Find research datasets worth reusing

opencc-by-4.0Jan 2018View details →

Time series used in the manuscript "Causal dependences between the coupled ocean-atmosphere dynamics over the Tropical Pacific, the North Pacific and the North Atlantic"

<p>These 6 files contain time series built using reanalyses datasets of the ECMWF as discussed in the manuscript "Causal dependences between the coupled ocean-atmosphere dynamics over the Tropical Pacific, the North Pacific and the North Atlantic" submitted for discussion in the journal "Earth System Dynamics".</p>

opencc-by-4.0Apr 2024View details →

Unified Relativistic Mean Field EoSs with density dependent couplings

<p>This dataset, containing 447345 equations of state, was used or the Bayesian analysis on the <br>properties of Neutron Stars in [1]. Each EoS includes an outer crust, an inner crust and a core.<br>The outer crust used for all the EoSs is the BsK22[2], while each pair of inner crust and core was calculated<br>using the same Relativistic Mean Field model and a Compressible Liquid Drop approximation for the <br>clusters in the inner crust. The crust-core transition is determined for each model by the crossing<br>of the energy density of crust and core.</p> <p>[1] L. Scurto, H. Pais and F. Gulminelli, arXiv:2402.15548 (2024)<br>[2] J.M. Pearson, N. Chamel, A.Y. Potekhin, A.F. Fantina, C. Ducoin, A.K. Dutta, and S. Goriely, MNRAS 481, 2994 (2018)</p>

opencc-by-4.0Apr 2024View details →

Dataset for the manuscript: Nonlinear Coupling of Kinetic Alfven Waves and Ion Acoustic waves in the Inner Heliosphere

<ol> <li>The file ef00 is the magnetic field profile data written for every t=0.5 starting from 0 to 200 with 128 by 128 grid point in space. It is used to produce Figure 1 of the paper plotted by using matlab m file filaments.m.</li> <li>The file dx00 is the density data taken at x=0 at different z. Figure 2 of the paper is plotted by matlab file density.m</li> <li>The data file esz0 is uesd in wavenumber.m to plot figure 3 of the paper</li> <li>The data file ekx0 is used to plot figure 4 in fullspectra.m </li> <li>The data file ekz0 is used to plot figure 5 in matlab m file bkspectkx.m</li> <li>The file eekz0 is used to plot figure 6 in the matlab m file spectrakx.m</li> <li>The matlab code heat.m is used to plot figure 7 of the paper</li> </ol>

Synchronous PIV measurements of a self-powered blood turbine and pump couple for right ventricle support

<p>A blood turbine-pump system (iATVA) was proposed as a self-driven, motorless mechanical right-heart assist device. In this study, the iATVA system is investigated with particular emphasis on the blood turbine flow dynamics. A time-resolved 2D particle image velocimetry (PIV) set-up allowed simultaneous recordings from both the turbine and pump impellers. Results showed that magnetically coupled impellers operated synchronously. As the turbine flow rate increased from 1.6 to 2.4 LPM, the rotational speed and relative inlet flow angle increased from 630 to 900 rpm, and 38 to 55% respectively. At the trailing edges, backflow region spanned 3/5 of the total passage outlet flow, and an extra leakage flow was observed at the leading edge. For this initial turbine design, ~75% of the turbine blade passage was not contributing to the impulse operation mode. The maximum non-wall shear rate was ~2288 s<sup>-1</sup> near to the inlet exit, which is significantly lower than the commercial blood pumps, encouraging further research and blood experiments of this novel concept. Experimental results will improve the hydrodynamic design of the turbine impeller and volute regions and will be useful in computational fluid dynamics validation studies.</p>

Enhancing Change Impact Prediction by Integrating Evolutionary Coupling with Software Change Relationships

