Riccardo Massei

The increasing complexity and volume of biological imaging data require structured and sustainable research data management to ensure reproducibility, reusability, and compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this context, Data Stewards (DaSts) support researchers throughout the bioimaging data lifecycle by facilitating data organization, documentation, standardization, preservation, and sharing. This work presents a curriculum for becoming a Data Steward in biological imaging. It is intended for educators, learners, and institutions. Openly available training materials were systematically identified through (i) targeted searches of domain-specific repositories and community platforms, (ii) keyword-based web searches, and (iii) citation and reference tracing. Searches were conducted between December 2025 and May 2026. Materials were evaluated using a qualitative scoring framework and mapped to defined learning objectives. This systematic review showed that many relevant educational resources exist but are distributed across diverse platforms and communities. The search focused on openly available English-language resources and may therefore underrepresent region-specific or non-English materials. Additionally, qualitative scoring involves subjective judgment despite the structured evaluation framework. This curriculum complements previous work defining the role of bioimaging Data Stewards by providing a structured overview of the interdisciplinary knowledge, competencies, and training resources required across the bioimaging data lifecycle. It systematically identifies and organizes existing resources into a coherent learning pathway while highlighting gaps, particularly in interdisciplinary collaboration and bioimaging-specific data standards. As bioimaging technologies, data infrastructures, and regulations evolve, the curriculum should remain a living framework, continuously updated through community-driven resources like the NFDI4BIOIMAGE Training Material Hub. This systematic review was conducted in accordance with the Prisma 2020 statement where applicable, was preregistered [https://doi.org/10.17605/OSF.IO/XEJNC] and financially supported by the German Research Foundation and the University of Konstanz.

Image analysis in the life sciences is constrained by fragmented software ecosystems, heterogeneous data formats, and limited reproducibility. These barriers hinder the reuse of image analysis methods and the sustainability of tools. In this article, we describe how the Galaxy platform enables FAIR (Findable, Accessible, Interoperable, and Reusable) image analysis by providing an integrated environment for data access, workflow execution, provenance tracing, and training. We present Galaxy as a computational workbench that supports diverse image formats and integrates with public, institutional, and private repositories. We describe a reference structure for FAIR image analysis workflows and illustrate how this pattern supports reproducibility, interoperability, and reuse. We also describe community‐driven training and sustainability practices that embed FAIR principles directly into executable tutorials and shared workflows. Together, these foundations position Galaxy as a reproducible, scalable, and community‐maintained platform for FAIR image analysis across the life sciences and beyond.

Current regulations aimed at safeguarding human health and the environment from chemical risks require toxicity testing, while regulatory bodies worldwide are phasing out the use of protected animals. Here, we systematically produced toxicity data from five alternative nonmammalian model organisms and a human cell line to demonstrate similarities and differences among distantly related species in their responses to chemical exposure (concentration range 1.8 pM to 2.1 M). We show that Daphnia magna and Danio rerio embryos are affected by more chemicals than Xenopus laevis embryos, Drosophila melanogaster and Caenorhabditis elegans. Lethality/immotility data assembled 72 diverse chemicals into 11 clusters, revealing species-specific outcomes and showing differential enrichment of the clusters for certain chemical structures and modes of action. The ranked relative toxicities of chemicals across the model organisms were highly correlated (Spearman's correlation, maximum rho = 0.86, p-value = 0; minimum rho = 0.57, p-value = 0.000312), and their average toxicity values closely matched published values for rodents (rat: rho = 0.88, p = 2.8e-06; mouse: rho = 0.88, p = 2.7e-06). Our findings suggest that chemical toxicity under standardized experimental conditions is broadly conserved across a phylogenetically diverse set of model organisms, providing a conservative estimate of mammalian toxicity.

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.

Zebrafish have emerged as a central model organism in toxicological research. Zebrafish embryos are exempt from certain animal testing regulations, which facilitates their use in toxicological testing. Next to the zebrafish embryo acute toxicity test (ZFET) according to the OECD TG 236, fish embryos are used in mechanistic investigations, chemical screenings, ecotoxicology, and drug development. However, inconsistencies in the applied test protocols and the monitored endpoints in addition to a lack of standardized data formats impede comprehensive meta-analyses and cross-study comparisons. To address these challenges, we developed the Integrated Effect Database for Toxicological Observations (INTOB), a comprehensive data management tool that standardizes the collection of metadata and phenotypic observations using a controlled vocabulary. By incorporating data from more than 600 experiments into the database and subsequent comprehensive data analyses, we demonstrate its utility in improving the comparability and interoperability of toxicity data. Our results show that the ZFET can detect toxicity spanning 7 orders of magnitude at the scale of effect concentrations. We also highlight the potential of read-across analyses based on morphological fingerprints and their connection to chemical modes of action, provide information on control variability of the ZFET, and highlight the importance of time for mechanistic understanding in chemical exposure-effect assessments. We provide the full Findable, Accessible, Interoperable, and Reusable (FAIR) data set as well as the analysis workflow and demonstrate how professional data management, as enabled with INTOB, marks a significant advancement by offering a comprehensive framework for the systematic use of zebrafish embryo toxicity data, thus paving the way for more reliable, data-driven chemical risk assessment.

