This dataset is a toxicity dataset consisting of several columns capturing various attributes of nanoparticles (NPs) and their toxicological effects. The dataset contains various features related to nanoparticles (NPs) and their properties, which are likely related to toxicity classification. Here is an overview of the columns in the dataset:

NPs: Type of nanoparticles (e.g., Al2O3). coresize: Core size of the nanoparticles. hydrosize: Hydrodynamic size of the nanoparticles. surfcharge: Surface charge of the nanoparticles. surfarea: Surface area of the nanoparticles. Ec: Electric Charge Expotime: Exposure time. Dosage: amount of material used. e: Energy-related feature. NOxygen: Number of oxygen atoms. class: Class label indicating whether the nanoparticles are toxic or non-toxic.

The unique behavior of engineered nanomaterials (ENM) in aqueous media and dynamic changes in particle settling, agglomeration and dissolution rates is a challenge to the consistency, reliability and interpretation of standard aquatic hazard bioassay results. While the toxicological endpoints (e.g., survival, growth, reproduction, etc.) in ecotoxicity bioassays are largely applicable to ENMs, the standard methods as written for dissolved substances are confounded by the dynamic settling, agglomeration and dissolution of particulate ENMs during the bioassay. A testing framework was designed to serve as a starting point to identify approaches for the consistent conduct of aquatic hazard tests that account for the behavior of ENMs in test media and suitable data collection to support representative exposure metrology. The framework was demonstrated by conducting three case studies testing ENMs with functionally distinct characteristics and behaviors. Pretests with a temporal sampling of particle concentration, agglomeration and dissolution were conducted on each ENM in test media. Results indicated that a silver nanoparticle (AgNP) powder was not dispersible, a nano-TiO2 powder was dispersible but unstable, and a polyvinylpyrrolidinone-coated AgNP was relatively stable in test media. Based on these functional results, Ceriodaphnia dubia bioassays were conducted to compare different exposure summary methods (nominal, arithmetic average, geometric average, time-weighted average) for calculating and expressing toxicity endpoints. Results indicated that while arithmetic means were effective for expressing the toxicity of more stable materials, time-weighted averaged concentrations were appropriate for the unstable nano-TiO2.

Additional file 2. Minimum information table.

This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018). The original dataset publication DOI: https://doi.org/10.1038/s41598-018-24483-z. The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun

Toxicity data for the impact of nano-silver on anaerobic degradation. This dataset is associated with the following publication: Gitipour, A., S. Thiel, K. Scheckel, and T. Tolaymat. Anaerobic Toxicity of Cationic Silver Nanoparticles. D. Barcelo Culleres, and J. Gan SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 557: 363-368, (2016).

Manufactured nanomaterials with unique properties have been extensively applied in various industrial, agricultural or medical fields. However, some of the properties have been identified to be closely related to nanomaterial toxicity. The “nano-paradox” has aroused concerns over the use and development of nanotechnology, which makes it difficult for regulatory agencies to regulate nanomaterials. The key to fulfilling proper nanomaterial regulation lies in the adequate understanding of the impact of nanomaterial properties on nano-bio interactions. To this end, we start the present work with a brief introduction to nano-bio interactions at different levels. Based on that, how key toxicity-associated properties of manufactured nanomaterials (i.e., size, shape, chemical composition, surface properties, biocorona formation, agglomeration and/or aggregation state, and biodegradability) impact their toxicokinetics, cellular uptake, trafficking and responses, and toxicity mechanisms is deeply explored. Moreover, advanced analytical methods for studying nano-bio interactions are introduced. Furthermore, the current regulatory and legislative frameworks for nanomaterial-containing products in different regions and/or countries are presented. Finally, we propose several challenges facing the nanotoxicology field and their possible solutions to shed light on the safety evaluation of nanomaterials.

This repository provides three curated nanotoxicity datasets — HaHa-Auto, HaHa-Manual, and Ha IIIB — comprising structured physicochemical and toxicological information on metal oxide nanoparticles. The datasets are intended for use in predictive modeling, risk assessment, and data-driven research in nanotoxicology.

HaHa-Auto: A semi-automatically generated dataset using large language models (LLMs) with a prompt-engineered data extraction pipeline. Contains 2696 entries with 15 descriptors, filtered for quality based on a physicochemical score.
HaHa-Manual: A manually curated counterpart to HaHa-Auto based on the same source articles. Offers a higher number of entries (3440) for benchmarking automated extraction performance.
Ha IIIB: A high-quality, smaller dataset (666 entries) curated from previously published training data, used to evaluate model applicability and robustness with limited but highly reliable data.

