The FAIR Data Fund

Architectural history

This dataset contains about 40 high-resolution historical maps and plans of Jaffa–Tel Aviv (1800–present) in TIFF/JPEG/PDF formats. Documentation is partial, with basic file naming and a preliminary spreadsheet listing sources, dates, titles, and limited scale/author details.

Geomechanics and Multiphase Flow in Subsurface Energy Storage

This dataset holds CT scans of geological materials with raw projections, 3D reconstructions, and instrument metadata in mixed formats. The project standardizes files into structured folders with JSON-LD metadata and READMEs, producing FAIR, AI-ready data and a reusable FAIRification workflow.

Phytopathology

This dataset includes raw long-read and Illumina sequencing reads, assembled genomes, variant files, and summary tables. Metadata follows MIxS standards in JSON/CSV, with QC reports and reproducible workflow scripts hosted on GitHub.

Agro-climatology

Bias-corrected and statistically downscaled seasonal climate hindcasts for Ghana, Kenya, and Zimbabwe are combined with LPJmL-generated crop yield hindcasts. Data include precipitation, temperature, radiation, and wind speed ensembles at multiple lead times, stored as NetCDF model outputs organized by variable, lead time, and country. Supporting bash and Python scripts are version-controlled but mainly internally documented with limited user metadata

Sustainable Value Chains

This project uses farm-level sustainability data (yields, fertilizer use, energy, water), Life Cycle Inventory (LCI) datasets for agricultural inputs, and supplier-reported KPIs from THESIS. It also includes modeled environmental impacts (GHG, water, land use) and metadata for provenance and quality.

Data Science, Traffic Study

TraffCOCO is a developing traffic dataset built on an existing 4TU.ResearchData deposit from the “Pedestrian Planet” project, which analyzed global dashcam footage from the CROWD dataset to produce processed research outputs and supporting materials.

Human Factors

This research collection totals ~18 TB, mainly dashcam videos, computer-vision derivatives, and models/logs. The curated FAIR subset for 4TU.ResearchData will include annotations, trajectories, segmentation outputs, configs, and metadata, estimated at ~2.1 TB, with extra storage requested if needed.

Forest Agriculture and Environment in the Spatial Sciences (FORAGES)

This research enhances drought monitoring in Kenya’s ASALs by testing LoRaWAN environmental sensors and crowd-sourced imagery, then integrating these in-situ data with satellite observations.

4TU.ResearchData believes in the added value and benefit of making research data FAIR. It also recognises that making data FAIR requires extra work. The ‘FAIR Data Fund’ offers researchers an opportunity to apply for financial aid to cover the costs of making datasets FAIR. This fund is for data that has already been created and is not for the creation of new data. It is intended for situations where there are no other resources available to make the data FAIR.

The applicant must have a demonstrable relationship with TU Delft, University of Twente, Eindhoven University, and Wageningen University and Research. Additionally, the dataset must have been created in affiliation with one of these institutions. The maximum financial contribution is €5000.

Efforts to make data FAIR that can be funded include:

• Identifying and implementing appropriate metadata standards to make data FAIR
• Generating (meta)data documentation or adding relevant documentation to datasets
• Anonymisation or aggregation of confidential data to make them publishable
• Shifting from a proprietary to open data format to make data interoperable
• Creating data visualisations (or other materials) to make datasets accessible and reusable
• Promotion of a FAIR dataset to increase its impact and reuse (e.g. delivering a presentation about a FAIR dataset and 4TU.ResearchData at a conference).

Refining datasets to make them FAIR and available to others may:

• Increase the quality and value of the data.
• Make them reusable over an extended period of time.
• Increase the visibility and the impact of the research.

• Only researchers or anyone working on a research project from TU Delft, University of Twente, Eindhoven University, and Wageningen University & Research can apply for the FAIR Data Fund.
• The research dataset to be FAIRified must have been collected/created in affiliation with TU Delft, University of Twente, Eindhoven University, and Wageningen University and Research.
• Applicants should present a detailed description of activities needed to make the dataset FAIR.
• If this includes the hiring of assistants, the applicant must provide details of the required hours per task and the hourly cost.
• Applicants should indicate an estimated time deadline by which they expect the process of making their data FAIR will be complete for follow-up by 4TU.ResearchData.
• The dataset(s) must be deposited in 4TU.ResearchData.
• Applicants should agree to have an interview / use case prepared together with a staff member at 4TU.ResearchData and have their project showcased on the  4TU.ResearchData communications channels.
• Applicants should provide 1-2 sentences describing their application.
• To promote diversity, successful applicants (or their research groups, unless they apply with a different project) are not eligible to re-apply for three years since the time of their funded application.
• Other resources (financial, or in-kind) are not otherwise available to make the data FAIR.

• The compensation for the costs is up to €5000 per application.
• The fund covers expenses for time costs (i.e. hiring a student assistance, data expert, translator), necessary services (i.e. software/equipment use) and/or promotional activities. However, the applicant commits to use all resources and technical devices already existing, and only requests funds for important new acquisitions.
• 4TU.ResearchData offers advice during the process of making data FAIR.
• 4TU.ResearchData will not provide support in the budgeting process.
• 4TU.ResearchData reserves the right to claim the subsidy back should the beneficiary not meet the requirements or satisfy the conditions as stated above.

• Applicants must complete the online form to apply for the FAIR Data Fund.
• Applicants must get in touch with the financial department of their Faculty to coordinate the budget transfer.

• After the submission deadline, applications will be anonymised to remove personal details (applicant name, position, institution, faculty, department and research discipline).

• Applications are scored by four independent reviewers based on the following criteria:
  • What efforts are required to make your dataset FAIR?
  • Summarise what you plan to achieve by making your dataset FAIR
  • Summarise how you will make your dataset FAIR beyond publishing it in 4TU.ResearchData (Maximum 500 words)
• Applications will be ranked based on their total score. If two or more applications receive the same score and only one can be accepted before the total budget for the call is maximised, reviewers will re-evaluate these application(s) and accept the application(s) from the under-represented research institution(s) within the 4TU.ResearchData repository. 
• If an applicant submits more than one application, the application with the highest score will be accepted unless the total budget for the call has not been reached.