Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
431
datasets available to search
ShareScore release 0.9.0
Dataset results
431 results for “nano”
Dataset: Nano-X Imaging Ltd. (NNOX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Nano Labs Ltd (NA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Three dimensional characterization of nickel coarsening in solid oxide cells via ex-situ ptychographic nano-tomography
<p>Three-dimensional dataset of a solid oxide cell (SOC) fuel electrode microstructure. Data were acquired using ptychographic X-ray computed tomography (PXCT). </p>
Data and code for article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides"
<p>This dataset contains the data presented in the figures of the article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides" (doi:10.1038/s41566-018-0144-1).</p> <p>The raw data in figures (curved plots) is packaged as an independent OriginLab project file (.opj). </p> <p>The layout of the design of the silicon nitride nano-photonic waveguide is presented. Fabrication process card (shown as a diagram) is provided as well.</p> <p>The source code for simulations presented in the article is also presented.</p>
The behaviour of copper at the nano-scale in an Al-Zn-Mg-Cu alloy, AA7010
<p>Data plots and images accompanying figures to paper.</p>
The effect of the graded bilayer design on the strain depth profiles and microstructure of CuW nano-multilayers
<p>In this document we share:</p> <p>-the XRD in-plane scans acquired on Cu/W multilayers at different incidence angle,</p> <p>-the in-situ stress curvature data acquired during multilayer growth,</p> <p>-the in plane d-spacing derived at different incidence angle, used for the simulation of the strain gradient.</p>
RDF version of the supplementary data from Shin, Hyun Kil and Seo et al. Meta-analysis of Daphnia magna nanotoxicity experiments in accordance with test guidelines. Environ. Sci.: Nano (2018)
<p>This is an RDF version of the dataset published by Shin, Hyun Kil and Seo et al. as a supplement of the study Meta-analysis of Daphnia magna nanotoxicity experiments in accordance with test guidelines. Environ. Sci.: Nano (2018).</p> <p>The original dataset is available online: <a href="https://ui.staging.kit.cloud.douglasconnect.com/dataexplorer?dataset=ab2bc1ee-99dc-4ddf-b1f9-9fdeb8a0f48c%3A1&q=%7B%7D">https://ui.staging.kit.cloud.douglasconnect.com/dataexplorer?dataset=ab2bc1ee-99dc-4ddf-b1f9-9fdeb8a0f48c%3A1&q=%7B%7D</a></p> <p>The original publication DOI: <a href="http://dx.doi.org/10.1039/C7EN01127J">http://dx.doi.org/10.1039/C7EN01127J</a></p> <p>GitHub repository of the datasets converted to RDF along with RML mappings: <a href="https://github.com/ammar257ammar/RDFied-datasets">https://github.com/ammar257ammar/RDFied-datasets</a></p>
BeMAGIC_Synthesis of magnetoelectric nano-objects/nanoparticles
<p>BeMAGIC ITN (GA861145) Synthesis of magnetoelectric nano-objects/nanoparticles by template assisted electrodeposition, lithography and other wet chemistry. Results from UCAM, INRIM, AALTO, TUC, ETHZ and ICN2.</p>
Supplementary materials to: Nano-Strainer: a workflow for identification of single-copy nuclear loci for plant systematic studies, using target capture kits and Oxford Nanopore long reads
<p>In the paper associated with this dataset, a workflow is presented which enables the identification of single-/low-copy nuclear molecular markers for a plant group of interest, by mining data from a small representative target capture experiment done using a commercial probe kit and Oxford Nanopore long-read sequencing. The proposed pipeline first assesses sequence variability contained in the data from targeted loci and assigns reads to their respective genes, via a combined BLAST/clustering procedure. Cluster consensus sequences are then examined based on four pre-defined criteria presumably indicative for absence of paralogy. This is done by calculating four specialized indices; loci are ranked according to their performance in these indices, and top-scoring loci are considered putatively single- or low-copy. The approach can be applied to any probe set. As it relies on long reads, the contribution also provides template workflows for processing Nanopore-based target capture data. Identified loci can be used for NGS amplicon sequencing. For detection of possibly remaining paralogy in these data, which might occur in groups with rampant paralogy, the long-read assembly tool CANU is employed. The presented workflow can be useful for researchers dealing with reticulate or polyploidization phylogenetic histories in plants.</p> <p>The present dataset contains several documents supplementing the original paper. Its most important elements are a detailed description (alongside two graphical workflow figures) of all methods employed in the study, suitable for reproducing the steps of the workflow and also the wet-lab work. The workflow employs a collection of BASH, Python and R scripts which is available here, together with a detailed account on command line use in Linux. Also, reference sequences for the identified markers can be found as well as sequence alignments derived from the amplicon sequencing.</p>
Supplementary materials to: Nano-Strainer: a workflow for identification of single-copy nuclear loci for plant systematic studies, using target capture kits and Oxford Nanopore long reads
Open the record for dataset details and reuse information.
