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1,045 results for “Generated Data”
pSCoPE: Prioritized Single-Cell Proteomics (data for generating publication figures)
<p>Major aims of single-cell proteomics include increasing the consistency, sensitivity, and depth of protein quantification, especially for proteins and modifications of biological interest. To simultaneously advance all these aims, we developed prioritized Single Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while analyzing identifiable peptides at full duty-cycle, thus increasing proteome depth. These strategies increased the sensitivity, data completeness, and proteome coverage over 2-fold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport. This protein covariation within a treatment condition was similar across the treatment conditions and coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at: <a href="http://scp.slavovlab.net/pSCoPE">scp.slavovlab.net/pSCoPE</a></p> <p> </p> <p>The files contained in this .zip directory are necessary for replicating the analysis and figures associated with the pSCoPE manuscript.</p> <p> </p>
Images of the data brushes generated for the ApPEARS deliverable D5.1 Database of vector based brush strokes and sample prints that demonstrate the range of printed materials
<p>These images are appendices of ApPEARS deliverable D5.1 Database of vector based brush strokes and sample prints that demonstrate the range of printed materials. They show the generated data brushes.</p>
Supporting Information for the Journal Article "Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning"
<p>This data set contains all data produced when exploring the Williamson ether synthesis starting from iodoethane and phenol.</p> <p><br> The set is structures as follows:</p> <ul> <li>analysis: Contains the script used to analyze the exploration and the output of said script</li> <li>check_barrier: Contains the output of the manual calculations done to check the barrier of the reaction</li> <li>exploration: Contains the scripts used to initialize and carry out the exploration as well as the two starting structures as XYZ files</li> <li>raw_data: a dump of the MongoDB database with all the data produced during the exploration</li> </ul>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk, Data
<p>This dataset accompanies the publication, "Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk", and can be used with the code located at https://zenodo.org/badge/latestdoi/248010532 to reproduce our results.</p>
Assemblies and alignment data generated for NAHRwhals manuscript.
<p>This repository contains 56 human assemblies used for a manuscript describing the NAHRwhals SV identifying tool (<a href="https://github.com/WHops/NAHRwhals" target="_new" rel="noreferrer">https://github.com/WHops/NAHRwhals</a>). All underlying raw data as well as half of the assemblies are directly taken from the Human Genome Structural Variation Consortium (HGSVC). Raw HiFi reads underlying the assemblies can be obtained from: <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/" target="_new" rel="noreferrer">http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/</a>.</p> <p>Assemblies were created with in two batches. Batch one (HG00512, HG00513, HG00514, HG00731, HG00732, HG00733, HG02818, HG03125, HG03486, NA12878, NA19238, NA19239, NA19240, NA24385) was created by the HGSVC (Ebert et al. 2021) and used in the NAHRwhals manuscript. Batch two (GM19129, GM19434, HG00171, HG00864, HG02018, HG02282, HG02769, HG02953, HG03452, HG03520, NA12329, NA19036, NA19983, NA20847) is based on HGSVC raw data but was created specifically for the manuscript by Tobias Rausch.</p> <p>For more information, please refer to the NAHRwhals paper "Impact and characterization of serial structural variations across humans and great apes" by Höps et al., 2024 for further details on assembly generation and intended usage.</p> <p>Contact: <a rel="noreferrer">wolfram.hoeps@gmail.com</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing
<p>This dataset contains the data for the publication " Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing "</p>
Data and code for: Failure to purge: Population and individual inbreeding effects on fitness across generations of wild Impatiens capensis
<p>Inbreeding exposes deleterious recessive alleles in homozygotes, lowering fitness and generating inbreeding depression (ID). Both purging (via selection) and fixation (via drift) should reduce segregating deleterious mutations and ID in more inbred populations. These theoretical predictions are not well-tested in wild populations, which is concerning given purging/fixation have opposite fitness outcomes. We examined how individual- and population-level inbreeding and genomic heterozygosity affected maternal and progeny fitness within and among 12 wild populations of <em>Impatiens capensis</em>. We quantified maternal fitness in home sites, maternal multilocus heterozygosity (using 12,560 SNPs), and lifetime fitness of selfed and predominantly outcrossed progeny in a common garden. These populations spanned a broad range of individual- (<span class="s1"><em>f</em></span><span class="s2"><sub>i</sub></span><em> </em>= -0.17–0.98) and population-level inbreeding (<span class="s1"><em>F</em></span><span class="s2"><sub>IS</sub></span> = 0.25–0.87). More inbred populations contained fewer polymorphic loci, less fecund mothers, and smaller progeny, suggesting higher fixed loads. However, despite appreciable ID (mean: 8.8 lethal equivalents per gamete), ID did not systematically decline in more inbred population. More heterozygous mothers were more fecund and produced fitter progeny in outcrossed populations, but this pattern unexpectedly reversed in highly inbred populations. These observations suggest that persistent overdominance or some other force acts to forestall purging and fixation in these populations.</p>
