![]() The German Research Foundation (DFG) funded this project through the Collaborative Research Center PolyTarget 1278 – project number 316213987, subproject Z01 (Z.C.). This work was financially supported by the International Leibniz Research School for Microbial and Biomolecular Interactions Jena – ILRS Jena (R.G.). This allows JIPipe to keep pace with the increasing complexity of new image-analysis tasks arising from the continuously improved set of imaging techniques. ![]() The range of JIPipe functionalities is extendable through plugins that are built upon the existing Java-based library ecosystem of ImageJ, thus opening our platform to all related ongoing community-driven and open-source efforts. The result is a highly flexible node model that unambiguously communicates the expected data types to the users. This is achieved by automatically assigning the data to appropriate text-metadata columns that are generated from the input files or by user input. The data model enables an intuitive solution for batch processing with multiple inputs without the reliance on structural loop nodes. Our software organizes all data through a constrained table model that enforces the existence of a primary data column of a specific suitable type. JIPipe imposes a strict standardization of nodes and their parameters, as well as their inputs and outputs, and facilitates the adoption of the FAIR (‘findability, accessibility, interoperability and reusability’) principles 5 by the implementation of standardized storage formats for pipeline projects, data and metadata. A full list of all dependency libraries and external tools is given in Supplementary Information, section 2. Aside from ImageJ functionalities, we also provide R and Python-based scripting, and an integration of cellpose 4. The assortment of nodes covers functionalities of ImageJ and various plugins that are captured automatically by our ImageJ2 integration or by dedicated JIPipe extensions (Fig. Our newly developed visual programming language, which we term Java image processing pipeline (JIPipe) ( ), provides a macro programming alternative that is particularly designed for ImageJ and supports the transition from interactive single-image manipulation to multi-image algorithmic batch processing (Fig. Existing tools contribute to this effort by providing a visual way to build pipelines or by simplifying the scripting procedure ( Supplementary Information, section 1). Visual programming languages that replace the writing of text commands with the design of a flowchart offer a solution. As programming skills are uncommon among experimentalists 3, the need for scripting contributes to an already-existing communication gap between life and computer scientists. On the other hand, the creation of reproducible batch-processing workflows is only possible using a macro language. The hallmark of ImageJ is its intuitive graphical user interface, which provides access to its many tools. Pillars among these tools are ImageJ 1 and its Fiji 2 distribution, which have been serving the imaging community for decades and continue to gain public support to keep up with the quantification needs of the newest and most-demanding microscopy techniques. ![]() The growth in microscopy adoption has led to a concomitant upsurge in the development of software tools for the automated analysis of image data.
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