For a common user, bioimages seem to be very easy to treat, to read, to understand and, therefore, to archive. Conversely, bioimage archiving require a very complex design and implementation process that needs skilled and trained technicians. We proposed to a class of bioengineering students at the Politecnico University of Milan the implementation of a hand image repository specifically designed for highlighting the main features that should be taken into account when treating bioimage archives. Students were required to build the archive with software tools they had previously learned in other programming language courses and available at the university informatics class-rooms.
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http://dx.doi.org/10.1109/IEMBS.2007.4353696 | DOI Listing |
We introduce bia-binder (BioImage Archive Binder), an open-source, cloud-architectured, and web-based coding environment tailored to bioimage analysis that is freely accessible to all researchers. The service generates easy-to-use Jupyter Notebook coding environments hosted on EMBL-EBI's Embassy Cloud, which provides significant computational resources. The bia-binder architecture is free, open-source and publicly available for deployment.
View Article and Find Full Text PDFCell
April 2024
Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden. Electronic address:
Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the cellular dynamics of MS by modeling temporal and regional rates of disease progression in mouse experimental autoimmune encephalomyelitis (EAE).
View Article and Find Full Text PDFF1000Res
March 2024
EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
Organised data is easy to use but the rapid developments in the field of bioimaging, with improvements in instrumentation, detectors, software and experimental techniques, have resulted in an explosion of the volumes of data being generated, making well-organised data an elusive goal. This guide offers a handful of recommendations for bioimage depositors, analysts and microscope and software developers, whose implementation would contribute towards better organised data in preparation for archival. Based on our experience archiving large image datasets in EMPIAR, the BioImage Archive and BioStudies, we propose a number of strategies that we believe would improve the usability (clarity, orderliness, learnability, navigability, self-documentation, coherence and consistency of identifiers, accessibility, succinctness) of future data depositions more useful to the bioimaging community (data authors and analysts, researchers, clinicians, funders, collaborators, industry partners, hardware/software producers, journals, archive developers as well as interested but non-specialist users of bioimaging data).
View Article and Find Full Text PDFJ Microsc
November 2024
European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge, UK.
Open access to data underpinning published results is a key pillar of scientific reproducibility. Making data available at scale also provides opportunities for data reuse, encouraging the development of new analysis approaches. In this poster article, accompanying a recorded talk, we will explain the benefits of publicly archiving your image data alongside your published manuscripts, as well as highlight what resources are available to do this.
View Article and Find Full Text PDFMethods Cell Biol
July 2023
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom.
Volume electron microscopy (vEM) techniques produce scientifically important datasets which are time and resource intensive to generate (Peddie et al., 2022). Public archival of such datasets, usually described in the literature, provides many benefits to the data depositors, to those making use of research results based on the datasets, and to the vEM community at large, both now and in the future.
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