The principles provide guidance for making data F indable, A ccessible, I nteroperable, and R eusable. 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. 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. The FAIR Data principles act as an international guideline for high quality data stewardship. FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. Share by WhatsApp. Data management in your project . In diesem Beitrag erläutern wir die jeweiligen Anforderungen und geben Beispiele. Für … I1. FAIR data are Findable, Accessible, Interoperable and Reusable. Researchers who apply for a grant … 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. Researchers can focus on adding value by interpreting the data rather than searching, collecting or re-creating existing data. (meta)data are assigned … The Association of European Research Libraries recommends the use of FAIR principles. A March 2016 publication by a consortium of scientists and organizations specified the "FAIR Guiding Principles for scientific data management and stewardship" in Scientific Data, using FAIR as an acronym and making the concept easier to discuss. [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]. (Meta)data meet domain-relevant community standards, The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. 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. a Digital Object Identifier (DOI). In this knowledge clip we have a look at FAIR data and what each of the FAIR principles mean (findable, accessible, interoperable and reusable). Published in 2016, the guidelines provide key requirements to make scientific data FAIR—findable, accessible, interoperable and reusable. The Council of the European Union emphasises that “the opportunities for the optimal reuse of research data can only be realised if data are consistent with the FAIR principles (findable, accessible, interoperable and re-usable) within a secure and trustworthy environment” (Council conclusions on the transition towards an open science system). (Meta)data are registered or indexed in a searchable resource. 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. Twee jaar later, na een open consultatieronde, zijn de FAIR-principes gepubliceerd. Open data may not be FAIR. 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. Adopting the FAIR data principles requires institutions to strengthen their policies around the sharing and management of research data. Why use the FAIR principles for your research data? Twee jaar later, na een open consultatieronde, zijn de FAIR-principes gepubliceerd. 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 ultimate goal of FAIR is to optimise the reuse of data. De principes dienen als richtlijn om wetenschappelijke data geschikt te maken voor hergebruik onder duidelijk beschreven condities, door zowel mensen als machines. FAIR data principles — making data Findable, Accessible, Interoperable and Reusable — are essential elements that allow R&D-intensive organizations to maximize the value of their digital assets. 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. A Fair Data company must meet the Fair Data principles. However, excluding matters of confidentiality they can be considered to extend far wider. Adopting FAIR Data Principles. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. 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. 2. (Meta)data are richly described with a plurality of accurate and relevant attributes, R1.1. Throughout the FAIR Principles, we use the phrase ‘ (meta)data ’ in cases where the Principle should be applied to both metadata and data. GDPR Compliance. The FAIR data principles (Wilkinson et al. [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. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. Findable The first step in (re)using data is to find them. Het vraagt immers om een herziening van het huidige datamanagement. FAIR: findable, accessible, interoperable, reusable) primarily focus on characteristics of data that will facilitate increased data sharing among entities while ignoring power differentials and historical contexts. Share on Twitter. Télécharger Voir le site. The FAIR Guiding Principles for scientific data management and stewardship. F1. 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. FAIR Data Principles. Want hoe beschermt u privacygevoelige informatie? Metadata and data should be easy to find for both humans and computers. 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. To facilitate this, datasets need to be Findable, Accessible, Interoperable and Reusable. 1. How reliable data is lies in the eye of the beholder and depends on the fore-seen application. Metadata are accessible, even when the data are no longer available. 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. Meta(data) are richly described with a plurality of accurate and relevant attributes, R1.1. FAIR Data Principles. The FAIR data principles (Wilkinson et al. The Principles define characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. 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. (Meta)data are released with a clear and accessible data usage license, R1.2. The first step in (re)using data is to find them. FAIR data principles: use cases. En wanneer u zelf gebruik maakt van andermans data, hoe weet u dan dat alles klopt? The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capable open license. Researchers need to consider data management and stewardship throughout the grant procedure and their research project. [13] The CARE principles extend principles outlined in FAIR data to include Collective benefit, Authority to control, Responsibility, and Ethics to ensure data guidelines address historical contexts and power differentials. Data scientists reported that this accounts for up to 80% of their working time. 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) . (Meta)data are registered or indexed in a searchable resource[2]. For example, data could meet the FAIR principles, but be private or only shared under certain restrictions. 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. Much of the data the biopharma and life sciences industry uses for its R&D processes are generated outside the company or in collaboration with external partners. R1. The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015. 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 Other international organisations active in the research data ecosystem, such as CODATA or Research Data Alliance (RDA) also support FAIR implementations by their communities. F1. Existing principles within the open data movement (e.g. (Meta)data include qualified references to other (meta)data[2]. For example, data could meet the FAIR principles, but be private or only shared under certain restrictions. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. 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. 3.2 FAIR data principles. 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. 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. 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. There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. Metadata and data should be easy to find for both humans and computers. 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. This includes working on policy, developing what FAIR means for specific disciplines, data and output types, supporting developers when developing code that enables FAIR outputs and building skills for research support staff and researchers. The resulting FAIR Principles for Heritage Library, Archive and Museum Collections focus on three levels: objects, metadata and metadata records. And research institutes are promoting measures to secure the transparency and accessibility of locally produced data sets. Principle 2: We will only use data for specified purposes and be open with individuals about the use of their data, respecting individuals’ wishes about the use of their data. Data are described with rich metadata (defined by R1 below), F3. Findability; Accessibility; Interoperability; Reusability; They are considered so important the G20 leaders, at the 2016 G20 Hangzhou summit, issued a statement endorsing the application of FAIR principles to research. A practical “how to” guidance to go FAIR can be found in the Three-point FAIRification Framework. FAIR data implementeren. [11], Before FAIR a 2007 paper was the earliest paper discussing similar ideas related to data accessibility.[12]. Share on Facebook. For the most part, these efforts are being led by research librarians, who have the unique skills and expertise needed to help their institutions become FAIR compliant. FAIR data principles — making data Findable, Accessible, Interoperable and Reusable — are essential elements that allow R&D-intensive organizations to maximize the value of their digital assets. (Meta)data are associated with detailed provenance, R1.3. On this website, we explain the principles (based on the DTLS website) and translate them into practical information for Radboud University researchers. Data can be FAIR but not open. These identifiers make it possible to locate and cite the dataset and its metadata. 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. (Meta)data are associated with detailed provenance, R1.3. 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’. Following the lead of the European Commission and Horizon 2020, Irish funders, including the Health Research Board (HRB) … This is what the FAIR principles are all about. FAIR Principles. FAIR data is all about reuse of data and emphasizes the ability of computers to find and use data. Principle 3: Once the user finds the required data, she/he needs to know how they can be accessed, possibly including authentication and authorisation. They were developed to help address common obstacles to data discovery and reuse – long recognized as an issue within scholarly research and beyond. (Meta)data use vocabularies that follow FAIR principles, I3. (Meta)data use vocabularies that follow FAIR principles, I3. A1. (Meta)data are released with a clear and accessible data usage license, R1.2. Most of the requirements for findability and accessibility can be achieved at the metadata level. 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