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.
124
datasets available to search
ShareScore release 0.9.0
Dataset results
124 results for “composite material”
Supplementary materials (processed data) for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies."
<p>Processed data for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies."</p>
Supplementary materials (raw data) for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies"
<p>Raw data (B&K 891, C60 and VNA instruments) for the paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies"</p>
NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations
<p>Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are mostly determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. The NIR-MFCO dataset aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs. The false-color images were created through the pixel-based classification of binary material mixtures using a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm – 1678 nm wavelength range) and the on-chip classification algorithm (CLASS 32). The resulting NIR-MFCO dataset includes <em>n</em> = 880 false-color images from three test series: (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) flakes, (T2a) post-consumer HDPE packaging and PET bottles, and (T2b) post-consumer HDPE packaging and beverage cartons for <em>n</em> = 11 different HDPE shares (0% - 50%) at four different material flow presentations (singled, monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to train machine learning algorithms, evaluate the accuracy of inline SBMC applications, and deepen the understanding of segregation effects of anthropogenic material flows, thus further advancing SBMC research and enhancing post-consumer plastic recycling.</p>
Supporting Information from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)
<p>Supporting Information (Open Data) from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)</p>
Supplementary Materials for "The Effects of Chromosome Doubling on Morphology, Salinity Tolerance, Essential Oil Composition, and Gene Expression of Biosynthesis Pathway in Peppermint (Mentha piperita L.)"
<p>Shandong Province Key Laboratory of Applied Microbiology, Ecology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250103, China; zhaozjfrances@163.com (Z.Z.); yanli_wei@163.com (Y.W.); menshenlai@163.com (K.Y.); sdkinghills@sina.com (B.L.); liling33802400@163.com (L.L.); yanght@sdas.org (H.Y)</p>
Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models
<p>This is supporting data for the paper titled "Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models" by Leight et al. (submitted to JGR-P 11/23). Table S1 lists each spectrum used to train PLS models, its source, and which training datasets the spectrum was included in. Zip files contain the MIR and VNIR PLS model files. Model files are .asc, and can be run using the code at Ytsma, (2022), https://doi.org/10.5281/zenodo.7347345. </p>
3D Printed Living Bacterial Cellulose and Biopolymer Composite Material Samples
<p>The images in this dataset were captured during an experiment initiated on 13 March 2023 at CITA, at the Royal Danish Academy, School of Architecture. The experiment was performed on 3d printed samples made from a material combining living cellulose-producing bacteria with a biopolymer composite. The biopolymer composite combines a xantham gum binder, glycerol and calcium chloride with cellulose fillers sourced from side and waste streams materials. During the experiment, the dormant bacterial cellulose was reawakened through the spraying of tea nutrient at timed intervals of 30 minutes for 2 seconds, for a period of 42 days.</p><p> </p>
The response of silicon carbide composites to He ion implantation and ramifications for use as a fusion reactor structural material
<p>Raw datasets for the publication "The response of silicon carbide composites to He ion implantation and ramifications for use as a fusion reactor structural material".</p>
Datasets for Material Modal Composite Analysis results in Duanjiapo loess section
<p>The dataset is affilicated to the manuscript titled "Genesis of Loess Particles on the Chinese Loess Plateau" that is submitted for publication in the journal of Geochemistry, Geophysics, Geosystems. The original instrumental data packages were provided in the excel files (Data1, Data2, Data3, and Data4).</p>
Supplementary material 1 from: Baum S, Weih M, Bolte A (2012) Stand age characteristics and soil properties affect species composition of vascular plants in short rotation coppice plantations. BioRisk 7: 51-71. https://doi.org/10.3897/biorisk.7.2699
Number of plots containing the respective species is stated.
Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation
<p>This dataset contains the raw data for the publication "Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation" by Fila et al, published in Chemical Engineering Journal. The upload includes raw data of physicochemical characterizations of calcium alginate and its composites, i.e. alginate-biochar and alginate-clinoptilolite, including BET, SEM, TG, XPS and XRD analyses. </p>
Evaluation of the Performance and Cost-Effectiveness of Engineered Cementitious Composites (ECC) Produced from Region 6 Local Materials
<p>Corresponding data set for Tran-SET Project No. 17CLSU05. Abstract of the final report is stated below for reference:</p> <p>"The project objective is to develop cost-effective Engineered Cementitious Composites (ECC) with locally available ingredients in Region 6 to address the deficiencies observed in ordinary concrete materials. The study explored the utilization of two types of river sands (coarse and fine), two types of PVA fibers (long and short), four levels of cement replacement with Class F fly ash, and the implementation of recycled crumb rubber in the performance of ECC materials. A total of 24 mix designs were prepared and evaluated in compression, tension, and bending to assess its mechanical properties. Furthermore, the cracking characteristics of the materials produced were evaluated to assess the durability potential of these composites. Lastly, the cost of each mix design and the feasibility of ECC implementation in transportation infrastructure were assessed. The experimental results showed that implementing crumb rubber and/or increasing contents of fly ash in the mixtures produced a positive impact in the ductility of the materials. However, a tradeoff between ductility and strength was observed. Furthermore, the utilization of the different types of sand evaluated in this study produced minor effects in the mechanical properties of ECCs evaluated. The properties of the materials developed in this study were exceedingly superior than that of regular concrete. It was concluded that ECC materials are promising for the future of transportation infrastructure."</p>
Data for "Nanoscale modification of MOC-based composites: The influence of alumina nanosheets on microstructure and material properties"
Open the record for dataset details and reuse information.