Each dataset includes detailed metadata such as:
1. Core size, hydrodynamic size, surface charge, surface area
2. Quantum-mechanical descriptors (e.g., band gap, enthalpy of formation)
3. Biological assay conditions (e.g., cell type, exposure time, dosage)
4. Toxicity endpoints (e.g., cell viability)

These datasets are suitable for use in automated machine learning (AutoML) applications, (Q)SAR model development, or validation of nanotoxicity prediction pipelines. All data entries were filtered using a scoring system to ensure quality and consistency, and are provided in CSV format with clear headers and descriptors.

As part of the research program on the aquatic ecotoxicology of nanomaterials, a study was carried out using the invertebrate Hydra vulgaris in order to determine the influence that the shape of nanoparticles (sphere, cube and prism) have on the toxic potential of silver nanoparticles. The selection of nanoparticles of similar size and identical surface properties (polyvinylpyrrolidone) allowed to highlight the effects related to the geometrical shape. This study also contributes to the expansion of knowledge on the toxic effects of nanoparticles in relation to their geometry—a topic that has very little examination in aquatic ecotoxicology. All data are a part subject of a publication containing method details, full QA/QC, interpretation and conclusions. Citation: Auclair J, Gagné F. Shape-Dependent Toxicity of Silver Nanoparticles on Freshwater Cnidarians. Nanomaterials. 2022; 12(18):3107. doi.org/10.3390/nano12183107 Supplemental Information Supporting programs: Chemicals Management Plan (CMP) The Chemicals Management Plan (CMP) is a Government of Canada initiative aimed at reducing the risks posed by chemicals to Canadians and their environment. A key element of the Chemicals Management Plan is the monitoring and surveillance of levels of harmful chemicals in Canadians and their environment. Monitoring and surveillance are essential to identify and track exposure to hazards in the environment and associated health implications. Monitoring and surveillance programs provide the basis for making sound and effective public health and environmental health policies and interventions, as well as measuring the efficacy of control measures. In support of the Chemicals Management Plan, monitoring and surveillance initiatives were established to support Health Canada and Environment and Climate Change Canada scientists, in collaboration with external partners and researchers, to advance our knowledge. This initiative has allowed the Government of Canada to increase its commitment to a number of existing monitoring initiatives, as well as to support new efforts. For more information on the Chemicals Management Plan, please visit https://www.canada.ca/en/health-canada/services/chemical-substances/chemicals-management-plan.html

This study aims to implement methods for toxicological profiling of engineered nanomaterials using toxicogenomics, proteomics, and metabolomics along with computational analyses. For all three omics layers the human cell lines A549 (lung epithelial cells) and THP1 (monocytes) were separately exposed to the nanomaterials TiO2 NM104, MWNCT NM401, and Ag NM300k. Proteomics and metabolomics samples have been performed as biological triplicates and were taken after 0, 6, 12, 24, and 48 hours. To assess ecotoxic effects C. elegans worms were grown in soil treated with NM300k. Ecotox samples were taken only at 0 and 24 hours. Integrating all three omics layers will enable the identification of (novel) ENM specific modes of action (MoA).

This repository contains the input data and the generated results utilized as part of the research article titled "Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials" by Saarimäki & Fratello et al.

Data

This dataset includes anonymized responses of a panel of experts to a questionnaire focused on nanomaterials safety.

Additional Data Sources

This entry further includes additional data from the following sources:

Saarimäki et al. (2021): Preprocessed data and primary physicochemical characteristics are available on Zenodo.

Gallud et al. (2020): Data is available from the NCBI Gene Expression Omnibus (GEO) under accession number GSE148705.

del Giudice et al. (2023): The advanced descriptors are accessible through the associated Zenodo repository.

Labouta et al. (2019): The harmonized dataset of ENMs cell viability assays is available as supporting information of the reasearch article.

Outputs

Statistical modeling: The inferred parameters of the statistical model developed to analyze the expert responses and identify the consensus among the experts.

Machine Learning classifiers: The performances of several machine learning classifiers trained on the consensus responses to learn models that can predict safety concerns of new nanomaterials based on transcriptomics and physicochemical descriptors data.

Feature Importance: The relevant features extracted from the models analyzed to understand which aspects of the molecular responses to ENMs and which physicochemical properties are most important in driving the predictions.

