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