KNAW open science meeting

Efficient organisation of research support

December 9, 2019

Dream

BIG

Science

2030

What would be the ideal open science situation in 2030?

1. Team science around the globe: Open science is more then data and knowledge sharing.
2. Infrastructure at the international, national vs local level more aligned
3. Culture and opportunity for change

Current status

Infrastructures not aligned

At the current moment everyone is ‘inventing the wheel’. There is no golden standard for infrastractures of data. Although there are great attempts, structures that are flexible enough for all different combinations of data types are lacking. Moreover, infrastructures at different levels are not align, for example infrastructure at the institute does not align to national data banks like DANS.

Rewarding and valuing skills

Open science is very much focused on sharing data and publications. Yet, sharing skills is underappreciated, although these skills are essential. Building infrastructure is complex and requires many skills including programming that require years to train. But these are not always rewarded with e.g. authorships and sometimes taken for granted.

Top 3 selected issues

Infrastructure

First infrastructure can be organized at different levels, e.g. at the institute, national or international. These each come with different challenges. For example, privacy regulations and laws differ (NL vs EU vs US).

Each project requires different infrastructure, so there is no golden standard, these systems need to be flexible. For example raw vs processed data.

Infrastructure is expensive and now often required to be covered by the individual researcher

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Initiate debate

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It is important to initiate a debate at the faculty and leadership level, as there is not always enough awareness of the urgency and issues related to infrastructure

There should also be a role for the participants in such a debate.

Skills sharing

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Open science is more then data and knowledge sharing it is also about sharing skills to be able to do good reproducible science.

Not everyone is skilled to work and build infrastructure, so it is essential to train specialists

There is an open question on how to reward other efforts besides writing (collecting or processing data)

Proposed deliverables

• Start debate at level of the leadership as well as bottom-up

• Look at examples outside of academia like CBS and NWA also with regard to privacy regulations

• Offer your programmer a valuable authorship position
• Give your students time to train skills

• Infrastructure at different levels are aligned and suitable for all types of data
• privacy regulations are clear and applied at each level
• Skill training for open science are implemented in study programs

The Team

Prof. Dr. Dorret Boomsma

VU University

Faruk Gulban

Maastricht University

Prof. Dr. Roshan Cools

Donders Institute

Dr. Lara Wierenga

Leiden University

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Dr. Rebecca Schaefer

Leiden University

Prof. Dr. Eveline Crone

Leiden University

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