Nano MOFs as targeted drug delivery agents to combat antibiotic resistant bacterial infections
<p>The drug resistance of bacteria is a significant threat to human civilization while the action of antibiotics against drug-resistant bacteria is severely limited due to the hydrophobic nature of drug molecules, which unquestionably inhibit its permanency for clinical applications. The antibacterial action of nanomaterials offers major modalities to combat drug resistance of bacteria. The current work reports, the use of nano MOFs encapsulating drug molecules to enhance its antibacterial activity against model drug-resistant free living bacteria and biofilm of the bacteria. We have attached rifampicin (RF), a well-documented antituberculosis drug with tremendous pharmacological significance, into the pore surface of zeolitic imidazolate framework 8 (ZIF8) by a <span><span>simple synthetic procedure</span></span><span>.</span> The synthesized ZIF8 has been characterized using X-ray diffraction (XRD) method before and after drug encapsulation. The electron microscopic strategies such as scanning electron microscope (SEM) and transmission electron microscope (TEM) methods was performed to characterize the binding between ZIF8 and RF. We have also performed picosecond resolved fluorescence spectroscopy to validate the formation of the ZIF8-RF nanohybrids (NHs). The drug release profile experiment demonstrates that ZIF8-RF depicts pH-responsive drug delivery and ideal for targeting bacterial disease corresponding to its inherent acidic nature. Most remarkably, ZIF8-RF gives enhanced antibacterial activity against methicillin-resistant <i>S. aureus</i> (MRSA) bacteria and also prompts entire damage of structurally robust bacterial biofilms. Overall, the present study depicts a detailed physical insight for manufactured antibiotic-encapsulated NHs presenting tremendous antimicrobial activity that can be beneficial for manifold practical applications.</p>
Shell biomass material supported nano-zero valent iron to remove Pb2+ and Cd2+ in water
Nanoscale zero-valent iron (NZVI) has a high adsorption capacity for heavy metals, but easily forms aggregates. Herein, preprocessed undulating venus shell (UVS) is used as support material to prevent NZVI from reuniting. The SEM and TEM results show that UVS had a porous layered structure and NZVI particles were evenly distributed on the UVS surface. A large number of adsorption sites on the surface of UVS-NZVI are confirmed by IR and XRD. UVS-NZVI is utilized for adsorption of Pb2+ and Cd2+ at pH=6 in aqueous solution, and the experimental adsorption capacities are 29.91 mg•g-1 and 38.99 mg•g-1 at optimal pH, respectively. Thermodynamic studies indicate that the adsorption of ions by UVS-NZVI is more in line with the Langmuir model when Pb2+ or Cd2+ existed alone. For the mixed solution of Pb2+ and Cd2+, only the adsorption of Pb2+ by UVS-NZVI conforms to the Langmuir model. In addition, the maximum adsorption capacities of UVS-NZVI for Pb2+ and Cd2+ are 93.01 mg•g-1 and 46.07 mg•g-1.Kinetic studies demonstrate that the determination coefficients (R2) of the pseudo first-order kinetic model for UVS-NZVI adsorption of Cd2+ and Pb2+ are higher than those of the pseudo second-order kinetic model and Elovich kinetic model. Highly efficient performance for metal removal makes UVS-NZVI show potential application to heavy metal ion adsorption.
