References Module

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Contributors Module Regular title

CEO

Max Mustermann

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CEO

Max Mustermann

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Dominic Weber, Innovator Fellow ETH Library Lab CEO

Max Mustermann

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Project owners Module

Ankit Dhall

Computer Vision & Machine Learning Specialist, Alumnus Innovator Fellowship Program

Bala Nivetha Kanakaraj

Ph.D. Biomedical Engineering, Indian Institute of Technology Madras

Barry Sunderland

Technical Engineer ETH Library Lab, Alumnus Innovator Fellowship Program

Blog teaser Module Regular title

Data Science
Algorithms, Artificial Intelligence (AI), Machine Learning (ML) – when I read about these subjects in the media, it always sounds a bit like magic to me: “human resources use machine learning” or “artificial intelligence can diagnose Parkinson’s disease earlier than specialists”. For me, these statements suggest something superhuman, incomprehensible – magic. However, it is not magic at all. Facial recognition algorithms are not “black boxes” and we do not pull self-driving cars out of a magician’s hat. Read on
© Phillip Ströbel
In science and research, there is an increasingly strong need to create, collect, federate and process ever larger amounts of data. Alongside this rapid development due to the digitalisation of information environments in research, scientific libraries are seeking to adapt and reframe their roles. On the one hand, they strive to grow into facilitators of scientific knowledge work in all its facets. On the other hand, they look for ways to better leverage the power of scientific data for the collective good. But can libraries move fast enough to realise these roles? This blog article attempts to find answers to this question by investigating and presenting both the researchers’ and the libraries’ perspectives. Read on
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