Data sets for heat generation and associated contact temperature during an oblique impact of a deformable particle and a rigid substrate
<p>This dataset contains essential data from the Finite Element Method model predicting heat generation due to friction and plastic deformation during the oblique impact of a deformable particle and a rigid substrate. Part of this data was processed and published in a journal article (<a href="https://doi.org/10.1016/j.powtec.2023.118481">https://doi.org/10.1016/j.powtec.2023.118481</a>). The following is the description of the data files and the associated Figure in the original paper.</p> <p>‘Heat_Elast_Vt.xlsx’ and ‘Temp_Elast_Vt.xlsx’ data for the evolution of heat and nodal contact temperature, respectively, for varying tangential velocity. Data was used in Figs. 7a and 7b in the associated paper</p> <p>‘Heat_Vt.xlsx’ and Heat_Vn.xlsx’ data for the evolution of heat for various tangential velocities and normal velocities, respectively. Data was used in Figs. 8a and 8b in the associated paper.</p> <p>‘Heat_YM.xlsx’ and ‘Temp_YM.xlsx’ data for the evolution of heat and nodal contact temperatures, respectively, for varying Young’s moduli. Data was used in Figs. 9 and 10 in the associated paper.</p> <p>‘Heat_YS.xlsx’ and ‘Temp_YS.xlsx’ data for the evolution of heat and nodal contact temperatures for varying yield strengths. Data was used in Figs. 11 and 12 in the associated paper.</p> <p>‘Heat_Den.xlsx’ and ‘Temp_Den.xlsx’ data for heat and nodal contact temperature evolution, respectively, for varying yield strengths. Data was used in Figs. 13 and 14 in the associated paper.</p> <p> ‘Temp_TC.xlsx’ data for the evolution of nodal contact temperatures for varying thermal conductivities. Data was used in Fig. 15 in the associated paper.</p> <p>‘Temp_HC.xlsx’ data for the evolution of nodal contact temperatures for varying specific heat capacities. Data was used in Fig. 16 in the associated paper.</p>
Data and code to next-generation ensemble projections reveal higher climate risks for marine ecosystems
<p>Data products: <strong>Tittensor et al. (2021). Next-generation ensemble projections reveal higher climate risks for marine ecosystems, Nature Climate Change. DOI: <a href="https://doi.org/10.1038/s41558-021-01173-9">https://doi.org/10.1038/s41558-021-01173-9</a> </strong></p> <p>This data was produced using R scripts available on the GitHub repository <a href="https://github.com/Fish-MIP/CMIP5vsCMIP6">https://github.com/Fish-MIP/CMIP5vsCMIP6</a>, and was used for analysis and plotting in Tittensor et al. (2021). These R scripts are also available here as CMIP5vsCMIP6_code.zip </p> <p>Data_CMIP5.Rdata and Data_CMIP6.RData include all data used to produce global maps of percentage change in total consumer biomass. </p> <p>Data_trends_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce temporal trends of percentage change in total consumer biomass. </p> <p>Data_inputs_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce global maps of percentage change in phytoplankton biomass, zooplankton biomass, net primary production and sea surface temperature. </p> <p>Data_trends_inputs_CMIP5.Rdata and Data_trends_CMIP6.RData include all data used to produce temporal trends of percentage change in phytoplankton biomass, zooplankton biomass, net primary production and sea surface temperature.</p> <p>The suffix _reducedModelSet refers to the case when only the subset of Fish-MIP models in Lotze et al. (2019) - Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change, PNAS, DOI: https://doi.org/10.1073/pnas.1900194116 - are considered. This data was used to produce some of the supplementary figures in Tittensor et al. (2021).</p> <p>Please contact Derek Tittensor (derek.tittensor@dal.ca), Camilla Novaglio (camilla.novaglio@gmail.com), or Julia Blanchard (julia.blanchard@utas.edu.au) for data interpretation and use. </p>
Data from: Fungal symbionts generate water-saver and water-spender plant drought strategies via diverse effects on host gene expression
