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.
2,885
datasets available to search
ShareScore release 0.9.0
Dataset results
2,885 results for “Transfers”
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
Doping of molecular semiconductors through proton-coupled electron transfer
<p>Doping of molecular semiconductors through proton-coupled electron transfer was conducted. Doping levels, electronic properties, and thin film structures of doped polymeric semiconductors were evaluated through conductivity, UV-Vis-NIR absorption, photoelectron yield, x-ray photoelectron, and x-ray diffraction measurements, where proton-coupled electron transfer reaction enable precise control of doping levels depending on pH of doping solutions under ambient conditions.</p>
Taxing reproduction: The full transfer cost of rearing children in Europe
<p>What are the intergenerational resource transfer contributions of parents and non-parents in Europe? Using National Transfer Accounts and National Time Transfer Accounts for twelve countries around 2010, we go beyond public transfers (net taxes) to also value two statistically much less visible transfer types in the family realm: of market goods (money) and of unpaid household labour (time). Non-parents contribute almost exclusively to public transfers. But parents additionally provide still larger private transfers: mothers mainly time, fathers mainly money. Estimating transfer stocks over the working life, the average parental/non-parental contribution ratio flips from 0.73 (public transfers alone) to 2.66 (all three transfers combined). The tax rates implicitly imposed thereby on rearing children are multiples of the value-added tax rates in place on consumption goods. The magnitude of these invisible transfer asymmetries carries multiple implications for policy debates. For instance, it raises the question whether European societies unwittingly tax their own reproduction too heavily.</p>
Accurate TCR-pMHC Interaction Prediction Using a BERT-based Transfer Learning Method
<p>The datasets used for the TABR-BERT. For the complete training and testing code of TABR-BERT, see <a href="https://github.com/Freshwind-Bioinformatics/TABR-BERT">Freshwind-Bioinformatics/TABR-BERT: TABR-BERT: an Accurate and Robust BERT-based Transfer Learning Model for TCR-pMHC Interaction Prediction (github.com)</a>.</p>
Data from: Robustness of Felsenstein's versus transfer bootstrap supports with respect to taxon sampling
<p><span>The bootstrap method is based on resampling sequence alignments and re-estimating trees. Felsenstein's bootstrap proportions (FBP) is the most common approach to assess the reliability and robustness of sequence-based phylogenies. However, when increasing taxon sampling (i.e., the number of sequences) to hundreds or thousands of taxa, FBP tends to return low supports for deep branches. The Transfer Bootstrap Expectation (TBE) has been recently suggested as an alternative to FBP. TBE is measured using a continuous transfer index in [0,1] for each bootstrap tree, instead of the binary {0,1} index used in FBP to measure the presence/absence of the branch of interest. TBE has been shown to yield higher and more informative supports, while inducing a very low number of falsely supported branches.</span> <span>Nonetheless, it has been argued that TBE must be used with care due to sampling issues, especially in datasets with high number of closely related taxa. In this study, we conduct multiple experiments by varying taxon sampling and comparing FBP and TBE support values on different phylogenetic depth, using empirical datasets. Our results show that the main critique of TBE stands in extreme cases with shallow branches and highly unbalanced sampling among clades, but that TBE is still robust in most cases, while FBP is inescapably negatively impacted by high taxon sampling. We suggest guidelines and good practices in TBE (and FBP) computing and interpretation.</span></p>
Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"
<p>Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere".</p> <p>Authors: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023</p> <p> </p> <ul> <li>Figure 1 has been plotted from two data files named Bx_By_Bz_prs_t_4783_C0.vtk and Bx_By_Bz_prs_t_4783_C1.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit</li> <li>Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 3 has been plotted using the data file named "visit_prs_Bx_By_Bz_t_4964s.vtk" and the visualisation toolkit VisIt.</li> <li>Figure 4 has been plotted using the associated data files and the visualisation toolkit VisIt.</li> <li>Figures 5 and 6 has been plotted using the ipython notebook named "new_fig_5_and_6.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb" and the associated data files have been uploaded alongwith. An example swarm CSV data file is added for the python script. The original SWARM data can be downloaded from <a href="https://swarm-diss.eo.esa.int/">https://swarm-diss.eo.esa.int/</a> and the FAC data from two spacecrafts, Swarm A and C, named SW_OPER_FAC_TMS_2F has been used. This data was first published in Dong et.al. 2023 [https://doi.org/10.1029/2022GL102460].</li> </ul>
Dataset and Software: Heat Transfer in Pyroclastic Density Current - Ice Interactions: Insights from Experimental and Numerical Simulations
