Collaborative Computing: Networking, Applications and Worksharing. 4th International Conference, CollaborateCom 2008, Orlando, FL, USA, November 13-16, 2008, Revised Selected Papers

Research Article

Collaboratively Sharing Scientific Data

Download
347 downloads
  • @INPROCEEDINGS{10.1007/978-3-642-03354-4_58,
        author={Fusheng Wang and Cristobal Vergara-Niedermayr},
        title={Collaboratively Sharing Scientific Data},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 4th International Conference, CollaborateCom 2008, Orlando, FL, USA, November 13-16, 2008, Revised Selected Papers},
        proceedings_a={COLLABORATECOM},
        year={2012},
        month={5},
        keywords={Scientific Data Sharing Scientific Data Integration Biomedical Data Management Computer Supported Collaborative Work Schema Sharing Schema Evolution},
        doi={10.1007/978-3-642-03354-4_58}
    }
    
  • Fusheng Wang
    Cristobal Vergara-Niedermayr
    Year: 2012
    Collaboratively Sharing Scientific Data
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-642-03354-4_58
Fusheng Wang1,*, Cristobal Vergara-Niedermayr2,*
  • 1: Siemens Corporate Research
  • 2: Freie Universität
*Contact email: fusheng.wang@siemens.com, vergara@mi.fu-berlin.de

Abstract

Scientific research becomes increasingly reliant on multi-disciplinary, multi-institutional collaboration through sharing experimental data. Indeed, data sharing is mandatory by government research agencies such as NIH. The major hurdles for data sharing come from: i) the lack of data sharing infrastructure to make data sharing convenient for users; ii) users’ fear of losing control of their data; iii) difficulty on sharing schemas and incompatible data from sharing partners; and iv) inconsistent data under schema evolution. In this paper, we develop a collaborative data sharing system , to support consistency preserved data sharing among multiple distributed organizations. The system first provides Central Server based lightweight data integration architecture, so data and schemas can be conveniently shared across multiple organizations. Through distributed schema management, schema sharing and evolution is made possible, while data consistency is maintained and data compatibility is enforced. With this data sharing system, distributed sites can now consistently share their research data and their associated schemas with much convenience and flexibility. SciPort has been successfully used for data sharing in biomedical research, clinical trials and large scale research collaboration.