Engender public trust in science and in research results.
How?[1]
- Increase incentives to replicate studies to ascertain reliability and validity
- Adhere to strict validation procedures and make validation transparent
- Provide or enforce new institutional formats as well as new normative and legal frameworks for the production, circulation, appropriation, evaluation, and use of scientific knowledge
- In scientific communication, the nuances of scientific findings must be presented in a manner that is adapted to the intended audience. These communications must avoid unfounded, though not necessary untrue, conclusions (exaggerations or yet unfounded correlations must be avoided).
- Ensure that transparency is applied in a nuanced way within OS that takes account of the contextual factors that complicate its use (eg privacy concerns). Science governance structures as well as science communication partners have an important role in this.
Take actions for epistemic justice.
How?
- Change the epistemic governance structure to ensure equal access, e.g., by actively adapting the incentive systems and funding requirements, and making adaptive adjustments to the assessment and impact metrics and peer review system.
- Improve research infrastructures in order to address skewed infrastructure effects, e,g., by counter-framing and providing compensations.
- Promote good communication and clear premises for collaboration in order to ascertain coherence between the different conceptions of openness.
- Amend and develop anti- and debiasing approaches in order to avoid and compensate for biases in various parts of the project.
Make your data transparent.
How?
- Provide transparent and repeatable data acquisition protocols in order to increase trustworthiness and reliability.
- Revise or provide new measures for data quality assurance.
- Better acknowledge the merit of data collection in research evaluation. The promotion of publishing peer-reviewed data papers might help in this endeavor.
- Institutionalise and incentivise data sharing (open or FAIR data)
- Incentivise the sharing of highly interoperable data
- RPOs should provide services to make open or FAIR data sharing and reuse easily accessible to researchers.
Manage data ethically.
How?
- Disciplinary and methodological constraints need to be taken into account, for example in research evaluation, where practicing OS is increasingly seen as an important criterion for academic career advancement.
- Investigate the probability of reidentification in the different fields and across fields.
- Establish guidance on data sharing based on evidence on probability of reidentification.
[1] Numerous recommendations of this document originate from Bjørn Hofmann, 2022. “Open Science Knowledge Production: Addressing Epistemological Challenges and Ethical Implications,” Publications, MDPI, vol. 10(3), pages 1-15, July.
This passage is part of D1.3: Conceptual and normative framework for tackling the ethical, epistemic, disciplinary and RI-related challenges of advancing OS-practices written by Kadri Simm, Søren Holm, Rosemarie de la Cruz Bernabe, Mathieu Rochambeau, Jaana Eigi, Bjørn Hofmann, Francois Jost, Olivier Le Gall, Ivars Neiders, Signe Mezinska, Ana Sofia Carvalho, Maria, Strecht, Nathalie Voarino