Contents

data/Combined_cleaned_responses_anon.xlsx: Contains the anonymized responses from the expert panel.

data/enms_grouping.txt: Contains a categorization of the ENMs based on their core material.

data/expert_bibliographies_anon.pickle: Contains the anonymized bibliography of the experts to assess the multidisciplinarity of the panel assembled.

data/gex.csc.gz: Contains the gene expression after exposure to the ENMs.

data/phenodata.txt: Contains the meta-data of the experiments

data/physicochemical_descriptors.txt: Contains the physicochemical descriptors of the ENMs.

data/external/nn8b07562_si_001.xlsx: Contains a panel of harmonized ENMs cytotoxicity assays.

outputs/concern_scores.csv: Contains the consensus scores inferred by the statistical model.

outputs/cross_validation/: Contains the logs and performances of all the machine learning classification runs .

outputs/important_{genes,physchem}_weighted.xlsx: Feature relevance scores of both views.

outputs/model_inference_anon.nc: Inferred parameters of the statistical model.

Supplementary materials for manuscript: Status quo in data availability and predictive models of nano-mixture toxicity. This table contains the list of 183 curated literature used in this study.

The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona. The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.

Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation. Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.

Comparison the performances for ethanol detection based on various nanomaterial fabricated electrodes.

Global Nanoparticle Toxicology Market is segmented by Application (Pharmaceutical Industry_Medical Devices_Consumer Products_Environmental Monitoring_Chemical Safety), Type (In Vitro Testing_In Vivo Testing_Biomarker Identification_Bio-Distribution Studies_Cytotoxicity Testing), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

In this model the property of a nanomaterial is predicted not on the basis of descriptors characterizing the chemical composition of nanoparticles or physical properties of the initial nanoforms, but on the basis of descriptors describing the dispersion medium (pH, IP, D3_HeteroNonMetals) and the property of nanoparticles dependent on it (Potential ζ). The observed small size of the hydrodynamic diameter of TiO2 in solvents of strong acids and bases compared to other solvents may indicate stronger repulsive interactions between nanoparticles than in the case of other systems. Moreover, in the case of salt solutions, the observed large size of the hydrodynamic diameter of TiO2 may be the result of a thicker electrical layer surrounding the particles in the dispersion system.

This deposit contains the proteomics and metabolomics data belonging to the publication with the title "A Multi-Omics Approach Reveals Mechanisms of Nanomaterial Toxicity and Structure-Activity-Relationships in Alveolar Macrophages". This project is part of the SIINN ERA-NET and is funded under the ERA-NET scheme of the Seventh Framework Program of the European Commission, BMBF Grant Agreement No. 03XP0008.

Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for nanomaterials, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures in vitro and in vivo.

The data set is a matrix of cellular biochemical (metabolites) in HepG2 cells treated with various metal oxide nanomaterials composed of CeO2, SiO2 and CuO. This dataset is associated with the following publication: Kitchin, K., S. Stirdivant, B. Robinette, B. Castellon, and X. Liang. Metabolomic effects of CeO2, SiO2 and CuO metal oxide nanomaterials on HepG2 cells. Particle and Fibre Toxicology. BioMed Central Ltd, London, UK, 14(50): 1-16, (2017).

As part of the research program on the aquatic ecotoxicology of nanomaterials, a study was carried out using the invertebrate Hydra vulgaris in order to determine the influence that the shape of nanoparticles (sphere, cube and prism) have on the toxic potential of silver nanoparticles. The selection of nanoparticles of similar size and identical surface properties (polyvinylpyrrolidone) allowed to highlight the effects related to the geometrical shape. This study also contributes to the expansion of knowledge on the toxic effects of nanoparticles in relation to their geometry—a topic that has very little examination in aquatic ecotoxicology. All data are a part subject of a publication containing method details, full QA/QC, interpretation and conclusions. Citation: Auclair J, Gagné F. Shape-Dependent Toxicity of Silver Nanoparticles on Freshwater Cnidarians. Nanomaterials. 2022; 12(18):3107. doi.org/10.3390/nano12183107 Supplemental Information Supporting programs: Chemicals Management Plan (CMP) The Chemicals Management Plan (CMP) is a Government of Canada initiative aimed at reducing the risks posed by chemicals to Canadians and their environment. A key element of the Chemicals Management Plan is the monitoring and surveillance of levels of harmful chemicals in Canadians and their environment. Monitoring and surveillance are essential to identify and track exposure to hazards in the environment and associated health implications. Monitoring and surveillance programs provide the basis for making sound and effective public health and environmental health policies and interventions, as well as measuring the efficacy of control measures. In support of the Chemicals Management Plan, monitoring and surveillance initiatives were established to support Health Canada and Environment and Climate Change Canada scientists, in collaboration with external partners and researchers, to advance our knowledge. This initiative has allowed the Government of Canada to increase its commitment to a number of existing monitoring initiatives, as well as to support new efforts. For more information on the Chemicals Management Plan, please visit https://www.canada.ca/en/health-canada/services/chemical-substances/chemicals-management-plan.html