FAIRification of External Data: Breaking Down the Data Silos
Whilst Artificial intelligence (AI) has recently been front and centre of the digital healthcare revolution, the principles of FAIR (Findable; Accessible; Inter-operable; Re-usable) data underpins the successful implementation of this important analytical tool.
The Pharma-Documentation-Ring (P-D-R) is holding an exploratory workshop to learn, discuss and debate the challenges and opportunities of the FAIRification process.
They will be focusing on the FAIRification of external data and the incredible impact FAIR is having across our industry.
The workshop will take place in London on 21st and 22nd May. The breakout sessions will explore how FAIR influences:
- Integration and Ontologies
- Technologies and Architecture
You will hear from a diverse range of speakers and thought leaders from CCC/Ixxus, Clarivate, Digital Science, Genomics England, GoFAIR, Janssen, Roche and Scibite. The workshop is designed to encourage strong interaction between participants and experts with a view to creating tangible next steps.
This is a unique opportunity for cross-industry collaboration and we look forward to seeing you there. Click here to register.
Figshare’s Mark Hahnel will be speaking at the workshop:
In recent years, we’ve seen the conversation move from data not only being open but being FAIR. This is a major shift considering we spent the early years of Figshare trying to convince researchers to share their data full stop. For every new feature we build at Figshare we have one eye on the FAIR principles so as a repository we are doing as much of the heavy lifting as possible for researchers. There is still a lot of work to be done to educate researchers on what is expected of them but the report highlights many new initiatives from across the research ecosystem, all pulling together in the same direction.
By making research outputs FAIR, there is the potential to move further, faster with research. By getting all outputs to a baseline when it comes to reusability, new correlations can be found in a systematic way by making use of Machine learning and AI.
This talk will focus on the balance between humans and machines when it comes to making research data FAIR. For example, ‘appropriate metadata’ is a very nuanced requirement. A metadata or subject-specific librarian may be able to determine this. Someone working in a similar field may be able to confirm that the research output has ‘all of the metadata required to understand and reproduce the research.’ However, for machines to be able to interpret this for every single field and subfield of research is a monumental task, one we will not see the results of any time soon. On the flip side, machine-readable licenses are a simple thing to implement, and a simple thing for a machine to check for (as the name suggests). It would be odd for a human to query this in a curation workflow. i.e. To check the API documentation or even landing page HTML.
There is a lot of data on the web today that adheres to some level of being FAIR. How do you find it all for your organisation? And what steps should you take to make the rest of it as FAIR as possible?
£319 includes conference fee, all meals + 1 night’s accommodation. Please register by 30th April 2019 at the latest. Click here to register.
For additional night(s) accommodation (bed & breakfast) at £152 per night. Please email Rebekah.Sidhu@marriotthotels.com.
The day rate will be the same as full conference fee including accommodation (£319). Please email firstname.lastname@example.org if you want to do this.