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”
Biomarker assessment of spatial and temporal changes in the composition of flocculent material (floc) in the subtropical wetland of the Florida Coastal Everglades (FCE) from May 2007 to December 2009
Flocculent material (floc) is an important energy source in wetlands. In the Florida Everglades, floc is present in both freshwater marshes and coastal environments and plays a key role in food webs and nutrient cycling. However, not much is known about its environmental dynamics, in particular its biological sources and bio-reactivity. We analysed floc samples collected from different environments in the Florida Everglades and applied biomarkers and pigment chemotaxonomy to identify spatial and seasonal differences in organic matter sources. An attempt was made to link floc composition with algal and plant productivity. Spatial differences were observed between freshwater marsh and estuarine floc. Freshwater floc receives organic matter inputs from local periphyton mats, as indicated by microbial biomarkers and chlorophyll-a estimates. At the estuarine sites, the floc is dominated by mangrove as well as diatom inputs from the marine end-member. The hydroperiod (duration and depth of inundation) at the freshwater sites influences floc organic matter preservation, where the floc at the short-hydroperiod site is more oxidised likely due to periodic dry-down conditions. Seasonal differences in floc composition were not consistent and the few that were observed are likely linked to the primary productivity of the dominant biomass (periphyton in the freshwater marshes and mangroves in the estuarine zone). Molecular evidence for hydrological transport of floc material from the freshwater marshes to the coastal fringe was also observed. With the on-going restoration of the Florida Everglades, it is important to gain a better understanding of the biogeochemical dynamics of floc, including its sources, transformations and reactivity.
Data set for the journal article: Colloidal-ALD Grown Metal Oxide Shells Enable the Synthesis of Photoactive Ligand/ Nanocrystal Composite Materials
<p>The data for each figure of the main manuscript is included in this folder.</p> <p>Figure 1 is not included as it contains no data.</p> <p>The folder for Figure 2 contains a sub-folder for the EDX and NMR data of 9-ACA/PbS@AlOx. The NMR data was processed by Mestrenova.</p> <p>The folder for Figure 3 contains optical absorption spectrum data of 9-ACA/PbS@AlOx.</p> <p>The folder for Figure 4 contains NMR data which was processed by Mestrenova. It contains the data for 9-ACA/CuInS2@AlOx, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx.</p> <p>The folder for Figure 5 is made of three sub-folders for figure 5A, 5B and 5C. 5A and 5B contain optical absorption for the CuInS2 and CsPbBr3 datasets while 5C contain time resolved data for CsPbBr3.</p> <p>The folder for Figure 6 contains time resolved PL for the as synthesized CsPbBr3, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx. The 9-PTA/CsPbBr3@AlOx data contain two decays that span 200 ns (short) or 13.5 us (long).</p> <p>The folder for Figure 7 contains time resolved PL for the as synthesized 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx. For both samples the data contain two decays that span 200 ns (short) or 13.5 us (long). Also an NMR folder is present with the 1H spectrum for 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx.</p> <p> </p> <p> </p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Data for Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates
<p>This repository contains data and scripts to reproduce results that are presented in the manuscript: Van Laere, J., Merckx, R., Hood-Nowotny, R., Dercon, G. (2023) Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates. <em>Front. Plant Sci</em>. 14:1222558 doi: 10.3389/fpls.2023.1222558</p>
Supplementary material 5: Bombus spp trapped in Palmer Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
1040 specimens of fourteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 3: Bombus spp trapped in Fairbanks Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
2,131 specimens of fifteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 2: Bombus spp trapped in Delta Junction Alaska, 2010 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
