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94 results for “nanomaterial”
WikiPathways Nanomaterials Portal
<p>Archive of the WikiPathways Nanomaterials Portal on 8 November 2022. Includes PNG images and original GPML files.</p>
Accompanying data for the PhD thesis 'Nanomaterial safety for microbially-colonized hosts'
<p><strong>These files include all data presented in chapter 6 of the dissertation:</strong></p> <p><strong>"Nanomaterial safety for microbially-colonized hosts: microbiota-mediated physisorption interactions and particle-specific toxicity" by Bregje Brinkmann (2022).</strong></p> <p>The data presented in chapters 2-5 have previously been published elsewhere:</p> <ul> <li>Chapter 2: <em>Zenodo</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.6800734&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=z4LB7Ziyiy%2BLXe0SllX67AJ%2F9zhfARBXp8QNzZsVg%2B4%3D&reserved=0">10.5281/zenodo.6800734</a>).</li> <li>Chapter 3: <em>Mendeley</em> <em>Data</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.17632%2F2d4hcr5cb5.1&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=x7LFz2OgiJ20%2BD18QlwR9qIwGH%2BCbju4BKqkUaqIoXs%3D&reserved=0">10.17632/2d4hcr5cb5.1</a>)</li> <li>Chapter 4: <em>Figshare</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.6084%2Fm9.figshare.c.4923261&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=n3wegOuHzKihCO%2FKbZhClnYHPyxWI0cALApBgLkUHnQ%3D&reserved=0">10.6084/m9.figshare.c.4923261</a>)</li> <li>Chapter 5: <em>Mendeley Data </em>(DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.17632%2F4nfg69v8hy.1&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=0A0ktYDmgfxwc7U%2BgzHzQUj8Q5wixi%2BUzoSzMctlbso%3D&reserved=0">10.17632/4nfg69v8hy.1</a>)</li> </ul> <p><br> <strong>1. Data presented in Figure 6.1:</strong> Survival_CFU_(...)<br> Tab-delimited file with zebrafish larvae survival, and the number of colony-forming units (CFUs) associated with zebrafish larvae, following exposure to silver nanoparticles (nAg) from 3-5 days-post fertilization (dpf):</p> <ul> <li><em>Concentration</em>: Nominal exposure concentration (mg nAg·L<sup>-1</sup>).</li> <li><em>Date</em>: The date at which the mortality was scored (Format: DD/MM/YYYY). </li> <li><em>Family</em>: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment. </li> <li><em>Survival</em>: Percentage of larvae that had survived the treatment.</li> <li><em>CFU</em>: Number of colony-forming units that was isolated per larva</li> </ul> <p>The methodology for toxicity tests and the procedures to determine CFU counts, have been published in <em>Nanotoxicology</em>:</p> <p>Brinkmann BW, Koch BEV, Spaink HP, Peijnenburg WJGM, Vijver MG. 2020. Colonizing microbiota protect zebrafish larvae against silver nanoparticle toxicity. Nanotoxicology. 14: 725-739. DOI: <a href="http://doi.org/10.1080/17435390.2020.1755469">10.1080/17435390.2020.1755469</a></p> <p> </p> <p><strong>2. Data presented in Figure 6.2:</strong> ABs_DoseResponses_(...)<br> Tab-delimited file with zebrafish larvae mortality following a pretreatment of 0, 6 or 72 hours with an antibiotic and antifungal cocktail, and subsequent exposure to nAg from 3-5 dpf:</p> <ul> <li><em>Concentration</em>: Nominal exposure concentration (mg nAg·L<sup>-1</sup>). </li> <li><em>Mortality</em>: Percentage of larvae that had died.</li> <li><em>Date</em>: The date at which the mortality was scored (Format: DD/MM/YYYY).</li> <li><em>Family</em>: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment. </li> <li><em>ABs</em>: Duration of the antibiotic/ antifungal pretreatment, either 0, 6 or 72 hours.</li> </ul> <p> </p> <p><strong>3. Data presented in Figure 6.3:</strong> il1beta_eGFP_(...)<br> Three folders comprising fluorescence microscopy images (TIFF format) of il1beta:eGFP reporter zebrafish larvae at 5 dpf:</p> <ul> <li><em>(...)_replicates1_20200226</em>: images for the first experimental replicate.</li> <li><em>(...)_replicates2_20200304</em>: images for the second experimental replicate.</li> <li><em>(...)