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849 results for “linear”
Data and code for "A Linear Time Solution to the Labeled Robinson-Foulds Distance Problem"
<p><strong>Data and code for "A Linear Time Solution to the Labeled Robinson-Foulds Distance Problem"</strong></p> <p>Samuel Briand, Christophe Dessimoz, Nadia El-Mabrouk, Yannis Nevers</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The __ALF\_Output__ directory contains the results obtained from ALF with parameters specified in the paper, as well as additional files generated in the downstream analysis (see below)</p> <p>The __Partitions__ directory contains one directory by partitioning of the 100 species from ALF in nested sets. Each contains three folder and a file.</p> <p> The summary.txt directory report which family are part of the nested set.</p> <p> The Allfamily directory contains the FASTA file of the 100 gene families generated with ALF, with only the species selected in the partition.</p> <p> The Aln directory contains the MSA for each gene family as generated with MAFFT with the selected species set</p> <p> The FTree directory contains the gene tree for each family as generated with FastTree with the selected species set</p> <p>The __Script__ directory containst the files used to generated the data from the ALF directory, as well as downstream analysis</p> <p> </p> <p> </p> <p>To reproduce the results start by runing __rewriteSeq.py__ , which is used for generating the Partitions. It takes as parameter the ALF directory, the directory in which you wish to generate the partitions, and the path to ALF's genomes FASTA files. If the partition file already exist, you can use the -r option to redo the random selection, otherwise it will generate file for the previous random selection. </p> <p>Example command :</p> <p>python rewriteSeq.py -i ../ALF\_output -o ../Partitions -g ../ALF\_output/DB</p> <p> </p> <p>Then, by runing __rewriteTree.py__ you will generated the reference trees used for the RF comparisons, as well as species tree used for each partitions. It takes as parameters the ALF directory , the Partitions directory and the species file of the partitions used to create the reference tree (smallest of all partitions)</p> <p>Example command :</p> <p>python rewriteTree.py -i ../ALF\_output/ -p ../Partitions/ -s ../Partitions/Part10/summary.txt</p> <p> </p> <p>Then, the script __launchFastTree.sh__ will, by partitions, generate a MSA using MAFFT and a phylogenetic tree using FastTree. It takes as parameter the Partitions directory and the number of the identifier of the partition for which you wish tu run it. Notes that the afforementionned software need to be installed before hand.</p> <p>Example command:</p> <p>bash launchFastTree.sh ../Partitions 10</p> <p> </p> <p>Finally, the __LRFAnalysis.ipynb__ file is a Jupyer Notebook used to run downstream analysis of RF and LRF on the different Partitions, including figure generation. Path to the data directory can be set in the 4th block of the Notebook.</p> <p> </p> <p><strong>Comparison of RF, LRF, and ELRF</strong></p> <p> </p> <p>The code to compare is provided as a Jupyter notebook in the directory "Comparison with RF and ELRF". The input NOX4 family from Ensembl version 99 is provided. The output figures are provided as PDF but they can be regenerated by running the notebook.</p> <p> </p>
Flow-to-fracture transition of linear Maxwell-type vs. yield strength fluids by air injection – implications for magma fracturing
<p>Datasets for Sánchez et al., Flow-to-fracture transition of linear Maxwell-type vs. yield strength fluids by air injection – implications for magma fracturing, accepted on Dec 16, 2022, in Geophysical Research Letters.</p>
Summary ouput data - Wasteaware Cities Benchmark Indicators - WABI 2023 - Global data analytics - Machine learning vs. Non-linear Regression
<p>This is the output dataset for the research publication "<em>Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning</em>". It features </p> <ul> <li>Metadata info used by R codes</li> <li>Summary of results for two modelling approaches (machine learning: Conditional random-forest and non-linear regression)</li> </ul> <p>The independent variables dataset analysed here refer to specific indicators of the WABI methodology (<a href="https://www.sciencedirect.com/science/article/pii/S0956053X14004905">https://www.sciencedirect.com/science/article/pii/S0956053X14004905</a>) that generates solid waste management and resource recovery profiles for cities. It was applied here for 40 cities around the world. The data input are available here: 10.5281/zenodo.7570174</p>
Figure 4. a, linear regression illustrating the relationship between log10 stride length and log10 stride speed. b in Morphological and performance modifications in the world's only marine lizard, the Galápagos marine iguana, Amblyrhynchus cristatus
Figure 4. a, linear regression illustrating the relationship between log10 stride length and log10 stride speed. b, linear regression illustrating the relationship between and log10 stride frequency and log10 stride speed for iguanids.
Figure 3. a, linear discriminant function illustrating shape variation between iguanids. Kernel density ellipses for each species illustrate 90 in Morphological and performance modifications in the world's only marine lizard, the Galápagos marine iguana, Amblyrhynchus cristatus
Figure 3. a, linear discriminant function illustrating shape variation between iguanids. Kernel density ellipses for each species illustrate 90% and 70% of the data distribution. b, graph of morphometric trait loadings from LD analysis.
