FAIR data Expert Group Call for Contributions The remit and objectives of the FAIR Data Expert Group The European Commission has established an Expert Group on FAIR data to support the Research and Innovation policy development on Open Science. 2 User Reviews . 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). Data Protection Act 1998 Principles Data Topic Fair Data Principles 1) Personal data shall be processed fairly and lawfully and, in particular, shall not be processed unless: (a) at least one of the conditions in Schedule 2 is met, and (b) in the case of sensitive personal data, at least one of the conditions in Schedule 3 is also met. There are certain core principles which drive a successful data governance implementation: Recognizing data as an asset: In any organization, data is the most important asset. 654065. The guidelines also provides the Horizon 2020 FAIR Data Management Plan template , which was designed to be applicable to any Horizon … The FAIR data prinicples are based on the four key corner stones of findability, accessibility, interoperability and reuse. Much more work to be done on relationship between FAIR data and FAIR repository Researchers must work according to the principles of FAIR data (Findable, Accessible, Interoperable and Re-usable. However, as this report argues, the FAIR principles do not just apply to data but to other digital objects including outputs of research. EUDAT & The FAIR Data Principles FAIR data Guiding Principles. Abstract: This is all articles of the special issue on Emerging FAIR Practices in a single PDF file. 1 Archivist Assessment . To provide input to the proposed European Open Science Cloud (EOSC) action plan on how to make data FAIR 4. The overall objective of the Expert Group is turning the FAIR data principles into an operational reality, to ensure that research data are … 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. This is how the ALCOA principle came to be. The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. In 2016, the FAIR data principles were published to offer guidelines to support communities’ needs on data sharing and improve data “Findability, Accessibility, Interoperability and Reuse”. 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. FAIR principles: proxy for data quality assessment • Operationalise the original principles to ensure no interactions among dimensions to ease scoring • Consider Reusability as the resultant of the other three: –the average FAIRness as an indicator of data quality –(F+A+I)/3=R • Manual and automatic scoring F A I R . Modern data protection law is built on “fair information practice principles” (FIPPS). Findable … 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. The FAIR Principles: First Generation Implementation Choices and Challenges (all articles in a single PDF) Barend Mons, Erik Schultes, Fenghong Liu, Annika Jacobsen. FAIR principles are deliberately vague - principles to be interpreted Nothing about back-up and preservation. How reliable data is lies in the eye of the beholder and depends on the fore-seen application. (meta)data TobeReusable: R1.meta(data) are richly described with a plurality of accurate and relevant attributes R1.1. At their inception in the 1970s and early 1980s, FIPPS were broad, aspirational, and included a blend of substantive (e.g., data quality, use limitation) and procedural (e.g., consent, access) principles. Prepare your (meta)data according to community stand-ards and best practices for data archiving and sharing in your research field. (meta)data meet domain‐relevant community standards FAIR Data Principles Data Fair Data Principles By becoming a Fair Data company you agree to adhere to ten core principles: We will make sure that customers have access to their personal data that we hold, and that we tell them how we use it. to as the FAIR Data Principles. Revised: May 2019. Access to research outputs will accord with international practices that are well defined, secure and trusted, and delivered through sustainable, fair, and efficient dissemination models. The Fair Data principles have been revised to reflect national and international developments in best practice in ethics and privacy including the revised data protection framework under the EU General Data Protection Regulation 2016 (GDPR). Principles of data security and integrity had to be standardized in order to regulate them and achieve better processes and higher quality products. We will protect personal data and keep it secure and confidential. to turn each component of the FAIR data principles into reality 2. 1. How can we facilitate data & information sharing (FAIR) Link datasets to publications (PubMed) • Develop criteria for open access NIH Data Sharing Repositories • Provide FAIR-enabled, open access for datasets that underly a publication resulting from NIH funded research 4. Relationship to Data Seal of Approval? The following established repositories fulfil SNSF’s requirements (allows publishing of FAIR data, non-commercial), and accept datasets from different research fields. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from con-temporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. There is a new (optional) template for DMPs which includes a section on FAIR data. (meta)data are released with a clear and accessible data usage license R1.2. PIPEDA’s 10 fair information principles form the ground rules for the collection, use and disclosure of personal information, as well as for providing access to personal information.They give individuals control over how their personal information is handled in the private sector. (meta)data are associated with detailed provenance R1.3. Findable F1. Measuring the Impact of Digital Repositories Interagency Workshop (2017) to identify current assessment metrics, … 24 Downloads . data and linked data [3]. Revised MRS Fair Data Principles (September 2018) GDPR Principles and concepts Current Fair Data Principle Revised Fair Data Principle Lawfulness, Fairness and Transparency Principle 1: We will ensure that all personal data is collected with customers’ consent. by the FAIR principles. The processing of personal data should be lawful and fair and done in a transparent manner. EUDAT receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license. Additionally, making digital objects FAIR requires a change in practices and the implementation of PSE. Abstract & Keywords. FAIR Principles About GO FAIR GO FAIR offers an open and inclusive ecosystem for anyone committed to defining and creating materials and tools as elements of the Internet of FAIR Data and Services. The Horizon 2020 FAIR Data Managaement Plan. : You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. Data has real, tangible and measurable value, so it must be recognized as a valued enterprise asset across different layers of an organization. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. 3484 405 0. The FAIR data principles in context. According to the ALCOA principle, the data should have the following five qualities to maintain data integrity: Attributable, Legible, Contemporaneous, Original and Accurate. It is a community-led and self-governed initiative working across disciplines and countries. “FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.” “Good data management is not a goal in itself, but rather is the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration While the “open data” movement represented a slightly utopian vision of the many possibilities of data sharing, the FAIR principles respect the reality of practical data sharing across different parties. To this end, we have adopted the Netherlands Institute for Permanent Access to Digital Research Resources (DANS) metrics for FAIR compliance2,3. However, those principles are not orthogonal and have not been designed for automated machine-based evaluation. We will ensure that ethical best practice in personal data is integral to our procurement process. The FAIR data principles represent the latest incarnation of a growing understanding of the implications of sharing data with other parties. FAIR DATA PRINCIPLES FOR CROSS-RESOURCE QUESTIONS IN EUROPEAN REFERENCE NETWORKS Marco Roos, Claudio Carta, David van Enckevort, Rajaram Kaliyaperumal, Annika Jacobsen, Rachel Thompson, Robert Reihs, Heimo Mueller, Pedro Sernadela, Marc Hanauer, Mark Thompson, Mark Wilkinson, Luiz Bonino, Marco Crimi, Peter-Bram ‘t Hoen, Domenica Taruscio … The FAIR Data Principles1 is a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable. This document outlines how the Mendeley Data platform supports each of these principles. To propose indicators to measure progress on each of the FAIR components 3. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. 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