Status quo in data availability and predictive models of nano-mixture toxicity
<p>Supplementary materials for manuscript: Status quo in data availability and predictive models of nano-mixture toxicity.</p> <p>This table contains the list of 183 curated literature used in this study.</p>
All NanoPUZZLES ISA-TAB-Nano datasets
<p>This file is a ZIP archive which contains ALL publicly released ISA-TAB-Nano datasets developed within the NanoPUZZLES EU project [http://www.nanopuzzles.eu]. The (meta)data in these datasets were extracted from literature references.</p> <p>These datasets are also available via FigShare (see below). ****Any necessary updates, e.g. to correct errors not spotted during the review of the datasets within the NanoPUZZLES project prior to their being released, will be uploaded to FigShare and the changes documented in the FigShare dataset descriptions. This Zenodo entry corresponds to the original publicly released versions of these datasets.****</p> <p>*****Before working with these datasets, you are strongly advised to read the following text - especially the "Disclaimers".*****</p> <p><br /> ISA-TAB-Nano [1,2,3] has been proposed as a nanomaterial data exchange standard. As is explained in the README file contained within each dataset, as well as the "Investigation Description" field of the Investigation file regarding dataset specific deviations, the manner in which certain data and metadata were recorded within these datasets deviates from the expectations of the generic ISA-TAB-Nano specification. Marchese Robinson et al. [3], distributed within each dataset, discusses this in more detail. However, some additional new business rules, going beyond those described in Marchese Robinson et al. [3], may also have been applied to each dataset - as documented in the README file.</p> <p>Each dataset was developed using Excel-based templates developed in the NanoPUZZLES project [4]. (N.B. The latest version of the templates, at the time of writing, was version 4 as opposed to version 3 which was described in Marchese Robinson et al. [3]. This latest version of the templates should be contained within the README file of each dataset.) Since these templates were iteratively updated, not all datasets may be perfectly consistent with the latest version - although efforts were made to minimise inconsistencies.</p> <p>The three copies of each dataset contained within each individual [DATASET ID]_all_copies.zip are as follows:<br /> (a) [DATASET ID].zip: the original dataset prepared within Excel<br /> (b) [DATASET ID]-txt_opt-N.zip: a tab-delimited text version of each dataset prepared using version 2.0 of the cited Python program [5], with the -N flag selected (designed to minimise inconsistencies with the latest version of the NanoPUZZLES templates)<br /> (c) [DATASET ID]-txt_opt-a_opt-c_opt-N.zip: a tab-delimited text version of each dataset prepared using version 2.0 of the cited Python program [5], with the -N, -a (truncate ontology IDs) and -c (remove Investigation file comments) flags selected, as required for submission to the nanoDMS online database system [3,6]. </p> <p>The original datasets prepared in Excel were prepared via manual curation. In some cases, it was necessary to extract data from graphs. In some cases, the GSYS software program was employed to facilitate estimation of the values of numerical data points reported in graphs [7,8].</p> <p><br /> Disclaimers:</p> <p>(1) this work has not undergone peer review<br /> (2) no endorsement by third parties should be inferred<br /> (3) *You are strongly advised to read the README file and the "Investigation Description" field of the Investigation file before working with anyone of these datasets. The latter field may document dataset specific caveats such as possible problems or uncertainties associated with curation from the original reference(s). *Other such comments may be found in Study, Material or Assay file "Comment" fields.