Fourteen applications have been awarded by the Board of Directors of NWO, for 4,137,517 euros. The awarded projects fall under the 2023 call Thematic Digital Competence Centers (TDCCs).
The aim of this call was to fund projects that help realize the digitization ambitions of the TDCCs and the communities they serve. The setup of the call was non-competitive and community-driven.
What are the TDCCs again?
The Thematic Digital Competence Centers are network organizations. The TDCCs were established in 2022 with funding from NWO. There are three TDCCs in the Netherlands: one for Life Science & Health (LSH), one for Natural and Engineering Sciences (NES), and one for Social Sciences & Humanities (SSH). Their ambitions are described in the TDCC roadmaps. For more information about the TDCCs, read here more.
Fourteen Allocations
Fourteen projects have been awarded within the TDCC 2023 call. The projects cover various topics such as harmonizing core data from long-term cohort studies or developing software that can assess the transparency of empirical research before it is published. Read all public summaries here:
RIGHTS: Responsible Implementation of Collecting, Processing, and Handling Sensitive Individual Digital Traces
TDCC: SSH; Main Applicant: Prof. Dr. T.B. Araujo (Amsterdam UMC)
The Social and Human Sciences have made significant strides in collecting the digital traces individuals leave in their daily lives. However, this wealth of data risks being locked away in unconnected local archives due to concerns about how sensitive data should be processed, published, and/or shared. RIGHTS leverages the power of the Dutch National Research Infrastructure to address these challenges. RIGHTS ensures that these crucial datasets about human behavior are findable, accessible, interoperable, and reusable in an ethical and privacy-protective manner.
Research Transparency Check
TDCC: SSH; Main Applicant: Prof. Dr. R.H.F.P. Bekkers (Vrije Universiteit Amsterdam)
We are developing software that automatically assesses the transparency of empirical research before it is published and makes recommendations for improving the documentation of data and methods. The software assists staff from universities and research institutes, scientific journals, and research funders. We involve seven groups of data users in the Social and Human Sciences in developing criteria for transparency and create checklists for these criteria.
SYNAPSIS: Synergy Network and Platform for Integration of Audiovisual Data Analysis and Archiving in the Social Sciences
TDCC: SSH; Main Applicant: Dr. M. Dingemanse (Radboud University)
SYNAPSIS is a collaborative project aimed at improving the management, sharing, and analysis of audiovisual data in the Social Sciences and Humanities. The project provides a secure, privacy-protective platform that integrates advanced masking tools for audiovisual data, allowing researchers to protect sensitive information while making data more accessible for analysis. In addition to the platform, extensive training programs are offered to equip researchers and data stewards with the digital skills necessary to integrate masking and multimodal analyses into their workflows, promoting open science and improving research transparency.
CLOUD-NES: Facilitating Cloud-Native Data Access and Processing for Natural and Engineering Sciences
TDCC: NES; Main Applicant: Dr. Ing. S. Girgin (University of Twente)
CLOUD-NES aims to stimulate the use of cloud-native methods to efficiently publish, open, and process research data. We do this by building a prototype of an open cloud-native data repository, converting selected datasets from traditional formats to cloud-native formats, demonstrating the benefits of cloud-native approaches with reliable and reproducible benchmarks compared to traditional methods, developing open training materials, and providing tailored training to researchers to enhance their skills in using cloud-native methods and share experiences and lessons learned with relevant (inter)national stakeholders.
FAIRify your Metabolomics Data
TDCC: LSH; Main Applicant: Prof. Dr. T. Hankemeier (Leiden University)
Metabolomics is a data-rich field that is rapidly growing. The FAIR principles provide guidelines for making data findable, accessible, automatically interoperable, and maximally reusable. Scalable solutions for making metabolomics data FAIR can only be built on widely accepted FAIR standards. In FAIRify, the Dutch Metabolomics Community will collaborate to develop a process for convergence to FAIR-facilitating standards for metabolomics. This process will start with high-priority data but will be reusable once the project is completed. Project FAIRify aims to initiate a culture of FAIR data creation and management for metabolomics.
LEARN-FAIR: Life Science & Health Educational Alignment for Research and Networking in FAIR Data Management
TDCC: LSH; Main Applicant: Dr. M.G. Kersloot (Amsterdam UMC)
The LEARN-FAIR project aims to promote collaboration and knowledge exchange among trainers involved in FAIR (Findable, Accessible, Interoperable, and Reusable) data. This is achieved by establishing a Dutch FAIR Trainers Community. Additionally, the project focuses on mapping training needs for researchers and data stewards, as well as existing training materials, to develop new Open Educational Resources based on this. To ensure that these materials meet the (future) needs of the community, the LEARN-FAIR project combines empirical research with active community engagement.
