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Fig. 1 Information management enhances the experimental and modeling cycle. Our 10 simple rules for managing laboratory information (green) augment the cycle of hypothesis formulation, design, data analysis, modeling, and decision-making (gray). The experimental design phase is improved by carefully tracking your inventory, samples, parameters, and variables. Proactive data management and the thoughtful use of databases facilitate statistical and exploratory analyses, as well as the development of conclusions that inform the next round of experiments. Frequent reevaluation of project, team, and workflow success is a critical component of refining experimental processes, developing a common culture, and positioning your research group in the greater scientific context.

Berezin, Casey-Tyler; Aguilera, Luis U.; Billerbeck, Sonja; Bourne, Philip E.; Densmore, Douglas; Freemont, Paul; Gorochowski, Thomas E.; Hernandez, Sarah I.; Hillson, Nathan J.; King, Connor R.; Köpke, Michael; Ma, Shuyi; Miller, Katie M.; Moon, Tae Seok; Moore, Jason H.; Munsky, Brian; Myers, Chris J.; Nicholas, Dequina A.; Peccoud, Samuel J.; Zhou, Wen; Peccoud, Jean (2023). "Ten simple rules for managing laboratory information". PLoS Computational Biology 19 (12): e1011652. doi:10.1371/journal.pcbi.1011652. 

2023

Berezin, Casey-Tyler; Aguilera, Luis U.; Billerbeck, Sonja; Bourne, Philip E.; Densmore, Douglas; Freemont, Paul; Gorochowski, Thomas E.; Hernandez, Sarah I.; Hillson, Nathan J.; King, Connor R.; Köpke, Michael; Ma, Shuyi; Miller, Katie M.; Moon, Tae Seok; Moore, Jason H.; Munsky, Brian; Myers, Chris J.; Nicholas, Dequina A.; Peccoud, Samuel J.; Zhou, Wen; Peccoud, Jean

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