<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>10(1)</volume><submitter>Wang H</submitter><pubmed_abstract>Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the lates</pubmed_abstract><journal>Scientific data</journal><pagination>858</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10693559</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects.</pubmed_title><pmcid>PMC10693559</pmcid><pubmed_authors>Phua SX</pubmed_authors><pubmed_authors>Guo T</pubmed_authors><pubmed_authors>Wang H</pubmed_authors><pubmed_authors>Gao H</pubmed_authors><pubmed_authors>Wong BJH</pubmed_authors><pubmed_authors>Lim KP</pubmed_authors><pubmed_authors>Kong W</pubmed_authors><pubmed_authors>Goh WWB</pubmed_authors></additional><is_claimable>false</is_claimable><name>MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects.</name><description>Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the lates</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Dec</publication><modification>2026-05-29T07:37:56.095Z</modification><creation>2025-05-29T21:46:38.984Z</creation></dates><accession>S-EPMC10693559</accession><cross_references><pubmed>38042886</pubmed><doi>10.1038/s41597-023-02779-8</doi></cross_references></HashMap>