Synthetic Data: Promoting the Use of Sensitive Data in SSH Research
TDCC: SSH; Main Applicant: Dr. E. van Kesteren MSc (Utrecht University)
Synthetic data is a dataset with (more or less) the same properties as an original dataset but without privacy-sensitive data. By making synthetic data available instead of (or prior to) the actual dataset, scientists gain faster and easier access to confidential data. In this project, two tools for creating synthetic data are employed to unlock existing datasets, including datasets stored at DANS.
Education for the Next Generation of FAIR-Conscious and AI-Savvy Data Scientists for the LWG Domain
TDCC: LSH; Main Applicant: Dr. Ir. P.D. Moerland (Amsterdam UMC)
For professionals in life sciences and health (LWG), it is increasingly crucial to (i) efficiently link findable, accessible, interoperable, and reusable (FAIR) data to computational workflows that process the data (FAIR workflows); (ii) ensure that data produced by FAIR workflows can be seamlessly used for artificial intelligence (AI) applications (AI-ready); (iii) construct AI models while integrating relevant domain-specific knowledge (AI-savvy). The aim of this project is to develop two new post-master courses on FAIR workflows and advanced machine learning/modern AI technology for life sciences. With this, we aim to address the existing shortage of researchers with these important skills.
A FAIR Tool Framework for Bioinformatics Services, Tools, and Workflows in Digital Life Sciences and Health (LSH) Research
TDCC: LSH; Main Applicant: Dr. H. Mouhib (Vrije Universiteit Amsterdam)
For data research, various tools and services are available, but users cannot easily find them or assess their quality. As a result, they are not used, or even similar tools are redeveloped. Together with developers, experts, and users, we are developing a toolkit for finding suitable tools and services. This will be applied to existing tools and those in development and can be used in the future for new tools. Through examples, training, interactive workshops, and collaboration with established platforms and communities, tools and services will be sustainably accessible to end-users and (re)development cycles for developers.
HPC-DAT: Breaking the Barrier in High-Performance Computing
TDCC: NES; Main Applicant: Dr. J.B.R. Oonk (SURF)
HPC-DAT trains NES researchers to effectively and easily utilize high-performance computing systems necessary to answer fundamental research questions. We organize multi-day hackathons, special training events, and build on the established Python-PyData-Jupyter-Dask ecosystem. Close collaboration with researchers is achieved through selected use cases, a broad user committee, and an open user forum. The hackathon approach will also enable the project to engage existing and future users from a wide range of disciplines and their evolving needs for large-scale data processing directly.
Towards a Modular Infrastructure for Comprehensive RDM
TDCC: SSH; Main Applicant: Prof. Dr. F.J. Oort (University of Amsterdam)
Many research institutes have RDM policies for the SSH domain but lack accessible infrastructure to fully implement that policy. Currently, they are developing various local RDM infrastructures, often with limited resources and independently. A joint national effort is necessary to create a policy-compliant, modular, and interoperable infrastructure from which institutes can choose the best-fitting components. To achieve this, the shortcomings in the current RDM infrastructure must be identified. We propose to identify existing policies and supporting infrastructures so that gaps can be made visible and recommendations can follow for future joint RDM innovations within the SSH domain.
FAIR4ChemNL: Breaking Down Barriers in Chemistry with Open and Accessible Data
TDCC: NES; Main Applicant: Prof. Dr. E.A. Pidko (Delft University of Technology)
Chemistry often has many barriers—between researchers, between disciplines, and even between data systems. FAIR4ChemNL aims to change this. By promoting open science and developing new data management solutions, this project makes chemistry more accessible, fosters collaboration, and becomes even more innovative. We bring together experts from across the Netherlands and beyond so that discoveries in one field of chemistry can also help others. By sharing data openly and facilitating collaboration, we aim to accelerate breakthroughs that are important for the future of energy, materials, and more.
A Core Dataset for Dutch Cohorts
TDCC: LSH; Main Applicant: Dr. K.J. van der Velde (UMC Groningen)
Dutch long-term cohort studies, which follow the health of large groups of people, collect data in various ways. This makes it more challenging to combine data and conduct research together. With this project, we aim to make data from Dutch long-term cohort studies more comparable. We will establish core data collected by each study, harmonize it with existing guidelines, and incorporate it into a national standard. This way, data from new cohorts can be more easily used together for research. We will also test whether our standard is usable for all existing cohort studies and for specific disease areas, such as depression and diabetes. This project contributes to better data for research.
Creating, Using, and Reusing Spatial Machine Learning Models
TDCC: NES; Main Applicant: Prof. Dr. R. Zurita-Milla (University of Twente)
Spatial machine learning (ML) models are now indispensable within technical and natural sciences (TNW). Developments are rapid, making it a significant challenge to keep the knowledge level of the scientific community for applying and developing these ML models up to par. An additional complexity is that working with spatial models requires specific knowledge and experience, which is not self-evident for every TNW researcher. In this project, various open training modules are developed for different post-graduate and PhD education programs. This lays a solid foundation for applying, reusing, publishing, and developing Spatial ML models by TNW researchers.