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1,557 results for “precision”
Output of ECHAM with radiation code in single precision
<p>We converted the radiation part of the atmospheric model </p> <p>ECHAM to single precision arithmetic. We analyzed different conversion strategies and finally used a step by step change of all modules, subroutines and functions. We found out that a small code portion still requires double precision arithmetic. We made use of Fortran interfaces to generate code that can be easily changed from double to single precision and vice versa, basically using a simple switch in one module. We compared the output of the single precision version in the CR configuration with observational data and with the original double precision code. The results of both versions are comparable. We extensively tested different parallelization options with respect to the possible performance gain, in both CR and LR configuration. The single precision radiation itself can be about 40\% faster, whereas the speed-up for the whole ECHAM model using the single precision radiation can be about 18\% in the best configuration. We further measured the energy consumption, which could also be reduced by about 17\%.</p> <p>This dataset contains the output data of an ECHAM AMIP experiment 1970-2010 with radiation part of the code in single precision arithmetic (in netcCDF file SPFINALDATA.nc). Corresponding output of a original double precision run is also provided (in netcCDF file DPFINALDATA.nc).</p> <p>The files <em>rad_dp_to_sp.sh</em> and <em>rad_sp_to_dp.sh</em> are conversion shell scripts that perform the necessary changes in the source code.</p> <p> </p>
Supplementary Datasets for the Paper "A new view of seismicity under Mt. Etna volcano, Italy, 2014-2023 from multi-scale high-precision earthquake relocations"
<p>Supplementary Datasets for the Paper <br><strong>Mapping finite-fault earthquake slip with spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax, Tiziana Tuvè, Elisabetta Giampiccolo, Ornella Cocina<br>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/xxxx</a></p> <p><strong>20240724A_Etna_Seismicity_2014-2023_INGV-OE_NLL-SC.csv</strong> is the catalog of NLL-SC relocations presented in the paper in CSV (.csv) format.</p> <p><strong>File_S1_catalog_config_run.zip</strong> includes the relocated NLL-SC catalog in CSV (.csv) and NLL-Hypocenter (.hyp) formats, along with pick data, configuration and other files used to run the NLL-SC relocations presented in the paper.</p>
Software, Dataset, and Techreport: Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration
<p>This upload contains a techreport titled "Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration" together with the software (with documentation) and dataset generating the results. The software is also available on GitHub at https://github.com/croci/mpfem-paper-experiments-2024/ . The GitHub version may be updated in the future. This upload corresponds to commit number 8506dd368b84655201c8c72b1307239b9b4e43fd . See README.md file for installation instructions. The manuscript is also available on the arXiv: https://arxiv.org/abs/2410.12614.</p>
Dataset for "Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia"
<p><strong>Article: Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia</strong></p> <p><em>Cancer Discovery</em>, <strong>DOI:</strong> 10.1158/2159-8290.CD-21-0410</p> <p> </p> <p>Data Types:</p> <p>1. Clinical summary</p> <p>2. Drug response data</p> <p>3. Exome-sequencing data</p> <p>4. RNA-sequencing data</p> <p> </p> <p><strong>Updates:</strong></p> <p>- <strong>FILE</strong>: File_3.2. <strong>DATE</strong>: 28.11.2022.</p> <p> </p> <p><strong>1. Clinical summary</strong></p> <p><strong>File_0: </strong>Common sample annotation including patient and sample IDs, stage of the disease, tissue type and availability of different data types.</p> <p><strong>File_1.1: </strong>Clinical data for 186 AML patients including clinical diagnosis, disease classification, gender, age at diagnosis, treatments, cytogenetic and molecular details. The description of the variables/column titles is given below the clinical data.</p> <p><strong>File_1.2</strong>: Description of the clinical variables in File_1.1.</p> <p> </p> <p><strong>2. Drug response data for 164 AML patient samples and 17 healthy samples</strong></p> <p><strong>File_2: </strong>Drug library details for 515 chemical compounds. The compound collection includes drugs names, drug class defined by molecular targets or mode of action, concentration range used for drug testing, supplier information, solvent information and vendor information.