Blog articles

Picture 'Macbook Air 13' by Sergey Galyonkin via Flickr _ THUMB

Written on June 9th, 2017 by Bérénice Wulbrecht

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Ontoforce at the Bio-IT World 2017 FAIR Hackathon

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.

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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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Typed Links_THUMB

Written on February 8th, 2017 by Kenny Knecht

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DISQOVER version 3.0 is launched

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!

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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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