Automating Research Lab Workflows
Siciliano Lab at Vanderbilt University transformed its in vivo animal study management by adopting Scispot’s Unified Platform. Previously hindered by manual spreadsheet tracking, scattered data, and inefficient analysis, the lab leveraged Labspaces, Labflows, and Labsheets to centralize and standardize workflows. Sample Manager’s barcode integration ensured accurate animal and reagent tracking, while Zapier automated metadata updates. Scispot’s Jupyter Hub and AI dashboard revolutionized data analysis, delivering nuanced insights for behavior studies, pharmacology, and analytical chemistry. This shift reduced errors, saved costs, and improved data continuity, enabling the lab to focus on groundbreaking addiction research with enhanced efficiency and productivity.
Siciliano Lab at Vanderbilt University’s Department of Pharmacology and Center for Addiction Research faced significant challenges in managing in vivo animal studies. Manual data logging in spreadsheets and scattered data across multiple systems led to siloed knowledge, time-consuming searches, and errors in tracking animal colony details, such as cage assignments and experimental protocols. These inefficiencies slowed research progress, increased costs due to unattended animals, and complicated data analysis for their diverse workflows involving behavior studies, pharmacology, analytical chemistry, and optical tools.
The lab required a centralized, user-friendly platform to automate animal colony management, streamline experimental workflows, and enhance data analysis. They needed a solution to track animals and reagents, integrate with existing tools, and provide insights through automated analysis, all while supporting their complex, multidisciplinary research without extensive technical expertise.
Scispot’s Unified Platform transformed Siciliano Lab’s operations by centralizing animal colony management and research workflows. Labspaces and Labflows organized animal study protocols, while Labsheets standardized data capture for in vivo experiments, including cage assignments and project associations. Sample Manager’s barcode integration enabled precise animal and reagent tracking, reducing errors and preventing double ordering. Zapier triggers automated metadata workflows, streamlining updates and notifications. Additionally, Scispot’s Jupyter Hub automated data analysis, and the AI-powered dashboard provided actionable insights, enhancing research outcomes across behavior studies, pharmacology, and analytical chemistry.
Scispot’s implementation led to significant improvements in Siciliano Lab’s operations. The platform reduced manual errors, saved costs by optimizing animal and reagent usage, and freed up staff time for core research. The centralized system improved data continuity despite personnel turnover, while the global search function and AI-driven analytics accelerated insights from complex datasets. These advancements enabled the lab to manage multiple in vivo experiments efficiently and advance their addiction research.
