I have worked on research data management since 2001, yes, way before this area was even considered a ‘thing’.
I was also told that there is no career to make in research data management.
Well, how wrong that comment was!
Formerly seen as an intersection between service provision and IT, data management has become a first-class citizen, recognised as a research and development subject, as it should be.
A lot of the credit for this change goes to the FAIR principles.
Love it or hate it, FAIR is now an internationally-known lighthouse brand.
Since we published the first article on the FAIR principles, enabling FAIR has been my focus.
The reuse of other people’s data is providing useful insights for new research questions and products, and driving new scientific discoveries.
To realise its potential, however, we need new mechanisms to manage the growing availability and scale of scholarly digital products, such as datasets, software, algorithms, articles.
FAIR has been specifically designed to emphasise the machine-readability of these digital objects.
Within my group of research software and knowledge engineers, we address the grand challenges related to information science and scholarly communications, where data quality and readiness for (re)use is a prerequisite for success.
I believe that better data means better science, and this underpins reusable research, aids scholarly publishing, and enables faster and reliable data-driven discoveries.
As I say in my one minute video, my vision is to transform the concept of data readiness into a powerful toolkit at the researchers’ fingertips to realize FAIR data by stealth.
FAIR is not a magic wand.
There is a lot to be done to enable and enact this transformation.
We need all hands on deck!
Researchers, service providers, journal publishers, library science experts, funders and learned societies in the academic as well as the commercial and governmental settings all play a role:
from providing use cases to drive policy and culture changes that motivates, rewards and credits researchers for disseminating and publishing high-quality, machine-readable data; to building tools and services, to inform, training and educate.
There are many community efforts around FAIR; keeping abreast with these is an activity in itself.
I spend considerable time to bring my group activities (such as ISA and FAIRsharing) in and under larger international umbrella organizations like GO FAIR and the RDA to interact with others, learn from them, compare and contrast efforts and build new collaborations.
I also play leading roles, sitting on boards and chairing working groups with colleagues, because you must get your hands dirty and lead by example.
In research data management, the history is the future.
The one I envision is a future where scientific evidence is routinely available in a transparent, trustworthy and persistent manner to support peer-review and withstand reproducibility, to underpin new results and discoveries, and effectively drive sciences forward.