Since the unfold of COVID-19 was first reported, researchers
of all sorts have mobilized to fulfill the challenges its causative agent,
SARS-CoV-2, presents to the world. Data scientists particularly have been
fast to use their experience to the issues of figuring out, monitoring and
predicting outbreaks; diagnosing COVID-19; figuring out contaminated people and
detecting non-compliance with virus countermeasures; discovering new
therapeutic interventions or repurposing current ones; and trying to find a
protected, efficient vaccine.
We’re studying extra about SARS-CoV-2 and COVID-19 daily,
and researchers have gotten extra subtle of their exploration of
every part from the virus’s fundamental biology to bettering affected person outcomes. Almost
each query they ask requires info from a number of sources to be discovered
and built-in, and nearly each time a query is answered, it prompts
one other query that requires info from yet one more supply.
The strategy of assembling information and knowledge to reply
vital analysis questions often begins with researchers assessing:
- What information can be found? Do the required information exist in any respect?
- How can the info be accessed as soon as we discover them? What rights will we
have to make use of the info?
- Are the info and metadata comprehensible? Can we put all of them
collectively in a significant method?
- Are all the info legitimate, or are there outliers or duplicates to
This iterative strategy of discovering info in all the locations it resides, bringing it collectively, cleansing it up and organizing it could possibly take up a lot of an information scientist’s time, maybe as a lot as 80% of their time in accordance with a 2016 survey. The remaining 20% is spent extra productively by really utilizing the info for evaluation or for coaching predictive fashions. The diagram under depicts a typical information science workflow on a timeline.
This strategy of placing information collectively to allow the evaluation and modeling that result in perception is often gradual and tedious as a result of nearly all of the info accessible to researchers at present is just not FAIR, which means that the info and metadata sometimes don’t adhere to the FAIR Guiding Principles of Findability, Accessibility, Interoperability, and Reusability. Adherence to the FAIR Principles makes information extra simply reusable, in order that they effectively will be utilized to any function, even unanticipated ones, compressing time-to-insight and rising the inherent worth of the info. Putting the hassle into FAIRifying information to make them effectively reusable permits for faster outcomes over a broader vary of functions.
At Elsevier, we’re dedicated to serving to scientists and clinicians discover new solutions, reshaping human information and tackling essentially the most pressing human crises—and we consider that information and knowledge are the keys to success. We are proud to assist efforts to assist researchers make use of the FAIR Principles to be the most effective information stewards they are often. We are notably proud to have participated within the growth of the Pistoia Alliance’s FAIR Toolkit, freely accessible to all information stewards, laboratory scientists, enterprise analysts and science managers.
The FAIR Toolkit comprises use circumstances to assist life sciences researchers higher perceive FAIR Data and the way to FAIRify their very own information. It additionally offers entry to FAIR instruments and coaching, in addition to containing info to assist organizations handle the change that adherence to the FAIR Principles requires. Like lots of the organizations we serve, we at Elsevier have made a dedication to FAIR Data and encourage researchers to take a look at the Pistoia Alliance’s FAIR Toolkit to be taught extra.
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