Supporting Researchers in Making Their Data FAIR: A Learning Resource for 4TU.ResearchData

Writ­ten by Christi­na Markanas­tasakis and edit­ed by Iulia Popes­cu

Over the past nine months, I had the oppor­tu­ni­ty to join the 4TU.ResearchData team as a learn­ing design­er to lead a project focused on sup­port­ing researchers in improv­ing the qual­i­ty of their meta­da­ta when sub­mit­ting datasets, soft­ware, and data col­lec­tions to the 4TU.ResearchData repos­i­to­ry. The aim was to clar­i­fy how meta­da­ta can sup­port the FAIR prin­ci­ples, mak­ing data Find­able, Acces­si­ble, Inter­op­er­a­ble, and Reusable, while con­sol­i­dat­ing exist­ing sub­mis­sion guid­ance into a sin­gle, acces­si­ble learn­ing resource. To meet this need, I cre­at­ed a Jupyter Book: a struc­tured Open Edu­ca­tion­al Resource (OER) host­ed on GitHub, where con­tent is writ­ten in Mark­down and designed to be updat­ed col­lab­o­ra­tive­ly by users. GitHub’s ver­sion con­trol makes it pos­si­ble to refine the con­tent over time, mak­ing this resource both sus­tain­able and co-con­struct­ed. What now exists is a first draft, devel­oped to accom­pa­ny researchers on their jour­ney toward shar­ing their data with the world and ensur­ing it remains mean­ing­ful and usable to oth­ers.

As the vol­ume of research data con­tin­ues to grow, it becomes increas­ing­ly impor­tant for researchers to under­stand how to make their out­puts dis­cov­er­able and impact­ful. As I often explain it, “Cre­at­ing ade­quate meta­da­ta for research out­puts is like prop­er­ly labelling box­es in a mas­sive stor­age unit or the attic of your grand­par­ents’ house. If box­es aren’t clear­ly marked, find­ing what you need takes time, effort, and a lot of guess­work. Some con­tents might be obvi­ous, but oth­ers are far less so. With­out clear labels, valu­able things may stay hid­den or go unused.”

The project was ground­ed in an under­stand­ing of researcher work­flows and devel­oped through an iter­a­tive feed­back process. It ben­e­fit­ed immense­ly from the exper­tise of the 4TU.ResearchData team and the thought­ful con­tri­bu­tions of data stew­ards, who pro­vid­ed insight at every stage. The learn­ing mate­ri­als were shaped not only by their input but also by ped­a­gog­i­cal design prin­ci­ples and adult learn­ing the­o­ry to ensure the con­tent is acces­si­ble, rel­e­vant, and prac­ti­cal.

Look­ing ahead, I have shared a series of rec­om­men­da­tions to sup­port the resource’s con­tin­ued devel­op­ment. These include inte­grat­ing the resource into the repos­i­to­ry inter­face, expand­ing the use of visu­al guid­ance, and pro­mot­ing it through train­ing ini­tia­tives, grad­u­ate school pro­grams, and research com­mu­ni­ties.

I am deeply grate­ful to the 4TU.ResearchData team for wel­com­ing me into the fold and for their will­ing­ness to con­tribute ideas, clar­i­fy process­es, and engage ful­ly in the review and refine­ment of the mate­ri­als. I also wish to thank the data stew­ards, whose engage­ment and insights were essen­tial to shap­ing a resource that reflects both the chal­lenges and real­i­ties of sub­mit­ting research data to a repos­i­to­ry in the cur­rent era.

The project ran from Sep­tem­ber 2024 to June 2025, as part of Christi­na’s col­lab­o­ra­tion with 4TU.ResearchData.