Findable, Accessible, Interoperable and Reusable. There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published on Scientific Data in 2016, are a set of guiding principles proposed by a consortium of scientists and organizations to support the reusability of digital assets. De internationale FAIR-principes zijn in 2014 geformuleerd tijdens een bijeenkomst in Leiden. SND strives to make data in the national research data catalogue as compliant as possible with the FAIR criteria, but as a researcher, you also play an important part in this work. For example, data could meet the FAIR principles, but be private or only shared under certain restrictions. [9], A 2017 paper by advocates of FAIR data reported that awareness of the FAIR concept was increasing among various researchers and institutes, but also, understanding of the concept was becoming confused as different people apply their own differing perspectives to it. Throughout the FAIR Principles, we use the phrase ‘ (meta)data ’ in cases where the Principle should be applied to both metadata and data. To facilitate this, datasets need to be Findable, Accessible, Interoperable and Reusable. The term FAIR was launched at a Lorentz workshop in 2014, the resulting FAIR principles were published in 2016. FAIR data Guiding Principles. The FAIR principles are designed to support knowledge discovery and innovation both by humans and machines, support data and knowledge integration, promote sharing and reuse of data, be applied across multiple disciplines and help data and metadata to be ‘machine readable’, support new discoveries through the harvest and analysis of multiple datasets and outputs. The FAIR Data Principles provide a set of guiding principles for successful research data management (RDM) in order to make data findable, accessible, interoperable and reusable [3]. The FAIR Data Principles represent a consensus guide on good data management from all key stakeholders in scientific research. The FAIR Guiding Principles for scientific data management and stewardship. Principle 2: Transparency and Accountability Involving producers in important decision making. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. Most of the requirements for findability and accessibility can be achieved at the metadata level. What is FAIR data? The FAIR data prinicples are based on the four key corner stones of findability, accessibility, interoperability and reuse. Twee jaar later, na een open consultatieronde, zijn de FAIR-principes gepubliceerd. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component). The FAIR data principles (Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship were first published in Scientific Data in 2016. 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