Computer Science > Machine Learning. arXiv:2006.06053 (cs). [Submitted on 10 Jun 2020]. Title:Fair Data Integration. Authors:Sainyam Galhotra, Karthikeyan

The FAIR principles, first published in 2016, contain guidelines for good data management practice that aim at making data FAIR: findable, accessible, interoperable, and reusable. "Data" refers in this context to all kinds of digital objects that are produced in research: research data in the strictest sense, code, software, presentations, etc.

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R1.1. (Meta)data are released with a clear and accessible data usage license In this context, during the Lorentz Workshop "Jointly Designing a Data FAIRport" (2014), participants formulated the FAIR data vision to optimise data sharing and reuse by humans and machines, which resulted in the publication of The FAIR Guiding Principles for scientific data management and stewardship, published in "Scientific Data". The FAIR principles describe how research outputs should be organised so they can be more easily accessed, understood, exchanged and reused. 2016-03-15 · The FAIR principles can equally be applied to these non-data assets, which need to be identified, described, discovered, and reused in much the same manner as data. research data meet the FAIR principles (their so-called "FAIRness"), but also as support and guidance in planning and conducting research and data management.

FAIR research data shall be Findable, Accessible, Interoperable, and Reusable. There are a total of 15 FAIR principles that can be applied to research in all scientific disciplines. The FAIR principles are mainly focused on machine readability, but also target human understanding of research data, in order to enable the reuse of data.

FAIR Data Principles. Preamble. One of the grand challenges of data-intensive science is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows.

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Sammanlagt Findable: hur kan man hitta data? Accessible: hur får man tillgång till data? Interoperable: är data och metadata interoperabla? Reusable: kan andra använda Ett exempel på detta är EU:s program för forskningsfinansiering, där FAIR hade en framträdande roll i mallen för datahanteringsplan för Horizon 2020. Sannolikt Den första principen är att data och forskningsoutput ska gå att hitta - vara sökbar eller "Findable".

Read more about Fair Principles 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. Twee jaar later, na een open consultatieronde, zijn de FAIR-principes gepubliceerd. De principes dienen als richtlijn om wetenschappelijke data geschikt te maken voor hergebruik onder duidelijk beschreven condities, door zowel mensen als machines. Het zijn met FAIR data is a set of principles to make sure that any data that has been collected is stored properly.
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Het zijn met FAIR data is a set of principles to make sure that any data that has been collected is stored properly. FAIR data was introduced for scientific data, but the principles are also useful for government data or company data. Any valuable data that is used by multiple organisations should be made FAIR. History of FAIR Met andere woorden data moeten vindbaar, toegankelijk, interoperabel, herbruikbaar en duurzaam opgeslagen zijn.

To meet concrete societal challenges such as human rights on the Internet, free flow of information, FAIR innebär att forskningsdata ska vara Findable (sökbara), Accessible (tillgängliga), Metadata är strukturerad och beskrivande information om data.
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remote sensing data… • FAIR data helps use of data at scale, by machines, harnessing technological potential. • Research data often have considerable potential for reuse, reinterpretation, use in different studies. • Open data foster innovation and accelerate scientific discovery through reuse of data within and outside the academic system.

In the article we tell you what these two types of data are, what they look like and how they differ. Information has become one of the basic resources of society.

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While the DSA focuses primarily on the responsibilities and conduct of data producers and repositories, FAIR focuses primarily on the data itself.

1. S a r a h J o n e s A s s o c i a t e D i r e c t o r , D i g i t a l C u r a t i o n C e n t r e R a p p o r t e u r o f F A I R Data stewardship as described in this handbook is adequate because it is based on the FAIR Principles, which have been adopted worldwide. It is natural Geoscience Australia is part of the international research community and as such supports FAIR data principles (data that is Findable, Accessible, Interoperable, The FAIR Data Principles feature 15 facets corresponding to the four letters of FAIR - Findable, Accessible, Interoperable, Reusable.

This video provides an introduction to the FAIR Data Point, explaining what it is and how metadata providers can use it to expose their metadata in a FAIR way.

The FAIR principles are structured around sub-categories, each containing guidelines regarding an aspect of FAIR. Data can be FAIR but not open.

Programme including registration: Draft Statement: You're invited to provide the Expert Group of the CIPM "Digital SI" with further use-cases for interoperable data based on the SI: FAIR Data Principles apply not only to data but also to metadata, and are supporting infrastructures (e.g., search engines). 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. FAIR Data Principles. Preamble.