Rec. 19: Encourage and incentivise data reuse

[sjDCC]

Funders should incentivise data reuse by promoting this in funding calls and requiring research communities to seek and build on existing data wherever possible.

• Researchers should be required to demonstrate in DMPs that existing FAIR data resources have been consulted and used where possible before creating new data. Stakeholders: Policymakers; Research communities.

• Appropriate levels of funding should be dedicated to reusing existing FAIR data by schemes that incentivise this. Stakeholders: Funders; Institutions.

[holubp]

BBMRI-ERIC Position: These principles should be extended to reuse of research resources: software and physical specimen (biological material in life sciences and medicine).

[katerbow]

DFG position: The reuse of data is considered as the top objective of the entire RDM-endeavour and scientists certainly should be encouraged to do so. There could be specific funding schemes to specifically aid this objective. However, it seems challenging to incentivise data reuse. Scientific work is based on and driven by individually developed ideas and research questions in order to gain knowledge. That is a very own approach by basic science and should not be controlled by incentives of any kind, especially the obligatory usage of a certain method or a specific infrastructure. If working on a research question, again, scientists should be encouraged to reuse existing data if possible.

[Falco-KUB]

The reuse of data is the foundation of FAIR, so I think that this recommendation is fundamental. However, there are disciplines, where there are no traditions for reusing data. What would their incentives for FAIR be?

[ajaunsen]

The premises for reuse of data is to train researchers to search for FAIR data and to make sure (through funding, data stewards, publication credits etc) that data is published according to FAIR in the first place. Ultimately, there should be no easier and cost-effective means of obtaining relevant data than to reuse FAIR data!

[ScienceEurope]

Science Europe welcomes this recommendation as this is in line with the DMP Core Requirements that will be published by Science Europe towards the end of 2018.

[eiroforum-it-wg]

EIROforum has published its input for the consultation which is available online (20180724-EIROforum-position-paper-EOSC.pdf). The paper highlights a number of practical points EIROforum members consider essential to ensure the EOSC can effectively interlink People, Data, Services and Training, Publications, Projects and Organisations, including aspects related to Rec. #19 “Encourage and incentivise data reuse”.

[ferag]

Remark: Reuse of good-quality data.

[RCN2018]

While this is an important goal, this seems a bit premature at the moment. It is important to incentivise data reuse but it is not possible to implement this today as a general rule. To do this we need measures to evaluate DMPs, and ensure that FAIR data become available through the EOSC or trusted repositories.

[pkdoorn]

Thumbs up.

[mromanie]

ESO position Astronomy as a discipline has a long tradition of one data and data re-use. We have many indications that incentivising data reuse is beneficial to the overall science output in a very cost-effective way.

[carrd]

Wellcome Trust position: We strongly support this recommendation and are in the process of trialling mechanisms ourselves to stimulate data re-use. However we weren't sure the sub-bullets were quite right, and could maybe be refined - in particular, I am not sure the DMP is necessarily the place to demonstrate that existing data resources have been used; it is not clear whether you are recommending the need for new funding mechanisms to support re-use; and there is a need to ensure that research that re-uses data is valued adequately in comparison to research which creates new data across the board (not just in schemes which incentivise this).

[npch]

SSI position:

We endorse others comments and suggest renaming to "Encourage and incentivise reuse of FAIR [outputs|objects]"

[gtoneill]

Fully support encouraging and incentivising the reuse of existing FAIR data. It is not clear though how this would actually work in research funding applications and reviews as well as in DMPs. It is also not clear what the relation will be to referring to previous peer-reviewed research. Will researchers now not only have to build upon peer-reviewed research results but also (un-)related FAIR data outputs?