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Data Quality for Medical Data Lakelands

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Future Data and Security Engineering (FDSE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12466))

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Abstract

Medical research requires biological material and data. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is only available for research after approval of ethics committees. These data sets, therefore, cannot be stored in central data warehouses or even in a common data lake but remain in a multitude of data lakes, which we call Data Lakelands. An example for such a Medical Data Lakelands are the collections of samples and their annotations in the European federation of biobanks (BBMRI-ERIC). We discuss the quality dimensions for data sets for medical research and the requirements for providers of data sets in terms of both quality of meta-data and meta-data of data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This work has been supported by the Austrian Bundesministerium für Bildung, Wissenschaft und Forschung within the project BBMRI.AT (GZ 10.470/0010-V/3c/2018).

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Authors and Affiliations

  1. Alpen-Adria-Universität Klagenfurt, Universitätsstraße 65-67, 9020, Klagenfurt am Wörthersee, Austria

    Johann Eder & Vladimir A. Shekhovtsov

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  1. Johann Eder
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  2. Vladimir A. Shekhovtsov
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Correspondence to Johann Eder .

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Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

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Eder, J., Shekhovtsov, V.A. (2020). Data Quality for Medical Data Lakelands. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. FDSE 2020. Lecture Notes in Computer Science(), vol 12466. Springer, Cham. https://doi.org/10.1007/978-3-030-63924-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-63924-2_2

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