_replicates3_20200318</em>: images for the third experimental replicate.</li> </ul> <p>For each of the replicates, the following images were acquired:</p> <ul> <li><em>nZnO_GFP</em>: GFP signal for larvae exposed to nZnO.</li> <li><em>Znion_GFP</em>: GFP signal for larvae exposed to zinc ions.</li> <li><em>nZnO_trans</em>: transmitted light images for larvae exposed to nZnO. </li> <li><em>Znion_trans</em>: transmitted light images for larvae exposed to zinc ions.</li> </ul> <p>Additionally, the following images have previously been deposited to <em>Mendeley Data </em>(DOI: <a href="http://doi.org/10.1016/j.ecoenv.2022.113522">10.17632/4nfg69v8hy.1</a>):</p> <ul> <li><em>nAg_GFP</em>: GFP signal for larvae exposed to nAg.</li> <li><em>nAg_trans</em>: transmitted light images for larvae exposed to nAg.</li> <li><em>Agion_GFP</em>: GFP signal for larvae exposed to silver ions.</li> <li><em>Agion_trans</em>: transmitted light images for larvae exposed to silver ions.</li> <li><em>control_GFP</em>: GFP signal for control larvae that had not been exposed to silver ions or nAg</li> <li><em>control_trans</em>: transmitted light images for control larvae that had not been exposed to silver ions or nAg.</li> </ul> <p>All image processing steps have been published in <em>Ecotoxicology and Environmental Safety</em>:</p> <p>Brinkmann BW, Koch BEV, Peijnenburg WJGM, Vijver MG. 2022. Microbiota-dependent TLR2 signaling reduces silver nanoparticle toxicity to zebrafish larvae. Ecotox Environ Saf. 237: 113522. DOI: <a href="http://doi.org/10.1016/j.ecoenv.2022.113522">10.1016/j.ecoenv.2022.113522</a></p> <p> </p> <p><strong>Abbreviations:</strong></p> <ul> <li><em>ABs</em>: antibiotics</li> <li><em>CFU</em>: colony-forming units</li> <li><em>dpf</em>: days post-fertilization</li> <li><em>il1beta</em>: interleukin-1beta</li> <li><em>nAg</em>: silver nanoparticles (NM-300 K)</li> <li><em>nZnO</em>: zinc oxide nanoparticles (NM-110)</li> </ul>
DPMFA_EU_ENM_2000-2020: Dynamic Probabilistic Material Flows of Engineered Nanomaterials from 2000 to 2020 - Raw results
<p>This dataset is related to the following publication:</p> <p>Title: Dynamic probabilistic material flow analysis of engineered nanomaterials in European waste treatment systems</p> <p>Authors: Sana Rajkovic, Nikolaus A. Bornhöf<span>t</span>, Renata van der Weijden, Bernd Nowack, Véronique Adam</p> <p>Submitted to the journal Waste Management in September 2019.</p> <p>The files contain key values of probability distributions associated with the emissions of selected engineered nanomaterials to the environment.</p>
RDF version of the data from 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)
<p>RDF version of the data from 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)</p>
SEM atlas of selected carbon nanomaterials
<p>Atlas of SEM images of commercially available carbon nanomaterials. Magnifications: x2000 - x100000.</p>
Dataset for "CVD growth of self-assembled 2D and 1D WS2 nanomaterials for the ultrasensitive detection of NO2"
<p>This file contains the raw data used in the paper entitled CVD growth of self-assembled 2D and 1D WS2 nanomaterials for the ultrasensitive detection of NO2 published in Sensors and Actuators: B. Chemical 326 (2021) 128813</p> <p>DOI: <a href="https://doi.org/10.1016/j.snb.2020.128813">10.1016/j.snb.2020.128813</a></p>
Data set for "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials"
<p>This excel file contains the raw data gathered with the measurements performed under different conditions of illumination for the different sensors. These data have been exploited in the paper "On the Use of Pulsed UV or Visible Light Activated Gas Sensing of Reducing and Oxidising Species with WO3 and WS2 Nanomaterials" DOI: 10.3390/s21113736</p>
Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials
<p>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 <em>in vitro</em> and <em>in vivo</em>.</p>
RDF version of the data from Choi, JS. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources (2018)