<div> <div><strong>Replication Package For Paper: Enhancing Change Impact Prediction by Integrating Evolutionary Coupling with Software Change Relationships</strong></div> <br> <div>IMPER is a change <strong>IM</strong>pact <strong>P</strong>rediction method that integrates <strong>E</strong>volutionary coupling with software change <strong>R</strong>elationships. The approach consists of following four main steps:</div> <br> <div><em>Step 1</em>: Relevant Transaction Filtering. Filtering the relevant transaction set based on the given query $<em>Q</em>$.</div> <div><em>Step 2</em>: Association Rule Generation. Generating association rules and obtaining the impact candidate set.</div> <div><em>Step 3</em>: Spatial-Temporal Window Processing. Considering the temporal and spatial change relationships to obtain the impact candidate set.</div> <div><em>Step 4</em>: Candidates Integrating and Ranking. Making a union of these two sets to find the final impact set.</div> <br> <div>The repo provides files for the paper replication. It includes::</div> </div> <div> </div> <div><strong>git_repo.zi</strong>p: Git repository used in the study.</div> <div><em><strong>***-rec-database-1107.tar.gz</strong>:</em> Neo4j dataset containing entity-level software relationship graphs and change information (changed classes, member member methods, member member variables, and files) from over 19,003 versions of six well-maintained projects.</div> <div><strong><em>code_entiy_genealogy</em></strong>: Data for code entity genealogy.<br> <div><em><strong>imper_jar.zip</strong>:</em>  Tool for the study, including: 1) Generating qualified queries; 2) Change prediction using four approaches (ROSE, TARMAQ, IMPER, IMPER-ST); 3) Evaluation. The tool outputs evaluation results for each query and average metrics (MAP, Precision, Recall, and F1-score).</div> <div><strong><em>evaluation_metrics_details.zip</em></strong>: Detailed evaluation metric results for different projects.</div> <div><strong><em>Imper_evaluation_results.xlsx</em></strong>: Summary of evaluation metric results for all projects.</div> <div> </div> <br> <div>Users are suggested to refer to the research paper for more detials.</div> <br> <div>This project is still in an early stage of development, and we are improving the implementation and documentation. Please feel free to contact ******(author) for the replication package.</div> <br><strong>Environment</strong><br> <div>- java 11+</div> <div>- <a href="https://neo4j.com/download-center/#community">neo4j 4.2</a> </div> <div>- memory 64G+</div> <br><strong>Quick Start</strong>:<br>To begin our study, follow these steps. Note that only one project can be analyzed at a time.</div> <div><br><strong><em>Step 1: Database Setup</em></strong></div> <div> </div> <div>Copy the relevant database to the folder `data` (in the root path of the Neo4j database), copy the plugins to the plugin folder, and open the database using Neo4j 4.2 (Recommended).</div> <div><br><strong><em>Step 2: Configure application-dev.yml</em></strong></div> <div> </div> <div>Adjust the application-dev.yml file in the folder `imper_jar` to match your configuration. If you're using an existing database, simply configure the database address, username, and password. <div> <p> </p> </div> </div> <div><strong><em>Step 3:  Run imper-study.jar</em></strong></div> <div><br>Command: `<em>java -jar imper-study.jar -imp -config ./application-dev.yml -rd /home/user/code_rec -pj commons-io -fi commons-io_file(Map)_1031.txt -mi commons-io_functionInfo(Map).txt -tw 35 -mc 20 -od /home/user/code_rec</em>`.</div> <div><br> <div>-imp:  Used to run IMPER</div> <div>-config:  Path of `<em>application-dev.yml</em>`.</div> <div>-rd:  Root path of code entiy genealogy, e.g., `<em>/home/user/code_rec</em>`.</div> <div>-pj:  Project name, e.g., `<em>commons-io</em>`.</div> <div>-fi:  the code entiy genealogy (file), e.g., `<em>commons-io_file(Map)_1031.txt</em>` (the absolute path is `<em>/home/user/code_rec/commons-io/commons-io_file(Map)_1031.txt</em>`).</div> <div>-mi:  the code entiy genealogy (method), e.g., `<em>commons-io_functionInfo(Map).txt</em>` (the absolute path is `<em>/home/user/code_rec/commons-io/commons-io_functionInfo(Map).txt</em>`).</div> <div>-tw:  Time window (half), e.g., <em>-tw 35 (the time window size is 70).</em></div> <div>-mc:  Maxium commit size in the time window.</div> <div>-od:  Output root path for evaluation results (MAP, Precison, Recall and F1-score), e.g., `<em>/home/user/code_rec</em>`.</div> </div>

Labile Carbon Triggers Microbial Priming of Deep Peat Carbon Breakdown Unveiled by Coupled DNA SIP-Metabolomics : NMR raw data