1812 specimens of sixteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 1: Bombus spp trapped in Delta Junction Alaska, 2009 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
2,446 specimens of sixteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 4: Bombus spp trapped in Fairbanks Alaska, 2010 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
57 specimens of seven species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Supplementary material 6: Bombus spp trapped in Palmer Alaska, 2010 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
764 specimens of fourteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Composition and electrical resistance results of a Ir-Pd-Pt-Rh-Ru composition spread thin film materials library
<p>The dataset contains the results of electrical resistance measurement and composition analysis of a thin film composition spread materials library. </p> <p>342 measurement areas were evaluated for chemical composition using energy dispersive X-ray spectroscopy and electrical resistance using a 4-point probe.</p> <p>CSV columns:</p> <p>x: x-coordinate of materials library in µm</p> <p>y: y-coordinate of materials library in µm</p> <p>Ir: relative chemical composition in at.%</p> <p>Pd: relative chemical composition in at.%</p> <p>Pt: relative chemical composition in at.%</p> <p>Rh: relative chemical composition in at.%</p> <p>Ru: relative chemical composition in at.%</p> <p>Resistance: electrical resistance in Ohm</p> <p> </p> <p>This dataset is supplementary information for an associated publication. A link to the publication will be provided after publishing.</p>
Figure 4: Spectro°uorimetric spectra of TiO2/CdS composites samples with a di®erent mass proportion-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Fig. 4 illustrates the fluorescence emission spectra of TiO2/CdS nanostructured ¯lm at the exciting<br> wavelength of 340 nm. The curves for TC 1, TC 2, TC 3 and TC 4 exhibited<br> the clear photoemission peaks at about 375 nm, and compared with emission of<br> bulk CdS (520 nm), 145 nm of blue shift was observed. These features indicate<br> the quantum-con¯ned e®ect of the TiO2/CdS nanocomposite ¯lms [25].</p>
PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS-Figure 2: The FT/IR microscopy images of the TiO2/CdS composites
<p>The microstructural properties of the deposited TiO2/CdS layers depend<br> on the mass proportions of TiO2:CdS. The proportion of CdS phase is di-<br> rect proportional with the CdS powder used in the preparation process. The<br> FT/IR microscopy images of the TiO2/CdS composites upon annealing at high<br> temperatures, are shown in Fig.2<br> The micrograph shows agglomerates with irregular morphology and size<br> ranging from few micrometers down to hundreds of nanometer.</p>
Figure 1: X-ray di®raction pattern for TiO2/CdS composites samples-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Fig. 1 shows the X-ray di®raction pattern for TiO2/CdS<br> composites (sample TC 1, TC 2, TC 3 and TC 4). The di®raction pattern of<br> the TiO2/CdS composites exhibits the di®raction peaks owning to CdS phase<br> (hexagonal and cubic). TiO2 (rutile and anatase) phases were also detected.</p>
Figure 3: Absorption spectra of TiO2/CdS composites samples with a di®erent mass proportion-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Optical properties of TiO2/CdS semi-<br> conductor composites have been investigated using optical absorption and pho-<br> toluminescence spectroscopy. UV-VIS absorption spectra of the as-prepared<br> photocatalysts were taken on a JASCO V-550 spectrometer. The °uorescence<br> spectra were obtained using an ABL&Jasco V 6500 spectro°uorimeter with<br> xenon lamp. The optical studies of the TiO2/CdS composites were performed<br> using the absorption spectra observed in the wavelength range 200-900 nm.<br> Fig. 3 shows the UV-Vis spectra of TiO2/CdS samples.</p>
Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades
<p>A scout and zoom dataset including video-versions of the figures behind the following paper to where the references should be given:</p> <p>Mikkelsen, L.P. Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades, IOP Conf. Series: Materials Science and Engineering <strong>380</strong> (2018) 012006 , http://iopscience.iop.org/article/10.1088/1757-899X/388/1/012006.</p> <p>The SFoV data-set is saved as both a 3D and a 2D (zipped) tiff stack.</p>