<p>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)</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1038/s41598-018-24483-z">https://doi.org/10.1038/s41598-018-24483-z</a></p> <p>The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun</p>
RDF version of the data from Saarimaki et al. Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials (Version 1.0.0) [Zenodo Dataset] (2020)
<p>This is an RDFied version of the dataset published by Saarimaki et al. Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials (Version 1.0.0) [Zebodo Dataset] (2020)</p> <p>The original dataset publication DOI: <a href="http://doi.org/10.5281/zenodo.4146981">http://doi.org/10.5281/zenodo.4146981</a></p> <p>The Original publication authors: Saarimaki, Laura Aliisa, Federico, Antonio, Lynch, Iseult, Papadiamantis, Anastasios G., Tsoumanis, Andreas, Melagraki, Georgia, Afantitis, Antreas, Serra, Angela, & Greco, Dario</p>
RDF dataset produced in the work "Exploring Adverse Outcome Pathways for Nanomaterials with semantic web technologies"
<p>Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge.</p>
Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials
<p>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.</p> <p>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.</p>
Data from: Influence of the properties of different graphene-based nanomaterials dispersed in polycaprolactone membranes on astrocytic differentiation
<p><strong>Abstract</strong></p> <p>Composites of polymer and graphene-based nanomaterials (GBNs) combine easy processing onto porous 3D membrane geometries due to the polymer and cellular differentiation stimuli due to GBNs fillers. Aimingto step forward to the clinical application of polymer/GBNs composites, this study performs a systematic and detailed comparative analysis of the influence of the properties of four different GBNs: i) graphene oxide obtained from graphite chemically processes (GO); ii) reduced graphene oxide (rGO); iii) multilayered graphene produced by mechanical exfoliation method (G<sub>mec</sub>); and iv) low-oxidized graphene via anodic exfoliation (G<sub>anodic</sub>); dispersed in polycaprolactone (PCL) porous membranes to induce astrocytic differentiation. PCL/GBN flat membranes were fabricated by phase inversion technique and broadly characterized in morphology and topography, chemical structure, hydrophilicity, protein adsorption, and electrical properties. Cellular assays with rat C6 glioma cells, as model for cell-specific astrocytes, were performed. Remarkably, low GBN loading (0.67 %wt.) caused an important difference in the response of the C6 differentiation among PCL/GBN membranes. PCL/rGO and PCL/GO membranes presented the highest biomolecule markers for astrocyte differentiation. Our results pointed to the chemical structural defects in rGO and GO nanomaterials and the protein adsorption mechanisms as the most plausible cause conferring distinctive properties to PCL/GBN membranes for the promotion of astrocytic differentiation. Overall, our systematic comparative study provides generalizable conclusions and new evidences to discern the role of GBNs features for future research on 3D PCL/graphene composite hollow fiber membranes for <em>in vitro</em>neural models.</p>
Data & code repository for the article "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". </p> <p>In detail the following data sources have been included:</p> <ul> <li>the relevant code and supporting data (code_to_upload.zip and supporting_data.zip);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results.zip);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results.zip);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures.zip)</li> </ul> </li> </ul>
Novel benzotriazole-nanomaterials for maritime applications: comparative anti-corrosion performance, environmental behavior, and hazard
