Standardisation of complex analysis processes across models and experiments

^{Copyright: Performing analysis of neuronal dynamics using the Elephant and Neo Python libraries (cf., https://ebrains.eu/service/elephant).}

The lack of standardisation in data acquisition also affects the downstream analysis process. With the development of increasingly sophisticated methodological approaches, a well-defined, traceable, and reproducible analysis pipeline is essential to strengthen confidence of analysis results, facilitate joint collaborative analysis on the same dataset, and enable the smooth transition from early interactive exploratory analysis processes to automated processing of large data volumes using high performance computing resources (Bouchard et al., 2016; Denker and Grün, 2016). Collaboratively, the team drives forward efforts coordinated through EBRAINS (http://ebrains.eu) to develop open-source community-centred elements for creating such analysis workflows, such as the Electrophysiology Analysis Toolkit (Elephant) for the analysis of concerted dynamics exhibited by spike train data and population signals (http://python-elephant.org) and Neo for unified data representations in simulations and experiments (http://neuralensemble.org/neo).

Publications for this project are:

• Bouchard KE., Aimone JB., Chun M., Dean T., Denker M., Diesmann M., Donofrio DD., Frank LM., Kasthuri N., Koch C., Ruebel O., Simon HD., Sommer FT., Prabhat (2016). High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination. Neuron 92, 628–631.
  DOI: 10.1016/j.neuron.2016.10.035
  
  
• Denker M., Grün S. (2016) Designing Workflows for the Reproducible Analysis of Electrophysiological Data. Brain-Inspired Computing, Springer International Publishing, Cham, pp. 58–72.
  DOI: 10.1007/978-3-319-50862-7_5