Details zur Publikation |
| Kategorie | Datenpublikation |
| DOI | 10.5281/zenodo.14205500 |
| Lizenz |
|
| Titel (primär) | Galaxy Training Data for "Overview of the Galaxy OMERO-suite" [Data set] |
| Autor | Massei, R. |
| Quelle | Zenodo |
| Erscheinungsjahr | 2024 |
| Department | MET |
| Sprache | englisch |
| Topic | T9 Healthy Planet T5 Future Landscapes |
| Abstract | High-content screening (HCS) for bioimaging is a powerful approach to studying biological processes, enabling the acquisition of large amounts of images from biological samples. However, it generates massive amounts of metadata, making HCS experiments a unique data management challenge. This data includes images, reagents, protocols, analytic outputs, and phenotypes, all of which must be stored, linked, and made accessible to users, scientists, collaborators, and the broader community to ensure sharable results. This study showcases different approaches using Workflow Management Systems (WMS) to create reusable semi-automatic workflows for HCS bioimaging data management, leveraging the image data management platform OMERO. The three developed workflows demonstrate the transition from a local file-based storage system to an automated and agile image data management framework. These workflows facilitate the management of large amounts of data, reduce the risk of human error, and improve the efficiency and effectiveness of image data management. We illustrate how applying WMS to HCS data management enables us to consistently transfer images across different locations in a structured and reproducible manner, reducing the risk of errors and increasing data consistency and reproducibility. Furthermore, we suggest future research direction, including developing new workflows and integrating machine learning algorithms for automated image analysis. This study provides a blueprint for developing efficient and effective image data management systems for HCS experiments and demonstrates how different WMS approaches can be applied to create reusable, semi-automated workflows for HCS bioimaging data management using OMERO. |
| Verknüpfte UFZ-Textpublikationen | |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30756 |
| Massei, R. (2024): Galaxy Training Data for "Overview of the Galaxy OMERO-suite" [Data set] Zenodo 10.5281/zenodo.14205500 |
|