<p><em>Panicum</em> <em>hallii</em> var <em>hallii</em> HAL2 plants were inoculated individually with six foliar fungal endophytes or fungus-free controls and subjected to 5% or 20% soil moisture treatments. The fungi were selected for their previously observed effects on plant drought physiology, inducing either a "water saver" or a "water spender" strategy in the host. Plants were grown in enclosed microcosms to prevent cross-contamination and each treatment and control included 6 replicates. All fungi were Ascomycetes isolated from plants in central Texas. Plants were monitored for height, wilt, water loss, and survival. At the harvest, we also measured biomass and leaf colonization by the fungi and flash-froze leaf tissue for transcriptomic analyses. Both plant response and gene expression data are provided.</p>
Data of "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator"
<p><strong>General</strong></p> <p>Data of <a href="http://doi.org/10.1016/j.ijsolstr.2023.112470">https://doi.org/10.1016/j.ijsolstr.2023.112470</a> related to MOAMMM project.</p> <p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br> journal = "International Journal of Solids and Structures",<br> year = "2023",<br> volume = "283",<br> pages = "112470",<br> doi = "10.1016/j.ijsolstr.2023.112470",<br> author = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels"</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <p><strong>Description</strong></p> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a>. The BI is described in [WU23] .The experimental results used in the BI are reported in [COB22,COB22b]. To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers.</p> <p><strong>Bibliography</strong></p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: https://doi.org/10.1016/j.ijsolstr.2023.112470</li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: https://doi.org/10.1016/j.polymertesting.2022.107556 (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” http://dx.doi.org/10.5281/zenodo.6136935 (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008, Open access: https://orbi.uliege.be/handle/2268/197898</li> </ul> <p><strong>Directories</strong></p> <p>All the codes and experimental results are in five directories:</p> <ol> <li>experimentalTests: experimental data, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VE_V2Step and VE_H: BI for viscoelastic properties of "V" specimen (VE_V2Step) and "H" specimen (VE_H) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>MCMC_VE_....dat in the VE_V2Step and VE_H directories are the BI results</li> <li>When proceeding in two steps in VE_V2Step, a first step generates MCMC_VE_VN8_1st.dat whose posterior is used as prior in the second step to generate MCMC_VE_VN8_2nd.dat</li> </ol> </li> <li>CheckBayRes: to visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> <li>uses as input VE_V2Step/MCMC_VE_....dat or VE_H/MCMC_VE_....dat</li> <li>uses local ViscoElasticTest.py, line.geo, line. msh as interface with https://gitlab.onelab.info/cm3/cm3Libraries code</li> <li>uses local functions plotExpLoad_Unload.py, plotExp.py</li> </ol> </li> <li>ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V2Step and VE_H to call the VEVP model</li> </ol> </li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VP_V2step and VP_H2step: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V2Step) and "H" specimen (VP_H2Step) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?_1of2Steps.dat (? being H or V)</li> <li>Then using MCMC_VP_?_1of2Steps.dat posterior to get a new prior, it generates MCMC_VP_?_2of2Steps.dat (? being H or V)</li> </ol> </li> <li>CheckBayRes: visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples ([28000, 45000,70000] by default, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> </ol> </li> <li>VEVPTest.py: interface with https://gitlab.onelab.info/cm3/cm3Libraries code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> </li> <li>RandomParametersGenerator: used to generate the parameters from the BI samples, with the same statistical content <ol> <li>Generator <ol> <li>DataProcess.py: creates normalized data for training from final inferred parameters in ../MCMC_ResData and creates ?_dirNormData (? being H or V)</li> <li>KmeanDataProcess.py: performs clustering for the data of H_dirNormDat and creates H_dirNormData_2cluster (no need for V direction because not bimodal)</li> <li>Gan_V.py and Gan_H.py are used to train the random material parameter generators and create the VDir_Gan or HDir_Gan200_0/HDir_Gan200_1</li> <li>GenerateParameters.py generates random parameters using the Gan files VDir_Gan or HDir_Gan200_0/HDir_Gan200_1 and checks the joint histograms of generated parameters, generated parameters are in V_GenData and H_GenData</li> <li>Ganlib.py is used by the generator</li> </ol> </li> <li>CheckRes <ol> <li>GenDataRes.py is used to check the numerical predictions with the generated parameter samples, see point 4) (using V_GenData and H_GenData).</li> <li>Plot_PropGen.py plots joints histograms of the generated parameters using the samples of V_GenData or H_GenData</li> </ol> </li> </ol> </li> <li>MCMC_ResData:All final data used in the paper (they can substitute the ones used here above) <ol> <li>H_direction and V_direction keep the MCMC random walk results of BI.</li> <li>RandomParameterGenerator keeps results of the generator Paper</li> </ol> </li> </ol> <p><strong>Figures of [WU23]</strong></p> <ul> <li>Fig. 5: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_V/plotExp_T.py or ./PrintDir_V/plotExp_C.py or ./PrintDir_V/plotExp_R.