FAIR DATA Fund use case: Fatigue running data in- and out of the lab

Authors: Frank J Wou­da (FAIR Data Fund grantee, Uni­ver­si­ty of Twente), Rob­bert P. van Mid­de­laar, Corné Dijk­stra.

Brief descrip­tion of the project

In the field of move­ment analy­sis a large amount of data is record­ed, each with their own pro­cess­ing and struc­ture. This makes it dif­fi­cult to com­bine and com­pare dif­fer­ent datasets. Our project focused on a spe­cif­ic dataset of 16 run­ners, doing a fatigue pro­to­col.

Key results

A dataset con­tain­ing motion cap­ture data (opti­cal and iner­tial) and ground reac­tion forces dur­ing run­ning until fatigued of 16 sub­jects was struc­tured and enriched by adding meta­da­ta to it (mak­ing it FAIR). The raw and dataset will be shared in an open for­mat on 4TU.ResearchData. The code to restruc­ture and process the data will be made open­ly avail­able as the move­ment analy­sis plat­form.

How FAIR Data Fund is sup­port­ing the project

The FAIR Data Fund allowed us to appoint a stu­dent assis­tant that worked on the pro­gram­ming required for the project. Addi­tion­al­ly, we got some great sup­port in how to pro­vide meta­da­ta fit­ting to the data, as we had some chal­lenges with the num­ber of devices involved in the dataset. Thank you to 4TU.ResearchData for this oppor­tu­ni­ty and sup­port offered!