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ZENODO
Dataset . 2023
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study

Authors: Oosterwegel, Max J.; Ibi, Dorina; Portengen, Lützen; Probst-Hensch, Nicole; Tarallo, Sonia; Naccarati, Alessio; Imboden, Medea; +11 Authors

Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study

Abstract

Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper DOI: 10.1021/acs.est.3c03233 . The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at https://github.com/moosterwegel/variability-metabolites-paper and may contain useful information/code to work with this data. `processed_covariate_data.csv`: ``` Rows: 298 Columns: 7 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ age_cat: indicates age category at the time of a PEM session $ sq_sex: indicates the sex of the participant (male, female) as filled in during the screening questionaire $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions. $ bmi_cat: indicates BMI category at the time of a PEM session ``` `processed_lcms_data data.csv` contains the processed LCMS data: ``` Rows: 298 Columns: 4297 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime. Non-detects (below limit of detection (LOD) are coded as 1 for the compounds. .... ``` In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables. The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively.

The study center in Basel was additionally funded by Grants from the Swiss National Science Foundation 33CS30-148470 and 33CS30-177506. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.

Keywords

lcms, reliability, variability, within-individual variability, biomarkers, metabolomics, blood, between-individual variability, cohort study, epidemiology, intraclass correlation coefficient (ICC), repeatability, liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS)

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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