Tutorial "Using open tools to build efficient workflows for data access, management and analysis" at CNS*2023

[Michael Denker]

We would like to take the opportunity to invite you to our hands-on
tutorial titled "Using open tools to build efficient workflows for data
access, management and analysis" which will be held in the morning
tutorial session on July 15, 2023 of this year's CNS*2023 conference in
Leipzig, Germany (https://www.cnsorg.org/cns-2023).

The tutorial is open to all registered participants, and assumes only
basic experience with the Python programming language. Tutorial
materials will be made available during the session. In order to
interactively follow the tutorials online, we suggest to create a free
EBRAINS account (https://www.ebrains.eu/page/sign-up) in advance.

Synopsis:
Neuroscientists today face challenges in managing the growing volume and
complexity of data generated through rapid technological and
methodological advancements and sophisticated experimental paradigms.
Data management tools and methods provide indispensable solutions for
researchers to efficiently handle, organize, and analyze datasets,
facilitating model validation, refinement, and simulation, while
fostering collaborations. This tutorial presents examples combining
multiple tools synergistically into a complete digitized workflow to
help researchers manage and control data and analysis processes.
- GIN is a platform for version-controlled (git and git-annex) data
management and collaboration. It supports any file types and folder
structure, provides web and command-line access, provides an option for
local installation, and services including format validation and data
publication (DOI).
- odML is an open, lightweight, and flexible format that provides a
common schema (with implementations in XML, JSON, and YAML) to collect,
organize and share metadata in a human- and machine-readable way.
- NIX is a lean data model and file format for storing fully annotated
scientific datasets, i.e., the data together with rich metadata (odML)
and their relations in a consistent, comprehensive format.
- Neo provides programmatic data objects for working with and
representing electrophysiological data and can read data from many
proprietary formats. In combination with NIX, Neo makes
electrophysiological data interoperable with generic analysis scripts,
tools, and services.
- Elephant provides a large portfolio of standard and advanced methods
for analyzing data from neuronal spike trains or time series data, such
as LFPs. The Neo data model makes them easily accessible to scientists
and applications.
- Alpaca enables simple capture of human-readable provenance of the data
processing workflow.

Organizers:
- Reema Gupta, Faculty of Biology, Ludwig-Maximilians-Universität München
- Moritz Kern, Institute of Neuroscience and Medicine (INM-6/10), Jülich
Research Centre
- Michael Denker, Institute of Neuroscience and Medicine (INM-6/10),
Jülich Research Centre
- Thomas Wachtler, Faculty of Biology, Ludwig-Maximilians-Universität
München