The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. Adopting the FAIR data principles requires institutions to strengthen their policies around the sharing and management of research data. The principles help data and metadata to be ‘machine readable’, supporting new discoveries through the harvest and analysis of multiple datasets. The context FAIR DATA – The role of scientists FAIR Repository – The role of the repository Each dataset is assigned a globally unique and persistent identifier (PID), e.g. The FAIR Guiding Principles for scientific data management and stewardship. The new Fair Data Principles are: Principle 1: We will ensure that all personal data is processed in line with the reasonable expectations of individuals of our use of their personal data. (meta)data are assigned … Die FAIR-Prinzipien erlauben auch eine Einschränkung des Datenzugangs, die in gewissen Fällen sinnvoll oder sogar erforderlich ist. The FAIR data principles (Wilkinson et al. To facilitate this, datasets need to be Findable, Accessible, Interoperable and Reusable. FAIR Data Stewardship combines the ideas of data management during research projects, data preservation after research projects, and the FAIR Principles for guidance on how to handle data. Share by e-mail. This is an initiative of the stakeholders in the research process including academics, industry, funders and scholarly publishers to design and implement a set of principles that are called the FAIR Data Principles. However, as this report argues, the FAIR principles do not just apply to data but to other digital objects including outputs of research. Reusable The ultimate goal of FAIR is to optimise the reuse of data. FAIR stands for Findable, Accessible, Interoperable and Reusable.The FAIR Data Principles were developed and endorsed by researchers, publishers, funding agencies and industry partners in 2016 and are designed to enhance the value of all digital resources. The FAIR data principles are an integral part of the work within open science, and describe some of the most central guidelines for good data management and open access to research data. For example, data could meet the FAIR principles, but be private or only shared under certain restrictions. Data scientists reported that this accounts for up to 80% of their working time. The FAIR Data Principles represent a consensus guide on good data management from all key stakeholders in scientific research. 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. Nevertheless at the core of the whole idea is the notion that your digital resouces (read documents) are described by clear meaningful additional information – referred to as metadata. Interoperable The data usually need to be integrated with other data. De FAIR-principles zijn geformuleerd door FORCE11 In Nederland worden de FAIR-principles in brede kring erkend. FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. Here, we describe FAIR - a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable. For example, publically available data may lack sufficient documentation to meet the FAIR principles… The FAIR Data Principles where published in 2016 by a consortium of organisations and researchers who not only wanted to enhance the reusability of datasets, but also related facets such as tools, workflows and algorithms. I1. For instance, FAIR principles are used in the template for data management plans that are mandatory for projects that receive funding from EU Horizon 2020. I2. In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. In the Data FAIRport, the embedded FAIR Data Points provide the relevant metadata to be indexed by the Data FAIRport’s data search engine as well as the accessibility to the data. It is therefore important that relevant data is findable, accessible, interoperable and re-usable (FAIR). FAIR Data Principles (Findable, Accessible, Interoperable, Re-usable) support knowledge discovery and innovation as well as data and knowledge integration, and promote sharing and reuse of data. [2], At the 2016 G20 Hangzhou summit, the G20 leaders issued a statement endorsing the application of FAIR principles to research. The principles were first published in 2016 (Wilkinson et al. Most of the requirements for findability and accessibility can be achieved at the metadata level, but interoperability and reuse require more efforts at the data level.This scheme depicts the FAIRification process adopted by GO FAIR. The FAIR Data Principles provide guidelines on how to achieve this however there are specific benefits to organisations and researchers. Open data may not be FAIR. [14], Data compliant with the terms of the FAIR Data Principles, Acceptance and implementation of FAIR data principles, Sandra Collins; Françoise Genova; Natalie Harrower; Simon Hodson; Sarah Jones; Leif Laaksonen; Daniel Mietchen; Rūta Petrauskaité; Peter Wittenburg (7 June 2018), "Turning FAIR data into reality: interim report from the European Commission Expert Group on FAIR data", Zenodo, doi:10.5281/ZENODO.1285272, GO FAIR International Support and Coordination Office, Association of European Research Libraries, "The FAIR Guiding Principles for scientific data management and stewardship", Creative Commons Attribution 4.0 International License, "G20 Leaders' Communique Hangzhou Summit", "European Commission embraces the FAIR principles - Dutch Techcentre for Life Sciences", "Progress towards the European Open Science Cloud - GO FAIR - News item - Government.nl", "Open Consultation on FAIR Data Action Plan - LIBER", "Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud", "Funding research data management and related infrastructures", "CARE Principles of Indigenous Data Governance", "FAIR Principles: Interpretations and Implementation Considerations", https://en.wikipedia.org/w/index.php?title=FAIR_data&oldid=994054954, Creative Commons Attribution-ShareAlike License, This page was last edited on 13 December 2020, at 21:54. Principle 3: Fair Trading Practices Trading fairly with concern for the social, economic and environmental well-being of producers. (Meta)data are released with a clear and accessible data usage license, R1.2. The FAIR principles can be seen as a consolidation of these earlier efforts and emerged from a multi-stakeholder vision of an infrastructure supporting machine-actionable data reuse, i.e., reuse of data that can be processed by computers , which was later coined the “Internet of FAIR Data and Services” (IFDS) . Both ideas are fundamentally aligned and can learn from each other. A Fair Data company must meet the Fair Data principles. It has since been adopted by research institutions worldwide. (Meta)data are released with a clear and accessible data usage license, R1.2. Ook de AVG-kwestie speelt een rol. FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. FAIR is een acroniem voor: Findable - vindbaar; Accessible - toegankelijk; Interoperable - uitwisselbaar; Reusable - herbruikbaar; De internationale FAIR-principes zijn in 2014 geformuleerd tijdens een bijeenkomst in Leiden. FAIR PRINCIPLES 1. Le mot Fair fait aussi référence au Fair use, fair trade, fair play, etc., il évoque un comportement proactif et altruiste du producteur de données, qui cherche à les rendre plus facilement trouvables et utilisables par tous, tout en facilitant en aval le sourçage (éventuellement automatique) par l'utilisateur des données. