Variations in Scientific Data Production: What Can We Learn from #Overlyhonestmethods?

Area of Science:

• Scientific methodology
• Sociology of science

Background:

• The hashtag #overlyhonestmethods is gaining traction on social media.
• Posts reveal discrepancies between idealized scientific practices and actual research methods.

Purpose of the Study:

• To analyze the implications of the #overlyhonestmethods trend for understanding scientific data generation.
• To explore the need for re-evaluating epistemic virtues in light of research realities.

Main Methods:

• Analysis of social media trends and associated scientific community reception.
• Conceptual examination of data generation, variability, and epistemic virtues.

Main Results:

• The #overlyhonestmethods hashtag reveals that data generation is often less standardized than assumed.
• The scientific community's blasé reception suggests these practices are common, not shocking.

Conclusions:

• Current conceptions of data may be limited by overlooking inherent variability in data generation.
• Re-evaluation of epistemic virtues like standardization and reproducibility is necessary.
• A framework of "moral professionalism" is proposed for data generation and sharing to address inherent variability.