<p>METHODS:</p> <p>180 µL of each DOM sample (two depths, 3 treatments (control, labeled glucose, unlabeled glucose,) and 6 times points (7, 14, 28, 42, 56, and 70 days); n=36) were combined with 2,2-dimethyl-2-silapentane- 5-sulfonate-d6 (DSS-d6) in D2O (20 µL, 5 mM) and thoroughly mixed prior to transfer to 3mm NMR tubes.  NMR spectra were acquired on a Varian 600 MHz VNMRS spectrometer equipped with a 5-mm triple-resonance (HCN) cold probe at a regulated temperature of 298K. The 90° 1H pulse was calibrated prior to the measurement of each sample. The one-dimensional (1D) 1H spectra were acquired using a nuclear Overhauser effect spectroscopy (NOESY) pulse sequence with a spectral width of 12 ppm and 512 transients. The NOESY mixing time was 100ms, and the acquisition time was 4s, followed by a relaxation delay of 1.5s during which pre-saturation of the water signal was applied. Time-domain free induction decays (57,472 total points) were zero filled to 131,072 total points prior to Fourier transform. Chemical shifts were referenced to the 1H methyl signal in DSS-d6 at 0 ppm. The 1D 1H spectra were manually processed, assigned metabolite identification, and quantified using Chenomx NMR Suite 8.3. Metabolite identification was based on matching the chemical shift, J-coupling, and the intensity of experimental signals to compound signals in the Chenomx and custom in-house databases. Quantification was based on fitted metabolite signals relative to the internal standard (DSS-d6). Signal-to-noise ratios (S/N) were measured using MestReNova 14 with the limit of quantification equal to an S/N of 10 and the limit of detection equal to an S/N of 3. 13C labeling was assessed by 13C satellite analysis from the 1D spectra described above or from a 1D-(13C-edited) HSQC experiment. In several cases further corroboration of metabolite identity was made using standard 2-D experiments such as 1H / 13C - heteronuclear correlation (HSQC) experiments or 2-D 1H/ 1H Total Correlation spectroscopy (TOCSY).</p> <p> </p> <p>FUNDING:</p> <p>This research was supported by U.S. Department of Energy Office of Science, Office of Biological and Environmental Research (BER), grant no. DE-SC0023297.</p>

opencc-by-4.0Mar 2024View details →

Core Binding Energy Calculations: A Scalable Approach with the Quantum Embedding Based Equation-of-Motion Coupled-Cluster Method

<p>This data includes the HF-optimized orbitals, coupled cluster amplitudes (T1, T2), and EOM-CCSD left and right eigenvectors at the CC-PCVDZ basis set. It can be used to reproduce the data for "Core Binding Energy Calculations: A Scalable Approach with the Quantum Embedding Based Equation-of-Motion Coupled-Cluster Method."</p>

All structures for electronic coupling

<p>These optimized structures provided the correct active space, enabling us to perform the coupling calculations accurately.</p>

Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"

<div> <p>This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"</p> <p> </p> </div>

opencc-by-4.0Dec 2023View details →

Coupled metalipidomics-metagenomics reveal structurally diverse sphingolipids produced by a wide variety of marine bacteria

