The dry lab microscopist or prompt microscopist: do we need them?

Abstract

In modern biological microscopy, the explosion of data volume and complexity highlights the urgent need for specialised data management support roles. While traditional microscopy focuses on visual data presentation, the rapid increase in big data acquisition and data mining demands advanced handling and analysis. This gap underscores the need for "dry lab microscopists" or data experts skilled in microscopy data management, software interoperability, and AI-driven solutions. Job markets reflect this demand, pointing to the necessity for dedicated training programs. Integrating these specialists into research institutions is crucial for addressing digital data challenges and maintaining high standards in data integrity and analysis. Their role is essential for advancing research in the data-driven era.

Keywords: Artificial intelligence; Automation; Bioinformatics; Biophysical microscopy; Computational biology; Deep learning; Structural biology.

Fig. 1

Diagram depicting the various contributions of the dry lab microscopist, who plays an enabling role in modern microscopy-driven research. The specialist contributes to the research project by providing support throughout the entire data cycle. As such, this support covers all stages of the project, including planning (orange), data acquisition (blue), and data processing (purple). Furthermore, the specialist also contributes to data archiving, assists in the preparation of data sets for publication to ensure they meet the highest scientific standards (green), and provides support to ongoing access to research data (black dotted double arrow). Note that the schematic drawing above is based on the ‘research user experience’ training and support model at Sydney Microscopy & Microanalysis, University of Sydney. This research user cycle has been implemented since the early 2000s as a good practice for training microscopists (Ratinac and Eichhorn ; Braet and Ratinac 2007)