Raw data for "A Composite Bayesian Optimisation Framework for Material and Structural Design under Uncertainty"
<p>This dataset contains the raw data for the paper "A Composite Bayesian Optimisation Framework for Material and Structural Design under Uncertainty" (submitted) by R. P. Cardoso Coelho, A. F. Carvalho Alves, T. M. Nogueira Pires and F. M. Andrade Pires (INEGI and Faculty of Engineering of the University of Porto, Portugal).</p> <p> </p> <p>The data has been generated with the development branch of piglot - an open-source optimisation toolbox (https://github.com/CM2S/piglot). The numerical simulations have been conducted with both an in-house finite element solver (Links) and with the open-source SCA implementation CRATE (https://github.com/bessagroup/CRATE).</p>
FIGURE 1. Selected lizard and amphisbaenian material from studied localities. 1-2 in First early Eocene lizards from Spain and a study of the compositional changes between late Mesozoic and early Cenozoic Iberian lizard assemblages
FIGURE 1. Selected lizard and amphisbaenian material from studied localities. 1-2, Geiseltaliellus sp.: 1, left dentary (IPS 49740); 2, maxilla (IPS 83552); 3-4, Iguanidae indet.: 3, fragment of dentary (IPS 83535) with one preserved tooth, 4, fragment of?maxilla with four preserved teeth (IPS 49756); 5-6, Agamidae indet.: 5, Fragment of toothbearing bone preserving one tooth (IPS 83546), 6, fragment of dentary preserving two teeth (IPS 83543). 7-8, Gekkota indet.: 7, posterior portion of left dentary (IPS 59559), 8, anterior portion of left dentary (IPS 83520); 9, Scincoidea (?Scincidae) indet., fragment of right dentary (IPS 49752); 10,?Lacertidae indet., fragment of tooth-bearing bone perserving two teeth (IPS 49762); 11, Amphisbaenia indet., vertebra (IPS 59529); 12, cf. Placosaurus sp., partial parietal with fused osteoderms (IPS 59567); 13, Glyptosaurini indet., skull osteoderm (IPS 83532); 14, Glyptosaurinae indet., body osteoderm (IPS 83533); 15-18, Anguinae indet.: 15, keeled body osteoderm (IPS 83540), 16, unkeeled body osteoderm (IPS 83533), 17, partial parietal (IPS 83557), 18, vertebra (IPS 59538); 19-20, "Necrosauridae" indet.: 19, partial left dentary (IPS 83545), 20, osteoderm (IPS 49741). 1, 2, 5, 6, 11, 15 and 17-20 from Masia de l'Hereuet (MP8+9); 3, 4, 7, 8, 10 and 14 from La Morera (MP10); 12 from Escarlà (MP10); 13 and 16 from Font del Torricó. 1-10 and 19 in labial view; 11-12, 17 in dorsal view; 13-16 and 20 in external view; 18 in ventral view.
Dataset of "Affordable metod for synthesis of composite nanoobjects from recycled materials by hot plasma arc from basic plasma cutter "
<p>Metal remnants from battery waste can be utilized in some advanced composite materials together with other materials like cerium dioxide nanoparticles which repeatedly show interesting application potential in the field of decomposition of environmental pollutants. It is important to avoid uncontrolled leakage of the nanoparticles into the environment. Therefore immobilization in composite is important. In this contribution, ceria nanoparticles were aggregated and immobilized with the addition of nanoparticles, metal sheets, and the usage of a plasma beam. The obtained material was characterized in detail using optical microscopy, XRD, SEM, and EDS. The composite was also compared to the non-treated ceria nanoparticles. The absorption of pollutant samples was performed in aqueous solutions of Eriochrome Black T. The results show promising potential considering the morphology change and aggregate formation, together with fast reaction times. </p>
Experimental investigation of composite materials for sliding friction dampers: data, plots, photos and videos of the tests
