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We present a standardized HTS-derived human cell-based testing protocol which combines the analysis of five assays into a broad toxic mode-of-action-based hazard value, termed Tox5-score. The overall protocol includes automated data FAIRification, preprocessing and score calculation. A newly developed Python module ToxFAIRy can be used independently or within an Orange Data Mining workflow that has custom widgets for fine-tuning, included in the custom-developed Orange add-on Orange3-ToxFAIRy. The created data-handling workflow has the advantage of facilitated conversion of the FAIR HTS data into the NeXus format, capable of integrating all data and metadata into a single file and multidimensional matrix amenable to interactive visualizations and selection of data subsets. The resulting FAIR HTS data includes both raw and interpreted data (scores) in machine-readable formats distributable as data archive, including into the eNanoMapper database and Nanosafety Data Interface. We overall present a HTS-driven FAIRifed computational assessment tool for hazard analysis of multiple agents simultaneously, including with broad potential applicability across diverse scientific communities.<\/jats:p>\n          <jats:p>\n            <jats:bold>Scientific Contribution<\/jats:bold> Our study represents significant tool development for analyzing multiple materials hazards rapidly and simultaneously, aligning with regulatory recommendations and addressing industry needs. The innovative integration of in vitro-based toxicity scoring with automated data preprocessing within FAIRification workflows enhances the applicability of HTS-derived data application in the materials development community. The protocols described increase the effectiveness of materials toxicity testing and mode-of-action research by offering an alternative to manual data processing, enrichment of HTS data with metadata, refining testing methodologies\u2014such as for bioactivity-based grouping\u2014and overall, demonstrates the value of reusing existing data.<\/jats:p>","DOI":"10.1186\/s13321-025-01001-8","type":"journal-article","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T16:01:54Z","timestamp":1745424114000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["High-throughput screening data generation, scoring and FAIRification: a case study on nanomaterials"],"prefix":"10.1186","volume":"17","author":[{"given":"Gergana","family":"Tancheva","sequence":"first","affiliation":[]},{"given":"Vesa","family":"Hongisto","sequence":"additional","affiliation":[]},{"given":"Konrad","family":"Patyra","sequence":"additional","affiliation":[]},{"given":"Luchesar","family":"Iliev","sequence":"additional","affiliation":[]},{"given":"Nikolay","family":"Kochev","sequence":"additional","affiliation":[]},{"given":"Penny","family":"Nymark","sequence":"additional","affiliation":[]},{"given":"Pekka","family":"Kohonen","sequence":"additional","affiliation":[]},{"given":"Nina","family":"Jeliazkova","sequence":"additional","affiliation":[]},{"given":"Roland","family":"Grafstr\u00f6m","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"key":"1001_CR1","unstructured":"Replacement, reduction and refinement (3Rs). https:\/\/nc3rs.org.uk\/who-we-are\/3rs. 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