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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Provenance Information for Biomedical Data and Workflows: Scoping Review.

Kerstin Gierend1, Frank Krüger2,3, Sascha Genehr3

  • 1Department of Biomedical Informatics, Mannheim Institute for intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.

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|August 23, 2024
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Summary
This summary is machine-generated.

This review identifies diverse data provenance tracking approaches in biomedical research. Implementing these can enhance data integrity, reproducibility, and collaboration in scientific studies.

Keywords:
biomedical researchdata managementhealth care dataprovenancescoping reviewsoftware life cycle

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Scientific Research Methodology

Background:

  • Data provenance is crucial for scientific interpretability, collaboration, and data sharing.
  • A lack of clear provenance strategies hinders best practices in clinical research.

Purpose of the Study:

  • To conduct a scoping review of provenance tracking approaches and criteria in the biomedical domain.
  • To identify state-of-the-art frameworks, artifacts, and methodologies for data provenance.

Main Methods:

  • Scoping review following Arksey and O'Malley framework.
  • Searched PubMed and Web of Science (2006-2022).
  • Screening by 4 independent reviewers using Rayyan; full-text review by 2.

Main Results:

  • 66 studies met inclusion criteria, revealing diverse provenance approaches.
  • Identified requirements for data integrity, reproducibility, quality assessment, and data protection.
  • Challenges include data annotation, modeling, performance, and achieving desired provenance quality.

Conclusions:

  • Automated and scalable provenance solutions are needed due to increasing data volumes and computational power.
  • Greater transparency in provenance systems is essential for meeting legal and scientific demands.
  • Recommendations support auditable and measurable provenance implementation in biomedical research.