Although the number of on-line image repositories is growing rapidly (e.g., 29–35) to the authors’ knowledge there is no data management service that can support the range of image data types, rich metadata, robust curation processes, and breadth of both human and computer access methods as TCIA.

Mar 1, 2024 · Our case-study is based on four scientific datasets: Protein translocation microscopy images, Airfoil angle of attack meshes, 1000 Genomes sequences, and Phenotypic screening images. The presented results highlight that our framework is optimal and can quickly adapt to new data access requirements.

May 31, 2023 · Research data management is essential for safeguarding and prospecting data generated in a scientific context. Specific issues arise regarding data in image format, as this data typology...

Feb 7, 2022 · We describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies.

Oct 7, 2022 · To meet all these requirements, we developed BioImageIT, a unique open-source system integrating imaging data management and analysis, and an operational solution for handling large datasets in...

Research data management is essential for safeguarding and prospecting data generated in a scientific context. Specific issues arise regarding data in image format, as this data typology poses particular challenges and opportunities; however, not much attention has been given to …

Aug 17, 2020 · Research data management is the basis for making data more Findable, Accessible, Interoperable and Reusable. In this context, little attention is given to research data in image format. This article presents the preliminary results of a study on the habits related to...

Managing Image Data. The growth of research image collection across the sciences, coupled with increasingly powerful instruments and devices for image capture, have created challenges and new opportunities for managing images across the research data lifecycle.

Scientific applications now have data management requirements that extend beyond the local computing environment. Scientific disciplines are assembling data collections that represent the primary source data for current research.

Our study explores scientists’ data sharing attitudes and practices by focusing on specific stages of the data lifecycle including describe, preserve, and discover.