Displaying articles related to "Datasets"

THUMB 3 IMAGE 170524_Bio-IT_World_Amgen_ONTOFORCE_co-presentation_FP_WH Fig 3

Written on July 6th, 2017 by Filip Pattyn

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Connected Data landscapes: a Self-Service Knowledge Platform at Amgen

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.

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FAIR data - thumb

Written on May 23rd, 2017 by Hans Constandt

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Why data should be FAIR

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.

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Picture 'CONNECTING LINES' by Lauren Manning Follow via Flickr _ THUMB

Written on January 24th, 2017 by Filip Pattyn

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Bring epidemiology data and disease genes in closer contact

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.

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Written on April 5th, 2016 by Filip Pattyn

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The gap shouldn’t be that wide between US and EU clinical trials

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?

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