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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
material quartet

Cards for Creativity

If you think the analysis of a database and creativity cannot go together, we want to prove you otherwise. In the process of finding and shaping our fellowship project idea, we explored the data and information of the Material Archive by developing an exciting card game. Thanks to this perhaps somewhat unusual approach, we not only found answers to our questions but also an unexpected new love for working with cards. Read on
Exploring new areas in technology and changing longstanding practices can be challenging. However, the knowledge and resources gained in this process can create many subsequent opportunities for improving innovation cycles. Read on
ETH Library Lab came to life as a collaboration between the ETH Library and the library of Karlsruhe Institute of Technology (KIT). The lab encourages young talents to take creative approaches and experiment with fresh ideas for finding, accessing, using and sharing scientific information and knowledge. The first cohort of Innovator Fellows started their fellowship at the ETH Library Lab in March 2019. Since then sixteen fellows across twelve disciplines from eight different countries have been part of the program. In this blog post the members of its Advisory Board discuss the relevance of today’s libraries, the importance of letting think “outside of the box” and what the library of the future might look like. Read on
Transcriptiones - platform for transcriptions of non-digitized manuscripts
Unreadable handwritings are the crux of many historical manuscripts. Unfortunately, the time-consuming transcriptions of such documents – especially when they are not digitised – will normally not get published. Thus, they need to be accomplished again and again. And that is where our journey towards building digital infrastructure for hosting, accessing and sharing transcriptions began. Read on
Employing Label-Hierarchy to Improve Image Classification
Image classification is one of the most widely tackled tasks in the field of computer vision. It involves predicting one or more relevant labels for a given image. Generally, when performing classification, the various labels are assumed to be independent of each other, which often is not the case and could mean that classification models are missing out on significant improvements in performance. Read on
Is it possible for an artist to respond to the current state of scientific research and manifest it artistically? This is one of the questions that I have been repeatedly asked while working on my project Beneath the Sea. I will give my answer to this question straightaway: “Yes, I believe this is possible!”. It all starts with finding and building a community dedicated to working towards a shared purpose – for instance, the purpose of meaningfully contributing towards coral reef rehabilitation. Read on
One for all and all for one. In these times of COVID-19, we are experiencing the true meaning of this phrase, maybe a little more than most of us are comfortable with. The containment measures related to COVID-19 have changed our daily lives. For example, for my fellowship at the ETH Library Lab the impact has been twofold: On one hand, I can no longer work side by side with my talented colleagues at Technopark and on the other hand it has shifted my perspective on the purpose of my project. And while the phrase ‘one for all and all for one’ is traditionally attributed to Alexandre Dumas’s novel ‘The Three Musketeers’, I recently learned that it is also known as ‘the unofficial motto of Switzerland’ [1]. So, what better way to introduce my project at the ETH Library Lab? Read on
Global environmental changes pose a serious threat to natural ecosystems. Responding to these threats with effective conservation efforts depends critically on the accurate historical records that are digitised and published by natural history collections. Despite increasing demand for digital access to physical specimens, the number of taxonomic experts working on this process has been declining for several decades. New technologies have the potential for natural history collections to compensate for this shortage of taxonomic expertise and can accelerate the publication of accurate biodiversity data. As a cooperation of ETH Library Lab and the Entomological Collection of ETH Zurich, our Automated Species Identification project aims to develop a practical solution for the classification of specimens based on artificial intelligence and computer vision. Read on

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