Poster Open Access

The Swiss Data Science Center on a mission to empower reproducible, traceable and reusable science

Schymanski, Stanislaus Josef; Eric Bouillet; Olivier Verscheure


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.581298", 
  "title": "The Swiss Data Science Center on a mission to empower reproducible, traceable and reusable science", 
  "issued": {
    "date-parts": [
      [
        2017, 
        4, 
        28
      ]
    ]
  }, 
  "abstract": "<p>Our abilities to collect, store and analyse scientific data have sky-rocketed in the past decades, but at the same\u00a0time, a disconnect between data scientists, domain experts and data providers has begun to emerge. Data scientists\u00a0are developing more and more powerful algorithms for data mining and analysis, while data providers are making\u00a0more and more data publicly\u00a0available, and yet many, if not most, discoveries are based on specific data and/or\u00a0algorithms that \"are available from the authors upon request\".<br>\nIn the strong belief that scientific progress would be much faster if reproduction and re-use of such data\u00a0and algorithms was made easier, the Swiss Data Science Center (SDSC) has\u00a0committed to provide an open framework for the handling and tracking of scientific data and algorithms, from raw data and first principle equations\u00a0to final data products and visualisations, modular simulation models and benchmark evaluation algorithms. Led\u00a0jointly by EPFL and ETH Zurich, the SDSC is composed of a distributed multi-disciplinary team of data scientists<br>\nand experts in select domains. The center aims to federate data providers, data and computer scientists, and\u00a0subject-matter experts around a cutting-edge analytics platform offering user-friendly tooling and services to help\u00a0with the adoption of Open Science, fostering research productivity and excellence.<br>\nIn this presentation, we will discuss our vision of a high-scalable open but secure community-based platform for sharing, accessing, exploring, and analyzing scientific data in easily reproducible workflows, augmented\u00a0by automated provenance and impact tracking, knowledge graphs, fine-grained access right and digital right\u00a0management, and a variety of domain-specific software tools. For maximum interoperability, transparency and\u00a0ease of use, we plan to utilize notebook interfaces wherever possible, such as Apache Zeppelin and Jupyter.<br>\nFeedback and suggestions from the audience will be gratefully considered.</p>", 
  "author": [
    {
      "family": "Schymanski, Stanislaus Josef"
    }, 
    {
      "family": "Eric Bouillet"
    }, 
    {
      "family": "Olivier Verscheure"
    }
  ], 
  "note": "Poster presented at  European Geosciences Union General Assembly 2017, id: EGU2017-12179.", 
  "type": "graphic", 
  "id": "581298"
}
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