A Capability Maturity Model for Research Data Management
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Last modified by Arden Kirkland on 2014/10/05 15:20
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38 38
39 39 == Three dimensions of data infrastructure services ==
40 40
41 -==== The Technology Dimension ====
41 +==== The Technology Dimension ====
42 42
43 43 The technology infrastructure covers a wide range of technologies for collecting, storing, processing, organizing, transmitting, and preserving data as well as platforms for communication and collaboration. Included in this dimension of the data infrastructure are networks, databases, authentication systems, and software applications. Scientific data and databases are different from conventional ones used for business transactions or employee records due to the idiosyncrasies of scientific data. Not only are scientific data collected from various sources such as observations, experiments, crowd-contributions (e.g., data generated from citizen science projects), or computer modeling /simulations, but also come with a wide variety of types and formats as well as varying levels of processing. Raw data collected from observations, experiments, modeling, or simulations often need to go through a series processing, transformation, and quality check before the data can be used for analysis. Differences in data types and formats cross disciplines or even within the same discipline field can become barriers for data sharing and reuse [5]. The technological dimension of data infrastructure, therefore, is not just a simple technical issue but rather, is closely tied with the policies and standards.
44 44
45 -==== The Dimension of Data and Metadata Standards ====
45 +==== The Dimension of Data and Metadata Standards ====
46 46
47 47 Another important dimension of a data infrastructure is data and metadata standards. Scientific data can be grouped into three large blocks based on discipline and type:
48 48
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