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Supporting Information
for 
Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using Machine Learning
R. L. Marchese Robinson,1 H. Sarimveis,2 P. Doganis,2 X. Jia,1 M. Kotzabasaki,2 C. Gousiadou,2 S.L. Harper,3,4,5 T. A.  Wilkins1,*

1.School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, United Kingdom
2. School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou str. Zografou Campus, 15780 Athens, Greece
3. School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon, USA
4. Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, USA
5. Oregon Nanoscience and Microtechnologies Institute, Eugene, Oregon, USA
*Corresponding author: T.A.Wilkins@leeds.ac.uk
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Copyright (c) 2010-2015 ONAMI & Oregon State University 
Copyright (c) 2019-2020 University of Leeds

This file was derived from experimental data records exported from the NBI Knowledgebase [http://nbi.oregonstate.edu/], developed at Oregon State University, using code written at the University of Leeds, followed by manual editing at the University of Leeds.

These data are distributed under the terms of the Creative Commons Attribution License Version 4.0	(CC BY 4.0) license. https://creativecommons.org/licenses/by/4.0/.
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Selection criteria:

1. Metal oxide NMs data records from the NBI Knowledgebase data reported by Karcher et al. (2016) which were not included in the original model development set, for which pseudo-external cross-validation results were generated for different modelling approaches

2. Only a single metal cation (including a single oxidation state) was included in the core material - save for allowing for small amounts of doping [unlike the model development set]. [See the "Particle Descriptor" and "Core Atomic Composition" columns for full details.]

3. Unlike the model development set, materials tested at maximum concentrations different from 250 ppm were included, which may mean the lack of a LOEL value could be an experimental false negative, as a LOEL may have been identifiable if the material was tested at concentrations up to and including 250 ppm.

4. Unlike the model development set, material records with missing physicochemical data, considered as inputs to the multi-descriptor models, were allowed.
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Descriptor input column: Core Atomic Composition

N.B. For linking to the Pauling metal atom electronegativity descriptor values reported in the code archive [BioRima_rlmr\prototype_da_models_workflow\desc_input_files\NBIK_CoreElementalDescs.csv], the entries "titanium dioxide [TiO2]; manganese [mn]" will need to be replaced with "titanium dioxide [TiO2]", as these refer to TiO2 NMs with small amounts of mannganese doping.

Endpoint column: 120 hpf evaluation_M_effect_loel
