Support for the enhancement and exchange of research data

Research thrives on the open exchange of knowledge – and of data that make knowledge possible in the first place. In order to support this exchange of data, the ETH Domain’s Open Research Data Program to open up research data has been in place since the beginning of 2022. Researchers can now once again apply for funding.

Two people point with their hands to a screen on which a network of white and blue lines can be seen.
Research data are the basis for Open Science. Researchers can now once again apply for funding to open up research data. (Photograph: ETH Zurich / Gian Marco Castelberg)

Together with the ETH Board, ETH Zurich, EPFL and the four research institutes (PSI, WSL, Empa, Eawag) have launched an Open Research Data (ORD) Program. A total of CHF 15 million is available for the ORD Program, half of which will be provided by the ETH Board and the other half of which will be matched by the six institutions. The ETH Zurich Executive Board provides CHF 2 million matching funds to support projects and sub-projects by ETH researchers and service units. Two calls for funding are currently open to researchers (see below).

The ORD Program envisages five measures to open up research data throughout the ETH Domain, amongst other criteria, in accordance with the FAIR Principles. These principles state that data should be findable, accessible, interoperable and reusable in order to support exchange between research groups and to allow other groups to reuse existing data for new research questions. Applying the FAIR Principles correctly enhances research data throughout the entire data life cycle, from planning, through collection and analysis, to publication and storage of the data.

The ambitions are high. The goal is to make researchers in the ETH Domain open science leader leaders in their areas of research.

Fifty-four research projects supported so far

Measure 1 of the ORD Program assists researchers, research groups and consortia in developing ORD practices with and for research communities. So far, 54 projects have been funded under Measure 1, including 39 smaller Contribute projects, 13 Explore projects and 2 Establish projects. The names are indicative: Contribute projects contribute to existing data communities by integrating their data into existing infrastructures. Explore projects break new ground, testing prototypes and new ways of making data findable, accessible, interoperable and reusable. Finally, Establish projects aim to take existing ORD practices to the next level, allowing the entire ETH Domain and ultimately the international research community to take part in them. At the halfway point of the programme, ETH Zurich is playing a leading role in a total of 16 Contribute and Explore projects, as well as participating in the 2 Establish projects.

Establish projects with the participation of ETH Zurich

The Establish projects are funded with CHF 1.5 million each.

The OPEM (Open Electron Microscope Data Network) Establish project includes ETH Zurich’s technology platform ScopeM. The project focuses on electron microscopy and involves also further major electron microscopy facilities of EPFL, PSI and Empa. The project - headed by EPFL - aims at providing standard best practices and workflows covering the collection of data and metadata, the processing and analysis of such data, as well as the flexible storage of data, metadata, processed data and models in institutional repositories and international databases. Thereby, it ensures that data can also be accessed by the broader scientific community throughout Switzerland according to open research data and FAIR principles. Data volumes in this field are rapidly increasing notably due to major developments in cryo-electron microscopy in the life sciences and 4D scanning transmission electron microscopy (4D STEM) in materials science. The solution is planned to be based on the existing PSI data management tool (SciCat Data Catalog).

The PREMISE (Open and Reproducible Materials Science Research) Establish project is being carried out under the leadership of PSI together with the Scientific IT Services of ETH Zurich and Empa. This project integrates existing data standards, workflows and data management systems for materials science, based on, among other things, the experiences of researchers and their ideas and standards of good data management. The aim is to understand data management as a substantial and advancing part of the research process and not as an additional obligation for researchers. This integration is fundamental for the further development of materials science. Autonomous laboratories, automated simulations and robot-controlled experiments can only be successful if they have access to open, reliable and/or reproducible data.

Other measures

The four other measures are also in full swing. Measure 2 are investigating the data management tools used by researchers in the ETH Domain during the different phases of the data lifecycle. Often, several software solutions are used for the same data project. In the future, data transfer between these systems needs to be improved, which will only be possible if the interoperability of the systems is ensured through standardised interfaces (APIs). Measure 3 develops online tutorials on research data management. These tutorials are tailored to the needs of researchers in the ETH Domain and are intended to be accessible to the public. Measure 4 provides legal clarification as to how the institutions of the ETH Domain need to adapt their regulations in order to ensure that the disclosure of research data is legally guaranteed. Finally, Measure 5 deals with the professionalisation and career opportunities of data specialists in the ETH Domain.

Open calls

The second and final call for Explore (deadline: 29 February 2024) and the sixth call for Contribute (deadline: 12 December 2023) are currently open. Researchers at ETH and in the ETH Domain can find information and guidelines on the external pageETH Board website. Contribute calls are aimed in particular at young researchers (doctoral students and postdocs) who want to add value to the data in their projects.

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