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1,007 results for “single use”
FIGURE 1 in Using integrative taxonomy to establish the status of Alpheus peasei (Armstrong, 1940) (Decapoda: Alpheidae) as a single species throughout its distribution
FIGURE 1. Alpheus peasei (Armstrong, 1940), ovigerous female, Recife, Pernambuco, Brazil, MOUFPE 19428: (A) frontal region and cephalic appendages, dorsal view; (B) the same, lateral view; (C) telson and uropods, dorsal view; (D) tooth on me- sioventral carina of first article of antennular peduncle, lateral view; (E) mandible, mesial view; (F) first maxilla, lateral view; (G) second maxilla, lateral view; (H) first maxilliped, lateral view; (I) second maxilliped, lateral view; (J) third maxilliped, lat- eral view. In the figures A–C setae omitted. Scale bars = 0.5 mm.
Fig. 2 ELAVI8 PCR yield and stringency across using a 1.1 in ELAV Intron 8: a single-copy sequence marker for shallow to deep phylogeny in Eupulmonata Hasprunar & Huber, 1990 and Hygrophila Férussac, 1822 (Gastropoda: Mollusca)
Fig. 2 ELAVI8 PCR yield and stringency across using a 1.1% Agarose gel in 1 × TBE buffer with GoldView Dye and 3 µl of PCR product from each reaction. Panel A represents 50° C annealing temperature for 40 cycles while B represents the modified touchdown procedure of 54° C anneal for 10 cycles followed by 50° C for 30 cycles. The
Datasets used in the papers: STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines & ODTE - An ensemble of multi-class SVM-based oblique decision trees
<p>These are the 49 datasets used in the benchmark. 45 of them are from the UCI machine learning repository, while the other 4 correspond to a problem about fecundity estimation for fisheries</p>
Dataset of "Wasting of Critical Raw Materials in Batteries for Single-Use E-Cigarettes "
<p>This paper focuses on the use of lithium-ion batteries in single-use e-cigarettes, examining their material composition and impact on the waste of critical raw materials. The consumption of these single-use e-cigarettes, which employ lithium-ion cells with a potential lifespan of several hundred cycles if reused, leads to the waste of critical and strategic raw materials in the Czech Republic alone. These discarded materials could have been used to manufacture several thousand batterypacks for electric vehicles annually.</p>
3D Real-Time Single Particle Tracking using two-photon fluorescence from bright dye-based organic nanoparticles
<p>Data set, and attached ReadMe files related to the production of the figures in the article "3D Real-Time Single Particle Tracking using two-photon fluorescence from bright dye-based organic nanoparticles" by Emperauger et al.</p> <p> </p> <p>The ReadMe files explaining how each data file is organised, the data are in txt format, organised by figure and sub-figures and compressed. </p>
Dataset containing over 7,400 activity cliffs produced by 10 widely used single-atom modifications.
<p>A dataset containing 7,407 activity cliffs generated by single-atom modifications is provided. For each compound, its ChEMBL ID and atom modifications are included, along with PDB IDs when available</p>
PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian
<p>Datasets that PHLOWER used:</p> <ol> <li>benchmarking data</li> <li>multiome kidney organoid data</li> <li>xenium kidney orgnoid data</li> </ol>
Data - Ultralow noise preamplified optical receiver using conventional single wavelength transmission
<p>This dataset contains measurement data and processing code for the results published in "Ultralow noise preamplified optical receiver using conventional single wavelength transmission". Raw measurement batches from the BER-measurement can be made available upon request, at rasmus.larsson@chalmers.se.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Synthetic Multimodal Dataset using ABB Studio: Single Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Löppenberg et al. (2024), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p>Frame '02655_frame' is corrupted. That means the corresponding values of the other modality needs to be removed as well.</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Löppenberg, M., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2024). Dynamic robot routing optimization: State-space decomposition for operations research-informed reinforcement learning. Robotics and Computer-Integrated Manufacturing, 90, 102812.</p>
Data from: Improving genomic prediction for two Yorkshire populations with a limited size using single-step method
In this study, we conducted genomic prediction for two Yorkshire purebred populations (Yichun and Chifeng) from two different provinces of China that both had a limited population size. Two growth traits (age adjusted to 100 kg weight, AGE; back‐fat thickness adjusted to 100 kg weight, BF) and one reproduction trait (total number of piglets born, TNB) were analyzed with four prediction strategies: one‐population BLUP, joint two‐population BLUP, one‐population single‐step BLUP (SSBLUP) and joint two‐population SSBLUP. Our results illustrate that accuracies of genomic estimated breeding values were improved for BF and TNB for the Yichun population and for BF for the Chifeng population by genomic prediction (one‐population SSBLUP and joint two‐population SSBLUP). The accuracy of TNB for the Yichun population was increased two fold when comparing the one‐population SSBLUP to the one‐population BLUP prediction. Meanwhile, prediction biases were dramatically reduced for AGE for the Yichun population and for TNB for the Chifeng population. The conclusions of this study are as follows: first, genomic prediction is useful for improving prediction accuracy for purebred pig breeding farms with a limited population size; second, joint genomic prediction for different populations of the same breed with certain genetic links has the trend to further improve prediction accuracy.
