Postdoctoral researcher in Biomechanical Engineering at the University of Twente | @Huawei_Wang_
Do you need FAIR human movement data with different sensor aspects? Huawei’s dataset comprises information from six different types of sensors (all synchronized) recording movement from 12 healthy young adults performing 13 daily activity trials. The funds will be used to openly publish the raw and processed data, processing pipeline (software), open hardware, metadata files, and documentation.
Assistant Professor in Chemical Engineering & Machine Learning at Delft University of Technology | @ASchweidtmann
Artur’s research project will enable the reuse of figures from open-access scientific journal articles in chemical engineering. His aim is to generate a big, linked public dataset of annotated scientific images that will enable the training of classification and information extraction algorithms for the chemical engineering field.
Darinka Czischke & Sara Brysch
Associate Professor at the Faculty of Architecture and the Built Environment, Delft University of Technology | @DarinkaCzischke
PhD researcher at the Faculty of Architecture and the Built Environment, Delft University of Technology
Quantitative data on collaborative housing are currently scattered (or even only available upon request) and lack standard definitions. The FAIR Data Fund will allow Darinka, Sara and their team to refine their existing datasets and make them FAIR. Their published datasets will create a solid basis for comparative and quantitative studies on collaborative housing.
Assistant Professor in Theoretical and Computational Catalysis at Eindhoven University of Technology | @IvoFilot
Ivo aims to build a compartmentalized data storage and distribution service for electronic structure calculations. The platform is based on Docker containers that allow for efficient and robust implementation and development. Uniquely, data storage and distribution is combined with metadata generation scripts and a web server that offers quick access and search functionality to retrieve single data items.
Assistant Professor in Psychology at Eindhoven University of Technology | @lakens
Daniël and Ruben’s research project aims to retrieve, organize, and openly release meta-analytical data from 854 independent meta-analyses of different psychological domains. Their dataset will represent summary study-level data from 38,468 different samples with the goal to create a standard to share individual meta-analytic databases in the future.
Postdoctoral researcher in Mechanical Engineering and Material Science at the University of Twente | @kia_tb
By tuning particle properties and species compositions in stiff-soft material mixtures using novel data science ideas, we aim to enhance the mechanical behaviour of particulate systems and design new materials. Kia’s published dataset will comprise computational (simulation inputs) and experimental data (X-ray images, in-situ wave propagation experiments).
The FAIR Data Fund offers researchers a budget (up to €3.500) to cover the costs of making their data Findable, Accessible, Interoperable and Reusable (FAIR data principles). Researchers from TU Delft, TU/Eindhoven and the University of Twente are eligible to apply for the fund.
Applications for the FAIR Data Fund Autumn call are now closed. Subscribe to our newsletter to stay updated about next year’s funds.