A Bay Area startup that streamlines the tech transfer process has raised $1.8 million in a seed round led by O’Reilly AlphaTech Ventures. Software company Riffyn helps build experimental process design and analytics software to make life science, chemical and materials R&D reproducible.
The company says it melds computer-aided design, real-time data acquisition, 24/7 cloud-based analytics and collaborative versioning to cut down error and hasten innovation.
“The root cause of irreproducible research is unmanaged experimental noise,” the company says. “One hundred years of industrial manufacturing around the world has shown that such noise can be conquered through a collaborative process of design, measurement and improvement. When noise is driven out, productivity is driven up.”
Indeed, the company claims that at present scientific research can only be reproduced some 10 percent of the time. An article last year in The Economist says:
A rule of thumb among biotechnology venture-capitalists is that half of published research cannot be replicated. Even that may be optimistic.
Last year researchers at one biotech firm, Amgen, found they could reproduce just six of 53 “landmark” studies in cancer research. Earlier, a group at Bayer, a drug company, managed to repeat just a quarter of 67 similarly important papers. A leading computer scientist frets that three-quarters of papers in his subfield are bunk. In 2000-10 roughly 80,000 patients took part in clinical trials based on research that was later retracted because of mistakes or improprieties.
Riffyn CEO Timothy Garnder penned an editorial in Science earlier this year on the topic. He said:
Behavioral economics has shown that removing even trivial hurdles can vastly improve the adoption of beneficial practices. In one randomized study, people were 29% more likely to attend college when their student aid forms were automatically populated.‡ Similarly, the digital capabilities in your pocket can lower the barriers to quality by automating most of its complexity. For example, my own company is building tools to automate data acquisition via mobile devices, break down lab procedures into modular steps that are evolvable and reusable like lines of software code, and automatically analyze data trends within and across experiments to catch errors and their root causes. Other companies are also contributing to a fast-growing ecosystem of related tools.
