EPFL Open Science Champions

In September 2018, EPFL President Martin Vetterli announced the creation of the Open Science Fund to support the best ideas from everyone on campus with a total of CHF 3 Mio over the period 2019-2021.

The first call for proposal attracted nearly 50 propositions submitted between mid-September and mid-December 2018. Eight projects were selected by the members of the open science strategic committee, joined for the occasion by representatives of the various EPFL school, and will receive support to develop ideas fostering open and reproducible research on campus, and beyond. You can find a short description of the laureate ideas below.

The web plays a major role in the adoption of open science best practice. But how can research outputs that cannot be shared as digital artefacts be made openly available? Starting with a cutting-edge structured illumination microscopy (SIM) developed in the Laboratory for Bio- and Nano-Instrumentation, this project will build and share a framework – including IP guidelines – to facilitate the broad dissemination of state-of-the-art instruments in the research community.

This dataset is invaluable for researchers in acoustics, signal processing and musicology, amongst other disciplines. Unfortunately, it cannot be made available due to copyright restrictions. This collaboration between the MetaMedia Center and the Digital and Cognitive Musicology Laboratory will build a secure computation platform allowing anyone to perform open and reproducible research on the archive without touching it.

Transmission electron microscopes (TEM) are widely used research tools that currently generate data and metadata in proprietary and undocumented formats, make it difficult to share results openly. With this project, Cécile Hébert from the Electron Spectrometry and Microscopy Laboratory will create a suitable open standard and metadata scheme for TEM with the help of researchers and manufacturers. The goal is also to develop open tools for reading, converting and manipulating TEM data.

Choosing a laboratory information management system (LIMS) or an electronic laboratory notebook (ELN) can be difficult. The many options available all have their pros and cons, and are not all suited for all research disciplines. Building on an existing open source tool, this project led by the POWERLab will develop a web solution adapted to the needs of the physical sciences, allowing users to adopt important open science best practices.

The Integrated Systems Laboratory at EPFL develops a collection of open software libraries and benchmarks to keep up the driving force in improving optimization algorithms in the field of logic synthesis.  This open science infrastructure can be used to tackle complex research problems. Making research software openly available has improved the reproducibility of experimental results and the comparability to the state of the art.  Overall, the developed libraries and benchmarks  have received positive feedback from academia and industry and have enabled several vivid research collaborations.

This joint project between NCCR MARVEL and Centre Européen de Calcul Atomique et Moléculaire (CECAM) will build an open and collaborative online hub to host existing simulation and data-analysis tools, effectively creating an open-science environment offering software tools as easy-to-use services, with minimal-to-zero setup time.

With one of the largest academic facility for micro- and nano-fabrication in the world, EPFL is well positioned to set some standards in the way information about processes is shared between researchers. This project initiated by the Laboratory of Photonics and Quantum Measurements will design, build and promote a knowledgebase to facilitate the exchange of best practice between users, covering both success and failures.

In an age of fake news and mistrust towards experts, weeding out misreporting of scientific studies is important.  The Distributed Information Systems Laboratory will develop SciLens, a platform that automatically generates indicators using weakly supervised learning, effectively helping non-experts to evaluate more accurately the quality of a science news article.