One and a half years ago we started to work with Amgen to help them improve the way they search and retrieve research data. They were looking for a solution that could aggregate and interlink their internal research data, enrich it with public data and provide an appealing, user-friendly interface for end-users.Read more
Displaying articles related to "Datasets"
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.Read more
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.Read more
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.Read more
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.Read more
Human beings, and to a greater extent scientists, try to order items for a purpose. In biomedical sciences, probably no other subject is more diversely ordered or classified as human conditions or diseases – hereinafter abbreviated as ‘diseases’. None of these classifications claims to be the one and only truth or is able to serve all purposes. Instead, many classifications co-exist and are widely accepted – or enforced – by the various stakeholders in life sciences: lab scientists, clinicians, pharmaceutical and biotech firms, regulatory bodies, governments, etc. With different classifications come different definitions of terms even if they are highly similar or have exactly the same meaning.
But what about searching, comparing and analyzing similar data where different diseases classifications are applied? Or what about compiling data about diseases that are produced and maintained for a specific purpose?Read more