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      <title-group>
        <article-title>How FAIR is EOSC? An Overview of FAIR Activities within the Various EOSC funded initiatives</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>© Damien Lecarpentier</string-name>
          <email>damien.lecarpentier@csc.fi</email>
          <xref ref-type="aff" rid="aff0">0</xref>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>CSC-IT Center for Science</institution>
          ,
          <addr-line>Espoo</addr-line>
          ,
          <country country="FI">Finland</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>Proceedings of the XX International Conference “Data Analytics and Management in Data Intensive Domains” (DAMDID/RCDL'2018)</institution>
          ,
          <addr-line>Moscow</addr-line>
          ,
          <country country="RU">Russia</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>The talk overviews FAIR activities within the various EOSC funded initiatives.</p>
      </abstract>
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    <sec id="sec-1">
      <title>-</title>
      <p>Science is changing, both in the way it is performed
and the way it is communicated. Driven by remarkable
advances in information and communication
technologies, today’s scientific infrastructures offer
researchers unprecedented access to data sources,
dataintensive sensors, and increasingly comprehensive
analysis and simulation facilities that have
revolutionized scientific methods in a remarkably short
space of time. Research services, processes and outputs
are becoming accessible to all levels of society.
Enormous amounts of data are being generated, bringing
extraordinary new opportunities for their innovative
reuse in novel scientific, commercial, and citizen-science
contexts. This is Open Science.</p>
      <p>To harness its full value and reap the fruits of public
and private investment, the European Commission has
launched the European Open Science Cloud (EOSC)
initiative. EOSC will foster an open, collaborative
platform for the management, analysis, sharing, reuse and
preservation of research data on which innovative services
can be developed and delivered. For this, Europe can build
on decades of public investment in scientific
infrastructures—experimental facilities, networking,
high-performance and high-throughput computing, cloud
services, scientific software and institutional and
community data repositories—by connecting national and
international infrastructures and services. The European
Open Science Cloud will provide a researcher with access
to all the varied distributed resources they are able to use
in the pursuit of their research—data storage, compute
cycles, and the packaged combinations of data and
compute we term services—in a way that is easy to use.
And by “easy to use” here we almost certainly mean
“familiar”: using the Science Cloud should be as much like
using a desktop computer as possible.</p>
      <p>Much of the technology to achieve the European Open
Science Cloud already exists. Challenges remain, of
course, but they are more ones of policy than technology,
of agreement between resource providers, of adoption of
common approaches, in particular concerning the
management of research data. A few years ago a limited
set of very clear principles were formulated and are now
getting wide acceptance from research funders, policy
makers and within the research communities: the FAIR
data principles. The principles essentially say that research
outputs, in particular data, should be Findable, Accessible,
Interoperable and Reusable. This should be true for reuse
by humans or by algorithms and other automated
processes. There is an overwhelming and general support
for the basic idea of FAIR data (and other research
resources, including software and data services) and some
work already in progress on how to accomplish and
operationalize FAIR.</p>
      <p>The FAIR principles have also penetrated EOSC and
are becoming a key component of it. The EOSC pilot [1]
and the EOSC-hub [2] projects are beginning to align
their activities around FAIR, the upcoming ESFRI
clusters address FAIR aspects at disciplinary levels, and
forthcoming projects are expected to work on FAIR
certification schemas to be adopted by EOSC. The
challenge is now to make FAIR the foundational
principles of EOSC and embed them into EOSC Rules of
Participation. This can be done on the basis of the FAIR
Guiding Principles [3] and the FAIR Data Action Plan
HLEG interim recommendations [4], but will require
concrete actions which should taken by all the EOSC
related initiatives, generic and thematic, and some
coordination. The overall objective is to accelerate the
realisation of EOSC by ensuring a wide uptake of the
FAIR principles and practices across disciplines, at
national, European, and global level.</p>
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  <back>
    <ref-list>
      <ref id="ref1">
        <mixed-citation>
          <string-name>
            <surname>Blomberg</surname>
          </string-name>
          , et al.
          <year>2016</year>
          . “
          <article-title>The FAIR Guiding Principles for scientific data management and stewardship</article-title>
          .”
          <source>Scientific Data</source>
          <volume>3</volume>
          (
          <issue>1</issue>
          ):
          <fpage>160018</fpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation>
          <source>doi:10</source>
          .1038/sdata.
          <year>2016</year>
          .
          <volume>18</volume>
          .
        </mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation>
          http://dx.doi.org/10.1038/sdata.
          <year>2016</year>
          .
          <volume>18</volume>
          .
        </mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation>
          <string-name>
            <surname>Collins</surname>
            ,
            <given-names>S.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Genova</surname>
            <given-names>F.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Harrower</surname>
            ,
            <given-names>N</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Hodson</surname>
            <given-names>S.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Jones</surname>
            <given-names>S.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Laaksonen</surname>
            <given-names>L.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Mietchen</surname>
            <given-names>D.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Petrauskaite</surname>
            <given-names>R.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>Wittenburg</surname>
            <given-names>P.</given-names>
          </string-name>
          ,
          <string-name>
            <surname>FAIR</surname>
          </string-name>
          <article-title>Data Action Plan: Interim recommendations and actions from the European Commission Expert Group on FAIR data</article-title>
          ,
          <year>June 2018</year>
          . https://doi.org/10.5281/zenodo.1120245
        </mixed-citation>
      </ref>
    </ref-list>
  </back>
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