Laboratory Data Management
In cooperation with leading industry specialists, Lab Manager will outline a strategic plan to the successful evaluation, implementation, and utilization of the most recent advances in informatics technology
Available on Demand
The adage of “adapt or die” applies to a laboratory information management system (LIMS) just as it does to evolution.
To help sort out the numerous choices available, the editors of Lab Manager have assembled a team of industry experts to discuss the latest trends in data management
Available on Demand
As one looks to the future of industrial innovation practices, two challenges—externalization and harmonization of incompatible infrastructure—are apparent.
Social media campaign #lyd16 asks: What will you do to care for your data?
A workflow management system can enhance your laboratory’s operation, no matter what type of lab you have.
Despite all the hype being tossed around, the cloud is a logical evolution of previous systems.
Associate editor Rachel Muenz speaks with research scientists Freya Freestone, MChem, and Helen O'Shea, BSc about data processing, analysis, and managing workflows in the lab.
Problem: According to the Allotrope Foundation, “Underpinning every experiment, every scientific decision, and every regulatory submission is data generated by a scientist using an instrument in the laboratory.” These mountains of analytical data are analyzed and interpreted in global R&D laboratories to help evaluate, identify, and characterize compounds and formulations.
Problem: Researchers and scientists generate and collect vast amounts of data in their work, whether they are running experiments in the lab or surveying and interviewing people on the street. Researchers typically don’t deal with their research outputs until towards the end of the research cycle, when poor organization and data management can be difficult to manage and address, but causes the most problems. Poor data management results in experiments that are harder to replicate and findings that may be called into question. Papers can be retracted, careers impacted and ultimately science can suffer. When researchers move on they may pass their work to others in their research group, where poor data management results in the group inheriting indecipherable written notes they cannot use.