Supplementary dataset for paper: "Approximate non-linear model predictive control with safety-augmented neural networks"
<p>Supplementary dataset for paper Henrik Hose and Johannes Koehler and Melanie N. Zeilinger and Sebastian Trimpe "Approximate non-linear model predictive control with safety-augmented neural networks".</p> <p>The code to use this dataset is publicly available at <a href="https://github.com/hshose/soeampc">https://github.com/hshose/soeampc</a></p> <p>The dataset contains training and testing data to train an NN controller for three standard benchmark systems, a stir tank reactor, a quadcopter, and a chain mass system.</p> <p>For each system, there are initial conditions as comma separated value in the `x0.txt` file, the MPC input trajectory in the `U.txt` file and the corresponding predicted state sequence in the `X.txt` file. MPC parameters are provided for each system. The dataset was computed using acados for SQP solving.</p> <p>The dataset also contains pretrained neural network approximations of the dataset.These are provided in the `pretrained_models.zip` file. The neural networks were trained with tensorflow.</p>
TCHES artefact dataset for paper : Efficient Regression-Based Linear Discriminant Analysis for Side-Channel Security Evaluations
<p>This dataset allows reproducing the results of the CHES 2023 paper : "Efficient Regression-Based Linear Discriminant<br> Analysis for Side-Channel Security Evaluations".</p> <p>It contains the side-channel measurements necessary to do so.</p> <p>Scripts and readme is available at the CHES artefact site : <link not yet alive></p> <p> </p>
FIGURE 5. Linear discriminant analysis plot that summarizes the total variation among five Ceratozamia species into two axes. Biplots A–I in Ceratozamia rosea (Zamiaceae): A new species from the Northern Mountains of Chiapas, Mexico
FIGURE 5. Linear discriminant analysis plot that summarizes the total variation among five Ceratozamia species into two axes. Biplots A–I correspond to traits listed in Table 2. Abbreviations: C. becerrae (bec), C. miqueliana (miq), C. rosea (ros), C. sancheziae (san), and C. zoquorum (zoq).
The linear baroclinic model ouput forced by Indian summer monsoonal diabatic heating.
<p>The linear baroclinic model (Watanabe and Kimoto 2000, 2001) ouput forced by Indian summer monsoonal diabatic heating. The LBM source code can be requested via https://ccsr.aori.u-tokyo.ac.jp/~lbm/sub/lbm.html.</p> <p>Forcing file: ISM_forcing.nc<br> Simulation output files: LBM_T42_xxx.nc; each file denotes different monthly mean atmohpsheric basic state is used from May to September.</p> <p>The data are generated and analyzed in the following study:</p> <p>Li, S., Sato, T., Nakamura, T. <em>et al.</em> East Asian summer rainfall stimulated by subseasonal Indian monsoonal heating. <em>Nat Commun</em> <strong>14</strong>, 5932 (2023). https://doi.org/10.1038/s41467-023-41644-5</p>
A new non-linear instability for scalar fields
<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "A new non-linear instability for scalar fields" (<a href="https://arxiv.org/abs/2107.14215">https://arxiv.org/abs/2107.14215</a>).</p> <p><br> This paper has also been published as a <em>letter </em>in Physical Review D and can be found in Volume 105, Issue 2, article id.L021304, which can be accessed at this link: <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.L021304">https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.L021304</a>. </p> <p>Directories</p> <ul> <li><strong>Codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution and Latfield2.</li> <li><strong>Simulations_data</strong>: This directory includes the data for the scalar field snapshots, information about the average of the scalar field, and power spectra.</li> <li><strong>Figure_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>Figure_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "codes" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "data" directory to access the simulation data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
Parametrising non-linear dark energy perturbations
<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Parametrising non-linear dark energy perturbations" (<a href="https://arxiv.org/abs/1910.01105">https://arxiv.org/abs/1910.01105</a>).</p> <p><br> This paper has also been published JCAP and can be found in Volume 2020, April 2020, which can be accessed at this link: <a href="https://iopscience.iop.org/article/10.1088/1475-7516/2020/04/039">https://iopscience.iop.org/article/10.1088/1475-7516/2020/04/039</a>.</p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory contains k-evolution code used to generate and post-process the simulation data.</li> <li><strong>notebooks_data</strong>: This directory includes the data and jupyter notebooks to reproduce the figures presented in the paper.</li> <li><strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>notebooks_data</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "<strong>notebooks_data</strong>" directory to access the simulation data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
