View source for National Academies of Sciences, Engineering, and Medicine 2018 Science data infrastructure

{{Publication
|title=National Academies of Sciences, Engineering, and Medicine (2018) International coordination for science data infrastructure: Proceedings of a workshop—in brief.. The National Academies Press, Washington DC doi: https://doi.org/10.17226/25015.
|info=[https://doi.org/10.17226/25015 Open Access]
|authors=National Academies of Sciences, Engineering, and Medicine
|year=2018
|journal=The National Academies Press, Washington DC
|abstract=Advances in science and technology have led to the creation of large amounts of data—data that could be harnessed to improve productivity, cure disease, and address many other critical issues. Consensus in the scientific community is growing that the transition to truly data-driven and open science is best achieved by the establishment of a globally interoperable research infrastructure.
A number of projects are looking to establish this infrastructure and exploit data to its fullest potential. Several projects in the United States, Europe, and China have made significant strides toward this effort. The goal of these projects is to make research data findable, accessible, interoperable, and reusable, or FAIR (see Box 1). The expected impact and benefits of FAIR data are substantial. To realize these benefits, there is a need to examine critical success factors for implementation, including training of a new generation of data experts to provide the necessary capacity.

On November 1, 2017, the Board on Research Data and Information (BRDI) of the National Academies of
Sciences, Engineering, and Medicine organized a symposium to explore these issues. Invited experts from China, Europe, and the United States were asked to:
* Review proposed science data infrastructure projects around the globe;
* Highlight, compare, and contrast the plans and capabilities of these projects; and
* Discuss the critical success factors for implementation and the role of international cooperation for scientific data management.
|editor=[[Gnaiger E]],
}}
== The FAIR Data Principles ==

:::: The FAIR Data Principles set out requirements for the sharing of scientific data

::::* '''F'''indable: Easy to find by both humans and computer systems and based on mandatory description of the metadata that allows the discovery of interesting datasets.
::::* '''A'''ccessible: Stored for long term such that they can be easily accessed and/or downloaded with well-defined license and access conditions (Open Access when possible), whether at the level of metadata, or at the level of the actual data content.
::::* '''I'''nteroperable: Ready to be combined with other datasets by humans as well as computer systems.
::::* '''R'''eusable: Ready to be used for future research and to be processed further using computational methods.
:::: Source: Barend Mons, presentation, November 1, 2017, Washington, DC. Credit given to the Dutch Techcentre for Life Sciences.

== H2020 ==
::::* [http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf H2020 Programme: Guidelines on FAIR Data Management in Horizon 2020]

== Cited by ==
{{Template:Cited by Gnaiger 2020 BEC MitoPhysiology}}

{{Labeling
|additional=Gentle Science, BEC 2020.1
}}