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311 results for “function dataset”
XPS, HAXPES, XRD and SEM datasets of functionalized graphene nanoplateletes
<p>The datasets from (Hard Energy) X-ray photoelectron spectroscopy, X-ray diffraction and Scanning Electron Microsopy are related to the publication </p> <p>G. Chemello, X. Knigge, D. Ciornii, B.P. Reed, A.J. Pollard, C.A. Clifford, T. Howe, N. Vyas, V.-D. Hodoroaba, J. Radnik</p> <p>"Influence of the morphology on the functionalization of graphene nanoplatelets analyzed by comparative photoelectron spectroscopy with soft and hard X-rays"</p> <p><em>Advanced Materials Interfaces </em>(2023), DOI: 10.1002/admi.202300116.</p> <p>Details of the materials and the experimental procedures are described in this publications. </p> <p>The filenames contain the material (g5 or g6), functionalization (Fluorine -f, Oxygen _o and raw material _raw), methods (XPS/HAXPES survey or high resolution, XRD, SEM). For the fitted spectra the peak is given: c1s, o1s, f1s. </p> <p>The data are given as tif-files for SEM (*tif). as asc ii files for XRD (*.xy) and the fitted XPS/HAXPES-spectra (*.dat) and as vamas data files for the XPS/HAXPES raw-data (*.npl).</p> <p> </p>
Dataset for: Intraspecific size shifts in generalist bumblebees and flowers lead to low functional consequences
<p>Body size is a trait that can affect plant-pollinator interaction efficiency and plant reproductive success. We explored the impact of intraspecific size shifts on the interactions between pollinators and flowering plants under controlled conditions. We considered two development conditions leading to the production of large and small individual flowers of <em>Borago</em> <em>officinalis</em> and <em>Echium</em> <em>plantagineum</em>. We also used the natural variability of worker size within bumblebee colonies to isolate small and large workers. We performed a fully crossed experiment with the two flower sizes of each plant species, and the two sizes of bumblebee workers. Our results show that the size of both partners did not affect bee foraging behavior in most of the evaluated parameters, and both bee sizes were equally efficient in depositing pollen. Significant differences were found only in the pollen deposition across the life of a flower in small flowers of <em>B. officinalis</em>, with the greatest quantity of pollen deposited by small bees. We did not find a relationship between pollinator size and plant fitness. Our results suggest that generalist plant-pollinator interactions may be resilient to future potential mismatches in the size of the partners, but remain to be tested if they are still resilient under the new environmental conditions resulting from global changes.</p>
Dataset of ``Plasma Distribution Solver: A Model for Field-Aligned Plasma Profiles Based on Spatial Variation of Velocity Distribution Functions"
<p>This dataset contains the plasma distribution data in the Jupiter–Io system, calculated from the Plasma Distribution Solver and used for figures in the paper “Plasma Distribution Solver: A model for field-aligned plasma profiles based on spatial variation of velocity distribution functions” by K. Saito et al. (2023).</p> <p> </p> <p>The contents of files ‘all_Case_1.csv’ and ‘all_Case_2.csv’ are as follows:</p> <ul> <li>Position along the magnetic field line (0 at the magnetic equator) [m] (column 1)</li> <li>Distance from the Jovian center [km] (column 2)</li> <li>Magnetic latitude [rad]([degree]) (column 3(4))</li> <li>Magnetic flux density [T] (column 5)</li> <li>The initial condition of electrostatic potential [V] (column 6)</li> <li>The result of electrostatic potential [V] (column 7)</li> <li>Number density profiles [m<sup>-3</sup>] (columns 8-17)</li> <li>Charge density profiles obtained from the integration of velocity distribution functions [C m<sup>-3</sup>] (column 18)</li> <li>Charge density profiles obtained from Poisson’s equation [C m<sup>-3</sup>] (column 19)</li> <li>Convergence value (column 20)</li> <li>Particle flux density [m<sup>-2</sup> s<sup>-1</sup>] (columns 21-30)</li> <li>Mean flow velocity parallel to the field line [m s<sup>-1</sup>] (columns 31-40)</li> <li>Plasma pressure perpendicular to the field line [Pa] (columns 41-50)</li> <li>Plasma pressure parallel to the field line [Pa] (columns 51-60)</li> <li>Plasma dynamic pressure [Pa] (columns 61-70)</li> <li>Perpendicular temperature [J] (columns 71-80)</li> <li>Parallel temperature [J] (columns 81-90)</li> <li>Alfvén speed considering the displacement current term in Ampère’s law [m s<sup>-1</sup>] (column 91)</li> <li>Alfvén speed per the speed of light (column 92)</li> <li>Ion inertial length using averaged mass [m] (column 93)</li> <li>Electron inertial length [m] (column 94)</li> <li>Ion Larmor radius using averaged mass [m] (column 95)</li> <li>Ion acoustic gyroradius using averaged mass [m] (column 96)</li> <li>Electron Larmor radius [m] (column 97)</li> <li>Current density [A m<sup>-2</sup>] (column 98)</li> </ul> <p>The Python codes ‘plot_all.py,’ ‘plot_plasma_beta_comparison.py,’ and ‘plot_Alfven_speed_comparison.py’ can plot Figures 5, 6, 7, and 9 of the paper using the above CSV files.