FAIR Data ****************************************************************************************** * ****************************************************************************************** In order to enhance the reusability of your research data, you should aim to make your dat In 2016, The FAIR Guiding Principles for scientific data management and stewardship [ URL www.nature.com/articles/sdata201618"]  were published in Scientific Data with the aim of o sharing and reuse by humans and machines. The FAIR principles describe how data should be can be more Findable, Accessible, Interoperable and Reusable, and they are promoted by som bodies such as the European Commision [ URL "https://ec.europa.eu/research/participants/da grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf"] . The authors formulated 15 princi data, which you can find in full below. ****************************************************************************************** * Open and/or FAIR data? ****************************************************************************************** It is a common belief that FAIR data equal open data; however, that does not necessarily n Open research data are such data that are freely available online to anyone and can be use shared for any purpose. Open data should be managed according to the FAIR principles so th easily understood be potential user; however, even data that are not managed to a standard open, if the access to them is not restricted.  FAIR data are such data that are managedin accordance with the FAIR principles, i.e., they Accessible, Interoperable and Reusable. FAIR data may or may not be open – restricting acc be in accordance with the FAIR principles under certain conditions.  Ideally, research data should be open and managed in accordance with the FAIR principles.  ****************************************************************************************** * 15 FAIR principles ****************************************************************************************** *========================================================================================= * 1. To be Findable: *========================================================================================= If you want to make your data reusable, the first step is ensuring that both humans and ma them - making the metadata machine-readable is key.  F1. (meta)data are assigned a globally unique and eternally persistent identifier. F2. data are described with rich metadata.  F3. (meta)data are registered or indexed in a searchable resource. F4. metadata specify the data identifier. *========================================================================================= * 2. To be Accessible: *========================================================================================= Your data should be freely available, ideally via a repository. Even if the access to the restricted, metadata should be open.  A1. (meta)data are retrievable by their identifier using a standardized communications A1.1. the protocol is open, free, and universally implementable. A1.2. the protocol allows for an authentication and authorization procedure, where necess A2. metadata are accessible, even when the data are no longer available. *========================================================================================= * 3. To be Interoperable: *========================================================================================= In order to allow for your data to be integrated with other data, you should use standardi describe the data.  I1. (meta)data use a formal, accessible, shared, and broadly applicable language for kn I2. (meta)data use vocabularies that follow FAIR principles.  I3.  (meta)data include qualified references to other (meta)data. *========================================================================================= * 4. To be Reusable: *========================================================================================= The ultimate goal of the FAIR principles is to enhance reusability of research data. In or this, it is important that the data are sufficiently described and shared under the least license, so that users know how the data were generated, what they describe and how they c R1. meta(data) have 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 their provenance. R1.3. (meta)data meet domain-relevant community standards. ****************************************************************************************** * FAIR self-assessment ****************************************************************************************** To assess the ‘FAIRness’ of your data, you can use the FAIR self-assessment tool [ URL "ht ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/"]  developed ANDS-Nectar-RDS initiative, or you can use this checklist [ URL "https://doi.org/10.5281/z  which was created for the EUDAT summer school by Sarah Jones and Marjan Grootveld.  ****************************************************************************************** * Useful Resources ****************************************************************************************** Jones, Sarah, & Grootveld, Marjan. 2017, November. How FAIR are yourdata?.Zenodo. http://d zenodo.3405141 [ URL "http://doi.org/10.5281/zenodo.3405141"]    Wilkinson, Mark D., Michel Dumontier, IJsbrand Aalbersberg et al. 2016. The FAIR Guiding P scientific data management and stewardship. Scientific Data 3. 160018. DOI: https://doi.or sdata.2016.18 [ URL "https://doi.org/10.1038/sdata.2016.18"]   GO FAIR [ URL "https://www.go-fair.org/fair-principles/"] initiative website   OpenAIRE Guide for Researchers: How to make your data FAIR [ URL "https://www.openaire.eu/ data-fair"]