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            Bioimage Data Analysis Workflows

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            Contributor(s)
            Miura, Kota (editor)
            Sladoje, Nataša (editor)
            Language
            English
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            Abstract
            This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows. The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/175983
            Keywords
            Medicine; Biomedical engineering; Cell biology; Bioinformatics; Biology—Technique; Systems biology; Biological systems; Textbook; thema EDItEUR::M Medicine and Nursing::MQ Nursing and ancillary services::MQW Biomedical engineering; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues; thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSF Cellular biology (cytology)
            DOI
            10.1007/978-3-030-22386-1
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            Cham, 2020
            Series
            Learning Materials in Biosciences,
            Pages
            170
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            Credits


            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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