=Paper=
{{Paper
|id=Vol-2680/invited
|storemode=property
|title=Deep FAIR - Knowledge Representation for Research Data about Complex Objects
|pdfUrl=https://ceur-ws.org/Vol-2680/invited.pdf
|volume=Vol-2680
|authors=Michael Kohlhase
|dblpUrl=https://dblp.org/rec/conf/ki/Kohlhase20
}}
==Deep FAIR - Knowledge Representation for Research Data about Complex Objects==
<pdf width="1500px">https://ceur-ws.org/Vol-2680/invited.pdf</pdf>
<pre>
  Proceedings of the 6th Workshop on Formal and Cognitive Reasoning



    Deep FAIR – Knowledge Representation for
      Research Data about Complex Objects
                 (Invited Talk)

                                 Michael Kohlhase

                              FAU Erlangen-Nürnberg
                             michael.kohlhase@fau.de



Abstract
The publication, management, archiving, and re-use of research data (RD) is one
of the current high-profile tasks in modern academia. Funding agencies mandate
comprehensive research data practices, and the National Research Data Infras-
tructure (NFDI) supports the development of RD infrastructures with almost a
billon Euro in the next decade in Germany alone.
    The bulk of research data in the natural and engineering sciences essentially
consists of ”arrays of numbers”, where the meaning of each data point can be
uniformly described by its coordinates and a unit (e.g. for for satelite images). For
such data sets, the meaning can be captured by relatively simple metadata. This
intuition is captured in the famous FAIR principles (Findability, Accessibility,
Interoperability, and Reusability) which constitute the gold standard for RD in
the current discussion.
    In this talk we will look at an class of data that is often overlooked in the RD
discussion: datasets of complex objects, such as the set of sculptures in a museum,
the tabulation of all known elliptic curves, or the mathematical models used in
a simulation. Such objects are complex in the sense that they are characterized
by a large set of properties and relations to other objects. Such objects are best
recorded by a formal description of their properties.
    We will formulate a notion of ”Deep FAIR Principles” for such object collec-
tions and look at the aspects of knowledge representation for the three examples
above.




 Copyright c 2020 for this paper by its authors. Use permitted under
 Creative Commons License Attribution 4.0 International (CC BY 4.0).

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