Embedding the Elephant data analysis framework into a collaborative environment

Heterogeneous data from electrophysiological experiments and spiking neural network simulations exhibits a level of complexity that puts tools to design workflows for managing data acquisition and analysis in a reproducible way in high demand [1]. Here, following [2], we demonstrate a use case in the framework of the Human Brain Project (HBP) that supports such analysis workflows in a collaborative fashion by combining various software components hosted on a common platform. The emerging open-source software tools that represent the basis of the pipelines are, in particular: (i) electrophysiological data of different origins are represented in a standard description using the Neo framework [3], (ii) complex metadata accumulating in the electrophysiological experiment [4] are recorded and organized using the open metadata markup language (odML) [5], and (iii) analysis is performed using the Electrophysiology Analysis Toolkit (Elephant, http://neuralensemble.org/elephant/) as a recent community-centered modular analysis framework for multi-scale activity data, such as massively parallel spike data or local field potentials. We outline how the integration of this workflow into the HBP Collaboratory infrastructure (http://www.collab.humanbrainproject.eu) facilitates interdisciplinary, collaborative work including access to high-performance computing for advanced analysis approaches.AcknowledgementsHelmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain (SMHB), EU grant 720270 (Human Brain Project, HBP).References Badia, R. et al. (2015) INCF Program on Standards for data sharing: new perspectives on workflows and data management for the analysis of electrophysiological data. Techn. Report. Denker, M., Grün, S. (2016). Designing Workflows for the Reproducible Analysis of Electrophysiological Data. In Brain-Inspired Computing, Amunts, K. et al., eds. (Cham: Springer International Publishing), pp. 58-72. Garcia, S. et al. (2014) Neo: an object model for handling electrophysiology data in multiple formats. Front Neuroinf 8, 10. Zehl, L. et al. (2016) Front Neuroinf, 10, 26. Grewe, J. et al. (2011) Front Neuroinf 5, 16