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@PHDTHESIS{Sprenger:793967,
author = {Sprenger, Julia},
othercontributors = {Grün, Sonja Annemarie and Kampa, Björn Michael},
title = {{T}ools and workflows for data $\&$ metadata management of
complex experiments : building a foundation for reproducible
$\&$ collaborative analysis in the neurosciences},
volume = {222},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH, Zentralbibliothek, Verlag},
reportid = {RWTH-2020-07304},
isbn = {978-3-95806-478-2},
series = {Schriften des Forschungszentrums Jülich. Reihe
Schlüsseltechnologien},
pages = {1 Online-Ressource (X, 168 Seiten) : Illustrationen,
Diagramme},
year = {2020},
note = {Druckausgabe: 2020. - Onlineausgabe: 2020. - Auch
veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2020},
abstract = {The scientific knowledge of mankind is based on the
verification of hypotheses by carrying out experiments. As
the construction and conduct of an experiment becomes
increasingly complex more and more scientists are involved
in a single project. In order to make the generated data
easily accessible to all scientists and, at best, to the
entire scientific community, it is essential to
comprehensively document the circumstances of the data
generation, as these contain essential information for later
analysis and interpretation. In this thesis, I present two
complex neuroscience projects and the strategies, tools, and
concepts that were used to comprehensively track, process,
organize, and prepare the collected data for joint analysis.
First, I describe the older of the two experiments and
explain in detail the generation of data and metadata and
the pipeline used for aggregating metadata. A hierarchical
approach based on the open source software odMLfor metadata
organization was implemented to capture the complex meta
information of this project. I evaluate the design concepts
and tools used and derive a general catalogue of
requirements for scientific collaboration in complex
projects. Also, I identify issues and requirements that were
not yet addressed by this pipeline. There were, in
particular, the difficulties in i) entering manual metadata
and structuring the metadata collection, ii) combining
metadata with the actual data, and iii) setting up the
pipeline in a modular generic and transparent manner. Guided
by this analysis, I describe concept and tool
implementations to address these identified issues. I
developed a complementary tool (odMLtables) to i) facilitate
the capture of metadata in a structured way and to ii)
convert these easily into the hierarchical, standardized
metadata format odML. odMLtables provides an interface
between the easy-to-read tabular metadata representation in
the formats commonly used in lab-oratory environments
(csv/xls) and the hierarchically organized odML format based
on xml, which is designed for a comprehensive collection of
complex metadata records in an easily machine-readable
manner. Supplementing the coordinated capture of metadata, I
contributed to and shaped the Neo toolbox for the
standardized representation of electrophysiological data.
This toolbox is a key component for electrophysiological
data analysis as it integrates different proprietary and
non-proprietary file formats and serves as a bridge between
different file formats. I emphasize new features that
simplify the process of data and metadata handling in the
data acquisition workflow. I introduce the concept of
workflow management into the field of scientific data
pro-cessing, based on the common Python-based snake make
package. For the second, more recent electrophysiological
experiment, I designed and implemented the workflow for
capturing and packaging metadata and data in a comprehensive
form. Here I used the generic neuroscience information
exchange format (Nix) for the user-friendly packaging of
data sets including data and metadata in combined form.
Finally, I evaluate the improved workflow against the
requirements of collaborative scientific work in complex
projects. I establish general guidelines for conducting such
experiments and workflows in a scientific environment. In
conclusion, I present the next development steps for the
presented workflow and potential avenues for deploying this
prototype as a production prototype to a wider scientific
community.},
cin = {163110 / 160000},
ddc = {570},
cid = {$I:(DE-82)163110_20180110$ / $I:(DE-82)160000_20140620$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2020-07304},
url = {https://publications.rwth-aachen.de/record/793967},
}
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