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The raw dataset obtained during performance tests of the proposed surrogate-based global optimization algorithm (KASRA) on all testing problems mentioned in the paper is included and stored in some zipped files. The dataset is relevant to the illustrative examples and Tables 1−4 in sections 5 and 6 of the paper. Besides, all MATLAB plots in fig-format are presented.
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This data set contains experimental data on characterization of the nature-inspired geometry for fluid flow through a solid porous regenerator used in active magentocaloric cooling applications. The data is sorted into several groups: 1. Passive data. This data was collected during the passive testing of 6 regenerators, of which 3 were made out of aluminum and 3 out of magnetocaloric material (MCM). The aim of these experiments was to determine the thermal effectiveness of the regenerators and the pressure drop though them. Since the aluminum regenerators had better surface quality and higher permeability than MCM ones, passive tests allowed to compare performance of different flow channel geometries. The passive testing on MCM regenerators was done in order to determine pressure drop and qualitatively evaluate surface roughness of the flow channels. 2. Active data. In this data set, the raw data from active magnetic testing are stored. MCM regenerators were characterized in a small-scale magnetocaloric demonstrator. The aim of this study was to investigate the behavior of the MCM regenerators under thermo-magnetic cycling. Each of the data files in this group contains full experimental data, but only the last 200s from the recorded data is used for the analysis, since the regenerators reached the periodic steady state condition. 3. Adiabatic temperature change measurements. This experiment was done under heating protocol. The sample and the cabinet were cooled to 273 K. The sample was thermally insulated from surroundings. Once the experiment was started, the cabinet temperature was increased to 302 K. The sample was cyclically magnetized and demagnetized until it reached thermal equilibrium with the cabinet. The data set contains recordings of sample temperature when it was magnetized and demagnetized, as well as induction effect, which must be eliminated before data analysis. 4. Microscopy images. There are microscopy images of randomly selected flow channels of each regenerator. 5. VSM and DSC data. These data are available only in Matlab figure format.
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Basic data
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3x2x2 design factor 1: condition (feature-alone color, feature-alone shape, binding) factor 2: block (two experimental runs/condition/participants) factor 3: group (old vs young participants) In this study, we compared the electrophysiological activity associated with the binding process in young (N=22, mean age=28.5) and old (N=22; mean age=67.4) participants in a change detection task. Analysis of event-related brain potentials (ERP) focused on the differences between feature-alone (color or shape) and feature-conjunction (color-shape) conditions in stimulus encoding. Independently of aging, discrimination ability was significantly attenuated in the binding condition. The effect, however, was more pronounced in old participants. ERP components related to the visual feature detection and processing (posterior N1 and frontal P2) were not modulated in the binding condition. Only in old participants, a late positive ERP component (LPC) was increased signaling the allocation of additional resources.
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Primary analysis files for bioRxiv manuscript with id 2019/859603 (https://www.biorxiv.org/content/10.1101/859603v1) to evaluate how common variant effect prediction methods capture effect determined by deep mutational scanning experiments. 'data' contains the deep mutational scanning data in a parsed format. See the manuscript for the original data sources which would then be processed with parseRawDatasets.py, followed by manual sequence mapping (resulting in the mapped_seqs.txt files) and then be processed with parseScores.py to result in the .npz files. 'predictionData' contains predictions from SIFT, PolyPhen-2, SNAP2 and Envision, parsed into .npz files. Additional folders are for dummy methods and while executing the below scripts. 'analysis' will contain most of the output files. See below for sample calls to reproduce e.g. Figure 1 from the paper. The scripts are written in Python3 and require, among others, numpy, pandas, scipy, sklearn, rpy2, svgutils and matplotlib. For all scripts the --normalization-scheme flag describes how the experimental scores are processed to fit on the same scale of values. The scheme used for the final manuscript is 'wt0_del_scaled' for deleterious effect variants and 'wt0_ben_scaled' for beneficial effect variants. For compareBinaryDMSToPredictions.py the --binarization-scheme flag describes how scores are binarized to neutral/effect. Possible values are the schemes outlined in the manuscript 'syn90', 'syn95' and 'syn99'.
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Deregulated expression of MYC induces a dependence on the NUAK1 kinase, but the molecular mechanisms underlying this dependence have not been fully clarified. Here we show that NUAK1 is a predominantly nuclear protein that associates with a network of nuclear protein phosphatase 1 (PP1) interactors and that PNUTS, a nuclear regulatory subunit of PP1, is phosphorylated by NUAK1. Both NUAK1 and PNUTS associate with the splicing machinery. Inhibition of NUAK1 abolishes chromatin association of PNUTS, reduces spliceosome activity and suppresses nascent RNA synthesis. Activation of MYC does not bypass the requirement for NUAK1 for spliceosome activity, but significantly attenuates transcription inhibition. Consequently, NUAK1 inhibition in MYC-transformed cells induces global accumulation of RNAPII both at the pause-site and at the first exon/intron boundary, but does not increase mRNA synthesis. We suggest that NUAK1 inhibition in the presence of deregulated MYC traps non-productive RNAPII due to the absence of correctly assembled spliceosomes.
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Data for "Does Renewable Energy Consumption Improve Sustainable Development? A Panel ARDL Approach"
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Clay enrichment experiments were carried out with the Chinese national standards “SYT 5163-2010” to semi-quantitative study of each type of clay mineral in samples. Contact resonance (CR) technique was performed to characterize micromechanics of shale after exposure to water. A total of 64 plus A16 test areas in samples of terrestrial shale in the Ordos Basin and of marine shale in the Sichuan Basin were selected for testing. The results indicated that the compositions of the terrestrial and marine shales were significantly different. Moreover, strength-related mechanical properties, such as storage modulus, of shale would decrease after shale interacts with water, but with different contributions from brittle minerals and clay, as well as variations depending on bedding plane orientation.
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The simulated and measured data in toroidal dipole metamaterials.
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The dataset contains information on the currents in the upper 15-meter layer of the Caspian Sea in 2003-2005. The velocity fields were reconstructed in an eddy-resolving ocean general circulation model SZ-COMPAS using a realistic forcing. The data arrays have a very high resolution: ~2 km in space and 4 hours in time. This is sufficient to resolve most of the mesoscale features of sea dynamics as well as a wide range of their temporal spectrum, including inertial oscillations, synoptic and seasonal variability. The NetCDF files are: 3 files with instantaneous currents and 3 files with monthly mean currents (suffix “mm” in the file names). Each file corresponds to one of the three horizons (depths): 1 m, 7 m, and 15 m. In the horizontal plane the data are defined on a uniform geographical grid (46.7625–54.2125°E, 36.5092–47.2892°N), dimensions of all of the arrays are 299 by 589. The period covered by the instantaneous data is 2003, time step is 4 hours; the first record corresponds to 2003-01-01 04:00:00 GMT. The period covered by the monthly mean data is 2003-2005 with one vector field for every month, defined on the last day of the month. All data dimension is cm/s. It should be noted that the model describes the upper 30-meter layer of the sea in sigma-coordinate, so the data values are actually defined on the 1st, 4th, and 8th sigma-horizons, rather than 1 m, 7 m, and 15 m depths. This means that, in the sea cells with bottom depth less than 30 m, the actual depth of the data nodes is ~3%, ~23% and 50% of real water column height, while in the rest of cells (with bottom depth greater than 30 m), the data nodes are located at ~1 m, ~7 m, and ~15 m of depth (± few centimeters depending on the local sea level).
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