OSGIS Conference: report on RDA/MPG Science workshop

RDA-Europe Forum 106/05/14

1st Science Workshop on Data10-11 February, Munich, Germany

Herman Stehouwer

Thanks to Raphael Ritz for the slides

Garching Compute Center, Max-Planck-Society, Germany

2

Science Workshop on Data

February 10-11, 2014, in Munich

15 leading scientists + data practitioners

Hosted by RDA-E and MPG

Key points:Rewards (“what’s in there for me?”)

Trust (“are these data and metadata correct?”)

Persistence (“what will be in 20 years?”)

Report available

RDA-Europe/MPG Science Workshop06/05/14

RDA-Europe/MPG Science Workshop 3

Participants - Scientists• Bernard Schutz - Gravitational Physics - Cardiff U / MPG • Bruce Allen - Gravitational Physics - MPI for Gravitationphysics, Hannover • Bruno Leibundgut - Astronomy - ESO, Garching • Cécile Callou - Archaezoology/Biodiversity-Ecology-Environment - Museum d'Histoire

Naturelle, Paris • Christine Gaspin - Bio-Informatics - INRA, Toulouse • Dick Dee - Meteorology - ECMWF • Jan Bjaalie - Neuroanatomy and Computer Science - University of Oslo• Francoise Genova – Astronomy – CNRS, Strasbourg• Jochem Marotzke - Climate Model - MPI for Meteorology, Hamburg • Manfred Laubichler - History of Science - New Mexico University • Marc Brysbaert - Psychology - Ghent University • Mark Hahnel - Biology - Figshare and Imperial College, London• Markku Kulmala - Atmospheric Sciences - University of Helsinki • Peter Coveney - Chemistry, biomedecine - UCL, London • Stefano Nativi – Earth System Science and Environmental Technologies – CNR, Rome

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Participants - Experts

• Carlos Morais-Pires - e-Infrastructures - European Commission • Donatella Castelli - RDA/E Member/Computer Science - ISTI-CNR, Pisa • Frank Sander - MPDL Director - MPDL, Munich • Herman Stehouwer - RDA Secretariat - MPI for Psycholinguistics, Nijmegen • Leif Laaksonen - RDA/E Coordinator - CSC, Helsinki • Peter Wittenburg - RDA-TAB Member/Linguistics - MPI for

Psycholinguistics, Nijmegen • Ramin Yahyapour - GWDG Director - GWDG, Göttingen • Raphael Ritz - RDA/E Member/NeuroInformatics - MPG, Garching • Stefan Heinzel - RZG Director - MPG, Garching • Reinhard Budich – Data Scientist – MPI Meterology, Hamburg • Riam Kanso – Data Policies/Cognitive Neuroscience – UCL, London• Ari Asmi – Atmospheric Sciences – University of Helsinki

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Multiple Dimensions

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Observations

Sharing and Re-use of Data

Publishing and Citing Data

Infrastructure and Repositories

Leading to

Recommendations

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Sharing and Re-use of Data

Still in its infancy

Lack of efficient, cross-disciplinary methods enabling re-use

Reference data is costly to establish and maintain

Trust needed in the identity, integrity, authenticity of data and the seriousness of all actors involved

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Publishing and Citing Data

Core of the scientific process

Referencing data (e.g. via PIDs) must be stable

Reproducible Science

Quality assessment (e.g. peer review)

Career building

Suitable infrastructure needed

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Infrastructure and Repositories

They are desperately needed

Researchers in the driving seat

Limits to Open Access

Services on data

Legacy data and systems

Companies more cost effective?

Registries and catalogues needed

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Recommendations to RDA

Come up with recommendations and specifications to overcome the one-shot, point solutions of today

Be really bottom-up

Engage a “middle layer” of data scientists

Compete or cooperate with commercial players

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Expectations towards RDA

Invest in training younger generations of data scientists

Push demo projects, act as clearing house, provide advice on data management, access and re-use

Have data experts visit and support institutes

Perform good quality assessment on own results

Don’t oversell

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A New Way of Doing Science?

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https://materialsproject.org/escience


Challenges of Data Intensive Science

Large Volumes

Accumulated at high rate (Velocity)

Integration across a Variety of sources

Trust/Validity/Veracity

This has implications on data capture, curation, storage, search, sharing, transfer, analysis and visualization

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Data Sharing != Open Access

Data Sharing starts in your own laboratory.

It continues with collaborators, consortia, peers, the scientific communities and maybe the general public.

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Are you willing to take the time to

prepare your data for sharing?

Is there somewhere for you to put your

data?

Can I read your data?

If so, can I understand it?(e.g., how it was acquired and

analysed?)

Will it be possible to still understand the data in

the future?

Making data sharing feasible

Develop methods to ensure

interoperability

Develop standardised

and comprehensive

metadata schemes

Develop automated methods for metadata

capture

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A Lot of Work

Managing data in a lab requires increasing amounts of time by the scientists involved.

Preparing for sharing requires additional work.

How can we make this as effective as possible?

RDA’s idea: adopt a bottom-up approach towards Research Data Sharing without barriers.

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Future

2015: CERN

2016: CNRS

Follow-up workshopsContinue Discussion

Deepen Themes

06/05/14

OSGIS Conference: report on RDA/MPG Science workshop

Science

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