ArticleScientific data2026
Preventing Proteomics Data Tombs Through Collective Responsibility and Community Engagement.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- DORSSAA: Drug-Target interactOmics Resource Based on Stability/Solubility Alteration Assay.Molecular & cellular proteomics : MCP · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
Abstract
Public proteomics repositories now host vast amounts of mass spectrometry data, yet much of it remains difficult to reuse, risking "data tombs" that are open access but not practically re-analyzable. In spring 2025, a graduate-level course at the University of Helsinki tasked six student teams with reanalyzing six projects from the Proteomics Identification Database (label-free quantification only) using a common R-based workflow (rpx, mzR, QFeatures, DEP/MSqRob2/limma/OmicsQ packages) that was shared across all teams. The teams reproduced identification, optional quantification, normalization, imputation, and differential expression analyses, and compared the outcomes to the original studies. As expected, systemic barriers recurred across cases: (i) no sample and data relationship format for proteomics metadata in any of the cases; (ii) missing details regarding decoy sets for false discovery rate assessment; (iii) proprietary-only outputs or software (e.g., Thermo.msf, Progenesis) that impeded open reanalysis in interoperable, community-standard formats; (iv) missing data-independent acquisition spectral libraries or protein sequences database files (FASTA); (v) absent or vague normalization/imputation/statistical parameters; (vi) inconsistent file naming; and (vii) insufficient biological/technical replication in at least one project. These shortcomings yielded large discrepancies in the analysis results (e.g., 13,068 vs. 4,923 proteins; 108 vs. 11 differentially expressed proteins), and, in one instance, a highlighted protein lacked robust support in the deposited identifications. We observed that reproducibility in mass spectrometry-based proteomics hinges less on instruments than on transparent metadata, open formats, and executable analysis provenance. We propose that data creators provide a minimum re-analysis package, including raw data and open formats, community standards, basic quality control summaries, data-independent acquisition spectral libraries, and complete parameter/code sets with pinned versions or containers. Moreover, we recommend repository-level nudges toward making such packages mandatory. This educational exercise simultaneously trains the students as well as stress-tests the community data practices to prevent proteomics "data tombs".
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.