Principle 3: Fair Trading Practices Trading fairly with concern for the social, economic and environmental well-being of producers. Adopting FAIR Data Principles. If you are in receipt of H2020 funding the EC requires a Data Management Plan (DMP) as part of the H2020 data pilot. Why should you make your data FAIR? The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015. How reliable data is lies in the eye of the beholder and depends on the fore-seen application. FAIR data support such collaborations and enable insight generation by facilitating the linking of data sources and enriching them with metadata. Want hoe beschermt u privacygevoelige informatie? Twee jaar later, na een open consultatieronde, zijn de FAIR-principes gepubliceerd. [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. FAIR Principles. (Meta)data include qualified references to other (meta)data[2]. 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. FOR THE ORGANISATION: A recognisable mark to show that your organisation can be trusted to use this personal data in an ethical way. 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. The FAIR data principles are guiding principles on how to make data Findable, Accessible, Interoperable and Reusable, formulated by Force11. Share on Facebook. Ook de AVG-kwestie speelt een rol. Reusable The ultimate goal of FAIR is to optimise the reuse of data. Principle 3: The FAIR principles emphasize 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.[2]. 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. FAIR data are Findable, Accessible, Interoperable and Reusable. Share on Twitter. Metadata and data should be easy to find for both humans and computers. The FAIR Data principles act as an international guideline for high quality data stewardship. The FAIR data principles (Wilkinson et al. Meta(data) are richly described with a plurality of accurate and relevant attributes, R1.1. 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 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. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. (Meta)data are registered or indexed in a searchable resource. Additionally, making digital objects FAIR requires a change in practices and the implementation of technologies and infrastructures. In this blog we will explain why this is in our view good news for Wageningen and why it will help to make our data more “FAIR”. Gemäß der FAIR-Prinzipien sollen Daten " F indable, A ccessible, I nteroperable, and R e-usable" sein. In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. It is therefore important that relevant data is findable, accessible, interoperable and re-usable (FAIR). To be Findable: F1. (Meta)data are assigned a globally unique and persistent identifier, F2. 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. The guidelines are timely as we see unprecedented volume, complexity, and … The principles have since received worldwide recognition by various organisations including FORCE11 , National Institutes of Health (NIH) and the European Commission as a useful framework for thinking about sharing data in a way that will enable maximum … FAIR stands for Findable, Accessible, Interoperable, Reusable. 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 Data and the FAIR Principles 1.5 - Language en 1.6 - Description This module provides five lessons to ensure that a researcher’s data is properly managed and published to ensure it enables reproducible research. by the FAIR principles. The principles developed addressed four key aspects of making data Finable, Accessible, Interoperable and Reusable (FAIR). The FAIR data prinicples are based on the four key corner stones of findability, accessibility, interoperability and reuse. The FAIR Guiding Principles for scientific data management and stewardship. In fact, if approached at the right moment, the FAIR principles should be taken into consideration so that data are Findable, Accessible, Interoperable and Reusable. The principles were first published in 2016 (Wilkinson et al. FAIR Principles. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. [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. De internationale FAIR-principes zijn in 2014 geformuleerd tijdens een bijeenkomst in Leiden. Data scientists reported that this accounts for up to 80% of their working time. R1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. The FAIR Data Principles apply to metadata, data, and supporting infrastructure (e.g., search engines). EN Research and results FAIR data and data management Data management in your project. Data Quality Principle. Het vraagt immers om een herziening van het huidige datamanagement. Die FAIR Data Principles, welche mittlerweile einen defacto-Standard des qualitätsbewussten Datenmanagements darstellen, verlangen nämlich, dass das Datenmanagement ständig darauf ausgerichtet sein soll, dass Forschungsdaten findable (auffindbar), accessible (zugänglich), interoperable (interoperabel) und reusable (nachnutzbar) gemacht werden und dauerhaft bleiben. Interoperable The data usually need to be integrated with other data. 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. These identifiers make it possible to locate and cite the dataset and its metadata. Published in 2016, the guidelines provide key requirements to make scientific data FAIR—findable, accessible, interoperable and reusable. [10], Guides on implementing FAIR data practices state that the cost of a data management plan in compliance with FAIR data practices should be 5% of the total research budget. Benefits to Researchers. Here, we describe FAIR - a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable. The resulting FAIR Principles for Heritage Library, Archive and Museum Collections focus on three levels: objects, metadata and metadata records. I1. The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015. 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. Het toepassen van de FAIR principes is een flinke kluif. On this website, we explain the principles (based on the DTLS website) and translate them into practical information for Radboud University researchers. The FAIR Data Principles represent a consensus guide on good data management from all key stakeholders in scientific research. Researchers need to consider data management and stewardship throughout the grant procedure and their research project. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. The first step in (re)using data is to find them. Les principes FAIR sont un ensemble de principes directeurs pour gérer les données de la recherche visant à les rendre faciles à trouver, accessibles, interopérables et réutilisables par l’homme et la machine. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. The CARE Principles for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event “Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop,” 8 November 2018, Gaborone, Botswana. 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. 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