<div>The present dataset contains chemical, characterization, and ecotoxicological data upon exposure of benzotriazole (BTA) in the soluble form and nanoforms (Mg-Al LDH-BTA and Zn-Al LDH-BTA). Chemical analyses data include the quantification of relevant anions (nitrites, nitrates, phosphates and chlorides, determined through High-Performance Liquid Chromatography) and target metals (Al, Mg and Zn) determined through Inductively Coupled Plasma Optical Emission Spectrometry). Characterization data include DLS (size) and zeta potential analysis. Ecotoxicological data includes acute and chronic endpoints determined in a wide-range of marine species.</div> <div> </div> <div> </div> <div> </div>
Environmental behavior of novel "smart" anti-corrosion nanomaterials in a global change scenario
<div>The present dataset contains dynamic light scattering data and quantification of anions (corrosion inhibitors) and target metals (Zn and Al) in saltwater dispersions aiming to assess and compare the environmental behavior of four anti-corrosion nanomaterials in the following conditions: “temperate seawater” (T=20 ºC, pH=8.0, without HA); “tropical seawater” (T=30 ºC, pH=8.0, without HA), “acidified temperate seawater” (T=20 ºC, pH=7.6, without HA), “acidified tropical seawater” (T=30 ºC, pH 7.6, without HA); “temperate seawater enriched with natural organic matter (NOM)” (T=20 ºC, pH=8.0, with HA); “tropical seawater enriched NOM” (T=30 ºC, pH=8.0, with HA).</div> <div> </div> <div> </div>
Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials - Data and results
<p>This repository contains the input data and the generated results utilized as part of the research article titled <em>"Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials"</em> by Saarimäki & Fratello et al.</p> <h1>Data</h1> <p>This dataset includes anonymized responses of a panel of experts to a questionnaire focused on nanomaterials safety.</p> <h2>Additional Data Sources</h2> <p>This entry further includes additional data from the following sources:</p> <ul> <li><strong>Saarimäki et al. (2021)</strong>: Preprocessed data and primary physicochemical characteristics are available on <a href="https://doi.org/10.5281/zenodo.6425445">Zenodo</a>.</li> <li><strong>Gallud et al. (2020)</strong>: Data is available from the NCBI Gene Expression Omnibus (GEO) under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148705">GSE148705</a>.</li> <li><strong>del Giudice et al. (2023)</strong>: The advanced descriptors are accessible through the associated <a href="https://doi.org/10.5281/zenodo.7674574">Zenodo repository</a>.</li> <li><strong>Labouta et al. (2019)</strong>: The harmonized dataset of ENMs cell viability assays is available as <a href="https://pubs.acs.org/doi/suppl/10.1021/acsnano.8b07562/suppl_file/nn8b07562_si_001.xlsx">supporting information of the reasearch article</a>.</li> </ul> <h1>Outputs</h1> <ul> <li><strong>Statistical modeling:</strong> The inferred parameters of the statistical model developed to analyze the expert responses and identify the consensus among the experts.</li> <li><strong>Machine Learning classifiers:</strong> 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.</li> <li><strong>Feature Importance:</strong> 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.</li> </ul> <h1>Contents</h1> <p><code>data/Combined_cleaned_responses_anon.xlsx</code>: Contains the anonymized responses from the expert panel.</p> <p><code>data/enms_grouping.txt</code>: Contains a categorization of the ENMs based on their core material.</p> <p><code>data/expert_bibliographies_anon.pickle</code>: Contains the anonymized bibliography of the experts to assess the multidisciplinarity of the panel assembled.</p> <p><code>data/gex.csc.gz</code>: Contains the gene expression after exposure to the ENMs.</p> <p><code>data/phenodata.txt</code>: Contains the meta-data of the experiments</p> <p><code>data/physicochemical_descriptors.txt</code>: Contains the physicochemical descriptors of the ENMs.</p> <p><code>data/external/nn8b07562_si_001.xlsx</code>: Contains a panel of harmonized ENMs cytotoxicity assays.</p> <p><code>outputs/concern_scores.csv</code>: Contains the consensus scores inferred by the statistical model.</p> <p><code>outputs/cross_validation/</code>: Contains the logs and performances of all the machine learning classification runs .</p> <p><code>outputs/important_{genes,physchem}_weighted.xlsx</code>: Feature relevance scores of both views.</p> <p><code>outputs/model_inference_anon.nc</code>: Inferred parameters of the statistical model.</p>
