The most popular and comprehensive Open Source ECM platform
Let us know more
Effective Collaboration with Data Scientists
Data science is a powerful tool for research, enabling new insights and discoveries across various disciplines. However, data science also poses unique challenges for interdisciplinary collaborations, such as miscommunication, unrealistic expectations, and lack of recognition. How can data scientists work together with other groups effectively and harmoniously?
Important elements of successful interactions with Data Scientists include communication, planning, documentation, and project lifecycle creativity. Some actions that can enable this include:
- Develop a communication plan that sets clear norms, records tasks and decisions, and ensures everyone has access to project records.
- To foster creativity and communication, learn the lingo of different disciplines within the team, encourage questions from both sides, and communicate creatively using diagrams, screenshots, and summaries.
- Establish a timeline that defines project goals, milestones, and deadlines collectively, considering project setup and exploration time.
- To ensure reproducibility and avoid ‘scope creep’, create a data-processing pipeline using coded alternatives, version control, and environment management tools, and assess new ideas to determine if they align with the original project goal.
- To plan for data storage and distribution, agree on file sharing platforms and discuss long-term storage, maintenance, and licensing if applicable, and document everything proactively, update documentation throughout the project and maintain it for future reference.
- To develop a publishing plan, discuss attribution, authorship, and publication responsibilities early in the project to ensure clarity, and stop gracefully by recognizing when a project has reached its end, regardless of success.
- Share the knowledge by deferring to domain experts when necessary, but teaching collaborators technical skills for balanced contributions.
- Embrace creativity by collaborating with diverse backgrounds, but prioritize ideas that align with project scope and timeline.
By following these guidelines, interdisciplinary collaborations that integrate data science can become genuine partnerships, enabling research that would otherwise be challenging to accomplish.