<p><strong>Abstract</strong></p> <p><span>Microbial lipids, used as taxonomic markers and physiological indicators, have mainly been studied through cultivation. However, this approach is limited due to the scarcity of cultures of environmental microbes, thereby restricting insights into the diversity of lipids and their ecological roles. Addressing this limitation, here we apply metalipidomics combined with metagenomics in the Black Sea, classifying and tentatively identifying 1</span><span>623 lipid-like species across 18 lipid classes. We discovered over 200 novel, abundant, and structurally diverse sphingolipids in euxinic waters, including unique 1-deoxysphingolipids with long-chain fatty acids and sulfur-containing groups. </span><span>Sphingolipids were thought to be rare in bacteria and their molecular and ecological functions in bacterial membranes remain elusive. However, </span><span>genomic analysis focused on sphingolipid biosynthesis genes revealed that members of 38 bacterial phyla in the Black Sea can synthesize sphingolipids, representing a fourfold increase from previously known capabilities and accounting for up to 25% of the microbial community. These sphingolipids appear to be involved in oxidative stress response, cell wall remodeling and are associated with the metabolism of nitrogen-containing molecules. Our findings underscore the effectiveness of multi-omics approaches in exploring microbial chemical ecology.</span></p> <div><br></div> <p><strong>Repository content:</strong></p> <p><strong>1) metalipidome_sphingolipids.zip: </strong>includes source data and code scripts used for figures regarding metalipidome and sphingolipids abundance, classification and diversity in this study. Files are organized as follows and are associated with the corresponding parts of the manuscript: Fig. 1a, Fig. 1b,  Fig. 2b,  Fig. 2c, Fig. 2e,  Fig. 2f, Fig. 2g, Fig. 2h, Fig. 4d, Supplementary Fig. 2, Supplementary Fig. 3.</p> <p><strong>2) Source data_major lipid classification.xlsx:</strong> includes original tables regarding metalipidome identification, abundance, precursor mass, retention time, classification as well as ID (name) in the molecular network.</p> <p><strong>3) Source data_sphingolipids information.xlsx:</strong> includes information about sphingolipids identification, precusor mass, retention time, peak intensity, elemental composition and etc.</p> <p><strong>4) Black_Sea_2013.code.tar.gz:</strong> contains the directory structure and code used for the metagenomics part of this project. Each directory contains a 'commands.sh', which contains the code to generate the content in that directory. Other shell and python scripts are always run from within 'commands.sh', with the exception of the files within the 'figures' directory which contains Jupyter labs and a python script that were run individually.</p> <p><strong>5) MAGs.tar.gz: </strong>all the MAGs generated by DAS Tool including CheckM and GTDB-Tk analyses (inside the 'binners' directory). Final taxonomic annotations of MAGs (see 'MAG2info.txt' file) are based on BAT annotatations with GTDB as a reference database, source data in the directory 'CAT_and_BAT_with_GTDB_refdb'.</p> <p><strong>6) abundance_profile.tar.gz:</strong> taxonomic annotation (based on CAT and BAT) and abundance of all scaffolds in the file 'big_table.txt'. Columns that start with 'mappings' are the read counts mapping to the scaffold in the sample from which it is assembled. Since the scaffolds were assembled per sample, only one of the 15 samples contains read mappings per scaffold. The last 15 columns (that start with 'BlackSea') are the depth per 1e8 mapped reads based on the all versus all mappings and were used for co-abundance analyses with sphingolipids. The file 'taxon2counts.txt' summarizes the taxonomic composition of the water column based on summing of the read mappings of the samples from which scaffolds were assembled (the 'mappings' columns in 'big_table.txt'), i.e. they represent all reads that could be associated with a certain taxon in that sample. The 'taxon2counts.txt' file used in Fig. 3c,d.</p> <p> </p> <p> </p> <p> </p>

opencc-by-4.0Jun 2024View details →

Data for "Strongly coupled edge states in a graphene quantum Hall interferometer"

<p>Repository containing the data sets and analysis code for the main figures of "Strongly coupled edge states in a graphene quantum Hall interferometer." </p> <p>Powerpoint files track data taken and notes recorded during measurement. Additional data and analysis codes are available upon request.</p>

Southern Hemisphere Circumpolar Wavenumber-4 Pattern Simulated in SINTEX-F2 Coupled Model

<p>These datasets are used to produce Figures in the paper by Senapati et al. (2024). </p> <p>Senapati, B., Morioka, Y., Behera, S. K., & Dash, M. K. (2024). Southern Hemisphere circumpolar wavenumber‐4 pattern simulated in SINTEX‐F2 coupled model. Journal of Geophysical Research: Oceans, 129, e2023JC020801. <a href="https://doi.org/10.1029/2023JC020801" rel="noopener">https://doi.org/10.1029/2023JC020801</a></p>

DYAMOND-II Model Outputs for "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations"

<p>This dataset contains outputs from the DYAMOND Phase-II simulations, focusing on Global Storm-Resolving Models (GSRMs) at various Atmospheric Radiation Measurement (ARM) sites. The dataset supports the analysis presented in the manuscript "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations."</p> <p>Included are high-resolution model outputs from nine GSRMs, detailing simulations of atmospheric processes at six ARM sites, including variables of clouds, temperature, humidity, wind, and surface fluxes. This dataset allows for a direct comparison between GSRM simulations and ARM field observations, facilitating the evaluation of PBL-coupled and decoupled clouds. For further inquiries or assistance regarding the dataset, please contact the corresponding author at su10@llnl.gov.</p>

opencc-by-4.0Jan 2024View details →

Investigating solute transport and reaction using a mechanistically coupled geochemical and geophysical modeling approach

