Collaborative Consciousness Science: Where do I Start?

Why do I need this?

Visit the motivation page to understand the importance of collaborative consciousness science, and what you can gain from it.

Collaborations are important to the field, but I know nothing about working in a collaboration.

Luckily, we do. The original ASSC25 tutorial was created to share the knowledge we wished we had when we started working in collaboration. Rony has built this website to share this helpful information with anyone considering taking part in a collaborative effort (or already are).

  • Learn about the importance of project management to collaborative science, even when not working in large groups. This includes tips and tricks for time management, planning, and working with others.
  • Understand how to establish the data infrastructure of your project. Data collection and analyses rely on having standardized data structures, processing pipelines, and analyses outputs. When working in a group, issues that most researchers face only when sharing data with the public arise much earlier - and can create a real mess if not addressed in time.
  • How can multiple researchers work on an analysis together? Visit the data analysis module to learn about collaborative coding and version control.
  • Check out the Glossary if you want to start with some basic terms.

Useful tools and platforms

The important thing to remember is that tools come and go, but the needs of collaborative science remain. Each tool has advantages and downsides; choose whatever suits your team best. Try not to have more than one tool for a single objective (e.g., if you communicate on slack, no need to open a discord space). The fewer the tools - the better. This prevents mess, duplication, and not knowing what to use / where to find things. The list below is far from being extensive; yet, we thought it might be useful for researchers who don’t know where to start.

Team Communication

Team Planning / Task Assignment

Knowledge Preservation

Useful Resources for Project Management

Version Control

Service Providers:

Programming Style Guidelines

IDEs

Python

R

C

Programming for Scientific Research

Code Testing Packages

Python

R

BIDS

Preregistration

Glossary

Project Management

Data Infrastructure

Data Analysis