Probing DNA - transcription factor interactions using single-molecule fluorescence detection in nanofluidic devices
<p>Readme.txt, 16.07.2021<br> Fontana et al.<br> “Probing DNA - transcription factor interactions using single-molecule fluorescence detection in nanofluidic devices”</p> <p>pre-print: https://doi.org/10.1101/2021.05.12.443786 (BioRxiv)</p> <p>The repository contains the raw data ("*.tif") files, data from subsequent analysis steps performed in Matlab ("*.mat") and obtained histograms.</p> <p>For further information, please contact<br> dr. Johannes Hohlbein @ Wageningen University & Research<br> (johannes.hohlbein@wur.nl)</p>
Spatial reconstruction of the early hepatic transcriptomic landscape after an acetaminophen overdose using single-cell RNA sequencing
<p>We leveraged single-cell RNA sequencing to understand the early molecular events that define the hepatocyte response to acetaminophen exposure at a subpopulation level. We spatially assigned hepatocytes along the portol-central vein axis by using established landmark genes. By spatially assigning the hepatocytes were were able to account for innate differences in gene expression that existed along this gradient. The excel files herein provide the full list of differentially expressed genes between key subpopulations of interest. Additionally, we classified genes as either pericentral zonated, periportal zonated, or non-zonated, the full list of genes and their spatial assignments are included in the appropriate excel file. </p>
Figure 5. Preferred single tree resulting from the analysis under implied weights with concavity constant k in Systematics of the genus Mayazomus (Arachnida: Schizomida): the relevance of using continuous characters and pedipalp setae patterns to schizomid phylogenetics
Figure 5. Preferred single tree resulting from the analysis under implied weights with concavity constant k = 100. Clade support is indicated above (Bremer support values) and below [symmetric resampling values; only significant values (over 50%) are presented] branches. Sensitivity plots ('Navajo rugs') indicate the recovery of the nodes in the analysis under implied weights with different values of k (black squares indicate monophyly; white squares indicate nonmonophyly).
Figure 6. Preferred single tree resulting from analyses under implied weights with concavity constant k in Systematics of the genus Mayazomus (Arachnida: Schizomida): the relevance of using continuous characters and pedipalp setae patterns to schizomid phylogenetics
Figure 6. Preferred single tree resulting from analyses under implied weights with concavity constant k = 100, 60, and 30, and equivalent to one of the most parsimonious trees recovered in the analysis with equal weights and continuous characters. Images on the tree represent the morphological congruence of the clades with the structures pictured (pedipalps and spermathecae). The genus Mayazomus is clearly separated from Rowlandius by the presence of robust pedipalps and trochanters with apical process; the clade (Mayazomus yaax + Mayazomus estor) is supported by the rounded distal margin of the pedipalp femur; the clade (Mayazomus loobil + Mayazomus infernalis) is supported by the presence of a patella curved in a 'U'-shape; and spermathecae with lobes slender and subequal in length; the clade (Mayazomus aluxe + Mayazomus kaamuul) is supported by setae Fv1 and Fv2 located distally and projected, with spermathecal lateral lobes reduced and wider than medial lobes.
Filtering and imaging of frequency-degenerate spin waves using nanopositioning of a single-spin sensor
<p>Data corresponding to the figures of the main text of "Filtering and imaging of frequency-degenerate spin waves using nanopositioning of a single-spin sensor"</p> <p> </p>
Model 4 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"
<p>Model 4 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"</p>
Model 2 dataset for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"
<p>Dataset produced by the model 2 neural network for the manuscript "Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution"</p>
Optimization used in in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>OptimizationResults contain the expanded input model for each data set, in the C field are the indices of the core reactions in the expanded input model used to build the multi-cell population for the 121 different parameters, A contains the indices of reaction in the multi-cell population model for the 121 runs, thresh the parameter setting for the cover and REI. The REI is given in %. So 5 REI means 0.05. While A might change because of alternative optimals when run on a different computer, C and the expanded Input model will remain unchanged.</p> <p> </p> <p>Multi_cell_population_CRC and Multi_cell_population_NM are the models obtained by scFASTCORMICS with the optimal setting for Dataset1 and Dataset2, respectively. </p> <p>Please, check the publication (scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data) for more details.</p>
Single grain crushing tests on two sand types used for wheel-rail sanding
<p>This data set contains the data of single grain crushing tests. Tested were two types of sand, which are used to increase friction in wheel-rail contacts.<br> Please see the following paper for a detailed description of experiments:<br> " Suhr, B.; Skipper, W.A.; Lewis, R.; Six, K. Sanded Wheel–Rail Contacts: Experiments on Sand Crushing Behaviour. <em>Lubricants</em> <strong>2023</strong>, <em>11</em>, 38. https://doi.org/10.3390/lubricants11020038 "</p> <p>Two types of experiments were conducted, both of them were single grain crushing tests. First, initial breakage tests were conducted. Second, single grain crushing tests were conducted under realistic wheel-rail load including subsequent grain/fragment crushing.<br> The material tested was two types of sand used in railways, called AT sand (used in Austria) and GB sand (used in the UK).<br> Both types of sand are tested under dry and wet contact conditions.<br> Please see the referenced paper for details on the conducted experiments.</p> <p> </p>
Quantum-optical excitations of semiconductor nanostructures in a microcavity using a two-band model and a single-mode quantum field
<p>Dataset of the publication "Quantum-optical excitations of semiconductor nanostructures in a microcavity using a two-band model and a single-mode quantum field" H. Rose, A. N. Vasil’ev, O. V. Tikhonova, T. Meier, and P. R. Sharapova, Phys. Rev. A <strong>107</strong>, 013703 (2023). ( https://doi.org/10.1103/PhysRevA.107.013703 ). The zip file includes the data on which the plots shown in figures 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11 are based.</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.