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 8: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires<a href="https://gitlab.onelab.info/cm3/cm3Libraries"> https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 11: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 12: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 13: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_H/plotExp_T.py or ./PrintDir_H/plotExp_C.py or ./PrintDir_H/plotExp_R.py</li> <li>Fig. 15C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 16C: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 19C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 21D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 22D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "H" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> </ul> <p> </p> <p> </p>
Data from: Latent generative landscapes as maps of functional diversity in protein sequence space
<p>Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.</p>
Data for Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties
<p>Data for the manuscript "Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties". The data set contains all the generated mechanisms, DFT data used for the construction of the RMG library, a backup of the RMG-database, all results and scripts for the evaluation of the results. </p>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
Data sets for temperature rise due to frictional heat generation during a sliding contact between an elastic particle and a rigid substrate
<p>This dataset contains essential data from the Finite Element Method model predicting heat generation due to friction during the sliding contact between an elastic particle and a rigid substrate. Part of this data was processed and presented in an article under review for journal publication. The following is the description of the data files and the associated Figure in the original paper.</p> <p>'Temp_CoeffFric_01_055.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various friction coefficient values (0.1-0.55).</p> <p>'Temp_Load_001_01.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various normal load values (0.01-0.1 N).</p> <p>'Temp_Vel_02_1.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various sliding velocity values (0.2-1 m/s).</p> <p>'Temp_TC_5_100.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various thermal conductivity values. (i.e. 5-100 W/m K).</p> <p>'Temp_HC_100_1600.xlsx' data for temperature evolution for the nodes in the contacts and along the particle radius for various thermal conductivity values. (i.e. 100-1600 J/kg K).</p> <p> </p>
Data and scripts to generate all figures for Communications Earth and Environment paper COMMSENV-21-0361D
<p>In this data set, it includes all the data and NCL scripts which are necessary to generate all figures used in the manuscript COMMSENV-21-0361D.E</p>
CEP-based Activity Detection Services generated from IoT Data
<p>Data set accompanying the paper "Data-driven Generation of Services for IoT-based Online Activity Detection" submitted to the International Conference on Service-Oriented Computing (ICSOC) 2023.</p> <p>The data set has 6 automatically generated activity detection services (*.siddhi files) for 6 different types of activities executed by 6 different production stations in a smart factory. The *.siddhi files have to be deployed to and activated on an instance of the <a href="http://siddhi.io">Siddhi</a> complex event processing platform.</p> <p>The data set includes the corresponding low-level IoT data for each activity (<strong>activity signature</strong>) that was used to generate the activity detection service (*.txt files). It also includes a visual representation of the activity signature for each type of activity (*.png files). To test the activity detection services, the *.txt files have to be read line by line, each line has to be send as one MQTT message to an MQTT broker and topic that the activity detection service is also listening to (standard: localhost).</p>
Data generated and analysed for Lambert, Santos Neves, Bilderbeek, Valente & Etienne 2022
<p>This repository contains the data for accompanying the publication Lambert et al. (2022). The effect of mainland dynamics on data and parameter estimates in island biogeography. The results files are .rda files for R which contain the results used in the manuscript. The logs files are text files that were obtained via computation at University of Groningen Hábrók High Performance Computing Cluster (HPCC). Data was generated and analysed using the DAISIEmainland and DAISIE R packages. The code for these packages is version controlled on GitHub and is freely available in open-source repositories. See the Related Identifiers section for links to relevant archived versions of both these packages.</p>
Paper data for DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles
<p>This repo contains the study and appendix data for "DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles". DOI 10.1109/TSE.2023.3301443.</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.