</p> <p><strong>File_3.1.: </strong>Drug response data including selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The DSS is modified area under the curve values and are calculated as shown in Yadav et al publication (1). The selective drug sensitivity scores (sDSS) is healthy control normalized DSS that gives estimated cancer-selective drug responses. The higher the sDSS values indicate drug sensitivities and negative sDSS values represent drug resistance.</p> <p><strong>File_3.2.: </strong>Drug response data including drug sensitivity scores (DSS) and selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The data is identical to the Supplementary Table 7 in the manuscript.</p> <p><em>Note: We recommend using selective DSS values instead of raw values (% inhibition, IC50, DSS). </em></p> <p><em>Note: If the value is missing, </em><em>the drug was not tested for </em><em>that</em><em> given sample</em><em>.</em></p> <p><strong>File_4: </strong>Drug sensitivity and resistance testing (DSRT) assay details for 181 samples (164 AML patient samples and 17 healthy control samples). The information includes medium (MCM or CM) used for the drug testing, % cell viability after 72 h without drug testing and blast cell percentage of each sample.</p> <p><em>Note: Column E is </em><em>the ratio of luminescence values at 72 h and 0 h. The fold change in the cell viability without drug treatment was calculated as % cell viability. That is why the value could be more than 100% e.g. 70% cell viability meaning that 30% cells died during 72 h and 300% cell viability meaning that cells grew 3 times in 72 h incubation period.</em></p> <p> </p> <p><strong>3. Exome-sequencing data for 225 AML patient samples</strong></p> <p><em>Note: The number of samples in the manuscript is 226. The correct number used in the analyses is 225.</em></p> <p>Mutation data. The cancer specific gene list was prepared by combining AML related genes from TCGA(2) (n=23), InToGen(3) (n=32), Papaemmanuil et al.(4) (n=111) and Census database(5) (n=616). Out of these genes, we found 340 genes as mutated across 225 AML patient samples. The mutation was called with P-values less than 0.05.</p> <p><strong>File_5: </strong>VAF (variant allele frequency) of 340 cancer-specific genes across 225 AML patient samples. The VAF was calculated using paired skin samples as a control from the same AML patient.</p> <p><strong>File_6:</strong> Binary data for 57 cancer specific genes frequently mutated (a given mutation detected in 5 or more samples) across 225 AML patient samples.</p> <p> </p> <p><strong>4. RNA-sequencing data for 163 AML patient samples and 4 healthy</strong></p> <p>CPM (count per million) data: The CPM values are batch corrected values used for direct comparison of gene expression.</p> <p><strong>File_7:</strong> Log2CPM values for 18,202 protein coding genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p><strong>File_8: </strong>Raw read count data RNA-seq library information for all 60,619 genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples). The raw read count data was used to calculate differential gene expression.</p> <p><strong>File_9: </strong>RNA-seq library information including RNA extraction method and sequencing library preparation information for 167 samples (163 AML patient samples and 4 healthy CD34+ samples).</p> <p> </p> <p><strong>References</strong></p> <p>1. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R<em>, et al.</em> Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports <strong>2014</strong>;4:5193.</p> <p>2. Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A<em>, et al.</em> Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med <strong>2013</strong>;368(22):2059-74.</p> <p>3. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A<em>, et al.</em> IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods <strong>2013</strong>;10(11):1081-2.</p> <p>4. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND<em>, et al.</em> Genomic classification and prognosis in acute myeloid leukemia. New England Journal of Medicine <strong>2016</strong>;374(23):2209-21.</p> <p>5. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N<em>, et al.</em> COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research <strong>2019</strong>;47(D1):D941-D7.</p> <p> </p>
High precision photogrammetry data of Lascar Volcano acquired by UAS survey in 2017 and 2020