Zebrafish have emerged as a central model organism in toxicological research. Zebrafish embryos are exempt from certain animal testing regulations which facilitates their use in toxicological testing. Next to the zebrafish embryo acute toxicity test (ZFET) according to the OECD TG 236, fish embryos are used in mechanistic investigations, chemical screenings, in ecotoxicology, and drug development. However, inconsistencies in the applied test protocols and the monitored endpoints in addition to a lack of standardized data formats, impede comprehensive meta-analyses and cross-study comparisons. To address these challenges, we developed the Integrated Effect Database for Toxicological Observations (INTOB), a comprehensive data management tool that standardizes collection of metadata and phenotypic observations using a controlled vocabulary. By incorporating data from more than 600 experiments into the database and subsequent comprehensive data analyses, we demonstrate its utility in improving the comparability and interoperability of toxicity data. Our results show that the ZFET can detect toxicity spanning seven orders of magnitude at the scale of effect concentrations. We also highlight the potential of read-across analyses based on morphological fingerprints and their connection to chemical modes of action, provide information on control variability of the ZFET, and highlight the importance of time for mechanistic understanding in chemical exposure-effect assessments. We provide the full FAIR dataset as well as the analysis workflow and demonstrate how professional data management, as enabled with INTOB, marks a significant advancement by offering a comprehensive framework for the systematic use of zebrafish embryo toxicity data, thus paving the way for more reliable, data-driven chemical risk assessment. Synopsis: This article shows how a novel data management tool for zebrafish embryo toxicity data advances comparative chemical risk assessment by enhancing data and metadata standardization, comparability, and cross-study analyses.

Internal concentrations (ICs) are crucial for linking exposure to effects in the development of New Approach Methodologies. ICs of chemicals in aquatic organisms are primarily driven by hydrophobicity and modulated by biotransformation and efflux. Comparing the predicted baseline to observed toxicity enables the estimation of effect specificity, but biological processes can lead to overestimating ICs and bias the specificity assessment. To evaluate the prediction of a mass balance model (MBM) and the impact of biotransformation on ICs, experimental ICs of 63 chemicals in zebrafish embryos were compared to predictions with physicochemical properties as input parameters. Experimental ICs of 79% (50 of 63) of the chemicals deviated less than 10-fold from predictions, and the remaining 13 deviated up to a factor of 90. Using experimental ICs changed the classification for 19 chemicals, with ICs 5 to 90 times lower than predicted, showing the bias of specificity classification. Uptake kinetics of pirinixic acid, genistein, dexamethasone, ethoprophos, atorvastatin, and niflumic acid were studied over a 96 h exposure period, and transformation products (TPs) were elucidated using suspect- and nontarget screening with UPLC-HRMS. 35 TPs (5 to 8 TPs per compound) were tentatively identified and semiquantified based on peak areas, suggesting that biotransformation may partly account for the overpredictions of ICs.

N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6-PPD) is a widely used antioxidant in tire rubber known to enter the aquatic environment via road runoff. The associated transformation product (TP) 6-PPD quinone (6-PPDQ) causes extreme acute toxicity in some fish species (e.g., coho salmon). To interpret the species-specific toxicity, information about biotransformation products of 6-PPDQ would be relevant. This study investigated toxicokinetics of 6-PPD and 6-PPDQ in the zebrafish embryo (ZFE) model. Over 96 h of exposure, 6-PPD and 6-PPDQ accumulated in the ZFE with concentration factors ranging from 140 to 2500 for 6-PPD and 70 to 220 for 6-PPDQ. A total of 22 TPs of 6-PPD and 12 TPs of 6-PPDQ were tentatively identified using liquid chromatography coupled to high-resolution mass spectrometry. After 96 h of exposure to 6-PPD, the TPs of 6-PPD comprised 47% of the total peak area (TPA), with 4-hydroxydiphenylamine being the most prominent in the ZFE. Upon 6-PPDQ exposure, >95% of 6-PPDQ taken up in the ZFE was biotransformed, with 6-PPDQ + O + glucuronide dominating (>80% of the TPA). Among other TPs of 6-PPD, a reactive N-phenyl-p-benzoquinone imine was found. The knowledge of TPs of 6-PPD and 6-PPDQ from this study may support biotransformation studies in other organisms.