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@PHDTHESIS{Simson:1015222,
author = {Simson, Anna},
othercontributors = {Kowalski, Julia and Wellmann, Jan Florian},
title = {{R}eusability in cryospheric sciences: fundamental concepts
and case studies},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-06267},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2025},
abstract = {Reusability of research products in form of digital
resources such as data sets and modeling software is of
utmost importance to science. It makes research transparent,
sustainable, and accelerates the scientific endeavor. Over
the past few decades, repositories for sharing research
products have been established, and standards for the
consistency of digital representations of resources have
been developed. The aim is to facilitate the reuse of data
and software. Since 2016, guidelines for Findable,
Accessible, Interoperable and Reusable research products
have been formulated, widely known as the FAIR Principles.
The FAIR vision describes a future automated state in which
humans assign tasks like data analysis and integration to
machines. In light of these FAIR developments, one might
think that reuse scenarios ranging from model validation
based on existing measurement data, to model coupling using
existing modeling software are smoothly executable. In
reality, despite FAIR's focus on machines, reuse is still
carried out mostly manually, i.e., humans find, integrate,
and analyze existing research products, and reuse efforts
remain time consuming and ineffective. Cryospheric sciences,
which study all occurrences of frozen water on Earth, would
particularly benefit from highly reusable research products.
Measurements of a constantly shrinking cryosphere cannot be
repeated, in-situ measurements during polar expeditions are
costly, and cryospheric modeling software must be easily
coupled with models from other geoscience domains. A
holistic understanding of the cryosphere is essential to
predict and mitigate the impacts of climate change. This
requires the combination of cryospheric data and models at
different scales, and their seamless, interdisciplinary
integration with data and models from other geoscience
domains. Therefore, the reusability of cryospheric research
products and their current leverage in reuse scenarios needs
to be investigated in depth. In this thesis, I investigate
the reusability of cryospheric research products based on
two case studies. Cryospheric Case Study I is a
physics-based process model for coupled water vapor
transport and settling in the snowpack. The model is
characterized by modularity and extensibility. The reuse
potential of the model's software is highlighted at the
example of a real-world reuse scenario in form of a model
comparison. Cryospheric Case Study II describes the approach
followed to compile sea ice core measurement data into a
comprehensive and analysis-ready database. This case study
demonstrates the challenges encountered when manually
harmonizing and combining distributed and heterogeneous data
sets. Both case studies put a special focus on the
transparency of the method and the reusability of the
generated research products. The case studies effectively
demonstrate that the reuse of cryospheric data and software
is not trivial and not yet executable by machines alone. The
sharing of cryospheric research products follows individual
preferences, and the products lack standardization of data
and metadata as well as quality information, both of which
affect their understandability and interoperability. The
case studies demonstrate: (1) the need to clearly
communicate reuse needs in the form of reuse scenarios; (2)
the large discrepancies between the many challenges faced
when manually reusing a resource and the FAIRness of the
resource, which reflects its machine reusability; and (3)
the challenges experienced in manual reuse will be inherited
by machines. In the future, reuse scenarios should be
documented more effectively to represent the reuse
perspective in a systematic way. A major focus should be on
improving reusability with transparent and comprehensive
documentation of data and software with metadata, the
development of community-agreed standards for terminology
and formats, and the prioritization and documentation of
data and software quality. Such developments will benefit
manual reusers, support the development of technologies that
enable autonomous reuse, and it will facilitate the
combination of small, heterogeneous, and distributed data
sets. To automate reuse scenarios, future research should
specifically investigate the application of large language
models, including exploring their limitations.},
cin = {422410 / 530000},
ddc = {550},
cid = {$I:(DE-82)422410_20210811$ / $I:(DE-82)530000_20140620$},
pnm = {HDS LEE - Helmholtz School for Data Science in Life, Earth
and Energy (HDS LEE) (HDS-LEE-20190612) / BMWK 50NA2009 -
Verbundvorhaben TRIPLE-nanoAUV 1, Teilvorhaben RWTH Aachen
University: nanoAUV GNC, Einschmelzsonde und Basisstation
(50NA2009) / BMWI 50NA1908 - EnEx-WISE: Eine intelligente,
thermo-fluiddynamische Wasser-Eis Simulationsumgebung zur
Unterstützung autonomer Eisexploration (50NA1908) / BMWI
50RK2351B - Entwicklung und Anwendung der wissenschaftlichen
Nutzlast für das TRIPLE-System zur
Habitabilitätsbeurteilung von unter-Eis Habitaten und der
nachhaltigen Nutzung der gewonnen Daten, Teilvorhaben RWTH
Aachen (50RK2351B) / Doktorandenprogramm
(PHD-PROGRAM-20170404)},
pid = {G:(DE-Juel1)HDS-LEE-20190612 / G:(BMWK)50NA2009 /
G:(BMWI)50NA1908 / G:(BMWI)50RK2351B /
G:(DE-HGF)PHD-PROGRAM-20170404},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-06267},
url = {https://publications.rwth-aachen.de/record/1015222},
}
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