We help research teams improve their productivity by leveraging the power of rule-based automation to improve the quality and quantity of data produced by their labs. Using our laboratory automation platform, they can standardize and automate their laboratory workflows to produce more and better data that leads to faster and stronger results.
Many accidental discoveries have shaped our world and many people have won the lottery.
However, relying on chance to make the next discovery would be as foolish as playing the lottery to make a living.
Large sets of high-quality data are the foundation of scientific success.
Small datasets cannot be generalized. Low-quality data are misleading.
Automation standardizes processes which reduces process failure and variability.
Reducing failure and uncontrolled variability reduces costs and improves quality.
Automation reduces costs by allowing people to do a few things very well and very cheaply. It enables division of labor and economies of scale, two proven methods to achieve greater efficiency. GenoFAB helps organizations adopt laboratory automation methods to extract actionable information from experimental data.
To remain competitive in the biotech marketplace, organizations must adopt new approaches that leverage the power of data analytics. The new bottleneck is no longer a lack of experimental capabilities; rather, it is the lack of processes that maximize the value of available resources.
Laboratory information management calls for the development of systems that best meet your lab's unique needs and distinctive workflows.
Check out the LIMS Strategy page.
An analysis of past studies indicates that the cumulative (total) prevalence of irreproducible preclinical research exceeds 50%, resulting in approximately US$28,000,000,000 (US$28B)/year spent on preclinical research that is not reproducible—in the United States alone. (PLOS Biology 16(4): e1002626)
More than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments. Those are some of the telling figures that emerged from Nature‘s survey of 1,576 researchers who took a brief online questionnaire on reproducibility in research. (Nature 533,452–454)
While problems with cell line misidentification have been known for decades, an unknown number of published papers remains in circulation reporting on the wrong cells without warning or correction. Here we attempt to make a conservative estimate of this ‘contaminated’ literature. We found 32,755 articles reporting on research with misidentified cells, in turn cited by an estimated half a million other papers. (PLoS ONE 12(10): e0186281)