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@INPROCEEDINGS{Ulianych:873932,
author = {Ulianych, Danylo and Gutzen, Robin and Sprenger, Julia and
Pastorelli, Elena and De Bonis, Giulia and Paolucci, Pier
Stanislao and Grün, Sonja and Denker, Michael},
title = {{D}esigning reproducible analysis workflows for
experimental and simulated activity using {E}lephant},
reportid = {FZJ-2020-01105},
year = {2020},
abstract = {Neuroscientists have a diversified and constantly growing
repertoire of methods to analyze neuronal activity data.
Moreover, the growing availability of open data sets
containing neuronal activity data puts modelers in a
position to perform a more in-depth validation of their
models (e.g., [1]) based on the statistical descriptions of
the activity observed in experiments. However, the increased
possibilities also come at the cost of higher complexity of
such analysis and validation processes. Here, we showcase
the state of HBP-enabled, tool-based workflow solutions that
implement rigorous and well-defined data handling and
analysis, as well as model validation schemes, for activity
data such as spike trains or local field potentials. We
demonstrate methods for data and metadata representation,
and its analysis using multiple emerging open-source
software tools (e.g., [2-4]). Analysis is performed using
the Electrophysiology Analysis Toolkit (Elephant,
http://neuralensemble.org/elephant/) as a community-centered
analysis framework for parallel, multi-scale activity data
developed within the HBP, while validation is carried out
using the HBP validation framework, and in particular the
NetworkUnit library [5-7]. The interplay between the tools
is showcased by integrating them into a robust workflow
solution. Concrete examples on how to utilize these tools
for scientific discovery in conjunction with the
Collaboratory and Knowledgegraph HBP infrastructure
components, as well as with the snakemake workflow tool, are
given in the context of the use cases of SP3
[8,9].References1. van Albada, S.J. et al. (2018). Front
Neuroinf 12, 291.2. Garcia, S. et al. (2014). Front Neuroinf
8, 10.3. Zehl, L. et al. (2016). Front Neuroinf 10, 26.4.
Grewe, J. et al. (2011). Front Neuroinf 5, 16.5. Gutzen, R.
et al. (2018) Front Neuroinf 12, 90.6. Omar, C. et al.
(2014). ICSE Companion 2014, 524–527.7. Sarma, G. P. et
al. (2016). F1000 Research, 5:1946.8. Pastorelli, E. et al.
(2019) Front. Syst. Neurosci 13, 339. De Bonis, G. et al.
(2019) Front. Syst. Neurosci. doi: 10.3389/fnsys.2019.00070
(in press)},
month = {Feb},
date = {2020-02-03},
organization = {Human Brain Project Summit, Athens
(Greece), 3 Feb 2020 - 6 Feb 2020},
subtyp = {Invited},
cin = {INM-6 / INM-10 / IAS-6},
cid = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
I:(DE-Juel1)IAS-6-20130828},
pnm = {571 - Connectivity and Activity (POF3-571) / 574 - Theory,
modelling and simulation (POF3-574) / HBP SGA2 - Human Brain
Project Specific Grant Agreement 2 (785907)},
pid = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574 /
G:(EU-Grant)785907},
typ = {PUB:(DE-HGF)24},
url = {https://juser.fz-juelich.de/record/873932},
}