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As a Neuroscientist and ETH Library Lab Innovator Fellow, I had the chance to combine two worlds: citizen science and information ecosystems. Bringing both together, I developed a digital toolkit and interdisciplinary collaborative framework for mental health and well-being. As a part of this project I also explored how to transform libraries into hubs for the meeting of knowledge, digital resources, and first-hand experiences. Concretely, I imagine scientists, mental health communities, and students coming together in libraries for open collaborative science. Let me explain why and how this could work. Read on
Some say material is only the solid and shapeable matter, others define it plainly as everything. How can we meaningfully discuss and learn about this all-encompassing term? Read on
The Feed4org app attempts to advance the scientific ecosystem by improving how feedback flows to and within the ETH Library. It is a fellowship project of the ETH Library Lab, and aims to contribute to the broader movement of transformative science. Read on
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

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