FAIR Data Fund Autumn Call 2021:
Meet the grantees 

Huawei Wang

Post­doc­tor­al researcher in Bio­me­chan­i­cal Engi­neer­ing at the Uni­ver­si­ty of Twente | @Huawei_Wang_

Do you need FAIR human move­ment data with dif­fer­ent sen­sor aspects? Huawei’s dataset com­pris­es infor­ma­tion from six dif­fer­ent types of sen­sors (all syn­chro­nized) record­ing move­ment from 12 healthy young adults per­form­ing 13 dai­ly activ­i­ty tri­als. The funds will be used to open­ly pub­lish the raw and processed data, pro­cess­ing pipeline (soft­ware), open hard­ware, meta­da­ta files, and doc­u­men­ta­tion. 

Artur Schweidtmann

Assis­tant Pro­fes­sor in Chem­i­cal Engi­neer­ing & Machine Learn­ing at Delft Uni­ver­si­ty of Tech­nol­o­gy | @ASchweidtmann

Artur’s research project will enable the reuse of fig­ures from open-access sci­en­tif­ic jour­nal arti­cles in chem­i­cal engi­neer­ing. His aim is to gen­er­ate a big, linked pub­lic dataset of anno­tat­ed sci­en­tif­ic images that will enable the train­ing of clas­si­fi­ca­tion and infor­ma­tion extrac­tion algo­rithms for the chem­i­cal engi­neer­ing field.

Darinka Czischke & Sara Brysch 

Darinka Czischke

Asso­ciate Pro­fes­sor at the Fac­ul­ty of Archi­tec­ture and the Built Envi­ron­ment, Delft Uni­ver­si­ty of Tech­nol­o­gy | @DarinkaCzischke

Sara Brysch 

PhD researcher at the Fac­ul­ty of Archi­tec­ture and the Built Envi­ron­ment, Delft Uni­ver­si­ty of Tech­nol­o­gy 

Quan­ti­ta­tive data on col­lab­o­ra­tive hous­ing are cur­rent­ly scat­tered (or even only avail­able upon request) and lack stan­dard def­i­n­i­tions. The FAIR Data Fund will allow Darin­ka, Sara and their team to refine their exist­ing datasets and make them FAIR. Their pub­lished datasets will cre­ate a sol­id basis for com­par­a­tive and quan­ti­ta­tive stud­ies on col­lab­o­ra­tive hous­ing.

Ivo Filot 

Assis­tant Pro­fes­sor in The­o­ret­i­cal and Com­pu­ta­tion­al Catal­y­sis at Eind­hoven Uni­ver­si­ty of Tech­nol­o­gy | @IvoFilot

Ivo aims to build a com­part­men­tal­ized data stor­age and dis­tri­b­u­tion ser­vice for elec­tron­ic struc­ture cal­cu­la­tions. The plat­form is based on Dock­er con­tain­ers that allow for effi­cient and robust imple­men­ta­tion and devel­op­ment. Unique­ly, data stor­age and dis­tri­b­u­tion is com­bined with meta­da­ta gen­er­a­tion scripts and a web serv­er that offers quick access and search func­tion­al­i­ty to retrieve sin­gle data items.

Daniël Lakens & Ruben Lopez

Daniël Lakens

Assis­tant Pro­fes­sor in Psy­chol­o­gy at Eind­hoven Uni­ver­si­ty of Tech­nol­o­gy  | @lakens

Ruben Lopez
PhD stu­dent in Method­ol­o­gy at the Uni­ver­si­ty of Murcia/Visiting researcher at Eind­hoven Uni­ver­si­ty of Tech­nol­o­gy | @neburmix

Daniël and Ruben’s research project aims to retrieve, orga­nize, and open­ly release meta-ana­lyt­i­cal data from 854 inde­pen­dent meta-analy­ses of dif­fer­ent psy­cho­log­i­cal domains. Their dataset will rep­re­sent sum­ma­ry study-lev­el data from 38,468 dif­fer­ent sam­ples with the goal to cre­ate a stan­dard to share indi­vid­ual meta-ana­lyt­ic data­bas­es in the future. 

Kianoosh Taghizadeh

Post­doc­tor­al researcher in Mechan­i­cal Engi­neer­ing and Mate­r­i­al Sci­ence at the Uni­ver­si­ty of Twente | @kia_tb

By tun­ing par­ti­cle prop­er­ties and species com­po­si­tions in stiff-soft mate­r­i­al mix­tures using nov­el data sci­ence ideas, we aim to enhance the mechan­i­cal behav­iour of par­tic­u­late sys­tems and design new mate­ri­als. Kia’s pub­lished dataset will com­prise com­pu­ta­tion­al (sim­u­la­tion inputs) and exper­i­men­tal data (X‑ray images, in-situ wave prop­a­ga­tion exper­i­ments).