<p>Here we present a high precision photogrammetry dataset of Lascar summit crater, which acquired by unmanned aircraft system (UAS) in Nov 2017 and Feb 2020 respectively, and reconstructed by Structure-from-Motion (SfM) method. In which, optical orthomosaic and DEM (Digital Elevation Model) were processed in Agisoft Metashape (version 1.7.3), preprocessing of thermal data was conducted in Thermoviewer (v3.0.7) and thermal mosaic was generated by Pix4Dmapper (v4.5.6). All data was projected to global coordinates (WGS 1984 UTM Zone 19 South). Thermal orthomosaic was georeferenced to 2020 orthomosaic in ArcMap (version 10.8). Employed UAS and SfM-derived product are given as follows:</p> <p>(1) 2017 orthomosaic (7.7 cm/pix) and DEM (15.6 cm/pix): DJI Mavic Pro Platinum </p> <p>(2) 2020 orthomosaic (7.0 cm/pix) and DEM (13.7 cm/pix): DJI Phantom 4 RTK</p> <p>(3) 2020 additional orthomosaic (5.3 cm/pix): DJI Mavic 2</p> <p>(4) 2020 thermal orthomosaic (spatial resolution: 45.0 cm/pix, radiometric resolution: 0.04 degree/pix): FLIR Tau 2 640 attached to DJI Phantom 4 RTK</p>
[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria
<p><strong>Highlights:</strong></p> <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul> <p> </p> <p><strong>Collector array description: </strong>The data is from a flat plate collector array with a total gross collector area of 516 m<sup>2</sup> (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.</p> <p> </p> <p><strong>Data files:</strong></p> <ul> <li><strong>FHW_ArcS__main__2017.csv</strong> – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li><strong>FHW_ArcS__main__2017.parquet</strong> – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li><strong>FHW_ArcS__parameters.json</strong> – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li><strong>FHW_ArcS__raw__2017.csv </strong>– Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul> <p> </p> <p><strong>Scripts: </strong></p> <ul> <li><strong>make_data.py</strong> – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li><strong>make_plots.py</strong> – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul> <p> </p> <p><strong>Data collection and preparation</strong>: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria</p> <p> </p> <p><strong>Data owner</strong>: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria</p> <p> </p> <p><strong>Additional information</strong> is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.</p> <p> </p> <p><strong>Note: </strong>A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a></p> <p> </p>
NLL-SSST-coherence high-precision earthquake location catalog for the 2023 Ojai, California earthquake sequence
<p><strong>Hypocenter catalog files and visualizations of high-precision, NLL-SSST-coherence earthquake locations for the 2023 M5.1 Ojai, California earthquake sequence and background seismicity (2128 events, 1980-01-01 to 2023-08-25).</strong></p> <p>NLL-SSST-coherence (<a href="https://doi.org/10.1029/2021JB023190">Lomax and Savvaidis, 2022</a>; <a href="https://doi.org/10.26443/seismica.v2i1.324">Lomax and Henry, 2023</a>) is an enhanced, absolute-timing earthquake location procedure which 1) iteratively generates spatially varying travel-time corrections to improve multi-scale location precision and 2) uses waveform similarity to improve fine-scale location precision.</p> <p>Relocations performed with phase arrival data available from <a href="http://service.scedc.caltech.edu">http://service.scedc.caltech.edu</a></p> <p>Visualizations include topography from <a href="https://opentopography.org">https://opentopography.org</a> and surface fault traces from <a href="https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=5a6038b3a1684561a9b0aadf88412fcf">https://usgs.maps.arcgis.com</a></p> <p> </p> <p>This repository contains:</p> <p><strong>Full catalog in CSV format</strong>:<br> CSV file data columns correspond to selected fields of the of NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Full catalog in NonLinLoc hyp format</strong>:<br> NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Key NLL-SSST-coherence configuration files</strong>: NLL-SSST-coherence_config/*</p> <p><strong>Selected Visualization images</strong></p> <p> </p>
Precise Lifetime Measurement of the Cesium 5²D₅⸝₂ State
<p>This repository contains data and software related to an experiment in which we determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state using atoms in a vapor cell. More information is available in the following paper:</p> <ul> <li>arXiv:1912.10089</li> </ul> <p>We provide the data and Python scripts for data evaluation in six folders. We zipped these folders with Windows 10 Enterprise, Version 1903. In the following, we describe how to use data and scripts to get the lifetime results published in our paper.