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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Preventing Proteomics Data Tombs Through Collective Responsibility and Community Engagement.

Uladzislau Vadadokhau1, Mai Soliman2,3,4, Leticia Castillon5

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Reanalyzing mass spectrometry proteomics data revealed significant barriers to reuse, including missing metadata and proprietary formats. Implementing standardized data packages is crucial for preventing "data tombs" and ensuring scientific reproducibility.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Data Science

Background:

  • Public proteomics repositories contain vast mass spectrometry data.
  • Much of this data is difficult to reuse, creating
  • data tombs
  • that hinder re-analysis.

Purpose of the Study:

  • To assess the re-analyzability of publicly available mass spectrometry proteomics data.
  • To identify systemic barriers hindering data reuse and reproducibility.
  • To propose solutions for improving data accessibility and re-analysis.

Main Methods:

  • A graduate-level course used a common R-based workflow to reanalyze six projects from the Proteomics Identification Database.
  • Student teams performed identification, quantification, normalization, imputation, and differential expression analyses.
  • Outcomes were compared to the original studies to identify discrepancies and barriers.

Main Results:

  • Systemic barriers included lack of metadata standards, missing decoy set details, proprietary software outputs, absent spectral libraries/FASTA files, vague parameters, inconsistent file naming, and insufficient replication.
  • These shortcomings led to significant discrepancies in analysis results (e.g., protein counts, differentially expressed proteins).
  • Reproducibility depends on transparent metadata, open formats, and executable analysis provenance, not just instrumentation.

Conclusions:

  • A minimum re-analysis package, including raw data, open formats, community standards, QC summaries, spectral libraries, and complete parameter/code sets, should be provided by data creators.
  • Repositories should incentivize or mandate the submission of such comprehensive data packages.
  • This approach trains students and improves community data practices to prevent proteomics
  • data tombs
  • .