Data from: 'Potential non-linearities in the high latitude circulation and ozone response to Stratospheric Aerosol Injection'
<p>Data used in: 'Potential non-linearities in the high latitude circulation and ozone response to Stratospheric Aerosol Injection' by Bednarz et al., 2023, submitted to Geophysical Research Letters. </p>
Publication data for article "Scientific and technological knowledge grows linearly over time"
<p>This dataset includes metadata for academic publications used in the article <em>Scientific and technological knowledge grows linearly over time</em>. The dataset contains citation relationships, publication dates, and academic fields for 213,715,816 publications from 1800 to 2020. These publications cover 292 secondary subjects in 19 major disciplines, including Economics, Biology, Computer Science, Physics, and more. The data are requested from Acemap (https://www.acemap.info, Shanghai Jiao Tong University) and sourced from the last snapshot of Microsoft Academic Graph (MAG) as of December 31, 2021.</p> <p>The dataset includes two gzip-compressed files, which contain all data in CSV format after decompression. Sample data is presented below:</p> <ol> <li>paper_date_refs.csv (paper_date_refs.tar.gz) <ol> <li>paper_id</li> <li>date</li> <li>reference_ids (separated by comma)</li> </ol> </li> <li>field_paper (field_paper.tar.gz) <ol> <li>field_paper/Computer science.csv <ol> <li>paper_id</li> </ol> </li> <li>field_paper/Biology.csv <ol> <li>paper_id</li> </ol> </li> <li>...</li> </ol> </li> </ol>
Data for: Non-linear optical phonon spectroscopy revealing polaronic signatures of the LaAlO3/SrTiO3 interface
<p>We report the direct observation of lattice phonons confined at LaAlO3/SrTiO3 (LAO/STO) interfaces and STO surfaces using the sum-frequency phonon spectroscopy. This interface-specific nonlinear optical technique unveiled phonon modes localized within a few monolayers at the interface, with inherent sensitivity to the coupling between lattice and charge degrees of freedom. Spectral evolution across the insulator-to-metal transition at LAO/STO interface revealed an electronic reconstruction at the sub-critical LAO thickness, as well as strong polaronic signatures upon formation of the two-dimensional electron gas. We further discovered a characteristic lattice mode from interfacial oxygen vacancies, enabling us to probe such important structural defects in situ. Our study provides a new perspective on many-body interactions at the correlated oxide interfaces.</p>
Output - Results of Random Forest and Multiple Linear regression analysis.
<p><strong>Hybrid streamflow modelling using machine learning and multi-model combination.</strong></p> <p> </p> <p><strong>Structure:</strong></p> <p><strong>MLR_output:</strong></p> <ul> <li>Validate <ul> <li>Different setups</li> </ul> </li> </ul> <p><strong>RF_output:</strong></p> <ul> <li>tune <ul> <li><em>all_stations</em></li> </ul> </li> <li>train <ul> <li><em>Different setups</em></li> </ul> </li> <li>Validate <ul> <li><em>Different setups</em></li> </ul> </li> </ul>
Linear-to-Circular Polarization Conversion with Full-Silica Meta-Optics to Reduce Nonlinear Effects in High-Energy Lasers - Nature Com - Dataset
<p>Data from the Nature Communication paper entitled "Linear-to-Circular Polarization Conversion with Full-Silica Meta-Optics to Reduce<br> Nonlinear Effects in High-Energy Lasers"</p> <p>.OPJU files can be opened with ORIGINLab</p> <p>. MAT files can be opened with MATLAB/SCILAB</p>
Perrot et al _ data _ Use of linear features by red-legged partridges in an intensive agricultural landscape
<p>Dataset about the article "<strong>Use of linear features by red-legged partridges in an intensive agricultural landscape: implications for landscape management in farmland"</strong></p>
Strategy of selection and optimization of single domain antibodies targeting the PHF6 linear peptide within the Tau intrinsically disordered protein
<p>Dataset pertaining to Strategy of selection and optimization of single domain antibodies targeting the PHF6 linear peptide within the Tau intrinsically disordered protein</p>
FIGURE 2. Peliosanthes linearifolia, general morphology. A in Peliosanthes linearifolia (Asparagaceae), a new species with linear leaves from Vietnam
FIGURE 2. Peliosanthes linearifolia, general morphology. A. Above-ground shoot with two inflorescences (side view). B. Apical portion of rhizome with bases of leaves and inflorescence (side view). C. Leaf blade, abaxial side. D. Inflorescence (side view). E. Portion of inflorescence showing flowers at different stages of anthesis. Nuraliev, Lyskov NUR 3457 (A–C, E) and Nuraliev, Lyskov NUR 3463 (D). Photos by M.S. Nuraliev.
FIGURE 1. Peliosanthes linearifolia. A, B in Peliosanthes linearifolia (Asparagaceae), a new species with linear leaves from Vietnam
FIGURE 1. Peliosanthes linearifolia. A, B. Plant in natural habitat. Nuraliev, Lyskov NUR 3457. Photos by M.S. Nuraliev.
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.