Where's the fanFAIR?
By Tim Gardner and Joann Calve
A consortium of scientists and organizations published in 2016 a potentially revolutionary framework for improving quality and collaboration in scientific research. The framework is called: “FAIR Guiding Principles for scientific data management and stewardship”. They chose FAIR (Findable, Accessible, Interoperable, Reusable) as an acronym to make the concept easier to discuss.
On the surface, FAIR seems almost absurdly obvious. After all, who wouldn’t want to be able to find and reuse their data? Who can argue with the authors’ opening statement: “Good data management is not a goal in itself, but rather is the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse by the community after the data publication process.”
FAIR nimbly outlines 15 principles that, when applied, should open the doors of the deepest research desires into discovery and more discovery. Fifteen principles for researchers and dataticians who sequence genomes, or produce mAbs, or conduct high throughput screening.
But while universities, scientific bodies, businesses, and leaders, including G20, have supported the initiative, the practical understanding and adoption of FAIR in everyday science has been a challenge, to say the least.
Despite all of the hope and potential that FAIR provides, it also seems to create new pain when researchers contemplate changing their data habits. The laboratory environment today is fundamentally at odds with itself. Current data tools aren’t designed to support the full scientific processes of the researcher. They only capture bits and pieces of it. As a result, the researcher’s scientific experiment, no matter how beautifully conceived, automatically becomes shortchanged within the limitations of the tools.
This mismatch produces poorly designed studies, unqualified assays, and bad data which leads to under-sampled, noisy, unannotated or under-annotated datasets, and datasets that simply aren’t structured appropriately for statistical analyses.
Riffyn’s goal is to change this equation. Permanently. We want to restore science so that researchers reap every bit of knowledge and insight that their inquiries and experiments reveal.
We have created the means for researchers to get the benefits of FAIR (and beyond): a paradigm-shifting SaaS technology, a best-practices transformation process, and a thought partnership. It’s a paradigm that borrows from the profound transformations that CAD and quality systems brought to other industries.
The backbone of the FAIR laboratory environment that Riffyn creates lies within our digital infrastructure – our SaaS platform. It understands and supports a researcher’s whole scientific experimentation and learning cycle. Our platform synthesizes scientific processes and data capture which ultimately enable machine learning in everyday routines. As a result, critical details are not lost, and effort is not wasted.
Getting started is often the biggest hurdle for a lab. We get this at a personal level, because we started out as laboratory scientists – not computer engineers. Our Scientific Services team joins our customers’ laboratory teams on day one. Riffyn scientists each have 5-20 years of experience working in the lab. They’ve all suffered in non-FAIR data environments. They’re determined to apply their knowhow to remove the pain of change and create permanent, supportive data environments.
They start by mapping the existing scientific process, making recommendations to improve it, and then creating a data strategy to support it. Our team aligns and merges process and measurement data – online, at line, and offline. We capture metadata and apply it to process control. We use all this information to analyze opportunities for improvements in experiment and process design, and we use the learnings to help our customer partners design better assays that produce higher quality, more usable data.
The end result is employees who spend their time in knowledge discovery, innovation, and integration. Who wouldn’t want to replace data housekeeping with this?
While change is never easy, there’s no reason not to be full of fanFAIR. We certainly are. If you check back here in a few weeks, we’ll start sharing stories from a global and growing fan base.
 Scientific Data 3, Article number: 160018 (2016)