Open Research Data Projects

The project aims to improve openness and interaction between research communities working with geospatial data. There is currently a significant gap in the absence of an application that enables research communities and Open Science stakeholders to publish, visualise, combine and extract research geospatial data in the formats desired by users, and to use them directly and openly in teaching and research. The project will focus on addressing key questions and working with research communities to better understand the needs and requirements of researchers for working with geospatial data in an open research data context. Key questions include the desired practices, data formats and standards for searching, combining, sharing and publishing open research geodata, and assessing the capabilities of existing geoportals such as GeoVITe to implement the developed ORD practices. Collaboration with the community, in particular with representatives of the geosciences, is essential to discuss and develop user-centred ORD practices. Participatory approaches aim to focus on user needs to make research geodata findable, accessible, interoperable and reusable in line with the FAIR principles. Based on the identified needs and processes, initial testing and technical implementation will be carried out on the portal. The long-term goal is to establish sustainable tools for the open research geodata community, based on existing open standards and an improved web-based geoportal.

The Atlas of Regenerative Materials, aims to be a non-profit website that assembles and interconnects knowledge about construction with bio- and geo-sourced building materials. The goal is to create an open tool that provides visibility to the entire value chain in sustainable construction, linking natural resources to exemplary buildings and involving the expert community. The Atlas includes information about natural resources, building materials, professionals in the sector, technical construction details, and buildings implementing sustainable construction. A critical factor for the success of this knowledge platform is the collaboration of researchers from academia and industry. The project seeks to gather research results and expertise from various sources to create a comprehensive and reliable repository. The involvement of renowned experts from academia and industry is seen as a key element in establishing a community-driven platform. The project will be a community-driven web repository gathering scientific knowledge and research on natural resources, building materials, technical constructions, professionals, and exemplary buildings. The project seeks to unite various stakeholders in the construction industry, providing academia with teaching support, the construction industry with a ready-to-use database, and third parties with insights into sustainable practices as an alternative to the current depletion of fossil resources.

The prototype of a new national database on intra-specific genetic diversity in populations of wild species in Switzerland, GenDiB, is currently being developed as part of a project co-financed by the Federal Office for the Environment (FOEN). With this ORD initiative proposal, we aim at (i) development of new tools to support simple procedures for dataset up-/download and to implement attractive visualization features and (ii) community building among researchers and stakeholders through various interactive communication means. Our dedicated activities, in parallel to further developing GenDiB as a beta version for subsequent permanent operation and maintenance, should promote the standardized use of GenDiB as the core repository for respective datasets within the community of researchers and stakeholders in conservation management in Switzerland, and possibly beyond. Integrating GenDiB into the national network of database holding species-level occurrence data (InfoSpecies) will complement these databases to cover the genetic level of biodiversity, which is fundamental for population and species persistence in the context of environmental change.

Highly detailed 3D characterization of trees and forests with close-range technologies offers great potential for modeling carbon, energy fluxes, habitat diversity and much more. Numerous research groups are collecting complex 3D data in forests, and some are making it available as open data to the community. However, there are currently very few open data repositories or even metadata for 3D forest data from laser scans that can be used by different groups on a European or global scale. In the Forest3DTwin project, we want to build a prototype for open storage of measured 3D data with reference data according to the FAIR principles (findability, accessibility, interoperability and reusability) and are convinced that this can be established at the Swiss Federal Institute for Forest Research WSL in the long term and lead to a engagement and commitment of the European and global community to open 3D forest data.

Tree-ring research has played a pivotal role in unraveling past environmental conditions, understanding climate variability, and providing valuable insights into ecological changes over time. In the current era of digitalization, driven by technological advancements, the field is undergoing a profound transformation, delivering unprecedented details crucial for enhanced comprehension and exploration across various research domains. However, the absence of a suitable repository and the emerging imbalance between resource-intensive data producers and users pose significant challenges to data-sharing practices. This project aims to address these challenges by showcasing an operational solution, exemplified through intra-annually resolved wood cell anatomical data and images, involving the establishment of a modern, robust, and flexible repository and simultaneous redefinition of incentives for data producers. The Xcell Hub, through the creation of a community-specific, interactive, visualizable, and user-friendly online data repository, aims to foster Open Research Data (ORD) practices. This solution integrates modern open-source technologies, emphasizing decentralized interactivity, rewarding mechanisms, and transparent data assessment to secure data archiving, engage data producers, stimulate contributions, and enhance data quality

