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Written on September 18th, 2017 by Hans Vergauwen

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Welcoming Paul Vauterin as VP Visual Analytics

We’re glad to have Paul Vauterin join ONTOFORCE as Vice-President Visual Analytics. Paul’s an expert in software product creation and management, including vision, strategy and execution for biotech, human diagnostics, data mining and visual analytics applications.

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man, hands, iphone, smartphone, laptop, digital, notebook, pencil, glass, macbook, technology, keyboard, reflection, glasses, doctor, workspace, thermometer, stethoscope

Written on August 30th, 2017 by Filip Pattyn

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Bringing down the virtual walls around hospital data

Integrating an organization’s internal data is challenging. It requires strategic vision by the organization’s leadership and an open mindset on different levels. In our experience, establishing such a solution has a greater chance of success if stakeholders quickly and easily notice the benefits. Small pragmatic initiatives with few data sources that solve a limited number of use-cases, can demonstrate how things can improve for the whole organization.

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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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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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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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