Architecture of the Mass Spectrometry Data Management Pipeline in the SMART-CARE Project
In the SMART-CARE project- a systems medicine approach to stratification of cancer recurrence in Heidelberg, Germany - a streamlined mass-spectrometry (MS) workflow for identification of cancer relapse was developed. This project has multiple partners from clinics, laboratories and computational teams. For optimal collaboration, consistent documentation and centralized storage, the linked data repository was designed. Clinical, laboratory and computational group members interact with this platform and store meta- and raw-data. The specific architectural choices, such as pseudonymization service, uploading process and other technical specifications as well as lessons learned are presented in this work. Altogether, relevant information in order to provide other research groups with a head-start for tackling MS data management in the context of systems medicine research projects is described.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:310 |
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| Enthalten in: |
Studies in health technology and informatics - 310(2024) vom: 25. Jan., Seite 1016-1020 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ringwald, Friedemann G [VerfasserIn] |
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| Links: |
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| Themen: |
Data management |
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| Anmerkungen: |
Date Completed 26.01.2024 Date Revised 26.01.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.3233/SHTI231118 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a In the SMART-CARE project- a systems medicine approach to stratification of cancer recurrence in Heidelberg, Germany - a streamlined mass-spectrometry (MS) workflow for identification of cancer relapse was developed. This project has multiple partners from clinics, laboratories and computational teams. For optimal collaboration, consistent documentation and centralized storage, the linked data repository was designed. Clinical, laboratory and computational group members interact with this platform and store meta- and raw-data. The specific architectural choices, such as pseudonymization service, uploading process and other technical specifications as well as lessons learned are presented in this work. Altogether, relevant information in order to provide other research groups with a head-start for tackling MS data management in the context of systems medicine research projects is described | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Czernilofsky, Felix |e verfasserin |4 aut | |
| 700 | 1 | |a Dietrich, Sascha |e verfasserin |4 aut | |
| 700 | 1 | |a Ganzinger, Matthias |e verfasserin |4 aut | |
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