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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The Data Tags Suite (DATS) model for discovering data access and use requirements.

George Alter1, Alejandra Gonzalez-Beltran2, Lucila Ohno-Machado3

  • 1University of Michigan, ICPSR 330 Packard Street, Ann Arbor MI 48104, USA.

Gigascience
|February 8, 2020
PubMed
Summary

Researchers need clear data access and reuse information. The Data Tags Suite (DATS) metadata schema provides elements for describing data access, use conditions, and consent, aiding data discovery and management.

Keywords:
confidential datadata accessdata discoverydata usemetadata

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Area of Science:

  • Data Science
  • Bioinformatics
  • Research Data Management

Background:

  • Data reuse is crucial but often restricted to protect subject and patient privacy.
  • Effective data discovery tools require clear communication of data access and re-use limitations.

Purpose of the Study:

  • To present metadata elements within the Data Tags Suite (DATS) schema for describing data access and use.
  • To explain how DATS metadata supports administrative, legal, and technical data protection systems.

Main Methods:

  • Developed metadata elements for the DATS schema.
  • Focused on describing data access, data use conditions, and consent information.
  • Explained metadata in the context of data protection systems.

Main Results:

  • Introduced DATS metadata elements for data access, use conditions, and consent.
  • Demonstrated how these metadata elements align with systems protecting confidential data.

Conclusions:

  • DATS metadata items are designed to facilitate researcher data discovery and reuse.
  • Called for standardized descriptions of informed consent and data use agreements to enable automated data management.