</p> <p> </p> <p><strong>Raw Time-Tags</strong></p> <p>Here, we provide the raw measurement data. We perform several experiment cycles. An excitation laser is switched on at the beginning of each cycle. In the middle of the cycle, it is switched off. We use two single-photon counting modules (SPCM): one detects fluorescence photons emitted by the atoms, the other reference light from the excitation laser beam. We record the arrival times of those photons with respect to the beginning of the cycle. These time delays can be used to create a histogram and to determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state.</p> <p>For each measurement, we provide two data files which are encoded in ‘UTF-8’:</p> <ul> <li>‘figx_xxx_reference_time_tags.dat’</li> <li>‘figx_xxx_fluorescence_time_tags.dat’</li> </ul> <p>where ‘figx_xxx’ is a unique tag indicating the figure and point to which this data corresponds in our paper. The ‘figx_xxx_fluorescence_raw_data.dat’ and ‘figx_xxx_reference_raw_data.dat’ files contain the raw time delays in picoseconds of the fluorescence and the reference photons, respectively.</p> <p>We provide raw time delays in the following folders:</p> <ul> <li>‘fig3_time_tags’: The data used in figure 3.</li> <li>‘fig4_time_tags’: The data used in figure 4. This folder has six subfolders, named ‘point_x’, where x indicates to which point of figure 4 the data belongs. The data of the subfolders ‘point_x_y’ was used for points x and y of figure 4 (the time-tags of the fluorescence photons were split into two sub-datasets with equal size).</li> <li>‘fig5_time_tags’: The data underlying figure 5. This folder has subfolders from ‘23C’ to ‘116C’ where the name indicates the temperature in units of °C of the vapor cell during the measurement. Note that the various measurements have different cycle lengths because reabsorption makes the decay of the fluorescence signal longer. For the lifetime value at a temperature of 23 °C, we used the lifetime which we found in figure 4. For some measurements, the time-tags of the reference SPCM are missing because only one SPCM was available for these measurements.</li> </ul> <p> </p> <p><strong>Histograms</strong></p> <p>Since the files of the raw measurement data are large, we also provide histograms of the time tags. For all datasets discussed above, we generated a histogram with a bin length of 5 ns. We save these histograms with the same file name as the files with the raw time tags but with the ending ‘_histo’ instead of ‘_time_tags’, e.g., ‘fig3_fluorescence_histo.dat’ and ‘fig3_reference_histo.dat’.</p> <p>We always provide two file formats:</p> <ul> <li>a data file (.dat), containing rows with the start time of a bin in microseconds, and the number of SPCM counts due to the fluorescence signal until the start of the next bin, separated by a comma. These files are encoded in ‘UTF-8’.</li> <li>a NumPy compressed array format file (.npz), which includes two arrays: The first array is called ‘time’ and contains the starting times of the bins. The second array is called ‘counts’ and includes the corresponding measured number of fluorescence photons per bin. It is possible to load the arrays into a Python script with numpy.load (tested with NumPy version 1.18.1).</li> </ul> <p> </p> <p><strong>Additional Information on the Measurements</strong></p> <p>We provide a JavaScript Object Notation file (.json) for each measurement. These files provide the following information about every measurement: temperature of the cell, number of detected photons, photons per cycle, and the total measurement duration. They are named ‘figx_xxx_info.json’, where ‘figx_xxx’ is the same indicator as discussed in section ‘Raw Time-Tags’.</p> <p> </p> <p><strong>Scripts</strong></p> <p>This folder contains two sample scripts to illustrate how our data can be processed with Python. The first Python script (generate_histograms.py) generates a histogram of the photon arrival times. The second Python script performs a fit in order to determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state. We wrote these scripts with Python 3.6.5. To avoid errors, one should download all zipped folders and extract them to the same folder.</p> <ul> <li>The script ‘generate_histograms.py’ processes the fluorescence photon detection events stored in the folder ‘fig3_time_tags’. The file ‘fig3 _fluorescence_time_tags.dat’ is read into the script, and a histogram is generated. To run the script, the following Python libraries are required: NumPy (version 1.18.1), os (version 0.1.4), and json (version 2.0.9).</li> <li>The script ‘fit_data.py’ loads the file ‘fig3_fluorescence_histogram.npz’ from the folder ‘histograms\ fig3_time_tags’ in NumPy arrays. We perform a least-square fit on the histogram of the fluorescence decay. From the fit, we get the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state. Optionally, it is possible to print a fit report and to plot the fit with its residuals. The following Python libraries are required to run the script: NumPy (version 1.18.1), os (version 0.1.4), json (version 2.0.9), pyplot from matplotlib (version 3.1.1), and Parameters, ExponentialModel, and ConstantModel from LmFit (version 1.0.0).</li> </ul> <p> </p> <p><strong>Figures</strong></p> <p>In the folder ‘figures’, we provide the values of the points which we used to generate figure 4 and figure 5. For both figures, we made a JavaScript Object Notation file (.json) where the data of every point is stored in a dictionary. This data contains the fit result of the lifetime and the temperature of the measurement. Additionally, it contains the corresponding errors and the units of every value.</p>