Custom instruments designed by researchers in academia are a key resource for advancing discovery and technology. However, they are very difficult and thus rarely shared openly with the scientific community. In the previous Explore project we have built an OpenSPM ecosystem around our custom OpenSPM prototypes. Through the rapid growth and success of this project, we have identified what will be required to sustain the growth of the OpenSPM ecosystem to become a global platform for future SPM research and development. In this second Explore grant call, we will continue to increase the available open technology, focus on maintenance and long-term viability by developing a professional development framework, and expand the community through international outreach and expansion beyond the field of SPM. The result will be a long-term sustainable, globally distributed network of users and developers from academia and industry

3D imaging is a cutting-edge method for digitizing natural history collections, offering immense potential for taxonomy, general biology, and education. By analyzing 3D models, specialists worldwide can instantly access rare reference objects from collections, aiding in field interpretation and various scientific and educational applications. As 3D scanning becomes more efficient and digitization initiatives invest heavily in generating 3D data, 3D models are anticipated to become widespread. Surprisingly, there is limited research on how these 3D data are being used for research and education in natural history collections. Initial comparisons suggest the current 3D models are complex sets of data, lack suitable tools for analysis and modification, and require linking to additional metadata for utility in taxonomic research and education. This proposal aims to establish additional standards for 3D data preparation and develop best practice guidelines to ensure the usability of natural history collections data. Rather than focusing on developing ready-to-use solutions, the emphasis will be on identifying needs, documenting recommendations, and testing them with expert user groups. The outcomes will directly impact data infrastructures in the U.S., Europe, and Switzerland, serving over 500 institutions. They will also enable expert groups worldwide, particularly in the Global South, to virtually access natural history collection specimens for various scientific purposes

While ORD practices become increasingly widespread, one area that remains a challenge is qualitative data. On the one hand, qualitative data is more difficult to process and make available in open practices. On the other, ethical norms in research practice require confidentiality of research subjects. Yet interview transcripts, workshops or other kinds of qualitative data are difficult or sometimes impossible to anonymize. This challenge comes to the fore when conducting transdisciplinary (Td) research, where new forms of engagement between science and society co-produce problem framings and project outputs. In this context, questions of who processes and stores data are increasingly important. Td research makes frequent use of qualitative methods, especially interviews and workshops. Sharing of this data could allow for improved learning between Td processes and increase engagement between science and society. How might this data be shared according to FAIR principles? What are appropriate protocols for determining what data to share and how to navigate the ethical issues of research participant protection and the benefits of sharing qualitative data? In this project we will prototype tools for FAIR qualitative data within Td research projects, develop standards and guidelines for other Td researchers and build a dialogue and community of practice within the Td research community around FAIR practices for qualitative data.

Hybrid models, which combine physics and machine learning (ML) based models, are becoming increasingly popular in hydrology and the broader Earth Science community due to their potential for improved prediction and process representation. However, hybrid models pose unique challenges to open research practices, including the widely accepted FAIR principles. Unlike physics-based models, the reusability of hybrid models is hindered by the integration of ML models which dynamically change with training data. Furthermore, existing model and data repositories are not designed to host hybrid models which contain code, ML models, and associated training data. To address these challenges, FRAME will collaboratively design, implement, and test a standardised FAIR protocol tailored for hydrological hybrid models. The protocol will consist of coding standards for interoperability between different model components, a unified metadata specification accounting for different types of physics and ML-based models, and a python package leveraging existing model and data repositories widely used in the hydrology (HydroShare) and ML (DLHub) communities to share and retrieve hybrid models. To ensure wider and long-term impact of the project beyond its lifetime, the developed protocol will be actively used and improved by participating groups in the ETH Domain and Europe and will ultimately be transitioned to a community-driven protocol, inviting participation from the wider scientific community.

Despite the presence of various Energy System Model (ESM) tools and platforms facilitating data sharing across models such as CROSSDat, the absence of a standardized structure poses a significant challenge, hindering the seamless reuse of data across different modules. MOTEL tackles this problem by applying Open Research Data (ORD) practices to technology-related data. The project aims to build an open-access data-base from unpublished data that is actively developed and used for internal model development by the Urban Energy Systems Lab (UESL) at Empa. This database will be supplemented with metadata to improve accessibil-ity, interoperability, and transparency of the underlying parameters when used in ESM studies. To achieve interoperability, we will identify and adhere to established ontologies. By connecting each parameter to a mathematically formulated model, we aim to develop a standardized methodology that explicitly defines each parameter, reducing the need for manual preprocessing and expert knowledge when using the data for ESM studies. The database will be published and maintained on the lab's existing infrastructure. In addition, we will establish a reproducible workflow demonstrating how the structured, ORD-compliant data can be seamlessly integrated into ESM tools. This will be achieved through a Python-based model builder that connects to the database and interfaces with our optimization-based ESM platform, ehubX.