</p> <p>Cited references:<br /> [1] Thomas, D.G. et al. BMC Biotechnol. 2013, 13, 2. doi:10.1186/1472-6750-13-2<br /> [2] https://wiki.nci.nih.gov/display/ICR/ISA-TAB-Nano (accessed 18th of December 2015)<br /> [3] Marchese Robinson, R.L. et al. Beilstein J. Nanotechnol. 2015, 6, 1978–1999. doi:10.3762/bjnano.6.202<br /> [4] http://www.myexperiment.org/files/1356.html (accessed 18th of December 2015)<br /> [5] https://github.com/RichardLMR/xls2txtISA.NANO.archive (accessed 18th of December 2015)<br /> [6] http://biocenitc-deq.urv.cat/nanodms (accessed 18th of December 2015)</p> <p>[7] http://www.jcprg.org/gsys/2.4/ (last accessed 11th of April 2016)</p> <p>[8] R. Suzuki, "Introduction, Design and Implementation of Digitization Software GSYS", IAEA Report INDC(NDS)-0629, p. 19, IAEA, Vienna, Austria (2013)</p> <p>FigShare versions:</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Cytotoxicity_and_some_physicochemical_data_reported_by_Wang_et_al_2014_DOI_10_3109_17435390_2013_796534_/2056140</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Zebrafish_mortality_and_basic_nanomaterial_composition_data_extracted_from_Kovriznych_et_al_2013_doi_10_2478_intox_2013_0012_/2056137</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Physicochemical_and_in_vitro_cytotoxicity_data_LDH_membrane_damage_extracted_from_Sayes_and_Ivanov_2010_DOI_10_1111_j_1539_6924_2010_01438_x_/2056134</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Cytotoxicity_and_physicochemical_data_for_nanomaterials_extracted_from_Murdock_et_al_2008_DOI_10_1093_toxsci_kfm240_/2056131</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Data_reported_in_Shaw_et_al_2008_DOI_10_1073_pnas_0802878105_/2056128</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Data_extracted_from_Puzyn_et_al_2011_DOI_10_1038_NNANO_2011_10_/2056125</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Toxicity_and_physicochemical_data_extracted_from_Zhang_et_al_2012_DOI_10_1021_nn3010087_/2056122</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Curation_of_carbon_nanotubes_experimental_data_reported_by_Zhou_et_al_2008_DOI_10_1021_nl0730155_supplemented_with_carbon_nanotubes_structure_files_3D_SDF_created_according_to_the_approach_described_by_Shao_et_al_/2056110</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_C60_fullerene_nanoparticle_Ames_test_and_in_vivo_micronucleus_data_extracted_from_Shinohara_et_al_2009_DOI_10_1016_j_toxlet_2009_09_012_/2056104</p> <p>https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Data_extracted_from_NanoCare_project_final_scientific_report/2056095</p> <p><br /> Funding:</p> <p>The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/ 2007-2013) under grant agreement no. 309837 (NanoPUZZLES project).</p>
Nano patents from Spain in Espacenet (2004-2014)
<p>This dataset include 3278 patents from Spain in field Nanotechnology, based in this query:</p> <p>#1 PD=2004:2014</p> <p>#2 PA=[ES] </p> <p>#3 IN=[ES]</p> <p>#4 IC=(B82 OR G01Q OR A61K9/51 OR H01F10/32 OR G02F1/017 OR B05D1/20 OR H01F41/30 OR C01B31/0206 OR H01L29/775)</p> <p>#5 TA=(nano* not nano2 not nano3 not nanog not nanosecond* not nanosegund* not nanomol* not nanogram* not nanoplankton* not nanoplancton* or "atom* scale" or "escala* atomic*" or "atomic layer deposition*" or "deposicion* de capa* atomic*" or "giant magnetoresist*" or "magnetorresistencia* gigante*" or graphen* or grafen* or dendrimer* or fulleren* or "c-60" or "langmuirblodgett*" or mesopor* or "molecul* assembl*" or "ensambla* molecul*" or "molecul* wire*" or "alambr*+ molecul*" or "hilo* molecul*" or "porous silicon*" or "silicon* porosa" or "quantum dot*" or "puntocuantic*" or "quantum well*" or "pozocuantic*" or "quantum comput*" or "computa* cuantic*" or "ordenador* cuantic*" or "quantum wire*" or "alambre* cuantic*" or "hilo* cuantic*" or qubit* or "self assembl*" or “autoensambla* or molecul*” or supramolecul* or supermolecul* or "ultrathin film*" or "ultra thin film*" or "lamina ultra-delgada*" or "lamina ultra delgada*")</p> <p>#1 AND (#2 OR #3) AND (#4 OR #5)</p> <p>The keyword query is based in Maghrebi 2010</p> <p>This dataset was used in a PhD dissertation in UGR (Björn Jürgens), two posters (NanoSpain and STI2016), and an article (WPI), more details in references.</p> <p>File format: MS-Excel</p>