Dataset for Supporting Information of the paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing"
<p>This dataset includes all results presented in the "Supporting information" of the paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing", published in Advanced Materials Technologies, vol.5, 2000659, 2020<br> DOI: 10.5281/zenodo.4099806, DOI: <a href="https://doi.org/10.1002/admt.202000659">10.1002/admt.202000659</a><br> URL:<br> https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fadmt.202000659&file=admt202000659-sup-0001-SuppMat.pdf</p> <p>List of data in this dataset:<br> Fig.S1-Theoretical Analysis.xlsx<br> Fig.S5-LM Coils Folding-Exp and NA.xlsx<br> Fig.S6-CoilFoldingDataARC.xlsx<br> Fig.S7-Cyclic Bending-1000 cycles.xlsx<br> Fig.S8-Cyclic Folding of FPC and LM Coils.xlsx</p> <p>All the data included in this dataset were collected and processed by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
Supplementary Video: Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing
<p>Supplementary Video for Adv. Mater. Technol., DOI: 10.1002/admt.202000659<br> Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing<br> H. Wang,* M. Totaro, S. Veerapandian,M. Ilyas, M. Kong, U. Jeong, L. Beccai*</p> <p>This video (.MP4) includes the following supporting movies:</p> <p>Movie S1. FE modeling of planar coil folding and bending<br> Movie S2. Numerical analysis of planar coil folding and bending<br> Movie S3. Dynamic bending test of FPC coil<br> Movie S4. Dynamic folding test of LM coil<br> Movie S5. Vibration detection with a folded FPC coil<br> Movie S6. Self-sensing origami<br> Movie S7. Sensorized soft pneumatic actuator<br> Movie S8. Wearable sensing</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
T-DNAreader: fast and precise identification of T-DNA insertion sites using RNA-seq data
<p><em><span>Agrobacterium</span></em><span>-mediated plant transformation, which enables the delivery of DNA using transfer DNA (T-DNA) binary vectors, is an essential technique in plant research. T-DNAs randomly integrate into the host genome and multiple T-DNAs can integrate in a single transformation event, necessitating the development of efficient, reliable tools to identify the T-DNA insertion sites (TISs). Here, we developed T-DNAreader to identify TISs from RNA-sequencing (RNA-seq) data with high precision, sensitivity, and speed, outperforming existing tools. T-DNAreader detected previously unknown TISs in characterized mutant plants. Overall, T-DNAreader enables the simple identification of TISs within transcribed regions and standardizes the characterization of T-DNA-containing transgenic plants.</span></p>
Data and code for the paper "Precision Groundwater Modeling: when cokriging meets evolutionary and iterative algorithms"
<ul> <li>exemplary dataset for 2019 yearly water table measurements in Northeaster Italy</li> <li>MATLAB code for the pre-processing GA-driven and the post-processing iterative validation part</li> </ul>
Raw data of "Proximity-Induced Superconductivity in Atomically Precise Nanographene on Ag/Nb(110)"
<p>E.M., R.P., and W.W. designed the experiments. P.Z., S.-X.L., R.H., and SD synthesized the molecule. J.-C.L. performed STM/AFM experiments and analyzed the data. W.W. provided the dilution STM and H.C. assisted the measurement. X.W. and U.A. performed the DFT calculations. J.-C.L. wrote the manuscript with the help of R.P. All authors discussed the results and revised the manuscript.</p>
Fecal-bbu-genes-quantification-predicts-L-carnitine-mediated-TMAO-production-and-serves-as-a-biomarker-for-precision-nutrition-code-20231201
<p>Custom code related to the original research article "Fecal bbu genes quantification predicts L-carnitine-mediated TMAO production and serves as a biomarker for precision nutrition"</p>
High-precision body mass estimators for small mammals: A case study in the Mesozoic
