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There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholdersârepresenting academia, industry, funding agencies, and scholarly publishersâhave come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wis
Translations Japanese â https://doi.org/10.18908/a.2019112601 (added 31.01.2020). 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. Here, we describe FAIR â a set of guiding
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