<p><strong>Folder DATA</strong></p> <p>This folder contains the data acquired by testing the friction pads M1, M2, M3, M4 and M5 under the following loading protocols:</p> <ul> <li>Linear static loading (M);</li> <li>Cyclic loading with constant amplitude (CA);</li> <li>Cyclic loading with decreasing amplitude at low rate (DA);</li> <li>Cyclic loading with increasing amplitude at low rate (IA);</li> <li>Cyclic loading with increasing amplitude at moderate rate (IA-H);</li> <li>Cyclic loading with increasing amplitude at high rate (IA-HH);</li> <li>Pulse-like loading protocol (PL);</li> <li>Mainshock-aftershock protocol (MS-AS): mainshock (MS), first aftershock (AS1) and second aftershock (AS2).</li> </ul> <p>The data include:</p> <ul> <li><em>Time</em>: time (unit: second);</li> <li><em>F</em>: axial force experienced by the sliding friction damper (unit: kN);</li> <li><em>N_bolt</em>: bolt preload (unit: kN);</li> <li><em>mu</em>: friction coefficient of the considered pad (unit: dimensionless);</li> <li><em>delta</em>: axial displacement experienced by the sliding friction damper (unit: mm);</li> <li><em>Cum. delta</em>: total cumulative displacement experienced by the sliding friction damper (unit: mm);</li> <li><em>Cum. E</em>: total cumulative energy dissipated by the sliding friction damper (unit: kJ);</li> <li><em>max Tin</em>: maximum temperature tracked close to the sliding interface (unit: Celsius);</li> <li><em>Tout</em>: temperature tracked at the surface of the inner slotted plate (unit: Celsius).</li> </ul> <p> The data are organized as follows:</p> <ul> <li>Folder <strong>T100</strong></li> </ul> <p>This folder contains the data acquired under the linear static loading protocol (M) for a tightening torque of 100 Nm. Each EXCEL file <strong>T100_M_Y</strong> saved in the folder <strong>T100</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol M.</p> <ul> <li>Folder <strong>T200</strong></li> </ul> <p>This folder contains the data acquired under the linear static loading protocol (M) for a tightening torque of 200 Nm. Each EXCEL file <strong>T200_M_Y</strong> saved in the folder <strong>T200</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol M.</p> <ul> <li>Folder <strong>Fs150</strong></li> </ul> <p>This folder contains the data acquired for an expected slip load of 150 kN. Each subfolder <strong>Fs150_X</strong> contains the data obtained under the loading protocol X (X = M, CA, DA, IA, IA-H). Each EXCEL file <strong>Fs150_X_Y</strong> saved in the subfolder <strong>Fs150_X</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol X.</p> <ul> <li>Folder <strong>Fs300</strong></li> </ul> <p>This folder contains the data acquired for an expected slip load of 300 kN. Each subfolder <strong>Fs300_X</strong> contains the data obtained under the loading protocol X (X = M, CA, DA, IA, IA-H, IA-HH, PL, MS, AS1, AS2). Each EXCEL file <strong>Fs300_X_Y</strong> saved in the subfolder <strong>Fs300_X </strong>contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol X.</p> <p><strong>Folder PHOTOS</strong></p> <p>This folder contains the following photos:</p> <ul> <li>Folder <strong>01_FrictionDamper</strong>: photos of the sliding friction damper and its components.</li> <li>Folder <strong>02_Instrumentation</strong>: photos of the instrumentation used for the data acquisition during the experimental campaign.</li> <li>Folder <strong>03_FrictionPads</strong>: <ul> <li>Subfolder <strong>BeforeTesting</strong>: photos of the friction pads before the experimental campaign.</li> <li>Subfolder <strong>AfterTesting</strong>: photos of the friction pads at the end of each loading protocol. The photo <strong>Fs150vs300_X_Y</strong> shows the condition of the pad Y (Y = M1, M2, M3, M4, M5) at the end of the loading protocol X (X = M, CA, DA, IA, IA-H, IA-HH, PL, MS, AS1, AS2) performed for an expected slip load of 150 kN and 300 kN (the pads shown at the top of each photo are those tested for an expected slip load of 150 kN). Similarly, the photo <strong>Fs300_X_Y</strong> shows the condition of the pad Y at the end of the loading protocol X performed for an expected slip load of 300 kN.</li> </ul> </li> <li>Folder <strong>04_Tests</strong>: photos taken from the east and north side of the sliding friction damper during the loading protocols that caused the fracture of the pads <ul> <li>Subfolder <strong>Fs150</strong>: photos taken during the tests conducted for an expected slip load of 150 kN. Each folder <strong>Fs150_X_Y</strong> contains the photos taken by testing the pad Y (Y = M1, M2, M4, M5) during the loading protocol X (X = CA, DA, IA, IA-H).