Datasets for "Comparison of multivariate ANOVA-based approaches for the determination of relevant variables in experimentally designed metabolomic studies"
<p><em><strong>Raw files of the LC-MS designed experiments.</strong></em></p> <p> </p> <p>* <strong>YEAST GROWTH DATASET</strong></p> <blockquote> <p><strong>A) Phospholipids extraction</strong></p> <p>- FosfoB1.raw</p> <p>- FosfoB3.raw</p> <p>- FosfoC2.raw</p> <p>- FosfoD1.raw</p> <p>- FosfoE1.raw</p> <p>- FosfoE2.raw</p> <p>- FosfoE3.raw</p> </blockquote> <p> </p> <blockquote> <p><strong>B) Sphingolipids extraction</strong></p> <p>- EsfingoB1.raw</p> <p>- EsfingoB2.raw</p> <p>- EsfingoC1.raw</p> <p>- EsfingoC2.raw</p> <p>- EsfingoC3.raw</p> <p>- EsfingoD1.raw</p> <p>- EsfingoD2.raw</p> <p>- EsfingoD3.raw</p> </blockquote> <p> </p> <p><strong>* ZEBRAFISH DATASET</strong></p> <blockquote> <p><strong>A) BPA study</strong></p> <p>Low BPA exposure</p> <p>- Low_BPA_A.d</p> <p>- Low_BPA_B.d</p> <p>- Low_BPA_C.d</p> <p>High_BPA_exposure</p> <p>- High_BPA_A.d</p> <p>- High_BPA_B.d</p> <p>- High_BPA_C.d</p> <p>Controls</p> <p>- Control_BPA_A.d</p> <p>-Control_BPA_B.d</p> <p>- Control_BPA_C.d</p> </blockquote> <p> </p> <blockquote> <p><strong>B) Estradiol study</strong></p> <p>Low E2 exposure</p> <p>- Low_E2_A.d</p> <p>- Low_E2_B.d</p> <p>- Low_E2_C.d</p> <p>High_E2_exposure</p> <p>- High_E2_A.d</p> <p>- High_E2_B.d</p> <p>- High_E2_C.d</p> <p>Controls</p> <p>- Control_E2_A.d</p> <p>-Control_E2_B.d</p> <p>- Control_E2_C.d</p> </blockquote> <p> </p> <p><strong>Funding:</strong> This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP). The 1290 LC system and 6545 XT QTOF instrumentation were provided to DS as gifts by Agilent Technologies through their Thought Leader program.</p>
ShareScore
40/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4