</p> <p> </p> <p>The files ‘boundary_conditions_Case_1.csv’ and ‘boundary_conditions_Case_2.csv’ contain the boundary conditions for Cases 1 and 2.</p> <p> </p> <p>The zip files ‘probability_density_function_Case_1_H_Io.zip’ and ‘probability_density_function_Case_1_H_Jupiter_North.zip’ are zipped CSV files with the same name. The contents of these files are as follows:</p> <ul> <li>Magnetic latitude [degree] (column 1)</li> <li>Perpendicular velocity at the particle position [m s<sup>-1</sup>] (column 2)</li> <li>Parallel velocity at the particle position [m s<sup>-1</sup>] (column 3)</li> <li>Perpendicular velocity at the boundary [m s<sup>-1</sup>] (column 4)</li> <li>Parallel velocity at the boundary [m s<sup>-1</sup>] (column 5)</li> <li>Probability density function [s<sup>3</sup> m<sup>-3</sup>] (column 6)</li> <li>Differential flux per number density [cm<sup>-2</sup> s<sup>-1</sup> sr<sup>-1</sup> keV<sup>-1</sup>] (column 7)</li> </ul> <p>The Python code ‘plot_velocity_distribution_function.py’ can plot Figure 8 of the paper using this CSV file.</p>
MD-Vibe Dataset: Footstep-Induced Floor Vibration Data for Functional Gait Assessment of Individuals with Muscular Dystrophy
<p>The purpose of this dataset is to evaluate the performance of floor vibration sensing in tracking the progression of muscular dystrophy through individuals’ gait patterns. We recruited human subjects (N=36) and conducted experiments at Stanford University and Nationwide Children's Hospital in Columbus, Ohio, with healthy human subjects (N=21) and children with MD (N=15).</p> <p> </p> <p>The experiments consist of two phases - Phase 1 is a pilot study to test the feasibility of floor vibration sensing in capturing gait characteristics in lab settings; Phase 2 has two hospital studies to evaluate our sensing method in real life. This dataset consists of floor vibration data (vertical velocity of floor vibration) induced by 9 healthy subjects’ footsteps from these two phases, named <strong>lab data.zip</strong>, <strong>hospital data 1.zip</strong>, and <strong>hospital data 2.zip</strong>, respectively. The complete dataset requires a data-sharing agreement with Nationwide Children's Hospital.</p> <p> </p> <p>The custom coding scripts (in MATLAB and Python) to process the data are included in the <strong>code.zip</strong> file, which includes 3 processing steps: 1) Preprocessing and Footstep Detection, 2) Feature Extraction, and 3) Model Prediction, the scripts of each step are grouped into a folder with their corresponding names. Detailed function descriptions are included in the scripts.</p> <p> </p> <p>The footstep-induced structural vibration data is stored as both raw data and as individual footsteps. The raw data files start with “raw_”, each consisting of a series of consecutive footsteps (see the sample plot). The individual footstep data files start with “detected_steps_”, each consisting of one single footstep detected from the raw data. The dataset is stored in MAT file format that can be accessed through MATLAB.</p> <p> </p> <p>The sensing unit consists of 5 components: 1) the geophone (SM-24), 2) the amplification module, 3) the processor board, 4) the data acquisition module (NI-Daq), and 5) the power cables. The sensing unit converts the structural vibration velocity into voltage records. The sampling frequency is 500 Hz for the lab study and 25600 Hz for the hospital studies.</p> <p> </p> <p>The experiment setup, sample data plot, and code usage can be found in <strong>Dataset Description.pdf</strong>. For more details about the hospital studies, please refer to the MD-Vibe paper at the following link: <a href="https://doi.org/10.1145/3410530.3414610">https://doi.org/10.1145/3410530.3414610</a></p> <p> </p> <p>Please cite this dataset as:</p> <p>Yiwen Dong, Megan Iammarino, Jingxiao Liu, Jesse Codling, Jonathon Fagert, Mostafa Mirshekari, Linda Lowes, Pei Zhang, and Hae Young Noh. 2023. The MD-Vibe Dataset: Footstep-Induced Floor Vibration Data for Functional Gait Assessment of Individuals with Muscular Dystrophy. Zenodo, DOI: <a href="https://doi.org/10.5281/zenodo.8125704">https://doi.org/10.5281/zenodo.8125744</a></p> <p> </p> <p>Yiwen Dong, Joanna Jiaqi Zou, Jingxiao Liu, Jonathon Fagert, Mostafa Mirshekari, Linda Lowes, Megan Iammarino, Pei Zhang, and Hae Young Noh. 2020. MD-Vibe: physics-informed analysis of patient-induced structural vibration data for monitoring gait health in individuals with muscular dystrophy. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (UbiComp-ISWC '20). Association for Computing Machinery, New York, NY, USA, 525–531. <a href="https://doi.org/10.1145/3410530.3414610">https://doi.org/10.1145/3410530.3414610</a></p>