Statua del nano
Una delle svariate statue di nani disseminate nel parco storico di Villa Pisani a Stra nella provincia di Venezia. Source: Objaverse 1.0 / Sketchfab
Thermal conductivity analysis of polymer-derived nano-composite via image-base structure reconstruction, computational homogenization and machine learning
<p>This dataset includes supplementary data and utilities for validating simulation results and training machine learning models as outlined in the publication titled "Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning" (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>).</p> <p>This dataset containes the microstructure images (identified by particle diameters size \(D_1\) and \(D_2\) volume fraction \(V_\mathrm{f}\) and aspect ratio \(A_\mathrm{r}\)) (see Table 1) and their corresponding homogenized thermal conductivity. these images resemble the microstructure of the monolithic \(\mathrm{(Hf,Ta)C/SiC}\) ceramic following FAST sintering, the material system of this work (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). White and black colors within the images represent distinct regions of the material system, respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work.</p> <p>Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis</p> <table> <tbody> <tr> <td>Param.</td> <td>Mean [unit]</td> <td>Std.</td> </tr> <tr> <td>\(D_{1}\)</td> <td>40, 50, 60 [μm]</td> <td>20%</td> </tr> <tr> <td>\(D_{2}\)</td> <td>20, 25, 26, 30, 33, 40 [μm]</td> <td>30%</td> </tr> <tr> <td>\(V_\mathrm{f}\)</td> <td>1.5, 2.0</td> <td>-</td> </tr> <tr> <td>\(A_\mathrm{r}\)</td> <td>35, 40, 45, 55, 60 [%]</td> <td>-</td> </tr> </tbody> </table> <p>This dataset contains:</p> <ul> <li><em>dataset.csv: </em>containing a summary of data including the names of microstructure images, their corresponding geometric details, as well as the first and third principal components of two-point statistics for all images, along with the effective thermal conductivity of the corresponding microstructures. Further details can be found in the associated publication.</li> <li><em>microstructures_images.zip</em>: containing binary cross-section images of the RVEs from synthetic microstructures。</li> <li><em>results.zip:</em> contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.</li> </ul>
Predicting zeta potential of liposomes from their structure: A nano-QSAR model for DOPE, DC-Chol, DOTAP, and EPC formulations
<p>Data set for publication: "<em>Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations.</em>"; Computational and Structural Biotechnology Journal 25 (2024) 3–8; https://doi.org/10.1016/j.csbj.2024.01.012 </p>
Nano-SMSI on Bimetallic FePt Clusters [doi: 10.1021/acs.jpcc.3c03896]
<p>Raw data, meta data and corresponding lists of figures are included. [Paper doi: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpcc.3c03896">10.1021/acs.jpcc.3c03896</a>]</p>
Data supporting the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.
<p>Reduced neutron reflectometry (NR) data associated with the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.. One folder contains the raw data for fitted parameters obtained from NR curves and supporting figure 3 in the study. The other contains a set of sub-folders which have reduced NR data along with python scripts which were used to create and fit the interfacial model to the data. Fitting bounds for each parameter are found in these scripts. </p>
ScienceDex guides
Understand access before you commit
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.