Open Science

resources.

Open science is the core component of the lab’s research practice.
We aim to make our work reproducible, reusable, and accessible through open-source software, open hardware, public documentation, and transparent scientific workflows.
The projects below reflect this commitment across instrumentation, experimental electrophysiology, and neurocomputational modeling.

1. Instrumentation

ANDI

ANDI is a Python library developed to control Digilent Analog Discovery instruments from Python.
It provides a lightweight software layer for rapid prototyping, acquisition, and signal generation in experimental instrumentation workflows.
Within the lab, ANDI supports the development of flexible measurement and control pipelines for research-grade setups.

Code: GitHub - Neuro-Interface-Lab/andi
Documentation: see the project documentation on Read the Docs

NVNA

NVNA is an ongoing instrumentation project currently under development.
It will be documented here as soon as the software and technical resources are stabilized for public release.

2. Open Hardware – Instrumentation

BIMMS

BIMMS is an open hardware and open software platform dedicated to bioimpedance measurements and electrode–tissue interface characterization.
The project was designed to support configurable experimental protocols for spectroscopy, stimulation interface assessment, and biological tissue characterization.
By combining hardware design files, embedded control, and a Python interface, BIMMS promotes reproducible instrumentation for bioelectronic research.

Code and hardware resources: GitHub - Neuro-Interface-Lab/BIMMS
Documentation: see the project documentation on Read the Docs

3. Experimentation in Electrophysiology

TAD

TAD is a Python package for handling and analyzing neural recordings.
It focuses on common electrophysiology analysis workflows, including spike rasters, burst detection, synchrony analysis, firing-rate estimation, avalanche analysis, and event-triggered processing.
The objective of TAD is to provide a transparent and reusable analysis environment for electrophysiology datasets, with an emphasis on readability, scripting, and reproducibility.

Code: GitHub - Neuro-Interface-Lab/TAD
Documentation: Read the Docs - TAD

4. Neurocomputational Models

Network models

This section is currently under construction.
Additional material will be added later to describe the network-modeling activities developed in the lab.

NRV Framework

NRV (NeuRon Virtualizer) is an open-source framework for the in silico modeling of peripheral nerve stimulation and bioelectronic interactions.
It enables multiscale simulations combining biophysical axon models, extracellular field computation, realistic electrode configurations, and optimization workflows.
The framework was initially initiated within the lab’s scientific environment and has since grown into an independent organization and software ecosystem with its own development structure and user community.

NRV is now maintained as a dedicated project and has been recognized by the 2025 Open Science Award for research software in the Documentation – Espoir category.
This distinction highlights both the scientific relevance of the framework and the quality of its open documentation for the broader research community.

Project website: NRV Framework
Code: GitHub - nrv-framework/NRV
Documentation: Read the Docs - NRV
Award: Official award page

Open science in practice

Across these projects, our objective is not only to publish results, but also to share the tools, methods, and technical environments that make those results possible.
This includes source code, hardware resources, documentation, and computational workflows that can be inspected, reused, adapted, and extended by other researchers.

We consider open science as a way to strengthen methodological rigor, accelerate collective progress, and foster durable scientific software and hardware for bioelectronics and neural engineering.