Metabolomics dataset relating to the pubblication: "Combining CRISPRi and metabolomics for functional annotation of compound libraries"
<p>Metabolomics dataset relating to the pubblication: "Combining CRISPRi and metabolomics for functional annotation of compound libraries"</p>
Datasets and code for "Multi-site transfer function approach for real-time modeling of the ground electric field induced by laterally-nonuniform ionospheric source" by Kruglyakov et al. (2023)
<ol> <li>Archive calculate_weights_for_rt.tgz contains the code for calculation of weights used for computation of electric fields based on multi-site transfer function approach following Kruglyakov et al. (2023). The code is written in Fortran 2003 and the only external dependency is LAPACK/BLAS -compatible library, for example OpenBLAS from https://www.openblas.net. See READ.ME for details.</li> <li>Files GICs*.dat contain observed and modelled geomagnetically induced currents (GICs) at Mäntsälä compressor station in southern Finland (60.6 N, 25.2 E) (https://space.fmi.fi/gic/) for three events (in 2000, 2001, and 2003).</li> <li>Files E_x*. E_y* contain corresponding components of measured (detrended and downsampled from 1s to 10s) and modeled electric fields at sites M02 and M05 from 05:15 to 06:15 UT, 11 Sep 2005.</li> <li>Files MS_TF*.dat contain multi-site transfer functions for different sets of IMAGE magnetometers (based on the data availability during the simulated events) in the frequency domain and the corresponding weights for calculation of electric field in the time domain.</li> <li>File E_to_GICs_W.dat contains coefficients for computation of GICs at Mäntsälä station from electric fields at 18 sites used in the simulation. See Equation (15) of Kruglyakov et al. (2023) for details.</li> </ol> <p> </p> <p> </p>
Dataset - Functional annotation for protein sequences
<p>These are the input and output files for the functional annotation of protein sequences workflow tests.</p>
Supplementary material and dataset for article "Structure-Function Relationship of Iron Oxide Nanoflowers: Optimal Sizes for Magnetic Hyperthermia Depending on Alternating Magnetic Field Conditions"
<p>The supporting information file contains additional measurements compared to the main manuscript of the related article: TEM size histograms, SAED and XRD patterns, absorption and fluorescence spectra, DC magnetization curves, AC hysteresis loops, HR-TEM, their FTT patterns and inverse FFT images after applying a mask in the reciprocal space, and various other plots of this multi-parametric study on the structure-properties relations of magnetic iron oxide nanoflowers. Whenever needed for comparison to other experimental results or theoretical fitting, the raw data of all DC magnetization curves, AC hysteresis loops, ZFC-FC magnetization curves vs. temperature (and their derivatives on temperature) are made freely available in this dataset.</p>
Supplementary datasets for eRNA community and Functional annotations
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The functional significance of tree species diversity in European forests - the FunDivEUROPE dataset
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Trafficking dataset for: A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2
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Dataset for: Intraspecific size shifts in generalist bumblebees and flowers lead to low functional consequences
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Dataset for Ongole at al. - Functional traits predict tree-level phenological strategies in a mesic Indian savanna
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Dataset for: Short flowers for long tongues: functional specialization in a nocturnal pollination network of an asclepiad in long-tongued hawkmoths (Biotropica)
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A specimen-level dataset of functional traits and DNA barcodes for Chinese bees
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Patch clamp dataset for: A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2
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Dataset related to article "Poly(I:C) stimulation is superior than Imiquimod to induce the antitumoral functional profile of tumor-conditioned macrophages."
