In May 2017, Bio-IT World hosted its first hackathon on FAIR data. As ONTOFORCE, we helped organize the event and devoted a team to the competition on ‘Aligning a dataset to FAIR principles’. Here’s our report.
FAIR is a fairly recent concept that stands for ‛Findable, Accessible, Interoperable and Reusable’. On the face of it, these principles don’t seem so remarkable. But what sets it apart, compared to other (earlier) open data models, is that the emphasis has shifted from the human researcher to machines.
In the last few years, the number of public data sources integrated into DISQOVER has grown steadily and we crossed the triple digit barrier in 2016. Nevertheless, the number of data sources on our waiting list for integration is growing just as fast.
One of our missions is to directly and indirectly help patients by aggregating and linking both private and public data. A direct help is to facilitate awareness about health and disease by providing proper information about prevention, diagnosis and treatment, amongst other things.
We are very excited to announce the release of version 3.0 of our DISQOVER platform. After introducing federation in versions 2.x, we succeeded in unlocking a whole new dimension of semantic data in this 3.0 version with the typed links. But wait, there is more! We have also greatly increased usability and flexibility in areas such as discovery management, exporting and dashboards. Can't wait to learn more? Read on!
In my previous blog, I tried to explain that the usage of different disease classifications or encodings in data sources like the US and EU clinical trial registries, doesn’t hamper the integration and linking of this kind of data.
Disease classifications are also used to precisely define diseases in other contexts like epidemiology, pharmacovigilance, toxicology, pharmacology, genetics, etc. This data is scattered across a plethora of data sources, maintained by different governmental and other non-profit organizations like research consortia and institutes or individual research groups. If they are keen on providing meaningful and useful data, data providers try to avoid using disease terms that aren’t defined precisely in an ontology.