Supplementary material for "A Rapid, Open-Source CCT Predictor for Low Alloy Steels, and its Application to Compositionally Heterogeneous Material"
<p>The complete collection of measured, modelled and analysed data associated with the work "A Rapid, Open-Source CCT Predictor for Low Alloy Steels, and its Application to Compositionally Heterogeneous Material" and includes:</p> <ol> <li>Measured and analysed dilatometry data.</li> <li>Optical micrographs of as-cooled microstructures.</li> <li>Microhardness measurements of the as-cooled samples.</li> <li>PAG size analysis.</li> <li>Modelled CCT data.</li> <li>SA-540 EPMA data.</li> <li>Modelled results from adapting the model to consider SA-540 chemical heterogeneity.</li> <li>Full chemical analysis for each alloy examined.</li> <li>Modelled Thermo-Calc CCT data.</li> <li>Modelled JMatPro CCT data.</li> </ol>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Video of Automated path planning for 3D robotic filament winding of high-performance composite materials
<p>Video showing the results of the reasearch article "Automated path planning for 3D robotic filament<br> winding of high-performance composite materials"</p>
Materials Datasets with 273 compositional and structural features extracted from Matminer
<p>Materials Datasets with 273 compositional and structural features extracted from <a href="https://github.com/hackingmaterials/matminer">Matminer</a>. Materials datasets are retrieved using the python package <a href="https://github.com/usnistgov/jarvis">jarvis-tools</a>.</p>
Internal large field of view observations with optical microscope of fatigue damage in composite materials during bending loading
<p>Stitched microscope pictures ...</p> <p>References to this data-set should include a reference to one of the following two papers in where the data has been used.</p> <p>Mortensen U., Andersen, T.L., Mikkelsen, L.P., Observation of Edge Effect in Flexural Fatigue Test of Composites using Large Field of View Microscopy, Journal of Composite Materials. https://doi.org/10.1177/0021998320902233, 2020</p> <p>Mortensen, U.A., Mikkelsen, L.P., Andersen, T.L. Observation of the interaction between transverse cracking and fibre breaks in uni-directional non-crimp fabric composites subjected to cyclic bending fatigue damage mechanism, submitted, Feb. 2022.</p> <p>The naming of the pictures are build up by the follwing elements</p> <p>EL_05 is the plate ID,<br> J01-J09 is the sample ID where the following load level are defined:</p> <ul> <li>J01: 1 000 cycles</li> <li>J02: 2 500 cycles</li> <li>J03: 5 000 cycles</li> <li>J04: 10 000 cycles</li> <li>J05: 50 000 cycles</li> <li>J06: 100 000 cycles</li> <li>J07: 250 000 cycles</li> <li>J08: 500 000 cycles</li> <li>J09: 1 000 000 cycles</li> </ul> <p>S01-04: is the 4 surfaces inside the sample as shown in the picture saved in: xxx. The picture show the sample where the red planes indicate the polished surfaces where the microscopy pictures from the internal surfaces are taken.</p> <p>The pictures are oriented with the compression side upward and the tensile side downward similar to the orientation of the actual 4-point fatigue bending test sample.</p>
Nanodiamond Modified Gutta Percha (NDGP) Composite for Non-surgical Root Canal Therapy (RCT) Filler Material
ClinicalTrials.gov study NCT02698163. IPD Sharing: NO. Countries: 1. Publications: 1.
Supplementary material 1 from: Huang J, Guo Z, Tang S, Ren W, Chu G, Wang L, Zhao L, Yu R, Xu Y, Ding Y, Zang R (2020) Floristic composition and plant diversity in distribution areas of native species congeneric with Betula halophila in Xinjiang, northwest China. Nature Conservation 42: 1-17. https://doi.org/10.3897/natureconservation.42.54735
Figure S1. The correlation between environmental variables in distribution areas of five congeneric species with Betula halophila
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.