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.