![](bg7.png)
plementation only allows for a single source of truth
for data input which is a limitation for RDM systems
where the datasets are scattered in multiple locations.
The seen complex process model shown in figure 4b
results from actual user behavior in an environment
where users are free to interact with a system with-
out a specific order. Thus, there is a need to extend
DA4RDM with a pre-processing pipeline suggested
by the authors in (Yazdi et al., 2021) to abstract event
logs to achieve structured and simple process mod-
els. We have to acknowledge that Coscine is a de-
veloping RDM platform and is currently in its pilot
phase; hence the sample dataset available is limited
to its beta users, and the current preliminary findings
may not entirely reflect the actual user behavior in a
mature RDM system.
The future work includes adding additional in-
terfaces for conformance checking of the user pro-
cess, identifying unfinished user journeys and trig-
gering automatic actions, and extending the collected
dataset to produce a FAIR maturity dashboard for ev-
ery research project and suggest user actions to in-
crease research data FAIRness. Although the current
DA4RDM UI allows for a PI to interact with the sys-
tem, the post-processing of data models proved to be
too advanced; therefore, we may need to pre-scope
the available features of our web application for the
target audience.
5 CONCLUSION
In this paper, we discussed the benefits of DA4RDM
for an RDM system. We began with a technical re-
view of the developed web application, its charac-
teristics, and functionalities. Furthermore, we elab-
orated on an RDM platform (Coscine) as a candidate
system under study and the approach used to obtain
a real dataset for the goal of data modeling and anal-
ysis. Preliminary findings demonstrated the benefits
of such a system toward user behavior studies and
discovering non-functional requirements. Although
the extracted data from Coscine is entirely based on
a service layer, DA4RDM is showing to be adapt-
able to any log format according to the needs of a
data modeling algorithm. Therefore, the contributions
of DA4RDM are to allow for a scalable web appli-
cation that enables non-technical staff to reuse pre-
defined pre-and post-processing pipelines to execute
data-driven studies without technical or scientific ex-
pertise.
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