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. Metadata clearly and explicitly include the identifier of the data they describe, F4. X. ANCHOR . In diesem Beitrag erläutern wir die jeweiligen Anforderungen und geben Beispiele. (Meta)data are associated with detailed provenance, R1.3. This is what the FAIR principles are all about. Adopting FAIR Data Principles. Twee jaar later, na een open consultatieronde, zijn de FAIR-principes gepubliceerd. FAIR data In order to make use of integrated data sets, we have to continuously validate their accuracy, their reliability, and their veracity with new forms of big data analytics. (Meta)data are richly described with a plurality of accurate and relevant attributes, R1.1. Except where otherwise noted, content on this website is licensed under a Creative Commons Attribution 4.0 License by GO FAIR, F1: (Meta) data are assigned globally unique and persistent identifiers, F2: Data are described with rich metadata, F3: Metadata clearly and explicitly include the identifier of the data they describe, F4: (Meta)data are registered or indexed in a searchable resource, A1: (Meta)data are retrievable by their identifier using a standardised communication protocol, A1.1: The protocol is open, free and universally implementable, A1.2: The protocol allows for an authentication and authorisation where necessary, A2: Metadata should be accessible even when the data is no longer available, I1: (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation, I2: (Meta)data use vocabularies that follow the FAIR principles, I3: (Meta)data include qualified references to other (meta)data, R1: (Meta)data are richly described with a plurality of accurate and relevant attributes, R1.1: (Meta)data are released with a clear and accessible data usage license, R1.2: (Meta)data are associated with detailed provenance, R1.3: (Meta)data meet domain-relevant community standards, FAIR Guiding Principles for scientific data management and stewardship’. However, excluding matters of confidentiality they can be considered to extend far wider. 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. [3][4], In 2016 a group of Australian organisations developed a Statement on FAIR Access to Australia's Research Outputs, which aimed to extend the principles to research outputs more generally.[5]. Hauptziel der FAIR Data Prinzipien ist sicherlich die optimale Aufbereitung der Forschungsdaten für Mensch und Maschine. There is a new experimental service, vest.agrisemantics.org that brings together different vocabularies that can be used as models for data in many subject fields that Wageningen is working on. The ARDC supports and encourages initiatives that enable making data and other related research outputs FAIR. In the FAIR Data approach, data should be: Findable – Easy to find by both humans and computer systems and based on mandatory description of the metadata that allow the discovery of interesting datasets These identifiers make it possible to locate and cite the dataset and its metadata. 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 Data FAIRport is an interoperability platform that allows data owners to publish their (meta)data and allows data users to search for and access data (if licenses allow). (Meta)data are retrievable by their identifier using a standardised communications protocol, A1.1 The protocol is open, free, and universally implementable, A1.2 The protocol allows for an authentication and authorisation procedure, where necessary, A2. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. 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. R1. [11], Before FAIR a 2007 paper was the earliest paper discussing similar ideas related to data accessibility.[12]. FAIR principles implementation assessment is being explored by FAIR Data Maturity Model Working Group of RDA,[7] CODATA's strategic Decadal Programme "Data for Planet: Making data work for cross-domain challenges"[8] mentions FAIR data principles as a fundamental enabler of data driven science. These guidelines are based on the FAIR Principles for scholarly output (FAIR data principles [2014]), taking into account a number of other recent initiatives for making data findable, accessible, interoperable and reusable. Researchers who apply for a grant … The term FAIR was launched at a Lorentz workshop in 2014, the resulting FAIR principles were published in 2016. Principle 2: Transparency and Accountability Involving producers in important decision making. Preamble: In the eScience ecosystem, the challenge of enabling optimal use of research data and methods is a complex one with multiple stakeholders: Researchers wanting to share their data and interpretations; Professional data publishers offering their services, software and tool-builders providing data analysis and processing services; Funding agencies They were developed to help address common obstacles to data discovery and reuse – long recognized as an issue within scholarly research and beyond. Télécharger Voir le site. Data sovereignty is the ability of a natural or legal person to exclusively and sovereignly decide concerning the usage of data as an economic asset. 1. F1: (Meta) data are assigned globally unique and persistent identifiers; F2: Data are described with rich metadata; F3: Metadata clearly and explicitly include the identifier of the data they describe; F4: (Meta)data are registered or indexed in a searchable resource The Principles define characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties. Benefits to Researchers. The FAIR data principles are guiding principles on how to make data Findable, Accessible, Interoperable and Reusable, formulated by Force11. Metadata and data should be easy to find for both humans and computers. 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