Open Source and Open Science - a happy partnership?
summary
Argues that open science must adopt open source practices but not blindly, showing concrete gaps: university incentives favor patents and prepublication peer review, labs lack sustainable maintainer roles, and wet‑lab implementation carries high marginal cost unlike software. Cites COVID as a rapid shared‑data success with real risks from premature claims. Recommends practical steps: open by default, build community governance, and fix licenses and data terms so code, datasets and notebooks are reusable and legally clear.
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chapters (13)
- 0:00Intro
- 5:45Objectives of Open Science
- 8:26Open Science - Open Source: Similarities
- 11:48Differences: Practices
- 13:53Differences: Protection
- 15:38Differences: Motivation
- 17:26Differences: Implementation
- 18:32Differences: Risk factors
- 19:24Uncertainty for open science
- 22:37Importance of Software in Science
- 23:36Open source adoption/maturity curve
- 25:08How to make open science work?
- 27:19Practical implications
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