<p>This repository encompasses the acquired data for the five experiments of reactive percolation in a column with geoelectrical monitoring. The input and output files of the reactive transport simulations are also given. In addition, five movies of the reaction rate evolving with time are also part of this dataset. The files are:</p> <ul> <li><strong>physicochemical_data.xlsx</strong>: the ionic concentrations, alkalinity, pH, and electrical conductivity from the collected water samples, the pH monitored at two locations in the column, and the inlet and outlet water conductivity and temperature. Porosity and formation factor calculated from the measured calcium concentration are also given.</li> <li><strong>sp-data-exp1-exp2-exp3.xlsx</strong>: the self-potential (SP) method has been used to monitor the reactive percolation for experiments 1, 2, and 3.</li> <li><strong>sip-data-exp4.xlsx </strong>and <strong>sip-data-exp5.xlsx</strong>: the spectral induced polarization (SIP) method has been used to monitor the reactive percolation for experiments 4 and 5.</li> <li><strong>output_exp</strong><em><strong>X</strong></em><strong>.zip</strong>: compressed folders of all output files for the reactive transport simulations using Crunchflow software.</li> <li><strong>1DcalciteFT_ex<em>X</em>.in</strong>: input files for the reactive transport simulation using CrnchFlow software.</li> <li><strong>OldRifleDatabaseLiLi_nicole.dbs</strong>: database for the reactive transport simulation using CrnchFlow software.</li> <li><strong>video-rate-exp</strong><em><strong>X</strong></em><strong>.avi</strong>: movies of the reaction rate along the column and evolving with time for the complete duration of each experiment.</li> </ul>

Dataset - Estimation of alginate purity and M/G ratio by methanolysis coupled with anion exchange chromatography

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Polaromechanics: photons, magnons and phonons in the triple strong-coupling regime

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flood modeling datas for Catastrophic outburst floods along the middle Yarlung Tsangpo River: responses to coupled fault and glacial activity on the southern Tibetan Plateau

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Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"

<p>This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"</p> <p>The code for plotting the figures is the notebook Plot_figures.ipynb</p> <p>Fig1/simulation_output/ : Model output necessary for plotting the first figure </p> <p>The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance).</p> <ul> <li><span>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</span></li> <li><span>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p>Fig2/simulation_output/ : Model output necessary for plotting the second figure </p> <p>The last timestep of each simulation is provided. </p> <ul> <li><span>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p><span>Fig3/simulation_output/ : Model output necessary for plotting the third figure </span></p> <ul> <li><span>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels,  pre-industrial insolation, coupling with the ice sheet model </span></li> <li><span>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels,  pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</span></li> </ul> <p> </p>

Bacterial cell surface characterization by phage display coupled to high-throughput sequencing

<p>This record contains the processed high-throughput sequencing data from the manuscript "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing." Data was generated using the <a href="https://github.com/caseygrun/phage-seq">Snakemake workflows and Jupyter notebooks in this repository</a> and is intended to be analyzed further using the notebooks in that repository</p> <p>Each tarball within this record, when expanded, populates the <code>results</code> and <code>intermediate</code> directories of one of those workflows: <code>alpaca-library</code> ,<code>panning-small</code>, <code>panning-massive</code>, or <code>panning-extended</code>. Clone the <a href="https://github.com/caseygrun/phage-seq"><code>phage-seq</code> repository</a>, then download one or more of these tarballs to the corresponding directory of that directory. For example:</p> <blockquote> <pre><code>git clone https://github.com/caseygrun/phage-seq.git cd panning-extended wget https://zenodo.org/records/11246658/files/panning-extended-results.tar.gz tar vzxf panning-extended-results.tar.gz</code></pre> </blockquote> <p>More detailed instructions are included in the README for the <a href="https://github.com/caseygrun/phage-seq"><code>phage-seq</code> repository</a>.</p>

Setup and Dataset for the Validation of an Eulerian-Lagrangian Coupling Method in the m-AIA framework

<p>This repository holds data files, code information and property files which are used to conduct performance analyses of a <br>Parallel Eulerian-Lagrangian Coupling Method. </p>