RDNL works on a national training and community platform for data stewards

RDNL, Research Data Nether­lands, received 4,8 mil­lion euros from Open Sci­ence NL to devel­op a train­ing and com­mu­ni­ty plat­form for data stew­ards over four years. ‘This allows them to expand their knowl­edge and skills, enabling research insti­tu­tions to make the tran­si­tion to open sci­ence more eas­i­ly and quick­ly,’ says Dorien Hui­js­er, close­ly involved in this RDNL project.

“The plat­form should become the go-to place for data stew­ards’ pro­fes­sion­al devel­op­ment.”

A national platform

The con­sor­tium form­ing RDNL con­sists of DANS, SURF, 4TU.ResearchData and Health-RI. These exper­tise cen­tres focus on archiv­ing and shar­ing FAIR data (find­able, acces­si­ble, inter­op­er­a­ble, and reusable), data stew­ard­ship, train­ing, and com­mu­ni­ty build­ing. Hui­js­er: ‘RDNL already offers two cours­es for data stew­ards, but beyond that, the train­ing oppor­tu­ni­ties are quite frag­ment­ed. In this project, we want to con­nect var­i­ous ini­tia­tives and train­ing options more close­ly through a nation­al plat­form.’

What exactly will change?

‘On the new nation­al train­ing and com­mu­ni­ty plat­form, data stew­ards can find all rel­e­vant infor­ma­tion about train­ing. We are not only devel­op­ing new cours­es, but also want to include cours­es from dif­fer­ent part­ners in our cur­ricu­lum. For exam­ple, the eScience Cen­ter offers a train­ing that is very suit­able for data stew­ards and would fit well in our pro­gramme. Because data stew­ards have such diverse train­ing needs, we aim to offer a wide range of cours­es. We are also con­sid­er­ing badg­ing for suc­cess­ful course com­ple­tion. This allows data stew­ards to show which spe­cif­ic skills they have.’

How do you know which training data stewards need?

‘Dur­ing the Open Sci­ence Fes­ti­val in Octo­ber 2025, we asked data stew­ards them­selves what train­ing they were miss­ing. This pro­duced a long list of train­ing needs, which we will use to update exist­ing cours­es and devel­op new ones. In addi­tion, we want to gath­er input from data stew­ards on a struc­tur­al basis so that the train­ing pro­gramme remains rel­e­vant for them. For exam­ple, there will be an annu­al com­mu­ni­ty event.’

Why are data stewards so important for the transition to open science?

‘Data stew­ards help researchers make their data FAIR and advise them on this. How was the research con­duct­ed? What data do they have? How are these data stored safe­ly and which tools are used? How do they ensure that the data remain find­able and share­able over time?

‘This does not hap­pen auto­mat­i­cal­ly. A link to a web­site might no longer work in a few years because the domain name has changed. Clean data are often not easy to under­stand; you need to pro­vide con­text so that oth­ers can use it. And for the researcher them­selves, it is also nec­es­sary, because in five or ten years they may no longer recall all the details. Data stew­ards spe­cialise in this.’

Are there specific themes that are particularly important for data stewards?

‘There are two areas where we could do more. First, more train­ing in trans­ver­sal skills, also known as soft skills. Researchers need to work more open­ly, but that is not their exper­tise. Data stew­ards must there­fore be able to guide researchers to move an organ­i­sa­tion more towards open sci­ence. Skills in advis­ing, per­sua­sion, com­mu­ni­ca­tion, and change man­age­ment are very impor­tant here.

‘Sec­ond, spe­cif­ic tech­nolo­gies are impor­tant. Nowa­days, inter­op­er­abil­i­ty is a hot top­ic. This means that data must be usable by both peo­ple and machines, and that more automa­tion in research is pos­si­ble. It is a fair­ly tech­ni­cal aspect of data man­age­ment that dif­fers great­ly between research dis­ci­plines. In math­e­mat­ics and life sci­ences, for exam­ple, there is already much more stan­dard­i­s­a­tion than in more qual­i­ta­tive dis­ci­plines such as social sci­ences and human­i­ties. Data stew­ards can act as a bridge by spe­cial­is­ing in a dis­ci­pline or tech­nol­o­gy and show­ing researchers its poten­tial.’

How do you hope data stewards will use the new training and community platform?

‘The plat­form should become the go-to place for data stew­ards’ pro­fes­sion­al devel­op­ment. They can fol­low the cours­es in the cur­ricu­lum, but they will always have addi­tion­al ques­tions. We hope they will use the com­mu­ni­ty part of the plat­form for this: to con­nect and help each oth­er with every­day prob­lems. For exam­ple, using a par­tic­u­lar tool, or how to inter­act with researchers. Some things are bet­ter learned by exchang­ing expe­ri­ences with col­leagues, which we do not need to for­malise into a train­ing. And we can­not, because the field is far too dynam­ic.’

Finally, what happens when the four years are over and the funding runs out?

‘We are work­ing on gov­er­nance sce­nar­ios and on increas­ing the com­mit­ment of stake­hold­ers: train­ing part­ners, the the­mat­ic research com­mu­ni­ties (TDCCs), the organ­i­sa­tions whose data stew­ards use the plat­form, and Open Sci­ence NL itself and its part­ners. In addi­tion, in con­sul­ta­tion with these stake­hold­ers, we are con­sid­er­ing a busi­ness mod­el so that the plat­form can con­tin­ue inde­pen­dent­ly.’