Do We Really Need All That Data: From Data to Agency in Automated Microscopy

Sergei V Kalinin; Kamyar Barakati; Boris N. Slautin; Austin Houston; Utkarsh Pratiush; Gerd Duscher; Charles Cao; Ioannis Sgouralis; Steven R. Spurgeon; Charles Yang; Georgia Channing

doi:10.1093/mictod/qaag035

March 19, 2026Microscopy Today

Do We Really Need All That Data: From Data to Agency in Automated Microscopy

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Key Points

Examine the narrative around data collection in automated microscopy and its potential drawbacks.
Analyzed current practices in automated microscopy
Evaluated the implications of data volume on experimental outcomes
Questioned the necessity of excessive data for effective model training
Identified potential negative impacts on research efficiency due to data overload

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Implication

Argument Dismisses Simplistic Views on Data Use in Automated Microscopy, Indicating Complex Implications.

Abstract

Microscopy is entering an era of automated laboratories and AI-enabled instruments, often justified by a simple narrative of automating experiments to collect more data and train better models. Here we argue that, for microscopy, this framing is incomplete and can be counterproductive.

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Cite This Study

Kalinin et al. (2026) studied this question.

synapsesocial.com/papers/6a04158679e20c90b44453ad https://doi.org/10.1093/mictod/qaag035

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Do We Really Need All That Data: From Data to Agency in Automated Microscopy

Key Points

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Implication

Abstract

Discussion

Cite This Study

Do We Really Need All That Data: From Data to Agency in Automated Microscopy

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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