<p>Body mass is a pivotal quantity in palaeobiology but must be estimated from an imperfect fossil record. We analyse the precision of skeletal predictors of mammalian body mass as a mean to inform the Mesozoic mammal record, including a new eutriconodont from North America. We focus on the critical small end of the size spectrum – critical because the earliest mammals were small, because small size persisted onto the stems of the major extant radiations, and because small mammals compose a large proportion of crown diversity. Linear regressions based on extant small mammals indicate a universal correlation of body mass with observed measurements, but with clear differences in precision. Postcranial predictors outperform jaw and dental metrics, with certain femoral joint dimensions providing surprisingly precise estimations. Overall, our data indicate small-mammal evolution during the Mesozoic unfolded in patterns of underappreciated complexity. Studying these dynamics is only possible when estimating body mass within a strict, highly focused phylogenetic context. The heuristic value of the estimators we provide here are not limited to the Mesozoic but are phylogenetically justified for any small-bodied mammal regardless of age.</p>
Precise determination of lightning plasma parameters based on the collisional-radiative model
<p>Fine spectrum of lightning have been simulated by collisional-radiative model (CRM), and the radiation of core channel is mainly dominated by N II ion. The line-intensities<br>of N II dependent on electron temperature (Te) and electron density (ne) were obtained. And it is found that the intensity proportion of 500.52 nm in the 500.5 nm multiple spectral lines and that of 567.96 nm in 568.0 nm one, i.e., I(500.52nm)/ I(500.5multiple) and I(567.96nm)/ I(568.0multiple), tend to stabilize in the Te-ne variation space, which is an inherent physical property that can be applied in determining the experimental intensity of characteristic line from the total intensity of overlapped lines. Through accurately matching the theoretical intensityratio with the experimental one, e.g., I(500.52nm)/ I(444.70nm) and I(567.96nm)/ I(444.70nm), an intersection of contours of line-intensity ratios in the two-dimensional plane of Te-ne is derived, and then, the Te and ne in lightning channel were diagnosed simultaneously. Precise spectral analysis method and plasma diagnostic technique are the basis of researching the transmission characteristics of lightning discharge, this work also has application value in articially triggered lightning, laser-guided lightning, arc discharge, etc.</p>
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility - Dataset
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.</p>
Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnosis of Depression: Data and code
<p><strong>In the archive are the datasets and code used to create the results in article "Application of Mass Multivariate Analysis on Neuroimaging Data Sets fo Precision Diagnosis of Depression". The methods is based on the Multivariate Linear toolbox mad F. Kherif’s lab and available on GitHub. <a href="https://github.com/LREN-CHUV/MLM">https://github.com/LREN-CHUV/MLM</a>. </strong></p> <p><strong>A total of 44 patients with a current psychotic (n=19) or depressive (n=25) episode were analyzed by MLM (details can be found in the article itself) utilizing resting sate fMRI, tasks fMRI, and anatomical T1w images. </strong></p> <p><strong>The data results are provided in MATLAB format, in a matrix called MLM.mat. The matrix includes the canonical variables for each of the three imaging modalities, chi-square statistics, and p-values for each brain region. Additionally, we provided a function MLM_plot_fun that allows mapping the statistics results to a canonical three-dimensional brain as per the article. </strong></p>
Molecular Signatures of Tumour and its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An Interna-tional Multi-cohort Diagnostic Validation Study
<p><strong>Supplementary Materials: </strong>The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, <strong>Table ST1</strong> – qMIDS<sup>V2 </sup>Gene panel primer sequences; <strong>Figure S1</strong> – qMIDS<sup>V1</sup> vs qMIDS<sup>V2</sup> 384-well assay format and protocols; <strong>Figure S2.</strong> Individual target gene expression pattern in 1761 samples; <strong>Figure S3.</strong> Various statistical methods used for gene selection analysis on 1761 clinical samples; <strong>Figure S4. </strong>Diagnostic performance comparison between qMIDS<sup>V2</sup> vs qMIDS<sup>V2* </sup>(with 4 less effective genes removed from the panel of 14 target genes of qMIDS<sup>V2</sup>); <strong>Figure S5</strong>. Effect of removing individual genes from the 14-target gene panel qMIDS<sup>V2</sup> (qV2) on diagnostic test performance based on the UK patient cohort data.</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.