How to make successful collaborations between computational biologists and experimentalists?

A 10 Simple Rules Educational Series

Matthias König
https://matthiaskoenig.github.io/edu-talks
konigmatt@gmail.com, https://livermetabolism.com

Humboldt-Universität zu Berlin, Faculty of Life Science, Institute of Biology, ITB
University of Stuttgart, Institute of Structural Mechanics and Dynamics in Aerospace Engineering

January 20, 2026

1: Collaboration1

You must choose your collaboration partners wisely.

  • most frictions arise from different expections
  • communication is key (early, often)

Actionable items

  • Openly discuss expectations
  • Be transparent
  • Agree on funding sources

2: Data/Metadata

Partners must agree on data and metadata.

  • Data and metadata are the items which are exchanged.

Actionable items

  • Follow the FAIR guidelines1
  • Define file formats and file naming policies
  • Follow good practises (e.g. for spreadsheets and sensitive data)

3: Publication

Publication policies must be clearly defined.

  • Agree on research dissemination.
  • Discuss authorships and contribution policies.

Actionable items

4: Experimental Design

Experimental designs must be defined by all partners.

  • Discuss the experimental designs early.
  • Involve computational biologists.

Actionable items

  • Include test and pilot experiments in your experimental design

5: Project Management

Partners must agree on project and time management.

  • Agree on milestones and timelines.
  • Work collaboratively.

Actionable items

  • Use online project planners (i.e., Trello, GitHub projects);
  • use multiuser online text editors (i.e., Google Docs, Overleaf) to draft manuscripts and other documents

6: Communication

Early and open communication is essential for success.

  • Communicate early, openly, and often enough.

Actionable items

  • Adhere to effective meetings1
  • Adhere to written interactions guidelines2

7: Reproducible Science

Open and reproducible Science is essential.

  • Follow open and reproducible science guidelines.

Actionable items

  • Use version control (i.e., GitHub, BitBucket, GitLab)
  • include reproducible and versioned software installation steps
  • create an analysis workflows that can easily be rerun

8: Transparency/Trust

Transparency and trust is important.

  • Establish the transparency and trust required for constructive feedback.

Actionable items

  • Share all raw and processed data (Open Data)
  • Listen to and acknowledge dissenting opinions

9: Respect

Respect and appreciation of partners is essential.

  • Be respectful and show appreciation.

Actionable items

  • Be respectful
  • Be aware of common cognitive biases (e.g. impostor syndrome1 and Dunning–Kruger effect2)

10: Learn as a Team

Collaboration is a learning process in a group.

  • Learn as a team

Actionable items

  • Add explanatory comments to the analysis code
  • Do not shy away from asking what you do not understand
  • Commit to answering basic and advanced questions about the analysis

References

Allen, Liz, Amy Brand, Jo Scott, Micah Altman, and Marjorie Hlava. 2014. Credit Where Credit Is Due.
Cechova, Monika. 2020. “Ten Simple Rules for Biologists Initiating a Collaboration with Computer Scientists.” PLOS Computational Biology 16 (10): e1008281. https://doi.org/10.1371/journal.pcbi.1008281.
Clance, Pauline Rose, and Suzanne Imes. 1978. “The Imposter Phenomenon in High Achieving Women: Dynamics and Therapeutic Intervention.” Psychotherapy: Theory, Research and Practice 15 (3): 1–8.
Kruger, J., and D. Dunning. 1999. “Unskilled and Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated Self-Assessments.” Journal of Personality and Social Psychology 77 (6): 1121–34. https://doi.org/10.1037//0022-3514.77.6.1121.
LeBlanc, Linda A., and Melissa R. Nosik. 2019. “Planning and Leading Effective Meetings.” Behavior Analysis in Practice 12 (3): 696–708. https://doi.org/10.1007/s40617-019-00330-z.
Robinson, Mark D., Peiying Cai, Martin Emons, et al. 2024. “Ten Simple Rules for Computational Biologists Collaborating with Wet Lab Researchers.” PLOS Computational Biology 20 (6): e1012174. https://doi.org/10.1371/journal.pcbi.1012174.
Stawarczyk, Bogna, and Małgorzata Roos. 2023. “Establishing Effective Cross-Disciplinary Collaboration: Combining Simple Rules for Reproducible Computational Research, a Good Data Management Plan, and Good Research Practice.” PLOS Computational Biology 19 (4): e1011052. https://doi.org/10.1371/journal.pcbi.1011052.
Wilkinson, Mark D., Michel Dumontier, I. Jsbrand Jan Aalbersberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (March): 160018. https://doi.org/10.1038/sdata.2016.18.

Contact

https://livermetabolism.com

koenigmx@hu-berlin.de

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