WHAT TO DO WITH THE DATA AFTER YOUR RESEARCH?

Every month the Dig­i­tal Com­pe­tence Cen­tre at the Uni­ver­si­ty of Twente organ­is­es a the­mat­ic ses­sion that focus­es on a hot research top­ic. In May, the ses­sion was all about data and what researchers should do with it after their research project is com­plete.

Ask the audience  

The ses­sion kicked off with a short sur­vey to ask researchers about their cur­rent research data prac­tices. Atten­dees were asked to answer the fol­low­ing ques­tion: ‘What do you cur­rent­ly do with the data after your research?’ 

Their respons­es revealed that whilst some researchers cur­rent­ly use a data repos­i­to­ry (16%) and/or an exter­nal hard dri­ve with­in group facil­i­ties (20%) to store their research data, the major­i­ty (59%) use a per­son­al device or shared net­work dri­ve.

The remain­der of the ses­sion explored the ben­e­fits and lim­i­ta­tions of these var­i­ous stor­age solu­tions, and focused on the best prac­tices for cre­at­ing data that is find­able, acces­si­ble, inter­op­er­a­ble and reusable (The FAIR prin­ci­ples).

A data steward’s perspective

Why should researchers think about what to do with their data after their research project is com­plete?

Data should be kept after the research project is com­plete for val­i­da­tion and ver­i­fi­ca­tion pur­pos­es. It’s impor­tant to make sure that oth­ers can trust the exper­i­men­tal results. In addi­tion, pre­serv­ing data in a secure and acces­si­ble loca­tion can help to make it avail­able for reuse which can lead to a larg­er impact of the research. 

Reuse doesn’t mean that data has to be reused by researchers work­ing out­side of a uni­ver­si­ty. Often, the researcher or a col­league from with­in the same research group can ben­e­fit from reusing the data.

It is impor­tant that researchers keep these con­sid­er­a­tions in mind from the begin­ning of their research project. They can do so by using open and sus­tain­able file for­mats, for exam­ple. 

What do oth­ers expect? 

In addi­tion to the require­ments for research ver­i­fi­ca­tion and data reuse, archiv­ing data for long-term preser­va­tion is also required by many fund­ing bod­ies, pub­lish­ers and research insti­tu­tions around the world. 

Fun­ders, such as NWO, expect researchers to pre­serve their data for at least 10 years in a trust­ed repos­i­to­ry, and prefer­ably with open access. 

Pub­lish­ers, such as Springer, encour­age researchers to share and cite research data in their pub­li­ca­tions. Some jour­nals require that a data avail­abil­i­ty state­ment is includ­ed in research pub­li­ca­tions which tells the read­er where the data asso­ci­at­ed with a paper is avail­able, and under what con­di­tions the data can be accessed. In some cas­es, a peer-review of the data is also required.

Addi­tion­al­ly, research insti­tu­tions may have their own data poli­cies and guide­lines in place, there­fore, researchers should always check these require­ments before archiv­ing the data. 

Which data should be pre­served? 

In prin­ci­ple, a pack­age con­sist­ing of the data, tools for col­lect­ing and analysing the data, and the analy­sis syntax/code should be archived togeth­er with the results.

Where can data be pre­served? And, does all data need to be pub­licly avail­able? 

Research data can be pre­served in trust­ed repos­i­to­ries (e.g. 4TU.ResearchData, DANS Easy), insti­tu­tion­al archive (e.g. Uni­ver­si­ty of Twente data archive: Are­da) and/or group stor­age (e.g. Uni­ver­si­ty of Twente net­work dri­ve). 

Archiv­ing your data in a trust­ed repos­i­to­ry will give you the oppor­tu­ni­ty to (open­ly) share your data with the world, and your data will get a per­sis­tent iden­ti­fi­er (e.g. DOI) which enables cita­tion of the data. How­ev­er, not every dataset can be made open­ly avail­able due to spe­cial restric­tions, e.g. pri­va­cy, com­mer­cial inter­ests, patents, data owned by oth­ers, data relat­ed to pub­lic secu­ri­ty, polit­i­cal inter­ests, etc.

A researcher’s perspective 

Kostas stud­ied his PhD on ‘Hand Neu­ro-Motor Char­ac­ter­i­za­tion and Motor Inten­tion Decod­ing in Duchenne Mus­cu­lar Dys­tro­phy’. Although the prac­tise of pub­lish­ing data was not com­mon with­in his research field, he pub­lished four data sets relat­ed to his PhD. 

Cur­rent­ly, his dataset pub­lished in 4TU.ResearchData, ‘Raw data col­lect­ed for the study of: Char­ac­ter­i­za­tion of fore­arm high-den­si­ty elec­tromyo­grams dur­ing wrist-hand tasks in indi­vid­u­als with Duchenne Mus­cu­lar Dys­tro­phy’, has 175 views and 75 down­loads. 

What were his moti­va­tions to pub­lish data? 

Kostas decid­ed to share his data to show that he is con­vinced of his results and not afraid of mak­ing the data avail­able to oth­ers. He believes that researchers should not fear crit­i­cism and accept that they can­not always be 100% right. 

For exam­ple, in one case, when he sub­mit­ted a man­u­script for peer-review, a review­er checked Kostas’s data and found a mis­take. Although the review process took slight­ly longer because of this, Kostas believes that it was worth shar­ing his data dur­ing the man­u­script review process as he had time to cor­rect the mis­take and could feel con­fi­dent to pub­lish his research results.

Anoth­er impor­tant rea­son why Kostas shares his data is that he under­takes research using data col­lect­ed from human par­tic­i­pants. He appre­ci­ates that col­lect­ing data from humans places a bur­den on the study par­tic­i­pants as well as the researchers. It costs a high amount of prepa­ra­tion time, resources and effort, and requires the recruit­ment of suit­able par­tic­i­pants, informed con­sent and eth­i­cal approval, for exam­ple.

Kostas believes that by shar­ing his data oth­er researchers can reuse it with­out dupli­cat­ing efforts and recruit­ing more human par­tic­i­pants. This reduces the time,  effort and resources spent by researchers and par­tic­i­pants. 

Choosing a repository 

The ses­sion closed with demon­stra­tions of how to upload data using the 4TU.ResearchData and DANS data repos­i­to­ries. The demon­stra­tions allowed researchers to fol­low the process of pub­lish­ing a dataset live with time for Q&A. 

• Watch the record­ing of the 4TU.ResearchData repos­i­to­ry demon­stra­tion by 4TU.ResearchData com­mu­ni­ty man­ag­er, Con­nie Clare (the demo starts at 41 min 20 s).

• Watch the record­ing of the DANS repos­i­to­ry demon­stra­tion by Uni­ver­si­ty of Twente data stew­ard, Qian Zhang.
  
  

https://vimeo.com/557549755/d2df70a4e2

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Writ­ten by Simone Fricke, Mar­it van Eck, Qian Zhang & Con­nie Clare