Service labs and R&D groups are harnessing the power of Next-Generation Sequencing (NGS) throughout the clinical continuum and using this technology to reach new heights in biological understanding and clinical decision support.
The movement to share data and research openly and freely for the sake of scientific advancement has been around for a while, with strong advocates and adherents that we count as some of our favorite people. Nonetheless, a huge driver in the rise in concern for data sharing and rigorous data management has come from the need to comply with data sharing requirements not only from the journals where the researcher will publish his or her paper, but from grant giving organizations themselves. We wondered what drove those increased requirements, and while we often talk to scientific researchers about their needs and frustrations, we were curious about the perspective of grant giving organizations themselves. So we got in touch with two of the biggest funders in the life sciences in the U.S., the National Institutes of Health (NIH) and the National Science Foundation (NSF) to learn more about what they actually required and why they required it.
The following is an interview conducted with Barry Wark, the co-founder and CEO of Ovation
The concept of data provenance in scientific research has grown in prevalence since the early 2000’s.
At Ovation, data management for researchers in the life sciences is our passion, and breaking down barriers to communicating great science and facilitating greater scientific collaboration is our obsession.
Doug Rains is the Chief Scientific Officer and Lab Manager of Quantigen Genomic Services in Indianapolis, Indiana.
Lab work can be a challenge. Compliance issues, training, and quality control measures, not to mention day-to-day hassles like tracking samples and incomplete assays, can overwhelm anybody, from the lab technician up through the lab’s management. Where does lab software fit in all of this? A necessary evil? A tool that makes life better, easier, more efficient? Or somewhere in between? As far as we can tell, lab software seems pretty broken, and it amazes us that in this era of agile software development and user-centered design, labs still have to deal with applications that look and feel like 1999, or require a whole team to manage. We’ve been doing some digging, interviewing experts and folks with years of experience in the lab, and uncovered a few primary ways in which lab software underserves the people who use it.
In recent years, companies across industries, and of all sizes, have embraced analytics and real-time data as essential to understanding and growing their business. Clinical labs have joined the movement as well. A variety of business intelligence software that connects with existing LIS has come on the market and, for lab managers, it’s no longer enough to think about only QA and audits. Lab managers must now think about growing the lab’s business as a whole, finding new efficiencies in lab workflows, informing partners or sales reps in real time, and reducing costly errors. Like many small businesses, small or startup labs view business analytics as the domain of the “big guys”, for those with resources that far exceed their own. However, it is even more incumbent upon small labs to take a hard look at their lab’s performance in near real time—less wiggle room in the budget means every mistake and inefficiency matters even more. What are some easy steps that labs without expensive business intelligence software can take to optimize their business, make more money, and perform better on important metrics, like turn around time?
At its core, Ovation is a tool for researchers to manage their data, collaborate with other researchers, and ensure that valuable metadata and relationships between data are conserved.
When we first got started with Ovation, we set out to better understand the problems of those on the front-lines of science, from the researchers to lab technicians.
Science answers questions by collecting data and telling the story of its evolution from hypothesis to conclusion.