EGU21-1614, updated on 03 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

A new distributed data analysis framework for better scientific collaborations

Philipp S. Sommer1, Viktoria Wichert1, Daniel Eggert2, Tilman Dinter3, Klaus Getzlaff4, Andreas Lehmann4, Christian Werner5, Brenner Silva3, Lennart Schmidt6, and Angela Schäfer3
Philipp S. Sommer et al.
  • 1Helmholtz-Zentrum Geesthacht (HZG), Institute of Coastal Research, Geesthacht, Germany
  • 2German Research Center for GeoSciences GFZ, Potsdam, Germany
  • 3Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
  • 4GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany
  • 5Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
  • 6Helmholtz-Zentrum für Umweltforschung GmbH - UFZ, Leipzig, Germany

A common challenge for projects with multiple involved research institutes is a well-defined and productive collaboration. All parties measure and analyze different aspects, depend on each other, share common methods, and exchange the latest results, findings, and data. Today this exchange is often impeded by a lack of ready access to shared computing and storage resources. In our talk, we present a new and innovative remote procedure call (RPC) framework. We focus on a distributed setup, where project partners do not necessarily work at the same institute, and do not have access to each others resources.

We present the prototype of an application programming interface (API) developed in Python that enables scientists to collaboratively explore and analyze sets of distributed data. It offers the functionality to request remote data through a comfortable interface, and to share and invoke single computational methods or even entire analytical workflows and their results. The prototype enables researchers to make their methods accessible as a backend module running on their own infrastructure. Hence researchers from other institutes may apply the available methods through a lightweight python or Javascript API. This API transforms standard python calls into requests to the backend process on the remote server. In the end, the overhead for both, the backend developer and the remote user, is very low. The effort of implementing the necessary workflow and API usage equalizes the writing of code in a non-distributed setup. Besides that, data do not have to be downloaded locally, the analysis can be executed “close to the data” while using the institutional infrastructure where the eligible data set is stored.

With our prototype, we demonstrate distributed data access and analysis workflows across institutional borders to enable effective scientific collaboration, thus deepening our understanding of the Earth system.

This framework has been developed in a joint effort of the DataHub and Digitial Earth initiatives within the Research Centers of the Helmholtz-Gemeinschaft Deutscher Forschungszentren e.V.  (Helmholtz Association of German Research Centres, HGF).

How to cite: Sommer, P. S., Wichert, V., Eggert, D., Dinter, T., Getzlaff, K., Lehmann, A., Werner, C., Silva, B., Schmidt, L., and Schäfer, A.: A new distributed data analysis framework for better scientific collaborations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1614,, 2021.

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