Nyhed på engelsk.
The focus of this Open Access week is research data citation and sharing. One thing is to make good citations of data, another is the incentives for sharing research data. Therefore, we asked Lars Pilgaard Mikkelsen, Associate Professor at DTU Wind, about his approach to open data.
Data journals
Lars has published more than 30 datasets, using the Zenodo data repository which is an alternative to DTU Data. Some of those data sets have been supplemented with a data journal publication in e.g. the WoS-indexed Elsevier journal Data in Brief or in the Software Impact journal regarding code publications.
To supplement main articles Lars uses data journals. In the articles in data journals, he can focus on a detailed explanation on the source of the data set. This improves the visibility of the data sets detached from the research subject addressed in the main article. Often data from experimental measurement or image-based data can be used in connections completely outside the scope of the main article.
Validation of the results
"When I share the whole data set, readers have the possibility to validate my results and perform own analyses on the data. Most often you only have space for showing a small representative part of a data set in scientific articles, which means that users must trust your data selection,” Lars elaborates, “With open data sharing, users can compare my analysis methodology with their own and that of others. This moves forward the scientific discussion and reproducibility of the research.”
To understand what is in his list of open datasets, Lars exemplifies:
- Numerical models behind simulations should be shared so others can validate and modify your analyses.
- Image-based data sets are often huge and difficult to share in traditional articles.
- Published datasets used in teaching are very useful for assignments. As an example, one dataset supplements my lecture on mechanical properties of glass and carbon fibers.
- Our work inside DTU Image Center creates extensive experimental data using X-ray tomography. Several PhD projects and other universities base their simulations on these data, which they would never be able to create themselves.
Open Science
When asked about the value of Open Science on innovation in his field, Lars replies: “The latter example shows how my efforts in data publication are rewarded. It does take resources to prepare data for publication, but when easily found, data can form the basis for PhD theses or new research.” Openness requires planning; therefore, Lars elaborates: “In collaborative projects, it’s a benefit to make an agreement about public data-sharing. It simplifies the continuation of the work after the project and the collaboration is finalized. Data publications with a clear license and a DOI ensure that we get credit for our work.”
Thank you Lars, for sharing.