A Supervised Machine-Learning Prediction of Textile's Antimicrobial Capacity Coated with Nanomaterials
<p>The dataset contains P-Chem properties of NMs and experimental conditions for assessing the antimicrobial properties of inorganic and organic NMs using machine learning tools.</p>
Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay - data from the article published in PlosOne
<p>Data that have been used in the manuscript "Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay" by M. Fontaine et al., published in PlosOne in 2023.</p>
Anisotropic Gold Nanomaterial Synthesis Using Peptide Facet Specificity and Timed Intervention: Experiment and simulation data, figures, and notebooks for recreating figures
<p><strong>/Data</strong></p> <p>Contains all experimental and simulation data used to generate figures in the manuscript. Each folder is labelled with the relevant technique: atomic force microscopy (AFM), dynamic light scattering (DLS), molecular dynamics (MD), small-angle X-ray scattering (SAXS), scanning electron microscopy (SEM), transmission electron microscopy and selected area electron diffraction (TEM-SAED), and ultraviolet-visible light spectroscopy (UV-Vis). Folders may contain subfolders from different experiments and should be unchanged for the notebooks to point to the correct path when loading data. Any file which contains SI in the filename indicates sample information, which includes sample concentration, delay associated with creating the sample (Delay4 is the relevant column name), unique identification (UID), etc.</p> <p><strong>/Data/AFM</strong></p> <p>Each filename is labelled with the number of delay associated with the sample or No Z2 Control in the case of the control sample. Each sample contains 3 files, the raw AFM data, a processed image, and an excel file with line profile measurements.</p> <p><strong>/Data/DLS</strong></p> <p>Contains exported data from Malvern Nano ZS DLS instrument.</p> <p><strong>/Data/Images</strong></p> <p>Images from the colloidal stability experiment.</p> <p><strong>/Data/MD</strong></p> <p>Molecular dynamics data and some notebooks for processing peptide and facet combinations.</p> <p><strong>/Data/SAXS</strong></p> <p>Reduced SAXS data from time-resolved study of nanoplatelet growth.</p> <p><strong>/Data/SEM</strong></p> <p>SEM images of samples prepared with and without dynamic intervention (control).</p> <p><strong>/Data/TEM_SAED</strong></p> <p>TEM images of samples prepared with and without dynamic intervention (control), and SAED image of a nanoplatelet.</p> <p><strong>/Data/UV-Vis</strong></p> <p>Spectroscopy data from different Z2 variants, the microplate based stability assay, cuvette based stability assay, and the time resolved (kinetics) measurements of particle growth as a result of manual dynamic intervention.</p> <p><strong>/Notebooks</strong></p> <p>This folder contains all of the Jupyter notebooks used to plot data and fit SAXS scattering profiles.</p> <p>See <strong>/Notebooks/environment.yml</strong> for packages necessary to execute the notebooks here and in <strong>/Synthesis_Protocol</strong>. We recommend installing this environment by using:</p> <p> </p> <p>Refer to <a href="https://github.com/SasView/sasmodels">https://github.com/SasView/sasmodels</a> and the first cell of <strong>/Notebooks/SAXS_Fitting.ipynb</strong> for specific instructions on how to complete installation of the sasmodels module (sasmodels will be installed by Pip if you correctly use the shared environment.yml file).</p> <p><strong>/Figures</strong></p> <p>Figures presented in the publication</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.