<p>Macrophage plasticity is the ability of mononuclear phagocytes to change phenotype, function, and genetic reprogramming upon encounter of specific local stimuli. In the tumor microenvironment, Tumor-Associated Macrophages (TAMs) acquire an immune-suppressive and tumor-promoting phenotype. With the aim to re-educate TAMs to antitumor effectors, in this study, we used two immunestimulatory compounds: the TLR7 agonist Imiquimod (IMQ) and the TLR3 agonist Poly(I:C). To better mimic in vitro the response of TAMs, we used Tumor-Conditioned Macrophages (TC-Mϕ) differentiated in the presence of tumor cell supernatants. Our results show that TC-Mϕ respond differently from conventional M2-polarized macrophages. Upon stimulation with IMQ, TC-Mϕ did not upregulate major histocompatibility complex (MHC II) molecules and unexpectedly expressed increased CD206. With both compounds, TC-Mϕ produced higher levels of inflammatory cytokines than M2 macrophages. IMQ and Poly(I:C) differed in the types of regulated genes and secreted mediators. Reflecting their signaling pathways, only IMQ significantly induced IL-1β and IL-6, while only Poly(I:C) stimulated CXCL10, and both upregulated CCL5. Of note, using a novel cytotoxicity assay, Poly(I:C), but not IMQ, was effective in triggering the cytotoxic activity of TC-Mϕ against cancer cells. Overall, the results demonstrate that Poly(I:C) stimulation of TC-Mϕ is superior than IMQ in terms of macrophage re-education toward antitumor effectors.</p>
Ruffed Grouse Resource Selection Function and Survival Datasets
<p>For overwintering species, individuals' ability to find refugia from inclement weather and predators likely confers strong fitness benefits. How animals use their environment can be mediated by their personality (e.g., risk-taking), but does personality mediate how overwintering species select refugia? Snow cover is a dynamic winter characteristic that can influence crypsis or provide below-the-snow refugia. We explored how wintering ruffed grouse (<i>Bonasa umbellus</i>) selected snow roosting sites, a behavior that reduces stress and cold exposure. We linked selection for ~700 roosts with survival of 42 grouse, and showed that grouse generally selected deeper snow and warmer areas. Grouse found in shallow snow were less likely to survive winter. However, individuals with personalities for selecting deep snow improved their survival, suggesting that demographic consequences of selecting winter refugia are mediated by differences in personality<i>.</i> Our study provides a crucial, and seldom addressed, link between personality in resource selection and resulting demographic consequences.</p>
Dataset associated to Electrocatalysis of N-doped carbons in the oxygen reduction reaction as a function of pH: N-sites and scaffold effects
<p>This dataset is associated to the following publication: "Electrocatalysis of N-doped carbons in the oxygen reduction reaction as a function of pH: N-sites and scaffold effects". It contains the raw data of the published article. This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. <a href="https://www.sciencedirect.com/science/article/pii/S0008622319302763#gs1">13/CDA/2213</a>. JAB acknowledges support from the Irish Research Council under Grant No. <a href="https://www.sciencedirect.com/science/article/pii/S0008622319302763#gs2">GOIPG/2014/399</a>. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements No. 748968 (FREMAB) and 799175 (HiBriCarbon). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains.</p>
Palma et al 2021 Plant functional traits reflect different dimensions of species invasiveness. Ecology. DATASET
<p>Dataset used in publication <em>Palma et al 2021 Plant functional traits reflect different dimensions of species invasiveness. Ecology.</em> It includes:</p> <p>(1) records of Vegetative height and Specific leaf area for 82 exotic species measured in to Victoria, Australia. The location where the plant samples were collected is also described. See tab 'Trait_field_collection'.</p> <p>(2) traits and other variables collected from the literature, including Seed mass, Longevity, Reproduction type, Seed morphology, Dispersion vector, Likely introduction pathway, Management. See tab 'Traits_literature'.</p> <p> </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.