</li> <li>Subfolder <strong>Fs300</strong>: photos taken during the tests conducted for an expected slip load of 300 kN. Each folder <strong>Fs300_X_Y</strong> contains the photos taken by testing the pad Y (Y = M1, M2, M4, M5) during the loading protocol X (X = CA, IA, IA-H). A video was recorded live during the loading protocols IA-HH, PL, MS, AS1 and AS2 (see folder <strong>VIDEOS</strong>).</li> </ul> </li> </ul> <p><strong>Folder PLOTS</strong></p> <p>This folder contains the following MATLAB plots:</p> <ul> <li><em>Force-Disp</em>: axial force – axial displacement response of the sliding friction damper;</li> <li><em>Preload-CumDisp</em>: bolt preload as a function of the total cumulative displacement experienced by the sliding friction damper;</li> <li><em>FrictionCoeff-CumDisp</em>: friction coefficient of the considered pad as a function of the total cumulative displacement experienced by the sliding friction damper;</li> <li><em>Temp-CumDisp</em>: rise in temperature as a function of the total cumulative displacement experienced by the sliding friction damper (the temperature values reported for the expected slip load of 150 kN correspond to “max Tin”, whereas those reported for the expected slip load of 300 kN correspond to “Tout”);</li> <li><em>FrictionCoeff-LoadingHistoryEffect</em>: friction coefficient of the considered pad as a function of the total cumulative displacement experienced by the sliding friction damper under different loading protocols;</li> <li><em>FrictionCoeff-RateEffect</em>: friction coefficient of the considered pad as a function of sliding velocity experienced by the sliding friction damper under different loading protocols;</li> <li><em>FrictionCoeff-TempEffect</em>: friction coefficient of the considered pad as a function of the rise in temperature tracked during different loading protocols;</li> <li><em>FrictionCoeff-PressureDependency</em>: mean and standard deviation of the friction coefficient of the considered pad obtained for different expected slip loads and loading protocols;</li> <li><em>FrictionCoeffStaticDynamic-PressureDependency</em>: mean of the static and dynamic friction coefficient of the considered pad obtained for different expected slip loads and loading protocols.</li> </ul> <p>The MATLAB plots are organized as follows:</p> <ul> <li>Folder <strong>T200</strong></li> </ul> <p>The MATLAB plots saved in this folder illustrate the data obtained by testing the friction pads M1, M2, M3, M4 and M5 under the linear static loading protocol (M) for a tightening torque of 200 Nm.</p> <ul> <li>Folder <strong>Fs150 and Fs300</strong></li> </ul> <p>The MATLAB plots saved in this folder illustrate the data obtained by testing the friction pads M1, M2, M3, M4 and M5 under the considered loading protocol (M, CA, DA, IA, IA-H, IA-HH, PL, MS-AS) for an expected slip load of 150 kN and 300 kN.</p> <p><strong>Folder VIDEOS</strong></p> <p>This folder contains the following videos:</p> <ul> <li>Folder <strong>T100</strong>: videos created from the photos taken during the tests conducted for a tightening torque of 100 Nm under the linear static loading protocol (M). The videos <strong>T100_M_Y_East</strong> and <strong>T100_M_Y_North</strong> show the test conducted on the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>T200</strong>: videos created from the photos taken during the tests conducted for a tightening torque of 200 Nm under the linear static loading protocol (M). The videos <strong>T200_M_Y_East</strong> and <strong>T200_M_Y_North</strong> show the test conducted on the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>Fs150</strong>: videos created from the photos taken during the tests conducted for an expected slip load of 150 kN. The videos <strong>Fs150_X_Y_East</strong> and <strong>Fs150_X_Y_North</strong> show the loading protocol X (X = M, CA, DA, IA, IA-H) applied to the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>Fs300</strong>: videos created from the photos taken during the tests conducted for an expected slip load of 300 kN. The videos <strong>Fs300_X_Y_East</strong> and <strong>Fs300_X_Y_North</strong> show the loading protocol X (X = M, CA, DA, IA, IA-H) applied to the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively. The videos obtained for the loading protocols IA-HH, PL, MS, AS1 and AS2 were recorded live during each test.</li> </ul>
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.