<p>This repository contains experiment data and executable MATLAB codes for the research article 'Heat Transfer in Pyroclastic Density Current - Ice Interactions: Insights from Experimental and Numerical Simulations'.</p> <p>Contained within the experiment data file Valeetal_Data.Zip are:</p> <ul> <li>Temperature, melt and steam data separated by particle type. See files: <ul> <li>Ballotini_Data_Valeetal</li> <li>Pumice_Data_Valeetal</li> <li>Ruapehu_Data_Valeetal</li> </ul> </li> <li>Melt and steam summary data plots. See file: <ul> <li>Data_Plots_Valeetal</li> </ul> </li> <li>Document with figures to demonstrate system behaviours. See file: <ul> <li>Valeetal_SI01_JGR</li> </ul> </li> </ul> <p>Contained within the model data file Model_Repository.zip are simulation data and codes that implement, process, and plot numerical solutions to the mathematical model. See README.txt for a brief description of each file.</p> <p> </p>
Lateral gene transfer generates accessory genes that accumulate at different rates within a grass lineage
<p><span></span></p> <p>Lateral gene transfer (LGT) is the <span>movement</span> of DNA between <span>organism</span> without sexual <span>reproduction. </span>The acquired genes represent genetic novelties that <span>have</span> independently evolved in the donor<span>'</span>s genome. <span>Phylogenetic</span> methods <span>have</span> shown that LGT is widespread across the entire grass family, although we know little about the underlying dynamics.</p> <p>We identify laterally acquired genes in five de novo reference genomes from the same grass genus (four <em>Alloteropsis semialata</em> and one <em>Alloteropsis angusta</em>). Using additional resequencing data for a further 40 Alloteropsis individuals, we place the acquisition of each gene onto a phylogeny using stochastic character mapping, and then infer rates of gains and losses.</p> <p>We detect 168 laterally acquired genes in the five reference genomes [32-100 per genome]. Exponential decay models indicate that the rate of LGT acquisitions [6-28 per Ma] and subsequent losses [11-24% per Ma] varied significantly among lineages. Laterally acquired genes were lost at a higher rate than vertically inherited loci [0.02-0.8% per Ma].</p> <p>This high turnover creates intraspecific gene content variation, with a preponderance of them occurring as accessory genes in the <em>Alloteropsis</em> pangenome. This rapid turnover generates standing variation that can ultimately fuel local adaptation.</p>
Light-driven reversible charge transfers from ITO Nanocrystals
<p>Complete dataset, comprising:</p> <p>- absorption spectra data, for all redox compounds tested and reference solutions and solvents.</p> <p>- TEM images of nanoparticles used</p> <p>- diffraction pattern of nanoparticles used</p>
Data-driven characterization and correction of the orientation dependence of magnetization transfer measures using diffusion MRI
<p>These are the produced plots for every subjects and every sessions of the study. Here are the characterization plots, the corrected plots, and the tractometry plots.</p>
Dataset from: The Impact of Angular Momentum Loss on the Outcomes of Binary Mass Transfer
<p>Data for the paper: The Impact of Angular Momentum Loss on the Outcomes of Binary Mass Transfer (<a href="https://arxiv.org/abs/2308.06666">https://arxiv.org/abs/2308.06666</a>).</p> <p>Associated data processing scripts can be found on github, at the fixed version linked here: <a href="https://github.com/reinhold-willcox/arXiv.2308.06666_scripts/tree/09b8ece5306d108b5890b74b7890b6e6455fe485">arXiv.2308.06666_scripts</a></p> <p>Each dataset corresponds to a variation in angular momentum loss value or mass transfer efficiency (often both), or in the mass transfer stability prescription. These all include the following COMPAS hdf5 groups: 'BSE_System_Parameters', 'BSE_RLOF', and 'BSE_Supernovae'. They are designed to be used in conjunction with the data processing scripts above, though you are free to try to write your own independent post-processing scripts to test the validity of the presented results. For any questions, please contact the corresponding author. </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>
200 Answers to Wealth Transfer
<p>Information about Wealth and wisdom currency to change the course of how you see wealth in your life. Book Cover Only</p>
Value of Technology to Transfer Discharge Information
ClinicalTrials.gov study NCT00101868. IPD Sharing: Not stated. Countries: 1. Publications: 54.
Proficiency Based Robotics Training Curriculum: Skill Acquisition & Transferability of Skills to Live Porcine Models
ClinicalTrials.gov study NCT02895347. IPD Sharing: YES. Countries: 1. Publications: 9.
Study of Adoptive Transfer of iNKT Cells Combined With TAE/TACE to Treat Unresectable HCC
ClinicalTrials.gov study NCT04011033. IPD Sharing: NO. Countries: 1. Publications: 1.
Gene Transfer for Recessive Dystrophic Epidermolysis Bullosa
ClinicalTrials.gov study NCT01263379. IPD Sharing: YES. Countries: 1. Publications: 3.
Endoscopic Flexor Hallucis Longus Transfer vs Minimally Invasive Repair in Acute Achilles Tendon Rupture
ClinicalTrials.gov study NCT06114368. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Neural Adaptation After Tendon Transfer and Training in Tetraplegia
ClinicalTrials.gov study NCT02768103. IPD Sharing: NO. Countries: 1. Publications: 1.
Gene Transfer for Severe Combined Immunodeficiency, X-linked (SCID-X1) Using a Self-inactivating (SIN) Gammaretroviral Vector
ClinicalTrials.gov study NCT01129544. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.